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 6: Structural Pattern Matching con DTW veloce.
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Idea diversa: invece di ML generico, cerca nel passato le finestre OHLC
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più simili alla finestra corrente usando una versione veloce (reduced DTW).
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Vota sulla direzione basandosi sui K-nearest neighbors nel passato.
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Usa features normalizzate (non DTW puro sul prezzo che è lento).
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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.neighbors import KNeighborsClassifier
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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 normalize_pattern_window
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print("=" * 60)
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print(" STRATEGIA 6: STRUCTURAL PATTERN KNN — 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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WINDOW = 24
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LOOKAHEAD = 6
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MIN_RETURN = 0.003
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def extract_structural_features(df: pd.DataFrame, idx: int, window: int) -> np.ndarray | None:
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"""Extract normalized structural features from OHLC window."""
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if idx < window:
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return None
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o = df["open"].values[idx - window : idx]
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h = df["high"].values[idx - window : idx]
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l = df["low"].values[idx - window : idx]
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c = df["close"].values[idx - window : idx]
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v = df["volume"].values[idx - window : idx]
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# Normalize price to [0,1]
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all_prices = np.concatenate([o, h, l, c])
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mn, mx = all_prices.min(), all_prices.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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# Body and shadow ratios (already normalized)
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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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upper_shadow = (h - np.maximum(o, c)) / total
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lower_shadow = (np.minimum(o, c) - l) / total
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direction = np.sign(c - o)
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# Returns
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log_c = np.log(np.where(c == 0, 1e-10, c))
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returns = np.diff(log_c)
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# Volume profile (normalized)
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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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# Downsample to fixed-size feature vector
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# Take every N-th candle if window is large
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step = max(1, window // 12)
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sampled_idx = np.arange(0, window, step)[:12]
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features = np.concatenate([
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c_n[sampled_idx], # 12: normalized close
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body[sampled_idx], # 12: body ratios
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direction[sampled_idx], # 12: direction
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upper_shadow[sampled_idx], # 12: upper shadow
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lower_shadow[sampled_idx], # 12: lower shadow
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v_n[sampled_idx], # 12: volume profile
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[np.mean(returns), np.std(returns), np.sum(returns)], # 3: return stats
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[np.mean(body), np.std(body)], # 2: body stats
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])
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return features
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print("Extracting features...")
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features_all = []
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labels_all = []
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indices_all = []
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for i in range(WINDOW, n - LOOKAHEAD, 1):
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feats = extract_structural_features(df, i, WINDOW)
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if feats is None:
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continue
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future_ret = (close[i + LOOKAHEAD - 1] - close[i - 1]) / close[i - 1]
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if abs(future_ret) < MIN_RETURN:
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continue
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features_all.append(feats)
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labels_all.append(1 if future_ret > 0 else 0)
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indices_all.append(i)
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X = np.array(features_all)
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y = np.array(labels_all)
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idx_arr = np.array(indices_all)
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X = np.nan_to_num(X, nan=0.0, posinf=1e6, neginf=-1e6)
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split = int(len(X) * 0.7)
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X_train, X_test = X[:split], X[split:]
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y_train, y_test = y[:split], y[split:]
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idx_test = idx_arr[split:]
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print(f"Samples: {len(X)} (train={split}, test={len(X)-split})")
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print(f"Label balance: up={np.mean(y)*100:.1f}%")
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scaler = StandardScaler()
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X_train_s = scaler.fit_transform(X_train)
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X_test_s = scaler.transform(X_test)
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# Test diversi K
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print("\n--- KNN SWEEP ---")
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for K in [5, 10, 20, 50, 100, 200]:
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knn = KNeighborsClassifier(n_neighbors=K, weights="distance", n_jobs=-1)
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knn.fit(X_train_s, y_train)
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proba = knn.predict_proba(X_test_s)
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up_idx = list(knn.classes_).index(1)
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for thr in [0.55, 0.60, 0.65, 0.70]:
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sigs = []
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accs = []
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for j in range(len(X_test)):
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p_up = proba[j][up_idx]
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i = idx_test[j]
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actual = (close[i + LOOKAHEAD - 1] - close[i - 1]) / close[i - 1]
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if p_up >= thr:
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sigs.append(1)
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accs.append(1 if actual > 0 else 0)
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elif p_up <= (1 - thr):
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sigs.append(-1)
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accs.append(1 if actual < 0 else 0)
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if not accs:
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continue
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acc = np.mean(accs) * 100
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# PnL
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capital = 1000
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for direction, j in zip(sigs, range(len(accs))):
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i_idx = idx_test[[k for k in range(len(X_test)) if proba[k][up_idx] >= thr or proba[k][up_idx] <= (1 - thr)][j]]
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entry = close[i_idx - 1]
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exit_ = close[i_idx + LOOKAHEAD - 1]
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if direction == 1:
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ret = (exit_ - entry) / entry
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else:
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ret = (entry - exit_) / entry
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ret -= 0.002
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capital *= (1 + ret * 0.5)
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total_ret = (capital - 1000) / 1000 * 100
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print(f" K={K:3d} thr={thr:.2f}: signals={len(accs):5d} acc={acc:.1f}% ret={total_ret:+.1f}%")
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# Best combo: try with Gradient Boosting on same features
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print("\n\n--- GRADIENT BOOSTING SU STRUCTURAL FEATURES ---")
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from sklearn.ensemble import GradientBoostingClassifier
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gb = 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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gb.fit(X_train_s, y_train)
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proba_gb = gb.predict_proba(X_test_s)
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up_idx_gb = list(gb.classes_).index(1)
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for thr in [0.50, 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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n_trades = 0
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for j in range(len(X_test)):
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p_up = proba_gb[j][up_idx_gb]
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i = idx_test[j]
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actual = (close[i + LOOKAHEAD - 1] - close[i - 1]) / close[i - 1]
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if p_up >= thr:
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accs.append(1 if actual > 0 else 0)
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ret = actual - 0.002
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capital *= (1 + ret * 0.5)
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n_trades += 1
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elif p_up <= (1 - thr):
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accs.append(1 if actual < 0 else 0)
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ret = -actual - 0.002
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capital *= (1 + ret * 0.5)
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n_trades += 1
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if not accs:
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continue
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acc = np.mean(accs) * 100
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total_ret = (capital - 1000) / 1000 * 100
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print(f" thr={thr:.2f}: trades={n_trades:5d} acc={acc:.1f}% ret={total_ret:+.1f}%")
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