Overview
Automated trading systems operate in an environment where software errors have direct financial consequences — a bug in order sizing logic, a race condition in position tracking, a failure mode in risk enforcement that allows a drawdown to exceed its intended limit. Unlike most software categories where an error causes an inconvenience that gets fixed in the next release, a trading system error can cause significant losses in minutes before anyone has time to intervene. The cost of a trading system audit is measured against the cost of the losses it prevents, not against the cost of ordinary software quality assurance.
An algorithmic trading audit is a structured technical review of an automated trading system by engineers with specific experience in trading system design and operation. It examines the correctness of the trading logic, the reliability of the execution infrastructure, the completeness of the risk controls, the quality of the position and P&L tracking, and the operational resilience of the system under the conditions that matter — network failures, exchange API disruptions, unexpected market conditions, and the edge cases that normal operation never exercises.
The audit is not a performance review of the trading strategy — whether the strategy has positive expected value is the strategy developer's domain. The audit focuses on whether the system implementing the strategy does so correctly and safely — whether the orders placed match the strategy's intentions, whether risk limits are enforced correctly, whether position tracking remains accurate under concurrent execution, whether the system recovers correctly from failures, and whether the operational visibility is sufficient to detect and respond to problems before they escalate.
We perform algorithmic trading audits for systematic trading firms, prop traders, fund managers, and technology companies that build and operate automated trading systems — covering the full range of trading system architectures from MetaTrader Expert Advisors and Python execution systems to custom Rust-based execution engines and multi-venue execution platforms.
What an Algorithmic Trading Audit Covers
Order logic review. The correctness of the order generation, sizing, and submission logic — the translation from strategy signal to executed order.
Order sizing logic: the calculation that determines position size — whether the sizing respects the account balance, the configured risk per trade, the instrument's contract size and tick value, and any portfolio-level constraints. The edge cases in sizing logic — what happens at round-number boundaries, at very small account balances, when the calculated size is below the broker's minimum order size. The precision handling that prevents floating-point rounding errors from producing incorrectly sized orders or causing accumulating discrepancies.
Order type correctness: whether the order types used match the strategy's execution requirements — market orders versus limit orders, stop orders versus stop-limit orders, the time-in-force parameters that control how long orders remain active. The order type selection in volatile market conditions where limit orders may not fill and market orders may execute at unexpected prices.
Entry and exit logic: whether the entry and exit signals are processed in the correct sequence — the race condition where an exit signal arrives before the entry order has been confirmed, the duplicate signal that could cause a double entry, the signal that arrives while a previous order for the same instrument is still open. The order state machine that correctly tracks each order through its lifecycle.
Order cancellation and replacement: the logic for cancelling open orders and replacing them with updated orders — the window where both the old and new orders could be simultaneously active, the handling of partial fills that occur during the replacement process.
Risk control review. The enforcement mechanisms that prevent the system from taking positions or incurring losses beyond the configured limits.
Drawdown limits: the maximum drawdown enforcement — whether the drawdown calculation is correct (realised plus unrealised losses, not just realised), whether the enforcement fires before the limit is breached rather than after, and whether enforcement correctly handles the case where a single trade's loss could exceed the drawdown limit. The daily drawdown reset logic that correctly resets the drawdown counter at the start of each trading day.
Position size limits: the maximum position size enforcement per instrument, per sector, and portfolio-wide. The enforcement that prevents the system from building a position exceeding the limit through multiple incremental entries. The limit check that runs before order submission, not after, to prevent breaches.
Exposure limits: the total market exposure limit that prevents excessive leverage. The exposure calculation that correctly accounts for notional value rather than margin requirement. The multi-instrument correlation that means two individually within-limit positions may together represent excessive correlated exposure.
Loss per trade limits: the maximum loss permitted on a single trade — the stop-loss placement that enforces this limit and the handling of gap risk where the market opens through the stop level.
Emergency halt: the kill switch that stops all trading immediately — the mechanism that a human operator can trigger and that the system can trigger automatically when anomalous conditions are detected. The halt that cancels all open orders and prevents new orders from being placed, and the confirmation that the halt has been successfully applied.
Position tracking accuracy. The correctness of the system's internal record of current positions — the source of truth that risk calculations and sizing decisions depend on.
Position reconciliation: the process that compares the system's internal position record against the broker's or exchange's actual position data. The reconciliation frequency, the discrepancy detection, and the resolution procedure when discrepancies are found. The initial position synchronisation at system startup that correctly reads the current state from the broker before the system begins trading.
Fill handling: the processing of execution reports — each fill correctly updating the internal position record. The partial fill handling that correctly accounts for partially executed orders. The fill deduplication that prevents the same fill from being processed twice when the execution report is delivered more than once.
Multi-instrument netting: for strategies that trade multiple instruments with offsetting exposure, the position netting that correctly calculates net exposure. The currency conversion that correctly translates positions in different denominations to a common base currency for portfolio-level calculations.
Concurrent access: the thread safety of the position tracking data structure — whether concurrent order fill processing, risk calculation, and signal generation access the position data in a way that could produce race conditions or inconsistent reads. The locking strategy that prevents concurrent access from corrupting position state.
P&L calculation. The accuracy of the system's profit and loss calculation — the metrics that tell the operator whether the system is performing as expected.
Realised P&L: the P&L from closed positions — the correct calculation of entry price, exit price, quantity, and commission for each closed trade. The handling of partial closes and position reversals. The currency conversion for trades in non-base-currency instruments.
Unrealised P&L: the mark-to-market P&L on open positions — using the correct current price (bid, ask, or mid depending on the position direction and accounting convention). The unrealised P&L that updates correctly as prices change.
Commission and fee accounting: the accurate inclusion of commissions, financing costs, and exchange fees in P&L calculations. The fee calculation that matches the broker's actual fee structure — the tiered fees, the spread markup, the overnight financing rates. The cumulative fee tracking that prevents P&L from appearing better than it is by excluding trading costs.
P&L attribution: the breakdown of P&L by strategy, instrument, time period, and trade — the attribution that allows understanding where the system's edge is concentrated and where it is losing money.
Execution infrastructure reliability. The robustness of the components that connect the trading logic to the market.
Connection management: the broker or exchange API connection lifecycle — the reconnection logic that re-establishes connections after network interruptions, the order state reconciliation after reconnection that correctly identifies which orders were placed, filled, or cancelled during the disconnection. The heartbeat monitoring that detects stale connections before they cause order submission failures.
Order submission reliability: the retry logic for order submission failures — the handling of timeout errors where the order may or may not have been accepted, the idempotency mechanism that prevents duplicate order submission on retry. The confirmation that each submitted order is tracked through to a terminal state.
Market data reliability: the detection and handling of stale market data — the timestamp check that identifies when the last price update was received, the staleness threshold beyond which the system stops trading, the reconnection and data recovery logic. The handling of data gaps that could cause indicators to produce incorrect values.
Clock synchronisation: the time synchronisation between the trading system's clock and the exchange's clock — the time discrepancy that can cause orders to be submitted with incorrect timestamps, affecting time-in-force calculations.
Backtesting and forward testing validity. The correctness of the testing infrastructure that validates strategy performance.
Lookahead bias: the check for future data leakage in the backtesting framework — the indicator calculation that accidentally uses data from after the decision point, the order fill logic that fills at the close price of the same bar that generated the signal. The temporal correctness validation that confirms the backtest processes data in strict chronological order.
Execution realism: the execution model that realistically reflects the actual costs and constraints of live trading — the slippage model that accounts for market impact, the fill model that does not assume fills at prices that the market only touched briefly, the commission model that matches the actual broker fee structure.
Overfitting detection: the strategy parameterisation that avoids curve-fitting to historical data — the out-of-sample validation, the walk-forward testing, and the sensitivity analysis that reveals whether performance is robust to small parameter changes.
Audit Process
System documentation review. The audit begins with reviewing available documentation — the strategy specification, the system architecture, the risk management rules, the operational procedures. Where documentation is incomplete or missing, the audit team works from code review and interviews.
Code review. Systematic review of the trading system's source code — the order management logic, the risk enforcement, the position tracking, the execution infrastructure, and the configuration management. The review that identifies correctness issues, missing edge case handling, and potential failure modes.
Live system observation. Where possible, observation of the system operating in a live or paper trading environment — the operational behaviour that code review alone cannot reveal. The order flow, the position updates, the risk checks, and the logging under actual market conditions.
Reconciliation testing. Comparison of the system's internal state against external records — the position records against broker statements, the P&L calculations against the actual account balance changes, the order records against the broker's order history.
Findings report. A structured report categorising identified issues by severity — critical issues that require immediate remediation, significant issues that should be addressed before the system's trading size is increased, and observations about areas for improvement. For each finding, a description of the issue, the risk it presents, and recommended remediation.
Technologies Covered
The audit covers trading systems built across the full range of trading system technologies:
- MetaTrader 4 and MT5 Expert Advisors — MQL4/MQL5 code review, execution logic, risk enforcement
- Python trading systems — strategy logic, execution libraries (CCXT, ib_insync, Alpaca), backtesting frameworks
- Rust execution systems — low-latency order management, position tracking, WebSocket connectivity
- C# trading platforms — .NET-based execution systems, Interactive Brokers TWS API integration
- Multi-venue execution — cross-exchange position aggregation, multi-broker order routing
- Cryptocurrency trading systems — exchange API integration, DeFi protocol interaction
Who Benefits from a Trading Audit
Systematic traders scaling up. The trader who has been running a system successfully at small size and wants to increase position sizes or add capital. The audit that validates whether the risk controls that worked adequately at small scale will hold at larger scale, and identifies the failure modes that become more consequential as the stakes increase.
New system deployment. The trading system that has been developed and backtested and is ready for live deployment. The pre-deployment audit that catches correctness issues before real capital is at risk.
Post-incident review. The system that experienced an unexpected loss, an unintended position, or anomalous behaviour. The audit that identifies what went wrong, why the risk controls did not prevent it, and what changes are required.
Investor and stakeholder due diligence. The fund or trading operation seeking investment or institutional backing where investors require independent verification that the trading infrastructure operates as described.
Operational handover. The trading system being handed over to a new team or operator — the audit that documents the system's behaviour, identifies undocumented dependencies and fragile components, and produces the technical knowledge transfer that the new operators need.
An Honest Assessment, Not a Rubber Stamp
A trading system audit is only useful if it is honest. The value is in finding the problems that the people closest to the system have stopped seeing — the assumption that has been encoded in the logic that turned out to be wrong, the edge case that was considered unlikely and never tested, the risk control that enforces the intended limit in normal conditions but has a bypass in an edge case.
The audit report will contain findings that are uncomfortable. That is the point. The discomfort of reading an audit finding is far preferable to the financial consequence of the issue it describes.