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Microsoft Qlib
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by microsoft
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backtestingfactor researchfeature engineeringfinancial machine learningpythonquant research
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Build the foundations
Suggested foundations for trading research and honest backtests and financial machine learning. Specialist prerequisites vary by resource.
Before you read
- Point-in-Time Data
Use only information available at the simulated decision time.
- Time-Ordered Train, Validation and Test Design
Preserve time order and avoid future information in training.
- Linear Regression
Establish an interpretable baseline before a flexible model.
Go further
- Positions, Trades, Fills and P&L
Reconcile signals, executions, positions and P&L.
- Financing, Borrow and Transaction Costs
Include the costs that can erase a paper strategy.
- Finance-aware validation
Account for dependence and overlapping observations.
Explore a related reading path
- From trading ideas to honest backtests
Choose a reproducible strategy, specify the trading rule, and test what remains after costs and estimation uncertainty.