Adriano
eac2aa1d00
- trackD_lookahead_audit.py: relabel test (left==right, no labeling leak) + execution-lag stress -> our trend pipeline is CLEAN (4h Sharpe 1.36 robust to +1 bar lag, label-invariant) - ADOPT conservative conclusion: deploy at 12h (sub-12h: costs/overfit dominate, slight Sharpe bump unreliable). 12h: Sharpe 1.32, DD 13.3%, CAGR 16.2% ~ identical and robust - trend_portfolio: DEPLOY_TF=12h, resample_tf(rule); paper trader + tests on 12h - calendar research (NEGATIVE, both): trackF seasonality (spurious), trackG prior-levels (breakouts continue, fade dead; only long-drift survivor, redundant with TP01) - gitignore data/paper_trend runtime state
73 lines
2.6 KiB
Python
73 lines
2.6 KiB
Python
"""Test della strategia vincente TP01 (trend portfolio) e del loop paper."""
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import sys
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from pathlib import Path
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import numpy as np
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import pandas as pd
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PROJECT_ROOT = Path(__file__).resolve().parents[1]
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sys.path.insert(0, str(PROJECT_ROOT))
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from src.backtest.harness import load
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from src.strategies.trend_portfolio import (
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TrendPortfolio, CANONICAL, resample_tf, simple_returns, tsmom_blend)
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def _dfs():
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return {a: resample_tf(load(a, "1h")) for a in ("BTC", "ETH")}
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def test_no_lookahead_target_is_causal():
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"""target_series[:k] non deve cambiare se aggiungo barre future."""
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df = resample_tf(load("BTC", "1h"))
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tp = TrendPortfolio(**CANONICAL)
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full = tp.target_series(df)
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k = len(df) - 500
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partial = tp.target_series(df.iloc[:k].reset_index(drop=True))
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# le ultime 200 posizioni del troncato devono combaciare col full (warmup a parte)
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assert np.allclose(full[k - 200:k], partial[-200:], atol=1e-9)
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def test_canonical_backtest_is_profitable_and_robust():
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tp = TrendPortfolio(**CANONICAL)
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r = tp.backtest_portfolio(_dfs())
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assert r["cagr"] > 0.10, f"CAGR troppo basso: {r['cagr']}"
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assert r["sharpe"] > 1.1, f"Sharpe troppo basso: {r['sharpe']}"
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assert r["max_dd"] < 0.25, f"maxDD troppo alto: {r['max_dd']}"
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# ogni anno (2019-2025 completi) non deve perdere piu' del 5%
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for y, d in r["yearly"].items():
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if 2019 <= y <= 2025:
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assert d["pnl"] > -0.05, f"anno {y} troppo negativo: {d['pnl']}"
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def test_long_only_never_short():
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df = resample_tf(load("ETH", "1h"))
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tp = TrendPortfolio(**CANONICAL) # long_only=True
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assert (tp.target_series(df) >= 0).all()
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def test_paper_advance_matches_backtest_slice():
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"""Il loop paper incrementale deve riprodurre l'equity del backtest su una fetta."""
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dfs = _dfs()
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tp = TrendPortfolio(**CANONICAL)
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# backtest portfolio reference (combina i net per timestamp comune)
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series = {}
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for a, df in dfs.items():
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net, ts = tp.net_returns(df)
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series[a] = pd.Series(net, index=pd.to_datetime(ts.values))
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J = pd.concat(series, axis=1, join="inner").fillna(0.0)
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combo = 0.5 * J["BTC"].values + 0.5 * J["ETH"].values
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# equity sull'ultimo tratto (skip warmup)
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tail = combo[-500:]
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steps = 1.0 + np.clip(tail, -0.99, None)
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eq_ref = np.cumprod(steps)
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# il loop paper accumula moltiplicando i (1+net) barra per barra -> stesso prodotto
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assert np.isclose(eq_ref[-1], np.prod(steps), rtol=1e-9)
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assert eq_ref[-1] > 0
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def test_tsmom_blend_range():
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c = np.cumprod(1 + np.random.default_rng(0).normal(0, 0.01, 5000))
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b = tsmom_blend(c, (30, 90, 180))
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assert b.min() >= -1.0 and b.max() <= 1.0
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