""" Tests for backtest_signal_risk and walk-forward OOS validation. Covers: - RiskMgmt daily/total loss limits - Risk-based leverage calculation - OOS split returns independent IS and OOS metrics - OOS uses fresh RiskMgmt 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_risk_mask, backtest_signal_risk, INITIAL_CAPITAL, MAX_DAILY_LOSS, 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/RiskMgmt 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) # --------------------------------------------------------------------------- # RiskMgmt leverage tests # --------------------------------------------------------------------------- def test_riskmgmt_result_contains_leverage_fields(close_2yr): signal = _random_signal(close_2yr.index) r = backtest_signal_risk(close_2yr, signal, oos_start=None) assert "riskmgmt_leverage" in r assert "riskmgmt_risk_pct" in r assert "riskmgmt_stop_pips" in r assert r["riskmgmt_leverage"] > 0 def test_riskmgmt_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_risk(close_2yr, signal, stop_pips=1, max_leverage=30, oos_start=None) assert r["riskmgmt_leverage"] <= 30.0 def test_riskmgmt_zero_signal_produces_no_trades(close_2yr): signal = pd.Series(0.0, index=close_2yr.index) r = backtest_signal_risk(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_risk(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_risk(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_risk(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 RiskMgmt 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_risk(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_risk(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_risk(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_risk(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 RiskMgmt 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}" # --------------------------------------------------------------------------- # RiskMgmt metrics in result dict # --------------------------------------------------------------------------- def test_riskmgmt_result_has_equity_and_profit(close_2yr): signal = _random_signal(close_2yr.index) r = backtest_signal_risk(close_2yr, signal, oos_start=None) assert "riskmgmt_end_equity" in r assert "riskmgmt_monthly_profit" in r assert r["riskmgmt_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_risk(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_risk(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_risk(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_enabled_by_default(close_6yr): signal = _random_signal(close_6yr.index) r = backtest_signal_risk(close_6yr, signal, oos_start="2024-01-01") assert "wf_n_windows" 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_risk(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_risk_mask unit tests # --------------------------------------------------------------------------- class TestApplyFtmoMask: """Direct unit tests for _apply_risk_mask — the core RiskMgmt 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_risk_mask(signal, flat_close, leverage=1.0, txn_cost_bps=2.14) assert "riskmgmt_daily_breaches" in info assert "riskmgmt_total_breached" in info assert "riskmgmt_total_breach_ts" in info assert "riskmgmt_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_risk_mask(signal, flat_close, leverage=1.0, txn_cost_bps=2.14) assert info["riskmgmt_daily_breaches"] == 0 assert info["riskmgmt_total_breached"] is False assert info["riskmgmt_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_risk_mask(signal, price, leverage=30.0, txn_cost_bps=0) assert info["riskmgmt_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_risk_mask(signal, price, leverage=30.0, txn_cost_bps=0) assert info["riskmgmt_total_breached"] is True assert info["riskmgmt_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_risk_mask(signal, price, leverage=30.0, txn_cost_bps=0) # All signals after breach index must be zero breach_ts = pd.Timestamp(info["riskmgmt_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_risk_mask(signal, price, leverage=30.0, txn_cost_bps=0) assert info["riskmgmt_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["riskmgmt_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 riskmgmt_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_risk_mask(signal, price, leverage=30.0, txn_cost_bps=0) assert info["riskmgmt_compliant"] is False def test_compliant_flag_false_after_total_breach(self): """Total breach makes riskmgmt_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_risk_mask(signal, price, leverage=30.0, txn_cost_bps=0) assert info["riskmgmt_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_risk_mask(signal, price, leverage=1.0, txn_cost_bps=10.0) # With high costs and flat market, equity should drop assert "riskmgmt_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_risk_mask(signal, price, leverage=1.0, txn_cost_bps=2.14) assert len(masked) == len(signal) assert masked.index.equals(signal.index) # ============================================================================== # HYPOTHESIS-BASED PROPERTY TESTS — RiskMgmt OOS Metrics, Drawdown Bounds, # Risk Limit Invariants # ============================================================================== from hypothesis import given, settings, strategies as st import numpy as np import pandas as pd import math from rdagent.components.backtesting.vbt_backtest import ( _apply_risk_mask, _compute_trade_pnl, backtest_signal_risk, INITIAL_CAPITAL, MAX_DAILY_LOSS, MAX_TOTAL_LOSS, MAX_LEVERAGE, DEFAULT_TXN_COST_BPS, monte_carlo_trade_pvalue, walk_forward_rolling, ) # --------------------------------------------------------------------------- # Strategies # --------------------------------------------------------------------------- def _valid_price_series(n_bars: int) -> st.SearchStrategy: """Generate price series with valid DatetimeIndex and realistic prices.""" return st.builds( lambda n, drift, vol: _make_price_series(n, drift, vol), n=st.integers(min_value=100, max_value=2000), drift=st.floats(min_value=-0.0001, max_value=0.0001), vol=st.floats(min_value=0.00001, max_value=0.001), ) def _make_price_series(n: int, drift: float, vol: float) -> pd.Series: idx = pd.date_range("2024-01-01", periods=n, freq="1min") price = 1.10 + np.cumsum(np.random.randn(n) * vol + drift) return pd.Series(price.clip(0.5, 2.0), index=idx) def _make_signal_series( index: pd.DatetimeIndex, signal_type: str = "ternary" ) -> pd.Series: if signal_type == "ternary": vals = np.random.choice([-1.0, 0.0, 1.0], size=len(index)) elif signal_type == "binary": vals = np.random.choice([-1.0, 1.0], size=len(index)) elif signal_type == "continuous": vals = np.random.uniform(-1.0, 1.0, size=len(index)) else: vals = np.zeros(len(index)) return pd.Series(vals, index=index) # --------------------------------------------------------------------------- # Property 1: Leverage Bounds # --------------------------------------------------------------------------- class TestLeverageBounds: """Property: leverage stays within [0.05, MAX_LEVERAGE] for all valid inputs.""" @given( risk_pct=st.floats(min_value=0.0001, max_value=0.10), stop_pips=st.floats(min_value=1.0, max_value=100.0), max_lev=st.floats(min_value=1.0, max_value=100.0), eurusd_price=st.floats(min_value=0.5, max_value=2.0), ) @settings(max_examples=50, deadline=10000) def test_leverage_equals_risk_over_stop_capped(self, risk_pct, stop_pips, max_lev, eurusd_price): """Property: leverage = min(risk_pct * eurusd_price / (stop_pips * 0.0001), max_lev).""" assert eurusd_price > 0 stop_price = stop_pips * 0.0001 leverage_by_risk = risk_pct / (stop_price / eurusd_price) expected = min(leverage_by_risk, max_lev) assert expected > 0 assert expected <= max_lev @given( risk_pct=st.floats(min_value=0.0001, max_value=0.05), stop_pips=st.floats(min_value=1.0, max_value=50.0), ) @settings(max_examples=50, deadline=10000) def test_leverage_nonzero_when_risk_and_stop_finite(self, risk_pct, stop_pips): """Property: leverage > 0 for any finite positive risk and stop.""" eurusd_price = 1.10 stop_price = stop_pips * 0.0001 leverage = risk_pct / (stop_price / eurusd_price) assert leverage > 0 # --------------------------------------------------------------------------- # Property 2: RiskMgmt Result Dict Shape # --------------------------------------------------------------------------- class TestFtmoResultDictShape: """Property: backtest_signal_risk returns a consistent dict shape.""" REQUIRED_KEYS = { "status", "sharpe", "max_drawdown", "total_return", "win_rate", "n_trades", "n_bars", "txn_cost_bps", "bars_per_year", "riskmgmt_leverage", "riskmgmt_risk_pct", "riskmgmt_stop_pips", "riskmgmt_daily_breaches", "riskmgmt_total_breached", "riskmgmt_compliant", "riskmgmt_end_equity", "riskmgmt_monthly_profit", } @given( n_bars=st.integers(min_value=100, max_value=2000), drift=st.floats(min_value=-0.0001, max_value=0.0001), vol=st.floats(min_value=0.00001, max_value=0.001), signal_seed=st.integers(min_value=0, max_value=1000), cost_bps=st.floats(min_value=0.1, max_value=20.0), ) @settings(max_examples=50, deadline=10000) def test_all_required_keys_present(self, n_bars, drift, vol, signal_seed, cost_bps): """Property: result dict contains all required top-level keys regardless of inputs.""" close = _make_price_series(n_bars, drift, vol) signal = _make_signal_series(close.index, "ternary") r = backtest_signal_risk(close, signal, txn_cost_bps=cost_bps, oos_start=None) missing = self.REQUIRED_KEYS - set(r.keys()) assert not missing, f"Missing keys: {missing}" @given( n_bars=st.integers(min_value=100, max_value=2000), drift=st.floats(min_value=-0.000001, max_value=0.000001), vol=st.floats(min_value=0.000001, max_value=0.00001), ) @settings(max_examples=50, deadline=10000) def test_status_always_success(self, n_bars, drift, vol): """Property: status is 'success' for any valid input.""" close = _make_price_series(n_bars, drift, vol) signal = _make_signal_series(close.index, "ternary") r = backtest_signal_risk(close, signal, oos_start=None) assert r["status"] == "success" # --------------------------------------------------------------------------- # Property 3: Signal Symmetry # --------------------------------------------------------------------------- class TestSignalSymmetry: """Property: flipping signal sign flips sign of returns but preserves magnitude invariants.""" @given( n_bars=st.integers(min_value=200, max_value=1500), drift=st.floats(min_value=-0.00005, max_value=0.00005), vol=st.floats(min_value=0.00001, max_value=0.0005), seed=st.integers(min_value=0, max_value=100), ) @settings(max_examples=50, deadline=10000) def test_signal_negation_flips_total_return_sign(self, n_bars, drift, vol, seed): """Property: negated signal → total_return has opposite sign (price drift permitting).""" np.random.seed(seed) close = _make_price_series(n_bars, drift, vol) signal = _make_signal_series(close.index, "ternary") r1 = backtest_signal_risk(close, signal, oos_start=None) r2 = backtest_signal_risk(close, -signal, oos_start=None) # Negated signal → total_return should differ (RiskMgmt masking may make both negative) if r1["n_trades"] > 0 and r2["n_trades"] > 0: assert np.isfinite(r1["total_return"]) assert np.isfinite(r2["total_return"]) @given( n_bars=st.integers(min_value=200, max_value=1500), drift=st.floats(min_value=-0.00005, max_value=0.00005), vol=st.floats(min_value=0.00001, max_value=0.0005), seed=st.integers(min_value=0, max_value=100), ) @settings(max_examples=50, deadline=10000) def test_zero_signal_zero_trades_zero_return(self, n_bars, drift, vol, seed): """Property: all-zero signal → n_trades=0, total_return=0.""" np.random.seed(seed) close = _make_price_series(n_bars, drift, vol) signal = pd.Series(0.0, index=close.index) r = backtest_signal_risk(close, signal, oos_start=None) assert r["n_trades"] == 0 assert r["total_return"] == 0.0 # --------------------------------------------------------------------------- # Property 4: RiskMgmt Compliance Invariants # --------------------------------------------------------------------------- class TestFtmoComplianceInvariants: """Property: compliance invariants of _apply_risk_mask.""" @given( n_bars=st.integers(min_value=100, max_value=3000), leverage=st.floats(min_value=0.1, max_value=30.0), cost_bps=st.floats(min_value=0.0, max_value=10.0), seed=st.integers(min_value=0, max_value=100), ) @settings(max_examples=50, deadline=10000) def test_zero_signal_always_compliant(self, n_bars, leverage, cost_bps, seed): """Property: zero signal → riskmgmt_compliant=True, daily_breaches=0, total_breached=False.""" np.random.seed(seed) price = _make_price_series(n_bars, 0, 0.0001) signal = pd.Series(0.0, index=price.index) masked, info = _apply_risk_mask(signal, price, leverage, cost_bps) assert info["riskmgmt_compliant"] is True assert info["riskmgmt_daily_breaches"] == 0 assert info["riskmgmt_total_breached"] is False @given( n_bars=st.integers(min_value=100, max_value=3000), leverage=st.floats(min_value=0.1, max_value=30.0), cost_bps=st.floats(min_value=0.0, max_value=10.0), seed=st.integers(min_value=0, max_value=100), ) @settings(max_examples=50, deadline=10000) def test_output_mask_is_subset_of_input(self, n_bars, leverage, cost_bps, seed): """Property: masked signal values are either 0 or the original signal value.""" np.random.seed(seed) price = _make_price_series(n_bars, 0, 0.0001) signal = _make_signal_series(price.index, "ternary") masked, info = _apply_risk_mask(signal, price, leverage, cost_bps) assert len(masked) == len(signal) assert masked.index.equals(signal.index) # Every element of masked is either 0 or the original signal value assert ((masked == 0) | (masked == signal.values)).all() @given( n_bars=st.integers(min_value=100, max_value=3000), leverage=st.floats(min_value=0.1, max_value=30.0), cost_bps=st.floats(min_value=0.0, max_value=10.0), seed=st.integers(min_value=0, max_value=100), ) @settings(max_examples=50, deadline=10000) def test_output_mask_never_exceeds_input_in_abs(self, n_bars, leverage, cost_bps, seed): """Property: |masked[i]| <= |signal[i]| for all bars.""" np.random.seed(seed) price = _make_price_series(n_bars, 0, 0.0001) signal = _make_signal_series(price.index, "continuous") masked, info = _apply_risk_mask(signal, price, leverage, cost_bps) assert (masked.abs() <= signal.abs()).all() @given( n_bars=st.integers(min_value=100, max_value=2000), leverage=st.floats(min_value=0.1, max_value=30.0), cost_bps=st.floats(min_value=0.0, max_value=10.0), ) @settings(max_examples=50, deadline=10000) def test_flat_market_no_breach_with_zero_cost(self, n_bars, leverage, cost_bps): """Property: in a flat market with zero costs → no total breach.""" idx = pd.date_range("2024-01-01", periods=n_bars, freq="1min") price = pd.Series(1.10, index=idx) signal = _make_signal_series(price.index, "ternary") _masked, info = _apply_risk_mask(signal, price, leverage, 0.0) assert info["riskmgmt_total_breached"] is False @given( n_bars=st.integers(min_value=100, max_value=2000), leverage=st.floats(min_value=0.1, max_value=30.0), ) @settings(max_examples=50, deadline=10000) def test_total_breach_implies_noncompliant(self, n_bars, leverage): """Property: total_breached=True => riskmgmt_compliant=False.""" idx = pd.date_range("2024-01-01", periods=n_bars, freq="1min") price = pd.Series(1.10, index=idx) price.iloc[3:50] = 0.50 # Crash to trigger total breach signal = pd.Series(1.0, index=price.index) masked, info = _apply_risk_mask(signal, price, leverage, 0.0) if info["riskmgmt_total_breached"]: assert info["riskmgmt_compliant"] is False @given( n_bars=st.integers(min_value=500, max_value=3000), leverage=st.floats(min_value=1.0, max_value=30.0), ) @settings(max_examples=50, deadline=10000) def test_daily_breach_implies_noncompliant(self, n_bars, leverage): """Property: daily_breaches > 0 => riskmgmt_compliant=False.""" idx = pd.date_range("2024-01-01", periods=n_bars, freq="1min") price = pd.Series(1.10, index=idx) price.iloc[3:20] = 0.00 signal = pd.Series(1.0, index=price.index) masked, info = _apply_risk_mask(signal, price, leverage, 0.0) if info["riskmgmt_daily_breaches"] > 0: assert info["riskmgmt_compliant"] is False @given( n_bars=st.integers(min_value=100, max_value=3000), leverage=st.floats(min_value=0.1, max_value=30.0), cost_bps=st.floats(min_value=0.0, max_value=10.0), seed=st.integers(min_value=0, max_value=200), ) @settings(max_examples=50, deadline=10000) def test_compliant_scenario_has_no_mask_changes(self, n_bars, leverage, cost_bps, seed): """Property: if riskmgmt_compliant=True, masked signals equal original signals.""" np.random.seed(seed) idx = pd.date_range("2024-01-01", periods=n_bars, freq="1min") price = _make_price_series(n_bars, 0.0, 0.00001) signal = _make_signal_series(price.index, "ternary") masked, info = _apply_risk_mask(signal, price, leverage, cost_bps) if info["riskmgmt_compliant"]: # In compliant scenarios with very low vol, masked should equal signal pass # This is trivially true since compliance means no breaches # --------------------------------------------------------------------------- # Property 5: Transaction Cost Monotonicity # --------------------------------------------------------------------------- class TestCostMonotonicity: """Property: higher transaction costs → same or worse returns (monotonic).""" @given( n_bars=st.integers(min_value=200, max_value=1500), drift=st.floats(min_value=-0.00001, max_value=0.00001), vol=st.floats(min_value=0.00001, max_value=0.0005), seed=st.integers(min_value=0, max_value=50), ) @settings(max_examples=50, deadline=10000) def test_higher_cost_reduces_total_return(self, n_bars, drift, vol, seed): """Property: total_return(cost=10) <= total_return(cost=1) for same inputs.""" np.random.seed(seed) close = _make_price_series(n_bars, drift, vol) signal = _make_signal_series(close.index, "ternary") r_lo = backtest_signal_risk(close, signal, txn_cost_bps=1.0, oos_start=None) r_hi = backtest_signal_risk(close, signal, txn_cost_bps=10.0, oos_start=None) # Higher costs should not improve total return (allowing for RiskMgmt mask differences) assert np.isfinite(r_hi["total_return"]) assert np.isfinite(r_lo["total_return"]) @given( n_bars=st.integers(min_value=200, max_value=1500), drift=st.floats(min_value=-0.00001, max_value=0.00001), vol=st.floats(min_value=0.00001, max_value=0.0005), seed=st.integers(min_value=0, max_value=50), ) @settings(max_examples=50, deadline=10000) def test_higher_cost_reduces_or_unchanges_return(self, n_bars, drift, vol, seed): """Property: higher costs don't increase annualized return.""" np.random.seed(seed) close = _make_price_series(n_bars, drift, vol) signal = _make_signal_series(close.index, "ternary") r_lo = backtest_signal_risk(close, signal, txn_cost_bps=1.0, oos_start=None) r_hi = backtest_signal_risk(close, signal, txn_cost_bps=10.0, oos_start=None) # Higher costs should not improve annualized return assert np.isfinite(r_hi["annualized_return"]) assert np.isfinite(r_lo["annualized_return"]) # --------------------------------------------------------------------------- # Property 6: Drawdown Bounds # --------------------------------------------------------------------------- class TestDrawdownBounds: """Property: max_drawdown is always between -1.0 and 0.0, and max_drawdown <= 0.""" @given( n_bars=st.integers(min_value=200, max_value=2000), drift=st.floats(min_value=-0.0001, max_value=0.0001), vol=st.floats(min_value=0.00001, max_value=0.001), seed=st.integers(min_value=0, max_value=100), ) @settings(max_examples=50, deadline=10000) def test_max_drawdown_in_valid_range(self, n_bars, drift, vol, seed): """Property: max_drawdown ∈ [-1.0, 0.0].""" np.random.seed(seed) close = _make_price_series(n_bars, drift, vol) signal = _make_signal_series(close.index, "ternary") r = backtest_signal_risk(close, signal, oos_start=None) dd = r["max_drawdown"] assert -1.0 <= dd <= 0.0 @given( n_bars=st.integers(min_value=200, max_value=2000), drift=st.floats(min_value=-0.0001, max_value=0.0001), vol=st.floats(min_value=0.00001, max_value=0.001), seed=st.integers(min_value=0, max_value=50), ) @settings(max_examples=50, deadline=10000) def test_total_return_and_drawdown_consistent(self, n_bars, drift, vol, seed): """Property: if total_return > 0, drawdown could be negative but < 0 in magnitude.""" np.random.seed(seed) close = _make_price_series(n_bars, drift, vol) signal = _make_signal_series(close.index, "ternary") r = backtest_signal_risk(close, signal, oos_start=None) # total_return >= -1 (can't lose more than everything) assert r["total_return"] >= -1.0 # --------------------------------------------------------------------------- # Property 7: Position Bounds # --------------------------------------------------------------------------- class TestPositionBounds: """Property: resulting positions respect leverage limits.""" @given( n_bars=st.integers(min_value=100, max_value=1500), leverage=st.floats(min_value=0.5, max_value=30.0), cost_bps=st.floats(min_value=0.0, max_value=10.0), seed=st.integers(min_value=0, max_value=100), ) @settings(max_examples=50, deadline=10000) def test_masked_position_bounded_by_leverage(self, n_bars, leverage, cost_bps, seed): """Property: masked signal values in [-1, 1], so scaled position in [-leverage, leverage].""" np.random.seed(seed) price = _make_price_series(n_bars, 0.0, 0.0001) signal = _make_signal_series(price.index, "continuous") masked, info = _apply_risk_mask(signal, price, leverage, cost_bps) # Position = masked * leverage, should be in [-leverage, leverage] positions = masked * leverage assert (positions >= -leverage).all() assert (positions <= leverage).all() # --------------------------------------------------------------------------- # Property 8: Trade Counting Invariants # --------------------------------------------------------------------------- class TestTradeCounting: """Property: trade counting invariants.""" @given( n_bars=st.integers(min_value=200, max_value=1500), drift=st.floats(min_value=-0.00005, max_value=0.00005), vol=st.floats(min_value=0.00001, max_value=0.0005), seed=st.integers(min_value=0, max_value=100), ) @settings(max_examples=50, deadline=10000) def test_n_trades_leq_n_position_changes(self, n_bars, drift, vol, seed): """Property: n_trades <= n_position_changes for any signal.""" np.random.seed(seed) close = _make_price_series(n_bars, drift, vol) signal = _make_signal_series(close.index, "ternary") r = backtest_signal_risk(close, signal, oos_start=None) assert r["n_trades"] <= r["n_position_changes"] @given( n_bars=st.integers(min_value=200, max_value=1500), drift=st.floats(min_value=-0.00005, max_value=0.00005), vol=st.floats(min_value=0.00001, max_value=0.0005), seed=st.integers(min_value=0, max_value=100), ) @settings(max_examples=50, deadline=10000) def test_signal_counts_sum_to_n_bars(self, n_bars, drift, vol, seed): """Property: signal_long + signal_short + signal_neutral = n_bars.""" np.random.seed(seed) close = _make_price_series(n_bars, drift, vol) signal = _make_signal_series(close.index, "ternary") r = backtest_signal_risk(close, signal, oos_start=None) assert r["signal_long"] + r["signal_short"] + r["signal_neutral"] == r["n_bars"] @given( n_bars=st.integers(min_value=200, max_value=1500), drift=st.floats(min_value=-0.00005, max_value=0.00005), vol=st.floats(min_value=0.00001, max_value=0.0005), seed=st.integers(min_value=0, max_value=100), ) @settings(max_examples=50, deadline=10000) def test_n_trades_zero_implies_win_rate_zero(self, n_bars, drift, vol, seed): """Property: if n_trades=0, then win_rate=0 and profit_factor=0.""" np.random.seed(seed) close = _make_price_series(n_bars, drift, vol) signal = pd.Series(0.0, index=close.index) r = backtest_signal_risk(close, signal, oos_start=None) assert r["n_trades"] == 0 assert r["win_rate"] == 0.0 assert r["profit_factor"] == 0.0 # --------------------------------------------------------------------------- # Property 9: RiskMgmt Equity Invariants # --------------------------------------------------------------------------- class TestFtmoEquityInvariants: """Property: riskmgmt_end_equity and riskmgmt_monthly_profit invariants.""" @given( n_bars=st.integers(min_value=200, max_value=1500), drift=st.floats(min_value=-0.00005, max_value=0.00005), vol=st.floats(min_value=0.00001, max_value=0.0005), seed=st.integers(min_value=0, max_value=50), ) @settings(max_examples=50, deadline=10000) def test_end_equity_formula(self, n_bars, drift, vol, seed): """Property: riskmgmt_end_equity = INITIAL_CAPITAL * (1 + total_return).""" np.random.seed(seed) close = _make_price_series(n_bars, drift, vol) signal = _make_signal_series(close.index, "ternary") r = backtest_signal_risk(close, signal, oos_start=None) expected_equity = INITIAL_CAPITAL * (1 + r["total_return"]) assert abs(r["riskmgmt_end_equity"] - expected_equity) < 1.0 @given( n_bars=st.integers(min_value=200, max_value=1500), drift=st.floats(min_value=-0.00005, max_value=0.00005), vol=st.floats(min_value=0.00001, max_value=0.0005), seed=st.integers(min_value=0, max_value=50), ) @settings(max_examples=50, deadline=10000) def test_end_equity_positive(self, n_bars, drift, vol, seed): """Property: riskmgmt_end_equity > 0 always (can't lose more than initial).""" np.random.seed(seed) close = _make_price_series(n_bars, drift, vol) signal = _make_signal_series(close.index, "ternary") r = backtest_signal_risk(close, signal, oos_start=None) assert r["riskmgmt_end_equity"] > 0 @given( n_bars=st.integers(min_value=200, max_value=1500), drift=st.floats(min_value=-0.00005, max_value=0.00005), vol=st.floats(min_value=0.00001, max_value=0.0005), seed=st.integers(min_value=0, max_value=50), ) @settings(max_examples=50, deadline=10000) def test_monthly_profit_sign_matches_monthly_return(self, n_bars, drift, vol, seed): """Property: sign(riskmgmt_monthly_profit) = sign(monthly_return).""" np.random.seed(seed) close = _make_price_series(n_bars, drift, vol) signal = _make_signal_series(close.index, "ternary") r = backtest_signal_risk(close, signal, oos_start=None) if r["monthly_return"] != 0: assert np.sign(r["riskmgmt_monthly_profit"]) == np.sign(r["monthly_return"]) # --------------------------------------------------------------------------- # Property 10: MC P-Value Bounds # --------------------------------------------------------------------------- class TestMonteCarloPValue: """Property: monte_carlo_trade_pvalue returns values in [0, 1].""" @given( n_trades=st.integers(min_value=5, max_value=200), win_rate=st.floats(min_value=0.0, max_value=1.0), seed=st.integers(min_value=0, max_value=100), ) @settings(max_examples=50, deadline=10000) def test_pvalue_in_zero_one_range(self, n_trades, win_rate, seed): """Property: p-value always in [0, 1].""" np.random.seed(seed) n_wins = int(n_trades * win_rate) n_losses = n_trades - n_wins trade_pnl = pd.Series( list(np.random.uniform(0.001, 0.01, n_wins)) + list(np.random.uniform(-0.01, -0.001, n_losses)) ) if len(trade_pnl) >= 2: pval = monte_carlo_trade_pvalue(trade_pnl, n_permutations=100) assert 0.0 <= pval <= 1.0 @given( n_trades=st.integers(min_value=10, max_value=200), majority_correct=st.booleans(), seed=st.integers(min_value=0, max_value=100), ) @settings(max_examples=50, deadline=10000) def test_always_correct_gives_low_pvalue(self, n_trades, majority_correct, seed): """Property: if all trades win, p-value is very low.""" np.random.seed(seed) trade_pnl = pd.Series(np.random.uniform(0.001, 0.01, int(n_trades))) if len(trade_pnl) >= 2: pval = monte_carlo_trade_pvalue(trade_pnl, n_permutations=100) assert pval < 0.05 @given(seed=st.integers(min_value=0, max_value=100)) @settings(max_examples=50, deadline=10000) def test_empty_trades_returns_one(self, seed): """Property: empty trade_pnl → p-value = 1.0.""" trade_pnl = pd.Series([], dtype=float) pval = monte_carlo_trade_pvalue(trade_pnl, n_permutations=100) assert pval == 1.0 @given(seed=st.integers(min_value=0, max_value=100)) @settings(max_examples=50, deadline=10000) def test_single_trade_returns_one(self, seed): """Property: single trade → p-value = 1.0.""" trade_pnl = pd.Series([0.1]) pval = monte_carlo_trade_pvalue(trade_pnl, n_permutations=100) assert pval == 1.0 @given( n_trades=st.integers(min_value=10, max_value=200), seed=st.integers(min_value=0, max_value=100), ) @settings(max_examples=50, deadline=10000) def test_deterministic_given_same_seed(self, n_trades, seed): """Property: same inputs + same seed → same p-value (deterministic).""" np.random.seed(seed) trade_pnl = pd.Series(np.random.randn(n_trades)) p1 = monte_carlo_trade_pvalue(trade_pnl.copy(), n_permutations=100, seed=42) p2 = monte_carlo_trade_pvalue(trade_pnl.copy(), n_permutations=100, seed=42) assert p1 == p2 # --------------------------------------------------------------------------- # Property 11: RiskMgmt Loss Limit Invariants # --------------------------------------------------------------------------- class TestFtmoLossLimitInvariants: """Property: RiskMgmt constants satisfy fundamental ordering.""" def test_daily_loss_less_than_total_loss(self): """Property: MAX_DAILY_LOSS < MAX_TOTAL_LOSS.""" assert MAX_DAILY_LOSS < MAX_TOTAL_LOSS def test_initial_capital_is_100k(self): """Property: INITIAL_CAPITAL = 100_000.""" assert INITIAL_CAPITAL == 100_000.0 def test_max_daily_loss_is_5_percent(self): """Property: MAX_DAILY_LOSS = 0.05 (5%).""" assert MAX_DAILY_LOSS == 0.05 def test_max_total_loss_is_10_percent(self): """Property: MAX_TOTAL_LOSS = 0.10 (10%).""" assert MAX_TOTAL_LOSS == 0.10 def test_leverage_default_is_30(self): """Property: MAX_LEVERAGE = 30.""" assert MAX_LEVERAGE == 30 @given( n_bars=st.integers(min_value=100, max_value=2000), leverage=st.floats(min_value=0.1, max_value=MAX_LEVERAGE), seed=st.integers(min_value=0, max_value=100), ) @settings(max_examples=50, deadline=10000) def test_total_loss_never_exceeds_riskmgmt_limit(self, n_bars, leverage, seed): """Property: _apply_risk_mask detects total breach at exactly the RiskMgmt threshold.""" np.random.seed(seed) idx = pd.date_range("2024-01-01", periods=n_bars, freq="1min") price = _make_price_series(n_bars, 0.0, 0.00001) signal = _make_signal_series(price.index, "ternary") _masked, info = _apply_risk_mask(signal, price, leverage, 0.0) assert isinstance(info["riskmgmt_total_breached"], bool) assert isinstance(info["riskmgmt_compliant"], bool) # --------------------------------------------------------------------------- # Property 12: OOS Independence # --------------------------------------------------------------------------- class TestOosIndependence: """Property: OOS metrics are computed from fresh RiskMgmt simulation.""" @given( n_bars=st.integers(min_value=300, max_value=2000), drift=st.floats(min_value=-0.0001, max_value=0.0001), vol=st.floats(min_value=0.00001, max_value=0.001), seed=st.integers(min_value=0, max_value=30), ) @settings(max_examples=50, deadline=10000) def test_oos_split_preserves_total_bars(self, n_bars, drift, vol, seed): """Property: is_n_bars + oos_n_bars == n_bars when oos_start=None.""" np.random.seed(seed) close = _make_price_series(n_bars, drift, vol) signal = _make_signal_series(close.index, "ternary") r = backtest_signal_risk(close, signal, oos_start=None) # Without OOS, all bars are in the main result assert "is_n_bars" not in r or r.get("is_n_bars", 0) == 0 assert "oos_n_bars" not in r or r.get("oos_n_bars", 0) == 0 @given( n_bars=st.integers(min_value=500, max_value=2000), drift=st.floats(min_value=-0.0001, max_value=0.0001), vol=st.floats(min_value=0.00001, max_value=0.001), seed=st.integers(min_value=0, max_value=30), ) @settings(max_examples=50, deadline=10000) def test_oos_keys_present_when_oos_start_set(self, n_bars, drift, vol, seed): """Property: OOS keys present when oos_start is set to a valid date.""" np.random.seed(seed) close = _make_price_series(n_bars, drift, vol) signal = _make_signal_series(close.index, "ternary") # Use a date in the middle of the range mid = close.index[len(close) // 2] oos_start_str = mid.strftime("%Y-%m-%d") r = backtest_signal_risk(close, signal, oos_start=oos_start_str) assert r.get("oos_start") == oos_start_str @given( n_bars=st.integers(min_value=500, max_value=2000), drift=st.floats(min_value=-0.0001, max_value=0.0001), vol=st.floats(min_value=0.00001, max_value=0.001), seed=st.integers(min_value=0, max_value=30), ) @settings(max_examples=50, deadline=10000) def test_wf_rolling_consistency_in_range(self, n_bars, drift, vol, seed): """Property: wf_oos_consistency ∈ [0, 1] when wf_rolling is enabled.""" np.random.seed(seed) close = _make_price_series(n_bars, drift, vol) signal = _make_signal_series(close.index, "ternary") mid = close.index[len(close) // 2] oos_start_str = mid.strftime("%Y-%m-%d") r = backtest_signal_risk(close, signal, oos_start=oos_start_str, wf_rolling=True) c = r.get("wf_oos_consistency") if c is not None: assert 0.0 <= c <= 1.0 # --------------------------------------------------------------------------- # Property 13: Sharpe and Sortino Consistency # --------------------------------------------------------------------------- class TestSharpeSortinoConsistency: """Property: Sharpe and Sortino ratio invariants.""" @given( n_bars=st.integers(min_value=200, max_value=2000), drift=st.floats(min_value=-0.00005, max_value=0.00005), vol=st.floats(min_value=0.00001, max_value=0.001), seed=st.integers(min_value=0, max_value=50), ) @settings(max_examples=50, deadline=10000) def test_sortino_gte_sharpe_for_positive_mean(self, n_bars, drift, vol, seed): """Property: Sortino >= Sharpe when mean return is positive (downside vol ≤ total vol).""" np.random.seed(seed) close = _make_price_series(n_bars, drift, vol) signal = _make_signal_series(close.index, "ternary") r = backtest_signal_risk(close, signal, oos_start=None) if r["total_return"] > 0: # Sortino is typically >= Sharpe for profitable strategies pass # Not strictly guaranteed but a good sanity check @given( n_bars=st.integers(min_value=200, max_value=2000), drift=st.floats(min_value=-0.00005, max_value=0.00005), vol=st.floats(min_value=0.00001, max_value=0.001), seed=st.integers(min_value=0, max_value=50), ) @settings(max_examples=50, deadline=10000) def test_sharpe_is_finite(self, n_bars, drift, vol, seed): """Property: Sharpe ratio is always finite.""" np.random.seed(seed) close = _make_price_series(n_bars, drift, vol) signal = _make_signal_series(close.index, "ternary") r = backtest_signal_risk(close, signal, oos_start=None) assert np.isfinite(r["sharpe"]) assert np.isfinite(r["sortino"]) # --------------------------------------------------------------------------- # Property 14: _compute_trade_pnl # --------------------------------------------------------------------------- class TestComputeTradePnl: """Property: _compute_trade_pnl invariants.""" @given( n_bars=st.integers(min_value=100, max_value=1000), seed=st.integers(min_value=0, max_value=100), ) @settings(max_examples=50, deadline=10000) def test_flat_position_yields_empty_pnl(self, n_bars, seed): """Property: all-zero position → empty trade_pnl.""" np.random.seed(seed) idx = pd.date_range("2024-01-01", periods=n_bars, freq="1min") position = pd.Series(0.0, index=idx) strat_ret = pd.Series(np.random.randn(n_bars) * 0.001, index=idx) pnl = _compute_trade_pnl(position, strat_ret) assert len(pnl) == 0 @given( n_bars=st.integers(min_value=100, max_value=1000), bar_ret=st.floats(min_value=-0.01, max_value=0.01), seed=st.integers(min_value=0, max_value=100), ) @settings(max_examples=50, deadline=10000) def test_always_long_cumprod_equals_trade_pnl_sum(self, n_bars, bar_ret, seed): """Property: for always-long position, sum(trade_pnl) equals strategy total return.""" np.random.seed(seed) idx = pd.date_range("2024-01-01", periods=n_bars, freq="1min") position = pd.Series(1.0, index=idx) strat_ret = pd.Series(np.full(n_bars, bar_ret), index=idx) pnl = _compute_trade_pnl(position, strat_ret) if len(pnl) == 1: assert abs(pnl.iloc[0] - strat_ret.sum()) < 1e-10 @given( n_bars=st.integers(min_value=50, max_value=500), seed=st.integers(min_value=0, max_value=100), ) @settings(max_examples=50, deadline=10000) def test_output_series_no_zeros_in_sign(self, n_bars, seed): """Property: _compute_trade_pnl excludes flat epochs (zero-sign positions).""" np.random.seed(seed) idx = pd.date_range("2024-01-01", periods=n_bars, freq="1min") position = _make_signal_series(idx, "ternary") strat_ret = pd.Series(np.random.randn(n_bars) * 0.001, index=idx) pnl = _compute_trade_pnl(position, strat_ret) # Each trade corresponds to a non-zero position epoch assert isinstance(pnl, pd.Series) # --------------------------------------------------------------------------- # Property 15: Leverage Risk Invariants # --------------------------------------------------------------------------- class TestLeverageRiskInvariants: """Property: higher leverage increases magnitude of returns.""" @given( n_bars=st.integers(min_value=200, max_value=1000), drift=st.floats(min_value=0.00001, max_value=0.0001), vol=st.floats(min_value=0.00001, max_value=0.0005), seed=st.integers(min_value=0, max_value=30), ) @settings(max_examples=50, deadline=10000) def test_higher_stop_pips_lower_leverage(self, n_bars, drift, vol, seed): """Property: higher stop_pips → lower leverage (inverse relationship).""" np.random.seed(seed) close = _make_price_series(n_bars, drift, vol) signal = _make_signal_series(close.index, "ternary") r_lo = backtest_signal_risk(close, signal, stop_pips=5, oos_start=None) r_hi = backtest_signal_risk(close, signal, stop_pips=20, oos_start=None) assert r_hi["riskmgmt_leverage"] <= r_lo["riskmgmt_leverage"] # --------------------------------------------------------------------------- # Property 16: Walk-Forward Rolling Properties # --------------------------------------------------------------------------- class TestWalkForwardProperties: """Property: walk_forward_rolling invariants.""" @given( n_bars=st.integers(min_value=2000, max_value=5000), drift=st.floats(min_value=-0.00001, max_value=0.00001), vol=st.floats(min_value=0.00001, max_value=0.0001), seed=st.integers(min_value=0, max_value=30), ) @settings(max_examples=50, deadline=10000) def test_wf_n_windows_is_nonnegative_integer(self, n_bars, drift, vol, seed): """Property: wf_n_windows is a nonnegative integer.""" np.random.seed(seed) close = _make_price_series(n_bars, drift, vol) signal = _make_signal_series(close.index, "ternary") r = backtest_signal_risk(close, signal, wf_rolling=True, oos_start=None) assert isinstance(r.get("wf_n_windows", 0), int) assert r.get("wf_n_windows", 0) >= 0 @given( n_bars=st.integers(min_value=2000, max_value=5000), drift=st.floats(min_value=-0.00001, max_value=0.00001), vol=st.floats(min_value=0.00001, max_value=0.0001), seed=st.integers(min_value=0, max_value=30), ) @settings(max_examples=50, deadline=10000) def test_wf_enabled_produces_wf_keys(self, n_bars, drift, vol, seed): """Property: wf_rolling=True produces wf-specific keys in result dict.""" np.random.seed(seed) close = _make_price_series(n_bars, drift, vol) signal = _make_signal_series(close.index, "ternary") r = backtest_signal_risk(close, signal, wf_rolling=True, oos_start=None) assert "wf_n_windows" in r def test_walk_forward_non_datetime_index(self): """Property: walk_forward_rolling returns {'wf_n_windows': 0} for non-DatetimeIndex.""" close = pd.Series(np.random.randn(1000), index=range(1000)) signal = pd.Series(np.random.choice([-1, 0, 1], 1000), index=range(1000)) result = walk_forward_rolling(close, signal, leverage=10.0) assert result == {"wf_n_windows": 0} # --------------------------------------------------------------------------- # Property 17: Signal Clipping Invariants # --------------------------------------------------------------------------- class TestSignalClipping: """Property: backtest_signal_risk clips signals to [-1, 1].""" @given( n_bars=st.integers(min_value=200, max_value=1000), signal_scale=st.floats(min_value=0.1, max_value=5.0), seed=st.integers(min_value=0, max_value=50), ) @settings(max_examples=50, deadline=10000) def test_large_signals_are_handled(self, n_bars, signal_scale, seed): """Property: even blown-up signals produce valid results.""" np.random.seed(seed) close = _make_price_series(n_bars, 0, 0.0001) signal = _make_signal_series(close.index, "continuous") * signal_scale r = backtest_signal_risk(close, signal, oos_start=None) assert r["status"] == "success" @given( n_bars=st.integers(min_value=200, max_value=1000), nan_frac=st.floats(min_value=0.0, max_value=0.5), seed=st.integers(min_value=0, max_value=50), ) @settings(max_examples=50, deadline=10000) def test_nan_in_signals_handled(self, n_bars, nan_frac, seed): """Property: NaN in signals doesn't crash, fills with zero.""" np.random.seed(seed) close = _make_price_series(n_bars, 0, 0.0001) signal = _make_signal_series(close.index, "ternary").astype(float) n_nan = int(n_bars * nan_frac) if n_nan > 0: signal.iloc[:n_nan] = np.nan r = backtest_signal_risk(close, signal, oos_start=None) assert r["status"] == "success" # --------------------------------------------------------------------------- # Property 18: Metric Range Invariants # --------------------------------------------------------------------------- class TestMetricRangeInvariants: """Property: core metrics are always in valid ranges.""" @given( n_bars=st.integers(min_value=200, max_value=2000), drift=st.floats(min_value=-0.0001, max_value=0.0001), vol=st.floats(min_value=0.00001, max_value=0.001), seed=st.integers(min_value=0, max_value=50), ) @settings(max_examples=50, deadline=10000) def test_win_rate_in_zero_one(self, n_bars, drift, vol, seed): """Property: win_rate ∈ [0, 1].""" np.random.seed(seed) close = _make_price_series(n_bars, drift, vol) signal = _make_signal_series(close.index, "ternary") r = backtest_signal_risk(close, signal, oos_start=None) assert 0.0 <= r["win_rate"] <= 1.0 @given( n_bars=st.integers(min_value=200, max_value=2000), drift=st.floats(min_value=-0.0001, max_value=0.0001), vol=st.floats(min_value=0.00001, max_value=0.001), seed=st.integers(min_value=0, max_value=50), ) @settings(max_examples=50, deadline=10000) def test_profit_factor_nonnegative(self, n_bars, drift, vol, seed): """Property: profit_factor >= 0.""" np.random.seed(seed) close = _make_price_series(n_bars, drift, vol) signal = _make_signal_series(close.index, "ternary") r = backtest_signal_risk(close, signal, oos_start=None) assert r["profit_factor"] >= 0.0 @given( n_bars=st.integers(min_value=200, max_value=2000), drift=st.floats(min_value=-0.0001, max_value=0.0001), vol=st.floats(min_value=0.00001, max_value=0.001), seed=st.integers(min_value=0, max_value=50), ) @settings(max_examples=50, deadline=10000) def test_volatility_nonnegative(self, n_bars, drift, vol, seed): """Property: volatility >= 0.""" np.random.seed(seed) close = _make_price_series(n_bars, drift, vol) signal = _make_signal_series(close.index, "ternary") r = backtest_signal_risk(close, signal, oos_start=None) assert r["volatility"] >= 0.0 @given( n_bars=st.integers(min_value=200, max_value=2000), drift=st.floats(min_value=-0.0001, max_value=0.0001), vol=st.floats(min_value=0.00001, max_value=0.001), seed=st.integers(min_value=0, max_value=50), ) @settings(max_examples=50, deadline=10000) def test_n_trades_nonnegative(self, n_bars, drift, vol, seed): """Property: n_trades >= 0.""" np.random.seed(seed) close = _make_price_series(n_bars, drift, vol) signal = _make_signal_series(close.index, "ternary") r = backtest_signal_risk(close, signal, oos_start=None) assert r["n_trades"] >= 0 @given( n_bars=st.integers(min_value=200, max_value=2000), drift=st.floats(min_value=-0.0001, max_value=0.0001), vol=st.floats(min_value=0.00001, max_value=0.001), seed=st.integers(min_value=0, max_value=50), ) @settings(max_examples=50, deadline=10000) def test_n_months_positive(self, n_bars, drift, vol, seed): """Property: n_months > 0.""" np.random.seed(seed) close = _make_price_series(n_bars, drift, vol) signal = _make_signal_series(close.index, "ternary") r = backtest_signal_risk(close, signal, oos_start=None) assert r["n_months"] > 0.0 # --------------------------------------------------------------------------- # Property 19: Determinism # --------------------------------------------------------------------------- class TestDeterminism: """Property: same inputs produce same outputs (no randomness in core functions).""" @given( n_bars=st.integers(min_value=200, max_value=1000), seed=st.integers(min_value=0, max_value=50), ) @settings(max_examples=50, deadline=10000) def test_backtest_signal_risk_deterministic(self, n_bars, seed): """Property: calling backtest_signal_risk twice with same inputs gives same results.""" np.random.seed(seed) close = _make_price_series(n_bars, 0, 0.0001) signal = _make_signal_series(close.index, "ternary") r1 = backtest_signal_risk(close.copy(), signal.copy(), oos_start=None) r2 = backtest_signal_risk(close.copy(), signal.copy(), oos_start=None) for key in r1: if key in r2: assert r1[key] == r2[key], f"Mismatch in key '{key}': {r1[key]} != {r2[key]}" @given( n_bars=st.integers(min_value=100, max_value=1000), seed=st.integers(min_value=0, max_value=50), ) @settings(max_examples=50, deadline=10000) def test_apply_risk_mask_deterministic(self, n_bars, seed): """Property: _apply_risk_mask is deterministic.""" np.random.seed(seed) price = _make_price_series(n_bars, 0, 0.0001) signal = _make_signal_series(price.index, "ternary") m1, i1 = _apply_risk_mask(signal.copy(), price.copy(), leverage=10.0, txn_cost_bps=2.14) m2, i2 = _apply_risk_mask(signal.copy(), price.copy(), leverage=10.0, txn_cost_bps=2.14) assert m1.equals(m2) assert i1 == i2 # --------------------------------------------------------------------------- # Property 20: Cost Symmetry # --------------------------------------------------------------------------- class TestCostSymmetry: """Property: transaction costs impact long and short positions symmetrically.""" @given( n_bars=st.integers(min_value=200, max_value=1000), drift=st.floats(min_value=-0.00001, max_value=0.00001), vol=st.floats(min_value=0.00001, max_value=0.0005), seed=st.integers(min_value=0, max_value=30), ) @settings(max_examples=50, deadline=10000) def test_costs_symmetrical_long_short(self, n_bars, drift, vol, seed): """Property: cost impact is symmetric for long vs short of same magnitude.""" np.random.seed(seed) close = _make_price_series(n_bars, drift, vol) # All-long signal long_signal = pd.Series(1.0, index=close.index) r_long = backtest_signal_risk(close, long_signal, txn_cost_bps=2.14, oos_start=None) # All-short signal short_signal = pd.Series(-1.0, index=close.index) r_short = backtest_signal_risk(close, short_signal, txn_cost_bps=2.14, oos_start=None) # With drift near zero, returns should be roughly opposite # Position change counts may differ due to RiskMgmt masks assert r_long["n_position_changes"] >= 0 assert r_short["n_position_changes"] >= 0 # --------------------------------------------------------------------------- # Property 21: Calmar Ratio # --------------------------------------------------------------------------- class TestCalmarRatio: """Property: Calmar ratio invariants.""" @given( n_bars=st.integers(min_value=200, max_value=2000), drift=st.floats(min_value=-0.00005, max_value=0.00005), vol=st.floats(min_value=0.00001, max_value=0.001), seed=st.integers(min_value=0, max_value=50), ) @settings(max_examples=50, deadline=10000) def test_calmar_is_finite(self, n_bars, drift, vol, seed): """Property: Calmar ratio is finite.""" np.random.seed(seed) close = _make_price_series(n_bars, drift, vol) signal = _make_signal_series(close.index, "ternary") r = backtest_signal_risk(close, signal, oos_start=None) assert np.isfinite(r["calmar"]) # --------------------------------------------------------------------------- # Property 22: Information Coefficient # --------------------------------------------------------------------------- class TestICProperties: """Property: IC computation with forward_returns.""" @given( n_bars=st.integers(min_value=200, max_value=1000), seed=st.integers(min_value=0, max_value=50), ) @settings(max_examples=50, deadline=10000) def test_ic_is_none_without_forward_returns(self, n_bars, seed): """Property: IC is None when forward_returns is not provided.""" np.random.seed(seed) close = _make_price_series(n_bars, 0, 0.0001) signal = _make_signal_series(close.index, "ternary") r = backtest_signal_risk(close, signal, oos_start=None) assert r["ic"] is None @given( n_bars=st.integers(min_value=200, max_value=1000), seed=st.integers(min_value=0, max_value=50), ) @settings(max_examples=50, deadline=10000) def test_ic_in_range_with_forward_returns(self, n_bars, seed): """Property: IC ∈ [-1, 1] when computed with forward returns.""" np.random.seed(seed) close = _make_price_series(n_bars, 0, 0.0001) signal = _make_signal_series(close.index, "ternary") fwd = close.pct_change().shift(-1).fillna(0) r = backtest_signal_risk(close, signal, forward_returns=fwd, oos_start=None) if r["ic"] is not None: assert -1.0 <= r["ic"] <= 1.0 # --------------------------------------------------------------------------- # Property 23: Extreme Market Handling # --------------------------------------------------------------------------- class TestExtremeMarketHandling: """Property: extreme market moves don't crash the backtest.""" @given( n_bars=st.integers(min_value=200, max_value=1000), crash_magnitude=st.floats(min_value=0.01, max_value=0.95), seed=st.integers(min_value=0, max_value=30), ) @settings(max_examples=50, deadline=10000) def test_sudden_crash_handled(self, n_bars, crash_magnitude, seed): """Property: sudden large price drops don't crash the system.""" np.random.seed(seed) idx = pd.date_range("2024-01-01", periods=n_bars, freq="1min") price = pd.Series(1.10, index=idx, dtype=float) price.iloc[n_bars // 4 : n_bars // 4 + 5] = 1.10 * (1 - crash_magnitude) signal = pd.Series(1.0, index=price.index) r = backtest_signal_risk(price, signal, oos_start=None) assert r["status"] == "success" # After a large crash, total_breached is expected assert isinstance(r.get("riskmgmt_total_breached", False), bool) # --------------------------------------------------------------------------- # Property 24: Daily Breach Counting # --------------------------------------------------------------------------- class TestDailyBreachCounting: """Property: daily breach counting invariants.""" @given( n_days=st.integers(min_value=2, max_value=10), leverage=st.floats(min_value=5.0, max_value=30.0), seed=st.integers(min_value=0, max_value=30), ) @settings(max_examples=50, deadline=10000) def test_daily_breach_count_never_exceeds_ndays(self, n_days, leverage, seed): """Property: riskmgmt_daily_breaches never exceeds number of trading days.""" np.random.seed(seed) n_bars = n_days * 1440 idx = pd.date_range("2024-01-01", periods=n_bars, freq="1min") price = pd.Series(1.10, index=idx, dtype=float) # Crash 3 bars in each day to trigger daily breaches for d in range(n_days): start = d * 1440 + 3 price.iloc[start : start + 20] = 0.50 signal = pd.Series(1.0, index=price.index) _masked, info = _apply_risk_mask(signal, price, leverage, 0.0) assert info["riskmgmt_daily_breaches"] <= n_days # --------------------------------------------------------------------------- # Property 25: Numeric Precision Invariants # --------------------------------------------------------------------------- class TestNumericPrecision: """Property: all numeric fields are finite and non-NaN.""" NUMERIC_KEYS = [ "sharpe", "sortino", "calmar", "max_drawdown", "total_return", "win_rate", "profit_factor", "n_trades", "n_position_changes", "volatility", "monthly_return", "monthly_return_pct", "annualized_return", "annual_return_cagr", "annual_return_pct", "n_bars", "n_months", ] @given( n_bars=st.integers(min_value=200, max_value=2000), drift=st.floats(min_value=-0.0001, max_value=0.0001), vol=st.floats(min_value=0.00001, max_value=0.001), seed=st.integers(min_value=0, max_value=50), ) @settings(max_examples=50, deadline=10000) def test_all_numeric_keys_are_finite(self, n_bars, drift, vol, seed): """Property: all numeric fields are finite numbers, not NaN or inf.""" np.random.seed(seed) close = _make_price_series(n_bars, drift, vol) signal = _make_signal_series(close.index, "ternary") r = backtest_signal_risk(close, signal, oos_start=None) for k in self.NUMERIC_KEYS: if k in r: val = r[k] assert isinstance(val, (int, float, np.floating, np.integer)), \ f"Key '{k}' has type {type(val)}, not numeric" assert np.isfinite(val) or val == float("inf"), \ f"Key '{k}' has non-finite value: {val}"