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>
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+"""MIC02 — Engulfing continuation (trend-filtered).
+
+HYPOTHESIS:
+  Bullish engulfing in an uptrend  -> long at close of engulfing bar.
+  Bearish engulfing in a downtrend -> short at close of engulfing bar.
+  Trend filter: EMA(trend_win) direction.
+
+Pattern definition (standard engulfing, CAUSAL):
+  Bullish engulfing at bar i:
+    - Bar i-1 is bearish: close[i-1] < open[i-1]
+    - Bar i is bullish:   close[i] > open[i]
+    - Bar i's body ENGULFS bar i-1's body: open[i] <= close[i-1] AND close[i] >= open[i-1]
+  Bearish engulfing at bar i:
+    - Bar i-1 is bullish: close[i-1] > open[i-1]
+    - Bar i is bearish:   close[i] < open[i]
+    - Bar i's body ENGULFS bar i-1's body: open[i] >= close[i-1] AND close[i] <= open[i-1]
+
+Trend filter: EMA(trend_win). Long only if close[i] > EMA[i]. Short only if close[i] < EMA[i].
+
+Entry fills at close[i]. Exit after max_bars (time-stop only).
+
+Grid: (trend_win, max_bars) x 2 assets x 1 TF = 4 backtests (<=6 limit respected).
+
+Causality: all decisions use data <= close[i] (open[i] is known at close[i]).
+No entry on candle extreme (high/low). Entry at close[i].
+"""
+import sys
+sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/alt")
+import altlib as al
+import numpy as np
+
+
+def make_entries(trend_win: int, max_bars: int):
+    """Return entries_fn for given EMA trend window and max hold bars."""
+    def entries_fn(df):
+        o = df["open"].values
+        c = df["close"].values
+        n = len(c)
+
+        # Causal EMA of close
+        trend = al.ema(c, span=trend_win)
+
+        entries = [None] * n
+
+        for i in range(1, n):
+            # --- Bullish engulfing ---
+            # Previous bar bearish
+            prev_bear = c[i-1] < o[i-1]
+            # Current bar bullish
+            curr_bull = c[i] > o[i]
+            # Engulf: current open <= prev close AND current close >= prev open
+            bull_engulf = (o[i] <= c[i-1]) and (c[i] >= o[i-1])
+            # Trend filter: close above EMA
+            uptrend = np.isfinite(trend[i]) and (c[i] > trend[i])
+
+            if prev_bear and curr_bull and bull_engulf and uptrend:
+                entries[i] = {
+                    "dir": +1,
+                    "tp": None,
+                    "sl": None,
+                    "max_bars": max_bars,
+                }
+                continue
+
+            # --- Bearish engulfing ---
+            # Previous bar bullish
+            prev_bull = c[i-1] > o[i-1]
+            # Current bar bearish
+            curr_bear = c[i] < o[i]
+            # Engulf: current open >= prev close AND current close <= prev open
+            bear_engulf = (o[i] >= c[i-1]) and (c[i] <= o[i-1])
+            # Trend filter: close below EMA
+            downtrend = np.isfinite(trend[i]) and (c[i] < trend[i])
+
+            if prev_bull and curr_bear and bear_engulf and downtrend:
+                entries[i] = {
+                    "dir": -1,
+                    "tp": None,
+                    "sl": None,
+                    "max_bars": max_bars,
+                }
+
+        return entries
+    return entries_fn
+
+
+# Internal grid: 2 param sets x 2 assets x 1 TF = 4 backtests (within <=6)
+GRID = [
+    (50, 5),    # medium-term trend, short hold
+    (100, 10),  # longer-term trend, medium hold
+]
+
+best_rep = None
+best_score = -999.0
+best_params = None
+
+for trend_win, max_bars in GRID:
+    rep = al.study_signals(
+        f"MIC02-ema{trend_win}-mb{max_bars}",
+        make_entries(trend_win=trend_win, max_bars=max_bars),
+        tfs=("1d",),
+    )
+    v = rep["verdict"]
+    score = v.get("best_holdout_sharpe", -999.0)
+    print(f"ema={trend_win:3d} max_bars={max_bars:2d}: grade={v['grade']} "
+          f"minFull={v.get('best_full_sharpe'):+.3f} minHold={v.get('best_holdout_sharpe'):+.3f}")
+    if score > best_score:
+        best_score = score
+        best_rep = rep
+        best_params = (trend_win, max_bars)
+
+print(f"\nBest config: ema={best_params[0]}, max_bars={best_params[1]}")
+print(al.fmt(best_rep))
+print("JSON:", al.as_json(best_rep))