TheQuantBridge — Open Source Finance Education

The CFA curriculum.
In Python.

Every formula in the CFA and FRM curriculum, implemented from scratch in Python. Built by a CFA charterholder, FRM, and data scientist who believes finance professionals deserve better tools to learn.

Explore Notebooks → View on GitHub
02
Notebooks live
∞
Always free
L1→L3
Full coverage
npf.pv(rate=0.08, nper=5, fv=1000) → $680.58 import numpy_financial as npf FV = PV * (1 + r)**n hypothesis_test(alpha=0.05) portfolio.optimize(method='min_variance') factor_model.fit(returns, factors) npf.pv(rate=0.08, nper=5, fv=1000) → $680.58 import numpy_financial as npf FV = PV * (1 + r)**n hypothesis_test(alpha=0.05) portfolio.optimize(method='min_variance') factor_model.fit(returns, factors)
Free Content

CFA Quant Methods
in Python

From the formula to working code. Each notebook implements the CFA curriculum exactly — then goes further with real financial applications.

● LIVE
NOTEBOOK 01 — CFA L1
Time Value of Money
PV, FV, annuities implemented from scratch. Includes a mortgage calculator, full amortization schedule, and CFA exam-style practice problems.
numpy_financialmatplotlibCFA L1
 ● LIVE
NOTEBOOK 02 — CFA L1
Organizing, Visualizing & Describing Data
Frequency distributions, descriptive statistics from scratch, skewness, kurtosis, and normality testing — applied to real market return data.
scipynumpyseabornCFA L1
SOON
NOTEBOOK 03 — CFA L1
Probability Concepts
Conditional probability, Bayes' theorem, expected value, and variance of a portfolio — implemented from scratch with real applications.
numpypandasCFA L1
SOON
NOTEBOOK 04 — CFA L1
Common Probability Distributions
Uniform, binomial, normal, lognormal, Student's t — every distribution the CFA tests, visualized and implemented in Python with financial examples.
scipy.statsmatplotlibCFA L1
SOON
NOTEBOOK 05 — CFA L1
Sampling & Estimation
Central limit theorem, confidence intervals, and standard error — built from scratch and verified on real return distributions.
numpyscipyCFA L1
SOON
NOTEBOOK 06 — CFA L1
Hypothesis Testing
t-tests, z-tests, chi-square — built from first principles, verified against scipy, applied to real return data.
scipy.statspandasCFA L1
SOON
NOTEBOOK 07 — CFA L1
Introduction to Linear Regression
OLS from scratch, assumptions testing, ANOVA table — the full CFA regression framework in Python with real equity data.
statsmodelssklearnCFA L1
SOON
NOTEBOOK 08 — CFA L2
Multiple Regression & Time Series
Multiple regression, serial correlation, heteroskedasticity — and ARIMA models applied to financial time series.
statsmodelspandasCFA L2
SOON
NOTEBOOK 09 — CFA L2
Machine Learning in Finance
Supervised and unsupervised ML applied to finance — exactly as the CFA L2 curriculum defines it, implemented end to end.
sklearnxgboostCFA L2
Research & Analysis

Data-Driven
Market Research

Rigorous quantitative analysis published on Substack. No noise. No hot takes. Just data, methodology, and insight.

May 2026 · French Equity Markets
The Effect of Inflation on Market Cap Cohorts in France
A data-driven analysis of how inflation regimes differentially impact small, mid, and large cap equities in the French market.
Coming soon · Factor Investing
Factor Premia in European Equity Markets — A Python Implementation
Replicating Fama-French factors on European data. How much of the premium survives transaction costs?
Coming soon · Risk Management
VaR vs CVaR — Which Risk Measure Actually Works?
A rigorous backtesting of Value at Risk and Conditional VaR across different market regimes using Python.
About

Built by someone
who lived both worlds.

Most finance education teaches you the formula. Most data science education has no idea what a Sharpe ratio is. TheQuantBridge exists in the gap between them.

Every notebook here is built by someone who has sat the CFA and FRM exams, worked as an equity analyst, teaches finance at a top-ranked business school, and holds an MSc in Data Science.

This is not a course platform. It is not a content farm. It is a serious technical resource built for finance professionals who want to think and build quantitatively.

All notebooks are free. Always.

CFA
CFA Charterholder
FRM
Financial Risk Manager (FRM)
MSc
MSc Data Science & Business Analytics — ESSEC
EQ
Former Equity Analyst
Prof
External Professor — ESSEC Business School
Stay Updated

New notebook every week.

Get notified when new notebooks drop. No spam. Just rigorous finance content in Python.

Roadmap

What's coming next.

✓
CFA L1
Quant Methods
02
CFA L2
Quant Methods
03
FRM
Risk Models
04
Portfolio
Construction
05
Factor
Investing