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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 and kept track of profit and loss (PnL). Read more...
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 object in order to make decisions on whether to send orders. As before it is natural to create a
Portfolio abstract base class (ABC) that all subsequent subclasses inherit from.
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 hierarchy will be outlined.
Strategy objects take market data as input and produce trading signal events as output.
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 data is utilised, both in a historical backtesting context and for live trade execution. Read more...
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