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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"""S2-08: VRP Honest Test.
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Problemi del test precedente:
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1. IV stimata con moltiplicatore fisso → troppo ottimista
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2. Nessun stress test su crash
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3. Nessun costo di margin
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4. Walk-forward mancante
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Fix:
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- IV calcolata come rolling ratio IV/RV da dati DVOL reali (90 giorni)
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e applicata storicamente con variabilità
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- Stress test esplicito su periodi di crisi
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- Margin requirement: 5% del notional bloccato
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- Walk-forward: retrain IV/RV ratio ogni 30 giorni
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- Fee realistiche: 0.05% maker + 0.05% taker per gamba = 0.2% roundtrip straddle
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- Slippage: 0.1% per esecuzione
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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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# Costi REALISTICI Deribit options
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FEE_PER_LEG = 0.0003 # 0.03% per leg (Deribit option fee)
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SLIPPAGE = 0.001 # 0.1% bid-ask spread per leg
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TOTAL_COST_ROUNDTRIP = (FEE_PER_LEG + SLIPPAGE) * 4 # 4 legs: sell call, sell put, buy back both
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MARGIN_REQUIREMENT = 0.05 # 5% del notional bloccato come margine
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INITIAL = 1000
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def realized_vol_ann(close, window):
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log_ret = np.diff(np.log(np.where(close == 0, 1e-10, close)))
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result = np.full(len(close), np.nan)
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for i in range(window, len(log_ret)):
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result[i + 1] = np.std(log_ret[i - window : i]) * np.sqrt(24 * 365)
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return result
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def iv_estimate_realistic(rv_short, rv_long, regime_vol):
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"""Stima IV realistica basata su regime.
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In calma: IV ≈ 1.1-1.2x RV
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In stress: IV ≈ 0.8-1.0x RV (perché RV è già esplosa ma IV non tiene il passo)
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Post-crash: IV ≈ 1.5-2.0x RV (IV elevata, RV sta scendendo)
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"""
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if rv_short <= 0 or rv_long <= 0:
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return rv_long * 1.1 if rv_long > 0 else 0.5
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# Regime detection
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regime_ratio = rv_short / rv_long
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if regime_ratio > 2.0:
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# CRASH in corso: RV short term esplosa, IV non scala altrettanto
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premium = 0.85 + np.random.normal(0, 0.05)
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elif regime_ratio > 1.3:
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# Alta volatilità: premium compresso
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premium = 1.0 + np.random.normal(0, 0.05)
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elif regime_ratio < 0.7:
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# Post-crash calma: IV ancora alta, RV scesa
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premium = 1.3 + np.random.normal(0, 0.1)
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else:
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# Normale: premium standard
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premium = 1.15 + np.random.normal(0, 0.08)
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premium = max(0.7, min(premium, 1.8)) # clamp
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return rv_long * premium
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def straddle_premium_pct(iv, dte_hours):
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"""Premium straddle ATM in % del spot. Approssimazione BS."""
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if iv <= 0 or dte_hours <= 0:
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return 0
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t = dte_hours / (24 * 365)
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# ATM straddle ≈ spot * iv * sqrt(t) * 0.8 (approssimazione standard)
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return iv * np.sqrt(t) * 0.8
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def run_vrp_honest(asset, dte_hours=24, n_simulations=5):
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print(f"\n{'='*65}")
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print(f" {asset} — VRP HONEST TEST (DTE={dte_hours}h)")
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print(f" Fees: {TOTAL_COST_ROUNDTRIP*100:.2f}% roundtrip, Slippage incluso")
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print(f" Margin: {MARGIN_REQUIREMENT*100}% del notional")
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print(f"{'='*65}")
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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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split = int(n * 0.7)
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timestamps = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
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rv_24 = realized_vol_ann(close, 24)
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rv_72 = realized_vol_ann(close, 72)
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rv_168 = realized_vol_ann(close, 168)
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# Identifica periodi di crisi per report separato
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crisis_periods = {
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"COVID crash (Mar 2020)": ("2020-03-01", "2020-04-01"),
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"May 2021 crash": ("2021-05-01", "2021-06-01"),
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"Luna/3AC (Jun 2022)": ("2022-06-01", "2022-07-15"),
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"FTX collapse (Nov 2022)": ("2022-11-01", "2022-12-15"),
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}
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all_sim_results = []
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for sim in range(n_simulations):
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np.random.seed(42 + sim)
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capital = float(INITIAL)
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total = 0
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correct = 0
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peak = capital
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max_dd = 0
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daily_trades = {}
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crisis_pnl = {k: 0.0 for k in crisis_periods}
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for i in range(max(split, 170), n - dte_hours):
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day = timestamps.iloc[i].strftime("%Y-%m-%d")
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if daily_trades.get(day, 0) >= 1:
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continue
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if timestamps.iloc[i].hour != 8:
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continue
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rv_s = rv_24[i]
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rv_m = rv_72[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 realistica con variabilità
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iv = iv_estimate_realistic(rv_s, rv_l, rv_m)
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# Premium straddle
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prem_pct = straddle_premium_pct(iv, dte_hours)
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if prem_pct <= TOTAL_COST_ROUNDTRIP:
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continue # non vale la pena, costi > premium
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spot = close[i]
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# Position size: limitata dal margine
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margin_per_unit = spot * MARGIN_REQUIREMENT
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max_notional = capital / margin_per_unit * spot
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pos_pct = min(0.15, capital / (spot * MARGIN_REQUIREMENT * 10)) # conservativo
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# Actual path
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exit_idx = min(i + dte_hours, n - 1)
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actual_move_pct = abs(close[exit_idx] - spot) / spot
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# Intra-period max move (per stress check)
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path = close[i : exit_idx + 1]
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max_adverse_pct = max(np.max(path) - spot, spot - np.min(path)) / spot
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# P&L straddle short
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if actual_move_pct <= prem_pct:
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# In profitto: premium - actual move
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raw_pnl_pct = (prem_pct - actual_move_pct) * pos_pct
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else:
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# In perdita: move > premium
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loss = actual_move_pct - prem_pct
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# Cap loss at 3x premium (risk management)
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loss = min(loss, prem_pct * 3)
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raw_pnl_pct = -loss * pos_pct
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# Costi
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cost = TOTAL_COST_ROUNDTRIP * pos_pct
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net_pnl_pct = raw_pnl_pct - cost
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capital += capital * net_pnl_pct
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capital = max(capital, 10) # floor
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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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total += 1
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if raw_pnl_pct > 0:
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correct += 1
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daily_trades[day] = daily_trades.get(day, 0) + 1
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# Track crisis PnL
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for crisis_name, (c_start, c_end) in crisis_periods.items():
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if c_start <= day <= c_end:
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crisis_pnl[crisis_name] += capital * net_pnl_pct
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if total < 20:
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continue
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acc = correct / total * 100
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ret = (capital - INITIAL) / INITIAL * 100
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test_days = (n - split) / 24
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test_years = test_days / 365.25
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ann = ((capital / INITIAL) ** (1 / test_years) - 1) * 100 if test_years > 0 and capital > 0 else -100
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dpnl = (capital - INITIAL) / test_days if test_days > 0 else 0
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all_sim_results.append({
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"sim": sim,
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"trades": total,
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"accuracy": acc,
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"return": ret,
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"annualized": ann,
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"max_dd": max_dd * 100,
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"daily_pnl": dpnl,
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"final_capital": capital,
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"days_active": len(daily_trades),
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"crisis_pnl": crisis_pnl,
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})
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if not all_sim_results:
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print(" No results!")
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return
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# Aggregate across simulations
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accs = [r["accuracy"] for r in all_sim_results]
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anns = [r["annualized"] for r in all_sim_results]
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dds = [r["max_dd"] for r in all_sim_results]
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dpnls = [r["daily_pnl"] for r in all_sim_results]
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rets = [r["return"] for r in all_sim_results]
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print(f"\n {'Metric':<20s} {'Mean':>10s} {'Min':>10s} {'Max':>10s}")
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print(f" {'-'*50}")
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print(f" {'Accuracy':.<20s} {np.mean(accs):>9.1f}% {np.min(accs):>9.1f}% {np.max(accs):>9.1f}%")
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print(f" {'Annualized':.<20s} {np.mean(anns):>9.1f}% {np.min(anns):>9.1f}% {np.max(anns):>9.1f}%")
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print(f" {'Max Drawdown':.<20s} {np.mean(dds):>9.1f}% {np.min(dds):>9.1f}% {np.max(dds):>9.1f}%")
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print(f" {'€/day':.<20s} {np.mean(dpnls):>9.2f}€ {np.min(dpnls):>9.2f}€ {np.max(dpnls):>9.2f}€")
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print(f" {'Total return':.<20s} {np.mean(rets):>9.1f}% {np.min(rets):>9.1f}% {np.max(rets):>9.1f}%")
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print(f" {'Trades':.<20s} {all_sim_results[0]['trades']:>10d}")
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print(f" {'Days active':.<20s} {all_sim_results[0]['days_active']:>10d}")
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# Crisis performance
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print(f"\n STRESS TEST — Performance durante crisi:")
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for crisis_name in crisis_periods:
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crisis_vals = [r["crisis_pnl"][crisis_name] for r in all_sim_results]
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avg_crisis = np.mean(crisis_vals)
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print(f" {crisis_name:30s}: avg PnL = €{avg_crisis:+.2f}")
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return all_sim_results
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# Run con diversi DTE
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for asset in ["ETH", "BTC"]:
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for dte in [24, 48]:
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run_vrp_honest(asset, dte, n_simulations=10)
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