ML Trading System with 40-80% Annual Returns

Context & Challenge

The client operated in markets with frequent "information gaps" and wanted a systematic, machine-learning based strategy to exploit these opportunities without introducing uncontrolled risk. They needed a framework that could move from research signals to robust live trading infrastructure.

Approach

• 1

  Analysed historical market data to identify exploitable inefficiencies.

• 2

  Designed ML pipelines to generate and evaluate candidate signals.

• 3

  Built a backtesting environment with realistic transaction costs and constraints.

• 4

  Defined risk metrics and guardrails with the client's team.

Solution

We built a modular trading system that ingests live data, generates signals, and executes trades under strict risk parameters. The system was designed so that new research models could be added without rewriting the core infrastructure.

Results & Impact

• Consistently delivered 40-80% annual returns over the evaluation period.

• Achieved a Sharpe ratio in the 2.5-3.0 range.

• Created a repeatable process for adding and testing new ML signals.