Python FX Backtesting Framework.
Basic instructions - BacktestRunFile.py
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Install necessary modules such as datetime, pandas, numpy as well as ta for the strategies.
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Data read - comment out either Backtest.readAndPrepData or Backtest.inputDataAndInfo. Further info below as well as context inside the functions.
a) Backtest.readAndPrepData method: Set up dates, data directory, time column label and delimitter.
b) Backtest.inputDataAndInfo method: Read data and handling formatting + input currency and frequency.
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Import the trading strategy from the module. Details on how this must be set up are below.
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Input other parameters for the backtest:
- Required minimum input rows
- limits (or skew by using stoploss/takeprofit variables)
- strategy itself
- pip size (0.0001 should be correct in most cases)
- guaranteed_sl
- broker_cost
- exportFolder
- runType
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Run and review output in the export folder once complete.
Important notes
- The Backtest.readAndPrepData functionality is useful as the user can keep the same dataset and simply tweak start dates, and the Backtest will format the rest. The example datasets and preloaded code are a good example of how this works.
It also removes the need for excess formatting in the BacktestRunFile.py file.
However, it assumes the filename is at least in the format for the string splitting to work correctly: "(CurrencyPair)_(Frequency)_XX.csv"
Export data from MT5 symbols will fit this format, as well as bid/ask concatenated files from the DataConcatenator function (but timecols and delimitter will need to be adjusted). - Trading strategy input must be the class itself for basic and not an instantiated object. The data input is handled in the strategy.run(data) method. The preloaded strategy examples are good to review for the required basic structuring.
Charting indicators and Deep learning methods can require some pre-instantiation when combined with basic indicators, but the main premise is that a strategy should be able to function and produce its signals simply by running strategy.run(data)
Acknowledgement
Many students have contributed to the final development of this work. The list is very long but Avinash, Mingyu and Patrick (https://github.com/pmclennan) are the few names that I could remember if you have worked under my supervision and had contributed to this work, please let me know and I shall happyily put your name under this acknowledgement here.