In the previous two articles of the series we discussed what an event-driven backtesting system is and the class hierarchy for the Event object. In… ( Read More )
Event-Driven Backtesting with Python - Part II
In the last article we described the concept of an event-driven backtester. The remainder of this series of articles will concentrate on each of the… ( Read More )
Event-Driven Backtesting with Python - Part I
We've spent the last couple of months on QuantStart backtesting various trading strategies utilising Python and pandas. The vectorised nature of pandas ensures that certain… ( Read More )
Successful Backtesting of Algorithmic Trading Strategies - Part II
In the first article on successful backtesting we discussed statistical and behavioural biases that affect our backtest performance. We also discussed software packages for backtesting,… ( Read More )
Successful Backtesting of Algorithmic Trading Strategies - Part I
This article continues the series on quantitative trading, which started with the Beginner's Guide and Strategy Identification. Both of these longer, more involved articles have… ( Read More )
Value at Risk (VaR) for Algorithmic Trading Risk Management
Value at Risk (VaR) for Algorithmic Trading Risk Management Estimating the risk of loss to an algorithmic trading strategy, or portfolio of strategies, is of… ( Read More )
Should You Build Your Own Backtester?
About This Post The post is suitable for those who are beginning quantitative trading as well as those who have had some experience with the… ( Read More )
Money Management via the Kelly Criterion
Risk and money management are absolutely critical topics in quantitative trading. We have yet to explore these concepts in any reasonable amount of detail beyond… ( Read More )
Sharpe Ratio for Algorithmic Trading Performance Measurement
When carrying out an algorithmic trading strategy it is tempting to consider the annualised return as the most useful performance metric. However, there are many… ( Read More )