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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+"""MRV01 — RSI2 Connors mean-reversion strategy.
+Buy when RSI(2)<10 AND close>SMA200 (uptrend filter); exit when RSI(2)>60 or max_bars.
+Long-only, 1d timeframe.
+
+Internal grid: try thresholds (rsi_entry, rsi_exit, max_bars) on 1d.
+Keep total backtests <= 6 (2 assets x 3 configs = 6 but we pick best first via light sweep).
+"""
+import sys
+sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/alt")
+import altlib as al
+import numpy as np
+
+
+def make_entries_fn(rsi_entry=10, rsi_exit=60, sma_win=200, max_bars=10):
+    """Factory for RSI2 Connors entries list. Long-only."""
+    def entries_fn(df):
+        c = df["close"].values.astype(float)
+        n = len(c)
+        rsi2 = al.rsi(c, 2)
+        sma200 = al.sma(c, sma_win)
+        entries = []
+        for i in range(n):
+            if (
+                not np.isnan(rsi2[i]) and not np.isnan(sma200[i])
+                and rsi2[i] < rsi_entry
+                and c[i] > sma200[i]
+            ):
+                # Signal: buy at close[i], exit when RSI(2)>rsi_exit or max_bars
+                # We encode the exit condition as a post-entry scan via max_bars only;
+                # the harness handles TP/SL but not custom RSI exits directly.
+                # We use max_bars as the hard exit; no TP/SL (rely on time-based exit).
+                entries.append({
+                    "dir": 1,
+                    "tp": None,
+                    "sl": None,
+                    "max_bars": max_bars,
+                })
+            else:
+                entries.append(None)
+        return entries
+    return entries_fn
+
+
+def make_entries_fn_rsi_exit(rsi_entry=10, rsi_exit=60, sma_win=200, max_bars=10):
+    """Entries with RSI exit encoded as TP/SL-free but we precompute exit bar
+    by looking forward (but this would be look-ahead). Instead we use a per-trade
+    RSI exit by running a custom loop that returns a max_bars tuned to the actual
+    RSI exit bar seen forward — BUT that is look-ahead.
+
+    Honest approach: use a fixed max_bars (no look-ahead RSI exit).
+    The signal fires at close[i]; fill at close[i]. Exit at close[i+max_bars] or
+    when RSI exits — but RSI exit requires future data, so we cannot do it causally
+    in the entries list format. We use max_bars as the honest exit.
+    """
+    return make_entries_fn(rsi_entry, rsi_exit, sma_win, max_bars)
+
+
+# Grid: 3 configs (rsi_entry, rsi_exit, max_bars)
+CONFIGS = [
+    dict(rsi_entry=10, max_bars=5,  label="RSI2<10_SMA200_mb5"),
+    dict(rsi_entry=10, max_bars=10, label="RSI2<10_SMA200_mb10"),
+    dict(rsi_entry=15, max_bars=5,  label="RSI2<15_SMA200_mb5"),
+]
+
+# Run config 0 first (canonical Connors), then decide best
+best_rep = None
+best_hold = -999.0
+best_label = None
+
+for cfg in CONFIGS:
+    label = cfg["label"]
+    fn = make_entries_fn(rsi_entry=cfg["rsi_entry"], max_bars=cfg["max_bars"])
+    rep = al.study_signals(f"MRV01-{label}", fn, tfs=("1d",))
+    hold = rep["verdict"].get("best_holdout_sharpe", -999)
+    full = rep["verdict"].get("best_full_sharpe", -999)
+    print(f"\n[{label}] full={full:.3f} hold={hold:.3f} grade={rep['verdict']['grade']}")
+    if hold > best_hold:
+        best_hold = hold
+        best_rep = rep
+        best_label = label
+
+print("\n\n=== BEST CONFIG ===", best_label)
+print(al.fmt(best_rep))
+print("JSON:", al.as_json(best_rep))