/crypto-algorithmic-trading

LSTM neural network predicting price movements of Bitcoin, backtesting and visualisations.

Primary LanguagePythonMIT LicenseMIT

Cryptocurrency algorithmic trading using neural networks

It is my first project of more extensive scope. This is where I acknowledged my interest in both Machine learning and algorythmic trading. This project consists of a rather simple LSTM recurrent neural network builder (using Keras). The goal of the neural network is to predict cryptocurenncy price movements - preferrably short term. And it succeeded to do so.

How do I know how well the model will perform when utilized for trading in the future? I don't. I can only look on historical data and try to determine whether it has potential to be effective and make money regardless of market behavior. A big part of my work on this project was to construct tools that best describe model's performance and robustness. Using plotly I made charts that visualise model's prediction pattern and it's accuracy. Also, I made a backtesting simulation of a simple strategies that can be based on model's prediction - and visualisation of how profitable they would be over time. Lastly, I implemented a simple trading bot that can preprocess data, run predictions and send orders to the exchange (Binance) via API.

The project's code and overall structure may seem messy. It is not a suprise since it's my first work of that kind and I was just a high school student. I've learnt a lot since and I will put my work into imporving the structure, code readability and speed along with ease of use . To be honest, while I'm still passionate about machine learning and its applications for algorithmic trading, I probably won't be maintaining and updating this particular project.

Project structure:

  • Data processing and model training:

    • getdata.py - tool used to download historical data from the exchange
    • new_preprocess.py - front to data preprocessing
    • compile.py - model training
    • vars.py - some constant variables
    • utils.py - utilities
    • data_processing.py - data processing functions - used by new_preprocess.py, dist_acc_graph.py, predictions_graph.py

  • Visualisation:

    • dist_acc_graph.py - model's distribution of accuracy chart
    • predictions_graph.py - model's chart of predictions plotted along price chart

  • Backtesting:

    • backtesting/brain.py - place to set a strategy and run a backtest
    • backtesting/heart.py - 'inner' processies of backtest
    • backtesting/statistics.py - analisys and saving trades
    • backtesting/chart.py - visualisation of strategy's performance over time
    • backtesting/chart_all.py - visualisation of multiple strategies' performance over time

  • Realtime - basically a trading bot for binance (probably messy and built for deprecated api :/)

    • realtime/BRAIN.py - place to set a strategy and run a bot
    • realtime/HEART.py - account endpoint, placing and monitoring orders
    • realtime/GENERAL.py - ulitities

Charts

dist_acc_graph.py

Bit more thorought analysis of model's accuracy based on how certain the model is of its prediction

dist and acc


predictions_graph.py

Visualisation of model's predictions along with the price chart at the time.

pred


backtesting/chart.py

Profitability chart of a strategy with paremeters:

    LEVERAGE = 7
    ORDER_SIZE = 0.1
    PYRAMID_MAX = 1
    THRESHOLD = 0.08

trades


backtesting/chart_all.py

Comparison on strategies with paremeters:

    thresholds = [0.04, 0.06, 0.08, 0.1, 0.12, 0.16]
    leverages = [7]
    order_sizes = [0.1]
    pyramid_maxes = [1]

strats