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findatapy Documentation

repository·master·Indexed 24 days ago

https://github.com/cuemacro/findatapy

A high-level Python API for downloading market data from diverse sources including ALFRED/FRED, Bloomberg, Yahoo, Google, Dukascopy, Quandl, and the Bank of England. It utilizes the Market and MarketDataRequest classes to fetch data and supports optional Redis-based caching to reduce redundant API calls.

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What's inside findatapy

  1. Install findatapy

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    You can install the latest release of findatapy via pip, or install the newest version directly from the GitHub repository.

    Requirements

    • Python: 3.10 or higher (Python 2 is not supported).
    • Dependencies: pandas, numpy, etc.
    • Recommended (Bloomberg): If using Bloomberg, install blpapi using: pip install --index-url=https://blpapi.bloomberg.com/repository/releases/python/simple blpapi
    • Recommended (Plotting): chartpy for interactive plots.

  2. Configure API keys for findatapy

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    To use various data sources (like Eikon, Quandl, or Twitter), you must provide API keys. You can manage these in several ways:

    1. On-demand: Pass keys directly via the MarketDataRequest object.
    2. Keyring: Run the set_api_keys.py script to store keys in your system keyring.
    3. Configuration File: Create a datacred.py file to overwrite default keys.
    4. Class Modification: Edit the dataconstants class directly.

  3. Troubleshoot 'Couldn't push MarketDataRequest' error

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    If you see the error Couldn't push MarketDataRequest, it means findatapy attempted to use its in-memory caching mechanism via Redis but could not find a running Redis instance.

    • Cause: Redis is used as a volatile cache to avoid redundant external API calls for identical MarketDataRequest parameters.
    • Impact: This is not a fatal error. All other functionality will work correctly; findatapy will simply bypass the cache and fetch data directly from the external provider every time.
    • Solution: To enable caching, install and run Redis on your system (available for Linux and older Windows versions).

  4. Fetch market data using Market and MarketDataRequest

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    To download market data, use the Market class initialized with a MarketDataGenerator. Define your query using MarketDataRequest and pass it to market.fetch_market().

    Example: Fetching ALFRED/FRED data

    from findatapy.market import Market, MarketDataRequest, MarketDataGenerator

market = Market(market_data_generator=MarketDataGenerator())

# Provide your FRED API key
fred_api_key = "WRITE YOUR KEY HERE" 

md_request = MarketDataRequest(start_date='year', category='fx', data_source='alfred', tickers=['AUDJPY'],
                               fred_api_key=fred_api_key)

df = market.fetch_market(md_request)
print(df.tail(n=10))

    Example: Fetching Dukascopy tick data

    md_request = MarketDataRequest(start_date='14 Jun 2016', finish_date='15 Jun 2016',
                                   category='fx', fields=['bid', 'ask'], freq='tick', 
                                   data_source='dukascopy', tickers=['EURUSD'])

df = market.fetch_market(md_request)
print(df.tail(n=10))
    from findatapy.market import Market, MarketDataRequest, MarketDataGenerator

market = Market(market_data_generator=MarketDataGenerator())

# Get you FRED API key from https://fred.stlouisfed.org/docs/api/api_key.html
fred_api_key = "WRITE YOUR KEY HERE" 

md_request = MarketDataRequest(start_date='year', category='fx', data_source='alfred', tickers=['AUDJPY'],
                               fred_api_key=fred_api_key)

df = market.fetch_market(md_request)
print(df.tail(n=10))

  5. Fetch data from Yahoo and Quandl

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    You can use the Market abstraction to fetch data from different vendors like yahoo or quandl by simply changing the data_source parameter in the MarketDataRequest object.

    # Yahoo Example
market = Market(market_data_generator=MarketDataGenerator())
md_request = MarketDataRequest(
        start_date="decade",
        data_source='yahoo',
        tickers=['Apple'],
        fields=['close'],
        vendor_tickers=['aapl'],
        vendor_fields=['Close'])

df = market.fetch_market(md_request)

# Quandl Example
md_request_quandl = MarketDataRequest(
        start_date="decade",
        data_source='quandl',
        tickers=['APPL'],
        fields=['close'],
        vendor_tickers=['APPL'],
        vendor_fields=['Close'])

df_quandl = market.fetch_market(md_request_quandl)

  6. Use DataVendorBOE to load Bank of England data

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    For Bank of England (BOE) data, you can use the DataVendorBOE class. This allows you to load specific tickers directly using a MarketDataRequest object via the load_ticker method.

    from findatapy.market.datavendorweb import DataVendorBOE
from findatapy.market import MarketDataRequest

md_request = MarketDataRequest(
        start_date="decade",
        data_source='boe',
        tickers=['IUMBV34', 'IUMBV37'],
        fields=['close'],
        vendor_tickers=['IUMBV34', 'IUMBV37'],
        vendor_fields=['Close'])

boe = DataVendorBOE()
boe.load_ticker(md_request)