AI Trading Prototype Backtester

This project is a backtester for the sentiment-analysis cryptocurrency trading strategy that pairs with the live trading bot, ai-trading-prototype. This backtester is designed to run accurate simulations for a given date range on a chosen trading symbol.

Disclaimer

This trading bot backtester does not provide financial advice or endorse trading strategies. Users assume all risks, as past results from historical data do not guarantee future returns; the creators bear no responsibility for losses. Please seek guidance from financial experts before trading or investing. By using this project you accept these conditions.

Features

• A flexible trading strategy builder.
• Customisable Backtesting strategy.
• Automated data downloader using data.binance.vision.
• Detailed backtest results and performance assessment, including HTML visualisations of all trades.

Installation

1. Clone the repository

git clone https://github.com/binance/ai-trading-prototype-backtester

2. Move into the cloned directory

cd ai-trading-prototype-backtester

3. Install dependencies

pip install -r requirements.txt

Usage

Configuration

All configurations are stored in config.yaml.example. You can specify:

• symbol: The trading symbol/ pair. Example: ETHUSDT.
• kline_interval: The interval of the candlesticks. Valid intervals are: 1m, 3m, 5m, 15m, 30m, 1h, 2h, 4h, 6h, 8h, 12h or 1d
• start_date and end_date: The range of dates to backtest on. Format: YYYY-MM-DD.
• sentiment_data: The path to the sentiment data file. Default: ./sentiment_data/sentiment_data.csv.
• start_balance: The starting balance in quote currency (eg. USDT). Default: 100000.
• order_size: The order size in base currency (eg. BTC). Default: 0.01.
• total_quantity_limit: The maximum quantity of base currency (eg. BTC) that can be held at any given time. Default: 1.
• commission: The trading fee/commission ratio. Default: 0.002.
• logging_level: The logging detail level. Default: INFO.

Please also ensure that the sentiment data file sentiment_data.csv is present and formatted as "headline source","headline collected timestamp (ms)","headline published timestamp (ms)","headline","sentiment".

Strategy Configuration Profiles

Within the strategy_configuration directory, there are 3 extra configuration files. Each file corresponds to a different strategy/risk level (Aggressive, Conservative and Standard). These can be used to quickly test different parameters with varying degrees of risk.

Run the backtester as module

python -m aitradingprototypebacktester

How it works

• During the backtesting process, the backtester checks /sentiment_data/sentiment_data.csv for a published headline at each kline/candlestick interval.
• If a headline was published during the current time-period, it reads the sentiment of the headline.
• By default, the backtester uses the successive_strategy which is defined in aitradingprototypebacktester/strategy/successive_strategy.py. The details of how this strategy works is as follows:
  • If the sentiment was "bullish" (meaning it is expected that the price would increase) it will attempt a BUY order of size = order_size of base currency, so long as the total_quantity_limit (max. quantity that can be held at any given time) has not yet been reached.
  • If the sentiment was "bearish" (meaning that a fall in price is expected), it will attempt to SELL order_size quantity of the base currency, so long as the current base currency balance is > 0.
• Once the backtest is complete, it will return a detailed table of results along with an HTML visualisation of all the buys and sells plotted against the trading symbol's price throughout the period.
  • An image, backtest_result.png, will also be generated. This is a static view of the HTML visualisation.
• If there is a position still open at the end of the backtest, it will be closed at the open price of the last kline in the backtesting period.

Backtest Results

• As outlined in the How it works section above, the backtester will output several files as a result of each backtest:
  • output/raw/backtest_result.txt - A summary of the backtest.
  • output/raw/backtest_trades.txt - A detailed list of each individual trade executed during the backtest.
  • output/visualisation/dynamic_report.html - HTML visualisation of all the buys and sells plotted against the trading symbol's price throughout the period.
  • output/visualisation/backtest_result.png - A static view of the HTML visualisation (image below).