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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"""S2-09: VRP test per-anno — verità nuda.
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Test su OGNI anno separatamente per vedere performance durante crash.
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Niente compounding — PnL medio per trade in punti percentuali.
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Costi realistici Deribit options.
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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 # 0.52% roundtrip (4 legs × 0.13%)
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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, dte_h):
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if iv <= 0 or dte_h <= 0:
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return 0
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return iv * np.sqrt(dte_h / (24 * 365)) * 0.8
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def run_per_year(asset, dte=24):
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print(f"\n{'='*70}")
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print(f" {asset} — VRP PER ANNO (DTE={dte}h, NO compounding)")
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print(f" Fee roundtrip: {FEE_ROUNDTRIP*100:.2f}%")
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print(f"{'='*70}")
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df = load_data(asset, "1h")
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close = df["close"].values
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n = len(close)
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ts = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
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rv_24 = rv_ann(close, 24)
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rv_168 = rv_ann(close, 168)
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# IV/RV premium: conservative estimate per regime
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# Storicamene crypto VRP ≈ 15-30% (IV/RV ≈ 1.15-1.30)
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# Ma durante crash VRP va NEGATIVO (RV > IV)
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years = sorted(set(ts.dt.year))
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print(f"\n {'Year':>6s} {'Trades':>7s} {'Wins':>5s} {'Acc%':>6s} {'AvgPnL%':>9s} {'TotPnL€':>9s} {'Worst%':>8s} {'MaxMove%':>9s}")
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print(f" {'-'*70}")
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all_pnls = []
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yearly_stats = []
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for year in years:
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year_mask = ts.dt.year == year
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year_indices = np.where(year_mask.values)[0]
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if len(year_indices) < 200:
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continue
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trades_pnl = []
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trades_detail = []
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for i in year_indices:
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if i < 170 or i + dte >= n:
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continue
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if ts.iloc[i].hour != 8:
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continue
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rv_s = rv_24[i]
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rv_l = rv_168[i]
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if np.isnan(rv_s) or np.isnan(rv_l) or rv_s < 0.05 or rv_l < 0.05:
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continue
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# IV estimate: regime-dependent
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regime = rv_s / rv_l if rv_l > 0 else 1.0
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if regime > 2.0:
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# CRASH: RV esplosa, IV probabilmente = RV o meno
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iv_premium_factor = 0.9
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elif regime > 1.5:
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iv_premium_factor = 1.0
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elif regime > 1.0:
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iv_premium_factor = 1.1
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else:
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# Calm: VRP positivo
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iv_premium_factor = 1.2
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iv = rv_l * iv_premium_factor
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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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# P&L (senza compounding — flat € su €1000)
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pos_size = INITIAL * 0.10 # 10% fisso, no leverage
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if actual_move <= prem:
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raw_pnl = (prem - actual_move) * pos_size
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else:
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raw_pnl = -(actual_move - prem) * pos_size
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raw_pnl = max(raw_pnl, -pos_size * 0.05) # cap loss
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cost = FEE_ROUNDTRIP * pos_size
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net_pnl = raw_pnl - cost
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trades_pnl.append(net_pnl)
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trades_detail.append({
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"prem": prem,
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"move": actual_move,
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"regime": regime,
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"rv_s": rv_s,
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"iv": iv,
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})
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all_pnls.append(net_pnl)
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if not trades_pnl:
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continue
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wins = sum(1 for p in trades_pnl if p > 0)
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acc = wins / len(trades_pnl) * 100
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avg_pnl = np.mean(trades_pnl)
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tot_pnl = np.sum(trades_pnl)
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worst = np.min(trades_pnl)
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max_move = max(t["move"] for t in trades_detail) * 100
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tag = ""
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if year in [2020, 2021, 2022]:
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tag = " ← CRASH YEAR"
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if acc >= 70 and avg_pnl > 0:
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tag += " ✅"
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print(f" {year:>6d} {len(trades_pnl):>7d} {wins:>5d} {acc:>5.1f}% {avg_pnl:>+8.2f}€ {tot_pnl:>+8.0f}€ {worst:>+7.2f}€ {max_move:>8.1f}% {tag}")
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yearly_stats.append({
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"year": year, "trades": len(trades_pnl), "acc": acc,
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"avg_pnl": avg_pnl, "tot_pnl": tot_pnl, "worst": worst,
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})
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# Summary
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if all_pnls:
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total_trades = len(all_pnls)
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total_wins = sum(1 for p in all_pnls if p > 0)
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print(f"\n {'TOTALE':>6s} {total_trades:>7d} {total_wins:>5d} {total_wins/total_trades*100:>5.1f}% {np.mean(all_pnls):>+8.2f}€ {np.sum(all_pnls):>+8.0f}€ {np.min(all_pnls):>+7.2f}€")
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# Con compounding realistico
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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 pnl in all_pnls:
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capital += pnl * (capital / INITIAL) # scala con capitale
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capital = max(capital, 10)
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if capital > peak:
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peak = capital
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dd = (peak - capital) / peak
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max_dd = max(max_dd, dd)
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years_total = (yearly_stats[-1]["year"] - yearly_stats[0]["year"] + 1)
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ann = ((capital / INITIAL) ** (1 / years_total) - 1) * 100 if capital > 0 else -100
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daily_avg = (capital - INITIAL) / (total_trades) # approx 1 trade/day
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print(f"\n CON COMPOUNDING:")
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print(f" Capitale finale: €{capital:,.0f}")
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print(f" ROI annualizzato: {ann:+.1f}%")
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print(f" Max Drawdown: {max_dd*100:.1f}%")
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print(f" €/trade medio: €{daily_avg:.2f}")
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# Worst year
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worst_year = min(yearly_stats, key=lambda x: x["tot_pnl"])
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best_year = max(yearly_stats, key=lambda x: x["tot_pnl"])
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print(f"\n Anno peggiore: {worst_year['year']} → {worst_year['tot_pnl']:+.0f}€ ({worst_year['acc']:.0f}% acc)")
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print(f" Anno migliore: {best_year['year']} → {best_year['tot_pnl']:+.0f}€ ({best_year['acc']:.0f}% acc)")
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for asset in ["ETH", "BTC"]:
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for dte in [24, 48]:
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run_per_year(asset, dte)
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