You can build a stock portfolio to replicate a market index.

Here is the step-by-step process to do it with Python: Market indices offer investors a quick look at the overall performance of a specific market or sector.

Index replication is a strategy that lets investors mimic the performance of market indices.

Here’s how you can replicate the S&P500 using Python and Riskfolio-Lib:

17 Python GitHub repos for quant finance: Goldman Sachs

github.com/goldmansachs

Machine learning is magic for building portfolios.

The problem?

You need a Ph.D. to figure out how to use it.

With RiskFolio-Lib, you can build your own cutting-edge portfolio with machine learning.

No Ph.D. required: By reading this thread, you’ll be able to optimize a portfolio using Hierarchical Risk Parity.

HRP:

1. Groups assets using hierarchical clustering
2. Applies risk parity within identified clusters
3. Allocates weights through bisection technique

Here’s how with Python…

Backtesting options strategies is impossible.

Why?

• Millions of contracts
• Weekly expirations
• Assignment if ITM

Not anymore.

Now, you can backtest options strategies in a few lines of code.

Here’s how: Optopsy is an analysis tool for options trading strategies.

• Generates strategies from raw historic data
• Uncover performance statistics for combos
• 12 strategies built in including strangles

We’ll use 5 years of historic options data on RUT for the analysis.

Let’s go!

I learned MATLAB and Octave (wtf?) during my master's degree.

So I studied 12,556 Python code repos and taught myself Python.

99.99% of them were a complete waste of time.

But these 9 are worth more than my $90,000 master's degree: Notebooks that replicate original quantitative finance papers from Emanuel Derman.

github.com/MarcosCarreira…

OpenAI.

OpenBB.

LangChain.

Cutting-edge sentiment analysis using generative pre-trained transformers.

All in a few lines of Python.

Here's the code: By reading this thread, you’ll use the OpenBB SDK to download news for a topic.

Then, you’ll build a prompt to predict the sentiment of a news headline.

You’ll bring it all together with LangChain.

Let's go!

In 2011, I committed my first code to GitHub.

Since then, I've curated the best Python code for quant finance on the internet.

Here are the 9 best (bookmark this to keep it handy): python-cheatsheet

A comprehensive Python cheatsheet.

github.com/gto76/python-c…

My PhD professors taught me MATLAB during my master's degree.

So I watched 200 YouTube videos to learn Python

96% of them were a complete waste of time.

But these 8 taught me more than all my PhD professors combined: Algorithmic Trading Using Python (4.5 hours)

Learn how to perform algorithmic trading using Python in this complete course. Algorithmic trading means using computers to make investment decisions.

I spent $90,000 on a master's degree to learn math for finance.

These are the 16 most important stochastic processes for asset pricing (bookmark this thread for later): 1/16 Bessel Process

Models the radial part of a particle's path in motion.

Used to quantify risk in mean-reverting financial instruments.

College completely failed to teach me market data analysis.

So I spent 10,000 hours with Python over the last 12 years.

Then, I picked the 15 best libraries for the markets.

But unlike college, these won't cost you $90,000.

Here they are for free: 1/15. Backtrader: A powerful platform for backtesting and trading strategies

• Create and backtest trading strategies
• Optimize strategies using machine learning
• Support for multiple data feeds and brokers

12 of the best books on building trading systems (bookmark this thread): Quantitative Trading

Introduction to basic quantitative trading on a retail level.

In 2011, I committed my first code to GitHub.

Since then, I've starred the best Python repos for quant finance on the internet.

Here's the 9 best (*definitely* bookmark this): python-cheatsheet

A comprehensive Python cheatsheet.

github.com/gto76/python-c…

A strategy with a Sharpe of 0.94 (and 513.8% cumulative return).

A strategy only Goldman Sachs can trade?

The secret behind their $8.3 billion 2022 net income?

Nope.

Backest a VIX-based futures strategy and demolishes the benchmark.

Just like the pros: With SigTech.

Here's a primer in case you're unfamiliar with SigTech:

• Quant tech for institutional investors
• Provides data and tools to hedge funds
• Customers with $5 trillion in assets under management

SigTech was built for institutions.

Now you can have it too:

20 Python libraries for market data everyone should know (that you probably never heard of): googlefinance

Python module to get stock data from Google Finance API. This module provides no delay, real time stock data in NYSE & NASDAQ.

github.com/hongtaocai/goo…

ChatGPT is overhyped for Python.

That's what I told myself after 6 months of frustration.

Turns out I was just a bad prompt writer.

But I've now spent 200+ hours with it.

And it's my Python developer.

Here's how: From the beginning, I expected ChatGPT to "know" how to code (and trade).

That was the critical mistake.

I gave it 1-sentence prompts and expected it to write code for me.

But garbage in = garbage out.

So I reframed it:

Use ChatGPT for sentiment analysis on stocks.

The step-by-step guide with code and prompts: After reading this thread, you’ll be able to:

• Download news headlines in one line of code
• Build a prompt to predict sentiment of the headline
• Bring it all together with LangChain

Let’s do it!

One way to get started with algorithmic trading:

Buying a $120,000 master's degree from MIT.

The other way:

YouTube.

The 10 YouTube videos to get you up and running today: Algorithmic Trading Using Python - Full Course

A comprehensive course on algorithmic trading using Python.

Google "algorithmic trading" and get 88,300,000 results.

Google "python tutorial" and get 717,000,000 results.

Utterly exhausting.

Impossible to navigate.

So I took 805,300,000 search results, added in 22+ years of experience, and had a baby.

This is what I named it: Getting Started With Python for Quant Finance.

Get up and running with Python fast:

• Copy-paste code to apply immediately
• Help to debug code and ask questions
• Practical experience you can use now
• Get the Python Quant Stack installed

Here's what's packed inside:

Hedge funds are secretive about their strategies.

This PDF exposes their most successful ones.

Grab it here: The paper discusses two types of strategies:

1. Arbitrage strategies
2. Macro fund strategies

Here are the details:

I learned MATLAB and Octave (wtf?) during my master's degree.

So I studied 82,112 Python code repos and taught myself Python.

99.9% of them were a complete waste of time.

But these 9 are worth more than my $90,000 master's degree: Notebooks that replicate original quantitative finance papers from Emanuel Derman.

github.com/MarcosCarreira…

The Python Quant Stack:

1. Research environments
2. Numerical computing & stats
3. Data acquisition & manipulation
4. Risk, pricing, & optimization
5. Backtesting & trading

The $0 cost Python Quant Staci: 1. Research environments

Where you go to find and test ideas.

• Jupyter Notebook
• OpenBB Terminal