Adriano Dal Pastro
14522262e6
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>
409 lines
14 KiB
Python
409 lines
14 KiB
Python
"""Strategia 13: Squeeze + ML ibrida.
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Squeeze breakout come PRE-FILTRO (quando tradare),
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ML come CONFERMA DIREZIONALE (quale direzione).
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Pipeline:
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1. Rileva squeeze release (Bollinger esce da Keltner)
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2. Estrai features frattali/strutturali dalla finestra
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3. ML predice direzione con confidenza
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4. Trade SOLO se squeeze + ML concordano
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Obiettivo: accuracy squeeze (>80%) + volume trade ML.
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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
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FEE = 0.001
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INITIAL = 1000
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def keltner_ratio(close, high, low, window=20):
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n = len(close)
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result = np.full(n, np.nan)
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for i in range(window, n):
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wc = close[i-window:i]
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wh = high[i-window:i]
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wl = low[i-window:i]
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ma = np.mean(wc)
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bb_std = np.std(wc)
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tr = np.maximum(wh - wl, np.maximum(np.abs(wh - np.roll(wc, 1)), np.abs(wl - np.roll(wc, 1))))
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atr = np.mean(tr[1:])
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kc_r = (ma + 1.5*atr) - (ma - 1.5*atr)
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bb_r = (ma + 2*bb_std) - (ma - 2*bb_std)
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if kc_r > 0:
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result[i] = bb_r / kc_r
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return result
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def detect_squeeze_releases(close, high, low, volume, bb_w, sq_thr, min_duration=5):
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kcr = keltner_ratio(close, high, low, bb_w)
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events = []
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in_sq = False
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sq_start = 0
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for i in range(bb_w + 1, len(close)):
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if np.isnan(kcr[i]):
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continue
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is_sq = kcr[i] < sq_thr
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if is_sq and not in_sq:
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in_sq = True
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sq_start = i
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elif not is_sq and in_sq:
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in_sq = False
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duration = i - sq_start
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if duration < min_duration:
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continue
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avg_vol = np.mean(volume[sq_start:i])
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events.append({
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"idx": i,
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"squeeze_start": sq_start,
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"duration": duration,
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"avg_vol_squeeze": avg_vol,
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"kcr_at_release": kcr[i],
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})
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return events
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def build_features_at(df, i, squeeze_info):
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"""Features per il punto di squeeze release."""
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if i < 100:
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return None
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o = df["open"].values
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h = df["high"].values
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l = df["low"].values
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c = df["close"].values
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v = df["volume"].values
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feats = []
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# Structural features multi-window
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for w in [12, 24, 48]:
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win_c = c[i-w:i]
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win_o = o[i-w:i]
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win_h = h[i-w:i]
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win_l = l[i-w:i]
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win_v = v[i-w:i]
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mn, mx = win_l.min(), max(win_h.max(), win_c.max())
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rng = mx - mn if mx - mn > 0 else 1e-10
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total = win_h - win_l
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total = np.where(total == 0, 1e-10, total)
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body = np.abs(win_c - win_o) / total
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direction = np.sign(win_c - win_o)
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log_c = np.log(np.where(win_c == 0, 1e-10, win_c))
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rets = np.diff(log_c)
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v_mean = np.mean(win_v)
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feats.extend([
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np.mean(rets) if len(rets) > 0 else 0,
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np.std(rets) if len(rets) > 0 else 0,
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np.sum(rets) if len(rets) > 0 else 0,
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float(pd.Series(rets).skew()) if len(rets) > 2 else 0,
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float(pd.Series(rets).kurtosis()) if len(rets) > 3 else 0,
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np.mean(body),
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np.std(body),
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np.mean(direction),
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np.mean(direction[-min(3, w):]),
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(win_c[-1] - mn) / rng,
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win_v[-1] / v_mean if v_mean > 0 else 1,
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np.corrcoef(rets[:-1], rets[1:])[0, 1] if len(rets) > 1 and np.std(rets) > 0 else 0,
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])
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# Squeeze-specific features
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sq = squeeze_info
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feats.extend([
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sq["duration"],
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sq["duration"] / 24, # durata in giorni
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sq["kcr_at_release"],
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v[i-1] / sq["avg_vol_squeeze"] if sq["avg_vol_squeeze"] > 0 else 1,
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np.mean(v[i:min(i+3, len(v))]) / sq["avg_vol_squeeze"] if sq["avg_vol_squeeze"] > 0 else 1,
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])
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# Price position
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h48 = np.max(h[max(0, i-48):i])
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l48 = np.min(l[max(0, i-48):i])
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r48 = h48 - l48
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feats.append((c[i-1] - l48) / r48 if r48 > 0 else 0.5)
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# ATR normalized
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tr = np.maximum(h[i-14:i] - l[i-14:i],
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np.maximum(np.abs(h[i-14:i] - np.roll(c[i-14:i], 1)),
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np.abs(l[i-14:i] - np.roll(c[i-14:i], 1))))
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atr = np.mean(tr[1:])
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feats.append(atr / c[i-1] if c[i-1] > 0 else 0)
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# First bar momentum (la barra di breakout)
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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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feats.append(first_ret)
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return np.nan_to_num(np.array(feats), nan=0, posinf=1e6, neginf=-1e6)
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def run_hybrid(asset, tf, bb_w, sq_thr, brk_bars, leverage, pos_pct):
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print(f"\n{'='*65}")
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print(f" {asset} {tf} — HYBRID Squeeze+ML (BBw={bb_w}, sq={sq_thr}, brk={brk_bars})")
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print(f" Leverage: {leverage}x, Position: {pos_pct*100:.0f}%")
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print(f"{'='*65}")
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df = load_data(asset, tf)
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close = df["close"].values
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high = df["high"].values
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low = df["low"].values
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volume = df["volume"].values
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n = len(df)
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events = detect_squeeze_releases(close, high, low, volume, bb_w, sq_thr)
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print(f" Squeeze releases totali: {len(events)}")
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# Build dataset: solo ai punti di squeeze
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X_all, y_all, ev_all = [], [], []
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for ev in events:
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i = ev["idx"]
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if i + brk_bars >= n or i < 100:
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continue
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feats = build_features_at(df, i, ev)
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if feats is None:
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continue
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actual_ret = (close[i + brk_bars - 1] - close[i - 1]) / close[i - 1]
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X_all.append(feats)
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y_all.append(1 if actual_ret > 0 else 0)
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ev_all.append(ev)
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if len(X_all) < 50:
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print(" Troppi pochi campioni.")
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return None
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X = np.array(X_all)
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y = np.array(y_all)
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print(f" Samples: {len(X)}, Up: {np.mean(y)*100:.1f}%")
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# Walk-forward
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TRAIN_SIZE = max(int(len(X) * 0.5), 50)
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STEP_SIZE = max(int(len(X) * 0.1), 10)
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results = {}
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for ml_thr in [0.50, 0.55, 0.60, 0.65, 0.70]:
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capital = float(INITIAL)
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equity = [capital]
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trades_list = []
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correct = 0
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total = 0
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start = 0
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while start + TRAIN_SIZE + STEP_SIZE <= len(X):
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train_end = start + TRAIN_SIZE
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test_end = min(train_end + STEP_SIZE, len(X))
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X_tr = X[start:train_end]
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y_tr = y[start:train_end]
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X_te = X[train_end:test_end]
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y_te = y[train_end:test_end]
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if len(np.unique(y_tr)) < 2:
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start += STEP_SIZE
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continue
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scaler = StandardScaler()
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X_tr_s = scaler.fit_transform(X_tr)
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X_te_s = scaler.transform(X_te)
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model = GradientBoostingClassifier(
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n_estimators=150, max_depth=4, min_samples_leaf=10,
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learning_rate=0.05, 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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up_idx = list(model.classes_).index(1) if 1 in model.classes_ else -1
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if up_idx < 0:
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start += STEP_SIZE
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continue
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for j in range(len(X_te)):
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proba = model.predict_proba(X_te_s[j:j+1])[0]
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p_up = proba[up_idx]
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ev = ev_all[train_end + j]
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i = ev["idx"]
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actual_ret = (close[i + brk_bars - 1] - close[i - 1]) / close[i - 1]
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# ML decide direction
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direction = None
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if p_up >= ml_thr:
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direction = "long"
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elif p_up <= (1 - ml_thr):
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direction = "short"
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if direction is None:
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continue
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is_correct = (direction == "long" and actual_ret > 0) or (direction == "short" and actual_ret < 0)
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total += 1
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if is_correct:
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correct += 1
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trade_ret = actual_ret if direction == "long" else -actual_ret
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net = trade_ret * leverage - FEE * 2 * leverage
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pnl = capital * pos_pct * net
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capital += pnl
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capital = max(capital, 0)
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equity.append(capital)
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trades_list.append({
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"idx": i,
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"direction": direction,
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"p_up": p_up,
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"actual_ret": actual_ret,
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"correct": is_correct,
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"pnl": pnl,
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})
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start += STEP_SIZE
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if total == 0:
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continue
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acc = correct / total * 100
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# Max drawdown
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peak = equity[0]
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max_dd = 0
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for v in equity:
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if v > peak:
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peak = v
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dd = (peak - v) / peak if peak > 0 else 0
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max_dd = max(max_dd, dd)
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# Annualized
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first_ev = ev_all[TRAIN_SIZE] if TRAIN_SIZE < len(ev_all) else ev_all[0]
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last_ev = ev_all[-1]
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test_candles = last_ev["idx"] - first_ev["idx"]
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if tf == "1h":
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test_days = test_candles / 24
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elif tf == "15m":
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test_days = test_candles / (24 * 4)
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else:
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test_days = test_candles / 24
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test_years = test_days / 365.25 if test_days > 0 else 1
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ann = ((capital / INITIAL) ** (1 / test_years) - 1) * 100 if test_years > 0 and capital > 0 else -100
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daily_pnl = (capital - INITIAL) / test_days if test_days > 0 else 0
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trades_yr = total / test_years if test_years > 0 else 0
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tag = ""
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if acc >= 80:
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tag = " ✅✅"
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elif acc >= 70:
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tag = " ✅"
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print(f" ml_thr={ml_thr:.2f}: trades={total:4d} acc={acc:.1f}%{tag} ret={(capital-INITIAL)/INITIAL*100:+.1f}% ann={ann:+.1f}% dd={max_dd*100:.1f}% sharpe= trades/yr={trades_yr:.0f} €/day={daily_pnl:.2f}")
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results[ml_thr] = {
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"trades": total, "accuracy": acc, "capital": capital,
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"annualized": ann, "max_dd": max_dd * 100, "daily_pnl": daily_pnl,
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"trades_yr": trades_yr,
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}
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# Modalità "squeeze puro" come baseline
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capital_sq = float(INITIAL)
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correct_sq = 0
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total_sq = 0
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split = int(len(X) * 0.5)
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for k in range(split, len(X)):
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ev = ev_all[k]
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i = ev["idx"]
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if i + brk_bars >= n:
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continue
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first_ret = (close[i] - close[i-1]) / close[i-1]
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if abs(first_ret) < 0.001:
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continue
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direction = 1 if first_ret > 0 else -1
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actual_ret = (close[i + brk_bars - 1] - close[i - 1]) / close[i - 1]
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is_correct = (direction == 1 and actual_ret > 0) or (direction == -1 and actual_ret < 0)
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total_sq += 1
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if is_correct:
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correct_sq += 1
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trade_ret = actual_ret * direction
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net = trade_ret * leverage - FEE * 2 * leverage
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capital_sq += capital_sq * pos_pct * net
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capital_sq = max(capital_sq, 0)
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if total_sq > 0:
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acc_sq = correct_sq / total_sq * 100
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print(f" BASELINE squeeze puro: trades={total_sq:4d} acc={acc_sq:.1f}% ret={(capital_sq-INITIAL)/INITIAL*100:+.1f}%")
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return results
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# ===== MAIN: test su multiple configurazioni =====
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print("=" * 70)
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print(" STRATEGIA 13: SQUEEZE + ML IBRIDA")
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print("=" * 70)
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configs = [
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# (asset, tf, bb_w, sq_thr, brk_bars, leverage, pos_pct)
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("ETH", "1h", 20, 0.8, 3, 3, 0.2),
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("ETH", "1h", 30, 0.9, 3, 3, 0.2),
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("ETH", "1h", 14, 0.8, 3, 3, 0.2),
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("ETH", "1h", 20, 0.9, 3, 3, 0.2),
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("BTC", "1h", 14, 0.8, 3, 3, 0.2),
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("BTC", "1h", 20, 0.8, 3, 3, 0.2),
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("BTC", "1h", 14, 0.9, 6, 3, 0.2),
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("ETH", "15m", 14, 0.8, 3, 3, 0.15),
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("ETH", "15m", 20, 0.9, 3, 3, 0.15),
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("BTC", "15m", 14, 0.9, 3, 3, 0.15),
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# Aggressive
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("ETH", "1h", 20, 0.8, 3, 5, 0.3),
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("ETH", "1h", 30, 0.9, 3, 5, 0.3),
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]
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all_results = []
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for cfg in configs:
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r = run_hybrid(*cfg)
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if r:
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for thr, data in r.items():
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all_results.append({
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"config": f"{cfg[0]} {cfg[1]} BBw={cfg[2]} sq={cfg[3]} brk={cfg[4]} lev={cfg[5]} pos={cfg[6]}",
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"ml_thr": thr,
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**data,
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})
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# Sort by accuracy
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print("\n\n" + "=" * 70)
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print(" CLASSIFICA PER ACCURACY (top 20)")
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print("=" * 70)
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sorted_acc = sorted(all_results, key=lambda x: x["accuracy"], reverse=True)
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for r in sorted_acc[:20]:
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tag = "✅✅" if r["accuracy"] >= 80 else "✅" if r["accuracy"] >= 70 else ""
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print(f" {r['config']:55s} ml={r['ml_thr']:.2f} → acc={r['accuracy']:.1f}% {tag:4s} trades={r['trades']:4d} ret={(r['capital']-INITIAL)/INITIAL*100:+.1f}% ann={r['annualized']:+.1f}% dd={r['max_dd']:.1f}% €/day={r['daily_pnl']:.2f}")
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print("\n" + "=" * 70)
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print(" CLASSIFICA PER ROI ANNUO (top 20, min 20 trades)")
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print("=" * 70)
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sorted_roi = sorted([r for r in all_results if r["trades"] >= 20], key=lambda x: x["annualized"], reverse=True)
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for r in sorted_roi[:20]:
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tag = "✅✅" if r["accuracy"] >= 80 else "✅" if r["accuracy"] >= 70 else ""
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print(f" {r['config']:55s} ml={r['ml_thr']:.2f} → acc={r['accuracy']:.1f}% {tag:4s} ann={r['annualized']:+.1f}% trades={r['trades']:4d} dd={r['max_dd']:.1f}% €/day={r['daily_pnl']:.2f}")
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print("\n" + "=" * 70)
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print(" SWEET SPOT: acc>=75% AND ann>=20% AND trades>=15")
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print("=" * 70)
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sweet = [r for r in all_results if r["accuracy"] >= 75 and r["annualized"] >= 20 and r["trades"] >= 15]
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sweet.sort(key=lambda x: x["accuracy"] * x["annualized"], reverse=True)
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for r in sweet:
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print(f" {r['config']:55s} ml={r['ml_thr']:.2f} → acc={r['accuracy']:.1f}% ann={r['annualized']:+.1f}% trades={r['trades']:4d} dd={r['max_dd']:.1f}% €/day={r['daily_pnl']:.2f}")
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