himanshuj16/algo-trading-skills/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.

1. The flipped-sample indices — so quant research can attribute which

inputs sit on the decision boundary.

1. 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.

1. Configure the perturbation budget. Construct an AdversarialRobustnessConfig:

```python 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, ) ```

1. Run the evaluation:

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

1. 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.

1. 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.

Decision Points

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: