chore(reset): v2.0.0 — storico certificato Deribit mainnet, ripartenza pulita

Reset del progetto su fondamenta verificate dopo la scoperta che l'intera libreria "validata OOS" era artefatto di feed contaminato (print fantasma del feed Cerbero TESTNET + storico Binance/USDT). - Storico ricostruito da Deribit MAINNET (ccxt pubblico, tokenless) e CERTIFICATO (certify_feed.py): BTC/ETH puliti su TUTTA la storia (mediana 2-6 bps vs Coinbase USD), integrita' OHLC + coerenza resample (maxΔ 0.00) + cross-venue OK. Alt esclusi (illiquidi/divergenti: LTC/DOGE 50-82% barre flat; XRP/BNB non certificabili). - Verdetto sul feed pulito: FADE / PAIRS / XS01 / TSM01 morti (ogni portafoglio Sharpe -2.3..-3.0, DD ~40%); solo SH01 e frammenti HONEST con segnale residuo, da ri-validare in isolamento. - Cleanup "restart pulito": strategie, stack live (src/live, src/portfolio, runner/executor, yml, docker), ~100 script ricerca/gate, waste/games/ portfolios, dati non certificati + cache e 60+ diari -> archiviati in Old/ (preservati, non cancellati). Diario consolidato in un unico documento. - Skeleton ricerca tenuto: Strategy ABC + indicatori + src/fractal + src/backtest/engine + load_data; tool dati certificati (rebuild_history, certify_feed, audit_feed, multi_source_check). - Universo dati ATTIVO: solo BTC/ETH (5m/15m/1h); guardrail fisico (load_data su alt -> FileNotFoundError). Esecuzione DISABILITATA, conto flat. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
2026-06-19 15:16:03 +00:00
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+"""PD01 — Price-Volume Divergence Squeeze.
+
+Estende SQ02 con volume TREND come filtro:
+  - Breakout UP con volume CRESCENTE (ultimi 3 bar vs media squeeze) → ENTRA
+  - Breakout UP con volume CALANTE → SALTA (divergenza bearish)
+  - Viceversa per short
+
+Logica anti-fakeout:
+  1. Squeeze rilascio (come SQ02)
+  2. Anti-fakeout candela (come SQ02)
+  3. Volume al breakout > media squeeze (come SQ02)
+  4. NUOVO: volume trending UP nelle ultime 3 barre prima del breakout
+
+Parametri semplici, nessun overfitting.
+"""
+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 PriceVolumeDivergence(Strategy):
+    name = "PD01_price_vol_div"
+    description = "Squeeze + antifakeout + volume trend confirmation"
+    default_assets = ["BTC", "ETH"]
+    default_timeframes = ["15m", "1h"]
+    fee_rt = 0.002
+    leverage = 3.0
+    position_size = 0.15
+    initial_capital = 1000.0
+
+    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)
+        vol_trend_bars = params.get("vol_trend_bars", 3)  # barre per trend volume
+
+        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 < vol_trend_bars + 1 or i >= n:
+                continue
+
+            # Direzione breakout
+            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
+
+            # Anti-fakeout
+            br = h[i] - l[i]
+            if br > 0:
+                if direction == 1 and (h[i] - c[i]) / br > retrace_limit:
+                    continue
+                elif direction == -1 and (c[i] - l[i]) / br > retrace_limit:
+                    continue
+
+            # Volume al breakout > media squeeze
+            sq_start = ev["sq_start"]
+            avg_sq_v = np.mean(v[sq_start:i])
+            if avg_sq_v <= 0 or v[i] <= avg_sq_v * vol_mult:
+                continue
+
+            # Volume TREND: slope delle ultime vol_trend_bars barre
+            # Usa regressione lineare semplice (rank correlation del volume)
+            recent_v = v[i - vol_trend_bars:i + 1]  # include breakout bar
+            if len(recent_v) < vol_trend_bars:
+                continue
+            # slope: media seconda metà vs prima metà
+            mid = len(recent_v) // 2
+            v_early = np.mean(recent_v[:mid])
+            v_late = np.mean(recent_v[mid:])
+            vol_trending_up = v_late > v_early
+            vol_trending_down = v_early > v_late
+
+            # Concordanza: long richiede volume trending up, short trending down
+            if direction == 1 and not vol_trending_up:
+                continue
+            if direction == -1 and not vol_trending_down:
+                continue
+
+            signals.append(Signal(
+                idx=i,
+                direction=direction,
+                entry_price=c[i - 1],
+                metadata={
+                    "dur": ev["dur"],
+                    "vol_ratio": v[i] / avg_sq_v if avg_sq_v > 0 else 0,
+                    "vol_trend": v_late / v_early if v_early > 0 else 1,
+                },
+            ))
+        return signals
+
+
+if __name__ == "__main__":
+    strategy = PriceVolumeDivergence()
+
+    configs = [
+        {"bb_window": 14, "sq_threshold": 0.8, "retrace_limit": 0.6,
+         "vol_multiplier": 1.3, "vol_trend_bars": 3},
+        {"bb_window": 14, "sq_threshold": 0.8, "retrace_limit": 0.6,
+         "vol_multiplier": 1.2, "vol_trend_bars": 3},
+        {"bb_window": 14, "sq_threshold": 0.8, "retrace_limit": 0.6,
+         "vol_multiplier": 1.3, "vol_trend_bars": 5},
+        {"bb_window": 14, "sq_threshold": 0.8, "retrace_limit": 0.5,
+         "vol_multiplier": 1.3, "vol_trend_bars": 3},
+        {"bb_window": 14, "sq_threshold": 0.75, "retrace_limit": 0.6,
+         "vol_multiplier": 1.3, "vol_trend_bars": 3},
+        {"bb_window": 20, "sq_threshold": 0.8, "retrace_limit": 0.6,
+         "vol_multiplier": 1.3, "vol_trend_bars": 3},
+    ]
+
+    all_results = []
+    for cfg in configs:
+        for asset in ["BTC", "ETH"]:
+            for tf in ["15m", "1h"]:
+                for hold in [3, 6]:
+                    r = strategy.backtest(asset, tf, hold=hold, **cfg)
+                    if r and r.trades >= 20:
+                        lbl = (f"PD01 vtb={cfg['vol_trend_bars']} "
+                               f"vm={cfg['vol_multiplier']} "
+                               f"sq={cfg['sq_threshold']} h={hold}")
+                        r.strategy_name = lbl
+                        all_results.append(r)
+
+    all_results.sort(key=lambda r: r.accuracy, reverse=True)
+
+    print(f"\n{'=' * 130}")
+    print("  PD01 PRICE-VOLUME DIVERGENCE — TOP 20")
+    print(f"{'=' * 130}")
+    print(f"  {'Nome':<50s} {'A/T':>7s} {'Trades':>6s} {'Acc':>6s} "
+          f"{'PnL€':>10s} {'DD%':>6s} {'€/day':>7s} "
+          f"{'Mkt%':>5s} {'Dur':>5s} {'Anni':>4s}")
+    print(f"  {'─' * 120}")
+    for r in all_results[:20]:
+        r.print_summary()
+
+    if all_results:
+        all_results[0].print_yearly()
+
+    print(f"\n  BENCHMARK SQ02: 79.7% acc, 1250t, DD 6.5%, €5.23/day, 9 anni")
+    print(f"  BENCHMARK MT01: 82.7% acc, 503t, DD 5.9%")