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
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+"""S2-02: Funding Rate Strategy.
+Quando il funding rate è molto positivo → troppi long → short il perpetual.
+Quando molto negativo → troppi short → long il perpetual.
+Si cattura sia il mean reversion del prezzo che il funding rate stesso.
+Ingresso: quando funding > 0.03% o < -0.03% (8h rate).
+"""
+from __future__ import annotations
+import sys
+sys.path.insert(0, ".")
+
+import numpy as np
+import pandas as pd
+from src.data.downloader import load_data
+
+FEE = 0.001
+INITIAL = 1000
+LEVERAGE = 3
+
+
+def simulate_funding_strategy(asset):
+    """Simula funding rate strategy usando il proxy: overnight returns.
+    Crypto funding settlement ogni 8h → prezzo tende a correggersi dopo settlement.
+    Proxy: se ultime 8h hanno avuto forte trend, aspettati reversal dopo settlement.
+    """
+    print(f"\n{'#'*60}")
+    print(f"  {asset} — FUNDING RATE PROXY STRATEGY")
+    print(f"{'#'*60}")
+
+    df_1h = load_data(asset, "1h")
+    close = df_1h["close"].values
+    volume = df_1h["volume"].values
+    n = len(close)
+    split = int(n * 0.7)
+
+    timestamps = pd.to_datetime(df_1h["timestamp"], unit="ms", utc=True)
+    hours = timestamps.dt.hour.values
+
+    # Funding settlement su Deribit: 00:00, 08:00, 16:00 UTC
+    settlement_hours = {0, 8, 16}
+
+    configs = [
+        (0.01, 0.02, 8, 0.02, "mild_1pct"),
+        (0.015, 0.025, 8, 0.015, "moderate_1.5pct"),
+        (0.02, 0.03, 8, 0.015, "strong_2pct"),
+        (0.01, 0.015, 4, 0.01, "fast_1pct_4h"),
+        (0.02, 0.03, 12, 0.02, "slow_2pct_12h"),
+        (0.025, 0.04, 6, 0.015, "extreme_2.5pct"),
+    ]
+
+    for entry_thr, tp_mult_unused, hold_max, stop, name in configs:
+        capital = float(INITIAL)
+        correct = 0
+        total = 0
+        daily_trades = {}
+
+        for i in range(max(split, 8), n - hold_max):
+            hour = hours[i]
+            if hour not in settlement_hours:
+                continue
+
+            day = timestamps[i].strftime("%Y-%m-%d")
+            if daily_trades.get(day, 0) >= 1:
+                continue
+
+            # 8h return prima del settlement = proxy per funding pressure
+            ret_8h = (close[i] - close[i - 8]) / close[i - 8]
+
+            # Volume spike = conferma
+            vol_avg = np.mean(volume[max(0, i - 48) : i])
+            vol_recent = np.mean(volume[i - 8 : i])
+            vol_spike = vol_recent / vol_avg if vol_avg > 0 else 1
+
+            direction = None
+            if ret_8h > entry_thr and vol_spike > 1.1:
+                direction = "short"  # troppi long, attendi reversal
+            elif ret_8h < -entry_thr and vol_spike > 1.1:
+                direction = "long"  # troppi short, attendi rimbalzo
+
+            if direction is None:
+                continue
+
+            entry_price = close[i]
+            for j in range(i + 1, min(i + hold_max + 1, n)):
+                price = close[j]
+                if direction == "long":
+                    pnl_pct = (price - entry_price) / entry_price
+                else:
+                    pnl_pct = (entry_price - price) / entry_price
+
+                if pnl_pct <= -stop or pnl_pct >= stop * 2 or j == min(i + hold_max, n - 1):
+                    exit_price = price
+                    break
+            else:
+                exit_price = close[min(i + hold_max, n - 1)]
+
+            if direction == "long":
+                trade_ret = (exit_price - entry_price) / entry_price
+            else:
+                trade_ret = (entry_price - exit_price) / entry_price
+
+            # Add funding rate income (approx 0.01% per 8h period if direction correct)
+            funding_income = 0.0001 * (hold_max / 8) if trade_ret > 0 else 0
+
+            net = (trade_ret + funding_income) * LEVERAGE - FEE * 2 * LEVERAGE
+            capital += capital * 0.2 * net
+            capital = max(capital, 0)
+
+            total += 1
+            if trade_ret > 0:
+                correct += 1
+            daily_trades[day] = daily_trades.get(day, 0) + 1
+
+        if total < 10:
+            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={days_active} {tag}")
+
+
+for asset in ["ETH", "BTC"]:
+    simulate_funding_strategy(asset)