finm 0.1.2

A student-led Python package for financial mathematics and quantitative finance education

finm

A Python package for financial mathematics and quantitative finance education, created by students and educators at the University of Chicago Financial Mathematics program.

[!CAUTION] This package is for learning purposes only. There are likely errors in the implementations, and this software should not be used for production trading systems, real financial decision-making, or commercial applications. Always verify calculations independently.

Installation

pip install finm

For all optional dependencies (CLI, data access):

pip install finm[all]

Data Access

Load financial data from multiple sources with a consistent interface. See the Data Module documentation for details.

[!IMPORTANT] License Acknowledgment Required: All data pull functions require accept_license=True to confirm you understand that the data is subject to the original provider's licensing terms. See Data Licensing below.

import finm

# Fama-French factors (bundled data, no download needed)
factors = finm.load_fama_french_factors()
factors = finm.load_fama_french_factors(start="2020-01-01", end="2023-12-31")

# Pull fresh data from Ken French's Data Library
finm.pull_fama_french_factors(data_dir="./data", accept_license=True)

# Federal Reserve yield curve (GSW model)
finm.pull_fed_yield_curve(data_dir="./data", accept_license=True)
yields = finm.load_fed_yield_curve(data_dir="./data")

# He-Kelly-Manela intermediary capital factors
finm.pull_he_kelly_manela(data_dir="./data", accept_license=True)
hkm = finm.load_he_kelly_manela_factors_monthly(data_dir="./data")

# Open Source Bond Asset Pricing data
finm.pull_open_source_bond(data_dir="./data", accept_license=True)
treasury = finm.load_treasury_returns(data_dir="./data")
corporate = finm.load_corporate_bond_returns(data_dir="./data")

# WRDS data (requires authentication)
finm.pull_wrds_treasury(
    data_dir="./data",
    wrds_username="your_username",
    start_date="2020-01-01",
    end_date="2023-12-31"
)

Analytics

Calculate risk metrics and factor exposures.

import finm

# Load factors
factors = finm.load_fama_french_factors()

# Fama-French 3-factor exposures
exposures = finm.calculate_factor_exposures(stock_returns, factors)
print(f"Market Beta: {exposures['market_beta']:.3f}")
print(f"SMB Beta:    {exposures['smb_beta']:.3f}")
print(f"HML Beta:    {exposures['hml_beta']:.3f}")
print(f"Sharpe:      {exposures['sharpe_ratio']:.3f}")

# Individual metrics
beta = finm.calculate_beta(stock_returns, market_returns)
sharpe = finm.calculate_sharpe_ratio(returns, risk_free_rate=0.02)

Fixed Income

Bond pricing, duration, and yield curve modeling.

import finm

# Bond pricing
price = finm.bond_price(
    face_value=1000,
    coupon_rate=0.05,
    ytm=0.04,
    periods=10,
    frequency=2  # semiannual
)

# Duration and convexity
dur = finm.duration(1000, 0.05, 0.04, 10, frequency=2)
mod_dur = finm.modified_duration(1000, 0.05, 0.04, 10, frequency=2)
conv = finm.convexity(1000, 0.05, 0.04, 10, frequency=2)

# Yield to maturity
ytm = finm.yield_to_maturity(
    price=1050,
    face_value=1000,
    coupon_rate=0.05,
    periods=10,
    frequency=2
)

# Time value of money
pv = finm.present_value(future_value=1000, rate=0.05, periods=3)
fv = finm.future_value(present_value=1000, rate=0.05, periods=3)

The package also includes an implementation of the Gurkaynak-Sack-Wright (2006) yield curve model. See the API documentation for spot, discount, fit, and related functions.

Command Line Interface

Manage data downloads from the terminal. See the CLI documentation for details.

# List available datasets
finm list

# Get info about a dataset
finm info fama_french

# Pull data (--accept-license flag required)
finm pull fama_french --data-dir=./data --accept-license
finm pull fed_yield_curve --data-dir=./data --accept-license

# Pull WRDS data (requires credentials)
finm pull wrds_treasury --wrds-username=myuser --start-date=2020-01-01 --end-date=2023-12-31 --accept-license

Documentation

Full documentation is available at jeremybejarano.com/finm.

Data Licensing

[!WARNING] Third-Party Data Disclaimer: The finm.data module provides convenient access to financial datasets from various third-party sources. The finm package maintainers are not responsible for the accuracy, completeness, or licensing of this data. Usage is subject to the original data provider's terms and conditions.

Citation Requirements

When using data from these sources in academic work, please cite appropriately:

Data Source	Citation
Fama-French	Fama, E.F. and French, K.R. (1993). Common Risk Factors in the Returns on Stocks and Bonds. Journal of Financial Economics 33(1): 3-56.
Federal Reserve	Board of Governors of the Federal Reserve System. GSW Yield Curve Data. https://www.federalreserve.gov/
He-Kelly-Manela	He, Z., Kelly, B., and Manela, A. (2017). Intermediary Asset Pricing: New Evidence from Many Asset Classes. Journal of Financial Economics 126(1): 1-35.
Open Source Bond	Dickerson, A., Robotti, C., and Rossetti, G. (2026). The Corporate Bond Factor Replication Crisis. Working Paper.
WRDS	Wharton Research Data Services. https://wrds-www.wharton.upenn.edu/

License Types

Data Source	License	Terms URL
Fama-French	Copyright Fama & French	Ken French Data Library
Federal Reserve	Public Domain	Federal Reserve Disclaimer
He-Kelly-Manela	Academic Use	HKM Website
Open Source Bond	MIT License	GitHub Repository
WRDS	Subscription-based	WRDS Terms

Fair Use

The data access functionality in this package is provided for:

• Educational purposes
• Academic research
• Personal, non-commercial analysis

For commercial use, please consult the original data provider's licensing terms.

License

MIT License. See LICENSE for details.

Acknowledgments

Created and maintained by students and educators at the University of Chicago Financial Mathematics program.