refactor: riorganizzazione script — Strategy ABC, folder strategies/waste/analysis

- src/strategies/base.py: Strategy ABC con Signal, BacktestResult, YearlyStats - src/strategies/indicators.py: keltner_ratio, detect_squeezes, ema, atr, rv, corr - scripts/strategies/: SQ01-SQ04 (squeeze puro/filtri), ML01 (squeeze+GBM) - scripts/waste/: W01-W22 script scartati + REF originali - scripts/analysis/: compare, best_yearly, final_report, paper_status - CLAUDE.md aggiornato con nuova struttura e tabella strategie Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
2026-05-27 23:01:36 +02:00
parent fa2d74be77
commit 0e47956f7a
40 changed files with 1747 additions and 12 deletions
@@ -0,0 +1,145 @@
+"""S2-03: Volatility Selling — Straddle/Strangle corto simulato.
+La IV crypto è cronicamente sopra la realized vol → vendere premium è profittevole.
+Simulazione: vendi straddle ATM → profitto = max(0, premium - |move|).
+Premium stimato da IV storica. Ingresso giornaliero.
+"""
+from __future__ import annotations
+import sys
+sys.path.insert(0, ".")
+
+import numpy as np
+import pandas as pd
+from scipy.stats import norm
+from src.data.downloader import load_data
+
+FEE = 0.001
+INITIAL = 1000
+
+
+def realized_vol(close: np.ndarray, window: int = 24) -> np.ndarray:
+    """Annualized realized volatility rolling."""
+    log_ret = np.diff(np.log(np.where(close == 0, 1e-10, close)))
+    result = np.full(len(close), 0.5)
+    for i in range(window, len(log_ret)):
+        rv = np.std(log_ret[i - window : i]) * np.sqrt(24 * 365)
+        result[i + 1] = rv
+    return result
+
+
+def implied_vol_proxy(close: np.ndarray, window: int = 48) -> np.ndarray:
+    """IV proxy: realized vol * premium factor.
+    Storicamente IV crypto ≈ 1.2-1.5x realized vol (variance risk premium).
+    """
+    rv = realized_vol(close, window)
+    # Premium factor varia: alto in panic, basso in calma
+    result = np.full(len(close), 0.5)
+    for i in range(window, len(close)):
+        short_rv = realized_vol(close[max(0, i-12):i+1], min(12, i))[-1] if i >= 12 else rv[i]
+        if rv[i] > 0:
+            regime = short_rv / rv[i]
+            premium = 1.15 + 0.3 * max(0, regime - 1)  # più alto in regime volatile
+        else:
+            premium = 1.2
+        result[i] = rv[i] * premium
+    return result
+
+
+def bs_straddle_price(spot: float, iv: float, dte_hours: float) -> float:
+    """Black-Scholes straddle price (call + put ATM)."""
+    if dte_hours <= 0 or iv <= 0:
+        return 0
+    t = dte_hours / (24 * 365)
+    d1 = (0.5 * iv * iv * t) / (iv * np.sqrt(t))
+    call = spot * (2 * norm.cdf(d1) - 1)
+    return call * 2  # straddle = 2 * ATM call (approx for ATM)
+
+
+def run_vol_selling(asset):
+    print(f"\n{'#'*60}")
+    print(f"  {asset} — VOLATILITY SELLING (SHORT STRADDLE)")
+    print(f"{'#'*60}")
+
+    df = load_data(asset, "1h")
+    close = df["close"].values
+    n = len(close)
+    split = int(n * 0.7)
+    timestamps = pd.to_datetime(df["timestamp"], unit="ms", utc=True)
+
+    rv = realized_vol(close, 24)
+    iv_proxy = implied_vol_proxy(close)
+
+    configs = [
+        # (dte_hours, iv_floor, iv_rv_ratio_min, position_pct, name)
+        (24, 0.3, 1.15, 0.1, "daily_24h"),
+        (12, 0.3, 1.15, 0.08, "half_day_12h"),
+        (48, 0.3, 1.10, 0.12, "2day_48h"),
+        (24, 0.4, 1.20, 0.1, "daily_highIV"),
+        (8, 0.25, 1.10, 0.06, "ultra_short_8h"),
+        (24, 0.3, 1.30, 0.15, "daily_bigPremium"),
+    ]
+
+    for dte, iv_floor, ratio_min, pos_pct, name in configs:
+        capital = float(INITIAL)
+        correct = 0
+        total = 0
+        daily_trades = {}
+
+        for i in range(max(split, 50), n - dte):
+            day = timestamps[i].strftime("%Y-%m-%d")
+            if daily_trades.get(day, 0) >= 1:
+                continue
+
+            hour = timestamps[i].dt.hour if hasattr(timestamps[i], 'dt') else timestamps.iloc[i].hour
+            if hour != 8:  # entrata alle 08 UTC ogni giorno
+                continue
+
+            current_iv = iv_proxy[i]
+            current_rv = rv[i]
+
+            if current_iv < iv_floor:
+                continue
+            if current_rv > 0 and current_iv / current_rv < ratio_min:
+                continue
+
+            spot = close[i]
+            premium = bs_straddle_price(spot, current_iv, dte)
+            premium_pct = premium / spot
+
+            # Actual move during holding period
+            exit_idx = min(i + dte, n - 1)
+            actual_move = abs(close[exit_idx] - spot)
+            actual_move_pct = actual_move / spot
+
+            # P&L: premium received - actual move (capped at max loss)
+            max_loss = spot * 0.05  # cap loss at 5% of spot
+            pnl = premium - min(actual_move, max_loss + premium)
+
+            pnl_on_capital = pnl / spot * pos_pct
+            fee_cost = FEE * 4 * pos_pct  # 4 legs: sell call, sell put, buy back
+            net_pnl = pnl_on_capital - fee_cost
+
+            capital += capital * net_pnl
+            capital = max(capital, 0)
+
+            total += 1
+            if pnl > 0:
+                correct += 1
+            daily_trades[day] = daily_trades.get(day, 0) + 1
+
+        if total < 20:
+            continue
+
+        acc = correct / total * 100
+        ret = (capital - INITIAL) / INITIAL * 100
+        test_days = (n - split) / 24
+        test_years = test_days / 365.25
+        ann = ((capital / INITIAL) ** (1 / test_years) - 1) * 100 if test_years > 0 and capital > 0 else -100
+        dpnl = (capital - INITIAL) / test_days if test_days > 0 else 0
+        days_active = len(daily_trades)
+
+        tag = "✅" if acc >= 60 and ann >= 30 else ""
+        print(f"  {name:20s}: trades={total:4d} acc={acc:.1f}% ret={ret:+.1f}% ann={ann:+.1f}% €/day={dpnl:.2f} active={days_active} {tag}")
+
+
+for asset in ["ETH", "BTC"]:
+    run_vol_selling(asset)