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>
91 lines
3.2 KiB
Python
91 lines
3.2 KiB
Python
"""SEA01 — Hour-of-day expectancy (seasonal/intraday pattern).
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IDEA: On 1h bars, compute per-UTC-hour mean return using an EXPANDING in-sample
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window (strictly causal). Go long during hours whose expanding-window mean is
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positive, flat otherwise. Position is vol-targeted.
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Causal guarantee:
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- At bar i (UTC hour h), we compute the mean return for hour h using all
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*prior* bars with that same hour: mean_r[h] = mean(r[j] for j < i where hour[j] == h).
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- We assign target[i] based on mean_r[h at bar i], which uses data up to i-1.
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- The lib then holds target[i] during bar i+1 (shift done by lib).
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Grid: we test different minimum-samples thresholds (how many past observations of
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that hour are required before we take a position): [30, 90].
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This keeps total backtests at 2 TFs x 2 params x 2 assets = 8, but study_weights
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handles BTC+ETH internally — so 2 TFs x 2 params = 4 calls 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 sea01_target(df: pd.DataFrame, min_samples: int = 30) -> np.ndarray:
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"""Compute vol-targeted position based on expanding per-hour mean return.
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For each bar i:
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- UTC hour = df['datetime'][i].hour
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- expanding mean of past returns for that same UTC hour (uses only j < i)
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- if expanding mean > 0 and count >= min_samples: direction = +1
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- else: flat = 0
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Then vol-target the direction signal.
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"""
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dt = pd.to_datetime(df["datetime"])
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c = df["close"].values.astype(float)
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r = al.simple_returns(c) # r[i] = c[i]/c[i-1] - 1
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n = len(df)
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# For each bar, compute expanding mean return per UTC hour
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hours = dt.dt.hour.values # 0..23
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# We'll compute causally using cumulative sums per hour
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# hour_cumsum[h], hour_count[h] track sum/count up to bar i-1 for hour h
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hour_cumsum = np.zeros(24, dtype=float)
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hour_count = np.zeros(24, dtype=int)
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direction = np.zeros(n, dtype=float)
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for i in range(n):
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h = hours[i]
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cnt = hour_count[h]
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if cnt >= min_samples:
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mean_r = hour_cumsum[h] / cnt
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direction[i] = 1.0 if mean_r > 0.0 else 0.0
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# else flat (direction[i] = 0)
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# Update with bar i's return (causal: used for bar i+1 onwards)
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hour_cumsum[h] += r[i]
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hour_count[h] += 1
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# Vol-target the binary direction signal
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tgt = al.vol_target(direction, df, target_vol=0.20, vol_win_days=30, leverage_cap=2.0)
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return tgt
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if __name__ == "__main__":
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best_rep = None
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best_sharpe = -999.0
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for min_samples in [30, 90]:
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name = f"SEA01-ms{min_samples}"
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rep = al.study_weights(
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name,
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lambda df, ms=min_samples: sea01_target(df, min_samples=ms),
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tfs=("1h",),
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)
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print(al.fmt(rep))
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print("JSON:", al.as_json(rep))
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# Track best by min_asset_full_sharpe
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s = rep["verdict"].get("best_full_sharpe", rep.get("min_asset_full_sharpe", -999))
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if s > best_sharpe:
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best_sharpe = s
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best_rep = rep
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print("\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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