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
08
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

The Full CFA & FRM Curriculum
in Python

From the formula to working code. Every notebook implements the curriculum exactly — then goes further with real market data.

CFA Level 1
∑ Quantitative Methods 8 / 8 live
● LIVE
QM 01 — CFA L1
Rates and Returns
HPR, arithmetic vs geometric mean, MWRR, TWRR, annualisation, real vs nominal returns — implemented with real FMP and FRED data.
numpypandasFREDCFA L1
 ● LIVE
QM 02 — 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
QM 03 — CFA L1
Statistical Measures of Asset Returns
Frequency distributions, descriptive statistics from scratch, skewness, kurtosis, and normality testing — applied to real market return data.
scipynumpyseabornCFA L1
 ● LIVE
QM 04 — CFA L1
Probability Concepts
Fed scenario probabilities, Bayes' theorem, portfolio expected return and variance — applied to real SPY/TLT data and macro regimes.
numpyFREDFMPCFA L1
 ● LIVE
QM 05 — CFA L1
Common Probability Distributions
Uniform, binomial, normal, lognormal, t-distribution and Monte Carlo — applied to real FX data across five currency pairs including USDTRY and USDJPY.
scipy.statsnumpyFMPCFA L1
 ● LIVE
QM 06 — CFA L1
Sampling & Estimation
Central limit theorem, confidence intervals, and standard error — built from scratch and verified on real return distributions.
numpyscipyCFA L1
 ● LIVE
QM 07 — 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
 ● LIVE
QM 08 — 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
◈ Equity Investments
Equity valuation, DDM, P/E, EV/EBITDA — implemented with real company data from FMP.
● LIVE
EI 06 — CFA L1
Equity Valuation — The Nike Case
DDM, FCFE, and price multiples applied to a real selloff. Built in June 2024 when NKE traded at $95. Price target: $41. NKE now trades at $44.
FMPDCFmultiplesCFA L1
≋ Fixed Income Coming soon
Bond pricing, duration, convexity, yield curves — built from scratch with real bond market data.
⟁ Derivatives Coming soon
Options pricing, Black-Scholes, Greeks — implemented from first principles with live market data.
CFA Level 2
∑ Quantitative Methods Coming soon
Multiple regression, time series, machine learning in finance — the full CFA L2 quant curriculum in Python.
FRM
⚠ Quantitative Analysis Coming soon
VaR, CVaR, copulas, extreme value theory — risk models implemented with real market stress scenarios.
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