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 5: Enhanced fractal features + binary classification + position management.
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Miglioramenti rispetto a #4:
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- Binary classification (up vs down, ignora flat)
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- Feature engineering esteso: multi-window fractal indicators
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- Migliore filtraggio segnali
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- Position sizing basato su confidenza
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- Trailing stop
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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.metrics import accuracy_score
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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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print("=" * 60)
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print(" STRATEGIA 5: ENHANCED FRACTAL — BTC + ETH 1H")
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print("=" * 60)
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LOOKAHEADS = [3, 6, 12]
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MIN_RETURN = 0.003 # 0.3% threshold for "up" label
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for asset in ["BTC", "ETH"]:
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for LOOKAHEAD in LOOKAHEADS:
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print(f"\n{'#'*60}")
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print(f" {asset} 1H — LOOKAHEAD={LOOKAHEAD}")
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print(f"{'#'*60}")
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df = load_data(asset, "1h")
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close = df["close"].values
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volume = df["volume"].values
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n = len(close)
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log_close = np.log(np.where(close == 0, 1e-10, close))
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returns = np.diff(log_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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WINDOWS = [24, 48, 96, 192]
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features_list = []
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labels = []
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indices = []
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max_window = max(WINDOWS) + 50
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for i in range(max_window, n - LOOKAHEAD, 2):
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feats = []
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for w in WINDOWS:
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ret_w = returns[i - w : i - 1]
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close_w = close[i - w : i]
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h = hurst_exponent(ret_w, max_lag=min(len(ret_w) // 4, 20))
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fd = fractal_dimension_higuchi(ret_w, k_max=min(6, len(ret_w) // 4))
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vr = volatility_ratio(close_w, fast=min(12, w // 4), slow=w)
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mom = np.sum(ret_w)
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vol = np.std(ret_w)
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skew = float(pd.Series(ret_w).skew()) if len(ret_w) > 2 else 0
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kurt = float(pd.Series(ret_w).kurtosis()) if len(ret_w) > 3 else 0
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ma = np.mean(close_w)
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price_vs_ma = close[i - 1] / ma if ma > 0 else 1
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# Autocorrelation lag-1
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if len(ret_w) > 1 and np.std(ret_w) > 0:
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ac1 = np.corrcoef(ret_w[:-1], ret_w[1:])[0, 1]
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if not np.isfinite(ac1):
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ac1 = 0
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else:
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ac1 = 0
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feats.extend([h, fd, vr, mom, vol, skew, kurt, price_vs_ma, ac1])
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# Self-similarity multi-scale
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large_window = close[max(0, i - 192 * 4) : i]
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ss = self_similarity_score(large_window, 48)
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feats.append(ss)
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# Candle pattern features (last 12 candles)
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ct = candle_types[i - 12 : i]
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br = body_ratios[i - 12 : i]
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sr = shadow_ratios[i - 12 : i]
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feats.extend([
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np.mean(ct[-3:]),
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np.mean(ct[-6:]),
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np.mean(ct[-12:]),
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np.std(br[-6:]),
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np.mean(br[-3:]),
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np.mean(sr[-6:]),
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np.max(br[-6:]),
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np.min(br[-6:]),
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])
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# Volume features
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vol_w = volume[i - 24 : i]
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if np.mean(vol_w) > 0:
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feats.append(volume[i - 1] / np.mean(vol_w))
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feats.append(np.std(vol_w) / np.mean(vol_w))
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else:
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feats.extend([1.0, 0.0])
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# Range/ATR proxy
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h_arr = df["high"].values[i - 14 : i]
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l_arr = df["low"].values[i - 14 : i]
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c_arr = close[i - 14 : i]
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tr = np.maximum(h_arr - l_arr, np.maximum(np.abs(h_arr - np.roll(c_arr, 1)), np.abs(l_arr - np.roll(c_arr, 1))))
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atr = np.mean(tr[1:])
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feats.append(atr / close[i - 1] if close[i - 1] > 0 else 0)
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# Label
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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 # skip flat zones
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label = 1 if future_ret > 0 else 0
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features_list.append(feats)
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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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X = np.nan_to_num(X, nan=0.0, posinf=1e6, neginf=-1e6)
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# Split
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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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# Train
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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_train, y_train)
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y_pred = model.predict(X_test)
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proba = model.predict_proba(X_test)
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base_acc = accuracy_score(y_test, y_pred)
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print(f"Base accuracy: {base_acc*100:.1f}%")
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# Threshold sweep
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print(f"\n Threshold sweep:")
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for thr in [0.50, 0.55, 0.60, 0.65, 0.70, 0.75, 0.80]:
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up_idx = model.classes_.tolist().index(1)
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sigs = []
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accs = []
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for k in range(len(X_test)):
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p_up = proba[k][up_idx]
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i = idx_test[k]
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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(("long", i))
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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(("short", i))
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accs.append(1 if actual < 0 else 0)
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if not accs:
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print(f" thr={thr:.2f}: no signals")
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continue
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acc = np.mean(accs) * 100
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# Simple PnL estimate
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pnl = 0
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capital = 1000
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for direction, i in sigs:
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entry = close[i - 1]
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exit_ = close[i + LOOKAHEAD - 1]
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if direction == "long":
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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 # fees round-trip
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pnl += capital * ret * 0.5 # 50% per trade
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capital += capital * ret * 0.5
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total_ret = (capital - 1000) / 1000 * 100
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trades_per_year = len(sigs) / ((n - max_window) / (24 * 365))
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print(f" thr={thr:.2f}: signals={len(sigs):5d} acc={acc:.1f}% ret={total_ret:+.1f}% trades/yr={trades_per_year:.0f}")
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