refactor: riorganizzazione script — Strategy ABC, folder strategies/waste/analysis
- src/strategies/base.py: Strategy ABC con Signal, BacktestResult, YearlyStats - src/strategies/indicators.py: keltner_ratio, detect_squeezes, ema, atr, rv, corr - scripts/strategies/: SQ01-SQ04 (squeeze puro/filtri), ML01 (squeeze+GBM) - scripts/waste/: W01-W22 script scartati + REF originali - scripts/analysis/: compare, best_yearly, final_report, paper_status - CLAUDE.md aggiornato con nuova struttura e tabella strategie Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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"""S2-11: VRP con DVOL REALE — unico test valido.
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Solo 90 giorni di dati, ma REALI.
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Confronta DVOL (IV reale Deribit) vs RV realizzata.
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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_ROUNDTRIP = 0.0052
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INITIAL = 1000
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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 straddle_prem(iv_pct, dte_h):
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"""iv_pct è la IV in decimale (es 0.50 = 50%)."""
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if iv_pct <= 0 or dte_h <= 0:
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return 0
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return iv_pct * np.sqrt(dte_h / (24 * 365)) * 0.8
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for asset in ["ETH", "BTC"]:
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print(f"\n{'='*70}")
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print(f" {asset} — VRP CON DVOL REALE (90 giorni)")
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print(f"{'='*70}")
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df_price = load_data(asset, "1h")
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df_dvol = pd.read_parquet(f"data/raw/{asset.lower()}_dvol.parquet")
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close = df_price["close"].values
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ts_price = df_price["timestamp"].values
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n = len(close)
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dvol_ts = df_dvol["timestamp"].values
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dvol_vals = df_dvol["dvol"].values / 100 # converti a decimale
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rv_24 = rv_ann(close, 24)
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rv_48 = rv_ann(close, 48)
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# Allinea DVOL ai candles 1h (DVOL è giornaliero)
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dvol_aligned = np.full(n, np.nan)
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for j in range(len(dvol_ts)):
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mask = (ts_price >= dvol_ts[j]) & (ts_price < dvol_ts[j] + 86400000)
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dvol_aligned[mask] = dvol_vals[j]
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valid_count = np.sum(~np.isnan(dvol_aligned))
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print(f" Candele con DVOL reale: {valid_count}")
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print(f" DVOL range: {np.nanmin(dvol_aligned)*100:.1f}% — {np.nanmax(dvol_aligned)*100:.1f}%")
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# Analisi IV vs RV reale
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iv_rv_ratios = []
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for i in range(n):
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if np.isnan(dvol_aligned[i]) or np.isnan(rv_24[i]) or rv_24[i] <= 0:
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continue
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iv_rv_ratios.append(dvol_aligned[i] / rv_24[i])
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if iv_rv_ratios:
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print(f"\n IV/RV ratio REALE:")
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print(f" Mean: {np.mean(iv_rv_ratios):.3f}")
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print(f" Median: {np.median(iv_rv_ratios):.3f}")
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print(f" Min: {np.min(iv_rv_ratios):.3f}")
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print(f" Max: {np.max(iv_rv_ratios):.3f}")
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print(f" >1 (VRP+): {sum(1 for r in iv_rv_ratios if r > 1)/len(iv_rv_ratios)*100:.0f}% del tempo")
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# Backtest VRP reale
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for dte in [24, 48]:
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print(f"\n --- DTE={dte}h ---")
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capital = float(INITIAL)
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trades = []
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daily_done = set()
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for i in range(100, n - dte):
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if np.isnan(dvol_aligned[i]) or np.isnan(rv_24[i]):
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continue
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ts_dt = pd.Timestamp(ts_price[i], unit="ms", tz="UTC")
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if ts_dt.hour != 8:
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continue
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day = ts_dt.strftime("%Y-%m-%d")
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if day in daily_done:
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continue
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iv = dvol_aligned[i]
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rv = rv_24[i]
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# Filtro regime: skip se RV > IV (no premium)
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if rv > iv:
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continue
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prem = straddle_prem(iv, dte)
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spot = close[i]
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exit_idx = min(i + dte, n - 1)
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actual_move = abs(close[exit_idx] - spot) / spot
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pos_pct = 0.10
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if actual_move <= prem:
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raw = (prem - actual_move) * pos_pct
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else:
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raw = -(actual_move - prem) * pos_pct
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raw = max(raw, -pos_pct * 0.05)
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net = raw - FEE_ROUNDTRIP * pos_pct
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capital += capital * net
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capital = max(capital, 10)
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trades.append({
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"day": day,
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"iv": iv * 100,
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"rv": rv * 100,
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"premium": prem * 100,
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"move": actual_move * 100,
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"pnl": net * capital,
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"win": raw > 0,
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})
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daily_done.add(day)
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if not trades:
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print(" Nessun trade!")
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continue
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wins = sum(1 for t in trades if t["win"])
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acc = wins / len(trades) * 100
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ret = (capital - INITIAL) / INITIAL * 100
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avg_iv = np.mean([t["iv"] for t in trades])
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avg_rv = np.mean([t["rv"] for t in trades])
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avg_prem = np.mean([t["premium"] for t in trades])
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avg_move = np.mean([t["move"] for t in trades])
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print(f" Trades: {len(trades)}")
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print(f" Accuracy: {acc:.1f}%")
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print(f" Return: {ret:+.1f}%")
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print(f" Capital: €{capital:.0f}")
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print(f" Avg IV: {avg_iv:.1f}%")
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print(f" Avg RV: {avg_rv:.1f}%")
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print(f" Avg Prem: {avg_prem:.2f}%")
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print(f" Avg Move: {avg_move:.2f}%")
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print(f" IV > Move (win condition): {sum(1 for t in trades if t['premium'] > t['move'])/len(trades)*100:.0f}%")
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# Worst trade
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worst = min(trades, key=lambda t: t["pnl"])
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print(f" Worst: day={worst['day']} iv={worst['iv']:.1f}% move={worst['move']:.2f}%")
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