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Machine-Learning FX Trading Platform: Model Audit → Cloud-Deployed Paper Trading

Foreign exchange Algorithmic trading Machine learning Backtesting Feature engineering Paper trading Cloud deployment MLOps Broker API integration

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

Client Private Fintech Investor, UAE/UK

Industry Financial Services (FX / Systematic Trading)

Region UAE & UK

Engagement Multi-phase research, validation, and deployment (sanitized)

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

Language & stack

Python (research orchestration, backtesting, reporting), lightweight services for scheduling and monitoring

Broker & data

Generic broker API (paper/live endpoints), institutional-grade market data adapters (providers anonymized)

Cloud

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

Quant Lead

ML Engineer

0.5

Cloud/DevOps Engineer

Research Analyst

Delivered as a coordinated, cross-functional team to move from audit to deployment without revealing client-specific tactics.

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.

Start your project → Schedule a consultation →

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