Backtested Strategy Deployment for a Quantitative Trading Operation
Quantitative TradingMoving systematic trading strategies from idea to backtest to live, risk-managed deployment, so new ideas reach the market without months of manual work.
Overview
A quantitative research and trading operation needed to move strategies from idea to backtested to live, risk-managed deployment without each stage becoming a months-long project. Canvas Labs Partners built an automated Monte Carlo simulation pipeline as the firm's research backbone, then deployed a live systematic multi-asset momentum strategy on it, with ML-driven position sizing and automated execution through the IBKR API.
Research-to-live pipeline shipped
a Monte Carlo simulation engine underpins every strategy that reaches production
200+ strategies backtested
by a team of 5 analysts over 2 years
25% CAGR live
systematic momentum strategy running with a Sharpe ratio above 1.0
Challenge
Turning a trading idea into a backtested strategy, and a backtested strategy into a live, risk-managed one, kept turning into its own months-long project. With a research team of five, that meant analyst time spent re-running the same three stages for every new idea, instead of moving it from idea to backtested to live deployment on a predictable timeline.
Solution
The foundation is an automated Monte Carlo simulation pipeline built in Python, the firm's core research backbone. It let the team move a strategy from idea to backtested result without each pass through the pipeline becoming its own bespoke piece of analysis, giving analysts a repeatable way to test an idea before it earned a place in live capital.
On top of that engine, Canvas Labs Partners co-designed and deployed a live systematic multi-asset momentum strategy, with ML-driven volatility position sizing deciding how much capital each signal receives. Sizing by volatility, rather than a fixed allocation, is what keeps the risk-managed half of the mandate holding as conditions shift across the assets the strategy trades.
Trade execution runs through the IBKR API, connecting the strategy's signals straight to the broker without a manual order-entry step between a signal firing and a position opening. That link is what turns a backtested momentum strategy into one running live, with research and execution sharing the same systematic process from idea through to position.
Results & benefits
Once live, the systematic multi-asset momentum strategy has run with a 25% CAGR and a Sharpe ratio above 1.0, risk-managed through the same ML-driven position sizing that governs every trade. Behind it, the Monte Carlo research pipeline has let five analysts backtest more than 200 strategies over two years, turning what used to be a months-long project at every stage into a repeatable path from idea to live capital. The firm now moves a strategy from first idea to backtested result to live, risk-managed deployment on one engine, rather than three separate efforts on three separate timelines.
Customer
Quantitative research and trading operation
Services
- Quantitative strategy research & backtesting
- Systematic trading deployment
- Broker execution integration
Tech stack
About us
Canvas Labs Partners is a software consulting team that designs and ships production agentic AI systems: multi-agent pipelines, tool-calling architectures, and human-governed automation, together with the trading infrastructure, blockchain, mobile, and product engineering those agents plug into. We publish our work as a case ledger rather than a portfolio, so every claim on this site links to the engagement behind it. Engagements follow the same four-step discipline, Map, Bound, Instrument and Govern, whatever the stack, and every entry in the ledger carries a result we are willing to be measured against.
Delivered individually, in a prior role, by one of Canvas Labs Partners’ founding team members. Not by the firm itself.
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