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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+"""TRD06 — Heikin-Ashi Trend Streak
+HYPOTHESIS: Build HA candles; long while HA close > HA open (green streak), flat on color flip.
+Also test vol-targeted variant and streak-length filter.
+
+Configs tested (<=4 param sets, total backtests = 4 configs * 2 assets * 2 TFs = 16):
+  1. Raw HA signal (long green, flat red) on 1d + 12h
+  2. Vol-targeted HA signal
+(We do 2 param sets * 2 TFs in study_weights call for a total of 8 runs x 2 assets = 16 cells)
+"""
+import sys
+sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/alt")
+import altlib as al
+import numpy as np
+
+
+def ha_candles(df):
+    """Compute Heikin-Ashi OHLC causally.
+    HA_close[i] = (open[i] + high[i] + low[i] + close[i]) / 4
+    HA_open[i]  = (HA_open[i-1] + HA_close[i-1]) / 2
+    This is causal: HA_open[i] uses only past HA values, HA_close[i] uses current bar data.
+    """
+    o = df["open"].values.astype(float)
+    h = df["high"].values.astype(float)
+    l = df["low"].values.astype(float)
+    c = df["close"].values.astype(float)
+    n = len(c)
+
+    ha_o = np.zeros(n)
+    ha_c = np.zeros(n)
+
+    # HA_close is just the average of OHLC — uses current bar only, causal
+    ha_c = (o + h + l + c) / 4.0
+
+    # HA_open: bootstrapped from first bar, then recursively
+    ha_o[0] = (o[0] + c[0]) / 2.0
+    for i in range(1, n):
+        ha_o[i] = (ha_o[i - 1] + ha_c[i - 1]) / 2.0
+
+    return ha_o, ha_c
+
+
+def trd06_base(df):
+    """Long when HA candle is green (ha_close > ha_open), flat otherwise."""
+    ha_o, ha_c = ha_candles(df)
+    # signal: +1 when green, 0 when red/doji
+    signal = np.where(ha_c > ha_o, 1.0, 0.0)
+    return signal
+
+
+def trd06_vt(df):
+    """Vol-targeted version of TRD06: scale green signal by vol target."""
+    ha_o, ha_c = ha_candles(df)
+    direction = np.where(ha_c > ha_o, 1.0, 0.0)
+    return al.vol_target(direction, df, target_vol=0.20, vol_win_days=30, leverage_cap=2.0)
+
+
+def trd06_streak2(df):
+    """Long only when HA has been green for >= 2 consecutive bars (reduces noise)."""
+    ha_o, ha_c = ha_candles(df)
+    green = (ha_c > ha_o).astype(float)
+    n = len(green)
+    streak = np.zeros(n)
+    cnt = 0
+    for i in range(n):
+        if green[i] > 0:
+            cnt += 1
+        else:
+            cnt = 0
+        streak[i] = cnt
+    # long only when streak >= 2
+    signal = np.where(streak >= 2, 1.0, 0.0)
+    return signal
+
+
+def trd06_streak2_vt(df):
+    """Vol-targeted streak>=2 variant."""
+    ha_o, ha_c = ha_candles(df)
+    green = (ha_c > ha_o).astype(float)
+    n = len(green)
+    streak = np.zeros(n)
+    cnt = 0
+    for i in range(n):
+        if green[i] > 0:
+            cnt += 1
+        else:
+            cnt = 0
+        streak[i] = cnt
+    direction = np.where(streak >= 2, 1.0, 0.0)
+    return al.vol_target(direction, df, target_vol=0.20, vol_win_days=30, leverage_cap=2.0)
+
+
+if __name__ == "__main__":
+    print("=== TRD06: Heikin-Ashi Trend Streak ===\n")
+
+    # Config 1: raw HA green/flat
+    print("--- Config 1: Raw HA green signal (1d, 12h) ---")
+    rep1 = al.study_weights("TRD06-base", trd06_base, tfs=("1d", "12h"))
+    print(al.fmt(rep1))
+    print("JSON:", al.as_json(rep1))
+
+    print()
+
+    # Config 2: vol-targeted HA
+    print("--- Config 2: Vol-targeted HA (1d, 12h) ---")
+    rep2 = al.study_weights("TRD06-VT", trd06_vt, tfs=("1d", "12h"))
+    print(al.fmt(rep2))
+    print("JSON:", al.as_json(rep2))
+
+    print()
+
+    # Config 3: streak>=2 filter
+    print("--- Config 3: HA streak>=2 (1d only) ---")
+    rep3 = al.study_weights("TRD06-streak2", trd06_streak2, tfs=("1d",))
+    print(al.fmt(rep3))
+    print("JSON:", al.as_json(rep3))
+
+    print()
+
+    # Config 4: streak>=2 vol-targeted
+    print("--- Config 4: HA streak>=2 vol-targeted (1d only) ---")
+    rep4 = al.study_weights("TRD06-streak2-VT", trd06_streak2_vt, tfs=("1d",))
+    print(al.fmt(rep4))
+    print("JSON:", al.as_json(rep4))
+
+    # Summary: pick best config
+    all_reps = [
+        ("TRD06-base-1d", rep1, "1d"),
+        ("TRD06-base-12h", rep1, "12h"),
+        ("TRD06-VT-1d", rep2, "1d"),
+        ("TRD06-VT-12h", rep2, "12h"),
+        ("TRD06-streak2-1d", rep3, "1d"),
+        ("TRD06-streak2-VT-1d", rep4, "1d"),
+    ]
+
+    print("\n=== SUMMARY ===")
+    for label, rep, tf in all_reps:
+        cell = next((c for c in rep["cells"] if c["tf"] == tf), None)
+        if cell:
+            print(f"{label:30s}: minFull={cell['min_asset_full_sharpe']:+.3f} "
+                  f"minHold={cell['min_asset_holdout_sharpe']:+.3f} "
+                  f"feeOK={cell['fee_survives']} grade={rep['verdict']['grade']}")