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>
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Adriano Dal Pastro
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"""MT01 — Squeeze + Multi-Timeframe Momentum.
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Problema SQ02: entra al breakout 15m ma non sa se il trend 1h è allineato.
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Soluzione: squeeze su 15m + conferma momentum su 1h.
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Anti-overfitting: usa solo 2 indicatori (squeeze + EMA slope),
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nessun parametro complesso.
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IN:
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- OHLCV 15m + 1h per lo stesso asset
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- Parametri: sq_threshold, ema_period_1h, min_slope
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OUT:
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- Signal al breakout 15m confermato da trend 1h
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- BacktestResult
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Logica:
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1. Squeeze release su 15m (come SQ01)
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2. Antifakeout filter (come SQ02)
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3. Check 1h: EMA slope positiva per long, negativa per short
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4. Check 1h: prezzo sopra/sotto EMA per conferma trend
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5. Entra solo se 15m e 1h concordano
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"""
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from __future__ import annotations
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import sys
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sys.path.insert(0, ".")
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import numpy as np
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import pandas as pd
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from src.strategies.base import Strategy, Signal, BacktestResult, YearlyStats, TF_MINUTES
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from src.strategies.indicators import keltner_ratio, detect_squeezes, ema
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from src.data.downloader import load_data
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class SqueezeMTFMomentum(Strategy):
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name = "MT01_squeeze_mtf"
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description = "Squeeze 15m + momentum trend 1h — multi-timeframe"
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default_assets = ["BTC", "ETH"]
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default_timeframes = ["15m"]
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fee_rt = 0.002
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def generate_signals(self, df, ts, **params):
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"""Genera segnali squeeze 15m confermati da trend 1h."""
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c = df["close"].values
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h = df["high"].values
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l = df["low"].values
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v = df["volume"].values
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n = len(c)
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asset = params.get("asset", "BTC")
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sq_thr = params.get("sq_threshold", 0.8)
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ema_period = params.get("ema_period", 50)
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min_slope_val = params.get("min_slope", 0.001)
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use_antifake = params.get("antifake", True)
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use_vol = params.get("vol_filter", False)
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kcr = keltner_ratio(c, h, l, 14)
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events = detect_squeezes(c, h, l, kcr, sq_thr)
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df_1h = params.get("df_1h")
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if df_1h is None:
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df_1h = load_data(asset, "1h")
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c1h = df_1h["close"].values
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ts1h_ms = df_1h["timestamp"].values
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n1h = len(c1h)
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ema_1h = ema(c1h, ema_period)
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ema_slope_arr = np.full(n1h, np.nan)
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for i in range(5, n1h):
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if not np.isnan(ema_1h[i]) and not np.isnan(ema_1h[i-5]) and ema_1h[i-5] > 0:
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ema_slope_arr[i] = (ema_1h[i] - ema_1h[i-5]) / ema_1h[i-5]
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ts_ms = df["timestamp"].values
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signals = []
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for ev in events:
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i = ev["idx"]
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if i < 1 or i >= n:
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continue
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first_ret = (c[i] - c[i-1]) / c[i-1] if c[i-1] > 0 else 0
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if abs(first_ret) < 0.001:
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continue
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if use_antifake:
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br = h[i] - l[i]
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if br > 0:
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if c[i] > c[i-1] and (h[i] - c[i]) / br > 0.6:
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continue
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elif c[i] <= c[i-1] and (c[i] - l[i]) / br > 0.6:
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continue
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if use_vol:
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avg_v = np.mean(v[ev["sq_start"]:i])
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if avg_v > 0 and v[i] <= avg_v * 1.3:
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continue
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direction = 1 if first_ret > 0 else -1
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i1h = np.searchsorted(ts1h_ms, ts_ms[i]) - 1
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if i1h < ema_period or i1h >= n1h:
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continue
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if np.isnan(ema_1h[i1h]) or np.isnan(ema_slope_arr[i1h]):
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continue
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if direction == 1:
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if c1h[i1h] < ema_1h[i1h] or ema_slope_arr[i1h] < min_slope_val:
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continue
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else:
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if c1h[i1h] > ema_1h[i1h] or ema_slope_arr[i1h] > -min_slope_val:
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continue
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signals.append(Signal(idx=i, direction=direction, entry_price=c[i-1]))
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return signals
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def backtest(self, asset, tf="15m", hold=3, **params):
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sq_thr = params.get("sq_threshold", 0.8)
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ema_period = params.get("ema_period", 50)
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min_slope = params.get("min_slope", 0.001)
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use_antifake = params.get("antifake", True)
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use_vol = params.get("vol_filter", False)
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# Carica 15m e 1h
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df_15m = load_data(asset, "15m")
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df_1h = load_data(asset, "1h")
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c15 = df_15m["close"].values
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h15 = df_15m["high"].values
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l15 = df_15m["low"].values
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v15 = df_15m["volume"].values
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n15 = len(c15)
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ts15 = pd.to_datetime(df_15m["timestamp"], unit="ms", utc=True)
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ts15_ms = df_15m["timestamp"].values
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c1h = df_1h["close"].values
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ts1h_ms = df_1h["timestamp"].values
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n1h = len(c1h)
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kcr = keltner_ratio(c15, h15, l15, 14)
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events = detect_squeezes(c15, h15, l15, kcr, sq_thr)
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# EMA su 1h
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ema_1h = ema(c1h, ema_period)
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# EMA slope (variazione percentuale su 5 barre)
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ema_slope = np.full(n1h, np.nan)
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for i in range(5, n1h):
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if not np.isnan(ema_1h[i]) and not np.isnan(ema_1h[i - 5]) and ema_1h[i - 5] > 0:
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ema_slope[i] = (ema_1h[i] - ema_1h[i - 5]) / ema_1h[i - 5]
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yearly = {}
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capital = float(self.initial_capital)
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peak = capital
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max_dd = 0.0
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total_bars = 0
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for ev in events:
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i = ev["idx"]
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if i + hold + 1 >= n15 or i < 1:
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continue
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first_ret = (c15[i] - c15[i - 1]) / c15[i - 1] if c15[i - 1] > 0 else 0
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if abs(first_ret) < 0.001:
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continue
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# Antifake
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if use_antifake:
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br = h15[i] - l15[i]
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if br > 0:
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if c15[i] > c15[i - 1] and (h15[i] - c15[i]) / br > 0.6:
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continue
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elif c15[i] <= c15[i - 1] and (c15[i] - l15[i]) / br > 0.6:
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continue
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# Volume filter
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if use_vol:
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avg_v = np.mean(v15[ev["sq_start"]:i])
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if avg_v > 0 and v15[i] <= avg_v * 1.3:
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continue
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direction = 1 if first_ret > 0 else -1
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# Trova indice 1h corrispondente
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i1h = np.searchsorted(ts1h_ms, ts15_ms[i]) - 1
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if i1h < ema_period or i1h >= n1h or np.isnan(ema_1h[i1h]) or np.isnan(ema_slope[i1h]):
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continue
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# Conferma trend 1h
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if direction == 1:
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if c1h[i1h] < ema_1h[i1h]:
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continue
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if ema_slope[i1h] < min_slope:
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continue
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else:
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if c1h[i1h] > ema_1h[i1h]:
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continue
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if ema_slope[i1h] > -min_slope:
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continue
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entry = c15[i - 1]
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exit_price = c15[min(i + hold - 1, n15 - 1)]
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actual = (exit_price - entry) / entry * direction
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net = actual * self.leverage - self.fee_rt * self.leverage
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capital += capital * self.position_size * net
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capital = max(capital, 10)
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if capital > peak: peak = capital
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dd = (peak - capital) / peak
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max_dd = max(max_dd, dd)
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total_bars += hold
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year = ts15.iloc[i].year
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if year not in yearly:
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yearly[year] = {"w": 0, "t": 0, "pnl": 0.0}
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yearly[year]["t"] += 1
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if actual > 0: yearly[year]["w"] += 1
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yearly[year]["pnl"] += net * self.initial_capital
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all_t = sum(d["t"] for d in yearly.values())
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all_w = sum(d["w"] for d in yearly.values())
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if all_t == 0:
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return None
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yearly_stats = [YearlyStats(y, d["t"], d["w"], d["pnl"]) for y, d in sorted(yearly.items())]
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return BacktestResult(
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strategy_name=self.name, asset=asset, timeframe="15m", params=params,
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trades=all_t, wins=all_w, pnl=sum(d["pnl"] for d in yearly.values()),
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capital=capital, initial_capital=self.initial_capital,
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max_dd=max_dd * 100, time_in_market_pct=total_bars / n15 * 100,
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avg_trade_duration_h=hold * 15 / 60, years_active=len(yearly), yearly=yearly_stats,
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)
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if __name__ == "__main__":
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strategy = SqueezeMTFMomentum()
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configs = [
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("ema50 sl0.1%", {"ema_period": 50, "min_slope": 0.001}),
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("ema50 sl0.05%", {"ema_period": 50, "min_slope": 0.0005}),
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("ema50 sl0.2%", {"ema_period": 50, "min_slope": 0.002}),
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("ema20 sl0.1%", {"ema_period": 20, "min_slope": 0.001}),
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("ema50 sl0.1%+vol", {"ema_period": 50, "min_slope": 0.001, "vol_filter": True}),
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("ema20 sl0.1%+vol", {"ema_period": 20, "min_slope": 0.001, "vol_filter": True}),
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("ema50 noAF", {"ema_period": 50, "min_slope": 0.001, "antifake": False}),
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("ema100 sl0.05%", {"ema_period": 100, "min_slope": 0.0005}),
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]
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all_results = []
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for label, params in configs:
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for asset in ["BTC", "ETH"]:
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for hold in [3, 6]:
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r = strategy.backtest(asset, "15m", hold=hold, **params)
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if r and r.trades >= 30:
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r.strategy_name = f"MT01 {label} h={hold}"
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all_results.append(r)
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all_results.sort(key=lambda r: r.accuracy, reverse=True)
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print(f"\n{'=' * 130}")
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print(f" MT01 SQUEEZE + MTF MOMENTUM — TOP 20")
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print(f"{'=' * 130}")
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for r in all_results[:20]:
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r.print_summary()
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if all_results:
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all_results[0].print_yearly()
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print(f"\n BENCHMARK SQ02: 79.7% acc, 1250t, DD 6.5%, 9 anni, €5.23/day")
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