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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"""S2-03: Volatility Selling — Straddle/Strangle corto simulato.
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La IV crypto è cronicamente sopra la realized vol → vendere premium è profittevole.
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Simulazione: vendi straddle ATM → profitto = max(0, premium - |move|).
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Premium stimato da IV storica. Ingresso giornaliero.
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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 scipy.stats import norm
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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(close: np.ndarray, window: int = 24) -> np.ndarray:
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"""Annualized realized volatility rolling."""
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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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rv = np.std(log_ret[i - window : i]) * np.sqrt(24 * 365)
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result[i + 1] = rv
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return result
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def implied_vol_proxy(close: np.ndarray, window: int = 48) -> np.ndarray:
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"""IV proxy: realized vol * premium factor.
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Storicamente IV crypto ≈ 1.2-1.5x realized vol (variance risk premium).
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"""
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rv = realized_vol(close, window)
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# Premium factor varia: alto in panic, basso in calma
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result = np.full(len(close), 0.5)
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for i in range(window, len(close)):
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short_rv = realized_vol(close[max(0, i-12):i+1], min(12, i))[-1] if i >= 12 else rv[i]
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if rv[i] > 0:
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regime = short_rv / rv[i]
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premium = 1.15 + 0.3 * max(0, regime - 1) # più alto in regime volatile
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else:
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premium = 1.2
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result[i] = rv[i] * premium
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return result
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def bs_straddle_price(spot: float, iv: float, dte_hours: float) -> float:
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"""Black-Scholes straddle price (call + put ATM)."""
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if dte_hours <= 0 or iv <= 0:
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return 0
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t = dte_hours / (24 * 365)
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d1 = (0.5 * iv * iv * t) / (iv * np.sqrt(t))
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call = spot * (2 * norm.cdf(d1) - 1)
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return call * 2 # straddle = 2 * ATM call (approx for ATM)
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def run_vol_selling(asset):
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print(f"\n{'#'*60}")
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print(f" {asset} — VOLATILITY SELLING (SHORT STRADDLE)")
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print(f"{'#'*60}")
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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 = realized_vol(close, 24)
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iv_proxy = implied_vol_proxy(close)
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configs = [
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# (dte_hours, iv_floor, iv_rv_ratio_min, position_pct, name)
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(24, 0.3, 1.15, 0.1, "daily_24h"),
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(12, 0.3, 1.15, 0.08, "half_day_12h"),
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(48, 0.3, 1.10, 0.12, "2day_48h"),
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(24, 0.4, 1.20, 0.1, "daily_highIV"),
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(8, 0.25, 1.10, 0.06, "ultra_short_8h"),
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(24, 0.3, 1.30, 0.15, "daily_bigPremium"),
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]
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for dte, iv_floor, ratio_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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for i in range(max(split, 50), n - dte):
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day = timestamps[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[i].dt.hour if hasattr(timestamps[i], 'dt') else timestamps.iloc[i].hour
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if hour != 8: # entrata alle 08 UTC ogni giorno
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continue
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current_iv = iv_proxy[i]
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current_rv = rv[i]
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if current_iv < iv_floor:
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continue
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if current_rv > 0 and current_iv / current_rv < ratio_min:
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continue
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spot = close[i]
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premium = bs_straddle_price(spot, current_iv, dte)
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premium_pct = premium / spot
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# Actual move during holding period
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exit_idx = min(i + dte, n - 1)
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actual_move = abs(close[exit_idx] - spot)
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actual_move_pct = actual_move / spot
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# P&L: premium received - actual move (capped at max loss)
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max_loss = spot * 0.05 # cap loss at 5% of spot
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pnl = premium - min(actual_move, max_loss + premium)
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pnl_on_capital = pnl / spot * pos_pct
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fee_cost = FEE * 4 * pos_pct # 4 legs: sell call, sell put, buy back
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net_pnl = pnl_on_capital - fee_cost
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capital += capital * net_pnl
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capital = max(capital, 0)
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total += 1
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if 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 >= 60 and ann >= 30 else ""
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print(f" {name:20s}: trades={total:4d} acc={acc:.1f}% ret={ret:+.1f}% ann={ann:+.1f}% €/day={dpnl:.2f} active={days_active} {tag}")
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for asset in ["ETH", "BTC"]:
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run_vol_selling(asset)
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