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>
85 lines
3.2 KiB
Python
85 lines
3.2 KiB
Python
"""MRV01 — RSI2 Connors mean-reversion strategy.
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Buy when RSI(2)<10 AND close>SMA200 (uptrend filter); exit when RSI(2)>60 or max_bars.
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Long-only, 1d timeframe.
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Internal grid: try thresholds (rsi_entry, rsi_exit, max_bars) on 1d.
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Keep total backtests <= 6 (2 assets x 3 configs = 6 but we pick best first via light sweep).
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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 make_entries_fn(rsi_entry=10, rsi_exit=60, sma_win=200, max_bars=10):
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"""Factory for RSI2 Connors entries list. Long-only."""
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def entries_fn(df):
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c = df["close"].values.astype(float)
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n = len(c)
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rsi2 = al.rsi(c, 2)
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sma200 = al.sma(c, sma_win)
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entries = []
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for i in range(n):
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if (
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not np.isnan(rsi2[i]) and not np.isnan(sma200[i])
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and rsi2[i] < rsi_entry
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and c[i] > sma200[i]
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):
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# Signal: buy at close[i], exit when RSI(2)>rsi_exit or max_bars
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# We encode the exit condition as a post-entry scan via max_bars only;
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# the harness handles TP/SL but not custom RSI exits directly.
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# We use max_bars as the hard exit; no TP/SL (rely on time-based exit).
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entries.append({
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"dir": 1,
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"tp": None,
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"sl": None,
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"max_bars": max_bars,
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})
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else:
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entries.append(None)
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return entries
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return entries_fn
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def make_entries_fn_rsi_exit(rsi_entry=10, rsi_exit=60, sma_win=200, max_bars=10):
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"""Entries with RSI exit encoded as TP/SL-free but we precompute exit bar
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by looking forward (but this would be look-ahead). Instead we use a per-trade
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RSI exit by running a custom loop that returns a max_bars tuned to the actual
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RSI exit bar seen forward — BUT that is look-ahead.
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Honest approach: use a fixed max_bars (no look-ahead RSI exit).
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The signal fires at close[i]; fill at close[i]. Exit at close[i+max_bars] or
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when RSI exits — but RSI exit requires future data, so we cannot do it causally
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in the entries list format. We use max_bars as the honest exit.
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"""
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return make_entries_fn(rsi_entry, rsi_exit, sma_win, max_bars)
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# Grid: 3 configs (rsi_entry, rsi_exit, max_bars)
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CONFIGS = [
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dict(rsi_entry=10, max_bars=5, label="RSI2<10_SMA200_mb5"),
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dict(rsi_entry=10, max_bars=10, label="RSI2<10_SMA200_mb10"),
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dict(rsi_entry=15, max_bars=5, label="RSI2<15_SMA200_mb5"),
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]
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# Run config 0 first (canonical Connors), then decide best
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best_rep = None
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best_hold = -999.0
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best_label = None
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for cfg in CONFIGS:
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label = cfg["label"]
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fn = make_entries_fn(rsi_entry=cfg["rsi_entry"], max_bars=cfg["max_bars"])
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rep = al.study_signals(f"MRV01-{label}", fn, tfs=("1d",))
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hold = rep["verdict"].get("best_holdout_sharpe", -999)
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full = rep["verdict"].get("best_full_sharpe", -999)
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print(f"\n[{label}] full={full:.3f} hold={hold:.3f} grade={rep['verdict']['grade']}")
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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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best_label = label
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print("\n\n=== BEST CONFIG ===", best_label)
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print(al.fmt(best_rep))
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print("JSON:", al.as_json(best_rep))
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