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ml/robustness.py — Robustness testing: Monte Carlo, stress tests, multi-scenario validation.
Capabilities:
1. Monte Carlo equity curve simulation (shuffled returns, bootstrapped trades)
2. Stress tests: higher costs, slippage spikes, execution delays, gap risk
3. Parameter sensitivity analysis (grid search over key params)
4. Walk-forward stability scoring
"""
from __future__ import annotations
import logging
from dataclasses import dataclass, field
import numpy as np
logger = logging.getLogger("trading_system.ml.robustness")
@dataclass
class MonteCarloResult:
"""Monte Carlo simulation results."""
n_simulations: int
median_return: float
p5_return: float # 5th percentile (worst case)
p25_return: float
p75_return: float
p95_return: float # 95th percentile (best case)
prob_positive: float # probability of positive total return
median_sharpe: float
median_max_dd: float
p95_max_dd: float # worst-case max drawdown (95th pct)
simulated_returns: np.ndarray # (n_sims,) final total returns
@dataclass
class StressTestResult:
"""Results from a single stress scenario."""
scenario_name: str
original_return: float
stressed_return: float
original_sharpe: float
stressed_sharpe: float
original_max_dd: float
stressed_max_dd: float
degradation_pct: float # % change in return
@dataclass
class RobustnessReport:
"""Full robustness test results."""
monte_carlo: MonteCarloResult | None = None
stress_tests: list[StressTestResult] = field(default_factory=list)
parameter_sensitivity: dict = field(default_factory=dict)
overall_score: float = 0.0 # 0-100 robustness score
class MonteCarloSimulator:
"""Monte Carlo equity curve simulation via return bootstrapping."""
def __init__(
self,
n_simulations: int = 1000,
block_size: int = 5, # block bootstrap to preserve autocorrelation
seed: int = 42,
):
self.n_simulations = n_simulations
self.block_size = block_size
self.rng = np.random.default_rng(seed)
def simulate(self, trade_returns: np.ndarray) -> MonteCarloResult:
"""Run Monte Carlo simulation on trade returns.
Uses block bootstrapping to preserve short-term return autocorrelation.
Args:
trade_returns: Array of per-trade returns (e.g., +0.02, -0.01).
Returns:
MonteCarloResult with distribution statistics.
"""
n_trades = len(trade_returns)
if n_trades < 10:
logger.warning("Too few trades for Monte Carlo (%d)", n_trades)
return MonteCarloResult(
n_simulations=0, median_return=0, p5_return=0, p25_return=0,
p75_return=0, p95_return=0, prob_positive=0, median_sharpe=0,
median_max_dd=0, p95_max_dd=0, simulated_returns=np.array([]),
)
n_blocks = max(1, n_trades // self.block_size)
sim_total_returns = np.zeros(self.n_simulations)
sim_sharpes = np.zeros(self.n_simulations)
sim_max_dds = np.zeros(self.n_simulations)
for i in range(self.n_simulations):
# Block bootstrap
block_starts = self.rng.integers(
0, n_trades - self.block_size + 1, size=n_blocks,
)
sampled = np.concatenate([
trade_returns[s:s + self.block_size] for s in block_starts
])[:n_trades] # match original length
# Compute equity curve
equity = np.cumprod(1 + sampled)
sim_total_returns[i] = equity[-1] - 1
# Sharpe
if np.std(sampled) > 0:
sim_sharpes[i] = (np.mean(sampled) / np.std(sampled)) * np.sqrt(252)
else:
sim_sharpes[i] = 0.0
# Max drawdown
peak = np.maximum.accumulate(equity)
dd = (peak - equity) / (peak + 1e-9)
sim_max_dds[i] = dd.max()
return MonteCarloResult(
n_simulations=self.n_simulations,
median_return=float(np.median(sim_total_returns)),
p5_return=float(np.percentile(sim_total_returns, 5)),
p25_return=float(np.percentile(sim_total_returns, 25)),
p75_return=float(np.percentile(sim_total_returns, 75)),
p95_return=float(np.percentile(sim_total_returns, 95)),
prob_positive=float(np.mean(sim_total_returns > 0)),
median_sharpe=float(np.median(sim_sharpes)),
median_max_dd=float(np.median(sim_max_dds)),
p95_max_dd=float(np.percentile(sim_max_dds, 95)),
simulated_returns=sim_total_returns,
)
class StressTester:
"""Apply stress scenarios to trade results."""
def __init__(self, base_return: float = 0.0, base_sharpe: float = 0.0, base_max_dd: float = 0.0):
self.base_return = base_return
self.base_sharpe = base_sharpe
self.base_max_dd = base_max_dd
def stress_higher_costs(
self,
trade_returns: np.ndarray,
cost_multiplier: float = 2.0,
base_cost_pct: float = 0.03,
) -> StressTestResult:
"""Stress: double transaction costs."""
extra_cost = base_cost_pct * (cost_multiplier - 1) / 100
stressed = trade_returns - extra_cost
return self._build_result("2x Transaction Costs", trade_returns, stressed)
def stress_slippage_spike(
self,
trade_returns: np.ndarray,
slippage_pct: float = 0.05,
) -> StressTestResult:
"""Stress: add extra slippage to all trades."""
stressed = trade_returns - slippage_pct / 100
return self._build_result(f"Slippage +{slippage_pct}bps", trade_returns, stressed)
def stress_execution_delay(
self,
trade_returns: np.ndarray,
delay_penalty_pct: float = 0.02,
) -> StressTestResult:
"""Stress: execution delay causes adverse price movement."""
penalties = np.abs(trade_returns) * delay_penalty_pct
stressed = trade_returns - penalties
return self._build_result("Execution Delay", trade_returns, stressed)
def stress_tail_events(
self,
trade_returns: np.ndarray,
n_worst: int = 5,
amplify_factor: float = 2.0,
) -> StressTestResult:
"""Stress: amplify the N worst trades (tail risk)."""
stressed = trade_returns.copy()
sorted_idx = np.argsort(trade_returns)
for i in sorted_idx[:n_worst]:
if stressed[i] < 0:
stressed[i] *= amplify_factor
return self._build_result("Tail Risk (2x worst 5)", trade_returns, stressed)
def stress_gap_risk(
self,
trade_returns: np.ndarray,
gap_probability: float = 0.02,
gap_size_pct: float = 3.0,
seed: int = 42,
) -> StressTestResult:
"""Stress: random overnight gap events."""
rng = np.random.default_rng(seed)
stressed = trade_returns.copy()
n = len(stressed)
n_gaps = max(1, int(n * gap_probability))
gap_idx = rng.choice(n, size=n_gaps, replace=False)
direction = rng.choice([-1, 1], size=n_gaps)
stressed[gap_idx] += direction * gap_size_pct / 100
return self._build_result("Random Gap Risk", trade_returns, stressed)
def _build_result(
self,
name: str,
original: np.ndarray,
stressed: np.ndarray,
) -> StressTestResult:
o_ret = float(np.sum(original))
s_ret = float(np.sum(stressed))
o_sharpe = self._sharpe(original)
s_sharpe = self._sharpe(stressed)
o_dd = self._max_dd(original)
s_dd = self._max_dd(stressed)
deg = ((s_ret - o_ret) / abs(o_ret) * 100) if abs(o_ret) > 1e-9 else 0
return StressTestResult(
scenario_name=name,
original_return=o_ret,
stressed_return=s_ret,
original_sharpe=o_sharpe,
stressed_sharpe=s_sharpe,
original_max_dd=o_dd,
stressed_max_dd=s_dd,
degradation_pct=deg,
)
@staticmethod
def _sharpe(returns: np.ndarray) -> float:
if len(returns) == 0 or np.std(returns) == 0:
return 0.0
return float((np.mean(returns) / np.std(returns)) * np.sqrt(252))
@staticmethod
def _max_dd(returns: np.ndarray) -> float:
equity = np.cumprod(1 + returns)
peak = np.maximum.accumulate(equity)
dd = (peak - equity) / (peak + 1e-9)
return float(dd.max()) if len(dd) > 0 else 0.0
def run_all(self, trade_returns: np.ndarray) -> list[StressTestResult]:
"""Run all stress scenarios."""
return [
self.stress_higher_costs(trade_returns),
self.stress_slippage_spike(trade_returns),
self.stress_execution_delay(trade_returns),
self.stress_tail_events(trade_returns),
self.stress_gap_risk(trade_returns),
]
def compute_robustness_score(
mc: MonteCarloResult,
stress_tests: list[StressTestResult],
) -> float:
"""Compute overall robustness score (0-100).
Scoring:
40% Monte Carlo: probability of positive returns + consistency
30% Stress resilience: avg degradation across scenarios
30% Drawdown control: worst-case drawdown
"""
score = 0.0
# MC component (40 pts)
if mc.n_simulations > 0:
# Prob positive: 0-20 pts
score += mc.prob_positive * 20
# Sharpe consistency: 0-10 pts
score += min(max(mc.median_sharpe, 0), 3.0) / 3.0 * 10
# P5 not too negative: 0-10 pts
p5_score = max(0, min(1.0, (mc.p5_return + 0.20) / 0.20))
score += p5_score * 10
# Stress component (30 pts)
if stress_tests:
degradations = [abs(st.degradation_pct) for st in stress_tests]
avg_deg = np.mean(degradations)
# Lower degradation = higher score
stress_score = max(0, 30 - avg_deg * 0.3)
score += stress_score
# Drawdown component (30 pts)
if mc.n_simulations > 0:
# P95 max DD: scale 0-30 (0% DD = 30, 30% DD = 0)
dd_score = max(0, 30 * (1 - mc.p95_max_dd / 0.30))
score += dd_score
return float(np.clip(score, 0, 100))
def print_robustness_report(report: RobustnessReport):
"""Pretty-print the robustness report."""
print("\n" + "=" * 70)
print(" ROBUSTNESS TESTING REPORT")
print("=" * 70)
if report.monte_carlo:
mc = report.monte_carlo
print(f"\n MONTE CARLO ({mc.n_simulations} simulations)")
print(f" {'Metric':<25s} {'Value':>10s}")
print(f" {'-'*35}")
print(f" {'Median Return':<25s} {mc.median_return:>9.2%}")
print(f" {'5th Percentile':<25s} {mc.p5_return:>9.2%}")
print(f" {'25th Percentile':<25s} {mc.p25_return:>9.2%}")
print(f" {'75th Percentile':<25s} {mc.p75_return:>9.2%}")
print(f" {'95th Percentile':<25s} {mc.p95_return:>9.2%}")
print(f" {'P(Return > 0)':<25s} {mc.prob_positive:>9.1%}")
print(f" {'Median Sharpe':<25s} {mc.median_sharpe:>9.2f}")
print(f" {'Median Max DD':<25s} {mc.median_max_dd:>9.2%}")
print(f" {'95th pct Max DD':<25s} {mc.p95_max_dd:>9.2%}")
if report.stress_tests:
print(f"\n STRESS TESTS")
print(f" {'Scenario':<25s} {'Orig':>8s} {'Stressed':>8s} {'Degrad':>8s}")
print(f" {'-'*49}")
for st in report.stress_tests:
print(
f" {st.scenario_name:<25s}"
f" {st.original_return:>7.2%}"
f" {st.stressed_return:>7.2%}"
f" {st.degradation_pct:>7.1f}%"
)
print(f"\n ROBUSTNESS SCORE: {report.overall_score:.0f}/100")
if report.overall_score >= 70:
print(" Status: ROBUST ✓")
elif report.overall_score >= 50:
print(" Status: MODERATE — review stress scenarios")
else:
print(" Status: WEAK — significant improvements needed")
print("=" * 70)
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