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 4: Regime-aware fractal ML.
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Combina:
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1. Hurst exponent per regime detection (trend vs mean-revert vs random)
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2. Feature engineering da indicatori frattali
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3. RandomForest per predizione direzione
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4. Trade filtering aggressivo (solo alta confidenza)
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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 RandomForestClassifier, GradientBoostingClassifier
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from sklearn.metrics import accuracy_score, classification_report
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from src.data.downloader import load_data
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from src.fractal.indicators import (
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hurst_exponent,
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fractal_dimension_higuchi,
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self_similarity_score,
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volatility_ratio,
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)
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from src.fractal.patterns import encode_candles, extract_body_ratios, extract_shadow_ratios
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from src.backtest.engine import run_backtest
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print("=" * 60)
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print(" STRATEGIA 4: REGIME-AWARE FRACTAL ML — BTC 1H")
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print("=" * 60)
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df = load_data("BTC", "1h")
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close = df["close"].values
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n = len(close)
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LOOKBACK = 48
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LOOKAHEAD = 6
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MIN_CONFIDENCE = 0.60
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print(f"\nDati: {n} candele")
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print(f"Lookback: {LOOKBACK}, Lookahead: {LOOKAHEAD}")
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# --- Feature engineering ---
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print("\nCalcolo features...")
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features_list = []
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labels = []
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indices = []
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returns = np.diff(np.log(np.where(close == 0, 1e-10, close)))
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candle_types = encode_candles(df)
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body_ratios = extract_body_ratios(df)
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shadow_ratios = extract_shadow_ratios(df)
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for i in range(LOOKBACK, n - LOOKAHEAD, 3):
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if i % 5000 == 0:
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print(f" Feature extraction: {i}/{n}")
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window = close[i - LOOKBACK : i]
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ret_window = returns[i - LOOKBACK : i - 1]
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if len(ret_window) < 10:
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continue
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h = hurst_exponent(ret_window, max_lag=min(len(ret_window) // 4, 20))
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fd = fractal_dimension_higuchi(ret_window, k_max=min(8, len(ret_window) // 4))
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larger_window = close[max(0, i - LOOKBACK * 6) : i]
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ss = self_similarity_score(larger_window, min(LOOKBACK, len(larger_window)))
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vr = volatility_ratio(window, fast=12, slow=LOOKBACK)
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# Candle pattern features
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ct = candle_types[i - 6 : i]
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br = body_ratios[i - 6 : i]
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sr = shadow_ratios[i - 6 : i]
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recent_returns = ret_window[-12:]
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momentum_short = np.sum(recent_returns[-3:])
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momentum_mid = np.sum(recent_returns[-6:])
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momentum_long = np.sum(recent_returns)
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vol_short = np.std(recent_returns[-6:]) if len(recent_returns) >= 6 else 0
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vol_long = np.std(ret_window) if len(ret_window) > 0 else 0
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volume_window = df["volume"].values[i - 12 : i]
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vol_avg = np.mean(volume_window) if len(volume_window) > 0 else 0
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vol_last = df["volume"].values[i - 1] if i > 0 else 0
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vol_ratio = vol_last / vol_avg if vol_avg > 0 else 1.0
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up_count_6 = np.sum(ct[-6:] == 1) / 6
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down_count_6 = np.sum(ct[-6:] == -1) / 6
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features = [
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h, # Hurst exponent
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fd, # Fractal dimension
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ss, # Self-similarity
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vr, # Volatility ratio
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momentum_short, # 3-candle momentum
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momentum_mid, # 6-candle momentum
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momentum_long, # Full window momentum
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vol_short, # Short-term volatility
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vol_long, # Long-term volatility
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vol_ratio, # Volume spike ratio
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up_count_6, # Bullish ratio (last 6)
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down_count_6, # Bearish ratio (last 6)
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np.mean(br[-6:]), # Avg body ratio
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np.mean(sr[-6:]), # Avg shadow ratio
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np.mean(br[-3:]), # Avg body ratio (last 3)
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np.std(br[-6:]), # Body ratio std
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close[i - 1] / np.mean(window), # Price vs MA
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]
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# Label: 1 if price goes up in next LOOKAHEAD candles, 0 otherwise
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future_ret = (close[i + LOOKAHEAD - 1] - close[i - 1]) / close[i - 1]
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label = 1 if future_ret > 0.002 else (0 if future_ret > -0.002 else -1)
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features_list.append(features)
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labels.append(label)
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indices.append(i)
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X = np.array(features_list)
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y = np.array(labels)
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idx_arr = np.array(indices)
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print(f"\nDataset: {len(X)} samples")
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print(f"Label distribution: up={np.sum(y==1)}, flat={np.sum(y==0)}, down={np.sum(y==-1)}")
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# Train/test split cronologico
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split_point = int(len(X) * 0.7)
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X_train, X_test = X[:split_point], X[split_point:]
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y_train, y_test = y[:split_point], y[split_point:]
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idx_train, idx_test = idx_arr[:split_point], idx_arr[split_point:]
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# Handle NaN/Inf
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X_train = np.nan_to_num(X_train, nan=0.0, posinf=1e6, neginf=-1e6)
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X_test = np.nan_to_num(X_test, nan=0.0, posinf=1e6, neginf=-1e6)
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# --- Modelli ---
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print("\n--- TRAINING ---")
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models = {
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"RandomForest": RandomForestClassifier(
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n_estimators=200, max_depth=8, min_samples_leaf=20,
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class_weight="balanced", random_state=42, n_jobs=-1,
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),
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"GradientBoosting": GradientBoostingClassifier(
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n_estimators=200, max_depth=5, min_samples_leaf=20,
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learning_rate=0.05, random_state=42,
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),
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}
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for name, model in models.items():
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print(f"\n{'='*40}")
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print(f" {name}")
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print(f"{'='*40}")
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model.fit(X_train, y_train)
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# Feature importance
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if hasattr(model, "feature_importances_"):
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feat_names = [
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"hurst", "fractal_dim", "self_sim", "vol_ratio",
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"mom_3", "mom_6", "mom_full", "vol_short", "vol_long",
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"vol_spike", "up_ratio", "down_ratio", "body_avg",
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"shadow_avg", "body_3", "body_std", "price_vs_ma"
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]
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imp = model.feature_importances_
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sorted_idx = np.argsort(imp)[::-1]
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print("\nFeature importance (top 10):")
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for j in sorted_idx[:10]:
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print(f" {feat_names[j]:15s}: {imp[j]:.4f}")
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# Prediction con probabilità
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y_pred = model.predict(X_test)
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proba = model.predict_proba(X_test)
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print(f"\nAccuracy: {accuracy_score(y_test, y_pred)*100:.1f}%")
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print(classification_report(y_test, y_pred, target_names=["down", "flat", "up"], zero_division=0))
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# Genera segnali filtrati per confidenza
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signals = pd.Series(0, index=df.index)
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accuracies_filtered = []
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classes = model.classes_
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up_class_idx = list(classes).index(1) if 1 in classes else -1
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down_class_idx = list(classes).index(-1) if -1 in classes else -1
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for k, i in enumerate(idx_test):
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p = proba[k]
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if up_class_idx >= 0 and p[up_class_idx] >= MIN_CONFIDENCE:
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signals.iloc[i] = 1
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actual = (close[i + LOOKAHEAD - 1] - close[i - 1]) / close[i - 1]
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accuracies_filtered.append(1 if actual > 0 else 0)
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elif down_class_idx >= 0 and p[down_class_idx] >= MIN_CONFIDENCE:
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signals.iloc[i] = -1
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actual = (close[i + LOOKAHEAD - 1] - close[i - 1]) / close[i - 1]
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accuracies_filtered.append(1 if actual < 0 else 0)
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n_signals = (signals != 0).sum()
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print(f"\nSegnali filtrati (conf>={MIN_CONFIDENCE}): {n_signals}")
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if accuracies_filtered:
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print(f"Accuratezza filtrata: {np.mean(accuracies_filtered)*100:.1f}%")
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# Backtest
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split_idx = int(len(df) * 0.7)
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test_df = df.iloc[split_idx:].reset_index(drop=True)
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test_signals = signals.iloc[split_idx:].reset_index(drop=True)
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result = run_backtest(test_df, test_signals, initial_capital=1000, fee_pct=0.001, max_hold_candles=LOOKAHEAD)
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print(f"\nBACKTEST:")
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for kk, v in result.summary().items():
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print(f" {kk}: {v}")
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# Prova con soglie diverse
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print(f"\n Varianti soglia:")
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for threshold in [0.55, 0.60, 0.65, 0.70, 0.75, 0.80]:
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sigs = pd.Series(0, index=df.index)
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accs = []
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for k, i in enumerate(idx_test):
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p = proba[k]
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if up_class_idx >= 0 and p[up_class_idx] >= threshold:
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sigs.iloc[i] = 1
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actual = (close[i + LOOKAHEAD - 1] - close[i - 1]) / close[i - 1]
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accs.append(1 if actual > 0 else 0)
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elif down_class_idx >= 0 and p[down_class_idx] >= threshold:
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sigs.iloc[i] = -1
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actual = (close[i + LOOKAHEAD - 1] - close[i - 1]) / close[i - 1]
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accs.append(1 if actual < 0 else 0)
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t_sigs = sigs.iloc[split_idx:].reset_index(drop=True)
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res = run_backtest(test_df, t_sigs, initial_capital=1000, fee_pct=0.001, max_hold_candles=LOOKAHEAD)
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acc = np.mean(accs) * 100 if accs else 0
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print(f" thr={threshold:.2f}: signals={len(accs):4d} acc={acc:.1f}% ret={res.total_return*100:+.1f}% wr={res.win_rate*100:.0f}% sharpe={res.sharpe_ratio:.2f}")
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