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You already know Python matters for quant finance. You just can't figure out where to start.

The complete, practitioner-built system for going from zero Python to backtesting strategies, analyzing market data, and automating trades — in less than 1 hour a day.

Start Using Python for Quant Finance →

Payment plan available. Join 1,500+ alumni.

⭐️ ⭐️ ⭐️ ⭐️ ⭐️
"This was the best course I have taken in my 28-year career in finance." — Zarko, Finance Professional

Here’s what probably happened.

You googled “learn Python for finance.”

You got 533 million results. You picked the first paid ad and spent $19 on a Udemy course.

Google search for Python quant finance

You learned how to print “Hello World” and build a tic-tac-toe game from someone who has never traded a share, priced a derivative, or sat on a risk desk.

Then you tried another course. More syntax drills. More toy examples. Still no idea how to pull market data, build a backtest, or connect to a broker.

You’re not failing because you’re bad at Python.

You’re failing because nobody taught you Python for the thing you actually want to do.

The generic courses teach you programming. They don’t teach you how a quant uses Python to analyze $20 billion in derivatives exposure, or how a trader uses it to automate execution on Interactive Brokers, or how an analyst uses it to screen 21,000+ equities with factor pipelines.

That’s what this course teaches.

But first, let me show you why I built it.

Hi, I’m Jason.

I traded my first stock and wrote my first line of code at 18.

Since then, I’ve spent 20+ years at the intersection of Python, trading, and quantitative finance:

Derivatives trader for a hedge fund and energy trading firm in Chicago, generating several million dollars in P&L

Credit quant managing $20 billion in derivatives exposure and a $100 million CVA book

Global team lead building all market risk analytics for a $7 billion derivatives business

Head of data engineering and quant analysis for a $60 billion metals trading operation

I taught myself Python in 2012 to avoid paying $2,000/year for a MATLAB license. Best trade I ever made.

I started PyQuant News in 2015 to share what I’d learned. Eleven years later, 1,500+ students and 200,000+ newsletter subscribers later, I’m still at it.

Here’s why I built this course:

Every week, someone asks me the same question:

“Jason, I know Python is important for quant finance. Where do I actually start?”

The honest answer used to be: nowhere good. The Python-for-finance courses out there fell into two camps — either generic programming tutorials with a finance coat of paint, or PhD-level theory with no practical code.

So I built the thing I wished existed when I was starting out. A complete, step-by-step system that teaches Python the way a quant actually uses it — with real market data, real backtesting frameworks, and real broker integration.

1,500+ students have gone through it. Here’s what they say.

Jason, PyQuant News instructor

What 1,500+ students say

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"This was the best course I have taken in my 28-year career in finance."

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"I've explored most of the quantitative finance courses out there and found this to be the most practical and efficient course currently available."

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"The course gave me the materials I needed with a mentor to guide me along the way to achieve my end goal of landing an active trader role."

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"I consider it one of the best investment decisions I have taken this year. Five weeks ago I didn't know what quant finance was. Since I took the course, a new world has opened to me which will boost my options trading."

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"I have no coding experience and no technical background. I went from no direction with Python to having a clear path in a month."

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"Great course whether you're a beginner or an experienced Python user. The shared notebooks alone are worth multiples of the course cost. I signed up for round 3!"

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"This was by far the best trading class I have ever taken."

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"I learned much from this course about both Python and quant finance, all of which was dependent upon the incredible support provided by Jason individually and by the PQN community."

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This is not another Python tutorial.

No theory without application. Every concept is taught with real market data you can run yourself.
No jargon for jargon's sake. Complex ideas explained by someone who's used them on a trading desk — not in a textbook.
No "Hello World." Your first line of code pulls live market data. Your second builds a backtest.

Getting Started With Python for Quant Finance is a complete practitioner system: 20 hours of instruction across 13 modules, 40+ code templates with real market data, and a 1,500-person community of finance professionals who use Python daily.

You’ll get the same tools I used to manage $20 billion in derivatives exposure, run a $100 million CVA book, and trade stocks and options from my home office.

Every module is designed around one principle: you should be able to use what you learn the same day you learn it.

Here’s exactly what’s inside.

What You’ll Build, Module by Module

Module 1: The 20% of Python That Does 80% of the Work in Quant Finance

If you’re brand new to Python, start here. We skip the computer science theory and cover exactly what a quant needs: data types, data structures, control flow, functions, and classes — with finance examples, not toy problems. You’ll be writing useful code by the end of the first day.

Python code for data import and setup

Module 2: The One Library That Handles 80%+ of Everything You’ll Do

Pandas is to quant finance what Excel wishes it could be. You’ll learn to load, clean, transform, and analyze market data — the same workflows I used across every quant role I’ve held. After this module, you’ll wonder how you ever worked without it.

Quant finance tools matrix

Module 3: Why Most Retail Traders Get Algorithmic Trading Wrong (And How to Get It Right)

The harsh truth: most people backtest wrong, form strategies wrong, and execute wrong. In this module, you’ll get the 8-step process for strategy formation that separates retail hobbyists from systematic traders. Yes, retail traders can compete — but only if they do this correctly.

Building trading strategies step by step

Module 4: The Statistical Test That Tells You If Your Backtest Is Skill or Luck

Most people optimize parameters to maximize profit. That’s exactly how you lose money. You’ll learn to treat backtests as experiments — with statistical rigor that reveals whether your strategy has genuine edge or just curve-fitted noise.

Trading pattern correlation heatmap

Module 5: How Professional Money Managers Engineer Alpha Factors

Most people have heard of alpha. Few can define it precisely, let alone capture it. You’ll build alpha factors from scratch, learn to hedge away beta exposure, and use the same factor engineering techniques used at institutional asset managers.

Strategy return quantiles analysis

Module 6: Run Millions of Strategy Simulations in Seconds with VectorBT

VectorBT is a vector-based backtesting framework that does in seconds what traditional frameworks do in hours. You’ll optimize entry and exit signals across massive parameter spaces and see exactly which combinations produce real edge.

Trading strategy returns histogram

Module 7: Build Factor Portfolios Across 21,000+ Equities with Zipline Reloaded

Zipline Reloaded is the most robust event-based backtesting framework available in Python. You’ll build factor pipelines that screen, rank, and allocate across a universe of 21,000+ equities — the same approach used by quantitative hedge funds.

3D visualization and Zipline pipeline flowchart

Module 8: The Risk Dashboard That Shows You Exactly Where Your Strategy Breaks

Cumulative returns look great until drawdowns destroy your capital. You’ll use Pyfolio and Alphalens to analyze risk-adjusted performance, factor exposures, alpha decay, and drawdown periods — so you know exactly when a strategy is working and when to cut it.

Financial analysis dashboard with returns and drawdowns

Module 9: Connect Python to Interactive Brokers and Automate Your First Trade

The final piece: execution. You’ll build the scaffolding for a live trading application using the Interactive Brokers API — downloading real-time data, submitting orders, and monitoring positions. This is where everything comes together.

Interactive Brokers trading platform

Module 10: Your Roadmap for What Comes Next

You’ve gone from zero to a working Python-based trading pipeline. In this final module, we map out your next steps — whether that’s advanced strategies, the QS Pro algorithmic trading system, or building your own research operation.

Live session with Jason

Also included: 40+ Code Templates You Can Use Today

Every template includes a 20-minute video walkthrough and real quant code. Copy, modify, and run — in Jupyter, on real market data.

Portfolio Risk & Performance

6 templates for building foundational risk metrics, cumulative return analysis, Sharpe ratios, and drawdown analysis.

Finance charts and cumulative returns
Options & Derivatives Pricing

4 templates for pricing options, building volatility surfaces, and forecasting implied volatility for trading edge.

3D implied volatility surface for AAPL
Factor Portfolios & Beta Hedging

4 templates for factor-based investing, beta-neutral portfolio construction, and risk decomposition.

3D financial data scatter plot
Algorithmic Trading Automation

5 templates for building, testing, and deploying systematic trading strategies with broker integration.

Trading platform with Python libraries

"But I can learn this for free on YouTube."

You can. And you’ll spend 6 months watching videos from people who’ve never sat on a trading desk, piecing together fragments that don’t connect, debugging code that was outdated 2 years ago. YouTube is a great place to learn what’s possible. It’s a terrible place to learn how to do it systematically. This course saves you months of wasted time by giving you the exact sequence, code, and frameworks — built by someone with 20 years of industry experience.

"I’m a complete beginner. Is this too advanced for me?"

Module 1 starts with the basics — data types, functions, and classes — with finance examples, not computer science theory. The pace ramps up, but you have lifetime access, 1,500+ community members to ask questions, and an instructor who’s taught people from zero-experience analysts to 30-year software engineers. Over 1,500 people have completed this course. Many started exactly where you are.

"I don’t have time. My schedule is packed."

The course is designed for less than 1 hour per day. All content is self-paced with lifetime access. No deadlines. No cohort schedule. If you miss a week, the content is still there. If you need 3 months instead of 6 weeks, take 3 months.

"Is this just another overpriced course?"

At $1,000, this costs less than 2 months of a Bloomberg terminal — and unlike Bloomberg, you keep access forever. The 40+ code templates alone took hundreds of hours to develop. The PQN Pro community has 1,500+ active members. The aggregate review score is 4.97 out of 5.0 across 100+ reviews, with a refund rate below 0.1%. Price-per-hour of instruction is under $50 for 20 hours of content — before counting the code templates, bonuses, and community access.

Over $4,500 in Bonuses — Included Free

Every bonus solves a specific problem you’ll hit as you build your Python-based trading operation.

Up to $1,000 of IBKR
Get up to $1,000 in IBRK stock when you open and fund an Interactive Brokers account.
Trading dashboard
Free cheatsheet
Breakevens are the prices of the underlying where you start to make or lose money.
Options trading strategies
30% off theory ebook
Get the theory behind the Black-Scholes model, binomial trees, the Greeks, and implied volatility.
Options pricing theory guide
50% off Python Foundations
If you need more help with Python, enjoy 50% off my new Python Foundations course.
Money bag icon
70% off options data
High-resolution, real-time, streaming equity and options data right to your Python API.
Thetadata cloud network
50% off access
Automatically analyze your trades to manage risk, monitor performance, and make more money.
Trade Blotter app
QuantConnect access
Access to hedge-fund quality data and multi-asset backtesting on an integrated platform.
Data analysis charts
Reference library
Dozens of the best books on Python, markets, trading, and quant finance at your fingertips.
Trading book collection

Everything you get — for life

13 modules, 134 lessons (20 hours)

Self-paced video instruction with Q&A. A private quant tutor runs $400/hr

$8,000

40+ Jupyter code templates

19 with video walkthroughs, real market data. Rich (hedge fund manager) said "the shared notebooks alone are worth multiples of the course cost."

$2,000

PQN Pro community (lifetime)

1,500+ finance professionals, developers, traders. Comparable Slack/Discord quant communities charge $100/month.

$1,500

Partner bonuses & credits

IBKR stock, data discounts, platform access. Load up on freebies.

$4,500

Real-time support & Q&A

Direct access to Jason + community debugging. Access to Jason + community for debugging and guidance. Equivalent of ~5 hours of consulting.

$1,000

Total value

$17,000

$1,000

One payment. Lifetime access. Everything above included.

You save $16,000 (94%)

Start Using Python for Quant Finance →

Payment plan available.

If you hired a quant consultant to teach you this material 1-on-1, you’d pay $400/hour. At 20 hours of instruction, that’s $8,000 — without the code, the community, or the bonuses.

A single semester of quantitative finance at a top university costs $15,000. A Bloomberg terminal costs $24,000/year.

Should you join? Here’s what I think.

Not everyone is right for this course and I don’t want to waste your time.

You’ll love this if:

✓ You want to use Python for market data analysis, backtesting, and automated trading

✓ You're tired of paying for generic courses that have nothing to do with quant finance

✓ You want an opinionated, structured path — not "figure it out yourself"

✓ You're brand new to Python, quant finance, or both

✓ You want step-by-step guidance from someone with 20+ years of industry experience

✓ You prefer hands-on instruction with zero fluff

✓ You don't have time to waste and want to know just what you need

This is not for you if:

✗ You want pure theory with no practical application

✗ You think buying a course will hand you profitable strategies without work

✗ You don't need Python in your field and won't anytime soon

✗ You're happy with Excel and unwilling to change tools

✗ You want a computer science fundamentals course (memory management, data structures, algorithms)

✗ You want to brute-force optimize backtests and data-mine the market

Frequently Asked Questions

What if I run out of time?

You have lifetime access. All modules, code notebooks, written curriculum, and the PQN Pro community — available whenever you’re ready. No deadlines. No expiration.

I'm completely new to Python. Will this work for me?

Module 1 covers the Python basics you need for quant finance. The pace increases from there, but you have lifetime access to revisit any module, and 1,500+ community members to help you through rough spots. Complete beginners have completed this course successfully.

I'm an experienced developer. Will this be too basic?

Several students have been software engineers for 20-30 years. They skip Module 1 and go straight to the quant-specific content — factor engineering, backtesting, risk analysis, broker integration. The code templates alone justify the investment for experienced devs.

Why does this cost $1,000 when Udemy courses are $19?

Udemy’s business model requires you to take course after course. They teach the broadest topics to the widest audience so you stay on their platform. Their incentives aren’t aligned with yours. Mine are: get you outcomes with Python for quant finance. I lose if you can’t use what you learn. The 4.97/5.0 aggregate rating across 100+ reviews and <0.1% refund rate speak to that alignment.

Do I need an Interactive Brokers account?

No. Only 3 of the 40+ code templates are specific to Interactive Brokers. 99% of the course applies to any broker or data source.

Does the code work in my market?

Techniques like conditional value at risk, factor investing, and options valuation work in all markets. The code templates are designed to be modified for your specific instruments and exchanges.

What's the time commitment?

Less than 1 hour per day. The course is fully self-paced with lifetime access.

Can I get a refund?

No. I’ve spent 11 years building a reputation for delivering the highest quality products. I have 1,500+ students, a <0.1% refund rate, and 100+ five-star reviews. This course is not for tire-kickers. It’s for people who are committed to the process. If that’s you, there’s zero chance you’ll be disappointed. If you’re just curious, please don’t buy the course.

How can I trust you?

I’m public online. My personal identity is tied to the PyQuant News brand across LinkedIn, Twitter, and every other platform. 1,500+ people have trusted me to get them outcomes. You can verify that through the reviews on this page.

The Python skills you need for quant finance. The practitioner system to learn them.

13 modules, 134 lessons (20 hours)

Self-paced video instruction with Q&A. A private quant tutor runs $400/hr

$8,000

40+ Jupyter code templates

19 with video walkthroughs, real market data. Rich (hedge fund manager) said "the shared notebooks alone are worth multiples of the course cost."

$2,000

PQN Pro community (lifetime)

1,500+ finance professionals, developers, traders. Comparable Slack/Discord quant communities charge $100/month.

$1,500

Partner bonuses & credits

IBKR stock, data discounts, platform access. Load up on freebies.

$4,500

Real-time support & Q&A

Direct access to Jason + community debugging. Access to Jason + community for debugging and guidance. Equivalent of ~5 hours of consulting.

$1,000

Total value

$17,000

$1,000

One payment. Lifetime access. Everything above included.

You save $16,000 (94%)

Start Using Python for Quant Finance →

Payment plan available.

1,500+ students. 4.97/5.0 rating. Less than 0.1% refund rate.