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Burns Statistics
Investment technology for the 21st century
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About Portfolio Probe
Software Quality Assurance
Applications of random portfolios
Assess Risk Models
Bid on a Portfolio
Evaluate Constraint Bounds
Performance Attribution
Performance Fees
Performance Measurement
Portfolio Construction Process Attribution
Quantitative Research
Test a Trading Strategy
Frequently Asked Questions
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Chief Investment Officer
Fund of Funds Manager
Fundamental Fund Manager
Hedge Fund Manager
Investment Consultant
Performance Measurement and Attribution
Plan Sponsor
Quantitative Fund Manager
Quantitative Researcher
Risk Manager
News
Random Portfolios in Finance
Key Features
Computing Engine
Constraints
Asset Trade Constraints
Cost Constraint
Count Constraints
Distance Constraints
Expected Return Constraints
Forced Trade Constraints
Linear Constraints
Long-only Constraint
Maximum Weight Constraints
Monetary Value Constraints
Number of Assets Held Constraints
Number of Assets Traded Constraint
Number of Closing Positions Constraint
Quadratic Constraints
Risk Fraction Constraints
Sum of Largest Weights Constraints
Threshold Constraints
Tracking Error Constraints
Trade Universe Constraint
Turnover Constraint
Volatility Constraints
Generate Random Portfolios
Portfolio Optimization
Transaction Costs
Utility-free Optimization
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Extra packages
Frequently Asked Support Questions
Documentation
Portfolio Probe Cookbook
1. Data basics
Add benchmark to variance matrix
Example data
Prices to returns
Read a comma-separated file into R
Read a tab-separated file into R
Returns to variance matrix
2. Generate Random Portfolios
Asset limits
Create and plot valuations
Give a range for turnover
Returns and realized volatility
Very simple long-only
Very simple long-short
Volatility and tracking error constraints
3. Optimize Trades
Active with benchmark
Active, no benchmark
Asset allocation
Asset limits
Compute a technical indicator
Control turnover
Create and plot portfolio valuations
Dollar neutral (and general case)
Impose transaction costs
Minimum variance with tracking error constraint
Passive with benchmark (minimum tracking error)
Passive, no benchmark (minimum variance)
Realized portfolio returns and volatility
Scenario optimization
First four moments utility
Generate historical scenarios
Generate statistical scenarios
Maximize the omega ratio
Maximize value
Write your own utility function
Write optimization results to a file
8. C++ and Portfolio Probe
9. R Notes
Using R packages
User’s Manual
Support policy
Some hints for the R beginner
Contact
Blog
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