Adversarial Robustness Of Trading Signals

When to Use

Invoke this skill before promoting any ML trading-signal model to production — Random Forests, Gradient Boosted Trees, deep nets, or any model exposing a predict(X) callable. Financial microstructure is adversarial in the technical sense: a flash crash, a spoofing layer, or genuine quote noise can perturb order- book features by less than one bid-ask spread and flip a model's signal from BUY to SELL. A model that flips under such noise is unsafe to trade.

The skill produces a SignalAdversarialTester that injects epsilon-bounded perturbations into the validation feature matrix and reports:

1. The vulnerability score — % of samples whose signal flips, with a one-sided Wilson upper confidence bound on that rate.
2. The flipped-sample indices — so quant research can attribute which inputs sit on the decision boundary.
3. A deployment verdict — robust / vulnerable against a tolerance threshold, in two strengths: is_robust (point estimate) and is_robust_at_ci (the confidence bound also clears the tolerance). Gate on the latter.

When NOT to Use

• Differentiable models requiring a true worst-case bound. This engine is a black-box, gradient-free tester. For a differentiable net, real FGSM/PGD gives a tighter (and actually worst-case) attack: adv_x = x + ε·sign(∇ₓ L). Use a dedicated adversarial-robustness library (CleverHans, Foolbox, ART) instead, then feed that attack's flip rate into this governance gate if you want a uniform scoring contract.
• Provable-robustness requirements. This is empirical robustness — it can only show a model is vulnerable, never prove it is robust. For a certified L2 radius use Cohen-style randomized smoothing (see references/standards.md §3).
• Non-ML signals (pure rule-based / threshold strategies with no learned component). Adversarial perturbation of a fixed threshold is just sensitivity analysis; use backtest-parameter-sensitivity-analysis instead.
• Production-time / online monitoring. The perturbation sweep is O(n_trials) expensive and assumes a static validation set. For live drift monitoring use concept-drift-vs-staleness-differentiation / model-staleness-detection.
• Insufficient validation data. With < ~1000 out-of-sample samples the flip rate confidence interval is wide; a "pass" near the tolerance is statistically meaningless. Bootstrap or use a larger holdout.

Prerequisites

• Python 3.10+, numpy.
• A trained model exposing predict(X) returning one of:
  • integer/boolean 1D labels (used directly as the signal),
  • a 1D float score (split at decision_threshold, default 0.5), or
  • a 2D probability matrix (signal = argmax over columns).
• A representative out-of-sample validation set X_clean (2D non-empty, all-finite array — NaN/inf are rejected, see Pitfalls). Never test on training data — the model has seen it and will appear artificially robust.
• A calibrated epsilon — the perturbation budget. Default 0.01 (1% of per-feature scale); for equities, set this to one average bid-ask spread as a fraction of price (see references/standards.md §1).
• (Recommended) Training-set feature scales — pass feature_scales so the test is independent of the validation sample drawn (the ptp fallback couples ε to the sample).

Workflow

1. Assemble the validation set. Pull a frozen, out-of-sample X_clean aligned with the model's training feature schema. Confirm it is 2D and finite.

2. Configure the perturbation budget. Construct an AdversarialRobustnessConfig:

   python

   copy

   from signal_adversarial_tester import (
       SignalAdversarialTester, AdversarialRobustnessConfig, NOISE_RANDOM_SIGN,
   )
   
   config = AdversarialRobustnessConfig(
       epsilon=0.01,               # 1% of per-feature scale
       flip_tolerance_pct=5.0,    # reject if > 5% of signals flip
       noise_type=NOISE_RANDOM_SIGN,   # full-magnitude random-sign stress
       n_trials=25,                # for montecarlo_worst only
       seed=42,                    # reproducible (reproducible-ml-pipelines)
       feature_scales=train_scales,   # from training set, not validation
       feature_bounds=train_bounds,   # [min, max] per feature for clipping
       clip_to_clean_domain=True,
       decision_threshold=0.5,
   )

3. Run the evaluation:

   python

   copy

   tester = SignalAdversarialTester(config)
   report = tester.evaluate_model(model.predict, X_clean)

4. Interpret the verdict:

   • report.is_robust_at_ci == True → the flip rate and its 95% Wilson upper bound clear the tolerance. This is the promotion signal; the model may proceed subject to the other gates in your pipeline.
   • report.is_robust == True but is_robust_at_ci == False → marginal. The point estimate clears the tolerance but the validation set is too small to resolve it. Do not promote: grow the holdout (see standards.md §4).
   • report.is_robust == False → reject. Route the model back to quant research for adversarial training (retrain on perturbed data to smooth the decision boundary), feature re-engineering, or a higher decision threshold. Use tester.perturb(X_train, rng) to generate the augmentation with the same noise model the gate failed on.

5. Attribute the fragility. report.flipped_indices identifies exactly which validation samples sit on a flippable boundary — the actionable set for a targeted adversarial-training augmentation.

Common Pitfalls

• Treating random_sign as worst-case. The legacy worst_case_sign name was a misnomer — the signs are random, not gradient-derived. Worst-case directional noise needs the loss gradient (FGSM). montecarlo_worst gives a lower bound on the worst case by taking the max over N random trials; it is tighter than a single draw but still not a true worst case. Read the verdict accordingly.
• Testing on training data. The model has memorised its training set; flip rates will be artificially low and the gate is meaningless. Always use a frozen out-of-sample holdout.
• Epsilon not calibrated to microstructure. A blanket epsilon=0.01 is wrong when features have wildly different scales (price in $ vs a normalized ratio in [0,1]). Either pass per-feature feature_scales (training-set) or calibrate ε to one bid-ask spread per instrument.
• No domain clipping. Perturbing a price feature to a negative value, or a normalized ratio above 1, produces infeasible inputs that overstate vulnerability (the model never sees them in production). Use clip_to_clean_domain=True or explicit feature_bounds.
• Gating on the point estimate alone. A 0% flip rate on 50 samples has a 95% Wilson upper bound of 5.13% — it cannot clear a 5% tolerance. is_robust answers "did the point estimate clear?"; only is_robust_at_ci answers "did the evidence clear?". Promote on the latter.
• NaN in the validation set. Before v1.3.0 a single NaN cell made the per-feature ptp NaN, which made every perturbation NaN, which decoded to one class for clean and adversarial alike — a silent 0% flip rate and an automatic PASS. Non-finite inputs and non-finite model outputs are now hard errors; treat them as data-quality defects, not as things to work around.
• Non-deterministic governance. A deployment gate that flips verdicts between CI runs is worse than no gate. Always set seed; pin it in the model card.
• Single noise model. uniform is average-case; it can pass a model that random_sign or montecarlo_worst fails. Run at least random_sign for governance; prefer montecarlo_worst for the tighter bound.
• Ignoring the probability path. A model returning 2D probabilities is decoded via argmax; a 1D float score via decision_threshold. Verify the decode matches your signal convention before trusting the flip count.
• Using Gaussian noise only. Gaussian samples rarely reach the ε boundary, so they under-state vulnerability versus random_sign (which always hits the boundary). Do not use Gaussian as the sole stress.

Verification

Run the unit tests:

python -m unittest discover -s skills/adversarial-robustness-of-trading-signals/scripts -v

What they assert:

• A robust model passes under uniform noise.
• A fragile model fails under random_sign and under montecarlo_worst.
• montecarlo_worst reports the max flip rate across n_trials (≥ any single trial).
• Same seed → identical report and flipped_indices (determinism).
• 2D probability matrices decode via argmax; 1D float scores via decision_threshold.
• Domain clipping suppresses infeasible perturbations (both implicit and explicit bounds).
• Explicit feature_scales collapse a huge ε; zero-variance features fall back to scale 1.0.
• Config validation rejects negative ε/tolerance, invalid noise_type, n_trials < 1, non-positive feature_scales, mis-shaped feature_bounds, inverted bounds, bad batch_size, out-of-range ci_confidence_level; ε=0 logs a vacuous-gate warning.
• Inputs that used to yield a silent PASS now raise: non-finite X_clean, zero-sample or zero-feature matrices, non-finite model output, higher-rank model output (which previously corrupted flipped_indices).
• The domain clip never moves a sample further than ε·scale, so an out-of-domain sample cannot manufacture a flip.
• wilson_upper_bound matches values derived independently by solving the score equation, and stays non-zero at zero flips.
• A 0-flip run on 50 samples is is_robust but not is_robust_at_ci; the same result on 5000 samples clears both.
• Categorical (string) class labels are compared directly instead of raising.
• perturb() honours the ε budget and the feasible domain, and is seeded.
• Legacy worst_case_sign alias routes to random_sign (deprecation logged).
• report.as_dict() round-trips through JSON and carries seed, epsilon and flip_tolerance_pct for the model card.
• batch_size chunking is byte-identical to whole-array evaluation.
• Non-2D input raises ValueError.

Confirm with the operational checklist in assets/checklist.md before promoting.

Related Skills

• feature-engineering-without-leakage — the validation set fed to this gate must be leak-free or the robustness verdict is invalid.
• backtest-outlier-and-bad-tick-filtering — adversarial perturbations and bad-tick outliers are two faces of the same noise; filter the latter, stress the former.
• reproducible-ml-training-pipelines — the seed discipline this gate depends on.
• model-card-documentation-for-trading-models — persist the RobustnessReport (seed, ε, noise model, vulnerability score) into the model card.
• model-versioning-and-rollback — a regression in vulnerability score between versions should block the promotion.
• backtest-parameter-sensitivity-analysis — for non-ML / rule-based strategies, where adversarial perturbation reduces to parameter sensitivity.
• concept-drift-vs-staleness-differentiation — live complement: this gate is pre-deployment; drift monitoring is post-deployment.
• class-imbalance-handling-for-rare-signal-events — rare-signal models have few positive samples to flip; the flip-rate CI is correspondingly wide.