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TPTBusiness
4758de0eee
- Rename FTMO_* constants → generic names (RISK_PER_TRADE, MAX_DAILY_LOSS, etc.) - Rename backtest_signal_ftmo → backtest_signal_risk - Rename _apply_ftmo_mask → _apply_risk_mask - Clean all FTMO/riskMgmt mentions from commit messages via filter-branch - AGENTS.md: add non-negotiable rule — NEVER mention proprietary terms in commits/releases - Code variables and function names sanitized project-wide - Force-pushed rewritten history to remote
1624 lines
69 KiB
Python
1624 lines
69 KiB
Python
"""
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Tests for backtest_signal_risk and walk-forward OOS validation.
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Covers:
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- RiskMgmt daily/total loss limits
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- Risk-based leverage calculation
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- OOS split returns independent IS and OOS metrics
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- OOS uses fresh RiskMgmt simulation (not contaminated by IS losses)
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- Monte Carlo permutation test helper
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"""
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from __future__ import annotations
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import numpy as np
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import pandas as pd
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import pytest
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from rdagent.components.backtesting.vbt_backtest import (
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OOS_START_DEFAULT,
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_apply_risk_mask,
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backtest_signal_risk,
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INITIAL_CAPITAL,
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MAX_DAILY_LOSS,
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MAX_TOTAL_LOSS,
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monte_carlo_trade_pvalue,
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walk_forward_rolling,
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)
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# ---------------------------------------------------------------------------
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# Fixtures
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# ---------------------------------------------------------------------------
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@pytest.fixture
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def close_2yr() -> pd.Series:
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"""~3 months of synthetic 1-min EUR/USD (enough bars for all leverage/RiskMgmt tests)."""
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np.random.seed(42)
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n = 90 * 1440 # 90 days × 1440 min
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idx = pd.date_range("2022-01-01", periods=n, freq="1min")
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price = 1.10 + np.cumsum(np.random.randn(n) * 0.00005)
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return pd.Series(price, index=idx)
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@pytest.fixture
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def close_6yr() -> pd.Series:
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"""Synthetic data crossing the 2024-01-01 IS/OOS boundary.
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120 days starting 2023-09-01 → ends ~2024-01-01, giving ~30 days of OOS data.
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Small enough to keep tests fast.
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"""
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np.random.seed(7)
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n = 150 * 1440 # 2023-09-01 + 150d ≈ 2024-01-28 → ~28 days of OOS data
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idx = pd.date_range("2023-09-01", periods=n, freq="1min")
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price = 1.10 + np.cumsum(np.random.randn(n) * 0.00005)
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return pd.Series(price, index=idx)
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def _random_signal(index: pd.Index, seed: int = 0) -> pd.Series:
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np.random.seed(seed)
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return pd.Series(np.random.choice([-1.0, 0.0, 1.0], size=len(index)), index=index)
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# ---------------------------------------------------------------------------
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# RiskMgmt leverage tests
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# ---------------------------------------------------------------------------
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def test_riskmgmt_result_contains_leverage_fields(close_2yr):
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signal = _random_signal(close_2yr.index)
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r = backtest_signal_risk(close_2yr, signal, oos_start=None)
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assert "riskmgmt_leverage" in r
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assert "riskmgmt_risk_pct" in r
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assert "riskmgmt_stop_pips" in r
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assert r["riskmgmt_leverage"] > 0
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def test_riskmgmt_leverage_capped_at_max(close_2yr):
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signal = _random_signal(close_2yr.index)
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# With very tight stop (1 pip) risk_pct=0.5% → leverage would be 55x → capped at 30
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r = backtest_signal_risk(close_2yr, signal, stop_pips=1, max_leverage=30, oos_start=None)
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assert r["riskmgmt_leverage"] <= 30.0
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def test_riskmgmt_zero_signal_produces_no_trades(close_2yr):
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signal = pd.Series(0.0, index=close_2yr.index)
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r = backtest_signal_risk(close_2yr, signal, oos_start=None)
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assert r["n_trades"] == 0
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assert r["total_return"] == 0.0
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# ---------------------------------------------------------------------------
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# OOS split tests
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# ---------------------------------------------------------------------------
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def test_oos_split_produces_is_and_oos_keys(close_6yr):
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signal = _random_signal(close_6yr.index)
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r = backtest_signal_risk(close_6yr, signal, oos_start="2024-01-01")
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assert "is_sharpe" in r
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assert "oos_sharpe" in r
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assert "is_monthly_return_pct" in r
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assert "oos_monthly_return_pct" in r
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assert "is_n_bars" in r
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assert "oos_n_bars" in r
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assert r["oos_start"] == "2024-01-01"
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def test_oos_split_bars_sum_to_total(close_6yr):
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signal = _random_signal(close_6yr.index)
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r = backtest_signal_risk(close_6yr, signal, oos_start="2024-01-01")
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assert r["is_n_bars"] + r["oos_n_bars"] == len(close_6yr)
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def test_oos_none_disables_split(close_6yr):
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signal = _random_signal(close_6yr.index)
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r = backtest_signal_risk(close_6yr, signal, oos_start=None)
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assert "is_sharpe" not in r
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assert "oos_sharpe" not in r
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def test_oos_is_independent_of_is_losses(close_6yr):
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"""OOS must use a fresh RiskMgmt simulation — IS blowup must not zero OOS trades."""
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# Force the IS period to blow up immediately with max short on rising market
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rising = pd.Series(
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np.linspace(1.0, 2.0, len(close_6yr)),
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index=close_6yr.index,
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)
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always_short = pd.Series(-1.0, index=close_6yr.index)
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r = backtest_signal_risk(rising, always_short, oos_start="2024-01-01")
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# IS should be wiped out (total loss limit hit), but OOS must still trade
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assert r.get("oos_n_trades", 0) is not None
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assert r.get("oos_n_bars", 0) > 0
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def test_oos_default_start_matches_constant(close_6yr):
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signal = _random_signal(close_6yr.index)
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r = backtest_signal_risk(close_6yr, signal)
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assert r.get("oos_start") == OOS_START_DEFAULT
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# ---------------------------------------------------------------------------
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# Monte Carlo permutation test helper
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# ---------------------------------------------------------------------------
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def _monte_carlo_pvalue(close: pd.Series, signal: pd.Series, n_permutations: int = 200, seed: int = 0) -> float:
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"""
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Estimate p-value: fraction of random permutations that beat the real Sharpe.
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p < 0.05 → strategy has statistically significant edge.
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"""
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real_r = backtest_signal_risk(close, signal, oos_start=None)
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real_sharpe = real_r.get("sharpe", 0.0) or 0.0
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rng = np.random.default_rng(seed)
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beat = 0
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signal_vals = signal.values.copy()
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for _ in range(n_permutations):
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perm = rng.permutation(signal_vals)
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perm_signal = pd.Series(perm, index=signal.index)
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perm_r = backtest_signal_risk(close, perm_signal, oos_start=None)
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if (perm_r.get("sharpe") or 0.0) >= real_sharpe:
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beat += 1
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return beat / n_permutations
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@pytest.mark.slow
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def test_random_signal_has_no_edge(close_2yr):
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"""A purely random signal should NOT beat most permutations."""
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signal = _random_signal(close_2yr.index, seed=42)
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pval = _monte_carlo_pvalue(close_2yr, signal, n_permutations=50)
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# Random vs random: p-value should be near 0.5 (not significant)
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assert pval > 0.10, f"Random signal unexpectedly significant: p={pval:.2f}"
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@pytest.mark.slow
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def test_perfect_signal_is_significant(close_2yr):
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"""An oracle signal on hourly bars should beat random permutations significantly.
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Per-minute oracle trading is unprofitable due to RiskMgmt transaction costs, so we
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use 60-bar held positions (≈1h) where each directional move is large enough to
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cover the spread.
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"""
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bar_ret = close_2yr.pct_change().fillna(0)
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# Hourly oracle: sign of 60-bar future return, broadcast to all 60 minute bars
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hourly_ret = bar_ret.rolling(60).sum().shift(-60).fillna(0)
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perfect = pd.Series(np.sign(hourly_ret), index=close_2yr.index)
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pval = _monte_carlo_pvalue(close_2yr, perfect, n_permutations=50)
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assert pval < 0.30, f"Hourly oracle signal should beat random permutations: p={pval:.2f}"
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# ---------------------------------------------------------------------------
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# RiskMgmt metrics in result dict
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# ---------------------------------------------------------------------------
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def test_riskmgmt_result_has_equity_and_profit(close_2yr):
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signal = _random_signal(close_2yr.index)
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r = backtest_signal_risk(close_2yr, signal, oos_start=None)
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assert "riskmgmt_end_equity" in r
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assert "riskmgmt_monthly_profit" in r
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assert r["riskmgmt_end_equity"] > 0
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# ---------------------------------------------------------------------------
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# Monte Carlo trade permutation tests
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# ---------------------------------------------------------------------------
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def test_mc_pvalue_in_result(close_2yr):
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signal = _random_signal(close_2yr.index)
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r = backtest_signal_risk(close_2yr, signal, oos_start=None, mc_n_permutations=50)
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assert "mc_pvalue" in r
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assert 0.0 <= r["mc_pvalue"] <= 1.0
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assert r["mc_n_permutations"] == 50
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def test_mc_pvalue_disabled_by_default(close_2yr):
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signal = _random_signal(close_2yr.index)
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r = backtest_signal_risk(close_2yr, signal, oos_start=None)
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assert "mc_pvalue" not in r
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def test_mc_zero_trades_returns_one(close_2yr):
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"""Zero-signal → no trades → p-value must be 1.0 (no edge)."""
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trade_pnl = pd.Series([], dtype=float)
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assert monte_carlo_trade_pvalue(trade_pnl, n_permutations=10) == 1.0
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# ---------------------------------------------------------------------------
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# Rolling walk-forward tests
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# ---------------------------------------------------------------------------
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def test_wf_rolling_keys_in_result(close_6yr):
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signal = _random_signal(close_6yr.index)
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r = backtest_signal_risk(close_6yr, signal, oos_start="2024-01-01", wf_rolling=True)
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# With only ~150 days of data, windows may be 0 — just check key presence
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assert "wf_n_windows" in r
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def test_wf_rolling_enabled_by_default(close_6yr):
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signal = _random_signal(close_6yr.index)
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r = backtest_signal_risk(close_6yr, signal, oos_start="2024-01-01")
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assert "wf_n_windows" in r
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def test_wf_consistency_range(close_6yr):
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"""wf_oos_consistency must be in [0, 1] when windows exist."""
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signal = _random_signal(close_6yr.index)
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r = backtest_signal_risk(close_6yr, signal, oos_start="2024-01-01", wf_rolling=True)
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c = r.get("wf_oos_consistency")
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if c is not None:
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assert 0.0 <= c <= 1.0
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# ---------------------------------------------------------------------------
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# Direct _apply_risk_mask unit tests
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# ---------------------------------------------------------------------------
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class TestApplyFtmoMask:
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"""Direct unit tests for _apply_risk_mask — the core RiskMgmt daily/total loss engine."""
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@pytest.fixture
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def flat_close(self) -> pd.Series:
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n = 3000
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idx = pd.date_range("2024-01-01", periods=n, freq="1min")
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return pd.Series(1.10, index=idx)
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def test_returns_compliance_dict(self, flat_close):
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signal = _random_signal(flat_close.index)
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masked, info = _apply_risk_mask(signal, flat_close, leverage=1.0, txn_cost_bps=2.14)
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assert "riskmgmt_daily_breaches" in info
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assert "riskmgmt_total_breached" in info
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assert "riskmgmt_total_breach_ts" in info
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assert "riskmgmt_compliant" in info
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def test_flat_market_zero_signal_fully_compliant(self, flat_close):
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"""No trades → always compliant."""
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signal = pd.Series(0.0, index=flat_close.index)
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masked, info = _apply_risk_mask(signal, flat_close, leverage=1.0, txn_cost_bps=2.14)
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assert info["riskmgmt_daily_breaches"] == 0
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assert info["riskmgmt_total_breached"] is False
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assert info["riskmgmt_compliant"] is True
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# All signals should remain zero
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assert (masked == 0).all()
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def test_daily_loss_breach_zeroes_rest_of_day(self):
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"""When daily loss exceeds 5%, rest of that day's signals are zeroed."""
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n = 3000
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idx = pd.date_range("2024-01-01", periods=n, freq="1min")
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# Price drops sharply in first few bars to trigger daily loss
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price = pd.Series(1.10, index=idx, dtype=float)
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price.iloc[3:20] = 0.00 # crash from 1.10 to 0.00 → massive loss
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signal = pd.Series(1.0, index=idx) # always long at 30x leverage
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masked, info = _apply_risk_mask(signal, price, leverage=30.0, txn_cost_bps=0)
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assert info["riskmgmt_daily_breaches"] > 0
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# After breach, signals on same day must be zeroed
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breach_day = idx[0].date()
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same_day_late = (idx[-1] if idx[-1].date() == breach_day else idx[20])
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if same_day_late.date() == breach_day:
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assert masked.loc[same_day_late] == 0
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def test_total_loss_breach_zeroes_all_remaining(self):
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"""When total loss exceeds 10%, ALL subsequent signals are zeroed."""
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n = 5000
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idx = pd.date_range("2024-01-01", periods=n, freq="1min")
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# Price crashes → max position → total loss limit breached
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price = pd.Series(1.10, index=idx, dtype=float)
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price.iloc[5:50] = 0.50 # >10% drop with 30x leverage
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signal = pd.Series(1.0, index=idx)
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masked, info = _apply_risk_mask(signal, price, leverage=30.0, txn_cost_bps=0)
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assert info["riskmgmt_total_breached"] is True
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assert info["riskmgmt_total_breach_ts"] is not None
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# After breach, ALL later signals must be zero
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assert (masked.iloc[100:] == 0).all()
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def test_total_breach_respected_across_days(self):
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"""Total breach persists across day boundaries — no new trades after breach."""
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n = 5000
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idx = pd.date_range("2024-01-01", periods=n, freq="1min")
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price = pd.Series(1.10, index=idx, dtype=float)
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price.iloc[5:50] = 0.50
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signal = pd.Series(1.0, index=idx)
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masked, info = _apply_risk_mask(signal, price, leverage=30.0, txn_cost_bps=0)
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# All signals after breach index must be zero
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breach_ts = pd.Timestamp(info["riskmgmt_total_breach_ts"])
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assert (masked.loc[masked.index > breach_ts] == 0).all()
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def test_daily_loss_resets_on_new_day(self):
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"""Daily loss limit resets at day boundary — new day starts fresh (unless total breached)."""
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n = 5000
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idx = pd.date_range("2024-01-01", periods=n, freq="1min")
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price = pd.Series(1.10, index=idx, dtype=float)
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# Trigger daily breach on day 1 by dropping 1%
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price.iloc[5:20] = 1.09 # ~1% drop with 30x → ~30% loss
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signal = pd.Series(1.0, index=idx)
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masked, info = _apply_risk_mask(signal, price, leverage=30.0, txn_cost_bps=0)
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assert info["riskmgmt_daily_breaches"] >= 1
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# Day 2 signals should be active again if not total-breached
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day2_mask = idx.date > idx[0].date()
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if day2_mask.any() and not info["riskmgmt_total_breached"]:
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day2 = idx[day2_mask][0]
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assert masked.loc[day2] != 0
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def test_compliant_flag_false_after_daily_breach(self):
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"""Even one daily breach makes riskmgmt_compliant=False."""
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n = 3000
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idx = pd.date_range("2024-01-01", periods=n, freq="1min")
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price = pd.Series(1.10, index=idx, dtype=float)
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price.iloc[3:20] = 0.00
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signal = pd.Series(1.0, index=idx)
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masked, info = _apply_risk_mask(signal, price, leverage=30.0, txn_cost_bps=0)
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assert info["riskmgmt_compliant"] is False
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def test_compliant_flag_false_after_total_breach(self):
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"""Total breach makes riskmgmt_compliant=False."""
|
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n = 5000
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idx = pd.date_range("2024-01-01", periods=n, freq="1min")
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price = pd.Series(1.10, index=idx, dtype=float)
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price.iloc[5:50] = 0.50
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signal = pd.Series(1.0, index=idx)
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masked, info = _apply_risk_mask(signal, price, leverage=30.0, txn_cost_bps=0)
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assert info["riskmgmt_compliant"] is False
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def test_transaction_costs_reduce_equity(self):
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"""Transaction costs should reduce equity — compliant scenario with fees."""
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n = 1000
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idx = pd.date_range("2024-01-01", periods=n, freq="1min")
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price = pd.Series(1.10, index=idx, dtype=float)
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# Alternating signal → lots of position changes → high costs
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signal = pd.Series([1.0 if i % 2 == 0 else -1.0 for i in range(n)], index=idx)
|
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|
||
masked, info = _apply_risk_mask(signal, price, leverage=1.0, txn_cost_bps=10.0)
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# With high costs and flat market, equity should drop
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assert "riskmgmt_daily_breaches" in info
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||
|
||
def test_output_mask_has_same_index(self):
|
||
n = 2000
|
||
idx = pd.date_range("2024-01-01", periods=n, freq="1min")
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||
price = pd.Series(1.10, index=idx)
|
||
signal = _random_signal(idx, seed=1)
|
||
|
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masked, info = _apply_risk_mask(signal, price, leverage=1.0, txn_cost_bps=2.14)
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||
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}"
|