Adriano Dal Pastro
5ac4e16af8
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>
96 lines
3.1 KiB
Python
96 lines
3.1 KiB
Python
"""TRD09 — Aroon Trend Strategy
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Aroon(period): long when AroonUp > AroonDown AND AroonUp > 70.
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Uses vol-targeting (TP01-style) for position sizing.
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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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def aroon(df, period: int = 25):
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"""Compute Aroon Up and Aroon Down (causal).
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AroonUp[i] = 100 * (bars since highest high in [i-period..i]) / period
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AroonDown[i] = 100 * (bars since lowest low in [i-period..i]) / period
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Both in [0, 100].
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"""
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high = df["high"].values.astype(float)
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low = df["low"].values.astype(float)
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n = len(high)
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aroon_up = np.full(n, np.nan)
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aroon_down = np.full(n, np.nan)
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# Vectorized using pandas rolling argmax/argmin
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import pandas as pd
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h_series = pd.Series(high)
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l_series = pd.Series(low)
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for i in range(period, n):
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window_h = high[i - period: i + 1]
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window_l = low[i - period: i + 1]
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# position of max/min within window (0=oldest, period=current)
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idx_max = np.argmax(window_h) # periods ago = period - idx_max
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idx_min = np.argmin(window_l)
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aroon_up[i] = 100.0 * idx_max / period
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aroon_down[i] = 100.0 * idx_min / period
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return aroon_up, aroon_down
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def make_target(period: int = 25, threshold: float = 70.0, use_vol_target: bool = True):
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"""Return a target function for al.study_weights."""
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def target_fn(df):
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up, dn = aroon(df, period)
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# Long signal: AroonUp > AroonDown AND AroonUp > threshold
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direction = np.where(
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(up > dn) & (up > threshold),
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1.0,
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0.0 # flat otherwise (long-flat, no short)
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)
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direction[~np.isfinite(up)] = 0.0
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if use_vol_target:
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return al.vol_target(direction, df, target_vol=0.20, vol_win_days=30, leverage_cap=2.0)
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else:
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return direction
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return target_fn
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if __name__ == "__main__":
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# Small grid: period x threshold (4 combos max)
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configs = [
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{"period": 25, "threshold": 70.0},
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{"period": 14, "threshold": 70.0},
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{"period": 25, "threshold": 60.0},
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{"period": 40, "threshold": 70.0},
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]
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best_rep = None
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best_score = -999.0
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for cfg in configs:
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name = f"TRD09_p{cfg['period']}_t{int(cfg['threshold'])}"
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print(f"\n=== Running {name} ===")
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fn = make_target(period=cfg["period"], threshold=cfg["threshold"])
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rep = al.study_weights(name, fn, tfs=("1d",))
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print(al.fmt(rep))
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# Score = min of BTC/ETH hold-out sharpe
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cells = rep.get("cells", [])
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if cells:
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cell = cells[0] # 1d
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pa = cell.get("per_asset", {})
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btc_ho = pa.get("BTC", {}).get("holdout", {}).get("sharpe", -999)
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eth_ho = pa.get("ETH", {}).get("holdout", {}).get("sharpe", -999)
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score = min(btc_ho, eth_ho)
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if score > best_score:
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best_score = score
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best_rep = rep
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best_cfg = cfg
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print("\n\n=== BEST CONFIG ===")
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print(f"Config: {best_cfg}")
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print(al.fmt(best_rep))
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print("JSON:", al.as_json(best_rep))
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