In this article we will make use of the machinery we introduced to carry out research on an actual strategy, namely the Moving Average Crossover on AAPL. Moving Average Crossover Strategy The Moving A...
In this article I want to introduce you to the methods by which I myself identify profitable algorithmic trading strategies. Our goal today is to understand in detail how to find, evaluate and select ...
It's been a while since we've considered the event-driven backtester, which we began discussing in this article. In Part VI I described how to code a stand-in ExecutionHandler model that worked for a ...
In the last article on the Event-Driven Backtester series we considered a basic ExecutionHandler hierarchy. In this article we are going to discuss how to assess the performance of a strategy post-bac...
This article continues the discussion of event-driven backtesters in Python. In the previous article we considered a portfolio class hierarchy that handled current positions, generated trading orders ...
In the previous article on event-driven backtesting we considered how to construct a Strategy class hierarchy. Strategies, as defined here, are used to generate signals, which are used by a portfolio ...
The discussion of the event-driven backtesting implementation has previously considered the event-loop, the event class hierarchy and the data handling component. In this article a Strategy class hier...
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 this article we are going to consider how market dat...
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 separate class hierarchies that make up the overall...
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 operations on large dataset...