refactor: riorganizzazione script — Strategy ABC, folder strategies/waste/analysis

- src/strategies/base.py: Strategy ABC con Signal, BacktestResult, YearlyStats
- src/strategies/indicators.py: keltner_ratio, detect_squeezes, ema, atr, rv, corr
- scripts/strategies/: SQ01-SQ04 (squeeze puro/filtri), ML01 (squeeze+GBM)
- scripts/waste/: W01-W22 script scartati + REF originali
- scripts/analysis/: compare, best_yearly, final_report, paper_status
- CLAUDE.md aggiornato con nuova struttura e tabella strategie

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

CLAUDE.md

@@ -0,0 +1,94 @@
+# PythagorasGoal — Istruzioni per agenti
+
+## Panoramica
+
+Progetto di ricerca: riconoscimento pattern frattali per trading algoritmico su criptovalute (BTC, ETH). L'obiettivo è arrivare a €50/giorno di profitto partendo da €1.000, entro 6–8 mesi.
+
+## Stack
+
+- **Linguaggio:** Python 3.11+
+- **Package manager:** uv (dipendenze in `pyproject.toml`, lock in `uv.lock`)
+- **Dati:** Parquet in `data/raw/` (non committati, ~70 MB)
+- **ML:** scikit-learn (GradientBoosting), PyTorch (LSTM)
+- **Analisi:** numpy, pandas, scipy
+- **API dati:** Cerbero MCP su `cerbero-mcp.tielogic.xyz` (Deribit, Bybit, Hyperliquid), ccxt/Binance come fallback
+
+## Struttura
+
+```
+src/data/              → download e caricamento dati (downloader.py)
+src/fractal/           → indicatori frattali (patterns.py, indicators.py, similarity.py)
+src/backtest/          → engine di backtesting (engine.py)
+src/strategies/        → classe base Strategy ABC + indicatori condivisi
+  base.py              → Strategy, Signal, BacktestResult, YearlyStats
+  indicators.py        → keltner_ratio, detect_squeezes, ema, atr, rv, correlation
+scripts/strategies/    → strategie attive (SQ01-SQ04, ML01)
+scripts/waste/         → strategie scartate (W01-W22 + REF originali)
+scripts/analysis/      → script di confronto e report
+docs/diary/            → diario di ricerca giornaliero
+data/raw/              → file .parquet OHLCV (gitignored)
+data/processed/        → modelli salvati (gitignored)
+```
+
+## Comandi
+
+```bash
+uv sync                                              # installa dipendenze
+uv run python -m src.data.downloader                  # scarica dati storici
+uv run python scripts/strategies/SQ02_squeeze_antifake_vol.py  # miglior strategia robusta
+uv run python scripts/strategies/ML01_squeeze_gbm.py  # squeeze + ML (GBM)
+uv run pytest                                         # test
+```
+
+## Dati storici
+
+Scaricati e salvati localmente in Parquet. Per rigenerarli:
+
+```python
+from src.data.downloader import download_all, load_data
+download_all()           # scarica BTC + ETH su 5m/15m/1h dal 2018
+df = load_data("ETH", "15m")  # carica un asset/timeframe
+```
+
+Fonte primaria: Cerbero MCP (endpoint `/mcp-deribit/tools/get_historical`).
+Token observer: nel file `secrets/observer.token` del progetto CerberoSuite.
+
+## Strategia vincente
+
+**Squeeze + ML ibrida** (script 13):
+
+1. Rileva squeeze di volatilità (Bollinger dentro Keltner)
+2. Al rilascio dello squeeze, estrai feature strutturali dalla finestra
+3. GradientBoosting predice direzione con walk-forward training
+4. Trade solo se modello ha confidenza ≥ 70%
+
+Configurazione migliore: ETH 15m, BBw=14, squeeze threshold=0.8, breakout=3 barre, leva 3x, position 15%.
+
+Risultato backtestato: 76.9% accuracy, 118% annuo, 4.2% drawdown, €13.78/giorno da €1.000.
+
+## Strategie attive
+
+| Codice | Nome | Tipo | Accuracy | Note |
+|--------|------|------|----------|------|
+| SQ01 | Squeeze Base | Regole | 76.7% | Squeeze breakout puro, baseline |
+| SQ02 | Antifake+Vol | Regole | 79.7% | **Miglior robusto** — 9 anni, Sharpe 5.01 |
+| SQ03 | All Filters | Regole | 79.2% | Cross-asset + timing + long squeeze |
+| SQ04 | Ultimate | Regole | 81.6% | Max accuracy ma concentrato 2018 |
+| ML01 | Squeeze+GBM | ML | 78.8% | Walk-forward, €12/day, DD basso |
+
+Tutte le strategie estendono `src.strategies.base.Strategy` con interfaccia comune:
+`generate_signals() → backtest() → report()`.
+
+## Convenzioni
+
+- Strategie in `scripts/strategies/` con codice univoco (SQ01, ML01, ...).
+- Script scartati in `scripts/waste/` con prefisso W01-W22.
+- Diario in `docs/diary/YYYY-MM-DD.md`. Aggiornare dopo ogni esperimento significativo.
+- Nessun dato sensibile nei commit (token, chiavi API). Usare `.gitignore`.
+- Verificare sempre assenza di data leakage prima di fidarsi dei risultati. In particolare: `returns[i-w : i]` include `close[i]` che è un candle nel futuro — usare `returns[i-w : i-1]`.
+
+## Attenzione
+
+- **Data leakage:** è stata trovata e corretta nello script 05. Ogni volta che si usano rendimenti logaritmici (`np.diff(np.log(close))`), ricordare che `returns[k]` usa `close[k+1]`. I feature devono fermarsi a `returns[i-2]` se il prezzo corrente è `close[i-1]`.
+- **Fee:** sempre 0.1% per lato (0.2% round-trip). Includere nel backtest.
+- **Leva:** testato con 3x. Aumentare a 5x migliora i rendimenti ma raddoppia il drawdown.

Dockerfile

@@ -0,0 +1,14 @@
+FROM python:3.11-slim
+
+COPY --from=ghcr.io/astral-sh/uv:latest /uv /usr/local/bin/uv
+
+WORKDIR /app
+
+COPY pyproject.toml uv.lock ./
+RUN uv sync --frozen --no-dev
+
+COPY src/ src/
+
+VOLUME /app/data
+
+CMD ["uv", "run", "python", "-m", "src.live.paper_trader"]

README.md

@@ -0,0 +1,120 @@
+# PythagorasGoal
+
+Sistema di riconoscimento pattern frattali e predizione per il trading di criptovalute (BTC, ETH), ispirato al framework teorico di Serleto & Malanga (*Pythagoras Trading Prediction*).
+
+## Obiettivo
+
+Partendo da un capitale iniziale di €1.000, raggiungere un profitto medio di €50 al giorno entro 6–8 mesi, tramite strategie algoritmiche che combinano analisi frattale, squeeze di volatilità e machine learning.
+
+## Risultati
+
+Tredici strategie testate su dati storici 2018–2026 (BTC e ETH, timeframe 5m / 15m / 1h). Le migliori cinque:
+
+| # | Strategia | Accuracy | ROI annuo | Max DD | €/giorno |
+|---|-----------|----------|-----------|--------|----------|
+| 1 | ETH 15m Squeeze + ML ibrida | 76.9% | 118% | 4.2% | €13.78 |
+| 2 | ETH 1h Squeeze + Vol | 83.9% | 22% | 2.0% | €0.71 |
+| 3 | BTC 15m Squeeze + ML ibrida | 78.8% | 69% | 7.0% | €5.51 |
+| 4 | ETH 1h Squeeze (BBw=30) | 82.8% | 47% | 3.2% | €1.77 |
+| 5 | ETH Walk-Forward ML | 57.7% | 38% | 47% | €3.12 |
+
+La strategia vincente (#1) opera su ETH a 15 minuti con ~1 trade al giorno, leva 3x e drawdown contenuto al 4.2%.
+
+## Come funziona
+
+### Volatility Squeeze Breakout
+
+Il meccanismo centrale sfrutta i cicli naturali di compressione ed espansione della volatilità:
+
+1. **Compressione** — le Bollinger Bands entrano dentro i Keltner Channel (il prezzo si muove sempre meno, accumulando "energia").
+2. **Breakout** — le bande escono dal canale. Un impulso direzionale parte.
+3. **Conferma ML** — un modello GradientBoosting, addestrato su feature strutturali e frattali della finestra precedente, conferma la direzione e filtra i segnali deboli.
+
+### Feature frattali
+
+- Rapporti body/shadow normalizzati su finestre multiple (12, 24, 48 candele)
+- Momentum, volatilità, skewness, kurtosis dei rendimenti logaritmici
+- Autocorrelazione lag-1
+- Profilo volumetrico e spike detection
+- Durata della fase di squeeze e rapporto di espansione Keltner
+- Posizione del prezzo rispetto al range recente e ATR normalizzato
+
+## Struttura progetto
+
+```
+PythagorasGoal/
+├── src/
+│   ├── data/          # Download e gestione dati storici (Cerbero MCP + Binance)
+│   ├── fractal/       # Indicatori frattali: Hurst, Higuchi FD, self-similarity
+│   ├── backtest/      # Motore di backtesting con fee e metriche
+│   ├── strategies/    # (predisposto per strategie modulari)
+│   ├── nn/            # (predisposto per reti neurali)
+│   └── utils/
+├── scripts/           # Script di analisi e test (01–13)
+├── data/
+│   ├── raw/           # Parquet OHLCV (non committati, ~70 MB)
+│   └── processed/     # Modelli salvati
+├── docs/
+│   └── diary/         # Diario di ricerca giornaliero
+├── tests/
+├── pyproject.toml
+└── README.md
+```
+
+## Setup
+
+```bash
+# Clona il repository
+git clone <repo-url> && cd PythagorasGoal
+
+# Installa dipendenze (richiede uv)
+uv sync
+
+# Scarica dati storici (~70 MB, richiede connessione)
+uv run python -m src.data.downloader
+
+# Esegui la strategia ibrida vincente
+uv run python scripts/13_squeeze_ml_hybrid.py
+```
+
+### Requisiti
+
+- Python ≥ 3.11
+- [uv](https://docs.astral.sh/uv/) come package manager
+- Accesso a Cerbero MCP (`cerbero-mcp.tielogic.xyz`) per i dati Deribit, oppure Binance via ccxt come fallback
+
+## Dati
+
+| Asset | Timeframe | Candele | Copertura |
+|-------|-----------|---------|-----------|
+| BTC | 5m / 15m / 1h | 883K / 294K / 74K | 2018-01 → oggi |
+| ETH | 5m / 15m / 1h | 882K / 294K / 74K | 2018-01 → oggi |
+
+Fonte primaria: Deribit perpetual via Cerbero MCP. Fallback per il periodo antecedente: Binance spot via ccxt. Formato: Apache Parquet.
+
+## Strategie testate
+
+| Script | Approccio | Esito |
+|--------|-----------|-------|
+| 01 | Pattern candlestick discreti (U/D/0) | Nessun edge |
+| 02 | DTW pattern matching | Troppo lento, edge minimo |
+| 03 | Proiezione FFT (ispirata al paper) | Random (49.8%) |
+| 04 | GBM su feature frattali (Hurst, FD) | 63.6% a soglia 0.65 |
+| 05 | GBM multi-window (corretto data leakage) | 58.9% |
+| 06 | GBM su feature strutturali normalizzate | 58.6%, +57.5% return |
+| 07 | LSTM su sequenze candele | 58.4%, comparabile a GBM |
+| 08 | Ensemble multi-timeframe (1h + 15m) | 59.2% (consensus 2/3) |
+| 09 | Walk-forward ML | 57.7%, Sharpe 7.4, €3.12/day |
+| 10 | Ensemble 5 modelli alta precisione | In corso |
+| 11 | **Volatility Squeeze Breakout** | **83.9%**, approccio strutturale |
+| 12 | Report finale e simulazione crescita | — |
+| 13 | **Squeeze + ML ibrida** | **76.9%**, 118% ann, €13.78/day |
+
+## Riferimenti
+
+- Serleto, L. & Malanga, C. — *Pythagoras Trading Prediction* (2024)
+- Serleto, L. & Malanga, C. — *Libro dei Frattali* (2024)
+
+## Licenza
+
+Uso privato. Non destinato alla distribuzione.

docker-compose.yml

@@ -0,0 +1,16 @@
+services:
+  paper-trader:
+    build: .
+    container_name: pythagoras-paper
+    restart: unless-stopped
+    volumes:
+      - ./data:/app/data
+    env_file:
+      - .env
+    environment:
+      - PYTHONUNBUFFERED=1
+    healthcheck:
+      test: ["CMD", "python", "-c", "import json; s=json.load(open('/app/data/paper_trades/status.json')); assert s['last_update']"]
+      interval: 120s
+      timeout: 10s
+      retries: 3

docs/diary/2026-05-27.md

@@ -64,9 +64,99 @@
 | ROI annuo >30% | max ~20% (structural) | serve +10% |
 | €50/giorno da €1000 | richiede ~5% daily | richiede crescita capitale su 6 mesi |
 
+### 01:00 — Strategia 5 corretta (senza leakage)
+
+**Reale dopo fix:** 53-58% accuracy (BTC LA=3 thr=0.65). Massimo 72.7% ma solo 11 trade. Conferma: senza leakage, edge tipico è 55-60%.
+
+### 01:15 — SVOLTA: Strategia 11 — Volatility Squeeze Breakout
+
+**Cosa:** approccio completamente diverso. Non predire la direzione direttamente. Identifica periodi di COMPRESSIONE (Bollinger dentro Keltner = squeeze), poi segui il breakout quando la volatilità ESPLODE.
+**Perché:** dopo compressione, il prezzo accumula "energia" e il breakout ha forte momentum direzionale. Approccio fisicamente motivato, non ML puro.
+**Atteso:** migliore di ML generico perché sfruttiamo un pattern strutturale ben definito
+**Reale:** **ECCEZIONALE**
+
+| Config | Asset | TF | Trades | Accuracy | Ann. Return |
+|---|---|---|---|---|---|
+| BBw=20 sqThr=0.8 +VOL | ETH | 1h | 87 | **83.9%** | 22.2% |
+| BBw=30 sqThr=0.9 | ETH | 1h | 203 | **82.8%** | 46.8% |
+| BBw=20 sqThr=0.8 | ETH | 1h | 285 | **79.3%** | **65.7%** |
+| BBw=14 sqThr=0.8 | BTC | 1h | 438 | **77.6%** | **53.3%** |
+| BBw=14 sqThr=0.8 +VOL | BTC | 15m | 315 | **75.6%** | 6.0% |
+
+**Lezione CRUCIALE:** gli approcci strutturali (compressione→espansione) battono ML generico di 20+ punti percentuali in accuracy. La struttura frattale del prezzo si manifesta nei cicli di compressione-espansione.
+
+### Target assessment
+
+| Target | Risultato | Status |
+|--------|-----------|--------|
+| Accuracy >80% | 83.9% (ETH 1h +VOL) | ✅ RAGGIUNTO |
+| ROI annuo >30% | 65.7% (ETH 1h) | ✅ RAGGIUNTO |
+| Fees considerate | 0.1% maker/taker | ✅ |
+
+### 01:30 — Strategia 9: Walk-forward ML — COMPLETATA
+
+**Cosa:** GBM con features structural+fractal, walk-forward validation (train 15K, step 3K), BTC e ETH su 2 lookahead × 4 threshold
+**Reale:**
+- BTC: max 58.4% acc, +75% ret, 8.8% ann, Sharpe 3.27 (LA=3, thr=0.70)
+- **ETH LA=3 thr=0.70: 57.7% acc, +758% ret, 38.1% ann, Sharpe 7.40, €3.12/giorno**
+- **ETH LA=6 thr=0.70: 56.5% acc, +1994% ret, 57.9% ann, Sharpe 6.72, €8.20/giorno**
+**Lezione:** walk-forward elimina il bias del singolo split. ETH più predittibile di BTC con ML. Sharpe >7 eccezionale per un sistema reale. Drawdown alto (47-52%) → servono nervi saldi.
+
+### TOP 5 DEFINITIVO (aggiornato con strategia 9)
+
+| # | Nome | Acc. | ROI ann | Sharpe | DD | €/day | Best for |
+|---|------|------|---------|--------|----|-------|----------|
+| 1 | ETH Squeeze+Vol (BBw=20) | **83.9%** | 22.2% | - | 2.0% | €0.71 | Precisione |
+| 2 | ETH Squeeze (BBw=30,sq=0.9) | **82.8%** | **46.8%** | - | 3.2% | €1.77 | Bilanciato |
+| 3 | ETH WF-ML (LA=3,thr=0.70) | 57.7% | **38.1%** | **7.40** | 47% | **€3.12** | Daily PnL |
+| 4 | ETH Squeeze aggressivo | 79.3% | **65.7%** | - | 3.6% | €2.79 | Max ROI |
+| 5 | ETH WF-ML (LA=6,thr=0.70) | 56.5% | **57.9%** | **6.72** | 53% | **€8.20** | Max growth |
+
+### Piano operativo consigliato
+
+**Fase 1 (mesi 1-3):** usa M2 (squeeze BBw=30, 82.8% acc, 3.2% DD) per crescita sicura
+**Fase 2 (mesi 4-6):** aggiungi M3 (WF-ML) per accelerare crescita con capitale più alto
+**Fase 3 (mese 6+):** combina entrambi — squeeze per trade sicuri, ML per volume
+
+### 02:00 — Strategia 13: Squeeze + ML IBRIDA — IL VINCITORE
+
+**Cosa:** squeeze breakout come pre-filtro (QUANDO tradare), GBM su features frattali/strutturali come conferma direzionale (QUALE direzione). Walk-forward validation. 12 configurazioni testate su BTC + ETH, 1h + 15m.
+**Atteso:** combinare accuratezza squeeze (>80%) con volume trade ML
+**Reale:**
+
+**Vincitore assoluto: ETH 15m BBw=14 sq=0.8 ml_thr=0.70**
+- 76.9% accuracy, 118.1% annualizzato, 4.2% max drawdown
+- **€13.78/giorno da €1000** (!!)
+- 1213 trades nel test, ~313/anno → ~1 trade/giorno
+- Con 3x leva, 15% position size
+
+**Runner-up: BTC 15m BBw=14 sq=0.9 ml_thr=0.70**
+- 78.8% accuracy, 68.8% ann, 7% DD, €5.51/day
+
+**Osservazioni chiave:**
+1. Il 15m batte il 1h per frequenza trade (più segnali di squeeze a timeframe basso)
+2. ML non migliora drammaticamente l'accuracy rispetto a squeeze puro (baseline ETH 15m squeeze: 79.5%) ma RIDUCE il drawdown (da ~8% a 4.2%)
+3. Il vero valore del ML è nel filtraggio: scarta i breakout deboli, tiene i forti
+4. ETH più predittibile di BTC in tutte le configurazioni
+
+**Piano per €50/giorno:**
+- Capitale attuale: €1000
+- Crescita stimata: 118% annuo
+- €1000 → €3600 in ~8 mesi
+- €3600 × €13.78/€1000 = €49.60/giorno ≈ target
+
+### TOP 5 DEFINITIVO FINALE
+
+| # | Config | Acc. | Ann. | DD | €/day | Tipo |
+|---|--------|------|------|----|-------|------|
+| 1 | ETH 15m Squeeze+ML (BBw=14,sq=0.8,ml=0.70) | 76.9% | **118%** | 4.2% | **€13.78** | Ibrido |
+| 2 | ETH 1h Squeeze+Vol (BBw=20,sq=0.8) | **83.9%** | 22% | 2.0% | €0.71 | Strutturale |
+| 3 | BTC 15m Squeeze+ML (BBw=14,sq=0.9,ml=0.70) | **78.8%** | 69% | 7.0% | €5.51 | Ibrido |
+| 4 | ETH 1h Squeeze (BBw=30,sq=0.9) | **82.8%** | **47%** | 3.2% | €1.77 | Strutturale |
+| 5 | ETH WF-ML (LA=3,thr=0.70) | 57.7% | 38% | 47% | €3.12 | ML puro |
+
 ### Prossimi passi
 
-1. Verificare strategia 5 corretta (senza leakage)
+1. Implementare sistema live con Cerbero MCP per segnali real-time
-2. Risultati strategia 9 (walk-forward) e 10 (high precision ensemble)
+2. Paper trading per 2-4 settimane prima di capitale reale
-3. Se accuracy ancora insufficiente: provare features da 5m aggregati, o approach completamente diverso (reinforcement learning?)
+3. Risk management: stop-loss, max daily loss, correlation filter
-4. Valutare combinazione: multi-asset (BTC+ETH) per diversificazione

scripts/analysis/best_yearly.py

@@ -0,0 +1,309 @@
+"""Confronto migliori strategie S1 e S2 — andamento per anno."""
+from __future__ import annotations
+import sys
+sys.path.insert(0, ".")
+
+import numpy as np
+import pandas as pd
+from src.data.downloader import load_data
+from src.fractal.patterns import encode_candles
+
+FEE_PERP = 0.002  # 0.1% taker roundtrip perpetual
+FEE_OPT = 0.0052  # options roundtrip
+INITIAL = 1000
+LEVERAGE = 3
+
+
+def keltner_ratio(close, high, low, window=14):
+    n = len(close)
+    r = np.full(n, np.nan)
+    for i in range(window, n):
+        wc, wh, wl = close[i-window:i], high[i-window:i], low[i-window:i]
+        ma = np.mean(wc)
+        bb_std = np.std(wc)
+        tr = np.maximum(wh-wl, np.maximum(np.abs(wh-np.roll(wc,1)), np.abs(wl-np.roll(wc,1))))
+        atr = np.mean(tr[1:])
+        kc = (ma+1.5*atr)-(ma-1.5*atr)
+        bb = (ma+2*bb_std)-(ma-2*bb_std)
+        if kc > 0:
+            r[i] = bb/kc
+    return r
+
+
+def rv_ann(close, window):
+    lr = np.diff(np.log(np.where(close==0, 1e-10, close)))
+    r = np.full(len(close), np.nan)
+    for i in range(window, len(lr)):
+        r[i+1] = np.std(lr[i-window:i]) * np.sqrt(24*365)
+    return r
+
+
+def rsi(close, period=14):
+    delta = np.diff(close)
+    gain = np.where(delta>0, delta, 0)
+    loss = np.where(delta<0, -delta, 0)
+    result = np.full(len(close), 50.0)
+    if len(gain) < period:
+        return result
+    ag = np.mean(gain[:period])
+    al = np.mean(loss[:period])
+    for i in range(period, len(delta)):
+        ag = (ag*(period-1)+gain[i])/period
+        al = (al*(period-1)+loss[i])/period
+        result[i+1] = 100 if al == 0 else 100-100/(1+ag/al)
+    return result
+
+
+def ema(arr, period):
+    r = np.full(len(arr), np.nan)
+    k = 2/(period+1)
+    r[period-1] = np.mean(arr[:period])
+    for i in range(period, len(arr)):
+        r[i] = arr[i]*k + r[i-1]*(1-k)
+    return r
+
+
+# =====================================================================
+#  S1 BEST: Squeeze Breakout ETH 1h (BBw=14, sq=0.8, brk=3)
+# =====================================================================
+def run_s1_squeeze(asset, tf):
+    df = load_data(asset, tf)
+    c, h, l, v = df["close"].values, df["high"].values, df["low"].values, df["volume"].values
+    n = len(c)
+    ts = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
+    kcr = keltner_ratio(c, h, l, 14)
+
+    yearly = {}
+    in_sq = False
+    sq_start = 0
+
+    for i in range(15, n):
+        if np.isnan(kcr[i]):
+            continue
+        is_sq = kcr[i] < 0.8
+        if is_sq and not in_sq:
+            in_sq = True
+            sq_start = i
+        elif not is_sq and in_sq:
+            in_sq = False
+            if i - sq_start < 5 or i + 3 >= n:
+                continue
+            first_ret = (c[i] - c[i-1]) / c[i-1]
+            if abs(first_ret) < 0.001:
+                continue
+            direction = 1 if first_ret > 0 else -1
+            actual = (c[i+2] - c[i-1]) / c[i-1]
+            trade_ret = actual * direction
+            net = trade_ret * LEVERAGE - FEE_PERP * LEVERAGE
+
+            year = ts.iloc[i].year
+            if year not in yearly:
+                yearly[year] = {"pnls": [], "wins": 0, "total": 0}
+            yearly[year]["pnls"].append(net)
+            yearly[year]["total"] += 1
+            if trade_ret > 0:
+                yearly[year]["wins"] += 1
+
+    return yearly
+
+
+# =====================================================================
+#  S1 BEST ALT: Squeeze+ML hybrid ETH 15m
+# =====================================================================
+# Troppo complesso da ricalcolare (serve ML training). Uso i dati S1 squeeze puro.
+
+
+# =====================================================================
+#  S2 BEST: VRP ETH 48h (con IV stimata, unico disponibile su 8 anni)
+# =====================================================================
+def run_s2_vrp(asset, dte=48):
+    df = load_data(asset, "1h")
+    c = df["close"].values
+    n = len(c)
+    ts = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
+    rv_24 = rv_ann(c, 24)
+    rv_168 = rv_ann(c, 168)
+
+    yearly = {}
+    for i in range(170, n - dte):
+        if ts.iloc[i].hour != 8:
+            continue
+        rv_s, rv_l = rv_24[i], rv_168[i]
+        if np.isnan(rv_s) or np.isnan(rv_l) or rv_s < 0.05 or rv_l < 0.05:
+            continue
+        regime = rv_s / rv_l
+        iv_pf = 0.9 if regime > 2 else (1.0 if regime > 1.5 else (1.1 if regime > 1 else 1.2))
+        iv = rv_l * iv_pf
+        prem = iv * np.sqrt(dte/(24*365)) * 0.8
+        spot = c[i]
+        move = abs(c[min(i+dte, n-1)] - spot) / spot
+        pos = 0.10
+        raw = (prem - move) * pos if move <= prem else max(-(move-prem)*pos, -pos*0.05)
+        net = raw - FEE_OPT * pos
+
+        year = ts.iloc[i].year
+        if year not in yearly:
+            yearly[year] = {"pnls": [], "wins": 0, "total": 0}
+        yearly[year]["pnls"].append(net)
+        yearly[year]["total"] += 1
+        if raw > 0:
+            yearly[year]["wins"] += 1
+    return yearly
+
+
+# =====================================================================
+#  S2 BEST PERPETUAL: Multi-TF 15m+1h BTC
+# =====================================================================
+def run_s2_multitf(asset):
+    df_1h = load_data(asset, "1h")
+    df_15m = load_data(asset, "15m")
+    c1h = df_1h["close"].values
+    ts1h = pd.to_datetime(df_1h["timestamp"], unit="ms", utc=True)
+    c15 = df_15m["close"].values
+    ts15 = df_15m["timestamp"].values
+    n15 = len(c15)
+
+    ema_50 = ema(c1h, 50)
+    rsi_15m = rsi(c15, 14)
+
+    yearly = {}
+    daily_done = set()
+
+    for i in range(100, n15 - 12):
+        ts_dt = pd.Timestamp(ts15[i], unit="ms", tz="UTC")
+        day = ts_dt.strftime("%Y-%m-%d")
+        if day in daily_done:
+            continue
+        if rsi_15m[i] > 35 and rsi_15m[i] < 65:
+            continue
+        h_idx = np.searchsorted(ts1h.values.astype("int64"), ts15[i]) - 1
+        if h_idx < 50 or h_idx >= len(c1h) or np.isnan(ema_50[h_idx]):
+            continue
+
+        direction = None
+        if rsi_15m[i] < 30 and c1h[h_idx] > ema_50[h_idx]:
+            direction = "long"
+        elif rsi_15m[i] > 70 and c1h[h_idx] < ema_50[h_idx]:
+            direction = "short"
+        if direction is None:
+            continue
+
+        entry = c15[i]
+        exit_price = c15[min(i+12, n15-1)]
+        trade_ret = (exit_price-entry)/entry if direction == "long" else (entry-exit_price)/entry
+        net = trade_ret * LEVERAGE - FEE_PERP * LEVERAGE
+
+        year = ts_dt.year
+        if year not in yearly:
+            yearly[year] = {"pnls": [], "wins": 0, "total": 0}
+        yearly[year]["pnls"].append(net)
+        yearly[year]["total"] += 1
+        if trade_ret > 0:
+            yearly[year]["wins"] += 1
+        daily_done.add(day)
+    return yearly
+
+
+# =====================================================================
+#  REPORT
+# =====================================================================
+strategies = {
+    "S1: Squeeze BTC 1h":       run_s1_squeeze("BTC", "1h"),
+    "S1: Squeeze ETH 1h":       run_s1_squeeze("ETH", "1h"),
+    "S1: Squeeze ETH 15m":      run_s1_squeeze("ETH", "15m"),
+    "S2: VRP ETH 48h (IV est)": run_s2_vrp("ETH", 48),
+    "S2: VRP BTC 48h (IV est)": run_s2_vrp("BTC", 48),
+    "S2: MultiTF BTC 15m+1h":   run_s2_multitf("BTC"),
+    "S2: MultiTF ETH 15m+1h":   run_s2_multitf("ETH"),
+}
+
+all_years = sorted(set(y for v in strategies.values() for y in v))
+
+print("=" * 120)
+print("  MIGLIORI STRATEGIE — ANDAMENTO PER ANNO")
+print("  Fee reali. PnL su €1000 flat (no compounding). Dati OHLCV reali 2018-2026.")
+print("  ⚠ VRP usa IV STIMATA (non reale) — fidarsi solo dei dati perpetual per backtest lungo")
+print("=" * 120)
+
+# Header
+hdr = f"  {'Anno':>6s}"
+for name in strategies:
+    short = name.split(": ")[1][:18]
+    hdr += f" | {short:>18s}"
+print(hdr)
+print(f"  {'-' * (len(hdr) - 2)}")
+
+# Per anno: accuracy / PnL totale
+for year in all_years:
+    row_acc = f"  {year:>6d}"
+    row_pnl = f"  {'':>6s}"
+    for name, yearly in strategies.items():
+        if year in yearly:
+            d = yearly[year]
+            acc = d["wins"]/d["total"]*100 if d["total"] > 0 else 0
+            pnl = sum(d["pnls"]) * INITIAL
+            tag = "▓" if acc >= 75 else "▒" if acc >= 65 else "░" if acc >= 55 else " "
+            row_acc += f" | {acc:>5.1f}% {tag} {d['total']:>3d}t"
+            row_pnl += f" | €{pnl:>+8.0f}      "
+        else:
+            row_acc += f" | {'—':>18s}"
+            row_pnl += f" | {'':>18s}"
+    print(row_acc)
+    print(row_pnl)
+
+# Totali
+print(f"  {'-' * (len(hdr) - 2)}")
+row_tot = f"  {'TOT':>6s}"
+for name, yearly in strategies.items():
+    all_pnls = [p for d in yearly.values() for p in d["pnls"]]
+    all_wins = sum(d["wins"] for d in yearly.values())
+    all_total = sum(d["total"] for d in yearly.values())
+    acc = all_wins/all_total*100 if all_total > 0 else 0
+    pnl = sum(all_pnls) * INITIAL
+    row_tot += f" | {acc:>5.1f}%   {all_total:>4d}t"
+print(row_tot)
+
+row_pnl_tot = f"  {'€TOT':>6s}"
+for name, yearly in strategies.items():
+    all_pnls = [p for d in yearly.values() for p in d["pnls"]]
+    pnl = sum(all_pnls) * INITIAL
+    row_pnl_tot += f" | €{pnl:>+8.0f}      "
+print(row_pnl_tot)
+
+# Compounding
+print(f"\n  {'':>6s}", end="")
+for name in strategies:
+    short = name.split(": ")[1][:18]
+    print(f" | {short:>18s}", end="")
+print()
+
+row_comp = f"  {'COMP':>6s}"
+for name, yearly in strategies.items():
+    cap = float(INITIAL)
+    for year in sorted(yearly):
+        for pnl in yearly[year]["pnls"]:
+            cap += cap * pnl
+            cap = max(cap, 10)
+    row_comp += f" | €{cap:>12,.0f}    "
+print(row_comp)
+
+# Drawdown
+row_dd = f"  {'MAXDD':>6s}"
+for name, yearly in strategies.items():
+    cap = float(INITIAL)
+    peak = cap
+    mdd = 0
+    for year in sorted(yearly):
+        for pnl in yearly[year]["pnls"]:
+            cap += cap * pnl
+            cap = max(cap, 10)
+            if cap > peak: peak = cap
+            dd = (peak - cap) / peak
+            mdd = max(mdd, dd)
+    row_dd += f" | {mdd*100:>12.1f}%   "
+print(row_dd)
+
+# Legenda
+print(f"\n  Legenda: ▓ ≥75% acc  ▒ ≥65% acc  ░ ≥55% acc")
+print(f"  ⚠ S2 VRP: IV stimata (rv_long × 1.0-1.2), NON dati reali opzioni")
+print(f"  S1 Squeeze e S2 MultiTF: dati OHLCV reali al 100%")

scripts/analysis/compare_strategies.py

@@ -0,0 +1,559 @@
+"""Analisi finale — S1 (squeeze puro) vs Script 13 (squeeze+ML GBM).
+Metriche: PnL, num trades, DD max, tempo medio a mercato, descrizione.
+"""
+from __future__ import annotations
+import sys
+sys.path.insert(0, ".")
+
+import numpy as np
+import pandas as pd
+from sklearn.ensemble import GradientBoostingClassifier
+from sklearn.preprocessing import StandardScaler
+from src.data.downloader import load_data
+from src.fractal.patterns import encode_candles
+
+FEE_PERP = 0.002
+FEE_ML = 0.001
+INITIAL = 1000
+LEVERAGE = 3
+
+TF_MINUTES = {"1m": 1, "5m": 5, "15m": 15, "1h": 60, "4h": 240, "1d": 1440}
+
+
+# ── helpers ──────────────────────────────────────────────────────────
+
+def keltner_ratio(close, high, low, window=14):
+    n = len(close)
+    r = np.full(n, np.nan)
+    for i in range(window, n):
+        wc, wh, wl = close[i-window:i], high[i-window:i], low[i-window:i]
+        ma = np.mean(wc)
+        bb_std = np.std(wc)
+        tr = np.maximum(wh-wl, np.maximum(np.abs(wh-np.roll(wc,1)), np.abs(wl-np.roll(wc,1))))
+        atr = np.mean(tr[1:])
+        kc = (ma+1.5*atr)-(ma-1.5*atr)
+        bb = (ma+2*bb_std)-(ma-2*bb_std)
+        if kc > 0:
+            r[i] = bb/kc
+    return r
+
+
+def detect_squeezes(close, high, low, kcr, sq_thr=0.8, min_dur=5):
+    events = []
+    in_sq = False
+    sq_start = 0
+    for i in range(1, len(close)):
+        if np.isnan(kcr[i]):
+            continue
+        is_sq = kcr[i] < sq_thr
+        if is_sq and not in_sq:
+            in_sq = True
+            sq_start = i
+        elif not is_sq and in_sq:
+            in_sq = False
+            dur = i - sq_start
+            if dur < min_dur:
+                continue
+            events.append({"idx": i, "dur": dur, "sq_start": sq_start,
+                           "avg_vol_squeeze": np.mean(close[sq_start:i]),
+                           "kcr_at_release": kcr[i]})
+    return events
+
+
+def _build_result(yearly, capital, max_dd, all_t, all_w, time_pct, avg_dur_h):
+    acc = all_w / all_t * 100
+    tot_pnl = sum(p for d in yearly.values() for p in d["pnls"])
+    years_active = len(yearly)
+    all_pnls = [p for d in yearly.values() for p in d["pnls"]]
+    sharpe = np.mean(all_pnls) / np.std(all_pnls) * np.sqrt(252) if len(all_pnls) > 1 and np.std(all_pnls) > 0 else 0
+
+    year_details = {}
+    for y in sorted(yearly):
+        d = yearly[y]
+        ya = d["w"] / d["t"] * 100 if d["t"] > 0 else 0
+        yp = sum(d["pnls"])
+        year_details[y] = {"trades": d["t"], "acc": ya, "pnl": yp}
+
+    valid_years = {y: d for y, d in year_details.items() if d["trades"] >= 10}
+    if valid_years:
+        worst_y = min(valid_years, key=lambda y: valid_years[y]["acc"])
+        worst_acc = valid_years[worst_y]["acc"]
+    elif year_details:
+        worst_y = min(year_details, key=lambda y: year_details[y]["acc"])
+        worst_acc = year_details[worst_y]["acc"]
+    else:
+        worst_y = "N/A"
+        worst_acc = 0
+
+    daily_pnl = tot_pnl / (years_active * 365) if years_active > 0 else 0
+
+    return {
+        "trades": all_t, "acc": acc, "pnl": tot_pnl, "capital": capital,
+        "max_dd": max_dd * 100, "sharpe": sharpe, "daily_pnl": daily_pnl,
+        "time_in_market_pct": time_pct, "avg_dur_h": avg_dur_h,
+        "years_active": years_active, "worst_year": str(worst_y),
+        "worst_acc": worst_acc, "year_details": year_details,
+    }
+
+
+# ── S1: Squeeze breakout puro ────────────────────────────────────────
+
+def run_s1_squeeze(asset, tf, hold=3):
+    df = load_data(asset, tf)
+    c, h, l, v = df["close"].values, df["high"].values, df["low"].values, df["volume"].values
+    n = len(c)
+    ts = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
+    kcr = keltner_ratio(c, h, l, 14)
+    events = detect_squeezes(c, h, l, kcr)
+
+    yearly = {}
+    capital = float(INITIAL)
+    peak = capital
+    max_dd = 0
+    total_bars = 0
+
+    for ev in events:
+        i = ev["idx"]
+        if i + hold + 1 >= n:
+            continue
+        first_ret = (c[i] - c[i-1]) / c[i-1] if c[i-1] > 0 else 0
+        if abs(first_ret) < 0.001:
+            continue
+        direction = 1 if first_ret > 0 else -1
+        entry = c[i-1]
+        exit_price = c[min(i + hold - 1, n - 1)]
+        actual = (exit_price - entry) / entry * direction
+        net = actual * LEVERAGE - FEE_PERP * LEVERAGE
+
+        capital += capital * 0.15 * net
+        capital = max(capital, 10)
+        if capital > peak: peak = capital
+        dd = (peak - capital) / peak
+        max_dd = max(max_dd, dd)
+        total_bars += hold
+
+        year = ts.iloc[i].year
+        if year not in yearly:
+            yearly[year] = {"w": 0, "t": 0, "pnls": []}
+        yearly[year]["t"] += 1
+        if actual > 0: yearly[year]["w"] += 1
+        yearly[year]["pnls"].append(net * INITIAL)
+
+    all_t = sum(d["t"] for d in yearly.values())
+    all_w = sum(d["w"] for d in yearly.values())
+    if all_t == 0: return None
+    return _build_result(yearly, capital, max_dd, all_t, all_w,
+                         total_bars / n * 100, hold * TF_MINUTES.get(tf, 60) / 60)
+
+
+def run_s1_antifake_vol(asset, tf, hold=3):
+    df = load_data(asset, tf)
+    c, h, l, v = df["close"].values, df["high"].values, df["low"].values, df["volume"].values
+    n = len(c)
+    ts = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
+    kcr = keltner_ratio(c, h, l, 14)
+    events = detect_squeezes(c, h, l, kcr)
+
+    yearly = {}
+    capital = float(INITIAL)
+    peak = capital
+    max_dd = 0
+    total_bars = 0
+
+    for ev in events:
+        i = ev["idx"]
+        if i + hold + 1 >= n:
+            continue
+        first_ret = (c[i] - c[i-1]) / c[i-1] if c[i-1] > 0 else 0
+        if abs(first_ret) < 0.001:
+            continue
+        br = h[i] - l[i]
+        if br > 0:
+            if c[i] > c[i-1]:
+                if (h[i] - c[i]) / br > 0.6:
+                    continue
+            else:
+                if (c[i] - l[i]) / br > 0.6:
+                    continue
+        avg_v = np.mean(v[ev["sq_start"]:i])
+        if avg_v > 0 and v[i] <= avg_v * 1.3:
+            continue
+
+        direction = 1 if first_ret > 0 else -1
+        entry = c[i-1]
+        exit_price = c[min(i + hold - 1, n - 1)]
+        actual = (exit_price - entry) / entry * direction
+        net = actual * LEVERAGE - FEE_PERP * LEVERAGE
+        capital += capital * 0.15 * net
+        capital = max(capital, 10)
+        if capital > peak: peak = capital
+        dd = (peak - capital) / peak
+        max_dd = max(max_dd, dd)
+        total_bars += hold
+
+        year = ts.iloc[i].year
+        if year not in yearly:
+            yearly[year] = {"w": 0, "t": 0, "pnls": []}
+        yearly[year]["t"] += 1
+        if actual > 0: yearly[year]["w"] += 1
+        yearly[year]["pnls"].append(net * INITIAL)
+
+    all_t = sum(d["t"] for d in yearly.values())
+    all_w = sum(d["w"] for d in yearly.values())
+    if all_t == 0: return None
+    return _build_result(yearly, capital, max_dd, all_t, all_w,
+                         total_bars / n * 100, hold * TF_MINUTES.get(tf, 60) / 60)
+
+
+# ── Script 13: Squeeze + ML ibrida (GBM walk-forward) ────────────────
+
+def build_features_at(df, i, squeeze_info):
+    if i < 100:
+        return None
+    o = df["open"].values
+    h = df["high"].values
+    l = df["low"].values
+    c = df["close"].values
+    v = df["volume"].values
+    feats = []
+    for w in [12, 24, 48]:
+        win_c = c[i-w:i]
+        win_o = o[i-w:i]
+        win_h = h[i-w:i]
+        win_l = l[i-w:i]
+        win_v = v[i-w:i]
+        mn, mx = win_l.min(), max(win_h.max(), win_c.max())
+        rng = mx - mn if mx - mn > 0 else 1e-10
+        total = win_h - win_l
+        total = np.where(total == 0, 1e-10, total)
+        body = np.abs(win_c - win_o) / total
+        direction = np.sign(win_c - win_o)
+        log_c = np.log(np.where(win_c == 0, 1e-10, win_c))
+        rets = np.diff(log_c)
+        v_mean = np.mean(win_v)
+        feats.extend([
+            np.mean(rets) if len(rets) > 0 else 0,
+            np.std(rets) if len(rets) > 0 else 0,
+            np.sum(rets) if len(rets) > 0 else 0,
+            float(pd.Series(rets).skew()) if len(rets) > 2 else 0,
+            float(pd.Series(rets).kurtosis()) if len(rets) > 3 else 0,
+            np.mean(body), np.std(body),
+            np.mean(direction), np.mean(direction[-min(3, w):]),
+            (win_c[-1] - mn) / rng,
+            win_v[-1] / v_mean if v_mean > 0 else 1,
+            np.corrcoef(rets[:-1], rets[1:])[0, 1] if len(rets) > 1 and np.std(rets) > 0 else 0,
+        ])
+    sq = squeeze_info
+    feats.extend([
+        sq["dur"], sq["dur"] / 24, sq["kcr_at_release"],
+        v[i-1] / sq["avg_vol_squeeze"] if sq["avg_vol_squeeze"] > 0 else 1,
+        np.mean(v[i:min(i+3, len(v))]) / sq["avg_vol_squeeze"] if sq["avg_vol_squeeze"] > 0 else 1,
+    ])
+    h48 = np.max(h[max(0, i-48):i])
+    l48 = np.min(l[max(0, i-48):i])
+    r48 = h48 - l48
+    feats.append((c[i-1] - l48) / r48 if r48 > 0 else 0.5)
+    tr = np.maximum(h[i-14:i] - l[i-14:i],
+                    np.maximum(np.abs(h[i-14:i] - np.roll(c[i-14:i], 1)),
+                               np.abs(l[i-14:i] - np.roll(c[i-14:i], 1))))
+    atr = np.mean(tr[1:])
+    feats.append(atr / c[i-1] if c[i-1] > 0 else 0)
+    first_ret = (c[i] - c[i-1]) / c[i-1] if c[i-1] > 0 else 0
+    feats.append(first_ret)
+    return np.nan_to_num(np.array(feats), nan=0, posinf=1e6, neginf=-1e6)
+
+
+def run_s13_hybrid(asset, tf, bb_w, sq_thr, brk_bars, leverage, pos_pct, ml_thr):
+    df = load_data(asset, tf)
+    close = df["close"].values
+    high = df["high"].values
+    low = df["low"].values
+    volume = df["volume"].values
+    n = len(df)
+    ts = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
+
+    kcr = keltner_ratio(close, high, low, bb_w)
+    events = detect_squeezes(close, high, low, kcr, sq_thr)
+
+    X_all, y_all, ev_all = [], [], []
+    for ev in events:
+        i = ev["idx"]
+        if i + brk_bars >= n or i < 100:
+            continue
+        feats = build_features_at(df, i, ev)
+        if feats is None:
+            continue
+        actual_ret = (close[i + brk_bars - 1] - close[i - 1]) / close[i - 1]
+        X_all.append(feats)
+        y_all.append(1 if actual_ret > 0 else 0)
+        ev_all.append(ev)
+
+    if len(X_all) < 50:
+        return None
+
+    X = np.array(X_all)
+    y = np.array(y_all)
+
+    TRAIN_SIZE = max(int(len(X) * 0.5), 50)
+    STEP_SIZE = max(int(len(X) * 0.1), 10)
+
+    yearly = {}
+    capital = float(INITIAL)
+    peak = capital
+    max_dd = 0
+    total_bars = 0
+    all_t = 0
+    all_w = 0
+
+    start = 0
+    while start + TRAIN_SIZE + STEP_SIZE <= len(X):
+        train_end = start + TRAIN_SIZE
+        test_end = min(train_end + STEP_SIZE, len(X))
+        X_tr, y_tr = X[start:train_end], y[start:train_end]
+        X_te = X[train_end:test_end]
+
+        if len(np.unique(y_tr)) < 2:
+            start += STEP_SIZE
+            continue
+
+        scaler = StandardScaler()
+        X_tr_s = scaler.fit_transform(X_tr)
+        X_te_s = scaler.transform(X_te)
+
+        model = GradientBoostingClassifier(
+            n_estimators=150, max_depth=4, min_samples_leaf=10,
+            learning_rate=0.05, subsample=0.8, random_state=42,
+        )
+        model.fit(X_tr_s, y_tr)
+
+        up_idx = list(model.classes_).index(1) if 1 in model.classes_ else -1
+        if up_idx < 0:
+            start += STEP_SIZE
+            continue
+
+        for j in range(len(X_te)):
+            proba = model.predict_proba(X_te_s[j:j+1])[0]
+            p_up = proba[up_idx]
+
+            ev = ev_all[train_end + j]
+            i = ev["idx"]
+            actual_ret = (close[i + brk_bars - 1] - close[i - 1]) / close[i - 1]
+
+            direction = None
+            if p_up >= ml_thr:
+                direction = 1
+            elif p_up <= (1 - ml_thr):
+                direction = -1
+            if direction is None:
+                continue
+
+            is_correct = (direction == 1 and actual_ret > 0) or (direction == -1 and actual_ret < 0)
+            trade_ret = actual_ret * direction
+            net = trade_ret * leverage - FEE_ML * 2 * leverage
+            capital += capital * pos_pct * net
+            capital = max(capital, 10)
+            if capital > peak: peak = capital
+            dd = (peak - capital) / peak
+            max_dd = max(max_dd, dd)
+            total_bars += brk_bars
+
+            all_t += 1
+            if is_correct: all_w += 1
+
+            year = ts.iloc[i].year
+            if year not in yearly:
+                yearly[year] = {"w": 0, "t": 0, "pnls": []}
+            yearly[year]["t"] += 1
+            if is_correct: yearly[year]["w"] += 1
+            yearly[year]["pnls"].append(net * INITIAL)
+
+        start += STEP_SIZE
+
+    if all_t == 0:
+        return None
+    return _build_result(yearly, capital, max_dd, all_t, all_w,
+                         total_bars / n * 100, brk_bars * TF_MINUTES.get(tf, 60) / 60)
+
+
+# ═══════════════════════════════════════════════════════════════════
+#  ESECUZIONE
+# ═══════════════════════════════════════════════════════════════════
+
+print("Calcolo in corso...\n")
+
+strategies = []
+
+def add(name, desc, cat, result):
+    if result and result["trades"] >= 20:
+        strategies.append({"name": name, "desc": desc, "cat": cat, **result})
+
+# ── S1: Squeeze puro ────────────────────────────────────────────
+add("S1 Squeeze BTC 15m",      "Squeeze breakout puro, BBw=14, hold 3×15m, leva 3x",
+    "S1", run_s1_squeeze("BTC", "15m"))
+add("S1 Squeeze ETH 15m",      "Squeeze breakout puro, BBw=14, hold 3×15m, leva 3x",
+    "S1", run_s1_squeeze("ETH", "15m"))
+add("S1 Squeeze BTC 1h",       "Squeeze breakout puro, BBw=14, hold 3×1h, leva 3x",
+    "S1", run_s1_squeeze("BTC", "1h"))
+add("S1 Squeeze ETH 1h",       "Squeeze breakout puro, BBw=14, hold 3×1h, leva 3x",
+    "S1", run_s1_squeeze("ETH", "1h"))
+add("S1 AF+Vol BTC 15m",       "Squeeze + antifakeout + volume confirm >1.3× media",
+    "S1", run_s1_antifake_vol("BTC", "15m"))
+add("S1 AF+Vol ETH 15m",       "Squeeze + antifakeout + volume confirm >1.3× media",
+    "S1", run_s1_antifake_vol("ETH", "15m"))
+add("S1 AF+Vol BTC 1h",        "Squeeze + antifakeout + volume confirm >1.3× media",
+    "S1", run_s1_antifake_vol("BTC", "1h"))
+add("S1 AF+Vol ETH 1h",        "Squeeze + antifakeout + volume confirm >1.3× media",
+    "S1", run_s1_antifake_vol("ETH", "1h"))
+
+# ── Script 13: Squeeze + ML (GBM walk-forward) ─────────────────
+print("  Training ML models...")
+add("S13 ETH 15m bb14 ml70",   "Squeeze+GBM walk-forward, BBw=14 sq=0.8 ml≥0.70, 3x leva 15% pos",
+    "S13", run_s13_hybrid("ETH", "15m", 14, 0.8, 3, 3, 0.15, 0.70))
+add("S13 ETH 15m bb14 ml65",   "Squeeze+GBM walk-forward, BBw=14 sq=0.8 ml≥0.65, 3x leva 15% pos",
+    "S13", run_s13_hybrid("ETH", "15m", 14, 0.8, 3, 3, 0.15, 0.65))
+add("S13 ETH 15m bb20 ml70",   "Squeeze+GBM walk-forward, BBw=20 sq=0.9 ml≥0.70, 3x leva 15% pos",
+    "S13", run_s13_hybrid("ETH", "15m", 20, 0.9, 3, 3, 0.15, 0.70))
+add("S13 BTC 15m bb14 ml70",   "Squeeze+GBM walk-forward, BBw=14 sq=0.9 ml≥0.70, 3x leva 15% pos",
+    "S13", run_s13_hybrid("BTC", "15m", 14, 0.9, 3, 3, 0.15, 0.70))
+add("S13 BTC 15m bb14 ml65",   "Squeeze+GBM walk-forward, BBw=14 sq=0.9 ml≥0.65, 3x leva 15% pos",
+    "S13", run_s13_hybrid("BTC", "15m", 14, 0.9, 3, 3, 0.15, 0.65))
+add("S13 BTC 1h bb14 ml70",    "Squeeze+GBM walk-forward, BBw=14 sq=0.8 ml≥0.70, 3x leva 20% pos",
+    "S13", run_s13_hybrid("BTC", "1h", 14, 0.8, 3, 3, 0.20, 0.70))
+add("S13 BTC 1h bb14 ml65",    "Squeeze+GBM walk-forward, BBw=14 sq=0.8 ml≥0.65, 3x leva 20% pos",
+    "S13", run_s13_hybrid("BTC", "1h", 14, 0.8, 3, 3, 0.20, 0.65))
+add("S13 ETH 1h bb14 ml70",    "Squeeze+GBM walk-forward, BBw=14 sq=0.8 ml≥0.70, 3x leva 20% pos",
+    "S13", run_s13_hybrid("ETH", "1h", 14, 0.8, 3, 3, 0.20, 0.70))
+
+strategies.sort(key=lambda x: x["acc"], reverse=True)
+
+
+# ═══════════════════════════════════════════════════════════════════
+#  TABELLA 1: Classifica
+# ═══════════════════════════════════════════════════════════════════
+
+W = 150
+print("=" * W)
+print("  S1 (SQUEEZE PURO) vs S13 (SQUEEZE + GBM) — CLASSIFICA FINALE")
+print(f"  Fee: 0.2% RT. Dati OHLCV reali 2018-2026. Position 15%. Leva 3x.")
+print("=" * W)
+hdr = (f"  {'#':>2s}  {'Cat':>3s}  {'Nome':<26s} {'Trades':>6s} {'Acc':>6s} "
+       f"{'PnL€':>9s} {'DD%':>6s} {'€/day':>7s} {'Sharpe':>7s} "
+       f"{'Mkt%':>5s} {'Dur':>6s} {'Worst':>12s} {'Anni':>4s}")
+print(hdr)
+print(f"  {'─'*(W-4)}")
+
+for idx, s in enumerate(strategies, 1):
+    worst = f"{s['worst_year']}({s['worst_acc']:.0f}%)"
+    dur_str = f"{s['avg_dur_h']:.0f}h" if s['avg_dur_h'] >= 1 else f"{s['avg_dur_h']*60:.0f}m"
+    tag = " ★★" if s["acc"] >= 78 else " ★" if s["acc"] >= 76 else ""
+    print(f"  {idx:>2d}  {s['cat']:>3s}  {s['name']:<26s} {s['trades']:>6d} {s['acc']:>5.1f}% "
+          f"€{s['pnl']:>+8.0f} {s['max_dd']:>5.1f}% {s['daily_pnl']:>+6.2f} {s['sharpe']:>7.2f} "
+          f"{s['time_in_market_pct']:>4.1f}% {dur_str:>6s} {worst:>12s} {s['years_active']:>4d}{tag}")
+
+
+# ═══════════════════════════════════════════════════════════════════
+#  TABELLA 2: Descrizione
+# ═══════════════════════════════════════════════════════════════════
+
+print(f"\n\n{'=' * W}")
+print("  DESCRIZIONE")
+print(f"{'=' * W}")
+print(f"  {'#':>2s}  {'Nome':<26s}  {'Descrizione'}")
+print(f"  {'─'*(W-4)}")
+for idx, s in enumerate(strategies, 1):
+    print(f"  {idx:>2d}  {s['name']:<26s}  {s['desc']}")
+
+
+# ═══════════════════════════════════════════════════════════════════
+#  TABELLA 3: Breakdown per anno
+# ═══════════════════════════════════════════════════════════════════
+
+top_n = min(12, len(strategies))
+top = strategies[:top_n]
+all_years = sorted(set(y for s in top for y in s["year_details"]))
+
+print(f"\n\n{'=' * W}")
+print(f"  BREAKDOWN PER ANNO — TOP {top_n} (accuracy% / trades)")
+print(f"{'=' * W}")
+
+header = f"  {'Nome':<26s}"
+for y in all_years:
+    header += f"  {y:>10d}"
+print(header)
+print(f"  {'─'*(W-4)}")
+
+for s in top:
+    line = f"  {s['name']:<26s}"
+    for y in all_years:
+        if y in s["year_details"]:
+            d = s["year_details"][y]
+            line += f"  {d['acc']:>4.0f}%/{d['trades']:<4d}"
+        else:
+            line += f"  {'—':>10s}"
+    print(line)
+
+
+# ═══════════════════════════════════════════════════════════════════
+#  TABELLA 4: Robustezza
+# ═══════════════════════════════════════════════════════════════════
+
+print(f"\n\n{'=' * W}")
+print(f"  ANALISI ROBUSTEZZA")
+print(f"{'=' * W}")
+print(f"  {'#':>2s}  {'Nome':<26s} {'MinAcc':>7s} {'MaxAcc':>7s} {'Spread':>7s} "
+      f"{'AnniOK':>7s} {'€/trade':>8s} {'Verdict':<12s}")
+print(f"  {'─'*90}")
+
+for idx, s in enumerate(strategies, 1):
+    yd = s["year_details"]
+    valid = {y: d for y, d in yd.items() if d["trades"] >= 10}
+    accs = [d["acc"] for d in (valid if valid else yd).values()]
+    if not accs:
+        continue
+    min_a, max_a = min(accs), max(accs)
+    spread = max_a - min_a
+    years_ok = sum(1 for a in accs if a >= 70)
+    avg_pnl = s["pnl"] / s["trades"] if s["trades"] > 0 else 0
+    n_valid = len(valid if valid else yd)
+
+    if n_valid < 4:
+        verdict = "⚠ CORTO"
+    elif min_a < 60:
+        verdict = "⚠ FRAGILE"
+    elif min_a >= 72 and s["acc"] >= 77:
+        verdict = "✅ SOLIDO"
+    elif min_a >= 65 and s["acc"] >= 74:
+        verdict = "~ BUONO"
+    else:
+        verdict = "~ OK"
+
+    print(f"  {idx:>2d}  {s['name']:<26s} {min_a:>6.1f}% {max_a:>6.1f}% {spread:>6.1f}% "
+          f"{years_ok:>3d}/{n_valid:<3d} €{avg_pnl:>+7.1f} {verdict:<12s}")
+
+
+# ═══════════════════════════════════════════════════════════════════
+#  VERDETTO
+# ═══════════════════════════════════════════════════════════════════
+
+print(f"\n\n{'=' * W}")
+print(f"  VERDETTO FINALE")
+print(f"{'=' * W}")
+
+solidi = [s for s in strategies if s["trades"] >= 200 and s["years_active"] >= 5 and s["worst_acc"] >= 65]
+solidi_s1 = [s for s in solidi if s["cat"] == "S1"]
+solidi_ml = [s for s in solidi if s["cat"] == "S13"]
+solidi_s1.sort(key=lambda x: x["acc"], reverse=True)
+solidi_ml.sort(key=lambda x: x["daily_pnl"], reverse=True)
+
+if solidi_s1:
+    b = solidi_s1[0]
+    print(f"\n  MIGLIORE S1 (regole pure, facile da deployare):")
+    print(f"     {b['name']} — {b['acc']:.1f}% acc, {b['trades']} trades, DD {b['max_dd']:.1f}%, €{b['daily_pnl']:+.2f}/day, Sharpe {b['sharpe']:.2f}")
+
+if solidi_ml:
+    m = solidi_ml[0]
+    print(f"\n  MIGLIORE S13 (squeeze+GBM, più complesso):")
+    print(f"     {m['name']} — {m['acc']:.1f}% acc, {m['trades']} trades, DD {m['max_dd']:.1f}%, €{m['daily_pnl']:+.2f}/day, Sharpe {m['sharpe']:.2f}")
+
+max_pnl = max(strategies, key=lambda x: x["pnl"])
+print(f"\n  MAX PnL: {max_pnl['name']} — €{max_pnl['pnl']:+,.0f}")

scripts/analysis/final_report.py

@@ -0,0 +1,298 @@
+"""Report finale: TOP 5 metodi + simulazione crescita capitale €1000 → €50/giorno."""
+from __future__ import annotations
+import sys
+sys.path.insert(0, ".")
+
+import numpy as np
+from src.data.downloader import load_data
+
+print("=" * 70)
+print("  REPORT FINALE — TOP 5 METODI")
+print("  Target: accuracy >80%, ROI annuo >30%, €50/giorno da €1000")
+print("=" * 70)
+
+# Metodo 1: Squeeze Breakout ETH 1h (BBw=20, sqThr=0.8, volume confirmed)
+# Metodo 2: Squeeze Breakout ETH 1h (BBw=30, sqThr=0.9, senza vol filter)
+# Metodo 3: Squeeze Breakout BTC+ETH combinato
+# Metodo 4: Squeeze Breakout 15m (alta frequenza)
+# Metodo 5: GBM Structural + Squeeze filter (ibrido ML + strutturale)
+
+FEE = 0.001
+LEVERAGE = 3
+INITIAL = 1000
+
+
+def bollinger_bandwidth(close, window=20):
+    n = len(close)
+    result = np.full(n, np.nan)
+    for i in range(window, n):
+        w = close[i-window:i]
+        ma = np.mean(w)
+        std = np.std(w)
+        if ma > 0:
+            result[i] = (2 * 2 * std) / ma
+    return result
+
+
+def keltner_ratio(close, high, low, window=20):
+    n = len(close)
+    result = np.full(n, np.nan)
+    for i in range(window, n):
+        wc = close[i-window:i]
+        wh = high[i-window:i]
+        wl = low[i-window:i]
+        ma = np.mean(wc)
+        bb_std = np.std(wc)
+        tr = np.maximum(wh - wl, np.maximum(np.abs(wh - np.roll(wc,1)), np.abs(wl - np.roll(wc,1))))
+        atr = np.mean(tr[1:])
+        kc_r = (ma + 1.5*atr) - (ma - 1.5*atr)
+        bb_r = (ma + 2*bb_std) - (ma - 2*bb_std)
+        if kc_r > 0:
+            result[i] = bb_r / kc_r
+    return result
+
+
+def run_squeeze_backtest(close, high, low, volume, bb_w, sq_thr, brk_bars, vol_filter, split_pct=0.7, leverage=3, pos_pct=0.2):
+    n = len(close)
+    split = int(n * split_pct)
+    kcr = keltner_ratio(close, high, low, bb_w)
+
+    in_sq = False
+    sq_start = 0
+    capital = float(INITIAL)
+    equity = [capital]
+    trades = []
+
+    for i in range(bb_w + 1, n):
+        if np.isnan(kcr[i]):
+            continue
+        is_sq = kcr[i] < sq_thr
+        if is_sq and not in_sq:
+            in_sq = True
+            sq_start = i
+        elif not is_sq and in_sq:
+            in_sq = False
+            duration = i - sq_start
+            if duration < 5 or i < split or i + brk_bars >= n:
+                continue
+
+            # Volume check
+            if vol_filter:
+                avg_v = np.mean(volume[sq_start:i])
+                brk_v = np.mean(volume[i:i+brk_bars])
+                if avg_v > 0 and brk_v < avg_v * 1.3:
+                    continue
+
+            first_ret = (close[i] - close[i-1]) / close[i-1]
+            if abs(first_ret) < 0.001:
+                continue
+
+            direction = 1 if first_ret > 0 else -1
+            actual = (close[i+brk_bars-1] - close[i-1]) / close[i-1]
+            is_correct = (direction == 1 and actual > 0) or (direction == -1 and actual < 0)
+
+            trade_ret = actual * direction
+            net = trade_ret * leverage - FEE * 2 * leverage
+            pnl = capital * pos_pct * net
+            capital += pnl
+            capital = max(capital, 0)
+            equity.append(capital)
+
+            trades.append({
+                "correct": is_correct,
+                "actual_ret": actual,
+                "net_pnl": pnl,
+                "capital_after": capital,
+            })
+
+    if not trades:
+        return None
+
+    correct = sum(1 for t in trades if t["correct"])
+    acc = correct / len(trades) * 100
+    total_ret = (capital - INITIAL) / INITIAL * 100
+    test_candles = n - split
+    test_days = test_candles / 24
+    test_years = test_days / 365.25
+    ann = ((capital / INITIAL) ** (1/test_years) - 1) * 100 if test_years > 0 and capital > 0 else -100
+    daily_pnl = (capital - INITIAL) / test_days if test_days > 0 else 0
+
+    peak = equity[0]
+    max_dd = 0
+    for v in equity:
+        if v > peak: peak = v
+        dd = (peak - v) / peak if peak > 0 else 0
+        max_dd = max(max_dd, dd)
+
+    return {
+        "trades": len(trades),
+        "accuracy": acc,
+        "total_return": total_ret,
+        "annualized": ann,
+        "max_drawdown": max_dd * 100,
+        "final_capital": capital,
+        "daily_pnl": daily_pnl,
+        "trades_per_year": len(trades) / test_years if test_years > 0 else 0,
+    }
+
+
+methods = []
+
+# --- Metodo 1: ETH 1h, BBw=20, sqThr=0.8, vol confirmed ---
+df_eth = load_data("ETH", "1h")
+r1 = run_squeeze_backtest(df_eth["close"].values, df_eth["high"].values, df_eth["low"].values, df_eth["volume"].values,
+                           bb_w=20, sq_thr=0.8, brk_bars=3, vol_filter=True)
+methods.append(("M1: ETH 1h Squeeze+Vol (BBw=20,sq=0.8)", r1))
+
+# --- Metodo 2: ETH 1h, BBw=30, sqThr=0.9, no vol ---
+r2 = run_squeeze_backtest(df_eth["close"].values, df_eth["high"].values, df_eth["low"].values, df_eth["volume"].values,
+                           bb_w=30, sq_thr=0.9, brk_bars=3, vol_filter=False)
+methods.append(("M2: ETH 1h Squeeze (BBw=30,sq=0.9)", r2))
+
+# --- Metodo 3: BTC+ETH combinato ---
+df_btc = load_data("BTC", "1h")
+r3a = run_squeeze_backtest(df_btc["close"].values, df_btc["high"].values, df_btc["low"].values, df_btc["volume"].values,
+                            bb_w=14, sq_thr=0.8, brk_bars=3, vol_filter=False, pos_pct=0.1)
+r3b = run_squeeze_backtest(df_eth["close"].values, df_eth["high"].values, df_eth["low"].values, df_eth["volume"].values,
+                            bb_w=20, sq_thr=0.8, brk_bars=3, vol_filter=False, pos_pct=0.1)
+
+if r3a and r3b:
+    combined_trades = r3a["trades"] + r3b["trades"]
+    combined_correct = int(r3a["accuracy"]/100 * r3a["trades"]) + int(r3b["accuracy"]/100 * r3b["trades"])
+    combined_acc = combined_correct / combined_trades * 100 if combined_trades > 0 else 0
+
+    # Simulate portfolio
+    cap = float(INITIAL)
+    # Rough estimate: alternate between assets
+    for r in [r3a, r3b]:
+        ret_per_trade = r["total_return"] / 100 / r["trades"] if r["trades"] > 0 else 0
+        for _ in range(r["trades"]):
+            cap *= (1 + ret_per_trade * 0.5)
+
+    r3 = {
+        "trades": combined_trades,
+        "accuracy": combined_acc,
+        "total_return": (cap - INITIAL) / INITIAL * 100,
+        "annualized": r3a["annualized"] * 0.5 + r3b["annualized"] * 0.5,
+        "max_drawdown": max(r3a["max_drawdown"], r3b["max_drawdown"]),
+        "final_capital": cap,
+        "daily_pnl": r3a["daily_pnl"] + r3b["daily_pnl"],
+        "trades_per_year": r3a["trades_per_year"] + r3b["trades_per_year"],
+    }
+    methods.append(("M3: BTC+ETH 1h Portafoglio Squeeze", r3))
+
+# --- Metodo 4: BTC 15m alta frequenza ---
+df_btc_15 = load_data("BTC", "15m")
+r4 = run_squeeze_backtest(df_btc_15["close"].values, df_btc_15["high"].values, df_btc_15["low"].values, df_btc_15["volume"].values,
+                           bb_w=14, sq_thr=0.9, brk_bars=3, vol_filter=True)
+methods.append(("M4: BTC 15m Squeeze+Vol alta freq", r4))
+
+# --- Metodo 5: ETH 1h squeeze aggressivo ---
+r5 = run_squeeze_backtest(df_eth["close"].values, df_eth["high"].values, df_eth["low"].values, df_eth["volume"].values,
+                           bb_w=20, sq_thr=0.8, brk_bars=3, vol_filter=False, leverage=3)
+methods.append(("M5: ETH 1h Squeeze aggressivo (no vol)", r5))
+
+# --- Print results ---
+print("\n")
+for i, (name, r) in enumerate(methods, 1):
+    if r is None:
+        print(f"  {name}: NO TRADES")
+        continue
+    print(f"  {'='*65}")
+    print(f"  #{i} — {name}")
+    print(f"  {'='*65}")
+    print(f"    Trades:           {r['trades']}")
+    print(f"    Accuracy:         {r['accuracy']:.1f}%  {'✅' if r['accuracy'] >= 80 else '⚠️' if r['accuracy'] >= 70 else '❌'}")
+    print(f"    Return totale:    {r['total_return']:+.1f}%")
+    print(f"    Return annuo:     {r['annualized']:+.1f}%  {'✅' if r['annualized'] >= 30 else '⚠️' if r['annualized'] >= 15 else '❌'}")
+    print(f"    Max Drawdown:     {r['max_drawdown']:.1f}%")
+    print(f"    Capitale finale:  €{r['final_capital']:.0f}")
+    print(f"    €/giorno media:   €{r['daily_pnl']:.2f}")
+    print(f"    Trades/anno:      {r['trades_per_year']:.0f}")
+    print()
+
+
+# --- Simulazione crescita 6 mesi ---
+print("\n" + "=" * 70)
+print("  SIMULAZIONE CRESCITA CAPITALE — 6 MESI")
+print("  Metodo: M1 (ETH 1h Squeeze+Vol) — il più preciso (83.9%)")
+print("=" * 70)
+
+# M1 params: ~87 trades in ~2.5 anni test = ~35 trades/anno = ~3 al mese
+# Accuracy: 83.9%, average return per trade with 3x leverage
+
+# Simulo con dati reali: prendo i trade dal test period
+close = df_eth["close"].values
+high = df_eth["high"].values
+low = df_eth["low"].values
+volume = df_eth["volume"].values
+n = len(close)
+split = int(n * 0.7)
+
+kcr = keltner_ratio(close, high, low, 20)
+in_sq = False
+sq_start = 0
+all_trade_rets = []
+
+for i in range(21, n):
+    if np.isnan(kcr[i]):
+        continue
+    is_sq = kcr[i] < 0.8
+    if is_sq and not in_sq:
+        in_sq = True
+        sq_start = i
+    elif not is_sq and in_sq:
+        in_sq = False
+        if i - sq_start < 5 or i < split or i + 3 >= n:
+            continue
+        avg_v = np.mean(volume[sq_start:i])
+        brk_v = np.mean(volume[i:i+3])
+        if avg_v > 0 and brk_v < avg_v * 1.3:
+            continue
+        first_ret = (close[i] - close[i-1]) / close[i-1]
+        if abs(first_ret) < 0.001:
+            continue
+        direction = 1 if first_ret > 0 else -1
+        actual = (close[i+2] - close[i-1]) / close[i-1]
+        trade_ret = actual * direction
+        all_trade_rets.append(trade_ret)
+
+avg_win = np.mean([r for r in all_trade_rets if r > 0]) if any(r > 0 for r in all_trade_rets) else 0
+avg_loss = np.mean([r for r in all_trade_rets if r <= 0]) if any(r <= 0 for r in all_trade_rets) else 0
+win_rate = sum(1 for r in all_trade_rets if r > 0) / len(all_trade_rets)
+
+print(f"\n  Statistiche trade:")
+print(f"    Win rate: {win_rate*100:.1f}%")
+print(f"    Avg win:  {avg_win*100:.2f}%")
+print(f"    Avg loss: {avg_loss*100:.2f}%")
+print(f"    Trades totali nel test: {len(all_trade_rets)}")
+print(f"    Trades/mese stimati: ~{len(all_trade_rets) / 30:.0f}")
+
+print(f"\n  Crescita simulata mese per mese (€1000 iniziali, leva 3x, 20% per trade):")
+capital = 1000.0
+monthly_trades = max(len(all_trade_rets) // 30, 3)
+
+# Shuffle trades to simulate different sequences
+np.random.seed(42)
+for month in range(1, 7):
+    n_trades = monthly_trades
+    month_rets = np.random.choice(all_trade_rets, size=n_trades, replace=True)
+
+    for ret in month_rets:
+        net = ret * LEVERAGE - FEE * 2 * LEVERAGE
+        capital += capital * 0.2 * net
+        capital = max(capital, 10)
+
+    daily_pnl = capital * 0.003  # stima conservativa 0.3% daily basata su performance
+    print(f"    Mese {month}: capitale €{capital:.0f}, €/giorno stima: €{daily_pnl:.1f}")
+
+print(f"\n  Capitale dopo 6 mesi: €{capital:.0f}")
+print(f"  €/giorno necessari: €50")
+print(f"  €/giorno ottenibili (0.5% daily su capitale): €{capital * 0.005:.1f}")
+
+if capital * 0.005 >= 50:
+    print(f"\n  ✅ TARGET RAGGIUNGIBILE: con €{capital:.0f} di capitale, 0.5% daily = €{capital*0.005:.0f}/giorno")
+else:
+    needed = 50 / 0.005
+    print(f"\n  ⚠️ Servono €{needed:.0f} di capitale per €50/giorno al 0.5% daily")
+    print(f"  Raggiungibile estendendo il periodo di crescita a ~{int(np.log(needed/1000) / np.log(1 + 0.15) + 0.5)} mesi")

scripts/analysis/paper_status.py

@@ -0,0 +1,79 @@
+"""Mostra lo stato del paper trader."""
+from __future__ import annotations
+import sys
+sys.path.insert(0, ".")
+
+import json
+from pathlib import Path
+from src.live.cerbero_client import CerberoClient
+
+LOG_DIR = Path("data/paper_trades")
+
+print("=" * 50)
+print("  PAPER TRADER STATUS")
+print("=" * 50)
+
+# Status file
+status_path = LOG_DIR / "status.json"
+if status_path.exists():
+    with open(status_path) as f:
+        status = json.load(f)
+    print(f"\n  In posizione: {status['in_position']}")
+    if status["in_position"]:
+        print(f"  Direzione:    {status['direction']}")
+        print(f"  Entry price:  {status['entry_price']}")
+        print(f"  Entry time:   {status['entry_time']}")
+        print(f"  Barre tenute: {status['bars_held']}")
+    print(f"  Ultimo update: {status['last_update']}")
+else:
+    print("\n  Nessun file di stato trovato.")
+
+# Account
+print("\n--- ACCOUNT DERIBIT TESTNET ---")
+c = CerberoClient()
+try:
+    acc = c.get_account_summary("USDC")
+    print(f"  Equity:  ${acc['equity']:,.2f}")
+    print(f"  Balance: ${acc['balance']:,.2f}")
+    print(f"  PnL:     ${acc['total_pnl']:,.2f}")
+except Exception as e:
+    print(f"  Errore: {e}")
+
+# Posizioni
+try:
+    pos = c.get_positions("USDC")
+    print(f"\n  Posizioni aperte: {len(pos)}")
+    for p in pos:
+        print(f"    {p.get('instrument','?')}: {p.get('size',0)} {p.get('direction','?')} @ ${p.get('avg_price',0)}")
+except Exception as e:
+    print(f"  Errore: {e}")
+
+# Ultimi log
+print("\n--- ULTIMI LOG ---")
+log_files = sorted(LOG_DIR.glob("trades_*.jsonl"))
+if log_files:
+    with open(log_files[-1]) as f:
+        lines = f.readlines()
+    for line in lines[-10:]:
+        entry = json.loads(line)
+        print(f"  [{entry['timestamp'][:19]}] {entry['event']}")
+else:
+    print("  Nessun log trovato.")
+
+# Statistiche trade
+all_trades = []
+for lf in log_files:
+    with open(lf) as f:
+        for line in f:
+            entry = json.loads(line)
+            if entry["event"] == "CLOSED":
+                all_trades.append(entry)
+
+if all_trades:
+    wins = sum(1 for t in all_trades if t.get("pnl_pct", 0) > 0)
+    total = len(all_trades)
+    total_pnl = sum(t.get("pnl_pct", 0) for t in all_trades)
+    print(f"\n--- STATISTICHE ---")
+    print(f"  Trade chiusi: {total}")
+    print(f"  Win rate:     {wins/total*100:.0f}%")
+    print(f"  PnL totale:   {total_pnl:.2f}%")

scripts/strategies/ML01_squeeze_gbm.py

@@ -0,0 +1,266 @@
+"""ML01 — Squeeze + GBM (Gradient Boosting Machine) Walk-Forward.
+
+Strategia ibrida: squeeze breakout come pre-filtro (QUANDO tradare),
+GradientBoosting su features strutturali come conferma (QUALE direzione).
+
+Pipeline:
+    1. Rileva squeeze release (Bollinger esce da Keltner)
+    2. Estrai 44 features dalla finestra (structural multi-window + squeeze
+       metadata + price position + ATR + momentum breakout)
+    3. GBM walk-forward: train su 50% rolling, step 10%, predice direzione
+    4. Trade solo se ML ha confidenza ≥ ml_threshold
+
+IN:
+    - OHLCV DataFrame
+    - Parametri: bb_window (14), sq_threshold (0.8), brk_bars (3),
+                 ml_threshold (0.70), leverage (3), position_pct (0.15)
+
+OUT:
+    - BacktestResult con metriche walk-forward (no data leakage)
+    - Solo periodo di test (seconda metà dati)
+
+Risultati tipici:
+    ETH 15m bb14 ml=0.70: 76.9% acc, 1213 trades, DD 4.2%, €13.78/day
+    BTC 15m bb14 ml=0.70: 78.8% acc, 1964 trades, DD 7.0%, €5.51/day
+    BTC 1h  bb14 ml=0.70: 77.3% acc,  617 trades, DD 6.7%, €3.85/day
+
+Note:
+    - GBM = GradientBoostingClassifier di scikit-learn
+    - Walk-forward: nessun look-ahead, train sempre prima di test
+    - Il baseline squeeze puro ha accuracy più alta (~79.5%) ma DD peggiore
+    - Il valore del ML è filtrare breakout deboli → DD ridotto
+"""
+from __future__ import annotations
+import sys
+sys.path.insert(0, ".")
+
+import numpy as np
+import pandas as pd
+from sklearn.ensemble import GradientBoostingClassifier
+from sklearn.preprocessing import StandardScaler
+
+from src.strategies.base import Strategy, Signal, BacktestResult, YearlyStats, TF_MINUTES
+from src.strategies.indicators import keltner_ratio, detect_squeezes
+from src.data.downloader import load_data
+
+
+def _build_features(df: pd.DataFrame, i: int, squeeze_info: dict) -> np.ndarray | None:
+    """44 features per il punto di squeeze release."""
+    if i < 100:
+        return None
+    o, h, l, c, v = (df["open"].values, df["high"].values, df["low"].values,
+                      df["close"].values, df["volume"].values)
+    feats = []
+    for w in [12, 24, 48]:
+        wc, wo = c[i-w:i], o[i-w:i]
+        wh, wl, wv = h[i-w:i], l[i-w:i], v[i-w:i]
+        mn, mx = wl.min(), max(wh.max(), wc.max())
+        rng = mx - mn if mx - mn > 0 else 1e-10
+        total = np.where(wh - wl == 0, 1e-10, wh - wl)
+        body = np.abs(wc - wo) / total
+        direction = np.sign(wc - wo)
+        log_c = np.log(np.where(wc == 0, 1e-10, wc))
+        rets = np.diff(log_c)
+        v_mean = np.mean(wv)
+        feats.extend([
+            np.mean(rets) if len(rets) > 0 else 0,
+            np.std(rets) if len(rets) > 0 else 0,
+            np.sum(rets) if len(rets) > 0 else 0,
+            float(pd.Series(rets).skew()) if len(rets) > 2 else 0,
+            float(pd.Series(rets).kurtosis()) if len(rets) > 3 else 0,
+            np.mean(body), np.std(body),
+            np.mean(direction), np.mean(direction[-min(3, w):]),
+            (wc[-1] - mn) / rng,
+            wv[-1] / v_mean if v_mean > 0 else 1,
+            np.corrcoef(rets[:-1], rets[1:])[0, 1] if len(rets) > 1 and np.std(rets) > 0 else 0,
+        ])
+    sq = squeeze_info
+    feats.extend([
+        sq["dur"], sq["dur"] / 24, sq["kcr_at_release"],
+        v[i-1] / sq.get("avg_vol", 1) if sq.get("avg_vol", 0) > 0 else 1,
+        np.mean(v[i:min(i+3, len(v))]) / sq.get("avg_vol", 1) if sq.get("avg_vol", 0) > 0 else 1,
+    ])
+    h48, l48 = np.max(h[max(0, i-48):i]), np.min(l[max(0, i-48):i])
+    r48 = h48 - l48
+    feats.append((c[i-1] - l48) / r48 if r48 > 0 else 0.5)
+    tr = np.maximum(h[i-14:i] - l[i-14:i],
+                    np.maximum(np.abs(h[i-14:i] - np.roll(c[i-14:i], 1)),
+                               np.abs(l[i-14:i] - np.roll(c[i-14:i], 1))))
+    feats.append(np.mean(tr[1:]) / c[i-1] if c[i-1] > 0 else 0)
+    feats.append((c[i] - c[i-1]) / c[i-1] if c[i-1] > 0 else 0)
+    return np.nan_to_num(np.array(feats), nan=0, posinf=1e6, neginf=-1e6)
+
+
+class SqueezeGBM(Strategy):
+    name = "ML01_squeeze_gbm"
+    description = "Squeeze + GBM walk-forward — ML filtra breakout deboli"
+    default_assets = ["BTC", "ETH"]
+    default_timeframes = ["15m", "1h"]
+    fee_ml = 0.001
+
+    def generate_signals(self, df, ts, **params):
+        raise NotImplementedError("ML01 usa backtest custom con walk-forward")
+
+    def backtest(self, asset: str, tf: str, hold: int = 3, **params) -> BacktestResult | None:
+        bb_w = params.get("bb_window", 14)
+        sq_thr = params.get("sq_threshold", 0.8 if tf == "1h" else 0.9)
+        brk = params.get("brk_bars", hold)
+        ml_thr = params.get("ml_threshold", 0.70)
+        lev = params.get("leverage", self.leverage)
+        pos = params.get("position_pct", self.position_size)
+
+        df = load_data(asset, tf)
+        close = df["close"].values
+        high = df["high"].values
+        low = df["low"].values
+        volume = df["volume"].values
+        n = len(df)
+        ts = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
+
+        kcr = keltner_ratio(close, high, low, bb_w)
+        raw_events = detect_squeezes(close, high, low, kcr, sq_thr)
+
+        # Aggiungi avg_vol a ogni evento
+        events = []
+        for ev in raw_events:
+            ev["avg_vol"] = float(np.mean(volume[ev["sq_start"]:ev["idx"]]))
+            events.append(ev)
+
+        X_all, y_all, ev_all = [], [], []
+        for ev in events:
+            i = ev["idx"]
+            if i + brk >= n or i < 100:
+                continue
+            feats = _build_features(df, i, ev)
+            if feats is None:
+                continue
+            actual_ret = (close[i + brk - 1] - close[i - 1]) / close[i - 1]
+            X_all.append(feats)
+            y_all.append(1 if actual_ret > 0 else 0)
+            ev_all.append(ev)
+
+        if len(X_all) < 50:
+            return None
+
+        X, y = np.array(X_all), np.array(y_all)
+        TRAIN_SIZE = max(int(len(X) * 0.5), 50)
+        STEP_SIZE = max(int(len(X) * 0.1), 10)
+
+        yearly: dict[int, dict] = {}
+        capital = float(self.initial_capital)
+        peak = capital
+        max_dd = 0.0
+        total_bars = 0
+        all_t = all_w = 0
+
+        start = 0
+        while start + TRAIN_SIZE + STEP_SIZE <= len(X):
+            train_end = start + TRAIN_SIZE
+            test_end = min(train_end + STEP_SIZE, len(X))
+            X_tr, y_tr = X[start:train_end], y[start:train_end]
+            X_te = X[train_end:test_end]
+
+            if len(np.unique(y_tr)) < 2:
+                start += STEP_SIZE
+                continue
+
+            scaler = StandardScaler()
+            X_tr_s = scaler.fit_transform(X_tr)
+            X_te_s = scaler.transform(X_te)
+
+            model = GradientBoostingClassifier(
+                n_estimators=150, max_depth=4, min_samples_leaf=10,
+                learning_rate=0.05, subsample=0.8, random_state=42,
+            )
+            model.fit(X_tr_s, y_tr)
+            up_idx = list(model.classes_).index(1) if 1 in model.classes_ else -1
+            if up_idx < 0:
+                start += STEP_SIZE
+                continue
+
+            for j in range(len(X_te)):
+                proba = model.predict_proba(X_te_s[j:j+1])[0]
+                p_up = proba[up_idx]
+                ev = ev_all[train_end + j]
+                i = ev["idx"]
+                actual_ret = (close[i + brk - 1] - close[i - 1]) / close[i - 1]
+
+                if p_up >= ml_thr:
+                    direction = 1
+                elif p_up <= (1 - ml_thr):
+                    direction = -1
+                else:
+                    continue
+
+                is_correct = (direction == 1 and actual_ret > 0) or (direction == -1 and actual_ret < 0)
+                trade_ret = actual_ret * direction
+                net = trade_ret * lev - self.fee_ml * 2 * lev
+                capital += capital * pos * net
+                capital = max(capital, 10)
+                if capital > peak:
+                    peak = capital
+                dd = (peak - capital) / peak
+                max_dd = max(max_dd, dd)
+                total_bars += brk
+
+                all_t += 1
+                if is_correct:
+                    all_w += 1
+
+                year = ts.iloc[i].year
+                if year not in yearly:
+                    yearly[year] = {"w": 0, "t": 0, "pnl": 0.0}
+                yearly[year]["t"] += 1
+                if is_correct:
+                    yearly[year]["w"] += 1
+                yearly[year]["pnl"] += net * self.initial_capital
+
+            start += STEP_SIZE
+
+        if all_t == 0:
+            return None
+
+        yearly_stats = [
+            YearlyStats(year=y, trades=d["t"], wins=d["w"], pnl=d["pnl"])
+            for y, d in sorted(yearly.items())
+        ]
+
+        return BacktestResult(
+            strategy_name=self.name,
+            asset=asset,
+            timeframe=tf,
+            params={"bb_w": bb_w, "sq_thr": sq_thr, "ml_thr": ml_thr,
+                    "brk": brk, "lev": lev, "pos": pos},
+            trades=all_t,
+            wins=all_w,
+            pnl=sum(d["pnl"] for d in yearly.values()),
+            capital=capital,
+            initial_capital=self.initial_capital,
+            max_dd=max_dd * 100,
+            time_in_market_pct=total_bars / n * 100,
+            avg_trade_duration_h=brk * TF_MINUTES.get(tf, 60) / 60,
+            years_active=len(yearly),
+            yearly=yearly_stats,
+        )
+
+
+if __name__ == "__main__":
+    strategy = SqueezeGBM()
+    print("Training ML models...\n")
+    results = []
+    for asset in ["ETH", "BTC"]:
+        for tf in ["15m", "1h"]:
+            for ml_thr in [0.65, 0.70]:
+                r = strategy.backtest(asset, tf, ml_threshold=ml_thr)
+                if r and r.trades >= 20:
+                    r.strategy_name = f"ML01 {asset} {tf} ml={ml_thr}"
+                    results.append(r)
+    results.sort(key=lambda r: r.accuracy, reverse=True)
+
+    print(f"{'=' * 120}")
+    print(f"  ML01 SQUEEZE+GBM — RISULTATI")
+    print(f"{'=' * 120}")
+    for r in results:
+        r.print_summary()
+    if results:
+        results[0].print_yearly()

scripts/strategies/SQ01_squeeze_base.py

@@ -0,0 +1,68 @@
+"""SQ01 — Squeeze Breakout Base.
+
+Strategia strutturale: rileva compressione di volatilità (Bollinger dentro
+Keltner Channel) e segue la direzione del breakout al rilascio.
+
+IN:
+    - OHLCV DataFrame (da load_data)
+    - Parametri: bb_window (14), sq_threshold (0.8), min_squeeze_dur (5)
+
+OUT:
+    - Lista di Signal con direzione breakout (+1/-1)
+    - BacktestResult con equity, yearly breakdown, metriche
+
+Risultati tipici:
+    BTC 15m: 76.7% acc, 4062 trades, DD 6.7%, €9.32/day
+    ETH 15m: 76.4% acc, 2948 trades, DD 6.2%, €10.31/day
+"""
+from __future__ import annotations
+import sys
+sys.path.insert(0, ".")
+
+import numpy as np
+import pandas as pd
+
+from src.strategies.base import Strategy, Signal
+from src.strategies.indicators import keltner_ratio, detect_squeezes
+
+
+class SqueezeBase(Strategy):
+    name = "SQ01_squeeze_base"
+    description = "Squeeze breakout puro — segui direzione al rilascio"
+    default_assets = ["BTC", "ETH"]
+    default_timeframes = ["15m", "1h"]
+
+    def generate_signals(self, df: pd.DataFrame, ts: pd.DatetimeIndex,
+                         **params) -> list[Signal]:
+        c = df["close"].values
+        h = df["high"].values
+        l = df["low"].values
+        n = len(c)
+
+        bb_w = params.get("bb_window", 14)
+        sq_thr = params.get("sq_threshold", 0.8)
+        min_dur = params.get("min_dur", 5)
+
+        kcr = keltner_ratio(c, h, l, bb_w)
+        events = detect_squeezes(c, h, l, kcr, sq_thr, min_dur)
+
+        signals = []
+        for ev in events:
+            i = ev["idx"]
+            if i < 1 or i >= n:
+                continue
+            first_ret = (c[i] - c[i - 1]) / c[i - 1] if c[i - 1] > 0 else 0
+            if abs(first_ret) < 0.001:
+                continue
+            signals.append(Signal(
+                idx=i,
+                direction=1 if first_ret > 0 else -1,
+                entry_price=c[i - 1],
+                metadata={"dur": ev["dur"], "kcr": ev["kcr_at_release"]},
+            ))
+        return signals
+
+
+if __name__ == "__main__":
+    strategy = SqueezeBase()
+    strategy.report()

scripts/strategies/SQ02_squeeze_antifake_vol.py

@@ -0,0 +1,87 @@
+"""SQ02 — Squeeze Breakout + Anti-Fakeout + Volume Confirmation.
+
+Migliora SQ01 con due filtri:
+  1. Anti-fakeout: scarta breakout dove la candela ritraccia >60% del range
+  2. Volume confirm: volume al breakout deve essere >1.3× la media durante squeeze
+
+IN:
+    - OHLCV DataFrame
+    - Parametri: bb_window (14), sq_threshold (0.8), retrace_limit (0.6),
+                 vol_multiplier (1.3)
+
+OUT:
+    - Lista di Signal filtrati
+    - BacktestResult
+
+Risultati tipici:
+    BTC 15m: 79.7% acc, 1250 trades, DD 6.5%, €5.23/day — SOLIDO 9/9 anni
+    ETH 15m: 78.6% acc, 942 trades, DD 3.4%, €4.33/day
+    BTC 1h:  78.0% acc, 473 trades, DD 3.5%, Sharpe 6.57
+"""
+from __future__ import annotations
+import sys
+sys.path.insert(0, ".")
+
+import numpy as np
+import pandas as pd
+
+from src.strategies.base import Strategy, Signal
+from src.strategies.indicators import keltner_ratio, detect_squeezes
+
+
+class SqueezeAntifakeVol(Strategy):
+    name = "SQ02_antifake_vol"
+    description = "Squeeze + antifakeout + volume confirmation"
+    default_assets = ["BTC", "ETH"]
+    default_timeframes = ["15m", "1h"]
+
+    def generate_signals(self, df: pd.DataFrame, ts: pd.DatetimeIndex,
+                         **params) -> list[Signal]:
+        c = df["close"].values
+        h = df["high"].values
+        l = df["low"].values
+        v = df["volume"].values
+        n = len(c)
+
+        bb_w = params.get("bb_window", 14)
+        sq_thr = params.get("sq_threshold", 0.8)
+        retrace_limit = params.get("retrace_limit", 0.6)
+        vol_mult = params.get("vol_multiplier", 1.3)
+
+        kcr = keltner_ratio(c, h, l, bb_w)
+        events = detect_squeezes(c, h, l, kcr, sq_thr)
+
+        signals = []
+        for ev in events:
+            i = ev["idx"]
+            if i < 1 or i >= n:
+                continue
+            first_ret = (c[i] - c[i - 1]) / c[i - 1] if c[i - 1] > 0 else 0
+            if abs(first_ret) < 0.001:
+                continue
+
+            br = h[i] - l[i]
+            if br > 0:
+                if c[i] > c[i - 1]:
+                    if (h[i] - c[i]) / br > retrace_limit:
+                        continue
+                else:
+                    if (c[i] - l[i]) / br > retrace_limit:
+                        continue
+
+            avg_v = np.mean(v[ev["sq_start"]:i])
+            if avg_v > 0 and v[i] <= avg_v * vol_mult:
+                continue
+
+            signals.append(Signal(
+                idx=i,
+                direction=1 if first_ret > 0 else -1,
+                entry_price=c[i - 1],
+                metadata={"dur": ev["dur"], "vol_ratio": v[i] / avg_v if avg_v > 0 else 0},
+            ))
+        return signals
+
+
+if __name__ == "__main__":
+    strategy = SqueezeAntifakeVol()
+    strategy.report()

scripts/strategies/SQ03_squeeze_all_filters.py

@@ -0,0 +1,175 @@
+"""SQ03 — Squeeze con filtri selezionabili.
+
+Ogni filtro è opzionale e attivabile via parametro. Di default attiva solo
+antifake + long_squeeze (i due filtri con miglior rapporto accuracy/trade).
+Esegue tutte le combinazioni utili e classifica.
+
+Filtri disponibili:
+    - antifake:  scarta breakout con retrace >60% (guadagna ~+1% acc)
+    - long_sq:   solo squeeze durata ≥10 barre (+1% acc, dimezza trade)
+    - timing:    solo ore 4-16 UTC (+0.5% acc)
+    - cross:     asset secondario in squeeze nelle ultime 10 barre (+0.5%)
+    - vol:       volume al breakout >1.3× media squeeze (+1% acc)
+
+IN:
+    - OHLCV DataFrame (primario + secondario per cross-check)
+    - Parametri: filters (lista), bb_window, sq_threshold
+
+OUT:
+    - BacktestResult per ogni preset di filtri
+
+Risultati tipici (BTC 15m):
+    antifake+long:    77.3% acc, 2179 trades
+    antifake+vol:     79.7% acc, 1250 trades — SOLIDO
+    ALL_FILTERS:      79.2% acc,  696 trades (restrittivo)
+"""
+from __future__ import annotations
+import sys
+sys.path.insert(0, ".")
+
+import numpy as np
+import pandas as pd
+
+from src.strategies.base import Strategy, Signal, BacktestResult, YearlyStats, TF_MINUTES
+from src.strategies.indicators import keltner_ratio, detect_squeezes
+from src.data.downloader import load_data
+
+
+PRESETS = {
+    "antifake":           ["antifake"],
+    "long_sq":            ["long_sq"],
+    "antifake+long":      ["antifake", "long_sq"],
+    "antifake+vol":       ["antifake", "vol"],
+    "antifake+timing":    ["antifake", "timing"],
+    "long+timing":        ["long_sq", "timing"],
+    "antifake+long+time": ["antifake", "long_sq", "timing"],
+    "antifake+cross":     ["antifake", "cross"],
+    "ALL_FILTERS":        ["antifake", "long_sq", "timing", "cross"],
+}
+
+
+class SqueezeFiltered(Strategy):
+    name = "SQ03_filtered"
+    description = "Squeeze + filtri selezionabili (antifake, long, timing, cross, vol)"
+    default_assets = ["BTC", "ETH"]
+    default_timeframes = ["15m", "1h"]
+
+    def generate_signals(self, df: pd.DataFrame, ts: pd.DatetimeIndex,
+                         **params) -> list[Signal]:
+        c = df["close"].values
+        h = df["high"].values
+        l = df["low"].values
+        v = df["volume"].values
+        n = len(c)
+
+        bb_w = params.get("bb_window", 14)
+        sq_thr = params.get("sq_threshold", 0.8)
+        filters = params.get("filters", ["antifake", "long_sq"])
+        asset = params.get("asset", "BTC")
+        tf = params.get("tf", "15m")
+
+        kcr = keltner_ratio(c, h, l, bb_w)
+        events = detect_squeezes(c, h, l, kcr, sq_thr)
+
+        kcr2 = None
+        ts2 = None
+        if "cross" in filters:
+            secondary = "ETH" if asset == "BTC" else "BTC"
+            df2 = load_data(secondary, tf)
+            kcr2 = keltner_ratio(df2["close"].values, df2["high"].values,
+                                 df2["low"].values, bb_w)
+            ts2 = df2["timestamp"].values
+
+        signals = []
+        for ev in events:
+            i = ev["idx"]
+            if i < 1 or i >= n:
+                continue
+            first_ret = (c[i] - c[i - 1]) / c[i - 1] if c[i - 1] > 0 else 0
+            if abs(first_ret) < 0.001:
+                continue
+
+            skip = False
+
+            if "antifake" in filters:
+                br = h[i] - l[i]
+                if br > 0:
+                    if c[i] > c[i - 1] and (h[i] - c[i]) / br > 0.6:
+                        skip = True
+                    elif c[i] <= c[i - 1] and (c[i] - l[i]) / br > 0.6:
+                        skip = True
+
+            if not skip and "long_sq" in filters:
+                if ev["dur"] < 10:
+                    skip = True
+
+            if not skip and "timing" in filters:
+                hour = ts.iloc[i].hour
+                if hour < 4 or hour > 16:
+                    skip = True
+
+            if not skip and "vol" in filters:
+                avg_v = np.mean(v[ev["sq_start"]:i])
+                if avg_v > 0 and v[i] <= avg_v * 1.3:
+                    skip = True
+
+            if not skip and "cross" in filters and kcr2 is not None and ts2 is not None:
+                i2 = np.searchsorted(ts2, ts.values[i].astype("int64") // 10**6)
+                i2 = min(i2, len(kcr2) - 1)
+                cross_ok = any(
+                    not np.isnan(kcr2[j]) and kcr2[j] < 0.85
+                    for j in range(max(0, i2 - 10), i2 + 1)
+                )
+                if not cross_ok:
+                    skip = True
+
+            if skip:
+                continue
+
+            signals.append(Signal(
+                idx=i,
+                direction=1 if first_ret > 0 else -1,
+                entry_price=c[i - 1],
+                metadata={"dur": ev["dur"], "filters": filters},
+            ))
+        return signals
+
+    def report_all_presets(self, assets=None, timeframes=None, hold=3):
+        """Esegue tutti i preset di filtri × asset × tf."""
+        assets = assets or self.default_assets
+        timeframes = timeframes or self.default_timeframes
+        all_results = []
+
+        for preset_name, filter_list in PRESETS.items():
+            for asset in assets:
+                for tf in timeframes:
+                    r = self.backtest(asset, tf, hold, filters=filter_list)
+                    if r and r.trades >= 20:
+                        r.strategy_name = f"SQ03 {preset_name}"
+                        all_results.append(r)
+
+        all_results.sort(key=lambda r: r.accuracy, reverse=True)
+
+        print(f"\n{'=' * 120}")
+        print(f"  SQ03 SQUEEZE FILTRATO — TUTTI I PRESET ({len(all_results)} config)")
+        print(f"  Fee: {self.fee_rt*100:.1f}% RT | Leva: {self.leverage:.0f}x | Pos: {self.position_size*100:.0f}%")
+        print(f"{'=' * 120}")
+        print(f"  {'Nome':<30s} {'A/T':>7s} {'Trades':>6s} {'Acc':>6s} "
+              f"{'PnL€':>10s} {'DD%':>6s} {'€/day':>7s} "
+              f"{'Mkt%':>5s} {'Dur':>5s} {'Worst':>12s} {'Anni':>4s}")
+        print(f"  {'─' * 110}")
+
+        for r in all_results:
+            r.print_summary()
+
+        if all_results:
+            print(f"\n  MIGLIORE: ", end="")
+            best = all_results[0]
+            best.print_yearly()
+
+        return all_results
+
+
+if __name__ == "__main__":
+    strategy = SqueezeFiltered()
+    strategy.report_all_presets()

scripts/strategies/SQ04_squeeze_ultimate.py

@@ -0,0 +1,204 @@
+"""SQ04 — Ultimate Squeeze — combinazione incrementale di tutti i filtri.
+
+Testa combinazioni di filtri (antifake, long_sq, timing, cross-asset,
+correlation, volume, trend alignment, volatility regime) e classifica
+per accuracy.
+
+IN:
+    - OHLCV DataFrame (primario + secondario)
+    - Parametri: bb_window, sq_threshold, lista filtri da attivare
+
+OUT:
+    - BacktestResult per ogni combinazione di filtri
+    - Classifica globale
+
+Risultati tipici:
+    BTC 15m antifake+corr: 81.6% acc (ma concentrato 2018)
+    BTC 15m antifake+vol: 79.7% acc, 1250 trades — robusto
+    ETH 1h antifake+corr: 80.7% acc (solo 2018)
+"""
+from __future__ import annotations
+import sys
+sys.path.insert(0, ".")
+
+import numpy as np
+import pandas as pd
+
+from src.strategies.base import Strategy, Signal
+from src.strategies.indicators import (
+    keltner_ratio, detect_squeezes, ema, rv_annualized, rolling_correlation,
+)
+from src.data.downloader import load_data
+
+
+class SqueezeUltimate(Strategy):
+    name = "SQ04_ultimate"
+    description = "Ultimate squeeze — tutti i filtri combinabili"
+    default_assets = ["BTC", "ETH"]
+    default_timeframes = ["15m", "1h"]
+
+    FILTER_PRESETS = {
+        "antifake+vol": ["antifake", "vol_confirm"],
+        "antifake+corr": ["antifake", "corr_high"],
+        "af+long+corr+trend": ["antifake", "long_sq", "corr_high", "trend_align"],
+        "ALL": ["antifake", "long_sq", "cross", "timing", "corr_high",
+                "vol_confirm", "trend_align", "low_rv"],
+    }
+
+    def generate_signals(self, df: pd.DataFrame, ts: pd.DatetimeIndex,
+                         **params) -> list[Signal]:
+        c = df["close"].values
+        h = df["high"].values
+        l = df["low"].values
+        v = df["volume"].values
+        n = len(c)
+
+        asset = params.get("asset", "BTC")
+        tf = params.get("tf", "15m")
+        filters = params.get("filters", ["antifake", "vol_confirm"])
+
+        kcr = keltner_ratio(c, h, l, 14)
+        events = detect_squeezes(c, h, l, kcr)
+
+        secondary = "ETH" if asset == "BTC" else "BTC"
+        df2 = load_data(secondary, tf)
+        c2 = df2["close"].values
+        kcr2 = keltner_ratio(c2, df2["high"].values, df2["low"].values, 14)
+        ts2 = df2["timestamp"].values
+
+        ema_50 = ema(c, 50)
+        rv_48 = rv_annualized(c, 48)
+        corr = rolling_correlation(c, c2)
+
+        signals = []
+        for ev in events:
+            i = ev["idx"]
+            if i < 1 or i >= n:
+                continue
+            first_ret = (c[i] - c[i - 1]) / c[i - 1] if c[i - 1] > 0 else 0
+            if abs(first_ret) < 0.001:
+                continue
+
+            skip = False
+            for f in filters:
+                if f == "antifake":
+                    br = h[i] - l[i]
+                    if br > 0:
+                        if c[i] > c[i-1] and (h[i] - c[i]) / br > 0.6:
+                            skip = True
+                        elif c[i] <= c[i-1] and (c[i] - l[i]) / br > 0.6:
+                            skip = True
+                elif f == "long_sq":
+                    if ev["dur"] < 10:
+                        skip = True
+                elif f == "timing":
+                    if ts.iloc[i].hour < 4 or ts.iloc[i].hour > 16:
+                        skip = True
+                elif f == "cross":
+                    i2 = np.searchsorted(ts2, ts.values[i].astype("int64") // 10**6)
+                    i2 = min(i2, len(kcr2) - 1)
+                    if not any(not np.isnan(kcr2[j]) and kcr2[j] < 0.85
+                               for j in range(max(0, i2 - 10), i2 + 1)):
+                        skip = True
+                elif f == "corr_high":
+                    if np.isnan(corr[i]) or abs(corr[i]) < 0.6:
+                        skip = True
+                elif f == "vol_confirm":
+                    avg_v = np.mean(v[ev["sq_start"]:i])
+                    if avg_v > 0 and v[i] <= avg_v * 1.3:
+                        skip = True
+                elif f == "trend_align":
+                    if not np.isnan(ema_50[i]):
+                        if first_ret > 0 and c[i] < ema_50[i]:
+                            skip = True
+                        elif first_ret < 0 and c[i] > ema_50[i]:
+                            skip = True
+                elif f == "low_rv":
+                    if not np.isnan(rv_48[i]) and rv_48[i] >= 1.5:
+                        skip = True
+                if skip:
+                    break
+
+            if skip:
+                continue
+
+            signals.append(Signal(
+                idx=i,
+                direction=1 if first_ret > 0 else -1,
+                entry_price=c[i - 1],
+                metadata={"dur": ev["dur"], "filters": filters},
+            ))
+        return signals
+
+    def backtest(self, asset: str, tf: str, hold: int = 3, **params):
+        params.setdefault("asset", asset)
+        params.setdefault("tf", tf)
+        df = load_data(asset, tf)
+        ts = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
+        signals = self.generate_signals(df, ts, **params)
+        # Usa il backtest della base ma passando i segnali già generati
+        from src.strategies.base import BacktestResult, YearlyStats, TF_MINUTES
+        c = df["close"].values
+        n = len(c)
+        yearly: dict[int, dict] = {}
+        capital = float(self.initial_capital)
+        peak = capital
+        max_dd = 0.0
+        total_bars = 0
+        for sig in signals:
+            i = sig.idx
+            if i + hold >= n or i < 1:
+                continue
+            entry = sig.entry_price
+            exit_price = c[min(i + hold - 1, n - 1)]
+            actual = (exit_price - entry) / entry * sig.direction
+            net = actual * self.leverage - self.fee_rt * self.leverage
+            capital += capital * self.position_size * net
+            capital = max(capital, 10)
+            if capital > peak: peak = capital
+            dd = (peak - capital) / peak
+            max_dd = max(max_dd, dd)
+            total_bars += hold
+            year = ts.iloc[i].year
+            if year not in yearly:
+                yearly[year] = {"w": 0, "t": 0, "pnl": 0.0}
+            yearly[year]["t"] += 1
+            if actual > 0: yearly[year]["w"] += 1
+            yearly[year]["pnl"] += net * self.initial_capital
+        all_t = sum(d["t"] for d in yearly.values())
+        all_w = sum(d["w"] for d in yearly.values())
+        if all_t == 0: return None
+        yearly_stats = [YearlyStats(y, d["t"], d["w"], d["pnl"]) for y, d in sorted(yearly.items())]
+        return BacktestResult(
+            strategy_name=self.name, asset=asset, timeframe=tf, params=params,
+            trades=all_t, wins=all_w, pnl=sum(d["pnl"] for d in yearly.values()),
+            capital=capital, initial_capital=self.initial_capital,
+            max_dd=max_dd * 100, time_in_market_pct=total_bars / n * 100,
+            avg_trade_duration_h=hold * TF_MINUTES.get(tf, 60) / 60,
+            years_active=len(yearly), yearly=yearly_stats,
+        )
+
+    def report_all_presets(self):
+        """Esegue tutte le combinazioni preset × asset × tf."""
+        all_results = []
+        for preset_name, filter_list in self.FILTER_PRESETS.items():
+            for asset in self.default_assets:
+                for tf in self.default_timeframes:
+                    r = self.backtest(asset, tf, filters=filter_list)
+                    if r and r.trades >= 20:
+                        r.strategy_name = f"SQ04 {preset_name}"
+                        all_results.append(r)
+
+        all_results.sort(key=lambda r: r.accuracy, reverse=True)
+
+        print(f"\n{'=' * 120}")
+        print(f"  SQ04 ULTIMATE — TUTTI I PRESET")
+        print(f"{'=' * 120}")
+        for r in all_results:
+            r.print_summary()
+        return all_results
+
+
+if __name__ == "__main__":
+    strategy = SqueezeUltimate()
+    strategy.report_all_presets()

scripts/waste/REF_11_volatility_breakout.py

@@ -0,0 +1,223 @@
+"""Strategia 11: Volatility compression → breakout.
+Approccio diverso: non predire la direzione direttamente.
+1. Identifica momenti di COMPRESSIONE (Bollinger squeeze, ATR basso, bassa fractal dim)
+2. Quando la volatilità ESPLODE dopo compressione, segui la direzione del breakout
+3. Alta precisione perché il breakout DOPO compressione ha forte momentum
+Target: pochi trade molto precisi, con leva.
+"""
+from __future__ import annotations
+import sys
+sys.path.insert(0, ".")
+
+import numpy as np
+import pandas as pd
+from src.data.downloader import load_data
+from src.fractal.indicators import volatility_ratio
+
+FEE_PCT = 0.001
+LEVERAGE = 3
+INITIAL_CAPITAL = 1000
+
+
+def bollinger_bandwidth(close: np.ndarray, window: int = 20) -> np.ndarray:
+    """Bandwidth = (upper - lower) / middle."""
+    result = np.full(len(close), np.nan)
+    for i in range(window, len(close)):
+        w = close[i - window : i]
+        ma = np.mean(w)
+        std = np.std(w)
+        if ma > 0:
+            result[i] = (2 * 2 * std) / ma
+    return result
+
+
+def keltner_channel_ratio(close: np.ndarray, high: np.ndarray, low: np.ndarray, window: int = 20) -> np.ndarray:
+    """Ratio of Bollinger to Keltner — squeeze when < 1."""
+    result = np.full(len(close), np.nan)
+    for i in range(window, len(close)):
+        w_c = close[i - window : i]
+        w_h = high[i - window : i]
+        w_l = low[i - window : i]
+
+        ma = np.mean(w_c)
+        bb_std = np.std(w_c)
+        bb_upper = ma + 2 * bb_std
+        bb_lower = ma - 2 * bb_std
+
+        tr = np.maximum(w_h - w_l, np.maximum(np.abs(w_h - np.roll(w_c, 1)), np.abs(w_l - np.roll(w_c, 1))))
+        atr = np.mean(tr[1:])
+        kc_upper = ma + 1.5 * atr
+        kc_lower = ma - 1.5 * atr
+
+        kc_range = kc_upper - kc_lower
+        bb_range = bb_upper - bb_lower
+        if kc_range > 0:
+            result[i] = bb_range / kc_range
+    return result
+
+
+def detect_squeeze_release(
+    close: np.ndarray,
+    high: np.ndarray,
+    low: np.ndarray,
+    volume: np.ndarray,
+    bb_window: int = 20,
+    squeeze_threshold: float = 0.8,
+    breakout_bars: int = 3,
+    volume_mult: float = 1.5,
+) -> list[dict]:
+    """Detect squeeze → breakout events."""
+    bw = bollinger_bandwidth(close, bb_window)
+    kcr = keltner_channel_ratio(close, high, low, bb_window)
+
+    events = []
+    in_squeeze = False
+    squeeze_start = 0
+
+    for i in range(bb_window + 1, len(close)):
+        if np.isnan(kcr[i]):
+            continue
+
+        is_squeeze = kcr[i] < squeeze_threshold
+
+        if is_squeeze and not in_squeeze:
+            in_squeeze = True
+            squeeze_start = i
+        elif not is_squeeze and in_squeeze:
+            in_squeeze = False
+            squeeze_duration = i - squeeze_start
+
+            if squeeze_duration < 5:
+                continue
+
+            # Check breakout direction using next few bars
+            if i + breakout_bars >= len(close):
+                continue
+
+            breakout_ret = (close[i + breakout_bars - 1] - close[i - 1]) / close[i - 1]
+
+            # Volume confirmation
+            avg_vol = np.mean(volume[squeeze_start:i])
+            breakout_vol = np.mean(volume[i:i + breakout_bars])
+            vol_confirmed = breakout_vol > avg_vol * volume_mult if avg_vol > 0 else False
+
+            # Momentum confirmation
+            mom_3 = (close[i + 2] - close[i - 1]) / close[i - 1] if i + 2 < len(close) else 0
+
+            events.append({
+                "idx": i,
+                "squeeze_duration": squeeze_duration,
+                "breakout_ret": breakout_ret,
+                "vol_confirmed": vol_confirmed,
+                "mom_3": mom_3,
+                "bb_expansion": bw[i] / bw[squeeze_start] if bw[squeeze_start] > 0 else 1,
+            })
+
+    return events
+
+
+def run_squeeze_strategy(asset: str, tf: str = "1h"):
+    print(f"\n{'#'*60}")
+    print(f"  {asset} {tf} — VOLATILITY SQUEEZE BREAKOUT")
+    print(f"{'#'*60}")
+
+    df = load_data(asset, tf)
+    close = df["close"].values
+    high = df["high"].values
+    low = df["low"].values
+    volume = df["volume"].values
+    n = len(df)
+
+    split_idx = int(n * 0.7)
+
+    for bb_w in [14, 20, 30]:
+        for sq_thr in [0.7, 0.8, 0.9]:
+            for brk_bars in [3, 6]:
+                events = detect_squeeze_release(close, high, low, volume,
+                                                bb_window=bb_w, squeeze_threshold=sq_thr,
+                                                breakout_bars=brk_bars, volume_mult=1.3)
+
+                test_events = [e for e in events if e["idx"] >= split_idx]
+                if len(test_events) < 10:
+                    continue
+
+                # Strategy: follow breakout direction, with volume confirmation
+                capital = float(INITIAL_CAPITAL)
+                correct = 0
+                total = 0
+
+                for e in test_events:
+                    i = e["idx"]
+                    if i + brk_bars * 2 >= n:
+                        continue
+
+                    # First 1-bar direction as signal
+                    first_bar_ret = (close[i] - close[i - 1]) / close[i - 1]
+                    if abs(first_bar_ret) < 0.001:
+                        continue
+
+                    direction = "long" if first_bar_ret > 0 else "short"
+
+                    # Actual result after holding for brk_bars more
+                    actual_ret = (close[i + brk_bars - 1] - close[i - 1]) / close[i - 1]
+
+                    is_correct = (direction == "long" and actual_ret > 0) or (direction == "short" and actual_ret < 0)
+                    total += 1
+                    if is_correct:
+                        correct += 1
+
+                    trade_ret = actual_ret if direction == "long" else -actual_ret
+                    net = trade_ret * LEVERAGE - FEE_PCT * 2 * LEVERAGE
+                    capital += capital * 0.2 * net
+                    capital = max(capital, 0)
+
+                    # Enhanced: volume-confirmed only
+                if total > 0:
+                    acc = correct / total * 100
+                    ret = (capital - INITIAL_CAPITAL) / INITIAL_CAPITAL * 100
+                    test_candles = n - split_idx
+                    test_years = test_candles / (24 * 365.25)
+                    ann = ((capital / INITIAL_CAPITAL) ** (1 / test_years) - 1) * 100 if test_years > 0 and capital > 0 else -100
+                    if acc >= 55 and total >= 15:
+                        print(f"  BBw={bb_w:2d} sqThr={sq_thr:.1f} brk={brk_bars}: trades={total:4d} acc={acc:.1f}% ret={ret:+.1f}% ann={ann:+.1f}%")
+
+                # Volume-confirmed only
+                cap_vc = float(INITIAL_CAPITAL)
+                correct_vc = 0
+                total_vc = 0
+
+                for e in test_events:
+                    if not e["vol_confirmed"]:
+                        continue
+                    i = e["idx"]
+                    if i + brk_bars * 2 >= n:
+                        continue
+
+                    first_bar_ret = (close[i] - close[i - 1]) / close[i - 1]
+                    if abs(first_bar_ret) < 0.001:
+                        continue
+
+                    direction = "long" if first_bar_ret > 0 else "short"
+                    actual_ret = (close[i + brk_bars - 1] - close[i - 1]) / close[i - 1]
+
+                    is_correct = (direction == "long" and actual_ret > 0) or (direction == "short" and actual_ret < 0)
+                    total_vc += 1
+                    if is_correct:
+                        correct_vc += 1
+
+                    trade_ret = actual_ret if direction == "long" else -actual_ret
+                    net = trade_ret * LEVERAGE - FEE_PCT * 2 * LEVERAGE
+                    cap_vc += cap_vc * 0.2 * net
+                    cap_vc = max(cap_vc, 0)
+
+                if total_vc >= 10:
+                    acc_vc = correct_vc / total_vc * 100
+                    ret_vc = (cap_vc - INITIAL_CAPITAL) / INITIAL_CAPITAL * 100
+                    ann_vc = ((cap_vc / INITIAL_CAPITAL) ** (1 / (test_candles/(24*365.25))) - 1) * 100 if cap_vc > 0 else -100
+                    if acc_vc >= 55:
+                        print(f"    +VOL BBw={bb_w:2d} sqThr={sq_thr:.1f} brk={brk_bars}: trades={total_vc:4d} acc={acc_vc:.1f}% ret={ret_vc:+.1f}% ann={ann_vc:+.1f}%")
+
+
+for asset in ["BTC", "ETH"]:
+    for tf in ["1h", "15m"]:
+        run_squeeze_strategy(asset, tf)

scripts/waste/REF_13_squeeze_ml_hybrid.py

@@ -0,0 +1,408 @@
+"""Strategia 13: Squeeze + ML ibrida.
+Squeeze breakout come PRE-FILTRO (quando tradare),
+ML come CONFERMA DIREZIONALE (quale direzione).
+
+Pipeline:
+1. Rileva squeeze release (Bollinger esce da Keltner)
+2. Estrai features frattali/strutturali dalla finestra
+3. ML predice direzione con confidenza
+4. Trade SOLO se squeeze + ML concordano
+
+Obiettivo: accuracy squeeze (>80%) + volume trade ML.
+"""
+from __future__ import annotations
+import sys
+sys.path.insert(0, ".")
+
+import numpy as np
+import pandas as pd
+from sklearn.ensemble import GradientBoostingClassifier
+from sklearn.preprocessing import StandardScaler
+from src.data.downloader import load_data
+from src.fractal.patterns import encode_candles
+
+FEE = 0.001
+INITIAL = 1000
+
+
+def keltner_ratio(close, high, low, window=20):
+    n = len(close)
+    result = np.full(n, np.nan)
+    for i in range(window, n):
+        wc = close[i-window:i]
+        wh = high[i-window:i]
+        wl = low[i-window:i]
+        ma = np.mean(wc)
+        bb_std = np.std(wc)
+        tr = np.maximum(wh - wl, np.maximum(np.abs(wh - np.roll(wc, 1)), np.abs(wl - np.roll(wc, 1))))
+        atr = np.mean(tr[1:])
+        kc_r = (ma + 1.5*atr) - (ma - 1.5*atr)
+        bb_r = (ma + 2*bb_std) - (ma - 2*bb_std)
+        if kc_r > 0:
+            result[i] = bb_r / kc_r
+    return result
+
+
+def detect_squeeze_releases(close, high, low, volume, bb_w, sq_thr, min_duration=5):
+    kcr = keltner_ratio(close, high, low, bb_w)
+    events = []
+    in_sq = False
+    sq_start = 0
+
+    for i in range(bb_w + 1, len(close)):
+        if np.isnan(kcr[i]):
+            continue
+        is_sq = kcr[i] < sq_thr
+        if is_sq and not in_sq:
+            in_sq = True
+            sq_start = i
+        elif not is_sq and in_sq:
+            in_sq = False
+            duration = i - sq_start
+            if duration < min_duration:
+                continue
+
+            avg_vol = np.mean(volume[sq_start:i])
+            events.append({
+                "idx": i,
+                "squeeze_start": sq_start,
+                "duration": duration,
+                "avg_vol_squeeze": avg_vol,
+                "kcr_at_release": kcr[i],
+            })
+    return events
+
+
+def build_features_at(df, i, squeeze_info):
+    """Features per il punto di squeeze release."""
+    if i < 100:
+        return None
+
+    o = df["open"].values
+    h = df["high"].values
+    l = df["low"].values
+    c = df["close"].values
+    v = df["volume"].values
+
+    feats = []
+
+    # Structural features multi-window
+    for w in [12, 24, 48]:
+        win_c = c[i-w:i]
+        win_o = o[i-w:i]
+        win_h = h[i-w:i]
+        win_l = l[i-w:i]
+        win_v = v[i-w:i]
+
+        mn, mx = win_l.min(), max(win_h.max(), win_c.max())
+        rng = mx - mn if mx - mn > 0 else 1e-10
+
+        total = win_h - win_l
+        total = np.where(total == 0, 1e-10, total)
+        body = np.abs(win_c - win_o) / total
+        direction = np.sign(win_c - win_o)
+
+        log_c = np.log(np.where(win_c == 0, 1e-10, win_c))
+        rets = np.diff(log_c)
+
+        v_mean = np.mean(win_v)
+
+        feats.extend([
+            np.mean(rets) if len(rets) > 0 else 0,
+            np.std(rets) if len(rets) > 0 else 0,
+            np.sum(rets) if len(rets) > 0 else 0,
+            float(pd.Series(rets).skew()) if len(rets) > 2 else 0,
+            float(pd.Series(rets).kurtosis()) if len(rets) > 3 else 0,
+            np.mean(body),
+            np.std(body),
+            np.mean(direction),
+            np.mean(direction[-min(3, w):]),
+            (win_c[-1] - mn) / rng,
+            win_v[-1] / v_mean if v_mean > 0 else 1,
+            np.corrcoef(rets[:-1], rets[1:])[0, 1] if len(rets) > 1 and np.std(rets) > 0 else 0,
+        ])
+
+    # Squeeze-specific features
+    sq = squeeze_info
+    feats.extend([
+        sq["duration"],
+        sq["duration"] / 24,  # durata in giorni
+        sq["kcr_at_release"],
+        v[i-1] / sq["avg_vol_squeeze"] if sq["avg_vol_squeeze"] > 0 else 1,
+        np.mean(v[i:min(i+3, len(v))]) / sq["avg_vol_squeeze"] if sq["avg_vol_squeeze"] > 0 else 1,
+    ])
+
+    # Price position
+    h48 = np.max(h[max(0, i-48):i])
+    l48 = np.min(l[max(0, i-48):i])
+    r48 = h48 - l48
+    feats.append((c[i-1] - l48) / r48 if r48 > 0 else 0.5)
+
+    # ATR normalized
+    tr = np.maximum(h[i-14:i] - l[i-14:i],
+                    np.maximum(np.abs(h[i-14:i] - np.roll(c[i-14:i], 1)),
+                               np.abs(l[i-14:i] - np.roll(c[i-14:i], 1))))
+    atr = np.mean(tr[1:])
+    feats.append(atr / c[i-1] if c[i-1] > 0 else 0)
+
+    # First bar momentum (la barra di breakout)
+    first_ret = (c[i] - c[i-1]) / c[i-1] if c[i-1] > 0 else 0
+    feats.append(first_ret)
+
+    return np.nan_to_num(np.array(feats), nan=0, posinf=1e6, neginf=-1e6)
+
+
+def run_hybrid(asset, tf, bb_w, sq_thr, brk_bars, leverage, pos_pct):
+    print(f"\n{'='*65}")
+    print(f"  {asset} {tf} — HYBRID Squeeze+ML (BBw={bb_w}, sq={sq_thr}, brk={brk_bars})")
+    print(f"  Leverage: {leverage}x, Position: {pos_pct*100:.0f}%")
+    print(f"{'='*65}")
+
+    df = load_data(asset, tf)
+    close = df["close"].values
+    high = df["high"].values
+    low = df["low"].values
+    volume = df["volume"].values
+    n = len(df)
+
+    events = detect_squeeze_releases(close, high, low, volume, bb_w, sq_thr)
+    print(f"  Squeeze releases totali: {len(events)}")
+
+    # Build dataset: solo ai punti di squeeze
+    X_all, y_all, ev_all = [], [], []
+    for ev in events:
+        i = ev["idx"]
+        if i + brk_bars >= n or i < 100:
+            continue
+        feats = build_features_at(df, i, ev)
+        if feats is None:
+            continue
+        actual_ret = (close[i + brk_bars - 1] - close[i - 1]) / close[i - 1]
+        X_all.append(feats)
+        y_all.append(1 if actual_ret > 0 else 0)
+        ev_all.append(ev)
+
+    if len(X_all) < 50:
+        print("  Troppi pochi campioni.")
+        return None
+
+    X = np.array(X_all)
+    y = np.array(y_all)
+
+    print(f"  Samples: {len(X)}, Up: {np.mean(y)*100:.1f}%")
+
+    # Walk-forward
+    TRAIN_SIZE = max(int(len(X) * 0.5), 50)
+    STEP_SIZE = max(int(len(X) * 0.1), 10)
+
+    results = {}
+
+    for ml_thr in [0.50, 0.55, 0.60, 0.65, 0.70]:
+        capital = float(INITIAL)
+        equity = [capital]
+        trades_list = []
+        correct = 0
+        total = 0
+
+        start = 0
+        while start + TRAIN_SIZE + STEP_SIZE <= len(X):
+            train_end = start + TRAIN_SIZE
+            test_end = min(train_end + STEP_SIZE, len(X))
+
+            X_tr = X[start:train_end]
+            y_tr = y[start:train_end]
+            X_te = X[train_end:test_end]
+            y_te = y[train_end:test_end]
+
+            if len(np.unique(y_tr)) < 2:
+                start += STEP_SIZE
+                continue
+
+            scaler = StandardScaler()
+            X_tr_s = scaler.fit_transform(X_tr)
+            X_te_s = scaler.transform(X_te)
+
+            model = GradientBoostingClassifier(
+                n_estimators=150, max_depth=4, min_samples_leaf=10,
+                learning_rate=0.05, subsample=0.8, random_state=42,
+            )
+            model.fit(X_tr_s, y_tr)
+
+            up_idx = list(model.classes_).index(1) if 1 in model.classes_ else -1
+            if up_idx < 0:
+                start += STEP_SIZE
+                continue
+
+            for j in range(len(X_te)):
+                proba = model.predict_proba(X_te_s[j:j+1])[0]
+                p_up = proba[up_idx]
+
+                ev = ev_all[train_end + j]
+                i = ev["idx"]
+                actual_ret = (close[i + brk_bars - 1] - close[i - 1]) / close[i - 1]
+
+                # ML decide direction
+                direction = None
+                if p_up >= ml_thr:
+                    direction = "long"
+                elif p_up <= (1 - ml_thr):
+                    direction = "short"
+
+                if direction is None:
+                    continue
+
+                is_correct = (direction == "long" and actual_ret > 0) or (direction == "short" and actual_ret < 0)
+                total += 1
+                if is_correct:
+                    correct += 1
+
+                trade_ret = actual_ret if direction == "long" else -actual_ret
+                net = trade_ret * leverage - FEE * 2 * leverage
+                pnl = capital * pos_pct * net
+                capital += pnl
+                capital = max(capital, 0)
+                equity.append(capital)
+
+                trades_list.append({
+                    "idx": i,
+                    "direction": direction,
+                    "p_up": p_up,
+                    "actual_ret": actual_ret,
+                    "correct": is_correct,
+                    "pnl": pnl,
+                })
+
+            start += STEP_SIZE
+
+        if total == 0:
+            continue
+
+        acc = correct / total * 100
+
+        # Max drawdown
+        peak = equity[0]
+        max_dd = 0
+        for v in equity:
+            if v > peak:
+                peak = v
+            dd = (peak - v) / peak if peak > 0 else 0
+            max_dd = max(max_dd, dd)
+
+        # Annualized
+        first_ev = ev_all[TRAIN_SIZE] if TRAIN_SIZE < len(ev_all) else ev_all[0]
+        last_ev = ev_all[-1]
+        test_candles = last_ev["idx"] - first_ev["idx"]
+        if tf == "1h":
+            test_days = test_candles / 24
+        elif tf == "15m":
+            test_days = test_candles / (24 * 4)
+        else:
+            test_days = test_candles / 24
+        test_years = test_days / 365.25 if test_days > 0 else 1
+        ann = ((capital / INITIAL) ** (1 / test_years) - 1) * 100 if test_years > 0 and capital > 0 else -100
+        daily_pnl = (capital - INITIAL) / test_days if test_days > 0 else 0
+        trades_yr = total / test_years if test_years > 0 else 0
+
+        tag = ""
+        if acc >= 80:
+            tag = " ✅✅"
+        elif acc >= 70:
+            tag = " ✅"
+
+        print(f"  ml_thr={ml_thr:.2f}: trades={total:4d} acc={acc:.1f}%{tag} ret={(capital-INITIAL)/INITIAL*100:+.1f}% ann={ann:+.1f}% dd={max_dd*100:.1f}% sharpe= trades/yr={trades_yr:.0f} €/day={daily_pnl:.2f}")
+
+        results[ml_thr] = {
+            "trades": total, "accuracy": acc, "capital": capital,
+            "annualized": ann, "max_dd": max_dd * 100, "daily_pnl": daily_pnl,
+            "trades_yr": trades_yr,
+        }
+
+    # Modalità "squeeze puro" come baseline
+    capital_sq = float(INITIAL)
+    correct_sq = 0
+    total_sq = 0
+    split = int(len(X) * 0.5)
+
+    for k in range(split, len(X)):
+        ev = ev_all[k]
+        i = ev["idx"]
+        if i + brk_bars >= n:
+            continue
+        first_ret = (close[i] - close[i-1]) / close[i-1]
+        if abs(first_ret) < 0.001:
+            continue
+        direction = 1 if first_ret > 0 else -1
+        actual_ret = (close[i + brk_bars - 1] - close[i - 1]) / close[i - 1]
+        is_correct = (direction == 1 and actual_ret > 0) or (direction == -1 and actual_ret < 0)
+        total_sq += 1
+        if is_correct:
+            correct_sq += 1
+        trade_ret = actual_ret * direction
+        net = trade_ret * leverage - FEE * 2 * leverage
+        capital_sq += capital_sq * pos_pct * net
+        capital_sq = max(capital_sq, 0)
+
+    if total_sq > 0:
+        acc_sq = correct_sq / total_sq * 100
+        print(f"  BASELINE squeeze puro: trades={total_sq:4d} acc={acc_sq:.1f}% ret={(capital_sq-INITIAL)/INITIAL*100:+.1f}%")
+
+    return results
+
+
+# ===== MAIN: test su multiple configurazioni =====
+print("=" * 70)
+print("  STRATEGIA 13: SQUEEZE + ML IBRIDA")
+print("=" * 70)
+
+configs = [
+    # (asset, tf, bb_w, sq_thr, brk_bars, leverage, pos_pct)
+    ("ETH", "1h", 20, 0.8, 3, 3, 0.2),
+    ("ETH", "1h", 30, 0.9, 3, 3, 0.2),
+    ("ETH", "1h", 14, 0.8, 3, 3, 0.2),
+    ("ETH", "1h", 20, 0.9, 3, 3, 0.2),
+    ("BTC", "1h", 14, 0.8, 3, 3, 0.2),
+    ("BTC", "1h", 20, 0.8, 3, 3, 0.2),
+    ("BTC", "1h", 14, 0.9, 6, 3, 0.2),
+    ("ETH", "15m", 14, 0.8, 3, 3, 0.15),
+    ("ETH", "15m", 20, 0.9, 3, 3, 0.15),
+    ("BTC", "15m", 14, 0.9, 3, 3, 0.15),
+    # Aggressive
+    ("ETH", "1h", 20, 0.8, 3, 5, 0.3),
+    ("ETH", "1h", 30, 0.9, 3, 5, 0.3),
+]
+
+all_results = []
+for cfg in configs:
+    r = run_hybrid(*cfg)
+    if r:
+        for thr, data in r.items():
+            all_results.append({
+                "config": f"{cfg[0]} {cfg[1]} BBw={cfg[2]} sq={cfg[3]} brk={cfg[4]} lev={cfg[5]} pos={cfg[6]}",
+                "ml_thr": thr,
+                **data,
+            })
+
+# Sort by accuracy
+print("\n\n" + "=" * 70)
+print("  CLASSIFICA PER ACCURACY (top 20)")
+print("=" * 70)
+sorted_acc = sorted(all_results, key=lambda x: x["accuracy"], reverse=True)
+for r in sorted_acc[:20]:
+    tag = "✅✅" if r["accuracy"] >= 80 else "✅" if r["accuracy"] >= 70 else ""
+    print(f"  {r['config']:55s} ml={r['ml_thr']:.2f} → acc={r['accuracy']:.1f}% {tag:4s} trades={r['trades']:4d} ret={(r['capital']-INITIAL)/INITIAL*100:+.1f}% ann={r['annualized']:+.1f}% dd={r['max_dd']:.1f}% €/day={r['daily_pnl']:.2f}")
+
+print("\n" + "=" * 70)
+print("  CLASSIFICA PER ROI ANNUO (top 20, min 20 trades)")
+print("=" * 70)
+sorted_roi = sorted([r for r in all_results if r["trades"] >= 20], key=lambda x: x["annualized"], reverse=True)
+for r in sorted_roi[:20]:
+    tag = "✅✅" if r["accuracy"] >= 80 else "✅" if r["accuracy"] >= 70 else ""
+    print(f"  {r['config']:55s} ml={r['ml_thr']:.2f} → acc={r['accuracy']:.1f}% {tag:4s} ann={r['annualized']:+.1f}% trades={r['trades']:4d} dd={r['max_dd']:.1f}% €/day={r['daily_pnl']:.2f}")
+
+print("\n" + "=" * 70)
+print("  SWEET SPOT: acc>=75% AND ann>=20% AND trades>=15")
+print("=" * 70)
+sweet = [r for r in all_results if r["accuracy"] >= 75 and r["annualized"] >= 20 and r["trades"] >= 15]
+sweet.sort(key=lambda x: x["accuracy"] * x["annualized"], reverse=True)
+for r in sweet:
+    print(f"  {r['config']:55s} ml={r['ml_thr']:.2f} → acc={r['accuracy']:.1f}% ann={r['annualized']:+.1f}% trades={r['trades']:4d} dd={r['max_dd']:.1f}% €/day={r['daily_pnl']:.2f}")

scripts/waste/REF_s3_01_squeeze_improved.py

@@ -0,0 +1,317 @@
+"""S3-01: Squeeze Migliorato — test per-anno, dati reali.
+Miglioramenti rispetto al squeeze base:
+1. Cross-asset: squeeze su BTC + ETH contemporaneo = segnale più forte
+2. Timing orario: accuracy per fascia oraria
+3. Squeeze duration weighted: squeeze lunghi → breakout più forti
+4. Dual-timeframe: squeeze su 1h confermato da 15m
+5. Anti-fakeout: skip se candela post-breakout ritraccia >50%
+6. Dynamic exit: trailing stop basato su ATR
+"""
+from __future__ import annotations
+import sys
+sys.path.insert(0, ".")
+
+import numpy as np
+import pandas as pd
+from src.data.downloader import load_data
+
+FEE_RT = 0.002
+INITIAL = 1000
+LEVERAGE = 3
+
+
+def keltner_ratio(close, high, low, window=14):
+    n = len(close)
+    r = np.full(n, np.nan)
+    for i in range(window, n):
+        wc, wh, wl = close[i-window:i], high[i-window:i], low[i-window:i]
+        ma = np.mean(wc)
+        bb_std = np.std(wc)
+        tr = np.maximum(wh-wl, np.maximum(np.abs(wh-np.roll(wc,1)), np.abs(wl-np.roll(wc,1))))
+        atr = np.mean(tr[1:])
+        kc = (ma+1.5*atr)-(ma-1.5*atr)
+        bb = (ma+2*bb_std)-(ma-2*bb_std)
+        if kc > 0:
+            r[i] = bb/kc
+    return r
+
+
+def atr_calc(high, low, close, period=14):
+    tr = np.maximum(high-low, np.maximum(np.abs(high-np.roll(close,1)), np.abs(low-np.roll(close,1))))
+    tr[0] = high[0]-low[0]
+    r = np.full(len(close), np.nan)
+    r[period-1] = np.mean(tr[:period])
+    k = 2/(period+1)
+    for i in range(period, len(close)):
+        r[i] = tr[i]*k + r[i-1]*(1-k)
+    return r
+
+
+def detect_squeezes(close, high, low, volume, kcr, sq_thr=0.8, min_dur=5):
+    """Ritorna lista di squeeze events con metadata."""
+    events = []
+    in_sq = False
+    sq_start = 0
+    n = len(close)
+
+    for i in range(1, n):
+        if np.isnan(kcr[i]):
+            continue
+        is_sq = kcr[i] < sq_thr
+        if is_sq and not in_sq:
+            in_sq = True
+            sq_start = i
+        elif not is_sq and in_sq:
+            in_sq = False
+            dur = i - sq_start
+            if dur < min_dur:
+                continue
+            avg_vol = np.mean(volume[sq_start:i])
+            # Range durante squeeze
+            sq_range = (np.max(high[sq_start:i]) - np.min(low[sq_start:i])) / close[sq_start] if close[sq_start] > 0 else 0
+            events.append({
+                "release_idx": i,
+                "duration": dur,
+                "avg_vol": avg_vol,
+                "squeeze_range": sq_range,
+                "kcr_at_release": kcr[i],
+            })
+    return events
+
+
+def run_improved_squeeze(primary_asset, tf="1h"):
+    # Carica asset primario
+    df = load_data(primary_asset, tf)
+    c, h, l, v = df["close"].values, df["high"].values, df["low"].values, df["volume"].values
+    n = len(df)
+    ts = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
+    ts_ms = df["timestamp"].values
+
+    kcr = keltner_ratio(c, h, l, 14)
+    atr_14 = atr_calc(h, l, c, 14)
+    events = detect_squeezes(c, h, l, v, kcr)
+
+    # Carica asset secondario per cross-check
+    secondary = "BTC" if primary_asset == "ETH" else "ETH"
+    df2 = load_data(secondary, tf)
+    c2, h2, l2 = df2["close"].values, df2["high"].values, df2["low"].values
+    ts2_ms = df2["timestamp"].values
+    kcr2 = keltner_ratio(c2, h2, l2, 14)
+
+    # Mappa ts2 → indici per allineare
+    def find_idx2(ts_val):
+        idx = np.searchsorted(ts2_ms, ts_val)
+        return min(idx, len(c2)-1)
+
+    # Carica 15m per dual-TF
+    if tf == "1h":
+        df_15m = load_data(primary_asset, "15m")
+        c15 = df_15m["close"].values
+        h15 = df_15m["high"].values
+        l15 = df_15m["low"].values
+        ts15 = df_15m["timestamp"].values
+        kcr_15m = keltner_ratio(c15, h15, l15, 14)
+    else:
+        kcr_15m = None
+        ts15 = None
+
+    # ================================================================
+    #  CONFIGURAZIONI
+    # ================================================================
+    configs = [
+        # (name, use_cross, use_timing, use_duration, use_dual_tf, use_antifake, use_trailing, hold, stop_atr)
+        ("BASE",                False, False, False, False, False, False, 3, 0),
+        ("cross_asset",         True,  False, False, False, False, False, 3, 0),
+        ("timing_filter",       False, True,  False, False, False, False, 3, 0),
+        ("long_squeeze",        False, False, True,  False, False, False, 3, 0),
+        ("dual_tf",             False, False, False, True,  False, False, 3, 0),
+        ("anti_fakeout",        False, False, False, False, True,  False, 3, 0),
+        ("trailing_stop",       False, False, False, False, False, True,  6, 1.5),
+        ("cross+timing",        True,  True,  False, False, False, False, 3, 0),
+        ("cross+long+timing",   True,  True,  True,  False, False, False, 3, 0),
+        ("cross+dual_tf",       True,  False, False, True,  False, False, 3, 0),
+        ("ALL_FILTERS",         True,  True,  True,  True,  True,  False, 3, 0),
+        ("ALL+trailing",        True,  True,  True,  True,  True,  True,  6, 1.5),
+        ("cross+antifake",      True,  False, False, False, True,  False, 3, 0),
+        ("timing+antifake",     False, True,  False, False, True,  False, 3, 0),
+        ("cross+timing+antifk", True,  True,  False, False, True,  False, 3, 0),
+        ("cross+timing+trail",  True,  True,  False, False, False, True,  6, 1.5),
+    ]
+
+    print(f"\n{'#'*75}")
+    print(f"  {primary_asset} {tf} — SQUEEZE MIGLIORATO")
+    print(f"{'#'*75}")
+
+    results = []
+
+    for name, f_cross, f_timing, f_dur, f_dual, f_antifake, f_trail, hold, stop_atr_m in configs:
+        yearly = {}
+        capital = float(INITIAL)
+        peak = capital
+        max_dd = 0
+
+        for ev in events:
+            i = ev["release_idx"]
+            if i + hold + 2 >= n:
+                continue
+
+            # --- FILTRI ---
+            skip = False
+
+            # Cross-asset: secondary deve anche essere in squeeze recente o breakout
+            if f_cross:
+                i2 = find_idx2(ts_ms[i])
+                if i2 >= 5:
+                    sec_in_squeeze = any(not np.isnan(kcr2[j]) and kcr2[j] < 0.85 for j in range(max(0,i2-10), i2+1))
+                    if not sec_in_squeeze:
+                        skip = True
+
+            # Timing: solo certe ore (testato: 6-14 UTC migliori)
+            if f_timing:
+                hour = ts.iloc[i].hour
+                if hour < 4 or hour > 16:
+                    skip = True
+
+            # Duration: solo squeeze > 10 barre
+            if f_dur:
+                if ev["duration"] < 10:
+                    skip = True
+
+            # Dual-TF: squeeze anche su 15m
+            if f_dual and kcr_15m is not None and ts15 is not None:
+                i15 = np.searchsorted(ts15, ts_ms[i])
+                if i15 >= 5:
+                    sq_15m = any(not np.isnan(kcr_15m[j]) and kcr_15m[j] < 0.85 for j in range(max(0,i15-20), i15+1))
+                    if not sq_15m:
+                        skip = True
+
+            # Anti-fakeout: prima candela post-breakout non deve ritracciare >50%
+            if f_antifake and i + 1 < n:
+                breakout_bar_range = h[i] - l[i]
+                if breakout_bar_range > 0:
+                    if c[i] > c[i-1]:  # breakout up
+                        retrace = (h[i] - c[i]) / breakout_bar_range
+                    else:  # breakout down
+                        retrace = (c[i] - l[i]) / breakout_bar_range
+                    if retrace > 0.6:
+                        skip = True
+
+            if skip:
+                continue
+
+            # --- DIREZIONE ---
+            first_ret = (c[i] - c[i-1]) / c[i-1]
+            if abs(first_ret) < 0.001:
+                continue
+            direction = 1 if first_ret > 0 else -1
+
+            # --- EXIT ---
+            entry = c[i-1]
+            if f_trail and not np.isnan(atr_14[i]):
+                # Trailing stop
+                trail_dist = atr_14[i] * stop_atr_m
+                best_price = entry
+                exit_price = c[min(i+hold, n-1)]
+                for j in range(i, min(i+hold+1, n)):
+                    if direction == 1:
+                        best_price = max(best_price, h[j])
+                        if l[j] <= best_price - trail_dist:
+                            exit_price = best_price - trail_dist
+                            break
+                    else:
+                        best_price = min(best_price, l[j])
+                        if h[j] >= best_price + trail_dist:
+                            exit_price = best_price + trail_dist
+                            break
+                    exit_price = c[j]
+            else:
+                exit_price = c[min(i+hold-1, n-1)]
+
+            actual = (exit_price - entry) / entry * direction
+            net = actual * LEVERAGE - FEE_RT * LEVERAGE
+
+            capital += capital * 0.15 * net
+            capital = max(capital, 10)
+            if capital > peak: peak = capital
+            dd = (peak - capital) / peak
+            max_dd = max(max_dd, dd)
+
+            year = ts.iloc[i].year
+            if year not in yearly:
+                yearly[year] = {"wins": 0, "total": 0, "pnls": []}
+            yearly[year]["total"] += 1
+            if actual > 0:
+                yearly[year]["wins"] += 1
+            yearly[year]["pnls"].append(net * INITIAL)
+
+        all_t = sum(d["total"] for d in yearly.values())
+        all_w = sum(d["wins"] for d in yearly.values())
+        if all_t < 30:
+            continue
+
+        acc = all_w / all_t * 100
+        all_pnls = [p for d in yearly.values() for p in d["pnls"]]
+        tot_pnl = sum(all_pnls)
+
+        # Worst year
+        worst_y_acc = 100
+        worst_y = ""
+        for y, d in yearly.items():
+            ya = d["wins"]/d["total"]*100 if d["total"] > 0 else 0
+            if ya < worst_y_acc:
+                worst_y_acc = ya
+                worst_y = str(y)
+
+        results.append({
+            "name": name, "trades": all_t, "acc": acc, "pnl": tot_pnl,
+            "max_dd": max_dd*100, "capital": capital,
+            "worst": f"{worst_y}({worst_y_acc:.0f}%)",
+            "yearly": yearly,
+        })
+
+    # Sort by accuracy
+    results.sort(key=lambda x: x["acc"], reverse=True)
+
+    print(f"\n  {'Name':.<26s} {'Trades':>7s} {'Acc':>6s} {'PnL€':>9s} {'DD%':>6s} {'Capital':>10s} {'Worst':>12s}")
+    print(f"  {'-'*80}")
+    for r in results:
+        tag = "✅✅" if r["acc"] >= 80 else "✅" if r["acc"] >= 76 else ""
+        print(f"  {r['name']:.<26s} {r['trades']:>7d} {r['acc']:>5.1f}% €{r['pnl']:>+8.0f} {r['max_dd']:>5.1f}% €{r['capital']:>9,.0f} {r['worst']:>12s} {tag}")
+
+    # Dettaglio per anno del migliore
+    if results:
+        best = results[0]
+        print(f"\n  MIGLIORE: {best['name']} → {best['acc']:.1f}% acc")
+        print(f"  {'Anno':>6s} {'Trades':>7s} {'Acc':>6s} {'PnL€':>9s}")
+        for y in sorted(best["yearly"]):
+            d = best["yearly"][y]
+            ya = d["wins"]/d["total"]*100 if d["total"] > 0 else 0
+            yp = sum(d["pnls"])
+            tag = " ← CRASH" if y in [2020,2021,2022] else ""
+            print(f"  {y:>6d} {d['total']:>7d} {ya:>5.1f}% €{yp:>+8.0f}{tag}")
+
+    return results
+
+
+# Run su entrambi gli asset e timeframe
+all_results = {}
+for asset in ["ETH", "BTC"]:
+    for tf in ["1h", "15m"]:
+        key = f"{asset}_{tf}"
+        all_results[key] = run_improved_squeeze(asset, tf)
+
+# Classifica globale
+print(f"\n\n{'='*75}")
+print(f"  CLASSIFICA GLOBALE — TOP 15")
+print(f"{'='*75}")
+
+global_list = []
+for key, results in all_results.items():
+    for r in results:
+        global_list.append({**r, "asset_tf": key})
+
+global_list.sort(key=lambda x: x["acc"], reverse=True)
+print(f"\n  {'Asset_TF':.<12s} {'Name':.<26s} {'Trades':>6s} {'Acc':>6s} {'PnL€':>9s} {'DD%':>5s} {'Worst':>12s}")
+for r in global_list[:15]:
+    tag = "✅✅" if r["acc"] >= 80 else "✅" if r["acc"] >= 76 else ""
+    print(f"  {r['asset_tf']:.<12s} {r['name']:.<26s} {r['trades']:>6d} {r['acc']:>5.1f}% €{r['pnl']:>+8.0f} {r['max_dd']:>4.1f}% {r['worst']:>12s} {tag}")

scripts/waste/REF_s3_02_lead_lag.py

@@ -0,0 +1,290 @@
+"""S3-02: Lead-lag multi-asset squeeze.
+Quando BTC fa squeeze breakout, ETH/SOL spesso seguono.
+Usa il breakout di BTC per anticipare entrata su ETH (e viceversa).
+Testa anche correlazione inter-asset per conferma segnale.
+"""
+from __future__ import annotations
+import sys
+sys.path.insert(0, ".")
+
+import numpy as np
+import pandas as pd
+from src.data.downloader import load_data
+
+FEE_RT = 0.002
+INITIAL = 1000
+LEVERAGE = 3
+
+
+def keltner_ratio(close, high, low, window=14):
+    n = len(close)
+    r = np.full(n, np.nan)
+    for i in range(window, n):
+        wc, wh, wl = close[i-window:i], high[i-window:i], low[i-window:i]
+        ma = np.mean(wc)
+        bb_std = np.std(wc)
+        tr = np.maximum(wh-wl, np.maximum(np.abs(wh-np.roll(wc,1)), np.abs(wl-np.roll(wc,1))))
+        atr = np.mean(tr[1:])
+        kc = (ma+1.5*atr)-(ma-1.5*atr)
+        bb = (ma+2*bb_std)-(ma-2*bb_std)
+        if kc > 0: r[i] = bb/kc
+    return r
+
+
+def load_aligned(assets, tf):
+    """Carica e allinea dati multi-asset per timestamp."""
+    dfs = {}
+    for asset in assets:
+        try:
+            if asset == "SOL":
+                df = pd.read_parquet(f"data/raw/sol_{tf}.parquet")
+                df["datetime"] = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
+            else:
+                df = load_data(asset, tf)
+            dfs[asset] = df
+        except Exception:
+            pass
+
+    if len(dfs) < 2:
+        return None
+
+    # Allinea per timestamp
+    common_ts = set(dfs[list(dfs.keys())[0]]["timestamp"].values)
+    for df in dfs.values():
+        common_ts &= set(df["timestamp"].values)
+    common_ts = sorted(common_ts)
+
+    aligned = {}
+    for asset, df in dfs.items():
+        mask = df["timestamp"].isin(common_ts)
+        aligned[asset] = df[mask].sort_values("timestamp").reset_index(drop=True)
+
+    return aligned
+
+
+def detect_breakouts(close, high, low, volume, kcr, sq_thr=0.8, min_dur=5):
+    """Detect squeeze breakout events."""
+    events = []
+    in_sq = False
+    sq_start = 0
+    for i in range(1, len(close)):
+        if np.isnan(kcr[i]):
+            continue
+        is_sq = kcr[i] < sq_thr
+        if is_sq and not in_sq:
+            in_sq = True
+            sq_start = i
+        elif not is_sq and in_sq:
+            in_sq = False
+            if i - sq_start < min_dur:
+                continue
+            first_ret = (close[i] - close[i-1]) / close[i-1] if close[i-1] > 0 else 0
+            if abs(first_ret) < 0.001:
+                continue
+            events.append({
+                "idx": i,
+                "duration": i - sq_start,
+                "direction": 1 if first_ret > 0 else -1,
+                "first_ret": first_ret,
+            })
+    return events
+
+
+print("=" * 75)
+print("  S3-02: LEAD-LAG MULTI-ASSET SQUEEZE")
+print("=" * 75)
+
+for tf in ["1h", "15m"]:
+    aligned = load_aligned(["BTC", "ETH", "SOL"], tf)
+    if aligned is None:
+        continue
+
+    n = len(aligned["BTC"])
+    ts = pd.to_datetime(aligned["BTC"]["timestamp"], unit="ms", utc=True)
+
+    print(f"\n  Timeframe: {tf}, Candles allineate: {n}")
+
+    # Calcola squeeze per ogni asset
+    asset_data = {}
+    for asset in aligned:
+        df = aligned[asset]
+        c, h, l, v = df["close"].values, df["high"].values, df["low"].values, df["volume"].values
+        kcr = keltner_ratio(c, h, l, 14)
+        events = detect_breakouts(c, h, l, v, kcr)
+        asset_data[asset] = {"close": c, "high": h, "low": l, "vol": v, "kcr": kcr, "events": events}
+        print(f"    {asset}: {len(events)} squeeze breakouts")
+
+    # ================================================================
+    #  STRATEGIA A: Leader-follower
+    #  Quando BTC fa breakout, entra su ETH/SOL nella stessa direzione
+    # ================================================================
+    print(f"\n  --- LEADER-FOLLOWER ({tf}) ---")
+
+    for leader, follower in [("BTC", "ETH"), ("BTC", "SOL"), ("ETH", "BTC"), ("ETH", "SOL")]:
+        if leader not in asset_data or follower not in asset_data:
+            continue
+
+        leader_events = asset_data[leader]["events"]
+        fc = asset_data[follower]["close"]
+
+        for hold in [3, 6]:
+            for delay in [0, 1, 2]:
+                yearly = {}
+
+                for ev in leader_events:
+                    i = ev["idx"] + delay
+                    if i + hold >= n:
+                        continue
+
+                    # Anti-fakeout su follower
+                    entry = fc[i]
+                    exit_price = fc[min(i + hold, n - 1)]
+                    direction = ev["direction"]
+                    actual = (exit_price - entry) / entry * direction
+                    net = actual * LEVERAGE - FEE_RT * LEVERAGE
+
+                    year = ts.iloc[min(i, n-1)].year
+                    if year not in yearly:
+                        yearly[year] = {"w": 0, "t": 0, "pnls": []}
+                    yearly[year]["t"] += 1
+                    if actual > 0:
+                        yearly[year]["w"] += 1
+                    yearly[year]["pnls"].append(net * INITIAL)
+
+                all_t = sum(d["t"] for d in yearly.values())
+                all_w = sum(d["w"] for d in yearly.values())
+                if all_t < 30:
+                    continue
+                acc = all_w / all_t * 100
+                pnl = sum(p for d in yearly.values() for p in d["pnls"])
+                worst_y = min(yearly.items(), key=lambda x: x[1]["w"]/x[1]["t"] if x[1]["t"]>0 else 0)
+                worst_acc = worst_y[1]["w"]/worst_y[1]["t"]*100 if worst_y[1]["t"]>0 else 0
+                tag = "✅" if acc >= 76 else ""
+                print(f"    {leader}→{follower} d={delay} h={hold}: trades={all_t:5d} acc={acc:.1f}% pnl=€{pnl:+.0f} worst={worst_y[0]}({worst_acc:.0f}%) {tag}")
+
+    # ================================================================
+    #  STRATEGIA B: Consensus multi-asset
+    #  Trade solo quando 2+ asset hanno squeeze breakout nello stesso momento
+    # ================================================================
+    print(f"\n  --- CONSENSUS MULTI-ASSET ({tf}) ---")
+
+    # Build event map: timestamp → list of (asset, direction)
+    event_map = {}
+    for asset, data in asset_data.items():
+        for ev in data["events"]:
+            idx = ev["idx"]
+            if idx not in event_map:
+                event_map[idx] = []
+            event_map[idx].append((asset, ev["direction"]))
+
+    for target in ["BTC", "ETH", "SOL"]:
+        if target not in asset_data:
+            continue
+        tc = asset_data[target]["close"]
+
+        for min_consensus in [2, 3]:
+            for window_bars in [1, 3, 5]:
+                yearly = {}
+                daily_done = set()
+
+                for idx in sorted(event_map.keys()):
+                    if idx + 6 >= n:
+                        continue
+
+                    day = ts.iloc[idx].strftime("%Y-%m-%d")
+                    if day in daily_done:
+                        continue
+
+                    # Count consensus within window
+                    nearby_events = []
+                    for j in range(max(0, idx - window_bars), idx + window_bars + 1):
+                        if j in event_map:
+                            nearby_events.extend(event_map[j])
+
+                    # Unique assets
+                    unique_assets = set(a for a, d in nearby_events)
+                    if len(unique_assets) < min_consensus:
+                        continue
+
+                    # Majority direction
+                    dirs = [d for a, d in nearby_events]
+                    majority = 1 if sum(dirs) > 0 else -1
+
+                    entry = tc[idx]
+                    exit_price = tc[min(idx + 3, n - 1)]
+                    actual = (exit_price - entry) / entry * majority
+                    net = actual * LEVERAGE - FEE_RT * LEVERAGE
+
+                    year = ts.iloc[idx].year
+                    if year not in yearly:
+                        yearly[year] = {"w": 0, "t": 0, "pnls": []}
+                    yearly[year]["t"] += 1
+                    if actual > 0:
+                        yearly[year]["w"] += 1
+                    yearly[year]["pnls"].append(net * INITIAL)
+                    daily_done.add(day)
+
+                all_t = sum(d["t"] for d in yearly.values())
+                all_w = sum(d["w"] for d in yearly.values())
+                if all_t < 20:
+                    continue
+                acc = all_w / all_t * 100
+                pnl = sum(p for d in yearly.values() for p in d["pnls"])
+                tag = "✅" if acc >= 76 else ""
+                print(f"    {target} consensus>={min_consensus} w={window_bars}: trades={all_t:4d} acc={acc:.1f}% pnl=€{pnl:+.0f} {tag}")
+
+    # ================================================================
+    #  STRATEGIA C: Correlation-weighted squeeze
+    #  Peso il segnale squeeze in base alla correlazione rolling con BTC
+    # ================================================================
+    print(f"\n  --- CORRELATION-WEIGHTED ({tf}) ---")
+
+    for target in ["ETH", "SOL"]:
+        if target not in asset_data:
+            continue
+        tc = asset_data[target]["close"]
+        btc_c = asset_data["BTC"]["close"]
+
+        # Rolling correlation
+        corr_window = 48  # 48 bars
+        rolling_corr = np.full(n, np.nan)
+        ret_t = np.diff(np.log(np.where(tc == 0, 1e-10, tc)))
+        ret_b = np.diff(np.log(np.where(btc_c == 0, 1e-10, btc_c)))
+        for i in range(corr_window, len(ret_t)):
+            c_val = np.corrcoef(ret_t[i-corr_window:i], ret_b[i-corr_window:i])[0, 1]
+            rolling_corr[i + 1] = c_val if np.isfinite(c_val) else 0
+
+        events = asset_data[target]["events"]
+
+        for corr_thr in [0.5, 0.6, 0.7, 0.8]:
+            yearly = {}
+            for ev in events:
+                i = ev["idx"]
+                if i + 3 >= n or np.isnan(rolling_corr[i]):
+                    continue
+
+                # Solo quando correlazione con BTC è alta
+                if abs(rolling_corr[i]) < corr_thr:
+                    continue
+
+                entry = tc[i - 1]
+                exit_price = tc[min(i + 2, n - 1)]
+                actual = (exit_price - entry) / entry * ev["direction"]
+                net = actual * LEVERAGE - FEE_RT * LEVERAGE
+
+                year = ts.iloc[i].year
+                if year not in yearly:
+                    yearly[year] = {"w": 0, "t": 0, "pnls": []}
+                yearly[year]["t"] += 1
+                if actual > 0:
+                    yearly[year]["w"] += 1
+                yearly[year]["pnls"].append(net * INITIAL)
+
+            all_t = sum(d["t"] for d in yearly.values())
+            all_w = sum(d["w"] for d in yearly.values())
+            if all_t < 20:
+                continue
+            acc = all_w / all_t * 100
+            pnl = sum(p for d in yearly.values() for p in d["pnls"])
+            tag = "✅" if acc >= 76 else ""
+            print(f"    {target} corr>={corr_thr}: trades={all_t:4d} acc={acc:.1f}% pnl=€{pnl:+.0f} {tag}")

scripts/waste/REF_s3_03_ultimate.py

@@ -0,0 +1,256 @@
+"""S3-03: Ultimate Squeeze — combina TUTTI i filtri migliori.
+Filtri che funzionano (testati singolarmente):
+- Anti-fakeout (+1% acc)
+- Long squeeze duration (+1% acc)
+- Cross-asset squeeze simultaneo (+0.5%)
+- Timing 4-16 UTC (+0.5%)
+- Correlation ETH-BTC alta per ETH trades (+1%)
+- Volume confirmation al breakout
+
+Nuovi filtri da testare:
+- Volume delta: up_volume - down_volume al breakout
+- Momentum confirmation: breakout nella direzione del trend 1h
+- Volatility regime: skip in regime estremo (RV > 100%)
+"""
+from __future__ import annotations
+import sys
+sys.path.insert(0, ".")
+
+import numpy as np
+import pandas as pd
+from src.data.downloader import load_data
+
+FEE_RT = 0.002
+INITIAL = 1000
+LEVERAGE = 3
+
+
+def keltner_ratio(close, high, low, window=14):
+    n = len(close)
+    r = np.full(n, np.nan)
+    for i in range(window, n):
+        wc, wh, wl = close[i-window:i], high[i-window:i], low[i-window:i]
+        ma = np.mean(wc)
+        bb_std = np.std(wc)
+        tr = np.maximum(wh-wl, np.maximum(np.abs(wh-np.roll(wc,1)), np.abs(wl-np.roll(wc,1))))
+        atr = np.mean(tr[1:])
+        kc = (ma+1.5*atr)-(ma-1.5*atr)
+        bb = (ma+2*bb_std)-(ma-2*bb_std)
+        if kc > 0: r[i] = bb/kc
+    return r
+
+
+def ema(arr, period):
+    r = np.full(len(arr), np.nan)
+    k = 2/(period+1)
+    r[period-1] = np.mean(arr[:period])
+    for i in range(period, len(arr)):
+        r[i] = arr[i]*k + r[i-1]*(1-k)
+    return r
+
+
+def rv_ann(close, window):
+    lr = np.diff(np.log(np.where(close == 0, 1e-10, close)))
+    r = np.full(len(close), np.nan)
+    for i in range(window, len(lr)):
+        r[i+1] = np.std(lr[i-window:i]) * np.sqrt(24*365)
+    return r
+
+
+def run_ultimate(primary, tf="15m"):
+    secondary = "ETH" if primary == "BTC" else "BTC"
+
+    df = load_data(primary, tf)
+    c, h, l, v = df["close"].values, df["high"].values, df["low"].values, df["volume"].values
+    n = len(df)
+    ts = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
+
+    df2 = load_data(secondary, tf)
+    c2, ts2 = df2["close"].values, df2["timestamp"].values
+
+    kcr = keltner_ratio(c, h, l, 14)
+    kcr2 = keltner_ratio(c2, df2["high"].values, df2["low"].values, 14)
+
+    ema_50 = ema(c, 50)
+    rv_48 = rv_ann(c, 48)
+
+    # Rolling correlation
+    ret1 = np.diff(np.log(np.where(c == 0, 1e-10, c)))
+    ret2 = np.diff(np.log(np.where(c2[:len(c)] == 0, 1e-10, c2[:len(c)])))
+    min_len = min(len(ret1), len(ret2))
+    ret1 = ret1[:min_len]
+    ret2 = ret2[:min_len]
+    corr = np.full(n, np.nan)
+    for i in range(48, min_len):
+        cv = np.corrcoef(ret1[i-48:i], ret2[i-48:i])[0,1]
+        corr[i+1] = cv if np.isfinite(cv) else 0
+
+    # Detect squeezes
+    events = []
+    in_sq = False
+    sq_start = 0
+    for i in range(15, n):
+        if np.isnan(kcr[i]): continue
+        is_sq = kcr[i] < 0.8
+        if is_sq and not in_sq:
+            in_sq = True
+            sq_start = i
+        elif not is_sq and in_sq:
+            in_sq = False
+            dur = i - sq_start
+            if dur < 5 or i + 6 >= n:
+                continue
+            events.append({"idx": i, "dur": dur, "sq_start": sq_start})
+
+    print(f"\n{'#'*70}")
+    print(f"  {primary} {tf} — ULTIMATE SQUEEZE ({len(events)} squeeze events)")
+    print(f"{'#'*70}")
+
+    filters_map = {
+        "antifake": lambda ev, i: not _antifake(c, h, l, i),
+        "long_sq": lambda ev, i: ev["dur"] >= 10,
+        "timing": lambda ev, i: 4 <= ts.iloc[i].hour <= 16,
+        "cross": lambda ev, i: _cross_squeeze(kcr2, i, ts, ts2),
+        "corr_high": lambda ev, i: not np.isnan(corr[i]) and abs(corr[i]) >= 0.6,
+        "vol_confirm": lambda ev, i: _vol_confirm(v, i, ev["sq_start"]),
+        "trend_align": lambda ev, i: _trend_align(c, ema_50, i),
+        "low_rv": lambda ev, i: not np.isnan(rv_48[i]) and rv_48[i] < 1.5,
+    }
+
+    def _antifake(c, h, l, i):
+        if i + 1 >= len(c): return False
+        br = h[i] - l[i]
+        if br <= 0: return False
+        if c[i] > c[i-1]:
+            return (h[i] - c[i]) / br > 0.6
+        return (c[i] - l[i]) / br > 0.6
+
+    def _cross_squeeze(kcr2, i, ts1, ts2_arr):
+        i2 = np.searchsorted(ts2_arr, ts.values[i].astype("int64") // 10**6)
+        i2 = min(i2, len(kcr2)-1)
+        return any(not np.isnan(kcr2[j]) and kcr2[j] < 0.85 for j in range(max(0,i2-10), i2+1))
+
+    def _vol_confirm(v, i, sq_start):
+        avg = np.mean(v[sq_start:i])
+        return avg > 0 and v[i] > avg * 1.3
+
+    def _trend_align(c, ema_val, i):
+        if np.isnan(ema_val[i]): return True
+        first_ret = (c[i] - c[i-1]) / c[i-1] if c[i-1] > 0 else 0
+        if first_ret > 0:
+            return c[i] > ema_val[i]
+        return c[i] < ema_val[i]
+
+    # Test combinazioni incrementali
+    combos = [
+        ("BASE", []),
+        ("antifake", ["antifake"]),
+        ("long_sq", ["long_sq"]),
+        ("antifake+long", ["antifake", "long_sq"]),
+        ("antifake+timing", ["antifake", "timing"]),
+        ("antifake+cross", ["antifake", "cross"]),
+        ("antifake+corr", ["antifake", "corr_high"]),
+        ("antifake+vol", ["antifake", "vol_confirm"]),
+        ("antifake+trend", ["antifake", "trend_align"]),
+        ("af+long+timing", ["antifake", "long_sq", "timing"]),
+        ("af+long+cross", ["antifake", "long_sq", "cross"]),
+        ("af+long+corr", ["antifake", "long_sq", "corr_high"]),
+        ("af+long+trend", ["antifake", "long_sq", "trend_align"]),
+        ("af+long+cross+time", ["antifake", "long_sq", "cross", "timing"]),
+        ("af+long+corr+time", ["antifake", "long_sq", "corr_high", "timing"]),
+        ("af+long+corr+trend", ["antifake", "long_sq", "corr_high", "trend_align"]),
+        ("ALL_NO_VOL", ["antifake", "long_sq", "cross", "timing", "corr_high", "trend_align", "low_rv"]),
+        ("ALL", ["antifake", "long_sq", "cross", "timing", "corr_high", "vol_confirm", "trend_align", "low_rv"]),
+        ("BEST_5", ["antifake", "long_sq", "corr_high", "trend_align", "low_rv"]),
+    ]
+
+    results = []
+    for combo_name, filter_names in combos:
+        yearly = {}
+        capital = float(INITIAL)
+        peak = capital
+        max_dd = 0
+
+        for ev in events:
+            i = ev["idx"]
+            first_ret = (c[i] - c[i-1]) / c[i-1] if c[i-1] > 0 else 0
+            if abs(first_ret) < 0.001:
+                continue
+
+            skip = False
+            for fn in filter_names:
+                if fn in filters_map and not filters_map[fn](ev, i):
+                    skip = True
+                    break
+            if skip:
+                continue
+
+            direction = 1 if first_ret > 0 else -1
+            entry = c[i-1]
+            exit_price = c[min(i+2, n-1)]
+            actual = (exit_price - entry) / entry * direction
+            net = actual * LEVERAGE - FEE_RT * LEVERAGE
+
+            capital += capital * 0.15 * net
+            capital = max(capital, 10)
+            if capital > peak: peak = capital
+            dd = (peak - capital) / peak
+            max_dd = max(max_dd, dd)
+
+            year = ts.iloc[i].year
+            if year not in yearly:
+                yearly[year] = {"w": 0, "t": 0, "pnls": []}
+            yearly[year]["t"] += 1
+            if actual > 0: yearly[year]["w"] += 1
+            yearly[year]["pnls"].append(net * INITIAL)
+
+        all_t = sum(d["t"] for d in yearly.values())
+        all_w = sum(d["w"] for d in yearly.values())
+        if all_t < 20: continue
+
+        acc = all_w / all_t * 100
+        pnl = sum(p for d in yearly.values() for p in d["pnls"])
+        worst = min(yearly.items(), key=lambda x: x[1]["w"]/x[1]["t"] if x[1]["t"]>0 else 0)
+        wa = worst[1]["w"]/worst[1]["t"]*100 if worst[1]["t"]>0 else 0
+
+        results.append({
+            "name": combo_name, "trades": all_t, "acc": acc, "pnl": pnl,
+            "dd": max_dd*100, "capital": capital, "worst": f"{worst[0]}({wa:.0f}%)",
+            "yearly": yearly,
+        })
+
+    results.sort(key=lambda x: x["acc"], reverse=True)
+
+    print(f"\n  {'Name':.<28s} {'Trades':>6s} {'Acc':>6s} {'PnL€':>9s} {'DD%':>5s} {'Worst':>12s}")
+    print(f"  {'-'*70}")
+    for r in results[:20]:
+        tag = "✅✅" if r["acc"] >= 80 else "✅" if r["acc"] >= 78 else ""
+        print(f"  {r['name']:.<28s} {r['trades']:>6d} {r['acc']:>5.1f}% €{r['pnl']:>+8.0f} {r['dd']:>4.1f}% {r['worst']:>12s} {tag}")
+
+    # Dettaglio migliore
+    if results:
+        best = results[0]
+        print(f"\n  MIGLIORE: {best['name']} → {best['acc']:.1f}% acc, DD {best['dd']:.1f}%")
+        for y in sorted(best["yearly"]):
+            d = best["yearly"][y]
+            ya = d["w"]/d["t"]*100 if d["t"]>0 else 0
+            tag = " ← CRASH" if y in [2020,2021,2022] else ""
+            print(f"    {y}: {d['t']:4d}t {ya:5.1f}% €{sum(d['pnls']):+.0f}{tag}")
+
+    return results
+
+
+all_r = []
+for asset in ["BTC", "ETH"]:
+    for tf in ["15m", "1h"]:
+        r = run_ultimate(asset, tf)
+        for x in r:
+            all_r.append({**x, "key": f"{asset}_{tf}"})
+
+all_r.sort(key=lambda x: x["acc"], reverse=True)
+print(f"\n\n{'='*70}")
+print(f"  TOP 10 GLOBALE")
+print(f"{'='*70}")
+for r in all_r[:10]:
+    tag = "✅✅" if r["acc"] >= 80 else "✅" if r["acc"] >= 78 else ""
+    print(f"  {r['key']:.<10s} {r['name']:.<28s} {r['trades']:>5d} {r['acc']:>5.1f}% €{r['pnl']:>+8.0f} DD {r['dd']:.1f}% {r['worst']:>12s} {tag}")

scripts/waste/W11_mean_reversion.py

@@ -0,0 +1,160 @@
+"""S2-01: Mean Reversion oraria con filtro orario.
+Idea: crypto ha bias di ritorno alla media nelle ore notturne (00-06 UTC)
+e di momentum nelle ore diurne USA (14-20 UTC).
+- Compra quando RSI < 30 in ore notturne
+- Vendi quando RSI > 70 in ore notturne
+- Hold max 4h, stop loss 1.5%
+Timeframe: 1h. Ingresso quasi giornaliero.
+"""
+from __future__ import annotations
+import sys
+sys.path.insert(0, ".")
+
+import numpy as np
+import pandas as pd
+from src.data.downloader import load_data
+
+FEE = 0.001
+INITIAL = 1000
+LEVERAGE = 3
+
+
+def rsi(close: np.ndarray, period: int = 14) -> np.ndarray:
+    delta = np.diff(close)
+    gain = np.where(delta > 0, delta, 0)
+    loss = np.where(delta < 0, -delta, 0)
+    result = np.full(len(close), 50.0)
+    avg_gain = np.mean(gain[:period])
+    avg_loss = np.mean(loss[:period])
+    for i in range(period, len(delta)):
+        avg_gain = (avg_gain * (period - 1) + gain[i]) / period
+        avg_loss = (avg_loss * (period - 1) + loss[i]) / period
+        if avg_loss == 0:
+            result[i + 1] = 100
+        else:
+            rs = avg_gain / avg_loss
+            result[i + 1] = 100 - 100 / (1 + rs)
+    return result
+
+
+def bollinger_pct(close: np.ndarray, window: int = 20) -> np.ndarray:
+    result = np.full(len(close), 0.5)
+    for i in range(window, len(close)):
+        w = close[i - window : i]
+        ma = np.mean(w)
+        std = np.std(w)
+        if std > 0:
+            result[i] = (close[i] - (ma - 2 * std)) / (4 * std)
+    return result
+
+
+def run_mean_reversion(asset, tf="1h"):
+    df = load_data(asset, tf)
+    close = df["close"].values
+    high = df["high"].values
+    low = df["low"].values
+    n = len(df)
+
+    timestamps = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
+    hours = timestamps.dt.hour.values
+
+    rsi_vals = rsi(close, 14)
+    bb_pct = bollinger_pct(close, 20)
+
+    split = int(n * 0.7)
+
+    configs = [
+        # (rsi_low, rsi_high, allowed_hours, hold_max, stop_pct, name)
+        (25, 75, list(range(0, 7)), 4, 0.015, "night_0-6_rsi25"),
+        (30, 70, list(range(0, 7)), 4, 0.015, "night_0-6_rsi30"),
+        (25, 75, list(range(0, 10)), 6, 0.02, "extended_0-9"),
+        (30, 70, list(range(20, 24)) + list(range(0, 6)), 4, 0.015, "late_night"),
+        (20, 80, list(range(0, 24)), 4, 0.015, "all_hours_rsi20"),
+        # Bollinger band mean reversion
+    ]
+
+    print(f"\n{'#'*60}")
+    print(f"  {asset} {tf} — MEAN REVERSION")
+    print(f"{'#'*60}")
+
+    for rsi_low, rsi_high, allowed, hold_max, stop, name in configs:
+        capital = float(INITIAL)
+        correct = 0
+        total = 0
+        daily_trades = {}
+
+        for i in range(max(split, 20), n - hold_max):
+            hour = hours[i]
+            if hour not in allowed:
+                continue
+
+            day = timestamps[i].strftime("%Y-%m-%d")
+            if daily_trades.get(day, 0) >= 2:
+                continue
+
+            direction = None
+            if rsi_vals[i] < rsi_low and bb_pct[i] < 0.2:
+                direction = "long"
+            elif rsi_vals[i] > rsi_high and bb_pct[i] > 0.8:
+                direction = "short"
+
+            if direction is None:
+                continue
+
+            entry = close[i]
+            best_exit = i + 1
+            for j in range(i + 1, min(i + hold_max + 1, n)):
+                price = close[j]
+                if direction == "long":
+                    pnl_pct = (price - entry) / entry
+                    if pnl_pct <= -stop:
+                        best_exit = j
+                        break
+                    if pnl_pct >= stop * 1.5:
+                        best_exit = j
+                        break
+                else:
+                    pnl_pct = (entry - price) / entry
+                    if pnl_pct <= -stop:
+                        best_exit = j
+                        break
+                    if pnl_pct >= stop * 1.5:
+                        best_exit = j
+                        break
+                best_exit = j
+
+            exit_price = close[best_exit]
+            if direction == "long":
+                trade_ret = (exit_price - entry) / entry
+            else:
+                trade_ret = (entry - exit_price) / entry
+
+            net = trade_ret * LEVERAGE - FEE * 2 * LEVERAGE
+            capital += capital * 0.15 * net
+            capital = max(capital, 0)
+
+            is_correct = trade_ret > 0
+            total += 1
+            if is_correct:
+                correct += 1
+            daily_trades[day] = daily_trades.get(day, 0) + 1
+
+        if total < 20:
+            continue
+
+        acc = correct / total * 100
+        ret = (capital - INITIAL) / INITIAL * 100
+        test_days = (n - split) / 24
+        test_years = test_days / 365.25
+        ann = ((capital / INITIAL) ** (1 / test_years) - 1) * 100 if test_years > 0 and capital > 0 else -100
+        dpnl = (capital - INITIAL) / test_days if test_days > 0 else 0
+        days_with_trades = len(daily_trades)
+        trades_per_day = total / days_with_trades if days_with_trades > 0 else 0
+
+        tag = "✅" if acc >= 60 and ann >= 30 else ""
+        print(f"  {name:25s}: trades={total:5d} acc={acc:.1f}% ret={ret:+.1f}% ann={ann:+.1f}% dd_est ~{abs(min(0, ret/3)):.0f}% €/day={dpnl:.2f} days_active={days_with_trades} {tag}")
+
+
+for asset in ["ETH", "BTC"]:
+    run_mean_reversion(asset, "1h")
+    run_mean_reversion(asset, "15m")

scripts/waste/W12_funding_rate.py

@@ -0,0 +1,129 @@
+"""S2-02: Funding Rate Strategy.
+Quando il funding rate è molto positivo → troppi long → short il perpetual.
+Quando molto negativo → troppi short → long il perpetual.
+Si cattura sia il mean reversion del prezzo che il funding rate stesso.
+Ingresso: quando funding > 0.03% o < -0.03% (8h rate).
+"""
+from __future__ import annotations
+import sys
+sys.path.insert(0, ".")
+
+import numpy as np
+import pandas as pd
+from src.data.downloader import load_data
+
+FEE = 0.001
+INITIAL = 1000
+LEVERAGE = 3
+
+
+def simulate_funding_strategy(asset):
+    """Simula funding rate strategy usando il proxy: overnight returns.
+    Crypto funding settlement ogni 8h → prezzo tende a correggersi dopo settlement.
+    Proxy: se ultime 8h hanno avuto forte trend, aspettati reversal dopo settlement.
+    """
+    print(f"\n{'#'*60}")
+    print(f"  {asset} — FUNDING RATE PROXY STRATEGY")
+    print(f"{'#'*60}")
+
+    df_1h = load_data(asset, "1h")
+    close = df_1h["close"].values
+    volume = df_1h["volume"].values
+    n = len(close)
+    split = int(n * 0.7)
+
+    timestamps = pd.to_datetime(df_1h["timestamp"], unit="ms", utc=True)
+    hours = timestamps.dt.hour.values
+
+    # Funding settlement su Deribit: 00:00, 08:00, 16:00 UTC
+    settlement_hours = {0, 8, 16}
+
+    configs = [
+        (0.01, 0.02, 8, 0.02, "mild_1pct"),
+        (0.015, 0.025, 8, 0.015, "moderate_1.5pct"),
+        (0.02, 0.03, 8, 0.015, "strong_2pct"),
+        (0.01, 0.015, 4, 0.01, "fast_1pct_4h"),
+        (0.02, 0.03, 12, 0.02, "slow_2pct_12h"),
+        (0.025, 0.04, 6, 0.015, "extreme_2.5pct"),
+    ]
+
+    for entry_thr, tp_mult_unused, hold_max, stop, name in configs:
+        capital = float(INITIAL)
+        correct = 0
+        total = 0
+        daily_trades = {}
+
+        for i in range(max(split, 8), n - hold_max):
+            hour = hours[i]
+            if hour not in settlement_hours:
+                continue
+
+            day = timestamps[i].strftime("%Y-%m-%d")
+            if daily_trades.get(day, 0) >= 1:
+                continue
+
+            # 8h return prima del settlement = proxy per funding pressure
+            ret_8h = (close[i] - close[i - 8]) / close[i - 8]
+
+            # Volume spike = conferma
+            vol_avg = np.mean(volume[max(0, i - 48) : i])
+            vol_recent = np.mean(volume[i - 8 : i])
+            vol_spike = vol_recent / vol_avg if vol_avg > 0 else 1
+
+            direction = None
+            if ret_8h > entry_thr and vol_spike > 1.1:
+                direction = "short"  # troppi long, attendi reversal
+            elif ret_8h < -entry_thr and vol_spike > 1.1:
+                direction = "long"  # troppi short, attendi rimbalzo
+
+            if direction is None:
+                continue
+
+            entry_price = close[i]
+            for j in range(i + 1, min(i + hold_max + 1, n)):
+                price = close[j]
+                if direction == "long":
+                    pnl_pct = (price - entry_price) / entry_price
+                else:
+                    pnl_pct = (entry_price - price) / entry_price
+
+                if pnl_pct <= -stop or pnl_pct >= stop * 2 or j == min(i + hold_max, n - 1):
+                    exit_price = price
+                    break
+            else:
+                exit_price = close[min(i + hold_max, n - 1)]
+
+            if direction == "long":
+                trade_ret = (exit_price - entry_price) / entry_price
+            else:
+                trade_ret = (entry_price - exit_price) / entry_price
+
+            # Add funding rate income (approx 0.01% per 8h period if direction correct)
+            funding_income = 0.0001 * (hold_max / 8) if trade_ret > 0 else 0
+
+            net = (trade_ret + funding_income) * LEVERAGE - FEE * 2 * LEVERAGE
+            capital += capital * 0.2 * net
+            capital = max(capital, 0)
+
+            total += 1
+            if trade_ret > 0:
+                correct += 1
+            daily_trades[day] = daily_trades.get(day, 0) + 1
+
+        if total < 10:
+            continue
+
+        acc = correct / total * 100
+        ret = (capital - INITIAL) / INITIAL * 100
+        test_days = (n - split) / 24
+        test_years = test_days / 365.25
+        ann = ((capital / INITIAL) ** (1 / test_years) - 1) * 100 if test_years > 0 and capital > 0 else -100
+        dpnl = (capital - INITIAL) / test_days if test_days > 0 else 0
+        days_active = len(daily_trades)
+
+        tag = "✅" if acc >= 60 and ann >= 30 else ""
+        print(f"  {name:20s}: trades={total:4d} acc={acc:.1f}% ret={ret:+.1f}% ann={ann:+.1f}% €/day={dpnl:.2f} active_days={days_active} {tag}")
+
+
+for asset in ["ETH", "BTC"]:
+    simulate_funding_strategy(asset)

scripts/waste/W13_vol_selling.py

@@ -0,0 +1,145 @@
+"""S2-03: Volatility Selling — Straddle/Strangle corto simulato.
+La IV crypto è cronicamente sopra la realized vol → vendere premium è profittevole.
+Simulazione: vendi straddle ATM → profitto = max(0, premium - |move|).
+Premium stimato da IV storica. Ingresso giornaliero.
+"""
+from __future__ import annotations
+import sys
+sys.path.insert(0, ".")
+
+import numpy as np
+import pandas as pd
+from scipy.stats import norm
+from src.data.downloader import load_data
+
+FEE = 0.001
+INITIAL = 1000
+
+
+def realized_vol(close: np.ndarray, window: int = 24) -> np.ndarray:
+    """Annualized realized volatility rolling."""
+    log_ret = np.diff(np.log(np.where(close == 0, 1e-10, close)))
+    result = np.full(len(close), 0.5)
+    for i in range(window, len(log_ret)):
+        rv = np.std(log_ret[i - window : i]) * np.sqrt(24 * 365)
+        result[i + 1] = rv
+    return result
+
+
+def implied_vol_proxy(close: np.ndarray, window: int = 48) -> np.ndarray:
+    """IV proxy: realized vol * premium factor.
+    Storicamente IV crypto ≈ 1.2-1.5x realized vol (variance risk premium).
+    """
+    rv = realized_vol(close, window)
+    # Premium factor varia: alto in panic, basso in calma
+    result = np.full(len(close), 0.5)
+    for i in range(window, len(close)):
+        short_rv = realized_vol(close[max(0, i-12):i+1], min(12, i))[-1] if i >= 12 else rv[i]
+        if rv[i] > 0:
+            regime = short_rv / rv[i]
+            premium = 1.15 + 0.3 * max(0, regime - 1)  # più alto in regime volatile
+        else:
+            premium = 1.2
+        result[i] = rv[i] * premium
+    return result
+
+
+def bs_straddle_price(spot: float, iv: float, dte_hours: float) -> float:
+    """Black-Scholes straddle price (call + put ATM)."""
+    if dte_hours <= 0 or iv <= 0:
+        return 0
+    t = dte_hours / (24 * 365)
+    d1 = (0.5 * iv * iv * t) / (iv * np.sqrt(t))
+    call = spot * (2 * norm.cdf(d1) - 1)
+    return call * 2  # straddle = 2 * ATM call (approx for ATM)
+
+
+def run_vol_selling(asset):
+    print(f"\n{'#'*60}")
+    print(f"  {asset} — VOLATILITY SELLING (SHORT STRADDLE)")
+    print(f"{'#'*60}")
+
+    df = load_data(asset, "1h")
+    close = df["close"].values
+    n = len(close)
+    split = int(n * 0.7)
+    timestamps = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
+
+    rv = realized_vol(close, 24)
+    iv_proxy = implied_vol_proxy(close)
+
+    configs = [
+        # (dte_hours, iv_floor, iv_rv_ratio_min, position_pct, name)
+        (24, 0.3, 1.15, 0.1, "daily_24h"),
+        (12, 0.3, 1.15, 0.08, "half_day_12h"),
+        (48, 0.3, 1.10, 0.12, "2day_48h"),
+        (24, 0.4, 1.20, 0.1, "daily_highIV"),
+        (8, 0.25, 1.10, 0.06, "ultra_short_8h"),
+        (24, 0.3, 1.30, 0.15, "daily_bigPremium"),
+    ]
+
+    for dte, iv_floor, ratio_min, pos_pct, name in configs:
+        capital = float(INITIAL)
+        correct = 0
+        total = 0
+        daily_trades = {}
+
+        for i in range(max(split, 50), n - dte):
+            day = timestamps[i].strftime("%Y-%m-%d")
+            if daily_trades.get(day, 0) >= 1:
+                continue
+
+            hour = timestamps[i].dt.hour if hasattr(timestamps[i], 'dt') else timestamps.iloc[i].hour
+            if hour != 8:  # entrata alle 08 UTC ogni giorno
+                continue
+
+            current_iv = iv_proxy[i]
+            current_rv = rv[i]
+
+            if current_iv < iv_floor:
+                continue
+            if current_rv > 0 and current_iv / current_rv < ratio_min:
+                continue
+
+            spot = close[i]
+            premium = bs_straddle_price(spot, current_iv, dte)
+            premium_pct = premium / spot
+
+            # Actual move during holding period
+            exit_idx = min(i + dte, n - 1)
+            actual_move = abs(close[exit_idx] - spot)
+            actual_move_pct = actual_move / spot
+
+            # P&L: premium received - actual move (capped at max loss)
+            max_loss = spot * 0.05  # cap loss at 5% of spot
+            pnl = premium - min(actual_move, max_loss + premium)
+
+            pnl_on_capital = pnl / spot * pos_pct
+            fee_cost = FEE * 4 * pos_pct  # 4 legs: sell call, sell put, buy back
+            net_pnl = pnl_on_capital - fee_cost
+
+            capital += capital * net_pnl
+            capital = max(capital, 0)
+
+            total += 1
+            if pnl > 0:
+                correct += 1
+            daily_trades[day] = daily_trades.get(day, 0) + 1
+
+        if total < 20:
+            continue
+
+        acc = correct / total * 100
+        ret = (capital - INITIAL) / INITIAL * 100
+        test_days = (n - split) / 24
+        test_years = test_days / 365.25
+        ann = ((capital / INITIAL) ** (1 / test_years) - 1) * 100 if test_years > 0 and capital > 0 else -100
+        dpnl = (capital - INITIAL) / test_days if test_days > 0 else 0
+        days_active = len(daily_trades)
+
+        tag = "✅" if acc >= 60 and ann >= 30 else ""
+        print(f"  {name:20s}: trades={total:4d} acc={acc:.1f}% ret={ret:+.1f}% ann={ann:+.1f}% €/day={dpnl:.2f} active={days_active} {tag}")
+
+
+for asset in ["ETH", "BTC"]:
+    run_vol_selling(asset)

scripts/waste/W14_momentum_micro.py

@@ -0,0 +1,159 @@
+"""S2-04: Momentum microstructure su 5m.
+Approccio: cattura micro-trend intraday.
+- Identifica breakout da consolidamento su 5m
+- Conferma con volume e acceleration
+- Hold breve (15-30 min), stop stretto
+- Target: molti piccoli guadagni, alta frequenza
+"""
+from __future__ import annotations
+import sys
+sys.path.insert(0, ".")
+
+import numpy as np
+import pandas as pd
+from src.data.downloader import load_data
+
+FEE = 0.001
+INITIAL = 1000
+LEVERAGE = 3
+
+
+def ema(arr: np.ndarray, period: int) -> np.ndarray:
+    result = np.full(len(arr), np.nan)
+    k = 2 / (period + 1)
+    result[period - 1] = np.mean(arr[:period])
+    for i in range(period, len(arr)):
+        result[i] = arr[i] * k + result[i - 1] * (1 - k)
+    return result
+
+
+def atr(high: np.ndarray, low: np.ndarray, close: np.ndarray, period: int = 14) -> np.ndarray:
+    tr = np.maximum(high - low, np.maximum(np.abs(high - np.roll(close, 1)), np.abs(low - np.roll(close, 1))))
+    tr[0] = high[0] - low[0]
+    return ema(tr, period)
+
+
+def run_momentum(asset):
+    print(f"\n{'#'*60}")
+    print(f"  {asset} 5m — MOMENTUM MICROSTRUCTURE")
+    print(f"{'#'*60}")
+
+    df = load_data(asset, "5m")
+    close = df["close"].values
+    high = df["high"].values
+    low = df["low"].values
+    volume = df["volume"].values
+    n = len(close)
+    split = int(n * 0.7)
+
+    timestamps = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
+
+    ema_fast = ema(close, 8)
+    ema_slow = ema(close, 21)
+    ema_trend = ema(close, 55)
+    atr_vals = atr(high, low, close, 14)
+
+    configs = [
+        # (consolidation_bars, breakout_atr_mult, hold_bars, stop_atr, tp_atr, min_vol_mult, name)
+        (12, 1.5, 3, 1.0, 2.0, 1.3, "tight_12bar"),
+        (12, 1.5, 6, 1.5, 2.5, 1.2, "medium_12bar"),
+        (24, 2.0, 6, 1.5, 3.0, 1.5, "wide_24bar"),
+        (6, 1.2, 3, 1.0, 1.5, 1.1, "fast_6bar"),
+        (12, 1.5, 3, 0.8, 2.0, 1.3, "tight_stop"),
+        (18, 1.8, 4, 1.2, 2.5, 1.4, "balanced_18bar"),
+    ]
+
+    for consol_bars, brk_mult, hold_bars, stop_m, tp_m, vol_mult, name in configs:
+        capital = float(INITIAL)
+        correct = 0
+        total = 0
+        daily_trades = {}
+
+        for i in range(max(split, 60), n - hold_bars):
+            if np.isnan(ema_fast[i]) or np.isnan(ema_slow[i]) or np.isnan(atr_vals[i]) or atr_vals[i] == 0:
+                continue
+
+            day = timestamps.iloc[i].strftime("%Y-%m-%d")
+            if daily_trades.get(day, 0) >= 5:
+                continue
+
+            # Consolidation: range delle ultime N barre < 1.5 ATR
+            consol_range = np.max(high[i - consol_bars : i]) - np.min(low[i - consol_bars : i])
+            if consol_range > 1.5 * atr_vals[i]:
+                continue
+
+            # Breakout: current bar breaks consolidation range
+            consol_high = np.max(high[i - consol_bars : i])
+            consol_low = np.min(low[i - consol_bars : i])
+
+            breakout_up = close[i] > consol_high + atr_vals[i] * (brk_mult - 1)
+            breakout_down = close[i] < consol_low - atr_vals[i] * (brk_mult - 1)
+
+            if not (breakout_up or breakout_down):
+                continue
+
+            # Volume confirmation
+            vol_avg = np.mean(volume[max(0, i - 24) : i])
+            if vol_avg > 0 and volume[i] < vol_avg * vol_mult:
+                continue
+
+            # Trend filter: only trade in direction of trend
+            if breakout_up and close[i] < ema_trend[i]:
+                continue
+            if breakout_down and close[i] > ema_trend[i]:
+                continue
+
+            direction = "long" if breakout_up else "short"
+            entry = close[i]
+            stop_price = entry - atr_vals[i] * stop_m if direction == "long" else entry + atr_vals[i] * stop_m
+            tp_price = entry + atr_vals[i] * tp_m if direction == "long" else entry - atr_vals[i] * tp_m
+
+            exit_price = close[min(i + hold_bars, n - 1)]
+            for j in range(i + 1, min(i + hold_bars + 1, n)):
+                if direction == "long":
+                    if low[j] <= stop_price:
+                        exit_price = stop_price
+                        break
+                    if high[j] >= tp_price:
+                        exit_price = tp_price
+                        break
+                else:
+                    if high[j] >= stop_price:
+                        exit_price = stop_price
+                        break
+                    if low[j] <= tp_price:
+                        exit_price = tp_price
+                        break
+                exit_price = close[j]
+
+            if direction == "long":
+                trade_ret = (exit_price - entry) / entry
+            else:
+                trade_ret = (entry - exit_price) / entry
+
+            net = trade_ret * LEVERAGE - FEE * 2 * LEVERAGE
+            capital += capital * 0.1 * net
+            capital = max(capital, 0)
+
+            total += 1
+            if trade_ret > 0:
+                correct += 1
+            daily_trades[day] = daily_trades.get(day, 0) + 1
+
+        if total < 30:
+            continue
+
+        acc = correct / total * 100
+        ret = (capital - INITIAL) / INITIAL * 100
+        test_days = (n - split) / (24 * 12)
+        test_years = test_days / 365.25
+        ann = ((capital / INITIAL) ** (1 / test_years) - 1) * 100 if test_years > 0 and capital > 0 else -100
+        dpnl = (capital - INITIAL) / test_days if test_days > 0 else 0
+        days_active = len(daily_trades)
+
+        tag = "✅" if acc >= 55 and ann >= 30 else ""
+        print(f"  {name:20s}: trades={total:5d} acc={acc:.1f}% ret={ret:+.1f}% ann={ann:+.1f}% €/day={dpnl:.2f} active={days_active} t/day={total/days_active:.1f} {tag}")
+
+
+for asset in ["ETH", "BTC"]:
+    run_momentum(asset)

scripts/waste/W15_gap_fade.py

@@ -0,0 +1,132 @@
+"""S2-05: Gap fade + overnight reversal.
+Crypto non ha gap di apertura classici, ma ha "gap di sessione":
+- Asia open (00 UTC): tende a continuare il trend USA precedente
+- EU open (07 UTC): spesso corregge eccessi notturni
+- USA open (13-14 UTC): alta volatilità, breakout o reversal
+
+Strategia: fai fade dell'overextension al cambio sessione.
+Se il prezzo ha fatto >1.5% nella sessione precedente, aspettati reversal.
+"""
+from __future__ import annotations
+import sys
+sys.path.insert(0, ".")
+
+import numpy as np
+import pandas as pd
+from src.data.downloader import load_data
+
+FEE = 0.001
+INITIAL = 1000
+LEVERAGE = 3
+
+
+def run_gap_fade(asset, tf="1h"):
+    print(f"\n{'#'*60}")
+    print(f"  {asset} {tf} — GAP FADE / SESSION REVERSAL")
+    print(f"{'#'*60}")
+
+    df = load_data(asset, tf)
+    close = df["close"].values
+    high = df["high"].values
+    low = df["low"].values
+    n = len(close)
+    split = int(n * 0.7)
+    timestamps = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
+    hours = timestamps.dt.hour.values
+
+    session_opens = {
+        "asia": 0,
+        "eu": 7,
+        "usa": 14,
+    }
+
+    configs = [
+        # (session_name, lookback_hours, entry_thr, hold_hours, stop_pct, name)
+        ("eu", 7, 0.015, 4, 0.012, "eu_fade_1.5pct"),
+        ("eu", 7, 0.02, 4, 0.015, "eu_fade_2pct"),
+        ("eu", 7, 0.01, 6, 0.01, "eu_fade_1pct_6h"),
+        ("usa", 7, 0.015, 4, 0.012, "usa_fade_1.5pct"),
+        ("usa", 7, 0.02, 4, 0.015, "usa_fade_2pct"),
+        ("asia", 8, 0.02, 6, 0.015, "asia_fade_2pct"),
+        ("eu", 7, 0.025, 3, 0.015, "eu_fade_2.5pct_fast"),
+        ("usa", 6, 0.015, 3, 0.01, "usa_fade_fast"),
+    ]
+
+    for session, lookback, entry_thr, hold, stop, name in configs:
+        capital = float(INITIAL)
+        correct = 0
+        total = 0
+        daily_trades = {}
+
+        session_hour = session_opens[session]
+
+        for i in range(max(split, lookback + 1), n - hold):
+            if hours[i] != session_hour:
+                continue
+
+            day = timestamps.iloc[i].strftime("%Y-%m-%d")
+            if daily_trades.get(day, 0) >= 1:
+                continue
+
+            prev_ret = (close[i] - close[i - lookback]) / close[i - lookback]
+
+            direction = None
+            if prev_ret > entry_thr:
+                direction = "short"  # fade the rally
+            elif prev_ret < -entry_thr:
+                direction = "long"  # fade the dump
+
+            if direction is None:
+                continue
+
+            entry = close[i]
+            exit_price = close[min(i + hold, n - 1)]
+
+            for j in range(i + 1, min(i + hold + 1, n)):
+                if direction == "long":
+                    if (close[j] - entry) / entry >= stop * 2:
+                        exit_price = close[j]
+                        break
+                    if (entry - close[j]) / entry >= stop:
+                        exit_price = close[j]
+                        break
+                else:
+                    if (entry - close[j]) / entry >= stop * 2:
+                        exit_price = close[j]
+                        break
+                    if (close[j] - entry) / entry >= stop:
+                        exit_price = close[j]
+                        break
+                exit_price = close[j]
+
+            if direction == "long":
+                trade_ret = (exit_price - entry) / entry
+            else:
+                trade_ret = (entry - exit_price) / entry
+
+            net = trade_ret * LEVERAGE - FEE * 2 * LEVERAGE
+            capital += capital * 0.2 * net
+            capital = max(capital, 0)
+
+            total += 1
+            if trade_ret > 0:
+                correct += 1
+            daily_trades[day] = daily_trades.get(day, 0) + 1
+
+        if total < 15:
+            continue
+
+        acc = correct / total * 100
+        ret = (capital - INITIAL) / INITIAL * 100
+        test_days = (n - split) / 24
+        test_years = test_days / 365.25
+        ann = ((capital / INITIAL) ** (1 / test_years) - 1) * 100 if test_years > 0 and capital > 0 else -100
+        dpnl = (capital - INITIAL) / test_days if test_days > 0 else 0
+        days_active = len(daily_trades)
+
+        tag = "✅" if acc >= 58 and ann >= 30 else ""
+        print(f"  {name:25s}: trades={total:4d} acc={acc:.1f}% ret={ret:+.1f}% ann={ann:+.1f}% €/day={dpnl:.2f} active={days_active} {tag}")
+
+
+for asset in ["ETH", "BTC"]:
+    run_gap_fade(asset)

scripts/waste/W16_iron_condor.py

@@ -0,0 +1,164 @@
+"""S2-06: Iron Condor simulato + Variance Risk Premium harvesting.
+Vendi un range: se il prezzo sta dentro il range a scadenza → profitto.
+Più sofisticato del vol selling puro:
+- Calcolo IV vs RV (variance risk premium)
+- Selezione larghezza condor in base a IV/RV ratio
+- Dynamic position sizing: più capital quando IV/RV ratio è alto
+- Ingresso giornaliero, scadenze 24h e 48h
+- Include: tail risk protection (chiudi se move > 2 ATR)
+"""
+from __future__ import annotations
+import sys
+sys.path.insert(0, ".")
+
+import numpy as np
+import pandas as pd
+from src.data.downloader import load_data
+
+FEE = 0.001
+INITIAL = 1000
+
+
+def realized_vol_ann(close: np.ndarray, window: int) -> np.ndarray:
+    log_ret = np.diff(np.log(np.where(close == 0, 1e-10, close)))
+    result = np.full(len(close), 0.5)
+    for i in range(window, len(log_ret)):
+        result[i + 1] = np.std(log_ret[i - window : i]) * np.sqrt(24 * 365)
+    return result
+
+
+def run_iron_condor(asset, tf="1h"):
+    print(f"\n{'#'*60}")
+    print(f"  {asset} {tf} — IRON CONDOR / VARIANCE PREMIUM")
+    print(f"{'#'*60}")
+
+    df = load_data(asset, tf)
+    close = df["close"].values
+    high = df["high"].values
+    low = df["low"].values
+    n = len(close)
+    split = int(n * 0.7)
+    timestamps = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
+
+    rv_24 = realized_vol_ann(close, 24)
+    rv_48 = realized_vol_ann(close, 48)
+    rv_168 = realized_vol_ann(close, 168)  # 1 week
+
+    IV_PREMIUM = 1.25  # IV typically 1.2-1.3x RV in crypto
+
+    configs = [
+        # (dte_hours, condor_width_mult, max_loss_pct, vrp_min, pos_pct, name)
+        (24, 1.0, 0.03, 1.10, 0.15, "24h_1x_std"),
+        (24, 1.5, 0.04, 1.10, 0.12, "24h_1.5x_safe"),
+        (24, 0.8, 0.025, 1.15, 0.18, "24h_0.8x_aggr"),
+        (48, 1.0, 0.035, 1.10, 0.15, "48h_1x_std"),
+        (48, 1.5, 0.05, 1.10, 0.12, "48h_1.5x_safe"),
+        (48, 0.7, 0.025, 1.20, 0.20, "48h_0.7x_highVRP"),
+        (72, 1.2, 0.04, 1.10, 0.12, "72h_1.2x"),
+        (24, 1.0, 0.03, 1.30, 0.20, "24h_veryHighVRP"),
+        (24, 1.2, 0.035, 1.10, 0.15, "24h_1.2x_balanced"),
+    ]
+
+    for dte, width_mult, max_loss, vrp_min, pos_pct, name in configs:
+        capital = float(INITIAL)
+        correct = 0
+        total = 0
+        daily_trades = {}
+        max_dd = 0
+        peak = capital
+
+        for i in range(max(split, 170), n - dte):
+            day = timestamps.iloc[i].strftime("%Y-%m-%d")
+            if daily_trades.get(day, 0) >= 1:
+                continue
+
+            hour = timestamps.iloc[i].hour
+            if hour != 8:
+                continue
+
+            rv_short = rv_24[i]
+            rv_long = rv_168[i]
+
+            if rv_short <= 0 or rv_long <= 0:
+                continue
+
+            iv_est = rv_long * IV_PREMIUM
+            vrp_ratio = iv_est / rv_short
+
+            if vrp_ratio < vrp_min:
+                continue
+
+            spot = close[i]
+            t_years = dte / (24 * 365)
+
+            # Condor range: spot ± width * daily_std * sqrt(t)
+            daily_std = rv_short / np.sqrt(365)
+            range_width = width_mult * daily_std * np.sqrt(dte / 24) * spot
+
+            upper_strike = spot + range_width
+            lower_strike = spot - range_width
+
+            # Premium collected (simplified BS for condor)
+            # Premium ≈ IV * sqrt(t) * (width factor)
+            premium_pct = iv_est * np.sqrt(t_years) * 0.4 * (1 / width_mult)
+
+            # Check if price stays in range
+            exit_idx = min(i + dte, n - 1)
+            price_path = close[i : exit_idx + 1]
+            max_move = max(np.max(price_path) - spot, spot - np.min(price_path))
+            final_price = close[exit_idx]
+
+            in_range = lower_strike <= final_price <= upper_strike
+            breached_hard = max_move > spot * max_loss
+
+            if breached_hard:
+                pnl_pct = -max_loss * pos_pct
+            elif in_range:
+                pnl_pct = premium_pct * pos_pct
+            else:
+                # Partial loss: exceeded range but not catastrophic
+                excess = max(0, final_price - upper_strike, lower_strike - final_price)
+                loss = min(excess / spot, max_loss)
+                pnl_pct = (premium_pct - loss) * pos_pct
+
+            fee_cost = FEE * 2 * pos_pct
+            net_pnl = pnl_pct - fee_cost
+
+            capital += capital * net_pnl
+            capital = max(capital, 0)
+
+            if capital > peak:
+                peak = capital
+            dd = (peak - capital) / peak if peak > 0 else 0
+            max_dd = max(max_dd, dd)
+
+            total += 1
+            if net_pnl > 0:
+                correct += 1
+            daily_trades[day] = daily_trades.get(day, 0) + 1
+
+        if total < 20:
+            continue
+
+        acc = correct / total * 100
+        ret = (capital - INITIAL) / INITIAL * 100
+        test_days = (n - split) / 24
+        test_years = test_days / 365.25
+        ann = ((capital / INITIAL) ** (1 / test_years) - 1) * 100 if test_years > 0 and capital > 0 else -100
+        dpnl = (capital - INITIAL) / test_days if test_days > 0 else 0
+        days_active = len(daily_trades)
+
+        tag = "✅✅" if acc >= 70 and ann >= 50 else "✅" if acc >= 65 and ann >= 30 else ""
+        print(f"  {name:22s}: trades={total:4d} acc={acc:.1f}% ret={ret:+.1f}% ann={ann:+.1f}% dd={max_dd*100:.1f}% €/day={dpnl:.2f} active={days_active} {tag}")
+
+
+for asset in ["ETH", "BTC"]:
+    run_iron_condor(asset)
+
+# === COMBINAZIONE: Iron Condor + Funding + Gap Fade ===
+print(f"\n{'#'*60}")
+print(f"  COMBINAZIONE: MULTI-STRATEGY PORTFOLIO")
+print(f"{'#'*60}")
+
+# Simula portafoglio: 50% iron condor ETH, 25% iron condor BTC, 25% gap fade ETH
+print("  (Dettagli nel prossimo script con backtest combinato)")

scripts/waste/W17_vol_premium.py

@@ -0,0 +1,252 @@
+"""S2-07: Variance Risk Premium harvesting — versione raffinata.
+Ottimizzazione del vol selling con:
+1. IV/RV ratio dinamico per entry timing
+2. Tail risk cutoff (chiudi se move > N sigma)
+3. Position sizing proporzionale al premium
+4. Combinazione con directional bias (da gap fade)
+5. Multi-asset portfolio (ETH + BTC)
+"""
+from __future__ import annotations
+import sys
+sys.path.insert(0, ".")
+
+import numpy as np
+import pandas as pd
+from scipy.stats import norm
+from src.data.downloader import load_data
+
+FEE = 0.001
+INITIAL = 1000
+
+
+def realized_vol(close, window=24):
+    log_ret = np.diff(np.log(np.where(close == 0, 1e-10, close)))
+    result = np.full(len(close), 0.5)
+    for i in range(window, len(log_ret)):
+        result[i + 1] = np.std(log_ret[i - window : i]) * np.sqrt(24 * 365)
+    return result
+
+
+def run_vrp(asset):
+    print(f"\n{'#'*60}")
+    print(f"  {asset} 1h — VARIANCE RISK PREMIUM REFINED")
+    print(f"{'#'*60}")
+
+    df = load_data(asset, "1h")
+    close = df["close"].values
+    high = df["high"].values
+    low = df["low"].values
+    n = len(close)
+    split = int(n * 0.7)
+    timestamps = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
+
+    rv_24 = realized_vol(close, 24)
+    rv_48 = realized_vol(close, 48)
+    rv_168 = realized_vol(close, 168)
+
+    configs = [
+        # (dte_h, iv_mult, cutoff_sigma, pos_base, entry_hour, dynamic_sizing, name)
+        (24, 1.20, 2.5, 0.10, 8, False, "24h_base"),
+        (24, 1.25, 2.5, 0.12, 8, False, "24h_highPrem"),
+        (24, 1.20, 2.0, 0.10, 8, False, "24h_tightCut"),
+        (24, 1.20, 3.0, 0.12, 8, False, "24h_wideCut"),
+        (48, 1.20, 2.5, 0.12, 8, False, "48h_base"),
+        (48, 1.25, 2.5, 0.15, 8, False, "48h_highPrem"),
+        (48, 1.30, 2.5, 0.15, 8, False, "48h_vhighPrem"),
+        (48, 1.20, 3.0, 0.15, 8, False, "48h_wideCut"),
+        (24, 1.20, 2.5, 0.10, 8, True, "24h_dynSize"),
+        (48, 1.20, 2.5, 0.12, 8, True, "48h_dynSize"),
+        (24, 1.20, 2.5, 0.10, 0, False, "24h_midnight"),
+        (24, 1.20, 2.5, 0.10, 16, False, "24h_afternoon"),
+        (36, 1.22, 2.5, 0.12, 8, False, "36h_medium"),
+        (24, 1.15, 2.5, 0.08, 8, False, "24h_lowPrem_safe"),
+        (48, 1.20, 2.0, 0.10, 8, True, "48h_tight_dyn"),
+    ]
+
+    for dte, iv_mult, cutoff, pos_base, entry_h, dyn_size, name in configs:
+        capital = float(INITIAL)
+        correct = 0
+        total = 0
+        daily_trades = {}
+        peak_capital = capital
+        max_dd = 0
+
+        for i in range(max(split, 170), n - dte):
+            day = timestamps.iloc[i].strftime("%Y-%m-%d")
+            if daily_trades.get(day, 0) >= 1:
+                continue
+
+            if timestamps.iloc[i].hour != entry_h:
+                continue
+
+            rv_s = rv_24[i]
+            rv_l = rv_168[i]
+            if rv_s <= 0.05 or rv_l <= 0.05:
+                continue
+
+            iv_est = rv_l * iv_mult
+            vrp = iv_est - rv_s
+
+            if vrp <= 0:
+                continue
+
+            spot = close[i]
+            t = dte / (24 * 365)
+            daily_std = rv_s / np.sqrt(365)
+
+            # Premium = IV * sqrt(t) * spot * factor
+            premium = iv_est * np.sqrt(t) * spot * 0.4
+            premium_pct = premium / spot
+
+            # Expected move based on IV
+            expected_move = iv_est * np.sqrt(t) * spot
+
+            # Cutoff: close if actual move > cutoff * expected_move
+            max_allowed_move = expected_move * cutoff
+
+            # Dynamic sizing: more when VRP is high
+            if dyn_size:
+                vrp_ratio = vrp / rv_s
+                pos_pct = min(pos_base * (1 + vrp_ratio), pos_base * 2)
+            else:
+                pos_pct = pos_base
+
+            # Check actual path
+            exit_idx = min(i + dte, n - 1)
+            actual_move = abs(close[exit_idx] - spot)
+
+            # Early exit: check if intra-period move exceeds cutoff
+            breached = False
+            for j in range(i + 1, exit_idx + 1):
+                intra_move = abs(close[j] - spot)
+                if intra_move > max_allowed_move:
+                    breached = True
+                    exit_idx = j
+                    actual_move = intra_move
+                    break
+
+            if breached:
+                loss = min(actual_move / spot, 0.05) * pos_pct
+                pnl = -loss
+            else:
+                profit = premium_pct * pos_pct
+                partial_loss = max(0, actual_move / spot - premium_pct) * pos_pct * 0.5
+                pnl = profit - partial_loss
+
+            fee_cost = FEE * 2 * pos_pct
+            net = pnl - fee_cost
+
+            capital += capital * net
+            capital = max(capital, 0)
+
+            if capital > peak_capital:
+                peak_capital = capital
+            dd = (peak_capital - capital) / peak_capital if peak_capital > 0 else 0
+            max_dd = max(max_dd, dd)
+
+            total += 1
+            if pnl > 0:
+                correct += 1
+            daily_trades[day] = daily_trades.get(day, 0) + 1
+
+        if total < 20:
+            continue
+
+        acc = correct / total * 100
+        ret = (capital - INITIAL) / INITIAL * 100
+        test_days = (n - split) / 24
+        test_years = test_days / 365.25
+        ann = ((capital / INITIAL) ** (1 / test_years) - 1) * 100 if test_years > 0 and capital > 0 else -100
+        dpnl = (capital - INITIAL) / test_days if test_days > 0 else 0
+        days_active = len(daily_trades)
+
+        tag = "✅✅" if acc >= 70 and ann >= 50 else "✅" if acc >= 65 and ann >= 30 else ""
+        print(f"  {name:22s}: trades={total:4d} acc={acc:.1f}% ret={ret:+.1f}% ann={ann:+.1f}% dd={max_dd*100:.1f}% €/day={dpnl:.2f} active={days_active} {tag}")
+
+    return daily_trades
+
+
+# Run both assets
+results = {}
+for asset in ["ETH", "BTC"]:
+    results[asset] = run_vrp(asset)
+
+# Multi-asset portfolio simulation
+print(f"\n{'#'*60}")
+print(f"  MULTI-ASSET PORTFOLIO: ETH + BTC")
+print(f"{'#'*60}")
+
+df_eth = load_data("ETH", "1h")
+df_btc = load_data("BTC", "1h")
+close_eth = df_eth["close"].values
+close_btc = df_btc["close"].values
+n = min(len(close_eth), len(close_btc))
+split = int(n * 0.7)
+ts = pd.to_datetime(df_eth["timestamp"].values[:n], unit="ms", utc=True)
+
+rv_eth = realized_vol(close_eth[:n], 168)
+rv_btc = realized_vol(close_btc[:n], 168)
+
+capital = float(INITIAL)
+total = 0
+correct = 0
+peak = capital
+max_dd = 0
+daily_trades = {}
+
+for i in range(max(split, 170), n - 48):
+    day = ts[i].strftime("%Y-%m-%d")
+    if daily_trades.get(day, 0) >= 1:
+        continue
+    if ts[i].hour != 8:
+        continue
+
+    for asset_close, rv_arr, name in [(close_eth[:n], rv_eth, "ETH"), (close_btc[:n], rv_btc, "BTC")]:
+        rv = rv_arr[i]
+        if rv <= 0.05:
+            continue
+        iv = rv * 1.22
+        spot = asset_close[i]
+        t = 48 / (24 * 365)
+        premium_pct = iv * np.sqrt(t) * 0.4
+        expected_move = iv * np.sqrt(t) * spot
+        max_move = expected_move * 2.5
+
+        exit_idx = min(i + 48, n - 1)
+        actual_move = abs(asset_close[exit_idx] - spot)
+
+        breached = False
+        for j in range(i + 1, exit_idx + 1):
+            if abs(asset_close[j] - spot) > max_move:
+                breached = True
+                actual_move = abs(asset_close[j] - spot)
+                break
+
+        pos_pct = 0.07  # 7% per asset = 14% total
+        if breached:
+            pnl = -min(actual_move / spot, 0.05) * pos_pct
+        else:
+            profit = premium_pct * pos_pct
+            partial = max(0, actual_move / spot - premium_pct) * pos_pct * 0.5
+            pnl = profit - partial
+
+        capital += capital * (pnl - FEE * 2 * pos_pct)
+        capital = max(capital, 0)
+        total += 1
+        if pnl > 0:
+            correct += 1
+
+    if capital > peak:
+        peak = capital
+    dd = (peak - capital) / peak if peak > 0 else 0
+    max_dd = max(max_dd, dd)
+    daily_trades[day] = daily_trades.get(day, 0) + 1
+
+if total > 0:
+    acc = correct / total * 100
+    ret = (capital - INITIAL) / INITIAL * 100
+    test_days = (n - split) / 24
+    test_years = test_days / 365.25
+    ann = ((capital / INITIAL) ** (1 / test_years) - 1) * 100 if test_years > 0 and capital > 0 else -100
+    dpnl = (capital - INITIAL) / test_days if test_days > 0 else 0
+    print(f"\n  ETH+BTC 48h portfolio: trades={total:4d} acc={acc:.1f}% ret={ret:+.1f}% ann={ann:+.1f}% dd={max_dd*100:.1f}% €/day={dpnl:.2f} active={len(daily_trades)}")

scripts/waste/W18_vrp_honest.py

@@ -0,0 +1,245 @@
+"""S2-08: VRP Honest Test.
+Problemi del test precedente:
+1. IV stimata con moltiplicatore fisso → troppo ottimista
+2. Nessun stress test su crash
+3. Nessun costo di margin
+4. Walk-forward mancante
+
+Fix:
+- IV calcolata come rolling ratio IV/RV da dati DVOL reali (90 giorni)
+  e applicata storicamente con variabilità
+- Stress test esplicito su periodi di crisi
+- Margin requirement: 5% del notional bloccato
+- Walk-forward: retrain IV/RV ratio ogni 30 giorni
+- Fee realistiche: 0.05% maker + 0.05% taker per gamba = 0.2% roundtrip straddle
+- Slippage: 0.1% per esecuzione
+"""
+from __future__ import annotations
+import sys
+sys.path.insert(0, ".")
+
+import numpy as np
+import pandas as pd
+from src.data.downloader import load_data
+
+# Costi REALISTICI Deribit options
+FEE_PER_LEG = 0.0003  # 0.03% per leg (Deribit option fee)
+SLIPPAGE = 0.001  # 0.1% bid-ask spread per leg
+TOTAL_COST_ROUNDTRIP = (FEE_PER_LEG + SLIPPAGE) * 4  # 4 legs: sell call, sell put, buy back both
+MARGIN_REQUIREMENT = 0.05  # 5% del notional bloccato come margine
+INITIAL = 1000
+
+
+def realized_vol_ann(close, window):
+    log_ret = np.diff(np.log(np.where(close == 0, 1e-10, close)))
+    result = np.full(len(close), np.nan)
+    for i in range(window, len(log_ret)):
+        result[i + 1] = np.std(log_ret[i - window : i]) * np.sqrt(24 * 365)
+    return result
+
+
+def iv_estimate_realistic(rv_short, rv_long, regime_vol):
+    """Stima IV realistica basata su regime.
+    In calma: IV ≈ 1.1-1.2x RV
+    In stress: IV ≈ 0.8-1.0x RV (perché RV è già esplosa ma IV non tiene il passo)
+    Post-crash: IV ≈ 1.5-2.0x RV (IV elevata, RV sta scendendo)
+    """
+    if rv_short <= 0 or rv_long <= 0:
+        return rv_long * 1.1 if rv_long > 0 else 0.5
+
+    # Regime detection
+    regime_ratio = rv_short / rv_long
+
+    if regime_ratio > 2.0:
+        # CRASH in corso: RV short term esplosa, IV non scala altrettanto
+        premium = 0.85 + np.random.normal(0, 0.05)
+    elif regime_ratio > 1.3:
+        # Alta volatilità: premium compresso
+        premium = 1.0 + np.random.normal(0, 0.05)
+    elif regime_ratio < 0.7:
+        # Post-crash calma: IV ancora alta, RV scesa
+        premium = 1.3 + np.random.normal(0, 0.1)
+    else:
+        # Normale: premium standard
+        premium = 1.15 + np.random.normal(0, 0.08)
+
+    premium = max(0.7, min(premium, 1.8))  # clamp
+    return rv_long * premium
+
+
+def straddle_premium_pct(iv, dte_hours):
+    """Premium straddle ATM in % del spot. Approssimazione BS."""
+    if iv <= 0 or dte_hours <= 0:
+        return 0
+    t = dte_hours / (24 * 365)
+    # ATM straddle ≈ spot * iv * sqrt(t) * 0.8 (approssimazione standard)
+    return iv * np.sqrt(t) * 0.8
+
+
+def run_vrp_honest(asset, dte_hours=24, n_simulations=5):
+    print(f"\n{'='*65}")
+    print(f"  {asset} — VRP HONEST TEST (DTE={dte_hours}h)")
+    print(f"  Fees: {TOTAL_COST_ROUNDTRIP*100:.2f}% roundtrip, Slippage incluso")
+    print(f"  Margin: {MARGIN_REQUIREMENT*100}% del notional")
+    print(f"{'='*65}")
+
+    df = load_data(asset, "1h")
+    close = df["close"].values
+    n = len(close)
+    split = int(n * 0.7)
+    timestamps = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
+
+    rv_24 = realized_vol_ann(close, 24)
+    rv_72 = realized_vol_ann(close, 72)
+    rv_168 = realized_vol_ann(close, 168)
+
+    # Identifica periodi di crisi per report separato
+    crisis_periods = {
+        "COVID crash (Mar 2020)": ("2020-03-01", "2020-04-01"),
+        "May 2021 crash": ("2021-05-01", "2021-06-01"),
+        "Luna/3AC (Jun 2022)": ("2022-06-01", "2022-07-15"),
+        "FTX collapse (Nov 2022)": ("2022-11-01", "2022-12-15"),
+    }
+
+    all_sim_results = []
+
+    for sim in range(n_simulations):
+        np.random.seed(42 + sim)
+        capital = float(INITIAL)
+        total = 0
+        correct = 0
+        peak = capital
+        max_dd = 0
+        daily_trades = {}
+        crisis_pnl = {k: 0.0 for k in crisis_periods}
+
+        for i in range(max(split, 170), n - dte_hours):
+            day = timestamps.iloc[i].strftime("%Y-%m-%d")
+            if daily_trades.get(day, 0) >= 1:
+                continue
+            if timestamps.iloc[i].hour != 8:
+                continue
+
+            rv_s = rv_24[i]
+            rv_m = rv_72[i]
+            rv_l = rv_168[i]
+
+            if np.isnan(rv_s) or np.isnan(rv_l) or rv_s <= 0.05 or rv_l <= 0.05:
+                continue
+
+            # IV realistica con variabilità
+            iv = iv_estimate_realistic(rv_s, rv_l, rv_m)
+
+            # Premium straddle
+            prem_pct = straddle_premium_pct(iv, dte_hours)
+
+            if prem_pct <= TOTAL_COST_ROUNDTRIP:
+                continue  # non vale la pena, costi > premium
+
+            spot = close[i]
+
+            # Position size: limitata dal margine
+            margin_per_unit = spot * MARGIN_REQUIREMENT
+            max_notional = capital / margin_per_unit * spot
+            pos_pct = min(0.15, capital / (spot * MARGIN_REQUIREMENT * 10))  # conservativo
+
+            # Actual path
+            exit_idx = min(i + dte_hours, n - 1)
+            actual_move_pct = abs(close[exit_idx] - spot) / spot
+
+            # Intra-period max move (per stress check)
+            path = close[i : exit_idx + 1]
+            max_adverse_pct = max(np.max(path) - spot, spot - np.min(path)) / spot
+
+            # P&L straddle short
+            if actual_move_pct <= prem_pct:
+                # In profitto: premium - actual move
+                raw_pnl_pct = (prem_pct - actual_move_pct) * pos_pct
+            else:
+                # In perdita: move > premium
+                loss = actual_move_pct - prem_pct
+                # Cap loss at 3x premium (risk management)
+                loss = min(loss, prem_pct * 3)
+                raw_pnl_pct = -loss * pos_pct
+
+            # Costi
+            cost = TOTAL_COST_ROUNDTRIP * pos_pct
+            net_pnl_pct = raw_pnl_pct - cost
+
+            capital += capital * net_pnl_pct
+            capital = max(capital, 10)  # floor
+
+            if capital > peak:
+                peak = capital
+            dd = (peak - capital) / peak
+            max_dd = max(max_dd, dd)
+
+            total += 1
+            if raw_pnl_pct > 0:
+                correct += 1
+
+            daily_trades[day] = daily_trades.get(day, 0) + 1
+
+            # Track crisis PnL
+            for crisis_name, (c_start, c_end) in crisis_periods.items():
+                if c_start <= day <= c_end:
+                    crisis_pnl[crisis_name] += capital * net_pnl_pct
+
+        if total < 20:
+            continue
+
+        acc = correct / total * 100
+        ret = (capital - INITIAL) / INITIAL * 100
+        test_days = (n - split) / 24
+        test_years = test_days / 365.25
+        ann = ((capital / INITIAL) ** (1 / test_years) - 1) * 100 if test_years > 0 and capital > 0 else -100
+        dpnl = (capital - INITIAL) / test_days if test_days > 0 else 0
+
+        all_sim_results.append({
+            "sim": sim,
+            "trades": total,
+            "accuracy": acc,
+            "return": ret,
+            "annualized": ann,
+            "max_dd": max_dd * 100,
+            "daily_pnl": dpnl,
+            "final_capital": capital,
+            "days_active": len(daily_trades),
+            "crisis_pnl": crisis_pnl,
+        })
+
+    if not all_sim_results:
+        print("  No results!")
+        return
+
+    # Aggregate across simulations
+    accs = [r["accuracy"] for r in all_sim_results]
+    anns = [r["annualized"] for r in all_sim_results]
+    dds = [r["max_dd"] for r in all_sim_results]
+    dpnls = [r["daily_pnl"] for r in all_sim_results]
+    rets = [r["return"] for r in all_sim_results]
+
+    print(f"\n  {'Metric':<20s} {'Mean':>10s} {'Min':>10s} {'Max':>10s}")
+    print(f"  {'-'*50}")
+    print(f"  {'Accuracy':.<20s} {np.mean(accs):>9.1f}% {np.min(accs):>9.1f}% {np.max(accs):>9.1f}%")
+    print(f"  {'Annualized':.<20s} {np.mean(anns):>9.1f}% {np.min(anns):>9.1f}% {np.max(anns):>9.1f}%")
+    print(f"  {'Max Drawdown':.<20s} {np.mean(dds):>9.1f}% {np.min(dds):>9.1f}% {np.max(dds):>9.1f}%")
+    print(f"  {'€/day':.<20s} {np.mean(dpnls):>9.2f}€ {np.min(dpnls):>9.2f}€ {np.max(dpnls):>9.2f}€")
+    print(f"  {'Total return':.<20s} {np.mean(rets):>9.1f}% {np.min(rets):>9.1f}% {np.max(rets):>9.1f}%")
+    print(f"  {'Trades':.<20s} {all_sim_results[0]['trades']:>10d}")
+    print(f"  {'Days active':.<20s} {all_sim_results[0]['days_active']:>10d}")
+
+    # Crisis performance
+    print(f"\n  STRESS TEST — Performance durante crisi:")
+    for crisis_name in crisis_periods:
+        crisis_vals = [r["crisis_pnl"][crisis_name] for r in all_sim_results]
+        avg_crisis = np.mean(crisis_vals)
+        print(f"    {crisis_name:30s}: avg PnL = €{avg_crisis:+.2f}")
+
+    return all_sim_results
+
+
+# Run con diversi DTE
+for asset in ["ETH", "BTC"]:
+    for dte in [24, 48]:
+        run_vrp_honest(asset, dte, n_simulations=10)

scripts/waste/W19_vrp_per_year.py

@@ -0,0 +1,181 @@
+"""S2-09: VRP test per-anno — verità nuda.
+Test su OGNI anno separatamente per vedere performance durante crash.
+Niente compounding — PnL medio per trade in punti percentuali.
+Costi realistici Deribit options.
+"""
+from __future__ import annotations
+import sys
+sys.path.insert(0, ".")
+
+import numpy as np
+import pandas as pd
+from src.data.downloader import load_data
+
+FEE_ROUNDTRIP = 0.0052  # 0.52% roundtrip (4 legs × 0.13%)
+INITIAL = 1000
+
+
+def rv_ann(close, window):
+    lr = np.diff(np.log(np.where(close == 0, 1e-10, close)))
+    r = np.full(len(close), np.nan)
+    for i in range(window, len(lr)):
+        r[i + 1] = np.std(lr[i - window : i]) * np.sqrt(24 * 365)
+    return r
+
+
+def straddle_prem(iv, dte_h):
+    if iv <= 0 or dte_h <= 0:
+        return 0
+    return iv * np.sqrt(dte_h / (24 * 365)) * 0.8
+
+
+def run_per_year(asset, dte=24):
+    print(f"\n{'='*70}")
+    print(f"  {asset} — VRP PER ANNO (DTE={dte}h, NO compounding)")
+    print(f"  Fee roundtrip: {FEE_ROUNDTRIP*100:.2f}%")
+    print(f"{'='*70}")
+
+    df = load_data(asset, "1h")
+    close = df["close"].values
+    n = len(close)
+    ts = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
+
+    rv_24 = rv_ann(close, 24)
+    rv_168 = rv_ann(close, 168)
+
+    # IV/RV premium: conservative estimate per regime
+    # Storicamene crypto VRP ≈ 15-30% (IV/RV ≈ 1.15-1.30)
+    # Ma durante crash VRP va NEGATIVO (RV > IV)
+
+    years = sorted(set(ts.dt.year))
+
+    print(f"\n  {'Year':>6s} {'Trades':>7s} {'Wins':>5s} {'Acc%':>6s} {'AvgPnL%':>9s} {'TotPnL€':>9s} {'Worst%':>8s} {'MaxMove%':>9s}")
+    print(f"  {'-'*70}")
+
+    all_pnls = []
+    yearly_stats = []
+
+    for year in years:
+        year_mask = ts.dt.year == year
+        year_indices = np.where(year_mask.values)[0]
+
+        if len(year_indices) < 200:
+            continue
+
+        trades_pnl = []
+        trades_detail = []
+
+        for i in year_indices:
+            if i < 170 or i + dte >= n:
+                continue
+            if ts.iloc[i].hour != 8:
+                continue
+
+            rv_s = rv_24[i]
+            rv_l = rv_168[i]
+            if np.isnan(rv_s) or np.isnan(rv_l) or rv_s < 0.05 or rv_l < 0.05:
+                continue
+
+            # IV estimate: regime-dependent
+            regime = rv_s / rv_l if rv_l > 0 else 1.0
+
+            if regime > 2.0:
+                # CRASH: RV esplosa, IV probabilmente = RV o meno
+                iv_premium_factor = 0.9
+            elif regime > 1.5:
+                iv_premium_factor = 1.0
+            elif regime > 1.0:
+                iv_premium_factor = 1.1
+            else:
+                # Calm: VRP positivo
+                iv_premium_factor = 1.2
+
+            iv = rv_l * iv_premium_factor
+            prem = straddle_prem(iv, dte)
+
+            spot = close[i]
+            exit_idx = min(i + dte, n - 1)
+            actual_move = abs(close[exit_idx] - spot) / spot
+
+            # P&L (senza compounding — flat € su €1000)
+            pos_size = INITIAL * 0.10  # 10% fisso, no leverage
+            if actual_move <= prem:
+                raw_pnl = (prem - actual_move) * pos_size
+            else:
+                raw_pnl = -(actual_move - prem) * pos_size
+                raw_pnl = max(raw_pnl, -pos_size * 0.05)  # cap loss
+
+            cost = FEE_ROUNDTRIP * pos_size
+            net_pnl = raw_pnl - cost
+
+            trades_pnl.append(net_pnl)
+            trades_detail.append({
+                "prem": prem,
+                "move": actual_move,
+                "regime": regime,
+                "rv_s": rv_s,
+                "iv": iv,
+            })
+            all_pnls.append(net_pnl)
+
+        if not trades_pnl:
+            continue
+
+        wins = sum(1 for p in trades_pnl if p > 0)
+        acc = wins / len(trades_pnl) * 100
+        avg_pnl = np.mean(trades_pnl)
+        tot_pnl = np.sum(trades_pnl)
+        worst = np.min(trades_pnl)
+        max_move = max(t["move"] for t in trades_detail) * 100
+
+        tag = ""
+        if year in [2020, 2021, 2022]:
+            tag = " ← CRASH YEAR"
+        if acc >= 70 and avg_pnl > 0:
+            tag += " ✅"
+
+        print(f"  {year:>6d} {len(trades_pnl):>7d} {wins:>5d} {acc:>5.1f}% {avg_pnl:>+8.2f}€ {tot_pnl:>+8.0f}€ {worst:>+7.2f}€ {max_move:>8.1f}% {tag}")
+
+        yearly_stats.append({
+            "year": year, "trades": len(trades_pnl), "acc": acc,
+            "avg_pnl": avg_pnl, "tot_pnl": tot_pnl, "worst": worst,
+        })
+
+    # Summary
+    if all_pnls:
+        total_trades = len(all_pnls)
+        total_wins = sum(1 for p in all_pnls if p > 0)
+        print(f"\n  {'TOTALE':>6s} {total_trades:>7d} {total_wins:>5d} {total_wins/total_trades*100:>5.1f}% {np.mean(all_pnls):>+8.2f}€ {np.sum(all_pnls):>+8.0f}€ {np.min(all_pnls):>+7.2f}€")
+
+        # Con compounding realistico
+        capital = float(INITIAL)
+        peak = capital
+        max_dd = 0
+        for pnl in all_pnls:
+            capital += pnl * (capital / INITIAL)  # scala con capitale
+            capital = max(capital, 10)
+            if capital > peak:
+                peak = capital
+            dd = (peak - capital) / peak
+            max_dd = max(max_dd, dd)
+
+        years_total = (yearly_stats[-1]["year"] - yearly_stats[0]["year"] + 1)
+        ann = ((capital / INITIAL) ** (1 / years_total) - 1) * 100 if capital > 0 else -100
+        daily_avg = (capital - INITIAL) / (total_trades)  # approx 1 trade/day
+
+        print(f"\n  CON COMPOUNDING:")
+        print(f"    Capitale finale: €{capital:,.0f}")
+        print(f"    ROI annualizzato: {ann:+.1f}%")
+        print(f"    Max Drawdown: {max_dd*100:.1f}%")
+        print(f"    €/trade medio: €{daily_avg:.2f}")
+
+        # Worst year
+        worst_year = min(yearly_stats, key=lambda x: x["tot_pnl"])
+        best_year = max(yearly_stats, key=lambda x: x["tot_pnl"])
+        print(f"\n    Anno peggiore: {worst_year['year']} → {worst_year['tot_pnl']:+.0f}€ ({worst_year['acc']:.0f}% acc)")
+        print(f"    Anno migliore: {best_year['year']} → {best_year['tot_pnl']:+.0f}€ ({best_year['acc']:.0f}% acc)")
+
+
+for asset in ["ETH", "BTC"]:
+    for dte in [24, 48]:
+        run_per_year(asset, dte)

scripts/waste/W20_vrp_filtered.py

@@ -0,0 +1,297 @@
+"""S2-10: VRP + filtri multipli per alzare accuracy.
+Filtri testati:
+1. NO vol sell se squeeze attivo (compressione → breakout imminente, NON vendere vol)
+2. NO vol sell se RV short-term > RV long-term (regime esplosivo)
+3. NO vol sell se move delle ultime 4h > 2% (momentum in corso)
+4. NO vol sell se volume spike > 2x media (evento in corso)
+5. COMBINAZIONI dei filtri sopra
+Test per-anno, NO compounding per PnL medio, compounding a fine report.
+"""
+from __future__ import annotations
+import sys
+sys.path.insert(0, ".")
+
+import numpy as np
+import pandas as pd
+from src.data.downloader import load_data
+
+FEE_ROUNDTRIP = 0.0052
+INITIAL = 1000
+
+
+def rv_ann(close, window):
+    lr = np.diff(np.log(np.where(close == 0, 1e-10, close)))
+    r = np.full(len(close), np.nan)
+    for i in range(window, len(lr)):
+        r[i + 1] = np.std(lr[i - window : i]) * np.sqrt(24 * 365)
+    return r
+
+
+def keltner_ratio(close, high, low, window=14):
+    n = len(close)
+    result = np.full(n, np.nan)
+    for i in range(window, n):
+        wc = close[i - window : i]
+        wh = high[i - window : i]
+        wl = low[i - window : i]
+        ma = np.mean(wc)
+        bb_std = np.std(wc)
+        tr = np.maximum(wh - wl, np.maximum(np.abs(wh - np.roll(wc, 1)), np.abs(wl - np.roll(wc, 1))))
+        atr = np.mean(tr[1:])
+        kc_r = (ma + 1.5 * atr) - (ma - 1.5 * atr)
+        bb_r = (ma + 2 * bb_std) - (ma - 2 * bb_std)
+        if kc_r > 0:
+            result[i] = bb_r / kc_r
+    return result
+
+
+def straddle_prem(iv, dte_h):
+    if iv <= 0 or dte_h <= 0:
+        return 0
+    return iv * np.sqrt(dte_h / (24 * 365)) * 0.8
+
+
+def run_filtered(asset, dte=48):
+    print(f"\n{'='*75}")
+    print(f"  {asset} — VRP + FILTRI (DTE={dte}h)")
+    print(f"{'='*75}")
+
+    df = load_data(asset, "1h")
+    close = df["close"].values
+    high = df["high"].values
+    low = df["low"].values
+    volume = df["volume"].values
+    n = len(close)
+    ts = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
+
+    rv_24 = rv_ann(close, 24)
+    rv_168 = rv_ann(close, 168)
+    kcr = keltner_ratio(close, high, low, 14)
+
+    # Pre-calcolo filtri
+    vol_avg_48 = np.full(n, np.nan)
+    for i in range(48, n):
+        vol_avg_48[i] = np.mean(volume[i - 48 : i])
+
+    ret_4h = np.full(n, 0.0)
+    for i in range(4, n):
+        ret_4h[i] = abs(close[i] - close[i - 4]) / close[i - 4]
+
+    filter_configs = [
+        # (name, use_squeeze, use_regime, use_momentum, use_volume, squeeze_thr, regime_thr, mom_thr, vol_thr)
+        ("BASELINE (no filter)",   False, False, False, False, 0, 0, 0, 0),
+        ("squeeze_only",           True,  False, False, False, 0.85, 0, 0, 0),
+        ("regime_only",            False, True,  False, False, 0, 1.3, 0, 0),
+        ("momentum_only",          False, False, True,  False, 0, 0, 0.02, 0),
+        ("volume_only",            False, False, False, True,  0, 0, 0, 2.0),
+        ("squeeze+regime",         True,  True,  False, False, 0.85, 1.3, 0, 0),
+        ("squeeze+momentum",       True,  False, True,  False, 0.85, 0, 0.02, 0),
+        ("squeeze+volume",         True,  False, False, True,  0.85, 0, 0, 2.0),
+        ("regime+momentum",        False, True,  True,  False, 0, 1.3, 0.02, 0),
+        ("ALL_FILTERS",            True,  True,  True,  True,  0.85, 1.3, 0.02, 2.0),
+        ("squeeze+regime+mom",     True,  True,  True,  False, 0.85, 1.3, 0.02, 0),
+        ("squeeze_tight",          True,  False, False, False, 0.80, 0, 0, 0),
+        ("squeeze_tight+regime",   True,  True,  False, False, 0.80, 1.3, 0, 0),
+        ("regime_strict",          False, True,  False, False, 0, 1.2, 0, 0),
+        ("mom_strict",             False, False, True,  False, 0, 0, 0.015, 0),
+        ("squeeze_tight+mom_strict", True, False, True, False, 0.80, 0, 0.015, 0),
+        ("ALL_TIGHT",              True,  True,  True,  True,  0.80, 1.2, 0.015, 1.8),
+    ]
+
+    results_table = []
+
+    for name, f_sq, f_reg, f_mom, f_vol, sq_thr, reg_thr, mom_thr, vol_thr in filter_configs:
+        all_pnls = []
+        yearly = {}
+
+        for i in range(170, n - dte):
+            if ts.iloc[i].hour != 8:
+                continue
+
+            rv_s = rv_24[i]
+            rv_l = rv_168[i]
+            if np.isnan(rv_s) or np.isnan(rv_l) or rv_s < 0.05 or rv_l < 0.05:
+                continue
+
+            # === FILTRI ===
+            skip = False
+
+            if f_sq and not np.isnan(kcr[i]):
+                in_squeeze = kcr[i] < sq_thr
+                # Controlla se squeeze nelle ultime 5 barre
+                recent_squeeze = any(not np.isnan(kcr[j]) and kcr[j] < sq_thr for j in range(max(0, i - 5), i + 1))
+                if recent_squeeze:
+                    skip = True
+
+            if f_reg and rv_l > 0:
+                if rv_s / rv_l > reg_thr:
+                    skip = True
+
+            if f_mom:
+                if ret_4h[i] > mom_thr:
+                    skip = True
+
+            if f_vol and not np.isnan(vol_avg_48[i]) and vol_avg_48[i] > 0:
+                if volume[i] > vol_avg_48[i] * vol_thr:
+                    skip = True
+
+            if skip:
+                continue
+
+            # === TRADE ===
+            regime = rv_s / rv_l if rv_l > 0 else 1.0
+            if regime > 2.0:
+                iv_pf = 0.9
+            elif regime > 1.5:
+                iv_pf = 1.0
+            elif regime > 1.0:
+                iv_pf = 1.1
+            else:
+                iv_pf = 1.2
+            iv = rv_l * iv_pf
+
+            prem = straddle_prem(iv, dte)
+            spot = close[i]
+            exit_idx = min(i + dte, n - 1)
+            actual_move = abs(close[exit_idx] - spot) / spot
+
+            pos_size = INITIAL * 0.10
+            if actual_move <= prem:
+                raw = (prem - actual_move) * pos_size
+            else:
+                raw = -(actual_move - prem) * pos_size
+                raw = max(raw, -pos_size * 0.05)
+
+            net = raw - FEE_ROUNDTRIP * pos_size
+            all_pnls.append(net)
+
+            year = ts.iloc[i].year
+            if year not in yearly:
+                yearly[year] = []
+            yearly[year].append(net)
+
+        if len(all_pnls) < 50:
+            continue
+
+        wins = sum(1 for p in all_pnls if p > 0)
+        acc = wins / len(all_pnls) * 100
+        avg_pnl = np.mean(all_pnls)
+        tot_pnl = np.sum(all_pnls)
+        worst_trade = np.min(all_pnls)
+        avg_win = np.mean([p for p in all_pnls if p > 0]) if wins > 0 else 0
+        avg_loss = np.mean([p for p in all_pnls if p <= 0]) if wins < len(all_pnls) else 0
+
+        # Worst year
+        worst_year_acc = 100
+        worst_year_name = ""
+        for y, ypnls in sorted(yearly.items()):
+            yw = sum(1 for p in ypnls if p > 0) / len(ypnls) * 100 if ypnls else 0
+            if yw < worst_year_acc:
+                worst_year_acc = yw
+                worst_year_name = str(y)
+
+        # Compounded return
+        capital = float(INITIAL)
+        peak = capital
+        max_dd = 0
+        for pnl in all_pnls:
+            capital += pnl * (capital / INITIAL)
+            capital = max(capital, 10)
+            if capital > peak:
+                peak = capital
+            dd = (peak - capital) / peak
+            max_dd = max(max_dd, dd)
+
+        n_years = len(yearly)
+        ann = ((capital / INITIAL) ** (1 / n_years) - 1) * 100 if capital > 0 and n_years > 0 else -100
+
+        results_table.append({
+            "name": name,
+            "trades": len(all_pnls),
+            "acc": acc,
+            "avg_pnl": avg_pnl,
+            "avg_win": avg_win,
+            "avg_loss": avg_loss,
+            "ann": ann,
+            "max_dd": max_dd * 100,
+            "worst_year": f"{worst_year_name}({worst_year_acc:.0f}%)",
+            "capital": capital,
+        })
+
+    # Sort by accuracy
+    results_table.sort(key=lambda x: x["acc"], reverse=True)
+
+    print(f"\n  {'Name':.<30s} {'Trades':>7s} {'Acc':>6s} {'AvgPnL':>8s} {'AvgWin':>8s} {'AvgLoss':>8s} {'Ann%':>7s} {'DD%':>5s} {'Worst_Yr':>12s} {'Capital':>10s}")
+    print(f"  {'-'*105}")
+    for r in results_table:
+        tag = "✅✅" if r["acc"] >= 75 else "✅" if r["acc"] >= 70 else ""
+        print(f"  {r['name']:.<30s} {r['trades']:>7d} {r['acc']:>5.1f}% {r['avg_pnl']:>+7.2f}€ {r['avg_win']:>+7.2f}€ {r['avg_loss']:>+7.2f}€ {r['ann']:>+6.1f}% {r['max_dd']:>4.1f}% {r['worst_year']:>12s} €{r['capital']:>9,.0f} {tag}")
+
+    # Dettaglio per anno del migliore
+    best = results_table[0]
+    print(f"\n  MIGLIORE: {best['name']} → {best['acc']:.1f}% acc, {best['ann']:+.1f}% ann, DD {best['max_dd']:.1f}%")
+
+    # Rerun best per year
+    best_name = best["name"]
+    best_cfg = None
+    for cfg in filter_configs:
+        if cfg[0] == best_name:
+            best_cfg = cfg
+            break
+
+    if best_cfg:
+        _, f_sq, f_reg, f_mom, f_vol, sq_thr, reg_thr, mom_thr, vol_thr = best_cfg
+        yearly_detail = {}
+
+        for i in range(170, n - dte):
+            if ts.iloc[i].hour != 8:
+                continue
+            rv_s = rv_24[i]
+            rv_l = rv_168[i]
+            if np.isnan(rv_s) or np.isnan(rv_l) or rv_s < 0.05 or rv_l < 0.05:
+                continue
+
+            skip = False
+            if f_sq:
+                if any(not np.isnan(kcr[j]) and kcr[j] < sq_thr for j in range(max(0, i - 5), i + 1)):
+                    skip = True
+            if f_reg and rv_l > 0 and rv_s / rv_l > reg_thr:
+                skip = True
+            if f_mom and ret_4h[i] > mom_thr:
+                skip = True
+            if f_vol and not np.isnan(vol_avg_48[i]) and vol_avg_48[i] > 0 and volume[i] > vol_avg_48[i] * vol_thr:
+                skip = True
+            if skip:
+                continue
+
+            regime = rv_s / rv_l if rv_l > 0 else 1.0
+            iv_pf = {True: 0.9, False: 1.2}[regime > 2.0] if regime > 2.0 else (1.0 if regime > 1.5 else (1.1 if regime > 1.0 else 1.2))
+            iv = rv_l * iv_pf
+            prem = straddle_prem(iv, dte)
+            spot = close[i]
+            exit_idx = min(i + dte, n - 1)
+            move = abs(close[exit_idx] - spot) / spot
+            pos_size = INITIAL * 0.10
+            if move <= prem:
+                raw = (prem - move) * pos_size
+            else:
+                raw = max(-(move - prem) * pos_size, -pos_size * 0.05)
+            net = raw - FEE_ROUNDTRIP * pos_size
+
+            year = ts.iloc[i].year
+            if year not in yearly_detail:
+                yearly_detail[year] = []
+            yearly_detail[year].append(net)
+
+        print(f"\n  Dettaglio per anno ({best_name}):")
+        for y in sorted(yearly_detail):
+            pnls = yearly_detail[y]
+            w = sum(1 for p in pnls if p > 0)
+            a = w / len(pnls) * 100
+            tag = " ← CRASH" if y in [2020, 2021, 2022] else ""
+            print(f"    {y}: trades={len(pnls):4d} acc={a:.1f}% avg=€{np.mean(pnls):+.2f} tot=€{np.sum(pnls):+.0f}{tag}")
+
+
+for asset in ["ETH", "BTC"]:
+    run_filtered(asset, dte=48)
+    run_filtered(asset, dte=24)

scripts/waste/W21_vrp_real_dvol.py

@@ -0,0 +1,152 @@
+"""S2-11: VRP con DVOL REALE — unico test valido.
+Solo 90 giorni di dati, ma REALI.
+Confronta DVOL (IV reale Deribit) vs RV realizzata.
+"""
+from __future__ import annotations
+import sys
+sys.path.insert(0, ".")
+
+import numpy as np
+import pandas as pd
+from src.data.downloader import load_data
+
+FEE_ROUNDTRIP = 0.0052
+INITIAL = 1000
+
+
+def rv_ann(close, window):
+    lr = np.diff(np.log(np.where(close == 0, 1e-10, close)))
+    r = np.full(len(close), np.nan)
+    for i in range(window, len(lr)):
+        r[i + 1] = np.std(lr[i - window : i]) * np.sqrt(24 * 365)
+    return r
+
+
+def straddle_prem(iv_pct, dte_h):
+    """iv_pct è la IV in decimale (es 0.50 = 50%)."""
+    if iv_pct <= 0 or dte_h <= 0:
+        return 0
+    return iv_pct * np.sqrt(dte_h / (24 * 365)) * 0.8
+
+
+for asset in ["ETH", "BTC"]:
+    print(f"\n{'='*70}")
+    print(f"  {asset} — VRP CON DVOL REALE (90 giorni)")
+    print(f"{'='*70}")
+
+    df_price = load_data(asset, "1h")
+    df_dvol = pd.read_parquet(f"data/raw/{asset.lower()}_dvol.parquet")
+
+    close = df_price["close"].values
+    ts_price = df_price["timestamp"].values
+    n = len(close)
+
+    dvol_ts = df_dvol["timestamp"].values
+    dvol_vals = df_dvol["dvol"].values / 100  # converti a decimale
+
+    rv_24 = rv_ann(close, 24)
+    rv_48 = rv_ann(close, 48)
+
+    # Allinea DVOL ai candles 1h (DVOL è giornaliero)
+    dvol_aligned = np.full(n, np.nan)
+    for j in range(len(dvol_ts)):
+        mask = (ts_price >= dvol_ts[j]) & (ts_price < dvol_ts[j] + 86400000)
+        dvol_aligned[mask] = dvol_vals[j]
+
+    valid_count = np.sum(~np.isnan(dvol_aligned))
+    print(f"  Candele con DVOL reale: {valid_count}")
+    print(f"  DVOL range: {np.nanmin(dvol_aligned)*100:.1f}% — {np.nanmax(dvol_aligned)*100:.1f}%")
+
+    # Analisi IV vs RV reale
+    iv_rv_ratios = []
+    for i in range(n):
+        if np.isnan(dvol_aligned[i]) or np.isnan(rv_24[i]) or rv_24[i] <= 0:
+            continue
+        iv_rv_ratios.append(dvol_aligned[i] / rv_24[i])
+
+    if iv_rv_ratios:
+        print(f"\n  IV/RV ratio REALE:")
+        print(f"    Mean:   {np.mean(iv_rv_ratios):.3f}")
+        print(f"    Median: {np.median(iv_rv_ratios):.3f}")
+        print(f"    Min:    {np.min(iv_rv_ratios):.3f}")
+        print(f"    Max:    {np.max(iv_rv_ratios):.3f}")
+        print(f"    >1 (VRP+): {sum(1 for r in iv_rv_ratios if r > 1)/len(iv_rv_ratios)*100:.0f}% del tempo")
+
+    # Backtest VRP reale
+    for dte in [24, 48]:
+        print(f"\n  --- DTE={dte}h ---")
+        capital = float(INITIAL)
+        trades = []
+        daily_done = set()
+
+        for i in range(100, n - dte):
+            if np.isnan(dvol_aligned[i]) or np.isnan(rv_24[i]):
+                continue
+
+            ts_dt = pd.Timestamp(ts_price[i], unit="ms", tz="UTC")
+            if ts_dt.hour != 8:
+                continue
+
+            day = ts_dt.strftime("%Y-%m-%d")
+            if day in daily_done:
+                continue
+
+            iv = dvol_aligned[i]
+            rv = rv_24[i]
+
+            # Filtro regime: skip se RV > IV (no premium)
+            if rv > iv:
+                continue
+
+            prem = straddle_prem(iv, dte)
+            spot = close[i]
+            exit_idx = min(i + dte, n - 1)
+            actual_move = abs(close[exit_idx] - spot) / spot
+
+            pos_pct = 0.10
+            if actual_move <= prem:
+                raw = (prem - actual_move) * pos_pct
+            else:
+                raw = -(actual_move - prem) * pos_pct
+                raw = max(raw, -pos_pct * 0.05)
+
+            net = raw - FEE_ROUNDTRIP * pos_pct
+            capital += capital * net
+            capital = max(capital, 10)
+
+            trades.append({
+                "day": day,
+                "iv": iv * 100,
+                "rv": rv * 100,
+                "premium": prem * 100,
+                "move": actual_move * 100,
+                "pnl": net * capital,
+                "win": raw > 0,
+            })
+            daily_done.add(day)
+
+        if not trades:
+            print("    Nessun trade!")
+            continue
+
+        wins = sum(1 for t in trades if t["win"])
+        acc = wins / len(trades) * 100
+        ret = (capital - INITIAL) / INITIAL * 100
+        avg_iv = np.mean([t["iv"] for t in trades])
+        avg_rv = np.mean([t["rv"] for t in trades])
+        avg_prem = np.mean([t["premium"] for t in trades])
+        avg_move = np.mean([t["move"] for t in trades])
+
+        print(f"    Trades:   {len(trades)}")
+        print(f"    Accuracy: {acc:.1f}%")
+        print(f"    Return:   {ret:+.1f}%")
+        print(f"    Capital:  €{capital:.0f}")
+        print(f"    Avg IV:   {avg_iv:.1f}%")
+        print(f"    Avg RV:   {avg_rv:.1f}%")
+        print(f"    Avg Prem: {avg_prem:.2f}%")
+        print(f"    Avg Move: {avg_move:.2f}%")
+        print(f"    IV > Move (win condition): {sum(1 for t in trades if t['premium'] > t['move'])/len(trades)*100:.0f}%")
+
+        # Worst trade
+        worst = min(trades, key=lambda t: t["pnl"])
+        print(f"    Worst:    day={worst['day']} iv={worst['iv']:.1f}% move={worst['move']:.2f}%")

scripts/waste/W22_perpetual_only.py

@@ -0,0 +1,320 @@
+"""S2-12: Strategie SOLO su perpetual — dati 100% reali.
+Niente opzioni, niente IV stimata. Solo prezzo OHLCV.
+Mix di approcci diversi da quelli già testati su main.
+
+1. Intraday range breakout con filtro volatilità
+2. Daily open range breakout (prima ora di trading)
+3. RSI divergence (prezzo fa nuovo min/max, RSI no)
+4. Close-to-close momentum filtrato da volatilità regime
+5. Multi-timeframe confirmation (15m signal + 1h trend)
+
+Test per-anno, onesto, con fee reali perpetual (0.05% taker × 2 = 0.1% roundtrip).
+"""
+from __future__ import annotations
+import sys
+sys.path.insert(0, ".")
+
+import numpy as np
+import pandas as pd
+from src.data.downloader import load_data
+
+FEE_RT = 0.002  # 0.1% taker roundtrip
+INITIAL = 1000
+LEVERAGE = 3
+
+
+def rsi(close, period=14):
+    delta = np.diff(close)
+    gain = np.where(delta > 0, delta, 0)
+    loss = np.where(delta < 0, -delta, 0)
+    result = np.full(len(close), 50.0)
+    if len(gain) < period:
+        return result
+    ag = np.mean(gain[:period])
+    al = np.mean(loss[:period])
+    for i in range(period, len(delta)):
+        ag = (ag * (period - 1) + gain[i]) / period
+        al = (al * (period - 1) + loss[i]) / period
+        if al == 0:
+            result[i + 1] = 100
+        else:
+            result[i + 1] = 100 - 100 / (1 + ag / al)
+    return result
+
+
+def ema(arr, period):
+    r = np.full(len(arr), np.nan)
+    k = 2 / (period + 1)
+    r[period - 1] = np.mean(arr[:period])
+    for i in range(period, len(arr)):
+        r[i] = arr[i] * k + r[i - 1] * (1 - k)
+    return r
+
+
+def run_all_perpetual(asset):
+    print(f"\n{'#'*70}")
+    print(f"  {asset} — STRATEGIE PERPETUAL (dati reali)")
+    print(f"  Fee: {FEE_RT*100}% roundtrip, Leva: {LEVERAGE}x")
+    print(f"{'#'*70}")
+
+    df_1h = load_data(asset, "1h")
+    df_15m = load_data(asset, "15m")
+    c1h = df_1h["close"].values
+    h1h = df_1h["high"].values
+    l1h = df_1h["low"].values
+    v1h = df_1h["volume"].values
+    n1h = len(c1h)
+    ts1h = pd.to_datetime(df_1h["timestamp"], unit="ms", utc=True)
+
+    rsi_14 = rsi(c1h, 14)
+    ema_20 = ema(c1h, 20)
+    ema_50 = ema(c1h, 50)
+
+    results = {}
+
+    # ======================================================
+    # STRAT 1: Daily Open Range Breakout
+    # Prima ora (08-09 UTC) definisce il range. Breakout = entrata.
+    # ======================================================
+    for hold, stop_m in [(6, 1.0), (12, 1.5), (4, 0.8)]:
+        name = f"ORB_h{hold}_s{stop_m}"
+        capital = float(INITIAL)
+        yearly = {}
+
+        for i in range(50, n1h - hold):
+            if ts1h.iloc[i].hour != 9:  # fine della prima ora
+                continue
+            day = ts1h.iloc[i].strftime("%Y-%m-%d")
+            if day in yearly and len(yearly[day]) >= 1:
+                continue
+
+            range_high = h1h[i - 1]
+            range_low = l1h[i - 1]
+            range_size = range_high - range_low
+            if range_size <= 0:
+                continue
+
+            # ATR per stop
+            atr_14 = np.mean(h1h[max(0,i-14):i] - l1h[max(0,i-14):i])
+            if atr_14 <= 0:
+                continue
+
+            # Breakout detection: la candela attuale rompe il range
+            if c1h[i] > range_high:
+                direction = "long"
+            elif c1h[i] < range_low:
+                direction = "short"
+            else:
+                continue
+
+            entry = c1h[i]
+            stop_dist = atr_14 * stop_m
+            exit_price = c1h[min(i + hold, n1h - 1)]
+
+            for j in range(i + 1, min(i + hold + 1, n1h)):
+                if direction == "long":
+                    if l1h[j] <= entry - stop_dist:
+                        exit_price = entry - stop_dist
+                        break
+                    if h1h[j] >= entry + stop_dist * 2:
+                        exit_price = entry + stop_dist * 2
+                        break
+                else:
+                    if h1h[j] >= entry + stop_dist:
+                        exit_price = entry + stop_dist
+                        break
+                    if l1h[j] <= entry - stop_dist * 2:
+                        exit_price = entry - stop_dist * 2
+                        break
+                exit_price = c1h[j]
+
+            trade_ret = ((exit_price - entry) / entry if direction == "long" else (entry - exit_price) / entry)
+            net = trade_ret * LEVERAGE - FEE_RT * LEVERAGE
+            capital += capital * 0.15 * net
+            capital = max(capital, 10)
+
+            year = ts1h.iloc[i].year
+            if year not in yearly:
+                yearly[year] = []
+            yearly[year].append(net > 0)
+            if day not in yearly:
+                yearly[day] = []
+
+        if sum(len(v) for v in yearly.values() if isinstance(v, list) and all(isinstance(x, bool) for x in v)) > 30:
+            all_wins = [w for v in yearly.values() if isinstance(v, list) for w in v if isinstance(w, bool)]
+            acc = sum(all_wins) / len(all_wins) * 100
+            ret = (capital - INITIAL) / INITIAL * 100
+            results[name] = {"acc": acc, "ret": ret, "trades": len(all_wins), "capital": capital}
+
+    # ======================================================
+    # STRAT 2: RSI Divergence
+    # Prezzo fa nuovo low, RSI no = bullish divergence → long
+    # ======================================================
+    for lookback, rsi_thr_low, rsi_thr_high, hold in [(20, 30, 70, 6), (14, 25, 75, 8), (10, 35, 65, 4)]:
+        name = f"RSIdiv_lb{lookback}_h{hold}"
+        capital = float(INITIAL)
+        trades_list = []
+
+        for i in range(max(50, lookback + 1), n1h - hold):
+            day = ts1h.iloc[i].strftime("%Y-%m-%d")
+
+            # Bullish divergence: price new low, RSI higher low
+            price_new_low = c1h[i] < np.min(c1h[i - lookback : i])
+            rsi_higher = rsi_14[i] > np.min(rsi_14[i - lookback : i]) and rsi_14[i] < rsi_thr_low
+
+            # Bearish divergence: price new high, RSI lower high
+            price_new_high = c1h[i] > np.max(c1h[i - lookback : i])
+            rsi_lower = rsi_14[i] < np.max(rsi_14[i - lookback : i]) and rsi_14[i] > rsi_thr_high
+
+            direction = None
+            if price_new_low and rsi_higher:
+                direction = "long"
+            elif price_new_high and rsi_lower:
+                direction = "short"
+
+            if direction is None:
+                continue
+
+            entry = c1h[i]
+            exit_price = c1h[min(i + hold, n1h - 1)]
+            trade_ret = ((exit_price - entry) / entry if direction == "long" else (entry - exit_price) / entry)
+            net = trade_ret * LEVERAGE - FEE_RT * LEVERAGE
+            capital += capital * 0.12 * net
+            capital = max(capital, 10)
+            trades_list.append({"year": ts1h.iloc[i].year, "win": trade_ret > 0})
+
+        if len(trades_list) > 30:
+            acc = sum(1 for t in trades_list if t["win"]) / len(trades_list) * 100
+            ret = (capital - INITIAL) / INITIAL * 100
+            results[name] = {"acc": acc, "ret": ret, "trades": len(trades_list), "capital": capital}
+
+    # ======================================================
+    # STRAT 3: Momentum regime — trend following solo in low-vol regime
+    # ======================================================
+    for fast, slow, vol_w, vol_thr, hold in [
+        (8, 21, 48, 0.8, 12),
+        (5, 13, 24, 0.8, 6),
+        (13, 34, 72, 0.7, 24),
+        (8, 21, 48, 0.9, 8),
+    ]:
+        name = f"MomReg_f{fast}s{slow}_h{hold}"
+        ema_f = ema(c1h, fast)
+        ema_s = ema(c1h, slow)
+
+        rv_short = np.full(n1h, np.nan)
+        rv_long = np.full(n1h, np.nan)
+        lr = np.diff(np.log(np.where(c1h == 0, 1e-10, c1h)))
+        for idx in range(vol_w, len(lr)):
+            rv_short[idx + 1] = np.std(lr[idx - min(12, vol_w) : idx])
+            rv_long[idx + 1] = np.std(lr[idx - vol_w : idx])
+
+        capital = float(INITIAL)
+        trades_list = []
+        daily_done = set()
+
+        for i in range(max(60, slow + 1), n1h - hold):
+            if np.isnan(ema_f[i]) or np.isnan(ema_s[i]) or np.isnan(rv_short[i]) or np.isnan(rv_long[i]):
+                continue
+            if rv_long[i] <= 0:
+                continue
+
+            day = ts1h.iloc[i].strftime("%Y-%m-%d")
+            if day in daily_done:
+                continue
+
+            # Only trade in low-vol regime
+            vol_ratio = rv_short[i] / rv_long[i]
+            if vol_ratio > vol_thr:
+                continue
+
+            # EMA crossover signal
+            cross_up = ema_f[i] > ema_s[i] and ema_f[i - 1] <= ema_s[i - 1]
+            cross_down = ema_f[i] < ema_s[i] and ema_f[i - 1] >= ema_s[i - 1]
+
+            if not (cross_up or cross_down):
+                continue
+
+            direction = "long" if cross_up else "short"
+            entry = c1h[i]
+            exit_price = c1h[min(i + hold, n1h - 1)]
+            trade_ret = ((exit_price - entry) / entry if direction == "long" else (entry - exit_price) / entry)
+            net = trade_ret * LEVERAGE - FEE_RT * LEVERAGE
+            capital += capital * 0.15 * net
+            capital = max(capital, 10)
+            trades_list.append({"year": ts1h.iloc[i].year, "win": trade_ret > 0})
+            daily_done.add(day)
+
+        if len(trades_list) > 30:
+            acc = sum(1 for t in trades_list if t["win"]) / len(trades_list) * 100
+            ret = (capital - INITIAL) / INITIAL * 100
+            results[name] = {"acc": acc, "ret": ret, "trades": len(trades_list), "capital": capital}
+
+    # ======================================================
+    # STRAT 4: Multi-TF confirmation (15m entry, 1h trend)
+    # ======================================================
+    c15 = df_15m["close"].values
+    h15 = df_15m["high"].values
+    l15 = df_15m["low"].values
+    ts15 = df_15m["timestamp"].values
+    n15 = len(c15)
+
+    ema_1h_50 = ema(c1h, 50)
+    rsi_15m = rsi(c15, 14)
+
+    capital = float(INITIAL)
+    trades_list = []
+    daily_done = set()
+
+    for i in range(100, n15 - 12):
+        ts_dt = pd.Timestamp(ts15[i], unit="ms", tz="UTC")
+        day = ts_dt.strftime("%Y-%m-%d")
+        if day in daily_done:
+            continue
+
+        # 15m signal: RSI extreme
+        if rsi_15m[i] > 35 and rsi_15m[i] < 65:
+            continue
+
+        # Find matching 1h candle
+        h_idx = np.searchsorted(ts1h.values.astype("int64"), ts15[i]) - 1
+        if h_idx < 50 or h_idx >= n1h or np.isnan(ema_1h_50[h_idx]):
+            continue
+
+        # 1h trend confirmation
+        trend_up = c1h[h_idx] > ema_1h_50[h_idx]
+        trend_down = c1h[h_idx] < ema_1h_50[h_idx]
+
+        direction = None
+        if rsi_15m[i] < 30 and trend_up:
+            direction = "long"  # oversold in uptrend
+        elif rsi_15m[i] > 70 and trend_down:
+            direction = "short"  # overbought in downtrend
+
+        if direction is None:
+            continue
+
+        entry = c15[i]
+        hold_bars = 12  # 12 × 15m = 3h
+        exit_price = c15[min(i + hold_bars, n15 - 1)]
+        trade_ret = ((exit_price - entry) / entry if direction == "long" else (entry - exit_price) / entry)
+        net = trade_ret * LEVERAGE - FEE_RT * LEVERAGE
+        capital += capital * 0.12 * net
+        capital = max(capital, 10)
+        trades_list.append({"year": ts_dt.year, "win": trade_ret > 0})
+        daily_done.add(day)
+
+    if len(trades_list) > 30:
+        acc = sum(1 for t in trades_list if t["win"]) / len(trades_list) * 100
+        ret = (capital - INITIAL) / INITIAL * 100
+        results["MultiTF_15m1h"] = {"acc": acc, "ret": ret, "trades": len(trades_list), "capital": capital}
+
+    # === PRINT RESULTS ===
+    print(f"\n  {'Strategy':<25s} {'Trades':>7s} {'Acc':>6s} {'Return':>10s} {'Capital':>10s}")
+    print(f"  {'-'*60}")
+    for name, r in sorted(results.items(), key=lambda x: x[1]["acc"], reverse=True):
+        tag = "✅" if r["acc"] >= 60 and r["ret"] > 30 else ""
+        print(f"  {name:<25s} {r['trades']:>7d} {r['acc']:>5.1f}% {r['ret']:>+9.1f}% €{r['capital']:>9,.0f} {tag}")
+
+
+for asset in ["ETH", "BTC"]:
+    run_all_perpetual(asset)

src/live/cerbero_client.py

@@ -0,0 +1,93 @@
+"""Client HTTP per Cerbero MCP — Deribit testnet."""
+from __future__ import annotations
+
+from dataclasses import dataclass
+from typing import Any
+
+import requests
+
+BASE_URL = "https://cerbero-mcp.tielogic.xyz"
+TOKEN = "_hm0FkyC67P9OXJTy7R9SE2lfhGz_Wa6i89KqH_uXrk"
+BOT_TAG = "pythagoras-paper"
+TIMEOUT = 15
+
+
+@dataclass
+class CerberoClient:
+    base_url: str = BASE_URL
+    token: str = TOKEN
+    bot_tag: str = BOT_TAG
+
+    def _headers(self) -> dict[str, str]:
+        return {
+            "Authorization": f"Bearer {self.token}",
+            "X-Bot-Tag": self.bot_tag,
+            "Content-Type": "application/json",
+        }
+
+    def _post(self, path: str, payload: dict | None = None) -> dict:
+        resp = requests.post(
+            f"{self.base_url}{path}",
+            headers=self._headers(),
+            json=payload or {},
+            timeout=TIMEOUT,
+        )
+        resp.raise_for_status()
+        return resp.json()
+
+    # --- Market data ---
+
+    def get_ticker(self, instrument: str = "ETH-PERPETUAL") -> dict:
+        return self._post("/mcp-deribit/tools/get_ticker", {"instrument": instrument})
+
+    def get_historical(self, instrument: str, start_date: str, end_date: str, resolution: str = "15") -> list[dict]:
+        data = self._post("/mcp-deribit/tools/get_historical", {
+            "instrument": instrument,
+            "start_date": start_date,
+            "end_date": end_date,
+            "resolution": resolution,
+        })
+        return data.get("candles", [])
+
+    # --- Account ---
+
+    def get_account_summary(self, currency: str = "USDC") -> dict:
+        return self._post("/mcp-deribit/tools/get_account_summary", {"currency": currency})
+
+    def get_positions(self, currency: str = "ETH") -> list[dict]:
+        return self._post("/mcp-deribit/tools/get_positions", {"currency": currency})
+
+    # --- Trading ---
+
+    def place_order(
+        self,
+        instrument: str,
+        side: str,
+        amount: float,
+        order_type: str = "market",
+        price: float | None = None,
+        leverage: int | None = 3,
+        label: str | None = None,
+    ) -> dict:
+        payload: dict[str, Any] = {
+            "instrument_name": instrument,
+            "side": side,
+            "amount": amount,
+            "type": order_type,
+        }
+        if price is not None:
+            payload["price"] = price
+        if leverage is not None:
+            payload["leverage"] = leverage
+        if label:
+            payload["label"] = label
+        return self._post("/mcp-deribit/tools/place_order", payload)
+
+    def close_position(self, instrument: str) -> dict:
+        return self._post("/mcp-deribit/tools/close_position", {"instrument_name": instrument})
+
+    def set_stop_loss(self, order_id: str, stop_price: float) -> dict:
+        return self._post("/mcp-deribit/tools/set_stop_loss", {"order_id": order_id, "stop_price": stop_price})
+
+    def set_take_profit(self, order_id: str, tp_price: float) -> dict:
+        return self._post("/mcp-deribit/tools/set_take_profit", {"order_id": order_id, "tp_price": tp_price})

src/live/paper_trader.py

@@ -0,0 +1,277 @@
+"""Paper trader: loop principale che monitora, segnala e opera su Deribit testnet."""
+from __future__ import annotations
+
+import json
+import time
+from datetime import datetime, timedelta, timezone
+from pathlib import Path
+
+import pandas as pd
+
+from src.live.cerbero_client import CerberoClient
+from src.live.signal_engine import SignalEngine
+from src.live.telegram_notifier import notify_event
+
+LOG_DIR = Path(__file__).resolve().parents[2] / "data" / "paper_trades"
+INSTRUMENT = "ETH_USDC-PERPETUAL"
+TRAIN_INSTRUMENT = "ETH-PERPETUAL"
+CURRENCY = "USDC"
+RESOLUTION = "15"
+LEVERAGE = 3
+POSITION_PCT = 0.15
+HOLD_BARS = 3
+POLL_SECONDS = 60
+LOOKBACK_DAYS = 60
+TRAIN_LOOKBACK_DAYS = 365
+VIRTUAL_CAPITAL = 1000.0  # simula capitale reale, ignora balance testnet
+
+
+class PaperTrader:
+    def __init__(self):
+        self.client = CerberoClient()
+        self.engine = SignalEngine(bb_w=14, sq_thr=0.8, ml_thr=0.70)
+
+        self.virtual_capital = VIRTUAL_CAPITAL
+        self.in_position = False
+        self.position_entry_time: datetime | None = None
+        self.position_direction: str | None = None
+        self.position_entry_price: float = 0
+        self.position_size: float = 0
+        self.bars_held = 0
+        self.last_bar_ts: int = 0
+
+        LOG_DIR.mkdir(parents=True, exist_ok=True)
+        self.log_path = LOG_DIR / f"trades_{datetime.now().strftime('%Y%m%d_%H%M%S')}.jsonl"
+        self.status_path = LOG_DIR / "status.json"
+
+    def log(self, event: str, data: dict | None = None):
+        entry = {
+            "timestamp": datetime.now(timezone.utc).isoformat(),
+            "event": event,
+            **(data or {}),
+        }
+        with open(self.log_path, "a") as f:
+            f.write(json.dumps(entry) + "\n")
+        print(f"  [{entry['timestamp'][:19]}] {event}: {json.dumps(data or {})}")
+        notify_event(event, data)
+
+    def save_status(self):
+        status = {
+            "virtual_capital": round(self.virtual_capital, 2),
+            "in_position": self.in_position,
+            "direction": self.position_direction,
+            "entry_price": self.position_entry_price,
+            "position_size": self.position_size,
+            "entry_time": self.position_entry_time.isoformat() if self.position_entry_time else None,
+            "bars_held": self.bars_held,
+            "last_update": datetime.now(timezone.utc).isoformat(),
+        }
+        with open(self.status_path, "w") as f:
+            json.dump(status, f, indent=2)
+
+    def fetch_candles(self, days: int = LOOKBACK_DAYS, instrument: str | None = None) -> pd.DataFrame:
+        end = datetime.now(timezone.utc)
+        start = end - timedelta(days=days)
+        candles = self.client.get_historical(
+            instrument or TRAIN_INSTRUMENT,
+            start.strftime("%Y-%m-%d"),
+            end.strftime("%Y-%m-%d"),
+            RESOLUTION,
+        )
+        if not candles:
+            return pd.DataFrame()
+        df = pd.DataFrame(candles)
+        df["timestamp"] = df["timestamp"].astype("int64")
+        df = df.sort_values("timestamp").reset_index(drop=True)
+        return df
+
+    def train_model(self):
+        self.log("TRAINING", {"lookback_days": TRAIN_LOOKBACK_DAYS, "instrument": TRAIN_INSTRUMENT})
+        df = self.fetch_candles(TRAIN_LOOKBACK_DAYS, TRAIN_INSTRUMENT)
+        if df.empty:
+            self.log("TRAINING_FAILED", {"reason": "no data"})
+            return False
+        result = self.engine.train(df, lookahead=HOLD_BARS)
+        self.log("TRAINING_DONE", result)
+        return "error" not in result
+
+    def open_position(self, direction: str, signal: dict):
+        ticker = self.client.get_ticker(INSTRUMENT)
+        price = ticker["last_price"]
+
+        notional = self.virtual_capital * POSITION_PCT * LEVERAGE
+        amount = round(notional / price, 3)
+        amount = max(amount, 0.001)
+
+        side = "buy" if direction == "buy" else "sell"
+
+        self.log("OPENING", {
+            "side": side,
+            "amount": amount,
+            "price": price,
+            "virtual_capital": round(self.virtual_capital, 2),
+            "notional": round(notional, 2),
+            "signal": signal,
+        })
+
+        try:
+            result = self.client.place_order(
+                instrument=INSTRUMENT,
+                side=side,
+                amount=amount,
+                order_type="market",
+                leverage=LEVERAGE,
+                label="pythagoras-squeeze",
+            )
+            self.in_position = True
+            self.position_direction = side
+            self.position_entry_price = price
+            self.position_size = amount
+            self.position_entry_time = datetime.now(timezone.utc)
+            self.bars_held = 0
+            self.log("OPENED", {"order_result": result})
+        except Exception as e:
+            self.log("OPEN_FAILED", {"error": str(e)})
+
+    def close_current_position(self, reason: str):
+        if not self.in_position:
+            return
+
+        ticker = self.client.get_ticker(INSTRUMENT)
+        exit_price = ticker["last_price"]
+
+        if self.position_direction == "buy":
+            trade_pnl = (exit_price - self.position_entry_price) * self.position_size
+        else:
+            trade_pnl = (self.position_entry_price - exit_price) * self.position_size
+
+        fee = self.position_size * (self.position_entry_price + exit_price) * 0.001
+        net_pnl = trade_pnl - fee
+        pnl_pct = net_pnl / self.virtual_capital * 100
+
+        self.log("CLOSING", {
+            "reason": reason,
+            "entry_price": self.position_entry_price,
+            "exit_price": exit_price,
+            "size": self.position_size,
+            "trade_pnl": round(trade_pnl, 2),
+            "fee": round(fee, 2),
+            "net_pnl": round(net_pnl, 2),
+            "pnl_pct": round(pnl_pct, 3),
+            "bars_held": self.bars_held,
+            "capital_before": round(self.virtual_capital, 2),
+        })
+
+        try:
+            result = self.client.close_position(INSTRUMENT)
+            self.virtual_capital += net_pnl
+            self.log("CLOSED", {
+                "result": result,
+                "net_pnl": round(net_pnl, 2),
+                "pnl_pct": round(pnl_pct, 3),
+                "virtual_capital": round(self.virtual_capital, 2),
+            })
+        except Exception as e:
+            self.log("CLOSE_FAILED", {"error": str(e)})
+
+        self.in_position = False
+        self.position_direction = None
+        self.position_entry_price = 0
+        self.position_size = 0
+        self.position_entry_time = None
+        self.bars_held = 0
+
+    def check_position_exit(self, df: pd.DataFrame):
+        if not self.in_position:
+            return
+
+        current_ts = df["timestamp"].iloc[-1]
+        if current_ts > self.last_bar_ts:
+            self.bars_held += 1
+            self.last_bar_ts = current_ts
+
+        if self.bars_held >= HOLD_BARS:
+            self.close_current_position("hold_limit")
+            return
+
+        price = df["close"].iloc[-1]
+        if self.position_direction == "buy":
+            pnl_pct = (price - self.position_entry_price) / self.position_entry_price
+        else:
+            pnl_pct = (self.position_entry_price - price) / self.position_entry_price
+
+        if pnl_pct <= -0.02:
+            self.close_current_position("stop_loss_2pct")
+
+    def run_once(self) -> str:
+        """Esegui un singolo ciclo. Ritorna lo stato."""
+        df = self.fetch_candles(LOOKBACK_DAYS, TRAIN_INSTRUMENT)
+        if df.empty:
+            return "no_data"
+
+        if self.in_position:
+            self.check_position_exit(df)
+            self.save_status()
+            if self.in_position:
+                return f"in_position_{self.position_direction}_bar{self.bars_held}"
+            return "position_closed"
+
+        signal = self.engine.check_signal(df)
+        if signal:
+            self.log("SIGNAL", signal)
+            self.open_position(signal["direction"], signal)
+            self.save_status()
+            return f"signal_{signal['direction']}"
+
+        self.save_status()
+        return "watching"
+
+    def run(self, retrain_hours: int = 24):
+        """Loop principale."""
+        print("=" * 60)
+        print(f"  PAPER TRADER — {INSTRUMENT} (margine {CURRENCY})")
+        print(f"  Segnali da: {TRAIN_INSTRUMENT} {RESOLUTION}m")
+        print(f"  Leva: {LEVERAGE}x, Position: {POSITION_PCT*100:.0f}%, Hold: {HOLD_BARS} barre")
+        print(f"  Poll: ogni {POLL_SECONDS}s")
+        print(f"  Log: {self.log_path}")
+        print("=" * 60)
+
+        account = self.client.get_account_summary()
+        self.log("STARTUP", {
+            "virtual_capital": self.virtual_capital,
+            "testnet_equity": account["equity"],
+            "testnet": account.get("testnet", True),
+        })
+
+        if not self.train_model():
+            print("Training fallito. Uscita.")
+            return
+
+        last_train = datetime.now(timezone.utc)
+
+        while True:
+            try:
+                now = datetime.now(timezone.utc)
+                if (now - last_train).total_seconds() > retrain_hours * 3600:
+                    self.train_model()
+                    last_train = now
+
+                status = self.run_once()
+                if status != "watching":
+                    print(f"  → {status}")
+
+            except KeyboardInterrupt:
+                self.log("SHUTDOWN", {"reason": "keyboard"})
+                if self.in_position:
+                    self.close_current_position("shutdown")
+                break
+            except Exception as e:
+                self.log("ERROR", {"error": str(e)})
+                print(f"  ERRORE: {e}")
+
+            time.sleep(POLL_SECONDS)
+
+
+if __name__ == "__main__":
+    trader = PaperTrader()
+    trader.run()

src/live/signal_engine.py

@@ -0,0 +1,232 @@
+"""Motore segnali: squeeze detection + ML confirmation su dati live."""
+from __future__ import annotations
+
+import numpy as np
+import pandas as pd
+from sklearn.ensemble import GradientBoostingClassifier
+from sklearn.preprocessing import StandardScaler
+
+
+def keltner_ratio(close: np.ndarray, high: np.ndarray, low: np.ndarray, window: int = 14) -> np.ndarray:
+    n = len(close)
+    result = np.full(n, np.nan)
+    for i in range(window, n):
+        wc = close[i - window : i]
+        wh = high[i - window : i]
+        wl = low[i - window : i]
+        ma = np.mean(wc)
+        bb_std = np.std(wc)
+        tr = np.maximum(wh - wl, np.maximum(np.abs(wh - np.roll(wc, 1)), np.abs(wl - np.roll(wc, 1))))
+        atr = np.mean(tr[1:])
+        kc_r = (ma + 1.5 * atr) - (ma - 1.5 * atr)
+        bb_r = (ma + 2 * bb_std) - (ma - 2 * bb_std)
+        if kc_r > 0:
+            result[i] = bb_r / kc_r
+    return result
+
+
+def build_features(df: pd.DataFrame, i: int, squeeze_duration: int, squeeze_avg_vol: float, kcr_val: float) -> np.ndarray | None:
+    if i < 100 or i >= len(df):
+        return None
+
+    o = df["open"].values
+    h = df["high"].values
+    l = df["low"].values
+    c = df["close"].values
+    v = df["volume"].values
+
+    feats = []
+    for w in [12, 24, 48]:
+        if i < w:
+            feats.extend([0] * 12)
+            continue
+
+        win_c = c[i - w : i]
+        win_o = o[i - w : i]
+        win_h = h[i - w : i]
+        win_l = l[i - w : i]
+        win_v = v[i - w : i]
+
+        mn, mx = win_l.min(), max(win_h.max(), win_c.max())
+        rng = mx - mn if mx - mn > 0 else 1e-10
+        total = win_h - win_l
+        total = np.where(total == 0, 1e-10, total)
+        body = np.abs(win_c - win_o) / total
+        direction = np.sign(win_c - win_o)
+        log_c = np.log(np.where(win_c == 0, 1e-10, win_c))
+        rets = np.diff(log_c)
+        v_mean = np.mean(win_v)
+
+        feats.extend([
+            np.mean(rets) if len(rets) > 0 else 0,
+            np.std(rets) if len(rets) > 0 else 0,
+            np.sum(rets) if len(rets) > 0 else 0,
+            float(pd.Series(rets).skew()) if len(rets) > 2 else 0,
+            float(pd.Series(rets).kurtosis()) if len(rets) > 3 else 0,
+            np.mean(body),
+            np.std(body),
+            np.mean(direction),
+            np.mean(direction[-min(3, w):]),
+            (win_c[-1] - mn) / rng,
+            win_v[-1] / v_mean if v_mean > 0 else 1,
+            np.corrcoef(rets[:-1], rets[1:])[0, 1] if len(rets) > 1 and np.std(rets) > 0 else 0,
+        ])
+
+    feats.extend([
+        squeeze_duration,
+        squeeze_duration / (24 * 4),
+        kcr_val,
+        v[i - 1] / squeeze_avg_vol if squeeze_avg_vol > 0 else 1,
+        np.mean(v[max(0, i - 3) : i]) / squeeze_avg_vol if squeeze_avg_vol > 0 else 1,
+    ])
+
+    h48 = np.max(h[max(0, i - 48) : i])
+    l48 = np.min(l[max(0, i - 48) : i])
+    r48 = h48 - l48
+    feats.append((c[i - 1] - l48) / r48 if r48 > 0 else 0.5)
+
+    tr = np.maximum(h[i - 14 : i] - l[i - 14 : i],
+                    np.maximum(np.abs(h[i - 14 : i] - np.roll(c[i - 14 : i], 1)),
+                               np.abs(l[i - 14 : i] - np.roll(c[i - 14 : i], 1))))
+    atr = np.mean(tr[1:])
+    feats.append(atr / c[i - 1] if c[i - 1] > 0 else 0)
+
+    first_ret = (c[i - 1] - c[i - 2]) / c[i - 2] if i >= 2 and c[i - 2] > 0 else 0
+    feats.append(first_ret)
+
+    return np.nan_to_num(np.array(feats), nan=0, posinf=1e6, neginf=-1e6)
+
+
+class SignalEngine:
+    """Rileva squeeze e genera segnali ML in real-time."""
+
+    def __init__(self, bb_w: int = 14, sq_thr: float = 0.8, ml_thr: float = 0.70, min_squeeze_bars: int = 5):
+        self.bb_w = bb_w
+        self.sq_thr = sq_thr
+        self.ml_thr = ml_thr
+        self.min_squeeze_bars = min_squeeze_bars
+
+        self.model: GradientBoostingClassifier | None = None
+        self.scaler: StandardScaler | None = None
+        self.in_squeeze = False
+        self.squeeze_start_idx = 0
+        self.trained = False
+
+    def train(self, df: pd.DataFrame, lookahead: int = 3) -> dict:
+        """Addestra il modello su dati storici."""
+        close = df["close"].values
+        high = df["high"].values
+        low = df["low"].values
+        volume = df["volume"].values
+        n = len(df)
+
+        kcr = keltner_ratio(close, high, low, self.bb_w)
+
+        X_all, y_all = [], []
+        in_sq = False
+        sq_start = 0
+
+        for i in range(self.bb_w + 1, n - lookahead):
+            if np.isnan(kcr[i]):
+                continue
+            is_sq = kcr[i] < self.sq_thr
+            if is_sq and not in_sq:
+                in_sq = True
+                sq_start = i
+            elif not is_sq and in_sq:
+                in_sq = False
+                duration = i - sq_start
+                if duration < self.min_squeeze_bars:
+                    continue
+
+                avg_vol = np.mean(volume[sq_start:i])
+                feats = build_features(df, i, duration, avg_vol, kcr[i])
+                if feats is None:
+                    continue
+
+                actual = (close[i + lookahead - 1] - close[i - 1]) / close[i - 1]
+                X_all.append(feats)
+                y_all.append(1 if actual > 0 else 0)
+
+        if len(X_all) < 30:
+            return {"error": "not enough training samples", "samples": len(X_all)}
+
+        X = np.array(X_all)
+        y = np.array(y_all)
+
+        self.scaler = StandardScaler()
+        X_s = self.scaler.fit_transform(X)
+
+        self.model = GradientBoostingClassifier(
+            n_estimators=150, max_depth=4, min_samples_leaf=10,
+            learning_rate=0.05, subsample=0.8, random_state=42,
+        )
+        self.model.fit(X_s, y)
+        self.trained = True
+
+        preds = self.model.predict(X_s)
+        train_acc = np.mean(preds == y) * 100
+
+        return {"samples": len(X), "up_ratio": np.mean(y) * 100, "train_accuracy": train_acc}
+
+    def check_signal(self, df: pd.DataFrame) -> dict | None:
+        """Controlla se c'è un segnale sulle ultime candele.
+        Ritorna dict con direzione e probabilità, oppure None.
+        """
+        if not self.trained:
+            return None
+
+        close = df["close"].values
+        high = df["high"].values
+        low = df["low"].values
+        volume = df["volume"].values
+        n = len(df)
+
+        kcr = keltner_ratio(close, high, low, self.bb_w)
+
+        if n < self.bb_w + 10:
+            return None
+
+        last_kcr = kcr[-1]
+        prev_kcr = kcr[-2] if n > 1 else np.nan
+
+        if np.isnan(last_kcr) or np.isnan(prev_kcr):
+            return None
+
+        was_squeeze = prev_kcr < self.sq_thr
+        is_released = last_kcr >= self.sq_thr
+
+        if not (was_squeeze and is_released):
+            self.in_squeeze = prev_kcr < self.sq_thr
+            if self.in_squeeze and not hasattr(self, '_sq_start_tracking'):
+                self._sq_start_tracking = n - 1
+            if not self.in_squeeze:
+                self._sq_start_tracking = None
+            return None
+
+        sq_start = getattr(self, '_sq_start_tracking', n - 10)
+        if sq_start is None:
+            sq_start = n - 10
+        duration = (n - 1) - sq_start
+        if duration < self.min_squeeze_bars:
+            self._sq_start_tracking = None
+            return None
+
+        avg_vol = np.mean(volume[max(0, sq_start) : n - 1])
+        feats = build_features(df, n - 1, duration, avg_vol, last_kcr)
+        self._sq_start_tracking = None
+
+        if feats is None:
+            return None
+
+        feats_s = self.scaler.transform(feats.reshape(1, -1))
+        proba = self.model.predict_proba(feats_s)[0]
+        up_idx = list(self.model.classes_).index(1)
+        p_up = proba[up_idx]
+
+        if p_up >= self.ml_thr:
+            return {"direction": "buy", "probability": p_up, "squeeze_duration": duration}
+        elif p_up <= (1 - self.ml_thr):
+            return {"direction": "sell", "probability": 1 - p_up, "squeeze_duration": duration}
+
+        return None

src/live/telegram_notifier.py

@@ -0,0 +1,39 @@
+"""Notifiche Telegram per il paper trader."""
+from __future__ import annotations
+
+import os
+import urllib.request
+import urllib.parse
+import json
+
+BOT_TOKEN = os.environ.get("TELEGRAM_BOT_TOKEN", "")
+CHAT_ID = os.environ.get("TELEGRAM_CHAT_ID", "")
+
+NOTIFY_EVENTS = {
+    "SIGNAL", "OPENED", "CLOSED", "OPEN_FAILED", "CLOSE_FAILED",
+    "ERROR", "STARTUP", "SHUTDOWN", "TRAINING_FAILED",
+}
+
+
+def send_telegram(text: str) -> bool:
+    if not BOT_TOKEN or not CHAT_ID:
+        return False
+    try:
+        url = f"https://api.telegram.org/bot{BOT_TOKEN}/sendMessage"
+        data = urllib.parse.urlencode({"chat_id": CHAT_ID, "text": text, "parse_mode": "HTML"}).encode()
+        urllib.request.urlopen(url, data, timeout=10)
+        return True
+    except Exception:
+        return False
+
+
+def notify_event(event: str, data: dict | None = None):
+    if event not in NOTIFY_EVENTS:
+        return
+    lines = [f"📊 <b>{event}</b>"]
+    if data:
+        for k, v in data.items():
+            if k in ("signal",):
+                continue
+            lines.append(f"  {k}: {v}")
+    send_telegram("\n".join(lines))

src/strategies/__init__.py

@@ -0,0 +1,11 @@
+"""Strategie di trading — classe base e indicatori condivisi."""
+from src.strategies.base import Strategy, Signal, BacktestResult, YearlyStats
+from src.strategies.indicators import (
+    keltner_ratio, detect_squeezes, ema, atr, rv_annualized, rolling_correlation,
+)
+
+__all__ = [
+    "Strategy", "Signal", "BacktestResult", "YearlyStats",
+    "keltner_ratio", "detect_squeezes", "ema", "atr",
+    "rv_annualized", "rolling_correlation",
+]

src/strategies/base.py

@@ -0,0 +1,243 @@
+"""Classe base astratta per tutte le strategie di trading."""
+from __future__ import annotations
+
+from abc import ABC, abstractmethod
+from dataclasses import dataclass, field
+
+import numpy as np
+import pandas as pd
+
+from src.data.downloader import load_data
+
+
+@dataclass
+class Signal:
+    """Segnale di trading generato da una strategia."""
+    idx: int
+    direction: int          # +1 long, -1 short
+    entry_price: float
+    metadata: dict = field(default_factory=dict)
+
+
+@dataclass
+class YearlyStats:
+    year: int
+    trades: int
+    wins: int
+    pnl: float
+
+    @property
+    def accuracy(self) -> float:
+        return self.wins / self.trades * 100 if self.trades > 0 else 0.0
+
+
+@dataclass
+class BacktestResult:
+    """Risultato completo di un backtest."""
+    strategy_name: str
+    asset: str
+    timeframe: str
+    params: dict
+
+    trades: int
+    wins: int
+    pnl: float
+    capital: float
+    initial_capital: float
+    max_dd: float
+    time_in_market_pct: float
+    avg_trade_duration_h: float
+    years_active: int
+    yearly: list[YearlyStats]
+
+    @property
+    def accuracy(self) -> float:
+        return self.wins / self.trades * 100 if self.trades > 0 else 0.0
+
+    @property
+    def sharpe(self) -> float:
+        pnls = []
+        for ys in self.yearly:
+            pnls.append(ys.pnl)
+        if len(pnls) < 2 or np.std(pnls) == 0:
+            return 0.0
+        return float(np.mean(pnls) / np.std(pnls) * np.sqrt(len(pnls)))
+
+    @property
+    def daily_pnl(self) -> float:
+        return self.pnl / (self.years_active * 365) if self.years_active > 0 else 0.0
+
+    @property
+    def worst_year(self) -> YearlyStats | None:
+        valid = [y for y in self.yearly if y.trades >= 10]
+        if not valid:
+            valid = self.yearly
+        return min(valid, key=lambda y: y.accuracy) if valid else None
+
+    def print_summary(self):
+        worst = self.worst_year
+        worst_str = f"{worst.year}({worst.accuracy:.0f}%)" if worst else "N/A"
+        dur = f"{self.avg_trade_duration_h:.0f}h" if self.avg_trade_duration_h >= 1 else f"{self.avg_trade_duration_h * 60:.0f}m"
+        print(f"  {self.strategy_name:<30s} {self.asset:>3s} {self.timeframe:>3s} "
+              f"{self.trades:>5d}t {self.accuracy:>5.1f}% "
+              f"€{self.pnl:>+9.0f} DD {self.max_dd:>4.1f}% "
+              f"€/d {self.daily_pnl:>+6.2f} "
+              f"Mkt {self.time_in_market_pct:>4.1f}% {dur:>5s} "
+              f"worst={worst_str} {self.years_active}y")
+
+    def print_yearly(self):
+        print(f"\n  {self.strategy_name} [{self.asset} {self.timeframe}] — per anno:")
+        print(f"  {'Anno':>6s} {'Trades':>7s} {'Acc':>6s} {'PnL€':>9s}")
+        for ys in sorted(self.yearly, key=lambda y: y.year):
+            print(f"  {ys.year:>6d} {ys.trades:>7d} {ys.accuracy:>5.1f}% €{ys.pnl:>+8.0f}")
+
+
+TF_MINUTES = {"1m": 1, "5m": 5, "15m": 15, "1h": 60, "4h": 240, "1d": 1440}
+
+
+class Strategy(ABC):
+    """Classe base per tutte le strategie.
+
+    Sottoclassi devono implementare:
+      - name, description, default_assets, default_timeframes
+      - generate_signals(df, timestamps, **params) -> list[Signal]
+    """
+
+    name: str = "unnamed"
+    description: str = ""
+    default_assets: list[str] = ["BTC", "ETH"]
+    default_timeframes: list[str] = ["15m", "1h"]
+
+    # Parametri di backtest
+    fee_rt: float = 0.002
+    leverage: float = 3.0
+    position_size: float = 0.15
+    initial_capital: float = 1000.0
+
+    @abstractmethod
+    def generate_signals(self, df: pd.DataFrame, ts: pd.DatetimeIndex,
+                         **params) -> list[Signal]:
+        """Genera segnali di trading dal dataframe OHLCV.
+
+        Args:
+            df: DataFrame con colonne open, high, low, close, volume, timestamp
+            ts: DatetimeIndex UTC dei timestamp
+            **params: parametri specifici della strategia
+
+        Returns:
+            Lista di Signal con idx, direction, entry_price
+        """
+        ...
+
+    def backtest(self, asset: str, tf: str, hold: int = 3,
+                 **params) -> BacktestResult | None:
+        """Esegue backtest su un asset/timeframe."""
+        df = load_data(asset, tf)
+        c = df["close"].values
+        n = len(c)
+        ts = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
+
+        sig_params = {**params, "asset": asset, "tf": tf}
+        signals = self.generate_signals(df, ts, **sig_params)
+        if not signals:
+            return None
+
+        yearly: dict[int, dict] = {}
+        capital = float(self.initial_capital)
+        peak = capital
+        max_dd = 0.0
+        total_bars = 0
+
+        for sig in signals:
+            i = sig.idx
+            if i + hold >= n or i < 1:
+                continue
+
+            entry = sig.entry_price
+            exit_price = c[min(i + hold - 1, n - 1)]
+            actual = (exit_price - entry) / entry * sig.direction
+            net = actual * self.leverage - self.fee_rt * self.leverage
+
+            capital += capital * self.position_size * net
+            capital = max(capital, 10)
+            if capital > peak:
+                peak = capital
+            dd = (peak - capital) / peak
+            max_dd = max(max_dd, dd)
+            total_bars += hold
+
+            year = ts.iloc[i].year
+            if year not in yearly:
+                yearly[year] = {"w": 0, "t": 0, "pnl": 0.0}
+            yearly[year]["t"] += 1
+            if actual > 0:
+                yearly[year]["w"] += 1
+            yearly[year]["pnl"] += net * self.initial_capital
+
+        all_t = sum(d["t"] for d in yearly.values())
+        all_w = sum(d["w"] for d in yearly.values())
+        if all_t == 0:
+            return None
+
+        yearly_stats = [
+            YearlyStats(year=y, trades=d["t"], wins=d["w"], pnl=d["pnl"])
+            for y, d in sorted(yearly.items())
+        ]
+
+        return BacktestResult(
+            strategy_name=self.name,
+            asset=asset,
+            timeframe=tf,
+            params=params,
+            trades=all_t,
+            wins=all_w,
+            pnl=sum(d["pnl"] for d in yearly.values()),
+            capital=capital,
+            initial_capital=self.initial_capital,
+            max_dd=max_dd * 100,
+            time_in_market_pct=total_bars / n * 100,
+            avg_trade_duration_h=hold * TF_MINUTES.get(tf, 60) / 60,
+            years_active=len(yearly),
+            yearly=yearly_stats,
+        )
+
+    def run_all(self, assets: list[str] | None = None,
+                timeframes: list[str] | None = None,
+                hold: int = 3, **params) -> list[BacktestResult]:
+        """Esegue backtest su tutte le combinazioni asset/timeframe."""
+        assets = assets or self.default_assets
+        timeframes = timeframes or self.default_timeframes
+        results = []
+        for asset in assets:
+            for tf in timeframes:
+                r = self.backtest(asset, tf, hold=hold, **params)
+                if r and r.trades >= 20:
+                    results.append(r)
+        results.sort(key=lambda r: r.accuracy, reverse=True)
+        return results
+
+    def report(self, results: list[BacktestResult] | None = None,
+               assets: list[str] | None = None,
+               timeframes: list[str] | None = None,
+               hold: int = 3, **params):
+        """Esegue e stampa report completo."""
+        if results is None:
+            results = self.run_all(assets, timeframes, hold, **params)
+
+        print(f"\n{'=' * 120}")
+        print(f"  {self.name} — {self.description}")
+        print(f"  Fee: {self.fee_rt*100:.1f}% RT | Leva: {self.leverage:.0f}x | Pos: {self.position_size*100:.0f}%")
+        print(f"{'=' * 120}")
+        print(f"  {'Nome':<30s} {'A/T':>7s} {'Trades':>6s} {'Acc':>6s} "
+              f"{'PnL€':>10s} {'DD%':>6s} {'€/day':>7s} "
+              f"{'Mkt%':>5s} {'Dur':>5s} {'Worst':>12s} {'Anni':>4s}")
+        print(f"  {'─' * 110}")
+
+        for r in results:
+            r.print_summary()
+
+        if results:
+            best = results[0]
+            best.print_yearly()
+
+        return results

src/strategies/indicators.py

@@ -0,0 +1,102 @@
+"""Indicatori tecnici condivisi tra tutte le strategie."""
+from __future__ import annotations
+
+import numpy as np
+
+
+def keltner_ratio(close: np.ndarray, high: np.ndarray, low: np.ndarray,
+                  window: int = 14) -> np.ndarray:
+    """Rapporto Bollinger / Keltner. Sotto 1 = squeeze (BB dentro KC)."""
+    n = len(close)
+    r = np.full(n, np.nan)
+    for i in range(window, n):
+        wc = close[i - window:i]
+        wh = high[i - window:i]
+        wl = low[i - window:i]
+        ma = np.mean(wc)
+        bb_std = np.std(wc)
+        tr = np.maximum(
+            wh - wl,
+            np.maximum(np.abs(wh - np.roll(wc, 1)), np.abs(wl - np.roll(wc, 1))),
+        )
+        atr = np.mean(tr[1:])
+        kc = (ma + 1.5 * atr) - (ma - 1.5 * atr)
+        bb = (ma + 2 * bb_std) - (ma - 2 * bb_std)
+        if kc > 0:
+            r[i] = bb / kc
+    return r
+
+
+def detect_squeezes(close: np.ndarray, high: np.ndarray, low: np.ndarray,
+                    kcr: np.ndarray, sq_thr: float = 0.8,
+                    min_dur: int = 5) -> list[dict]:
+    """Rileva squeeze events: periodi dove BB sta dentro KC."""
+    events: list[dict] = []
+    in_sq = False
+    sq_start = 0
+    for i in range(1, len(close)):
+        if np.isnan(kcr[i]):
+            continue
+        is_sq = kcr[i] < sq_thr
+        if is_sq and not in_sq:
+            in_sq = True
+            sq_start = i
+        elif not is_sq and in_sq:
+            in_sq = False
+            dur = i - sq_start
+            if dur < min_dur:
+                continue
+            events.append({
+                "idx": i, "dur": dur, "sq_start": sq_start,
+                "kcr_at_release": kcr[i],
+            })
+    return events
+
+
+def ema(arr: np.ndarray, period: int) -> np.ndarray:
+    """Exponential Moving Average."""
+    r = np.full(len(arr), np.nan)
+    k = 2 / (period + 1)
+    r[period - 1] = np.mean(arr[:period])
+    for i in range(period, len(arr)):
+        r[i] = arr[i] * k + r[i - 1] * (1 - k)
+    return r
+
+
+def atr(high: np.ndarray, low: np.ndarray, close: np.ndarray,
+        period: int = 14) -> np.ndarray:
+    """Average True Range (EMA-smoothed)."""
+    tr = np.maximum(
+        high - low,
+        np.maximum(np.abs(high - np.roll(close, 1)), np.abs(low - np.roll(close, 1))),
+    )
+    tr[0] = high[0] - low[0]
+    r = np.full(len(close), np.nan)
+    r[period - 1] = np.mean(tr[:period])
+    k = 2 / (period + 1)
+    for i in range(period, len(close)):
+        r[i] = tr[i] * k + r[i - 1] * (1 - k)
+    return r
+
+
+def rv_annualized(close: np.ndarray, window: int) -> np.ndarray:
+    """Realized volatility annualizzata (hourly data assumed)."""
+    lr = np.diff(np.log(np.where(close == 0, 1e-10, close)))
+    r = np.full(len(close), np.nan)
+    for i in range(window, len(lr)):
+        r[i + 1] = np.std(lr[i - window:i]) * np.sqrt(24 * 365)
+    return r
+
+
+def rolling_correlation(close_a: np.ndarray, close_b: np.ndarray,
+                        window: int = 48) -> np.ndarray:
+    """Correlazione rolling tra rendimenti logaritmici di due asset."""
+    n = max(len(close_a), len(close_b))
+    ret_a = np.diff(np.log(np.where(close_a == 0, 1e-10, close_a)))
+    ret_b = np.diff(np.log(np.where(close_b[:len(close_a)] == 0, 1e-10, close_b[:len(close_a)])))
+    min_len = min(len(ret_a), len(ret_b))
+    corr = np.full(n, np.nan)
+    for i in range(window, min_len):
+        cv = np.corrcoef(ret_a[i - window:i], ret_b[i - window:i])[0, 1]
+        corr[i + 1] = cv if np.isfinite(cv) else 0
+    return corr