research(alt): sweep 104 strategie alternative su Deribit (153 agenti) + marginal scorer

Ondata di ricerca onesta a largo spettro su BTC/ETH+DVOL certificati: 104 ipotesi distinte (11 famiglie), un agente-finder per ipotesi, verifica avversariale a 3 scettici sui promettenti, sintesi (153 agenti totali). Esito: NIENTE di nuovo regge -> conferma del soffitto strutturale ~1.3 BTC/ETH-direzionale; lo stack TP01+XS01+VRP01 resta imbattuto. - altlib.py: harness condiviso vettoriale leak-free (eval_weights/study_weights, fee-sweep, both-asset + hold-out 2025+). Riproduce i numeri canonici di TP01. - MARGINAL SCORER (study_marginal/marginal_vs_tp01): Sharpe INCREMENTALE vs baseline TP01 (corr, blend uplift OOS, alpha residua) + jackknife OOS (clean-year + drop-best-month). earns_slot = abs!=FAIL & ADDS & robust_oos. Smaschera gli overlay su TSMOM con PASS assoluti fasulli (CMB04, VOL11, ...) e il falso positivo KAMA (ADDS ma muore al jackknife). - runs/*.py (104) script riproducibili per ipotesi; wf_altstrat.js workflow. - Verdetto: 0 candidati deployabili; 2 LEAD fragili (VOL08, STA05_LS) da forward-monitor. - test_marginal_scorer.py blocca baseline + invarianti. Suite: 32 verde. Diario: docs/diary/2026-06-20-alt-strategies-100agent-sweep.md Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
2026-06-20 19:50:39 +00:00
parent bf84bc91e2
commit 5ac4e16af8
111 changed files with 16924 additions and 0 deletions
@@ -0,0 +1,108 @@
+"""CMB05 — BB Squeeze -> Breakout (honest, leak-free).
+
+HYPOTHESIS: Bollinger Bandwidth at a multi-bar low (squeeze) then close > upper BB
+-> enter long at that close (entry at close[i], direction decided with data<=close[i]).
+Exit when close drops back below middle band, or max_bars reached, or SL hit.
+
+Tested on 1d only (study_signals, discrete). Small grid on:
+  - BB window: 20 vs 30
+  - Squeeze lookback: 50 vs 100
+  Total configs: 4 — two assets each => 8 backtests. Within budget.
+"""
+import sys
+sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/alt")
+import altlib as al
+import numpy as np
+import pandas as pd
+
+
+def make_entries(bb_win: int = 20, squeeze_lb: int = 50, squeeze_pct: float = 0.20, sl_mult: float = 2.0, max_bars: int = 30):
+    """
+    Returns entries_fn(df) -> list[dict|None] for study_signals.
+
+    Squeeze = BB bandwidth (upper-lower)/middle in lowest squeeze_pct quantile over squeeze_lb bars.
+    Breakout = close[i] > upper[i] AND bandwidth is in compressed regime.
+    Entry: long at close[i], honest (direction decided with close[i]).
+    Exit: close < middle band (continuous) OR max_bars OR SL at entry - sl_mult*ATR.
+    """
+    def entries_fn(df):
+        c = df["close"].values.astype(float)
+        n = len(c)
+
+        # BB bands - causal (uses data up to i)
+        upper, mid, lower = al.bbands(c, win=bb_win, k=2.0)
+
+        # Bandwidth
+        bw = np.where(mid != 0, (upper - lower) / mid, np.nan)
+
+        # Squeeze: bandwidth in lowest squeeze_pct percentile over squeeze_lb bars (causal)
+        # Use rolling quantile to flag "compressed" bandwidth
+        bw_series = pd.Series(bw)
+        bw_lo = bw_series.rolling(squeeze_lb, min_periods=squeeze_lb).quantile(squeeze_pct).values
+
+        # ATR for SL
+        atr_arr = al.atr(df, win=14)
+
+        entries = [None] * n
+        in_trade = False
+
+        for i in range(squeeze_lb + bb_win, n):
+            if np.isnan(upper[i]) or np.isnan(bw_lo[i]) or np.isnan(atr_arr[i]):
+                continue
+            if not np.isfinite(bw[i]):
+                continue
+
+            # Squeeze: bandwidth <= its rolling low-percentile threshold
+            is_squeeze = bw[i] <= bw_lo[i]
+
+            # Breakout: close[i] > upper[i]  (decided at close[i], honest)
+            breakout = c[i] > upper[i]
+
+            if (not in_trade) and is_squeeze and breakout:
+                sl_px = c[i] - sl_mult * atr_arr[i]
+                entries[i] = {
+                    "dir": +1,
+                    "tp": None,
+                    "sl": sl_px,
+                    "max_bars": max_bars,
+                }
+                in_trade = True
+            elif in_trade:
+                # Exit signal: close falls below middle band -> reset flag
+                if c[i] < mid[i]:
+                    in_trade = False
+
+        return entries
+
+    return entries_fn
+
+
+# Grid: 4 configs (2 bb_win x 2 squeeze_pct) with fixed squeeze_lb=100
+configs = [
+    dict(bb_win=20, squeeze_lb=100, squeeze_pct=0.20),
+    dict(bb_win=20, squeeze_lb=100, squeeze_pct=0.30),
+    dict(bb_win=30, squeeze_lb=100, squeeze_pct=0.20),
+    dict(bb_win=30, squeeze_lb=100, squeeze_pct=0.30),
+]
+
+best_rep = None
+best_score = -999.0
+
+print("=== CMB05: BB Squeeze -> Breakout ===")
+print(f"Grid: {len(configs)} configs x 2 assets x fee_sweep = honest eval\n")
+
+for cfg in configs:
+    name = f"CMB05-BB{cfg['bb_win']}-SQ{cfg['squeeze_lb']}-P{int(cfg['squeeze_pct']*100)}"
+    fn = make_entries(bb_win=cfg["bb_win"], squeeze_lb=cfg["squeeze_lb"], squeeze_pct=cfg["squeeze_pct"])
+    rep = al.study_signals(name, fn, tfs=("1d",))
+    v = rep["verdict"]
+    score = v.get("best_holdout_sharpe", -9)
+    print(f"  {name}: grade={v['grade']} fullSh={v.get('best_full_sharpe'):.3f} holdSh={v.get('best_holdout_sharpe'):.3f}")
+    if score > best_score:
+        best_score = score
+        best_rep = rep
+        best_rep["_cfg"] = cfg
+
+print("\n--- BEST CONFIG ---")
+print(al.fmt(best_rep))
+print("JSON:", al.as_json(best_rep))