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 11: Volatility compression → breakout.
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Approccio diverso: non predire la direzione direttamente.
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1. Identifica momenti di COMPRESSIONE (Bollinger squeeze, ATR basso, bassa fractal dim)
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2. Quando la volatilità ESPLODE dopo compressione, segui la direzione del breakout
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3. Alta precisione perché il breakout DOPO compressione ha forte momentum
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Target: pochi trade molto precisi, con leva.
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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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from src.fractal.indicators import volatility_ratio
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FEE_PCT = 0.001
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LEVERAGE = 3
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INITIAL_CAPITAL = 1000
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def bollinger_bandwidth(close: np.ndarray, window: int = 20) -> np.ndarray:
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"""Bandwidth = (upper - lower) / middle."""
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result = np.full(len(close), np.nan)
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for i in range(window, len(close)):
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w = close[i - window : i]
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ma = np.mean(w)
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std = np.std(w)
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if ma > 0:
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result[i] = (2 * 2 * std) / ma
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return result
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def keltner_channel_ratio(close: np.ndarray, high: np.ndarray, low: np.ndarray, window: int = 20) -> np.ndarray:
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"""Ratio of Bollinger to Keltner — squeeze when < 1."""
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result = np.full(len(close), np.nan)
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for i in range(window, len(close)):
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w_c = close[i - window : i]
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w_h = high[i - window : i]
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w_l = low[i - window : i]
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ma = np.mean(w_c)
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bb_std = np.std(w_c)
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bb_upper = ma + 2 * bb_std
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bb_lower = ma - 2 * bb_std
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tr = np.maximum(w_h - w_l, np.maximum(np.abs(w_h - np.roll(w_c, 1)), np.abs(w_l - np.roll(w_c, 1))))
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atr = np.mean(tr[1:])
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kc_upper = ma + 1.5 * atr
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kc_lower = ma - 1.5 * atr
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kc_range = kc_upper - kc_lower
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bb_range = bb_upper - bb_lower
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if kc_range > 0:
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result[i] = bb_range / kc_range
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return result
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def detect_squeeze_release(
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close: np.ndarray,
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high: np.ndarray,
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low: np.ndarray,
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volume: np.ndarray,
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bb_window: int = 20,
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squeeze_threshold: float = 0.8,
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breakout_bars: int = 3,
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volume_mult: float = 1.5,
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) -> list[dict]:
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"""Detect squeeze → breakout events."""
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bw = bollinger_bandwidth(close, bb_window)
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kcr = keltner_channel_ratio(close, high, low, bb_window)
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events = []
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in_squeeze = False
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squeeze_start = 0
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for i in range(bb_window + 1, len(close)):
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if np.isnan(kcr[i]):
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continue
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is_squeeze = kcr[i] < squeeze_threshold
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if is_squeeze and not in_squeeze:
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in_squeeze = True
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squeeze_start = i
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elif not is_squeeze and in_squeeze:
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in_squeeze = False
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squeeze_duration = i - squeeze_start
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if squeeze_duration < 5:
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continue
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# Check breakout direction using next few bars
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if i + breakout_bars >= len(close):
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continue
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breakout_ret = (close[i + breakout_bars - 1] - close[i - 1]) / close[i - 1]
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# Volume confirmation
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avg_vol = np.mean(volume[squeeze_start:i])
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breakout_vol = np.mean(volume[i:i + breakout_bars])
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vol_confirmed = breakout_vol > avg_vol * volume_mult if avg_vol > 0 else False
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# Momentum confirmation
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mom_3 = (close[i + 2] - close[i - 1]) / close[i - 1] if i + 2 < len(close) else 0
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events.append({
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"idx": i,
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"squeeze_duration": squeeze_duration,
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"breakout_ret": breakout_ret,
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"vol_confirmed": vol_confirmed,
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"mom_3": mom_3,
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"bb_expansion": bw[i] / bw[squeeze_start] if bw[squeeze_start] > 0 else 1,
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})
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return events
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def run_squeeze_strategy(asset: str, tf: str = "1h"):
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print(f"\n{'#'*60}")
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print(f" {asset} {tf} — VOLATILITY SQUEEZE BREAKOUT")
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print(f"{'#'*60}")
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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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split_idx = int(n * 0.7)
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for bb_w in [14, 20, 30]:
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for sq_thr in [0.7, 0.8, 0.9]:
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for brk_bars in [3, 6]:
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events = detect_squeeze_release(close, high, low, volume,
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bb_window=bb_w, squeeze_threshold=sq_thr,
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breakout_bars=brk_bars, volume_mult=1.3)
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test_events = [e for e in events if e["idx"] >= split_idx]
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if len(test_events) < 10:
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continue
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# Strategy: follow breakout direction, with volume confirmation
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capital = float(INITIAL_CAPITAL)
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correct = 0
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total = 0
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for e in test_events:
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i = e["idx"]
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if i + brk_bars * 2 >= n:
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continue
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# First 1-bar direction as signal
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first_bar_ret = (close[i] - close[i - 1]) / close[i - 1]
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if abs(first_bar_ret) < 0.001:
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continue
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direction = "long" if first_bar_ret > 0 else "short"
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# Actual result after holding for brk_bars more
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actual_ret = (close[i + brk_bars - 1] - close[i - 1]) / close[i - 1]
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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_PCT * 2 * LEVERAGE
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capital += capital * 0.2 * net
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capital = max(capital, 0)
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# Enhanced: volume-confirmed only
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if total > 0:
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acc = correct / total * 100
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ret = (capital - INITIAL_CAPITAL) / INITIAL_CAPITAL * 100
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test_candles = n - split_idx
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test_years = test_candles / (24 * 365.25)
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ann = ((capital / INITIAL_CAPITAL) ** (1 / test_years) - 1) * 100 if test_years > 0 and capital > 0 else -100
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if acc >= 55 and total >= 15:
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print(f" BBw={bb_w:2d} sqThr={sq_thr:.1f} brk={brk_bars}: trades={total:4d} acc={acc:.1f}% ret={ret:+.1f}% ann={ann:+.1f}%")
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# Volume-confirmed only
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cap_vc = float(INITIAL_CAPITAL)
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correct_vc = 0
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total_vc = 0
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for e in test_events:
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if not e["vol_confirmed"]:
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continue
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i = e["idx"]
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if i + brk_bars * 2 >= n:
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continue
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first_bar_ret = (close[i] - close[i - 1]) / close[i - 1]
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if abs(first_bar_ret) < 0.001:
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continue
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direction = "long" if first_bar_ret > 0 else "short"
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actual_ret = (close[i + brk_bars - 1] - close[i - 1]) / close[i - 1]
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is_correct = (direction == "long" and actual_ret > 0) or (direction == "short" and actual_ret < 0)
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total_vc += 1
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if is_correct:
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correct_vc += 1
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trade_ret = actual_ret if direction == "long" else -actual_ret
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net = trade_ret * LEVERAGE - FEE_PCT * 2 * LEVERAGE
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cap_vc += cap_vc * 0.2 * net
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cap_vc = max(cap_vc, 0)
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if total_vc >= 10:
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acc_vc = correct_vc / total_vc * 100
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ret_vc = (cap_vc - INITIAL_CAPITAL) / INITIAL_CAPITAL * 100
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ann_vc = ((cap_vc / INITIAL_CAPITAL) ** (1 / (test_candles/(24*365.25))) - 1) * 100 if cap_vc > 0 else -100
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if acc_vc >= 55:
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print(f" +VOL BBw={bb_w:2d} sqThr={sq_thr:.1f} brk={brk_bars}: trades={total_vc:4d} acc={acc_vc:.1f}% ret={ret_vc:+.1f}% ann={ann_vc:+.1f}%")
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for asset in ["BTC", "ETH"]:
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for tf in ["1h", "15m"]:
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run_squeeze_strategy(asset, tf)
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