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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"""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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