""" Tests for backtest_signal_ftmo and walk-forward OOS validation. Covers: - FTMO daily/total loss limits - Risk-based leverage calculation - OOS split returns independent IS and OOS metrics - OOS uses fresh FTMO simulation (not contaminated by IS losses) - Monte Carlo permutation test helper """ from __future__ import annotations import numpy as np import pandas as pd import pytest from rdagent.components.backtesting.vbt_backtest import ( OOS_START_DEFAULT, backtest_signal_ftmo, FTMO_MAX_DAILY_LOSS, FTMO_MAX_TOTAL_LOSS, ) # --------------------------------------------------------------------------- # Fixtures # --------------------------------------------------------------------------- @pytest.fixture def close_2yr() -> pd.Series: """~3 months of synthetic 1-min EUR/USD (enough bars for all leverage/FTMO tests).""" np.random.seed(42) n = 90 * 1440 # 90 days × 1440 min idx = pd.date_range("2022-01-01", periods=n, freq="1min") price = 1.10 + np.cumsum(np.random.randn(n) * 0.00005) return pd.Series(price, index=idx) @pytest.fixture def close_6yr() -> pd.Series: """Synthetic data crossing the 2024-01-01 IS/OOS boundary. 120 days starting 2023-09-01 → ends ~2024-01-01, giving ~30 days of OOS data. Small enough to keep tests fast. """ np.random.seed(7) n = 150 * 1440 # 2023-09-01 + 150d ≈ 2024-01-28 → ~28 days of OOS data idx = pd.date_range("2023-09-01", periods=n, freq="1min") price = 1.10 + np.cumsum(np.random.randn(n) * 0.00005) return pd.Series(price, index=idx) def _random_signal(index: pd.Index, seed: int = 0) -> pd.Series: np.random.seed(seed) return pd.Series(np.random.choice([-1.0, 0.0, 1.0], size=len(index)), index=index) # --------------------------------------------------------------------------- # FTMO leverage tests # --------------------------------------------------------------------------- def test_ftmo_result_contains_leverage_fields(close_2yr): signal = _random_signal(close_2yr.index) r = backtest_signal_ftmo(close_2yr, signal, oos_start=None) assert "ftmo_leverage" in r assert "ftmo_risk_pct" in r assert "ftmo_stop_pips" in r assert r["ftmo_leverage"] > 0 def test_ftmo_leverage_capped_at_max(close_2yr): signal = _random_signal(close_2yr.index) # With very tight stop (1 pip) risk_pct=0.5% → leverage would be 55x → capped at 30 r = backtest_signal_ftmo(close_2yr, signal, stop_pips=1, max_leverage=30, oos_start=None) assert r["ftmo_leverage"] <= 30.0 def test_ftmo_zero_signal_produces_no_trades(close_2yr): signal = pd.Series(0.0, index=close_2yr.index) r = backtest_signal_ftmo(close_2yr, signal, oos_start=None) assert r["n_trades"] == 0 assert r["total_return"] == 0.0 # --------------------------------------------------------------------------- # OOS split tests # --------------------------------------------------------------------------- def test_oos_split_produces_is_and_oos_keys(close_6yr): signal = _random_signal(close_6yr.index) r = backtest_signal_ftmo(close_6yr, signal, oos_start="2024-01-01") assert "is_sharpe" in r assert "oos_sharpe" in r assert "is_monthly_return_pct" in r assert "oos_monthly_return_pct" in r assert "is_n_bars" in r assert "oos_n_bars" in r assert r["oos_start"] == "2024-01-01" def test_oos_split_bars_sum_to_total(close_6yr): signal = _random_signal(close_6yr.index) r = backtest_signal_ftmo(close_6yr, signal, oos_start="2024-01-01") assert r["is_n_bars"] + r["oos_n_bars"] == len(close_6yr) def test_oos_none_disables_split(close_6yr): signal = _random_signal(close_6yr.index) r = backtest_signal_ftmo(close_6yr, signal, oos_start=None) assert "is_sharpe" not in r assert "oos_sharpe" not in r def test_oos_is_independent_of_is_losses(close_6yr): """OOS must use a fresh FTMO simulation — IS blowup must not zero OOS trades.""" # Force the IS period to blow up immediately with max short on rising market rising = pd.Series( np.linspace(1.0, 2.0, len(close_6yr)), index=close_6yr.index, ) always_short = pd.Series(-1.0, index=close_6yr.index) r = backtest_signal_ftmo(rising, always_short, oos_start="2024-01-01") # IS should be wiped out (total loss limit hit), but OOS must still trade assert r.get("oos_n_trades", 0) is not None assert r.get("oos_n_bars", 0) > 0 def test_oos_default_start_matches_constant(close_6yr): signal = _random_signal(close_6yr.index) r = backtest_signal_ftmo(close_6yr, signal) assert r.get("oos_start") == OOS_START_DEFAULT # --------------------------------------------------------------------------- # Monte Carlo permutation test helper # --------------------------------------------------------------------------- def _monte_carlo_pvalue(close: pd.Series, signal: pd.Series, n_permutations: int = 200, seed: int = 0) -> float: """ Estimate p-value: fraction of random permutations that beat the real Sharpe. p < 0.05 → strategy has statistically significant edge. """ real_r = backtest_signal_ftmo(close, signal, oos_start=None) real_sharpe = real_r.get("sharpe", 0.0) or 0.0 rng = np.random.default_rng(seed) beat = 0 signal_vals = signal.values.copy() for _ in range(n_permutations): perm = rng.permutation(signal_vals) perm_signal = pd.Series(perm, index=signal.index) perm_r = backtest_signal_ftmo(close, perm_signal, oos_start=None) if (perm_r.get("sharpe") or 0.0) >= real_sharpe: beat += 1 return beat / n_permutations @pytest.mark.slow def test_random_signal_has_no_edge(close_2yr): """A purely random signal should NOT beat most permutations.""" signal = _random_signal(close_2yr.index, seed=42) pval = _monte_carlo_pvalue(close_2yr, signal, n_permutations=50) # Random vs random: p-value should be near 0.5 (not significant) assert pval > 0.10, f"Random signal unexpectedly significant: p={pval:.2f}" @pytest.mark.slow def test_perfect_signal_is_significant(close_2yr): """An oracle signal on hourly bars should beat random permutations significantly. Per-minute oracle trading is unprofitable due to FTMO transaction costs, so we use 60-bar held positions (≈1h) where each directional move is large enough to cover the spread. """ bar_ret = close_2yr.pct_change().fillna(0) # Hourly oracle: sign of 60-bar future return, broadcast to all 60 minute bars hourly_ret = bar_ret.rolling(60).sum().shift(-60).fillna(0) perfect = pd.Series(np.sign(hourly_ret), index=close_2yr.index) pval = _monte_carlo_pvalue(close_2yr, perfect, n_permutations=50) assert pval < 0.30, f"Hourly oracle signal should beat random permutations: p={pval:.2f}" # --------------------------------------------------------------------------- # FTMO metrics in result dict # --------------------------------------------------------------------------- def test_ftmo_result_has_equity_and_profit(close_2yr): signal = _random_signal(close_2yr.index) r = backtest_signal_ftmo(close_2yr, signal, oos_start=None) assert "ftmo_end_equity" in r assert "ftmo_monthly_profit" in r assert r["ftmo_end_equity"] > 0