ohlc-toolkit 0.4.0
A flexible toolkit for working with OHLC data and generating custom timeframes from minute data.
License: MIT
Requires Python: <4.0,>=3.10
Keywords: ohlc, price, candlestick, financial, market-data, time-series

# OHLC Toolkit

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

## Installation

The project is available on [PyPI](https://pypi.org/project/ohlc-toolkit/):

```bash
pip install ohlc-toolkit
```

## Features

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

  ```py
    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](https://github.com/ff137/bitstamp-btcusd-minute-data)):

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

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

  ```py
    # Convert 1-minute data into 5-minute data, maintaining a 1-minute resolution
    df_5m = transform_ohlc(df_1min, timeframe=5, step_size_minutes=1)

    # Convert 1-minute data into 1-day data, with a 1-day resolution
    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")
  ```

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

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

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

- Calculate future price changes:

  ```py
    # 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](examples/README.md) directory for examples of how to use the toolkit.

Run the example script to see how the toolkit works:

```bash
# 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! ⭐