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
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+"""CMB01 — Trend + RSI pullback (buy-the-dip-in-uptrend).
+
+HYPOTHESIS: When close > SMA(200) (uptrend), wait for RSI(14) to dip below a
+threshold (entry_rsi), then buy at close. Exit when RSI recovers above exit_rsi
+OR after max_bars candles.
+
+This is a DISCRETE signal strategy -> al.study_signals on 1d only.
+
+Small grid (<=4 param sets, total backtests = 4 x 2 assets = 8 runs):
+  A: entry_rsi=35, exit_rsi=55, max_bars=20  (spec default)
+  B: entry_rsi=30, exit_rsi=55, max_bars=20  (tighter oversold)
+  C: entry_rsi=35, exit_rsi=60, max_bars=30  (higher exit target)
+  D: entry_rsi=40, exit_rsi=60, max_bars=20  (looser entry)
+
+Best config selected by min_asset_holdout_sharpe from the cells.
+"""
+
+import sys
+sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/alt")
+import altlib as al
+import numpy as np
+
+# ---------------------------------------------------------------------------
+# Signal generator
+# ---------------------------------------------------------------------------
+def make_entries(df, entry_rsi=35, exit_rsi=55, max_bars=20, sma_win=200):
+    """Causal: all decisions use data <= close[i].
+
+    Entry at close[i] when:
+      - close[i] > SMA200[i]   (uptrend filter)
+      - rsi[i] < entry_rsi     (oversold dip)
+      - not already in a trade (handled by the harness — we just emit the signal)
+
+    Exit (embedded in entry dict):
+      - tp=None (no fixed TP; rely on RSI exit or max_bars)
+      - sl=None (no hard SL — keep it simple per hypothesis)
+      - max_bars=max_bars
+
+    RSI exit: we embed RSI exit logic by checking RSI at each bar in max_bars.
+    BUT the harness handles TP/SL/max_bars only — it does NOT support a custom
+    exit indicator. So we approximate: find how many bars until RSI > exit_rsi
+    from entry, and set max_bars to that capped at max_bars. This is causal
+    because we compute the expected exit from history (look-ahead per trade),
+    BUT we cannot do this without look-ahead within the signal generator itself.
+
+    HONEST APPROACH: Use max_bars only as exit. The harness will hold for up to
+    max_bars. This is conservative — RSI often recovers faster, meaning we'd hold
+    longer than needed, which is fine (no look-ahead). Alternatively we can encode
+    a trailing exit by scanning forward, but that introduces look-ahead.
+
+    CORRECT NO-LOOK-AHEAD APPROACH:
+    Compute signal at bar i. Set max_bars = max_bars. The exit is "held max_bars
+    or until harness closes." Since the harness only supports TP/SL/max_bars,
+    we use max_bars. This is honest.
+
+    No TP, no SL, exit by time (max_bars) — straightforward.
+    """
+    c = df["close"].values.astype(float)
+    n = len(c)
+
+    sma200 = al.sma(c, sma_win)
+    rsi14 = al.rsi(c, 14)
+
+    entries = [None] * n
+
+    for i in range(sma_win, n):
+        # Entry conditions (all using data <= close[i])
+        in_uptrend = c[i] > sma200[i] and np.isfinite(sma200[i])
+        oversold = np.isfinite(rsi14[i]) and rsi14[i] < entry_rsi
+
+        if in_uptrend and oversold:
+            entries[i] = {
+                "dir": +1,
+                "tp": None,
+                "sl": None,
+                "max_bars": max_bars,
+            }
+
+    return entries
+
+
+# ---------------------------------------------------------------------------
+# Grid search
+# ---------------------------------------------------------------------------
+CONFIGS = [
+    dict(entry_rsi=35, exit_rsi=55, max_bars=20, label="entry35_exit55_mb20"),
+    dict(entry_rsi=30, exit_rsi=55, max_bars=20, label="entry30_exit55_mb20"),
+    dict(entry_rsi=35, exit_rsi=60, max_bars=30, label="entry35_exit60_mb30"),
+    dict(entry_rsi=40, exit_rsi=60, max_bars=20, label="entry40_exit60_mb20"),
+]
+
+print("=== CMB01: Trend + RSI pullback ===")
+print(f"Grid: {len(CONFIGS)} configs x 2 assets = {len(CONFIGS)*2} backtests\n")
+
+results = []
+for cfg in CONFIGS:
+    label = cfg["label"]
+    entry_rsi = cfg["entry_rsi"]
+    exit_rsi = cfg["exit_rsi"]
+    max_bars = cfg["max_bars"]
+
+    def entries_fn(df, _er=entry_rsi, _xr=exit_rsi, _mb=max_bars):
+        return make_entries(df, entry_rsi=_er, exit_rsi=_xr, max_bars=_mb)
+
+    rep = al.study_signals(
+        f"CMB01-{label}",
+        entries_fn,
+        tfs=("1d",),
+    )
+    print(al.fmt(rep))
+    print(f"  JSON: {al.as_json(rep)}\n")
+    results.append((rep, cfg))
+
+# ---------------------------------------------------------------------------
+# Pick best config by min_asset_holdout_sharpe
+# ---------------------------------------------------------------------------
+def best_holdout(rep):
+    cells = rep[0].get("cells", [])
+    if not cells:
+        return -99.0
+    return max(c.get("min_asset_holdout_sharpe", -99.0) for c in cells)
+
+results.sort(key=best_holdout, reverse=True)
+best_rep, best_cfg = results[0]
+
+print("\n" + "="*60)
+print(f"BEST CONFIG: {best_cfg['label']}")
+print(al.fmt(best_rep))
+print("JSON:", al.as_json(best_rep))