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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"""S2-07: Variance Risk Premium harvesting — versione raffinata.
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Ottimizzazione del vol selling con:
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1. IV/RV ratio dinamico per entry timing
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2. Tail risk cutoff (chiudi se move > N sigma)
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3. Position sizing proporzionale al premium
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4. Combinazione con directional bias (da gap fade)
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5. Multi-asset portfolio (ETH + BTC)
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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, window=24):
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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_vrp(asset):
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print(f"\n{'#'*60}")
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print(f" {asset} 1h — VARIANCE RISK PREMIUM REFINED")
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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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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(close, 24)
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rv_48 = realized_vol(close, 48)
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rv_168 = realized_vol(close, 168)
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configs = [
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# (dte_h, iv_mult, cutoff_sigma, pos_base, entry_hour, dynamic_sizing, name)
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(24, 1.20, 2.5, 0.10, 8, False, "24h_base"),
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(24, 1.25, 2.5, 0.12, 8, False, "24h_highPrem"),
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(24, 1.20, 2.0, 0.10, 8, False, "24h_tightCut"),
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(24, 1.20, 3.0, 0.12, 8, False, "24h_wideCut"),
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(48, 1.20, 2.5, 0.12, 8, False, "48h_base"),
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(48, 1.25, 2.5, 0.15, 8, False, "48h_highPrem"),
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(48, 1.30, 2.5, 0.15, 8, False, "48h_vhighPrem"),
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(48, 1.20, 3.0, 0.15, 8, False, "48h_wideCut"),
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(24, 1.20, 2.5, 0.10, 8, True, "24h_dynSize"),
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(48, 1.20, 2.5, 0.12, 8, True, "48h_dynSize"),
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(24, 1.20, 2.5, 0.10, 0, False, "24h_midnight"),
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(24, 1.20, 2.5, 0.10, 16, False, "24h_afternoon"),
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(36, 1.22, 2.5, 0.12, 8, False, "36h_medium"),
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(24, 1.15, 2.5, 0.08, 8, False, "24h_lowPrem_safe"),
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(48, 1.20, 2.0, 0.10, 8, True, "48h_tight_dyn"),
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]
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for dte, iv_mult, cutoff, pos_base, entry_h, dyn_size, 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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peak_capital = capital
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max_dd = 0
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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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if timestamps.iloc[i].hour != entry_h:
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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 rv_s <= 0.05 or rv_l <= 0.05:
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continue
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iv_est = rv_l * iv_mult
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vrp = iv_est - rv_s
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if vrp <= 0:
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continue
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spot = close[i]
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t = dte / (24 * 365)
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daily_std = rv_s / np.sqrt(365)
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# Premium = IV * sqrt(t) * spot * factor
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premium = iv_est * np.sqrt(t) * spot * 0.4
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premium_pct = premium / spot
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# Expected move based on IV
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expected_move = iv_est * np.sqrt(t) * spot
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# Cutoff: close if actual move > cutoff * expected_move
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max_allowed_move = expected_move * cutoff
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# Dynamic sizing: more when VRP is high
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if dyn_size:
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vrp_ratio = vrp / rv_s
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pos_pct = min(pos_base * (1 + vrp_ratio), pos_base * 2)
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else:
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pos_pct = pos_base
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# Check actual path
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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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# Early exit: check if intra-period move exceeds cutoff
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breached = False
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for j in range(i + 1, exit_idx + 1):
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intra_move = abs(close[j] - spot)
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if intra_move > max_allowed_move:
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breached = True
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exit_idx = j
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actual_move = intra_move
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break
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if breached:
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loss = min(actual_move / spot, 0.05) * pos_pct
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pnl = -loss
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else:
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profit = premium_pct * pos_pct
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partial_loss = max(0, actual_move / spot - premium_pct) * pos_pct * 0.5
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pnl = profit - partial_loss
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fee_cost = FEE * 2 * pos_pct
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net = pnl - fee_cost
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capital += capital * net
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capital = max(capital, 0)
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if capital > peak_capital:
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peak_capital = capital
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dd = (peak_capital - capital) / peak_capital if peak_capital > 0 else 0
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max_dd = max(max_dd, dd)
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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 >= 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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return daily_trades
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# Run both assets
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results = {}
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for asset in ["ETH", "BTC"]:
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results[asset] = run_vrp(asset)
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# Multi-asset portfolio simulation
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print(f"\n{'#'*60}")
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print(f" MULTI-ASSET PORTFOLIO: ETH + BTC")
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print(f"{'#'*60}")
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df_eth = load_data("ETH", "1h")
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df_btc = load_data("BTC", "1h")
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close_eth = df_eth["close"].values
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close_btc = df_btc["close"].values
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n = min(len(close_eth), len(close_btc))
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split = int(n * 0.7)
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ts = pd.to_datetime(df_eth["timestamp"].values[:n], unit="ms", utc=True)
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rv_eth = realized_vol(close_eth[:n], 168)
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rv_btc = realized_vol(close_btc[:n], 168)
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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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for i in range(max(split, 170), n - 48):
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day = ts[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 ts[i].hour != 8:
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continue
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for asset_close, rv_arr, name in [(close_eth[:n], rv_eth, "ETH"), (close_btc[:n], rv_btc, "BTC")]:
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rv = rv_arr[i]
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if rv <= 0.05:
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continue
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iv = rv * 1.22
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spot = asset_close[i]
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t = 48 / (24 * 365)
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premium_pct = iv * np.sqrt(t) * 0.4
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expected_move = iv * np.sqrt(t) * spot
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max_move = expected_move * 2.5
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exit_idx = min(i + 48, n - 1)
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actual_move = abs(asset_close[exit_idx] - spot)
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breached = False
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for j in range(i + 1, exit_idx + 1):
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if abs(asset_close[j] - spot) > max_move:
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breached = True
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actual_move = abs(asset_close[j] - spot)
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break
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pos_pct = 0.07 # 7% per asset = 14% total
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if breached:
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pnl = -min(actual_move / spot, 0.05) * pos_pct
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else:
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profit = premium_pct * pos_pct
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partial = max(0, actual_move / spot - premium_pct) * pos_pct * 0.5
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pnl = profit - partial
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capital += capital * (pnl - FEE * 2 * pos_pct)
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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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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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daily_trades[day] = daily_trades.get(day, 0) + 1
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if total > 0:
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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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print(f"\n ETH+BTC 48h portfolio: trades={total:4d} acc={acc:.1f}% ret={ret:+.1f}% ann={ann:+.1f}% dd={max_dd*100:.1f}% €/day={dpnl:.2f} active={len(daily_trades)}")
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