ohlc-toolkit

OHLC Toolkit

A flexible Python toolkit for working with OHLC (Open, High, Low, Close) market data.

Installation

The project is available on PyPI:

pip install ohlc-toolkit

Features

• Read OHLC data from CSV files into pandas DataFrames, with built-in data quality checks:

    df = read_ohlc_csv(csv_file_path, timeframe="1d")
  

• Download BTCUSD 1-minute candle data in one line (using data from ff137/bitstamp-btcusd-minute-data):

    df_1min = DatasetDownloader().download_bitstamp_btcusd_minute_data(bulk=True)
  

• Transform your candle data into any desired timeframe and resolution:

    # Convert 1-minute candle data into 5-minute candles (with candle updated every minute)
    df_5m = transform_ohlc(df_1min, timeframe=5, step_size_minutes=1)
  
    # Convert 1-minute candle data into 1-day candles (with candle updated every day)
    df_1d = transform_ohlc(df_1min, timeframe="1d", step_size_minutes=1440)  
    
    # Support for arbitrary timeframes is available!
    df_arb = transform_ohlc(df_1min, timeframe="1d3h7m", step_size_minutes=33)
  

• Convert timeframe strings to the number of minutes, and vice versa:

    # From string to minutes
    parse_timeframe("1h15m") == 75
  
    # From minutes to string
    format_timeframe(minutes=75) == "1h15m"
  

• Calculate future price changes:

    # For every minute, compute the 1-hour percentage price change
    df_future_returns = calculate_percentage_return(
        df_1min["close"],  # Use the close price column
        timestep_size=1,  # Compute returns at 1-minute resolution (same as input)
        future_return_length=60,  # Compute price changes over 60 minutes
    )
  

🚧 Coming soon™️:

• Calculate technical indicators

All of the above features will enable you to generate extensive training data for machine learning models, whether for research or trading, to predict future price changes based on technical indicators.

Examples

See the examples directory for examples of how to use the toolkit.

Run the example script to see how the toolkit works:

# Clone the repository
git clone https://github.com/ff137/ohlc-toolkit.git
cd ohlc-toolkit

# Create and activate a virtual environment
python -m venv .venv
source .venv/bin/activate

# Install dependencies
pip install poetry
poetry install

# Run the example script
python examples/basic_usage.py

Support

If you need any help or have any questions, please feel free to open an issue or contact me directly.

We hope this repo makes your life easier! If it does, please give us a star! ⭐

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