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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"""S3-03: Ultimate Squeeze — combina TUTTI i filtri migliori.
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Filtri che funzionano (testati singolarmente):
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- Anti-fakeout (+1% acc)
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- Long squeeze duration (+1% acc)
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- Cross-asset squeeze simultaneo (+0.5%)
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- Timing 4-16 UTC (+0.5%)
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- Correlation ETH-BTC alta per ETH trades (+1%)
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- Volume confirmation al breakout
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Nuovi filtri da testare:
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- Volume delta: up_volume - down_volume al breakout
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- Momentum confirmation: breakout nella direzione del trend 1h
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- Volatility regime: skip in regime estremo (RV > 100%)
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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 src.data.downloader import load_data
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FEE_RT = 0.002
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INITIAL = 1000
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LEVERAGE = 3
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def keltner_ratio(close, high, low, window=14):
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n = len(close)
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r = np.full(n, np.nan)
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for i in range(window, n):
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wc, wh, wl = close[i-window:i], high[i-window:i], 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 = (ma+1.5*atr)-(ma-1.5*atr)
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bb = (ma+2*bb_std)-(ma-2*bb_std)
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if kc > 0: r[i] = bb/kc
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return r
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def ema(arr, period):
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r = np.full(len(arr), np.nan)
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k = 2/(period+1)
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r[period-1] = np.mean(arr[:period])
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for i in range(period, len(arr)):
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r[i] = arr[i]*k + r[i-1]*(1-k)
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return r
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def rv_ann(close, window):
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lr = np.diff(np.log(np.where(close == 0, 1e-10, close)))
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r = np.full(len(close), np.nan)
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for i in range(window, len(lr)):
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r[i+1] = np.std(lr[i-window:i]) * np.sqrt(24*365)
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return r
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def run_ultimate(primary, tf="15m"):
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secondary = "ETH" if primary == "BTC" else "BTC"
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df = load_data(primary, tf)
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c, h, l, v = df["close"].values, df["high"].values, df["low"].values, df["volume"].values
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n = len(df)
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ts = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
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df2 = load_data(secondary, tf)
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c2, ts2 = df2["close"].values, df2["timestamp"].values
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kcr = keltner_ratio(c, h, l, 14)
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kcr2 = keltner_ratio(c2, df2["high"].values, df2["low"].values, 14)
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ema_50 = ema(c, 50)
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rv_48 = rv_ann(c, 48)
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# Rolling correlation
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ret1 = np.diff(np.log(np.where(c == 0, 1e-10, c)))
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ret2 = np.diff(np.log(np.where(c2[:len(c)] == 0, 1e-10, c2[:len(c)])))
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min_len = min(len(ret1), len(ret2))
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ret1 = ret1[:min_len]
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ret2 = ret2[:min_len]
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corr = np.full(n, np.nan)
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for i in range(48, min_len):
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cv = np.corrcoef(ret1[i-48:i], ret2[i-48:i])[0,1]
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corr[i+1] = cv if np.isfinite(cv) else 0
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# Detect squeezes
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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(15, n):
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if np.isnan(kcr[i]): continue
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is_sq = kcr[i] < 0.8
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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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dur = i - sq_start
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if dur < 5 or i + 6 >= n:
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continue
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events.append({"idx": i, "dur": dur, "sq_start": sq_start})
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print(f"\n{'#'*70}")
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print(f" {primary} {tf} — ULTIMATE SQUEEZE ({len(events)} squeeze events)")
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print(f"{'#'*70}")
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filters_map = {
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"antifake": lambda ev, i: not _antifake(c, h, l, i),
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"long_sq": lambda ev, i: ev["dur"] >= 10,
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"timing": lambda ev, i: 4 <= ts.iloc[i].hour <= 16,
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"cross": lambda ev, i: _cross_squeeze(kcr2, i, ts, ts2),
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"corr_high": lambda ev, i: not np.isnan(corr[i]) and abs(corr[i]) >= 0.6,
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"vol_confirm": lambda ev, i: _vol_confirm(v, i, ev["sq_start"]),
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"trend_align": lambda ev, i: _trend_align(c, ema_50, i),
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"low_rv": lambda ev, i: not np.isnan(rv_48[i]) and rv_48[i] < 1.5,
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}
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def _antifake(c, h, l, i):
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if i + 1 >= len(c): return False
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br = h[i] - l[i]
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if br <= 0: return False
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if c[i] > c[i-1]:
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return (h[i] - c[i]) / br > 0.6
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return (c[i] - l[i]) / br > 0.6
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def _cross_squeeze(kcr2, i, ts1, ts2_arr):
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i2 = np.searchsorted(ts2_arr, ts.values[i].astype("int64") // 10**6)
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i2 = min(i2, len(kcr2)-1)
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return any(not np.isnan(kcr2[j]) and kcr2[j] < 0.85 for j in range(max(0,i2-10), i2+1))
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def _vol_confirm(v, i, sq_start):
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avg = np.mean(v[sq_start:i])
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return avg > 0 and v[i] > avg * 1.3
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def _trend_align(c, ema_val, i):
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if np.isnan(ema_val[i]): return True
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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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if first_ret > 0:
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return c[i] > ema_val[i]
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return c[i] < ema_val[i]
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# Test combinazioni incrementali
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combos = [
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("BASE", []),
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("antifake", ["antifake"]),
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("long_sq", ["long_sq"]),
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("antifake+long", ["antifake", "long_sq"]),
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("antifake+timing", ["antifake", "timing"]),
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("antifake+cross", ["antifake", "cross"]),
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("antifake+corr", ["antifake", "corr_high"]),
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("antifake+vol", ["antifake", "vol_confirm"]),
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("antifake+trend", ["antifake", "trend_align"]),
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("af+long+timing", ["antifake", "long_sq", "timing"]),
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("af+long+cross", ["antifake", "long_sq", "cross"]),
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("af+long+corr", ["antifake", "long_sq", "corr_high"]),
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("af+long+trend", ["antifake", "long_sq", "trend_align"]),
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("af+long+cross+time", ["antifake", "long_sq", "cross", "timing"]),
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("af+long+corr+time", ["antifake", "long_sq", "corr_high", "timing"]),
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("af+long+corr+trend", ["antifake", "long_sq", "corr_high", "trend_align"]),
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("ALL_NO_VOL", ["antifake", "long_sq", "cross", "timing", "corr_high", "trend_align", "low_rv"]),
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("ALL", ["antifake", "long_sq", "cross", "timing", "corr_high", "vol_confirm", "trend_align", "low_rv"]),
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("BEST_5", ["antifake", "long_sq", "corr_high", "trend_align", "low_rv"]),
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]
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results = []
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for combo_name, filter_names in combos:
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yearly = {}
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capital = float(INITIAL)
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peak = capital
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max_dd = 0
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for ev in events:
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i = ev["idx"]
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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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if abs(first_ret) < 0.001:
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continue
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skip = False
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for fn in filter_names:
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if fn in filters_map and not filters_map[fn](ev, i):
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skip = True
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break
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if skip:
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continue
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direction = 1 if first_ret > 0 else -1
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entry = c[i-1]
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exit_price = c[min(i+2, n-1)]
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actual = (exit_price - entry) / entry * direction
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net = actual * LEVERAGE - FEE_RT * LEVERAGE
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capital += capital * 0.15 * net
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capital = max(capital, 10)
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if capital > peak: peak = capital
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dd = (peak - capital) / peak
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max_dd = max(max_dd, dd)
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year = ts.iloc[i].year
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if year not in yearly:
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yearly[year] = {"w": 0, "t": 0, "pnls": []}
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yearly[year]["t"] += 1
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if actual > 0: yearly[year]["w"] += 1
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yearly[year]["pnls"].append(net * INITIAL)
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all_t = sum(d["t"] for d in yearly.values())
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all_w = sum(d["w"] for d in yearly.values())
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if all_t < 20: continue
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acc = all_w / all_t * 100
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pnl = sum(p for d in yearly.values() for p in d["pnls"])
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worst = min(yearly.items(), key=lambda x: x[1]["w"]/x[1]["t"] if x[1]["t"]>0 else 0)
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wa = worst[1]["w"]/worst[1]["t"]*100 if worst[1]["t"]>0 else 0
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results.append({
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"name": combo_name, "trades": all_t, "acc": acc, "pnl": pnl,
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"dd": max_dd*100, "capital": capital, "worst": f"{worst[0]}({wa:.0f}%)",
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"yearly": yearly,
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})
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results.sort(key=lambda x: x["acc"], reverse=True)
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print(f"\n {'Name':.<28s} {'Trades':>6s} {'Acc':>6s} {'PnL€':>9s} {'DD%':>5s} {'Worst':>12s}")
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print(f" {'-'*70}")
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for r in results[:20]:
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tag = "✅✅" if r["acc"] >= 80 else "✅" if r["acc"] >= 78 else ""
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print(f" {r['name']:.<28s} {r['trades']:>6d} {r['acc']:>5.1f}% €{r['pnl']:>+8.0f} {r['dd']:>4.1f}% {r['worst']:>12s} {tag}")
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# Dettaglio migliore
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if results:
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best = results[0]
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print(f"\n MIGLIORE: {best['name']} → {best['acc']:.1f}% acc, DD {best['dd']:.1f}%")
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for y in sorted(best["yearly"]):
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d = best["yearly"][y]
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ya = d["w"]/d["t"]*100 if d["t"]>0 else 0
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tag = " ← CRASH" if y in [2020,2021,2022] else ""
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print(f" {y}: {d['t']:4d}t {ya:5.1f}% €{sum(d['pnls']):+.0f}{tag}")
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return results
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all_r = []
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for asset in ["BTC", "ETH"]:
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for tf in ["15m", "1h"]:
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r = run_ultimate(asset, tf)
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for x in r:
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all_r.append({**x, "key": f"{asset}_{tf}"})
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all_r.sort(key=lambda x: x["acc"], reverse=True)
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print(f"\n\n{'='*70}")
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print(f" TOP 10 GLOBALE")
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print(f"{'='*70}")
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for r in all_r[:10]:
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tag = "✅✅" if r["acc"] >= 80 else "✅" if r["acc"] >= 78 else ""
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print(f" {r['key']:.<10s} {r['name']:.<28s} {r['trades']:>5d} {r['acc']:>5.1f}% €{r['pnl']:>+8.0f} DD {r['dd']:.1f}% {r['worst']:>12s} {tag}")
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