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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"""Strategia 8: Ensemble multi-timeframe.
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Combina i migliori approcci:
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1. GBM su structural features (miglior singolo finora: 58.6%/+57.5%)
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2. GBM su fractal indicators
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3. Multi-timeframe: 1h features + 15m aggregati
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Vota con consensus: trade solo quando almeno 2/3 modelli 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 sklearn.ensemble import GradientBoostingClassifier
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from sklearn.preprocessing import StandardScaler
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from src.data.downloader import load_data
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from src.fractal.patterns import encode_candles, extract_body_ratios, extract_shadow_ratios
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print("=" * 60)
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print(" STRATEGIA 8: ENSEMBLE MULTI-TF — BTC")
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print("=" * 60)
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# Load both timeframes
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df_1h = load_data("BTC", "1h")
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df_15m = load_data("BTC", "15m")
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close_1h = df_1h["close"].values
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ts_1h = df_1h["timestamp"].values
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WINDOW_1H = 24
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LOOKAHEAD = 6
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MIN_RETURN = 0.003
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def structural_features_1h(df: pd.DataFrame, i: int, window: int = 24) -> np.ndarray | None:
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if i < window:
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return None
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o = df["open"].values[i - window : i]
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h = df["high"].values[i - window : i]
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l = df["low"].values[i - window : i]
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c = df["close"].values[i - window : i]
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v = df["volume"].values[i - window : i]
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all_p = np.concatenate([o, h, l, c])
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mn, mx = all_p.min(), all_p.max()
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if mx - mn == 0:
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return None
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o_n = (o - mn) / (mx - mn)
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h_n = (h - mn) / (mx - mn)
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l_n = (l - mn) / (mx - mn)
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c_n = (c - mn) / (mx - mn)
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total = h - l
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total = np.where(total == 0, 1e-10, total)
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body = np.abs(c - o) / total
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u_shadow = (h - np.maximum(o, c)) / total
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l_shadow = (np.minimum(o, c) - l) / total
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direction = np.sign(c - o)
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log_c = np.log(np.where(c == 0, 1e-10, c))
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rets = np.diff(log_c)
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v_mean = np.mean(v)
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v_n = v / v_mean if v_mean > 0 else np.ones_like(v)
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step = max(1, window // 12)
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idx = np.arange(0, window, step)[:12]
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features = np.concatenate([
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c_n[idx], body[idx], direction[idx],
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u_shadow[idx], l_shadow[idx], v_n[idx],
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[np.mean(rets), np.std(rets), np.sum(rets),
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np.mean(body), np.std(body),
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np.max(body[-6:]) - np.min(body[-6:])],
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])
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return features
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def multi_tf_features(ts_current: int, df_15m: pd.DataFrame, n_bars: int = 48) -> np.ndarray | None:
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"""Extract aggregated features from 15m data aligned to current 1h candle."""
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ts_15m = df_15m["timestamp"].values
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mask = ts_15m <= ts_current
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end_idx = np.sum(mask)
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if end_idx < n_bars:
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return None
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start = end_idx - n_bars
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chunk = df_15m.iloc[start:end_idx]
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c = chunk["close"].values
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h = chunk["high"].values
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l = chunk["low"].values
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v = chunk["volume"].values
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if len(c) < n_bars:
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return None
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log_c = np.log(np.where(c == 0, 1e-10, c))
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rets = np.diff(log_c)
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# Micro-structure features
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mom_12 = np.sum(rets[-12:])
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mom_24 = np.sum(rets[-24:])
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vol_12 = np.std(rets[-12:])
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vol_48 = np.std(rets)
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# Candle pattern stats
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ct = encode_candles(chunk)
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up_ratio_12 = np.mean(ct[-12:] == 1)
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up_ratio_24 = np.mean(ct[-24:] == 1)
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# Intra-bar volatility (high-low range)
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ranges = (h - l) / np.where(c == 0, 1e-10, c)
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avg_range_12 = np.mean(ranges[-12:])
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avg_range_48 = np.mean(ranges)
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# Volume profile
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v_mean = np.mean(v)
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v_recent = np.mean(v[-12:])
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vol_surge = v_recent / v_mean if v_mean > 0 else 1.0
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# Autocorrelation
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if np.std(rets) > 0 and len(rets) > 1:
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ac1 = np.corrcoef(rets[:-1], rets[1:])[0, 1]
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ac1 = 0 if not np.isfinite(ac1) else ac1
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else:
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ac1 = 0
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return np.array([
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mom_12, mom_24, vol_12, vol_48,
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up_ratio_12, up_ratio_24,
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avg_range_12, avg_range_48,
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vol_surge, ac1,
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vol_12 / vol_48 if vol_48 > 0 else 1.0,
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])
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print("Extracting features...")
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n_1h = len(df_1h)
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X_struct = []
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X_multi = []
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y_all = []
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indices = []
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for i in range(WINDOW_1H, n_1h - LOOKAHEAD, 1):
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if i % 5000 == 0:
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print(f" {i}/{n_1h}")
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sf = structural_features_1h(df_1h, i, WINDOW_1H)
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if sf is None:
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continue
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mf = multi_tf_features(ts_1h[i - 1], df_15m)
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if mf is None:
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continue
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future_ret = (close_1h[i + LOOKAHEAD - 1] - close_1h[i - 1]) / close_1h[i - 1]
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if abs(future_ret) < MIN_RETURN:
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continue
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X_struct.append(sf)
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X_multi.append(mf)
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y_all.append(1 if future_ret > 0 else 0)
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indices.append(i)
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X_s = np.nan_to_num(np.array(X_struct), nan=0, posinf=1e6, neginf=-1e6)
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X_m = np.nan_to_num(np.array(X_multi), nan=0, posinf=1e6, neginf=-1e6)
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X_combined = np.hstack([X_s, X_m])
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y = np.array(y_all)
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idx_arr = np.array(indices)
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print(f"\nSamples: {len(y)}, struct_feats: {X_s.shape[1]}, multi_feats: {X_m.shape[1]}, combined: {X_combined.shape[1]}")
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print(f"Up ratio: {np.mean(y)*100:.1f}%")
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split = int(len(y) * 0.7)
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# 3 models
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configs = {
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"M1_structural": X_s,
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"M2_multi_tf": X_m,
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"M3_combined": X_combined,
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}
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probas = {}
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for name, X_data in configs.items():
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X_tr, X_te = X_data[:split], X_data[split:]
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y_tr, y_te = y[:split], y[split:]
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sc = StandardScaler()
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X_tr_s = sc.fit_transform(X_tr)
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X_te_s = sc.transform(X_te)
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model = GradientBoostingClassifier(
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n_estimators=300, max_depth=5, min_samples_leaf=30,
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learning_rate=0.03, subsample=0.8, random_state=42,
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)
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model.fit(X_tr_s, y_tr)
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proba = model.predict_proba(X_te_s)
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up_idx = list(model.classes_).index(1)
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probas[name] = proba[:, up_idx]
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# Individual results
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for thr in [0.55, 0.60, 0.65, 0.70]:
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accs = []
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capital = 1000
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for j in range(len(X_te)):
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p = proba[j][up_idx]
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i = idx_arr[split + j]
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actual = (close_1h[i + LOOKAHEAD - 1] - close_1h[i - 1]) / close_1h[i - 1]
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if p >= thr:
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accs.append(1 if actual > 0 else 0)
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capital *= (1 + (actual - 0.002) * 0.5)
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elif p <= (1 - thr):
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accs.append(1 if actual < 0 else 0)
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capital *= (1 + (-actual - 0.002) * 0.5)
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if accs:
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acc = np.mean(accs) * 100
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ret = (capital - 1000) / 1000 * 100
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test_days = (idx_arr[-1] - idx_arr[split]) / 24
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years = test_days / 365.25
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ann = ((capital / 1000) ** (1 / years) - 1) * 100 if years > 0 and capital > 0 else -100
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print(f" {name:15s} thr={thr:.2f}: trades={len(accs):5d} acc={acc:.1f}% ret={ret:+.1f}% ann={ann:+.1f}%")
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# Ensemble voting
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print("\n\n--- ENSEMBLE VOTING ---")
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y_test = y[split:]
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idx_test = idx_arr[split:]
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for min_agree in [2, 3]:
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for thr in [0.55, 0.60, 0.65, 0.70]:
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accs = []
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capital = 1000
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for j in range(len(y_test)):
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votes_up = sum(1 for p in probas.values() if p[j] >= thr)
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votes_down = sum(1 for p in probas.values() if p[j] <= (1 - thr))
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i = idx_test[j]
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actual = (close_1h[i + LOOKAHEAD - 1] - close_1h[i - 1]) / close_1h[i - 1]
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if votes_up >= min_agree:
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accs.append(1 if actual > 0 else 0)
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capital *= (1 + (actual - 0.002) * 0.5)
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elif votes_down >= min_agree:
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accs.append(1 if actual < 0 else 0)
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capital *= (1 + (-actual - 0.002) * 0.5)
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if accs:
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acc = np.mean(accs) * 100
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ret = (capital - 1000) / 1000 * 100
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test_days = (idx_test[-1] - idx_test[0]) / 24
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years = test_days / 365.25
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ann = ((capital / 1000) ** (1 / years) - 1) * 100 if years > 0 and capital > 0 else -100
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trades_yr = len(accs) / years if years > 0 else 0
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print(f" agree>={min_agree} thr={thr:.2f}: trades={len(accs):5d} acc={acc:.1f}% ret={ret:+.1f}% ann={ann:+.1f}% trades/yr={trades_yr:.0f}")
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# Average probability ensemble
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print("\n--- ENSEMBLE AVERAGE PROBABILITY ---")
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avg_proba = np.mean([p for p in probas.values()], axis=0)
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for thr in [0.55, 0.60, 0.65, 0.70, 0.75]:
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accs = []
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capital = 1000
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for j in range(len(y_test)):
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p = avg_proba[j]
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i = idx_test[j]
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actual = (close_1h[i + LOOKAHEAD - 1] - close_1h[i - 1]) / close_1h[i - 1]
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if p >= thr:
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accs.append(1 if actual > 0 else 0)
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capital *= (1 + (actual - 0.002) * 0.5)
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elif p <= (1 - thr):
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accs.append(1 if actual < 0 else 0)
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capital *= (1 + (-actual - 0.002) * 0.5)
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if accs:
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acc = np.mean(accs) * 100
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ret = (capital - 1000) / 1000 * 100
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test_days = (idx_test[-1] - idx_test[0]) / 24
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years = test_days / 365.25
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ann = ((capital / 1000) ** (1 / years) - 1) * 100 if years > 0 and capital > 0 else -100
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trades_yr = len(accs) / years if years > 0 else 0
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daily_ret = capital ** (1 / (test_days)) - 1 if test_days > 0 and capital > 0 else 0
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daily_pnl_on_1k = 1000 * daily_ret
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print(f" thr={thr:.2f}: trades={len(accs):5d} acc={acc:.1f}% ret={ret:+.1f}% ann={ann:+.1f}% trades/yr={trades_yr:.0f} daily_pnl=€{daily_pnl_on_1k:.2f}")
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