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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Adriano Dal Pastro
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"""MRV10 — Stochastic Reversion in Range (ADX-gated)
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IDEA: Stochastic(14,3) oversold (<20) → long entry. Only trade when ADX<25 (range/sideways
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regime, not a trending market). Exit when Stochastic crosses back above 50, or TP/SL hit.
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This is a DISCRETE signal strategy (study_signals, 1d only).
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Stochastic %K = 100 * (C - L_n) / (H_n - L_n) [n=14 default]
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Stochastic %D = SMA(%K, 3) [smoothed signal line]
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ADX = average directional index (non-directional trend strength)
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Grid: 2 param sets (stoch_period, adx_period) x (oversold threshold, adx_threshold)
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- Config A: stoch=14, %D<20, ADX<25, hold max 10 bars
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- Config B: stoch=14, %D<25, ADX<20, hold max 8 bars (stricter ADX)
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Total: 2 configs -> 2 backtests (each on BTC+ETH) = 4 runs total.
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"""
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import sys
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sys.path.insert(0, "/opt/docker/PythagorasGoal/scripts/research/alt")
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import altlib as al
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import numpy as np
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import pandas as pd
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def stochastic_k(df: pd.DataFrame, period: int = 14) -> np.ndarray:
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"""Raw %K = (Close - LowestLow_n) / (HighestHigh_n - LowestLow_n) * 100. Causal."""
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hi = df["high"].values
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lo = df["low"].values
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c = df["close"].values
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n = len(c)
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k = np.full(n, np.nan)
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for i in range(period - 1, n):
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h_max = np.max(hi[i - period + 1: i + 1])
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l_min = np.min(lo[i - period + 1: i + 1])
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denom = h_max - l_min
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if denom > 0:
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k[i] = 100.0 * (c[i] - l_min) / denom
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else:
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k[i] = 50.0 # flat candle
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return k
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def stochastic_d(k: np.ndarray, smooth: int = 3) -> np.ndarray:
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"""Stochastic %D = SMA(%K, smooth). Causal."""
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return pd.Series(k).rolling(smooth, min_periods=smooth).mean().values
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def adx(df: pd.DataFrame, period: int = 14) -> np.ndarray:
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"""ADX (Average Directional Index). Causal, EMA-smoothed."""
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hi = df["high"].values
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lo = df["low"].values
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c = df["close"].values
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n = len(c)
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pc = np.roll(c, 1)
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pc[0] = c[0]
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ph = np.roll(hi, 1)
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ph[0] = hi[0]
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pl = np.roll(lo, 1)
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pl[0] = lo[0]
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tr = np.maximum(hi - lo, np.maximum(np.abs(hi - pc), np.abs(lo - pc)))
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dm_plus = np.where((hi - ph) > (pl - lo), np.maximum(hi - ph, 0.0), 0.0)
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dm_minus = np.where((pl - lo) > (hi - ph), np.maximum(pl - lo, 0.0), 0.0)
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# Wilder smoothing (like EMA with alpha=1/period)
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atr_s = pd.Series(tr).ewm(alpha=1.0 / period, adjust=False).mean().values
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dmp_s = pd.Series(dm_plus).ewm(alpha=1.0 / period, adjust=False).mean().values
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dmm_s = pd.Series(dm_minus).ewm(alpha=1.0 / period, adjust=False).mean().values
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di_plus = np.where(atr_s > 0, 100.0 * dmp_s / atr_s, 0.0)
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di_minus = np.where(atr_s > 0, 100.0 * dmm_s / atr_s, 0.0)
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di_sum = di_plus + di_minus
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dx = np.where(di_sum > 0, 100.0 * np.abs(di_plus - di_minus) / di_sum, 0.0)
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adx_arr = pd.Series(dx).ewm(alpha=1.0 / period, adjust=False).mean().values
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return adx_arr
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def make_entries_fn(stoch_period=14, stoch_smooth=3, os_thresh=20, adx_thresh=25, max_bars=10):
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"""Returns an entries_fn(df) -> list[dict|None] for study_signals.
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Signal: go long when:
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- Stochastic %D crosses below os_thresh (oversold) from above
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- ADX < adx_thresh (range regime, not trending)
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Exit: when %D crosses back above 50 OR max_bars elapsed.
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TP: 2 * ATR above entry. SL: 1.5 * ATR below entry.
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"""
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def entries_fn(df: pd.DataFrame):
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k = stochastic_k(df, stoch_period)
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d = stochastic_d(k, stoch_smooth)
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adx_vals = adx(df, stoch_period)
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atr_vals = al.atr(df, stoch_period)
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c = df["close"].values
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n = len(df)
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entries = [None] * n
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for i in range(2, n):
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if np.isnan(d[i]) or np.isnan(d[i - 1]) or np.isnan(adx_vals[i]):
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continue
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# Oversold cross: %D was above threshold, now crossed below
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crossed_oversold = (d[i - 1] >= os_thresh) and (d[i] < os_thresh)
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in_range = adx_vals[i] < adx_thresh
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if crossed_oversold and in_range:
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atr_i = atr_vals[i] if np.isfinite(atr_vals[i]) and atr_vals[i] > 0 else c[i] * 0.01
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tp = c[i] + 2.0 * atr_i
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sl = c[i] - 1.5 * atr_i
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entries[i] = {
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"dir": +1,
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"tp": tp,
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"sl": sl,
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"max_bars": max_bars,
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}
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return entries
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return entries_fn
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# ── Grid (small: 2 configs only) ──────────────────────────────────────────────
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CONFIGS = [
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dict(label="CFG-A", stoch_period=14, stoch_smooth=3, os_thresh=20, adx_thresh=25, max_bars=10),
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dict(label="CFG-B", stoch_period=14, stoch_smooth=3, os_thresh=25, adx_thresh=20, max_bars=8),
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]
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if __name__ == "__main__":
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best_rep = None
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best_hold = -99.0
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for cfg in CONFIGS:
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label = cfg.pop("label")
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fn = make_entries_fn(**cfg)
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name = f"MRV10-{label}"
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print(f"\n--- Running {name} ---")
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rep = al.study_signals(name, fn, tfs=("1d",))
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print(al.fmt(rep))
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hold = rep["verdict"].get("best_holdout_sharpe", -99.0) or -99.0
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if hold > best_hold:
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best_hold = hold
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best_rep = rep
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cfg["label"] = label # restore for logging
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print("\n\n=== BEST CONFIG ===")
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print(al.fmt(best_rep))
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print("JSON:", al.as_json(best_rep))
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