ftgo 1.1.0

A Python library for fetching financial data from FT Markets

FTMarkets

A Python library for fetching financial data from Financial Times Markets, including historical stock prices, ETF holdings, fund profiles, and allocation breakdowns.

Features

• Historical Data: Fetch historical OHLCV data for stocks and ETFs
• Holdings Data: Get ETF/fund holdings, asset allocation, and sector breakdowns
• Fund Profiles: Access fund information, statistics, and investment details
• Symbol Search: Find FT Markets XIDs by ticker symbols
• Concurrent Processing: Fast data retrieval using multithreading
• Pandas Integration: Returns data as pandas DataFrames for easy analysis

Installation

pip install ftgo

Quick Start

from ftgo import search_securities, get_xid, get_historical_prices, get_holdings

# Search for a security
results = search_securities('AAPL')
print(results)

# Get XID for a ticker
xid = get_xid('AAPL')

# Fetch historical data
df = get_historical_prices(xid, "01012024", "31012024")
print(df.head())

# Get ETF holdings
spy_xid = get_xid('SPY')
holdings = get_holdings(spy_xid, "top_holdings")
print(holdings)

API Reference

Search Functions

search_securities(query)

Search for securities on FT Markets.

Parameters:

• query (str): Search term for securities (ticker symbol or company name)

Returns: pandas.DataFrame with search results containing xid, name, symbol, asset_class, url

# Search for Apple
results = search_securities('Apple')
print(results)

# Search by ticker
results = search_securities('AAPL')

get_xid(ticker, display_mode="first")

Get FT Markets XID for given ticker symbol.

Parameters:

• ticker (str): Ticker symbol
• display_mode (str): "first" to return first match XID, "all" to return all matches

Returns: String XID (if display_mode="first") or DataFrame (if display_mode="all")

# Get XID for Apple
xid = get_xid('AAPL')
print(xid)  # Returns XID string

# Get all matches
all_results = get_xid('AAPL', display_mode='all')
print(all_results)

Historical Data

get_historical_prices(xid, date_from, date_to)

Get historical price data for a security with full OHLCV data.

Parameters:

• xid (str): The FT Markets XID
• date_from (str): Start date in DDMMYYYY format (e.g., "01012024")
• date_to (str): End date in DDMMYYYY format (e.g., "31122024")

Returns: pandas.DataFrame with columns: date, open, high, low, close, volume

xid = get_xid('AAPL')
df = get_historical_prices(xid, "01012024", "31012024")
print(df.head())

get_multiple_historical_prices(xids, date_from, date_to)

Get historical data for multiple securities concurrently.

Parameters:

• xids (list): List of FT Markets XIDs
• date_from (str): Start date in DDMMYYYY format
• date_to (str): End date in DDMMYYYY format

Returns: pandas.DataFrame with concatenated data for all securities

xids = [get_xid('AAPL'), get_xid('MSFT')]
df = get_multiple_historical_prices(xids, "01012024", "31012024")

Holdings Data

get_holdings(xid, holdings_type="all")

Get holdings and allocation data for ETFs and funds.

Parameters:

• xid (str): The FT Markets XID
• holdings_type (str): Type of holdings data:
  • "asset_allocation": Asset class breakdown (stocks, bonds, cash)
  • "sector_weights": Sector allocation
  • "geographic_allocation": Geographic allocation
  • "top_holdings": Top holdings by weight
  • "all": All holdings data types as a tuple

Returns: pandas.DataFrame or tuple of DataFrames

# Get top holdings for SPY ETF
spy_xid = get_xid('SPY')
top_holdings = get_holdings(spy_xid, "top_holdings")

# Get asset allocation
allocation = get_holdings(spy_xid, "asset_allocation")

# Get all holdings data
all_data = get_holdings(spy_xid, "all")
asset_alloc, sectors, regions, holdings = all_data

get_fund_breakdown(xid)

Get complete fund breakdown with all allocation data.

Parameters:

• xid (str): The FT Markets XID

Returns: Dictionary with all DataFrames

qqq_xid = get_xid('QQQ')
breakdown = get_fund_breakdown(qqq_xid)
print(breakdown['asset_allocation'])
print(breakdown['top_holdings'])

Fund Profile Data

get_fund_profile(xid)

Get profile and investment information for ETFs and funds.

Parameters:

• xid (str): The FT Markets XID

Returns: pandas.DataFrame with Field and Value columns

xid = get_xid('SPY')
profile = get_fund_profile(xid)
print(profile)

# Filter for specific information
fees = profile[profile['Field'].str.contains('fee', case=False)]

get_fund_stats(xid)

Get fund profile data as a dictionary for easy access.

Parameters:

• xid (str): The FT Markets XID

Returns: Dictionary with all available fund fields and values

xid = get_xid('QQQ')
stats = get_fund_stats(xid)

# Access any available field safely
inception = stats.get('Inception date', 'Not available')
fees = stats.get('Ongoing charge', 'Not available')

get_available_fields(xid)

Get list of all available profile fields for a fund.

Parameters:

• xid (str): The FT Markets XID

Returns: List of all field names available

xid = get_xid('SPY')
fields = get_available_fields(xid)
print("Available fields:")
for field in fields:
    print(f"  - {field}")

search_profile_field(xid, search_term)

Search for specific fields in the fund profile data.

Parameters:

• xid (str): The FT Markets XID
• search_term (str): Term to search for in field names (case-insensitive)

Returns: pandas.DataFrame with matching fields and values

xid = get_xid('SPY')
fees = search_profile_field(xid, 'fee')
inception = search_profile_field(xid, 'inception')

Complete Example

from ftgo import get_xid, get_historical_prices, get_holdings, get_fund_profile
import matplotlib.pyplot as plt

# Search for QQQ ETF
qqq_xid = get_xid('QQQ')

# Get 1 year of historical data
historical_data = get_historical_prices(qqq_xid, "01012023", "31122023")

# Get fund information
profile = get_fund_profile(qqq_xid)
top_holdings = get_holdings(qqq_xid, "top_holdings")
asset_allocation = get_holdings(qqq_xid, "asset_allocation")

# Plot price chart
historical_data.set_index('date')['close'].plot(title='QQQ Price History')
plt.show()

# Display fund information
print("Fund Profile:")
print(profile.head(10))

print("\nTop 10 Holdings:")
print(top_holdings.head(10))

print("\nAsset Allocation:")
print(asset_allocation)

Error Handling

The library includes logging and error handling, but you should wrap calls in try-except blocks for production use:

try:
    xid = get_xid('INVALID_TICKER')
    data = get_historical_prices(xid, "01012024", "31012024")
except ValueError as e:
    print(f"Error: {e}")
except Exception as e:
    print(f"Unexpected error: {e}")

Requirements

• Python 3.7+
• cloudscraper >= 1.2.68
• pandas >= 1.3.0
• beautifulsoup4 >= 4.11.0

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Disclaimer

This library is for educational and research purposes.

License

This project is licensed under the MIT License - see the LICENSE file for details.