A machine learning program in python to generate cryptocurrency trading strategies using machine learning. The script is inspired by both the pytrader project https://github.com/owocki/pytrader, and the auto-sklearn project https://automl.github.io/auto-sklearn/stable/.
The information in this repository is provided for information purposes only. The Information is not intended to be and does not constitute financial advice or any other advice, is general in nature and not specific to you.
Minor changes were made to the Poloniex API python wrapper which is inluded in the repository https://github.com/s4w3d0ff/python-poloniex. Data is retrieved via the Poloniex API in OHLC (open, high, low, close) candlestick format.
Alternatively data can be supplied in the form of .csv files by including them in the working directory, setting web_flag as false and supplying the filenames as filename1 and filename2, (filename1 will be the currency pair used for trading).
A series of technical indicators are calculated and provided as inputs to the machine learning optimisation, exponential moving averages and exponential moving volatilities over a series of windows. A kalman filter is also provided as an input.
An ideal trading strategy is generated based on past data, every candlestick is given a score which represent the potential profit or loss before the next price reversal exceeding the combined transaction fee and bid ask spread. This minimum price reversal is represented by Δp in the diagram below.
A buy threshold and sell threshold are selected which maximise profit based on the score returned for the training data, where a sell or buy signal is generated if the respective threshold is crossed.
The machine learning optimisation is based on a two layer random search, as outlined in the diagram below. The meta-fitting selects a machine learning and preprocessing pair, the selected machine learning model is then optimised using a second random grid search to fit the hyperparameters for that particular machine learning model. (Without GPU support the tensorflow fitting may take a long time!)
With none of the different automated machine learning optimisation strategies was I able to get a set of fitting parameters which was consistently profitable at multiple offsets. Some of the offsets would be profitable an example is included below.
In order to estimate the amount of overfitting, a series of offset hyperparameter fittings are performed. If the trading strategy is not overfit, fitting should be approximately consistent across at all offsets in terms of profit fraction and fitting error.
With none of the different automated machine learning optimisation strategies was I able to get a set of fitting parameters which was consistently profitable at multiple offsets.
- Add none price data.
https://conda.io/docs/installation.html
https://conda.io/docs/_downloads/conda-cheatsheet.pdf
conda create -n tensorflow-p2 python=2.7
source activate tensorflow-p2
conda install numpy pandas matplotlib tensorflow jupyter notebook scipy scikit-learn nb_conda
conda install -c auto multiprocessing statsmodels arch
pip install arch polyaxon
conda create -n tensorflow-p2 python=2.7
activate tensorflow-p2
conda install numpy pandas matplotlib tensorflow jupyter notebook scipy scikit-learn nb_conda
conda install -c auto multiprocessing statsmodels arch
pip install arch polyaxon