This document discusses backtesting strategies for algorithmic trading. It outlines several methods for backtesting including using historical data, optimizing parameters, and sensitivity analysis. It also describes using bootstrapped data and Monte Carlo simulations with different models like random walk, AR, and GARCH to further analyze strategies. The document notes backtesting requires significant computing power to run all tests in parallel on a grid.

1. Backtesting
Haksun Li
haksun.li@numericalmethod.com
www.numericalmethod.com

2. Speaker Profile
 Dr. Haksun Li
 CEO, Numerical Method Inc.
 (Ex-)Adjunct Professors, Industry Fellow, Advisor,
Consultant with the National University of Singapore,
Nanyang Technological University, Fudan University,
the Hong Kong University of Science and Technology.
 Quantitative Trader/Analyst, BNPP, UBS
 PhD, Computer Sci, University of Michigan Ann Arbor
 M.S., Financial Mathematics, University of Chicago
 B.S., Mathematics, University of Chicago
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3. Strategy
AlgoQuant Framework
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SignalData
on depth update on news update
standarized P&L report

4. Backtesting
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 Backtesting with historical data.
 Backtesting with optimized parameters.
 Sensitivity analysis for all parameters.
 Backtesting with carefully chosen parameters.
 Identify the P&L sources.
 Backtesting with bootstrapped data.

5. Historical P&L
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6. Optimized P&L
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7. Sensitivity Analysis of Parameters (1)
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8. Sensitivity Analysis of Parameters (2)
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9. Sensitivity Analysis of Parameters (3)
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10. Backtesting Using Recommended Parameters
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11. Monte Carlo Simulation – Random Walk with
Drift
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12. Monte Carlo Simulation – AR(1)
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13. Monte Carlo Simulation – ARMA(1,1)
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14. Monte Carlo Simulation – AR(1)+GARCH(1,1), φ
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15. Monte Carlo Simulation – AR(1)+GARCH(1,1), b
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16. Bootstrapping
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 Expected P&L and Variance of P&L
 Politis, N. Dimitris, White Halbert, "Automatic Block-
Length Selection for the Dependent Bootstrap",
Econometric Reviews , 2004.
 Politis, D., White, H., Patton Andrew,"CORRECTION
TO 'Automatic Block-Length Selection for the
Dependent Bootstrap'", Econometric Reviews,
28(4):372–375, 2009.

17. Computing Power
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 Very demanding!
 Need to run all different types of backtesting in
parallel on a grid.