Thus it is possible to "drip feed" an event-driven backtester with market data, replicating how an order management and portfolio system would behave.
#Python event driven programming syslog update#
Redraw_screen() # Update the screen to provide animation # Based on the event type, perform an actionĮlif new_event.type = "ESCAPE_KEY_PRESS": New_event = get_new_event() # Get the latest event Here is some example pseudo-code: while True: # Run the loop forever Depending upon the nature of the event, which could include a key-press or a mouse click, some subsequent action is taken, which will either terminate the loop or generate some additional events. This is handled by running the entire set of calculations within an "infinite" loop known as the event-loop or game-loop.Īt each tick of the game-loop a function is called to receive the latest event, which will have been generated by some corresponding prior action within the game. A video game has multiple components that interact with each other in a real-time setting at high framerates.
#Python event driven programming syslog software#
Video games provide a natural use case for event-driven software and provide a straightforward example to explore. Event-Driven Softwareīefore we delve into development of such a backtester we need to understand the concept of event-driven systems. In this series of articles we are going to discuss a more realistic approach to historical strategy simulation by constructing an event-driven backtesting environment using Python. However the forms of vectorised backtester that we have studied to date suffer from some drawbacks in the way that trade execution is simulated. The vectorised nature of pandas ensures that certain operations on large datasets are extremely rapid. We've spent the last couple of months on QuantStart backtesting various trading strategies utilising Python and pandas.