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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"""S2-06: Iron Condor simulato + Variance Risk Premium harvesting.
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Vendi un range: se il prezzo sta dentro il range a scadenza → profitto.
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Più sofisticato del vol selling puro:
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- Calcolo IV vs RV (variance risk premium)
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- Selezione larghezza condor in base a IV/RV ratio
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- Dynamic position sizing: più capital quando IV/RV ratio è alto
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- Ingresso giornaliero, scadenze 24h e 48h
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- Include: tail risk protection (chiudi se move > 2 ATR)
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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 = 0.001
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INITIAL = 1000
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def realized_vol_ann(close: np.ndarray, window: int) -> np.ndarray:
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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), 0.5)
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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 run_iron_condor(asset, tf="1h"):
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print(f"\n{'#'*60}")
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print(f" {asset} {tf} — IRON CONDOR / VARIANCE PREMIUM")
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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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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_48 = realized_vol_ann(close, 48)
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rv_168 = realized_vol_ann(close, 168) # 1 week
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IV_PREMIUM = 1.25 # IV typically 1.2-1.3x RV in crypto
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configs = [
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# (dte_hours, condor_width_mult, max_loss_pct, vrp_min, pos_pct, name)
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(24, 1.0, 0.03, 1.10, 0.15, "24h_1x_std"),
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(24, 1.5, 0.04, 1.10, 0.12, "24h_1.5x_safe"),
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(24, 0.8, 0.025, 1.15, 0.18, "24h_0.8x_aggr"),
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(48, 1.0, 0.035, 1.10, 0.15, "48h_1x_std"),
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(48, 1.5, 0.05, 1.10, 0.12, "48h_1.5x_safe"),
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(48, 0.7, 0.025, 1.20, 0.20, "48h_0.7x_highVRP"),
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(72, 1.2, 0.04, 1.10, 0.12, "72h_1.2x"),
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(24, 1.0, 0.03, 1.30, 0.20, "24h_veryHighVRP"),
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(24, 1.2, 0.035, 1.10, 0.15, "24h_1.2x_balanced"),
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]
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for dte, width_mult, max_loss, vrp_min, pos_pct, name in configs:
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capital = float(INITIAL)
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correct = 0
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total = 0
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daily_trades = {}
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max_dd = 0
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peak = capital
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for i in range(max(split, 170), n - dte):
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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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hour = timestamps.iloc[i].hour
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if hour != 8:
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continue
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rv_short = rv_24[i]
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rv_long = rv_168[i]
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if rv_short <= 0 or rv_long <= 0:
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continue
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iv_est = rv_long * IV_PREMIUM
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vrp_ratio = iv_est / rv_short
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if vrp_ratio < vrp_min:
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continue
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spot = close[i]
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t_years = dte / (24 * 365)
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# Condor range: spot ± width * daily_std * sqrt(t)
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daily_std = rv_short / np.sqrt(365)
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range_width = width_mult * daily_std * np.sqrt(dte / 24) * spot
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upper_strike = spot + range_width
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lower_strike = spot - range_width
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# Premium collected (simplified BS for condor)
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# Premium ≈ IV * sqrt(t) * (width factor)
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premium_pct = iv_est * np.sqrt(t_years) * 0.4 * (1 / width_mult)
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# Check if price stays in range
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exit_idx = min(i + dte, n - 1)
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price_path = close[i : exit_idx + 1]
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max_move = max(np.max(price_path) - spot, spot - np.min(price_path))
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final_price = close[exit_idx]
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in_range = lower_strike <= final_price <= upper_strike
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breached_hard = max_move > spot * max_loss
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if breached_hard:
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pnl_pct = -max_loss * pos_pct
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elif in_range:
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pnl_pct = premium_pct * pos_pct
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else:
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# Partial loss: exceeded range but not catastrophic
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excess = max(0, final_price - upper_strike, lower_strike - final_price)
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loss = min(excess / spot, max_loss)
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pnl_pct = (premium_pct - loss) * pos_pct
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fee_cost = FEE * 2 * pos_pct
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net_pnl = pnl_pct - fee_cost
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capital += capital * net_pnl
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capital = max(capital, 0)
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if capital > peak:
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peak = capital
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dd = (peak - capital) / peak if peak > 0 else 0
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max_dd = max(max_dd, dd)
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total += 1
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if net_pnl > 0:
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correct += 1
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daily_trades[day] = daily_trades.get(day, 0) + 1
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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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days_active = len(daily_trades)
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tag = "✅✅" if acc >= 70 and ann >= 50 else "✅" if acc >= 65 and ann >= 30 else ""
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print(f" {name:22s}: trades={total:4d} acc={acc:.1f}% ret={ret:+.1f}% ann={ann:+.1f}% dd={max_dd*100:.1f}% €/day={dpnl:.2f} active={days_active} {tag}")
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for asset in ["ETH", "BTC"]:
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run_iron_condor(asset)
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# === COMBINAZIONE: Iron Condor + Funding + Gap Fade ===
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print(f"\n{'#'*60}")
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print(f" COMBINAZIONE: MULTI-STRATEGY PORTFOLIO")
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print(f"{'#'*60}")
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# Simula portafoglio: 50% iron condor ETH, 25% iron condor BTC, 25% gap fade ETH
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print(" (Dettagli nel prossimo script con backtest combinato)")
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