deepdow
by jankrepl
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Build the foundations
Suggested foundations for portfolio construction and financial machine learning. Specialist prerequisites vary by resource.
Before you read
- Eigenvalues and Covariance Matrices
Understand the covariance structure of portfolio risk.
- Constrained and Convex Optimisation
Read objectives, constraints and optimality conditions.
- 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
- Mean-Variance Optimisation
Construct allocations and examine sensitivity to estimated inputs.
- Portfolio Constraints
Turn mathematical weights into feasible portfolio decisions.
- Finance-aware validation
Account for dependence and overlapping observations.
Explore a related reading path
- Portfolio construction and risk
Connect diversification, common risk factors, investor views, and the properties of a risk measure.
- From trading ideas to honest backtests
Choose a reproducible strategy, specify the trading rule, and test what remains after costs and estimation uncertainty.