ib-data-sdk 0.1.1

Interactive Brokers Data SDK

IB Data SDK

A Python SDK for retrieving historical market data from Interactive Brokers with a clean, extensible interface.

Features

• 🚀 Easy to use: Simple, intuitive API for data retrieval
• 🔧 Extensible: Plugin architecture for custom data processors
• 📊 Pandas integration: Built-in DataFrame support
• 🛡️ Type safe: Full type hints and Pydantic validation
• 📱 CLI included: Command-line interface for quick data retrieval
• 🏗️ Production ready: Proper error handling, logging, and testing

Installation

pip install ib-data-sdk

For development:

pip install ib-data-sdk[dev]

Quick Start

Environment Setup

Set up your IB connection parameters:

export TWS_HOST=127.0.0.1
export TWS_PORT=7497  # or 7496 for live trading
export TWS_CLIENT_ID=1

Basic Usage

from ib_data_sdk import IBDataClient, HistoricalDataRequest
from ib_data_sdk.enums import Duration, BarSize, DataType

# Create client
client = IBDataClient()

# Create request
request = HistoricalDataRequest(
    symbol="AAPL",
    duration=Duration.DAY_1, 
    bar_size=BarSize.MIN_5,
    data_type=DataType.TRADES
)

# Get data as DataFrame
df = client.get_historical_data_as_dataframe(request)
print(df.head())

# Clean up
client.disconnect_from_ib()

Using the CLI

# Get daily AAPL data
ib-data-sdk get-data AAPL --duration DAY_1 --bar-size MIN_5

# Save to file
ib-data-sdk get-data AAPL --output aapl_data.csv

# List available options
ib-data-sdk list-enums

Advanced Usage

Custom Data Processors

from ib_data_sdk import DataProcessor, DataType

class VolumeWeightedDataProcessor(DataProcessor):
    def process_bar_data(self, price):
        return {
            "date": price.date,
            "vwap": (price.open + price.close) / 2 * price.volume,
            "volume": price.volume,
        }
    
    def get_data_type(self):
        return DataType.TRADES

# Register the processor
from ib_data_sdk.processors import DataProcessorFactory
DataProcessorFactory.register_processor(DataType.TRADES, VolumeWeightedDataProcessor)

Options Data

request = HistoricalDataRequest(
    symbol="AAPL",
    asset_type=AssetType.OPT,
    expiry="20240119",
    strike=150.0,
    right="C"  # Call option
)

df = client.get_historical_data_as_dataframe(request)

Error Handling

from ib_data_sdk.exceptions import ConnectionError, DataRequestError, TimeoutError

try:
    df = client.get_historical_data_as_dataframe(request)
except ConnectionError:
    print("Failed to connect to IB")
except DataRequestError as e:
    print(f"Data request failed: {e}")
except TimeoutError:
    print("Request timed out")

API Reference

Classes

• IBDataClient: Main client class for data retrieval
• HistoricalDataRequest: Request model with validation
• HistoricalDataResponse: Response model with metadata
• DataProcessor: Abstract base for custom processors

Enums

• AssetType: STK, OPT, FUT, CASH, IND
• BarSize: 1 sec to 1 month intervals
• Duration: 1 D to 2 Y periods
• DataType: TRADES, BID_ASK, MIDPOINT, etc.

Development

Setup

git clone https://github.com/quantphantom/ib-data-sdk
cd ib-data-sdk
pip install -e .[dev]
pre-commit install

Testing

pytest tests/ -v --cov=src/ib_data_sdk

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests
5. Submit a pull request

License

MIT License - see LICENSE file for details.

Support

• 📖 Documentation: https://ib-data-sdk.readthedocs.io/
• 🐛 Issues: https://github.com/quantphantom/ib-data-sdk/issues
• 💬 Discussions: https://github.com/quantphantom/ib-data-sdk/discussions

Disclaimer

This project is not affiliated with Interactive Brokers. Use at your own risk. Trading