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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"""SQ04 — Ultimate Squeeze — combinazione incrementale di tutti i filtri.
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Testa combinazioni di filtri (antifake, long_sq, timing, cross-asset,
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correlation, volume, trend alignment, volatility regime) e classifica
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per accuracy.
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IN:
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- OHLCV DataFrame (primario + secondario)
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- Parametri: bb_window, sq_threshold, lista filtri da attivare
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OUT:
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- BacktestResult per ogni combinazione di filtri
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- Classifica globale
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Risultati tipici:
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BTC 15m antifake+corr: 81.6% acc (ma concentrato 2018)
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BTC 15m antifake+vol: 79.7% acc, 1250 trades — robusto
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ETH 1h antifake+corr: 80.7% acc (solo 2018)
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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.strategies.base import Strategy, Signal
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from src.strategies.indicators import (
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keltner_ratio, detect_squeezes, ema, rv_annualized, rolling_correlation,
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)
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from src.data.downloader import load_data
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class SqueezeUltimate(Strategy):
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name = "SQ04_ultimate"
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description = "Ultimate squeeze — tutti i filtri combinabili"
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default_assets = ["BTC", "ETH"]
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default_timeframes = ["15m", "1h"]
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FILTER_PRESETS = {
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"antifake+vol": ["antifake", "vol_confirm"],
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"antifake+corr": ["antifake", "corr_high"],
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"af+long+corr+trend": ["antifake", "long_sq", "corr_high", "trend_align"],
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"ALL": ["antifake", "long_sq", "cross", "timing", "corr_high",
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"vol_confirm", "trend_align", "low_rv"],
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}
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def generate_signals(self, df: pd.DataFrame, ts: pd.DatetimeIndex,
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**params) -> list[Signal]:
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c = df["close"].values
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h = df["high"].values
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l = df["low"].values
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v = df["volume"].values
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n = len(c)
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asset = params.get("asset", "BTC")
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tf = params.get("tf", "15m")
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filters = params.get("filters", ["antifake", "vol_confirm"])
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kcr = keltner_ratio(c, h, l, 14)
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events = detect_squeezes(c, h, l, kcr)
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secondary = "ETH" if asset == "BTC" else "BTC"
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df2 = load_data(secondary, tf)
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c2 = df2["close"].values
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kcr2 = keltner_ratio(c2, df2["high"].values, df2["low"].values, 14)
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ts2 = df2["timestamp"].values
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ema_50 = ema(c, 50)
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rv_48 = rv_annualized(c, 48)
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corr = rolling_correlation(c, c2)
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signals = []
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for ev in events:
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i = ev["idx"]
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if i < 1 or i >= n:
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continue
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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 f in filters:
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if f == "antifake":
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br = h[i] - l[i]
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if br > 0:
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if c[i] > c[i-1] and (h[i] - c[i]) / br > 0.6:
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skip = True
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elif c[i] <= c[i-1] and (c[i] - l[i]) / br > 0.6:
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skip = True
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elif f == "long_sq":
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if ev["dur"] < 10:
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skip = True
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elif f == "timing":
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if ts.iloc[i].hour < 4 or ts.iloc[i].hour > 16:
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skip = True
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elif f == "cross":
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i2 = np.searchsorted(ts2, ts.values[i].astype("int64") // 10**6)
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i2 = min(i2, len(kcr2) - 1)
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if not any(not np.isnan(kcr2[j]) and kcr2[j] < 0.85
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for j in range(max(0, i2 - 10), i2 + 1)):
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skip = True
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elif f == "corr_high":
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if np.isnan(corr[i]) or abs(corr[i]) < 0.6:
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skip = True
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elif f == "vol_confirm":
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avg_v = np.mean(v[ev["sq_start"]:i])
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if avg_v > 0 and v[i] <= avg_v * 1.3:
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skip = True
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elif f == "trend_align":
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if not np.isnan(ema_50[i]):
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if first_ret > 0 and c[i] < ema_50[i]:
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skip = True
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elif first_ret < 0 and c[i] > ema_50[i]:
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skip = True
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elif f == "low_rv":
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if not np.isnan(rv_48[i]) and rv_48[i] >= 1.5:
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skip = True
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if skip:
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break
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if skip:
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continue
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signals.append(Signal(
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idx=i,
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direction=1 if first_ret > 0 else -1,
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entry_price=c[i - 1],
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metadata={"dur": ev["dur"], "filters": filters},
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))
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return signals
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def backtest(self, asset: str, tf: str, hold: int = 3, **params):
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params.setdefault("asset", asset)
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params.setdefault("tf", tf)
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df = load_data(asset, tf)
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ts = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
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signals = self.generate_signals(df, ts, **params)
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# Usa il backtest della base ma passando i segnali già generati
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from src.strategies.base import BacktestResult, YearlyStats, TF_MINUTES
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c = df["close"].values
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n = len(c)
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yearly: dict[int, dict] = {}
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capital = float(self.initial_capital)
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peak = capital
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max_dd = 0.0
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total_bars = 0
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for sig in signals:
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i = sig.idx
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if i + hold >= n or i < 1:
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continue
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entry = sig.entry_price
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exit_price = c[min(i + hold - 1, n - 1)]
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actual = (exit_price - entry) / entry * sig.direction
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net = actual * self.leverage - self.fee_rt * self.leverage
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capital += capital * self.position_size * 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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total_bars += hold
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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, "pnl": 0.0}
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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]["pnl"] += net * self.initial_capital
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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 == 0: return None
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yearly_stats = [YearlyStats(y, d["t"], d["w"], d["pnl"]) for y, d in sorted(yearly.items())]
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return BacktestResult(
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strategy_name=self.name, asset=asset, timeframe=tf, params=params,
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trades=all_t, wins=all_w, pnl=sum(d["pnl"] for d in yearly.values()),
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capital=capital, initial_capital=self.initial_capital,
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max_dd=max_dd * 100, time_in_market_pct=total_bars / n * 100,
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avg_trade_duration_h=hold * TF_MINUTES.get(tf, 60) / 60,
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years_active=len(yearly), yearly=yearly_stats,
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)
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def report_all_presets(self):
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"""Esegue tutte le combinazioni preset × asset × tf."""
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all_results = []
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for preset_name, filter_list in self.FILTER_PRESETS.items():
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for asset in self.default_assets:
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for tf in self.default_timeframes:
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r = self.backtest(asset, tf, filters=filter_list)
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if r and r.trades >= 20:
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r.strategy_name = f"SQ04 {preset_name}"
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all_results.append(r)
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all_results.sort(key=lambda r: r.accuracy, reverse=True)
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print(f"\n{'=' * 120}")
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print(f" SQ04 ULTIMATE — TUTTI I PRESET")
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print(f"{'=' * 120}")
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for r in all_results:
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r.print_summary()
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return all_results
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if __name__ == "__main__":
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strategy = SqueezeUltimate()
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strategy.report_all_presets()
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