Machine-Learning FX Trading Platform: Model Audit → Cloud-Deployed Paper Trading
A private investor engaged Marsbridge to validate and harden a third-party FX trading model and stand up a production-style paper-trading environment. We identified and removed sources of data leakage, rebuilt the evaluation/reporting stack, broadened the feature pipeline, and deployed a resilient cloud runtime integrated with a broker API simulator/live gateway—with automation and monitoring for long unattended runs.
Customer
Scope: (a) audit the delivered strategy, (b) establish credible backtests and risk reporting, (c) recommend model and process improvements, and (d) bring up a paper-trading stack with a path to live trading—without disclosing proprietary trading heuristics.
Challenge
- Model validity & bias control: Detect and remove look-ahead/data-leakage issues; re-establish clean train/test splits.
- Backtesting realism: Produce auditable portfolio and risk metrics with reproducible runs and clear assumptions.
- Cost-aware design: Select a prediction/trading cadence that balances signal quality and transaction costs, without revealing timing specifics.
- Cloud & broker integration: Provision a fault-tolerant runtime and integrate a broker API for paper execution, with minimal operational burden.
- Data integrity & ops stability: Ensure logging correctness, safe restarts, and recoverability after interruptions—avoiding disclosure of internal runbooks.
Solution
Approach: A compact Marsbridge squad—Quant Lead, ML Engineer, Cloud/DevOps Engineer, and Research Analyst—delivered iterative drops from model audit to stable paper trading.
Model audit & bias removal
Replicated baseline results; identified leakage/label drift; refactored pipelines to enforce causal ordering and proper evaluation windows.
Backtesting & MLOps
Implemented a vectorized backtester with portfolio-level analytics and added process supervision, health checks, and periodic automated restarts for MLOps resilience.
Paper trading & broker/API integration
Deployed a cloud runtime with broker API connectivity (paper environment), synchronized positions/orders, and implemented defensive order templates.
Technologies & tools
Python (research orchestration, backtesting, reporting), lightweight services for scheduling and monitoring
Generic broker API (paper/live endpoints), institutional-grade market data adapters (providers anonymized)
Virtualized compute with secure access and automated start/stop, storage for artifacts and logs (providers anonymized)
Process
- Discovery & replication: Confirm scope, reproduce baseline results, and map potential leakage paths.
- Bias control & refactor: Enforce causal data flow; re-baseline the model on corrected pipelines.
- Research iteration: Broaden features and targets; run controlled experiment batches with consistent reporting.
- Backtesting & analytics: Deliver portfolio-level analytics and governance-ready summaries.
- Paper-trading deployment: Bring up cloud runtime; connect broker API; validate end-to-end orders in a safe environment.
- Hardening & runbooks: Add observability, automated restarts, and recovery protocols; finalize handover materials.
Team
Results
- Credible baselines: Eliminated leakage; established auditable backtests and clear performance accounting.
- Operational readiness: Stable paper-trading workflows with recoverable processes and standardized runbooks (details withheld).
- Decision clarity: Comparable experiment results and governance-grade reports simplified go/no-go decisions for live staging.
- Future-proofing: A modular path to live trading with configurable timing/parameters—kept private to the client.
All strategy parameters, trade horizons, instrument lists, and PnL snapshots have been intentionally removed or generalized to protect client confidentiality.
Stand Up a Credible ML Trading Stack
Need an ML strategy you can run (and defend)? We turn models into audit-ready backtests and resilient paper-trading systems—with clean MLOps and zero exposure of your proprietary rules.
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