""" 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, _apply_ftmo_mask, backtest_signal_ftmo, FTMO_INITIAL_CAPITAL, FTMO_MAX_DAILY_LOSS, FTMO_MAX_TOTAL_LOSS, monte_carlo_trade_pvalue, walk_forward_rolling, ) # --------------------------------------------------------------------------- # 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 # --------------------------------------------------------------------------- # Monte Carlo trade permutation tests # --------------------------------------------------------------------------- def test_mc_pvalue_in_result(close_2yr): signal = _random_signal(close_2yr.index) r = backtest_signal_ftmo(close_2yr, signal, oos_start=None, mc_n_permutations=50) assert "mc_pvalue" in r assert 0.0 <= r["mc_pvalue"] <= 1.0 assert r["mc_n_permutations"] == 50 def test_mc_pvalue_disabled_by_default(close_2yr): signal = _random_signal(close_2yr.index) r = backtest_signal_ftmo(close_2yr, signal, oos_start=None) assert "mc_pvalue" not in r def test_mc_zero_trades_returns_one(close_2yr): """Zero-signal → no trades → p-value must be 1.0 (no edge).""" trade_pnl = pd.Series([], dtype=float) assert monte_carlo_trade_pvalue(trade_pnl, n_permutations=10) == 1.0 # --------------------------------------------------------------------------- # Rolling walk-forward tests # --------------------------------------------------------------------------- def test_wf_rolling_keys_in_result(close_6yr): signal = _random_signal(close_6yr.index) r = backtest_signal_ftmo(close_6yr, signal, oos_start="2024-01-01", wf_rolling=True) # With only ~150 days of data, windows may be 0 — just check key presence assert "wf_n_windows" in r def test_wf_rolling_disabled_by_default(close_6yr): signal = _random_signal(close_6yr.index) r = backtest_signal_ftmo(close_6yr, signal, oos_start="2024-01-01") assert "wf_n_windows" not in r def test_wf_consistency_range(close_6yr): """wf_oos_consistency must be in [0, 1] when windows exist.""" signal = _random_signal(close_6yr.index) r = backtest_signal_ftmo(close_6yr, signal, oos_start="2024-01-01", wf_rolling=True) c = r.get("wf_oos_consistency") if c is not None: assert 0.0 <= c <= 1.0 # --------------------------------------------------------------------------- # Direct _apply_ftmo_mask unit tests # --------------------------------------------------------------------------- class TestApplyFtmoMask: """Direct unit tests for _apply_ftmo_mask — the core FTMO daily/total loss engine.""" @pytest.fixture def flat_close(self) -> pd.Series: n = 3000 idx = pd.date_range("2024-01-01", periods=n, freq="1min") return pd.Series(1.10, index=idx) def test_returns_compliance_dict(self, flat_close): signal = _random_signal(flat_close.index) masked, info = _apply_ftmo_mask(signal, flat_close, leverage=1.0, txn_cost_bps=2.14) assert "ftmo_daily_breaches" in info assert "ftmo_total_breached" in info assert "ftmo_total_breach_ts" in info assert "ftmo_compliant" in info def test_flat_market_zero_signal_fully_compliant(self, flat_close): """No trades → always compliant.""" signal = pd.Series(0.0, index=flat_close.index) masked, info = _apply_ftmo_mask(signal, flat_close, leverage=1.0, txn_cost_bps=2.14) assert info["ftmo_daily_breaches"] == 0 assert info["ftmo_total_breached"] is False assert info["ftmo_compliant"] is True # All signals should remain zero assert (masked == 0).all() def test_daily_loss_breach_zeroes_rest_of_day(self): """When daily loss exceeds 5%, rest of that day's signals are zeroed.""" n = 3000 idx = pd.date_range("2024-01-01", periods=n, freq="1min") # Price drops sharply in first few bars to trigger daily loss price = pd.Series(1.10, index=idx, dtype=float) price.iloc[3:20] = 0.00 # crash from 1.10 to 0.00 → massive loss signal = pd.Series(1.0, index=idx) # always long at 30x leverage masked, info = _apply_ftmo_mask(signal, price, leverage=30.0, txn_cost_bps=0) assert info["ftmo_daily_breaches"] > 0 # After breach, signals on same day must be zeroed breach_day = idx[0].date() same_day_late = (idx[-1] if idx[-1].date() == breach_day else idx[20]) if same_day_late.date() == breach_day: assert masked.loc[same_day_late] == 0 def test_total_loss_breach_zeroes_all_remaining(self): """When total loss exceeds 10%, ALL subsequent signals are zeroed.""" n = 5000 idx = pd.date_range("2024-01-01", periods=n, freq="1min") # Price crashes → max position → total loss limit breached price = pd.Series(1.10, index=idx, dtype=float) price.iloc[5:50] = 0.50 # >10% drop with 30x leverage signal = pd.Series(1.0, index=idx) masked, info = _apply_ftmo_mask(signal, price, leverage=30.0, txn_cost_bps=0) assert info["ftmo_total_breached"] is True assert info["ftmo_total_breach_ts"] is not None # After breach, ALL later signals must be zero assert (masked.iloc[100:] == 0).all() def test_total_breach_respected_across_days(self): """Total breach persists across day boundaries — no new trades after breach.""" n = 5000 idx = pd.date_range("2024-01-01", periods=n, freq="1min") price = pd.Series(1.10, index=idx, dtype=float) price.iloc[5:50] = 0.50 signal = pd.Series(1.0, index=idx) masked, info = _apply_ftmo_mask(signal, price, leverage=30.0, txn_cost_bps=0) # All signals after breach index must be zero breach_ts = pd.Timestamp(info["ftmo_total_breach_ts"]) assert (masked.loc[masked.index > breach_ts] == 0).all() def test_daily_loss_resets_on_new_day(self): """Daily loss limit resets at day boundary — new day starts fresh (unless total breached).""" n = 5000 idx = pd.date_range("2024-01-01", periods=n, freq="1min") price = pd.Series(1.10, index=idx, dtype=float) # Trigger daily breach on day 1 by dropping 1% price.iloc[5:20] = 1.09 # ~1% drop with 30x → ~30% loss signal = pd.Series(1.0, index=idx) masked, info = _apply_ftmo_mask(signal, price, leverage=30.0, txn_cost_bps=0) assert info["ftmo_daily_breaches"] >= 1 # Day 2 signals should be active again if not total-breached day2_mask = idx.date > idx[0].date() if day2_mask.any() and not info["ftmo_total_breached"]: day2 = idx[day2_mask][0] assert masked.loc[day2] != 0 def test_compliant_flag_false_after_daily_breach(self): """Even one daily breach makes ftmo_compliant=False.""" n = 3000 idx = pd.date_range("2024-01-01", periods=n, freq="1min") price = pd.Series(1.10, index=idx, dtype=float) price.iloc[3:20] = 0.00 signal = pd.Series(1.0, index=idx) masked, info = _apply_ftmo_mask(signal, price, leverage=30.0, txn_cost_bps=0) assert info["ftmo_compliant"] is False def test_compliant_flag_false_after_total_breach(self): """Total breach makes ftmo_compliant=False.""" n = 5000 idx = pd.date_range("2024-01-01", periods=n, freq="1min") price = pd.Series(1.10, index=idx, dtype=float) price.iloc[5:50] = 0.50 signal = pd.Series(1.0, index=idx) masked, info = _apply_ftmo_mask(signal, price, leverage=30.0, txn_cost_bps=0) assert info["ftmo_compliant"] is False def test_transaction_costs_reduce_equity(self): """Transaction costs should reduce equity — compliant scenario with fees.""" n = 1000 idx = pd.date_range("2024-01-01", periods=n, freq="1min") price = pd.Series(1.10, index=idx, dtype=float) # Alternating signal → lots of position changes → high costs signal = pd.Series([1.0 if i % 2 == 0 else -1.0 for i in range(n)], index=idx) masked, info = _apply_ftmo_mask(signal, price, leverage=1.0, txn_cost_bps=10.0) # With high costs and flat market, equity should drop assert "ftmo_daily_breaches" in info def test_output_mask_has_same_index(self): n = 2000 idx = pd.date_range("2024-01-01", periods=n, freq="1min") price = pd.Series(1.10, index=idx) signal = _random_signal(idx, seed=1) masked, info = _apply_ftmo_mask(signal, price, leverage=1.0, txn_cost_bps=2.14) assert len(masked) == len(signal) assert masked.index.equals(signal.index)