py-ftmarkets

Financial Times (markets.ft.com) data source for Python. Provides a high-level API and CLI to search for securities, fetch historical data, and validate prices.

Installation

uv add py-ftmarkets
# or
pip install py-ftmarkets

CLI Usage

The package provides a CLI tool named ftmarkets.

Lookup a Security

Resolve an ISIN or Symbol to the Financial Times symbol format (e.g., AAPL:NSQ).

# Basic lookup by ISIN
ftmarkets lookup --isin DE000A0S9GB0

# Lookup with price and date validation (Returns 1 best matching security)
ftmarkets lookup --isin DE000A0S9GB0 --price 117.81 --date 2025-12-12 --limit 1

# Lookup with filters (currency, country, asset-class)
ftmarkets lookup --isin DE000A0S9GB0 --currency EUR --country DE --asset-class ETF

# Return all matching results in JSON format
ftmarkets lookup --isin DE000A0S9GB0 --limit 0 --format json

Fetch History and Validate

Fetch historical data for a resolved security and optionally validate a trade price on a specific date.

# Fetch 1 month of history for an ISIN
ftmarkets history --isin DE000A0S9GB0

# Fetch 1 year of history and validate a price
ftmarkets history --isin DE000A0S9GB0 --period 1y --price 120.50 --date 2025-01-15

Library Usage

py-ftmarkets implements the DataSource interface from pydantic-market-data.

from ftmarkets.api import FTDataSource
from pydantic_market_data.models import SecurityCriteria

source = FTDataSource()

# Resolve a security
criteria = SecurityCriteria(isin="DE000A0S9GB0")
security = source.resolve(criteria)
print(f"Symbol: {security.symbol}")

# Fetch history
history = source.history(security.symbol, period="1mo")
df = history.to_pandas()
print(df.tail())

# Validate price
is_valid = source.validate(security.symbol, target_date="2025-01-15", target_price=120.50)
print(f"Price valid: {is_valid}")

Features

• Robust Resolution: Searches by ISIN, Symbol, or Description.
• Smart Mapping: Prioritizes results based on preferred exchanges and currency.
• Price Validation: Verifies if a security traded within a range or near a specific price on a given date.
• Pandas Integration: Historical data is easily convertible to Pandas DataFrames.
• Modern Python: Built with Pydantic v2 and async-ready architecture (though currently synchronous).