Getting Started With Python For Quant Finance By Jason Strimpel – Free Download Course
Learn from a complete system for getting started with Python for quant finance from scratch. No theory. No jargon. Just practical Python you can use for Algo trading!
✅ About This Course:
✅ Course Author: Jason Strimpel
✅ Official Course Price: $1000
✅ Free For Our VIP Members? : Yes
✅ Download Links : Mega & Google Drive
✅ Course Size : 19.79 GB
✅ Updatable? : Yes, all future updates included.
✅ Sales Page : You can check at the bottom of this page.
🏆 Here’s What You Get & Learn With This Course:
PQN Pro
Support from 1.5K others like you
Get personalized answers fast, detailed code walkthroughs, strategy ideas, and help fixing code bugs. All from thousands of like-minded people.
Screenshot of a large virtual meeting with dozens of participants, each shown in their own frame, smiling and gesturing to the camera, representing a diverse group of individuals from various backgrounds.
Onboarding
Get ready for the course
Install the Python Quant Stack, download market data, and connect to Interactive Brokers—all with step-by-step instructions.
Module 1:
Getting the Python Basics Right
If you’re brand new to Python, you’ll fast-track your learning with exactly what you need to know—no overwhelm, no complexity.
Screenshot of Python code: It shows imports for pandas and a module from openbb_terminal.sdk. Variables are defined for storing stock and futures data paths with ‘stocks.h5’ and ‘futures.h5’ respectively, and setting up tickers for ‘SPY’ and root for ‘ES’.
Module 2:
The Python Quant Stack
Get familiar with the the most important Python libraries for algo trading and data analysis—Pandas—so you can work with market data.
Infographic displaying a matrix of categories and tools used in quantitative finance. Categories include Research Environments, Numerical Computing, Data Visualization, Machine Learning, Risk & Optimization, and Algo Trading, each paired with relevant tools like Jupyter, NumPy, pandas, and Interactive Brokers.
Module 3:
Algorithmic Trading, Backtesting, and Strategy Formation
Yes! Retail traders can compete. Get a framework to form trading ideas, test them, and get them executed.
Title slide from a presentation: ‘How to Build Trading Strategies: Step-by-Step’, branded with the PyQuant News logo in the corner.
Module 4:
Treat Your Backtest Like an Experiment
Understand why most people get backtesting wrong—and the secret of avoiding losing money because of a backtest.
Color-coded heatmap displaying the relationship between various bullish and bearish trading patterns. Each cell represents the correlation between patterns, ranging from dark purple (low correlation) to bright yellow (high correlation).
Module 5:
How to Engineer Alpha Factors With Python
Get the tools and techniques professional money managers use to manage portfolios and hedge away unwanted risk.
Graph titled ‘Strategy – Return Quantiles’ displaying box plots for return distributions on daily, weekly, monthly, quarterly, and yearly intervals from January 2010 to August 2023. Each time period shows variability and median returns through box and whisker plots in different colors.
Module 6:
Prototyping and Optimizing Strategies with VectorBT
Get working code to run millions of simulations with the cutting-edge VectorBT backtesting library.
Data visualization slide featuring a histogram of total returns and box plots for different trading strategies, color-coded as SL, TS, and TP. The slide includes Python and Veles logos, emphasizing the use of these tools in analysis.
Module 7:
How to Backtest A Trading Strategy with Zipline Reloaded
Build factor pipelines to screen and sort a universe of 21,000+ equities to build and backtest real-life factor portfolios.
Analytical visualization featuring a 3D color density plot to represent data dimensions, accompanied by a flowchart explaining the process flow in data analysis using Zipline library. The flowchart details steps involving data input, processing with functions like ‘AverageDollarVolume’ and ‘MeanReversion’, and output based on specific conditions.
Module 8:
Risk and Performance Analysis with PyFolio and AlphaLens
Get the code to quickly asses strategy risk and performance—including factor performance—and assess alpha decay.
Financial analysis dashboard featuring a series of charts and graphs: Cumulative Returns vs. Benchmark, Distribution of Monthly Returns, Daily Active Returns, Rolling Beta to Benchmark, and Strategy’s Worst 5 Drawdown Periods. Each chart provides detailed metrics comparing a strategy against a benchmark from 2010 to 2023.
Module 9:
Automate Trade Execution with Python
Connect to your broker, download high-resolution market data, historical data, and automate your trades so you can get to trading, faster.
Screenshot of the Interactive Brokers trading platform displaying detailed market data for Facebook (FB) stock. The interface includes multiple panels showing the stock’s bid and ask prices, a chart of 10-minute candlesticks, and various market indicators. Additional panels show real-time market news and a summary of other major stocks and currencies.
Module 10:
Double Down on Your Success With More Help and Support
Get expert guidance to take your experience to the next level. More strategies. More code. More support.
Code for portfolio risk and performance optimization
6 code templates and video walkthroughs to build the foundational risk and performance metrics for improving your trading performance.
Collection of finance graphs and charts presenting various metrics such as cumulative returns, volatility, Sharpe ratio, and drawdown periods over a span from 2010 to 2024.
Code to price options and derivatives with Python
4 code templates and video walkthroughs to price options and forecast implied volatility for trading edge.
3D visualization of implied volatility surface for AAPL stock, showing a mesh of blue lines with historical data points marked in red, on a dark grid background.
Code to build factor portfolios and hedge beta
4 code templates and video walkthroughs to reduce risk and build portfolios that make money.
3D scatter plot visualizing financial data points in various colors to represent different metrics, with arrows indicating trends. Adjacent is a table of performance metrics comparing a benchmark and a strategy.
Code to automate your trading strategies
5 code templates and video walkthroughs to demonstrate an algo trading system you can modify for your own purposes.
Up to $1,000 of IBKR
Get up to $1,000 in IBRK stock when you open and fund an Interactive Brokers account.
Diagram showing options trading strategies, including Long Call, Short Call, Long Put, and Short Put, with potential gains and losses detailed.
Free cheatsheet
Breakevens are the prices of the underlying where you start to make or lose money.
Cover of ‘The 47-Page Ultimate Guide to Options Pricing Theory’, featuring a bright yellow background with mathematical equations.
30% off theory ebook
Get the theory behind the Black-Scholes model, binomial trees, the Greeks, and implied volatility.
Icon of a money bag with a dollar sign, set against a gradient orange background.
50% off Python Foundations
If you need more help with Python, enjoy 50% off my new Python Foundations course.
Graphic illustrating thetadata’s cloud network connected to data sources from Nasdaq, IEX, Cboe, and NYSE.
70% off options data
High-resolution, real-time, streaming equity and options data right to your Python API.
Advertisement for Trade Blotter showing a laptop screen with the app interface for managing investment risks and profits.
50% off access
Automatically analyze your trades to manage risk, monitor performance, and make more money.
Screenshot of data analysis software showing multiple charts, including a line graph tracking model predictions and bar charts of model accuracy over time.
QuantConnect access
Access to hedge-fund quality data and multi-asset backtesting on an integrated platform.
Collage of book covers about trading and investment strategies, including titles like ‘How to Make Money in Stocks’ and ‘Algorithmic Trading’.
Reference library
Dozens of the best books on Python, markets, trading, and quant finance at your fingertips!
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You can find more info on the sales page here.
