Your Algorithms Trade Billions.
Can You Explain Why?

How does an algo compliance system handle SEC Rule 15c3-5 and MiFID II RTS 6 at the same time?

The core challenge is that SEC Rule 15c3-5 and MiFID II RTS 6 overlap in intent but diverge in specifics. Rule 15c3-5 requires pre-trade risk controls (capital thresholds, credit limits, erroneous order filters) with annual CEO certification. RTS 6 requires algorithmic trading firms to maintain records of algo decisions for five years, submit strategy descriptions to competent authorities, and demonstrate that compliance staff can technically review algo processes.

We build a unified control layer where each risk control maps to both frameworks simultaneously. A single pre-trade capital threshold check, for example, satisfies Rule 15c3-5's market access requirement and RTS 6's risk control documentation requirement. The system maintains dual audit trails: one formatted for FINRA examination and one for FCA/national competent authority review. When you modify a control parameter, the system shows the compliance impact across both jurisdictions before the change takes effect.

For firms also operating under DORA, we add a third mapping layer covering ICT risk management and incident reporting obligations. The alternative, maintaining separate compliance processes per jurisdiction, is how most firms operate today. It works until a cross-border incident hits and three regulators ask different questions about the same event.

What does an explainable algo audit actually produce when a regulator asks why an algorithm executed a specific trade?

When a regulator asks why your algorithm sold 200,000 shares of a particular stock at 9:47 AM during a volatility spike, they need more than order logs showing the trade happened. They need the decision chain.

Our audit system reconstructs four layers for any flagged trade. First, the market state snapshot: what data the algorithm received at that timestamp, including order book depth, spread width, volatility readings, and cross-asset signals. Second, the model attribution: which features drove the decision and by how much, using SHAP values that show, for example, that 43% of the sell signal came from a VIX spike, 31% from order book imbalance, and 26% from a cross-asset correlation shift. Third, the rule evaluation log: which deterministic constraints were checked (position limits, capital thresholds, instrument restrictions) and whether any triggered. Fourth, the counterfactual: what the algorithm would have done under different conditions, such as if the VIX reading had been 10% lower or if the cross-asset signal had been absent.

This produces a structured report that a compliance officer can hand to an examiner. The reconstruction runs on historical data, so you can audit trades from weeks or months ago, not just real-time events.

How do you reduce false positives in trade surveillance without missing real manipulation?

The 25%+ false positive rate that 70% of banks report comes from a fundamental design problem: most surveillance systems apply static thresholds to individual trades or patterns without considering context. A large block trade looks identical to layering if you only examine order size and cancellation rate.

We build context-aware alert scoring across three dimensions. First, trader behavioral baselines: the system learns each trader's normal patterns (instruments, timing, size distribution, cancellation rates) and flags deviations from their own baseline, not a generic threshold. A market maker who routinely places and cancels large orders generates different alerts than a portfolio manager who does the same thing. Second, market regime adjustment: during high-volatility periods like the August 2024 flash crash, normal algorithmic behavior looks abnormal by static standards. The system adjusts thresholds based on current regime (bull, bear, crisis, low-liquidity) so a volatility-driven sell-off doesn't generate hundreds of false spoofing alerts. Third, cross-reference scoring: before escalating an alert, the system checks whether the pattern appears across multiple venues, whether the trader has counterparty relationships that explain the activity, and whether similar patterns are occurring market-wide (suggesting regime-driven behavior rather than manipulation).

Each alert gets a composite confidence score. Compliance teams review high-confidence alerts first, and the system continuously refines scoring based on which alerts lead to genuine investigations versus dismissals.

What does the EU AI Act mean for algorithmic trading systems, and what is the August 2026 deadline?

The EU AI Act classifies certain AI applications as high-risk, requiring conformity assessments, technical documentation, CE marking, and registration in the EU database. Financial credit scoring is explicitly listed as high-risk. Whether algorithmic trading AI qualifies was expected to be clarified in the European Commission's February 2026 guidelines on high-risk classification.

Regardless of the final classification, firms using AI in trading face three practical requirements by August 2, 2026. First, documentation: you need technical documentation describing your AI system's purpose, architecture, training data, performance metrics, and known limitations. Most algo trading firms have code repositories but not the structured documentation the Act requires. Second, risk management: you need a documented risk management system that identifies and mitigates risks throughout the AI lifecycle, including testing under conditions that differ from training data, which matters enormously for trading algorithms that may have been developed during low-volatility periods. Third, human oversight: the system must allow human intervention, including the ability to override or stop the AI system. For trading, this means documented kill switch procedures, escalation paths, and evidence that human operators can actually interpret what the system is doing.

The EBA published a report in November 2025 on AI Act implications for banking, which we use as the baseline for gap assessments. The Act has global reach: if your AI system interacts with EU-based users or markets, you are in scope regardless of where your firm is incorporated.

How long does it take to build a custom algo compliance system, and what does a typical engagement look like?

A typical engagement runs 4 to 8 months from kickoff to production, depending on scope and the state of your existing infrastructure. The first phase (4 to 6 weeks) is a compliance architecture assessment. We audit your current algorithmic trading controls against every jurisdiction you operate in, map your existing surveillance tools and their gaps, review your algo inventory and documentation state, and identify which regulatory deadlines are most pressing. This phase produces a gap report and a prioritized build plan.

The second phase (8 to 16 weeks) is system build. We work within your existing infrastructure, not replacing your OMS or surveillance vendor, but building the compliance intelligence layer that sits between them. This includes the explainability pipeline (decision chain reconstruction, SHAP attribution), the multi-jurisdictional control mapping, and the alert triage system. Integration with existing data feeds (order management, market data, execution records) is typically the longest single task.

The third phase (4 to 6 weeks) is validation and regulatory alignment. We run the system against historical data, validate audit outputs against actual regulatory examination formats, tune alert thresholds against your false positive baseline, and document everything to EU AI Act and RTS 6 standards.

One caveat: if your algo inventory is poorly documented (the FCA found this at most firms they reviewed), add 3 to 4 weeks for the discovery and cataloging work that must happen before any compliance system can be built on top of it.

Can this work with our existing surveillance vendor (NICE Actimize, Eventus, Nasdaq), or does it replace them?

It works alongside your existing vendor. Replacing an enterprise surveillance platform is a multi-year, multi-million dollar project that most firms should not undertake unless the platform is fundamentally broken.

What we build fills the gaps your current vendor does not cover. NICE Actimize, Eventus, and Nasdaq Surveillance are strong at pattern-based detection: spoofing, layering, wash trading, front-running. They scan order flow for known manipulation signatures and generate alerts. What they typically lack is three things. First, algo decision explainability: when a regulator asks why your algorithm did something, your surveillance vendor can show that the trade happened and whether it matched a manipulation pattern, but not why the algorithm made that decision. The explainability layer we build reconstructs the decision chain from your algorithm's own logic, model features, and market state. Second, cross-regulatory compliance mapping: your surveillance vendor detects market abuse, but it does not map your algorithmic trading controls to SEC Rule 15c3-5 requirements, MiFID II RTS 6 self-assessment criteria, EU AI Act documentation obligations, and DORA resilience standards simultaneously. That mapping is what we build. Third, intelligent circuit breakers: your vendor generates alerts after trades execute. The circuit breaker system we build intervenes before or during execution with graduated responses based on the severity and pattern of deviation.

The integration is typically through your vendor's API or alert feed. We consume their alerts as one input into the broader compliance picture, adding context, cross-referencing, and explainability on top of what they already detect.