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Quant Science Profile picture
Quant Science
@quantscience_
Develop profitable trading strategies, build a systematic trading process, and trade your ideas with Python—even if you’ve never done it before.
11 subscribers
Jul 25 • 7 tweets • 3 min read
🚨 Introducing Goldman Sachs GS-Quant

A Python quant toolkit made by Goldman Sachs.

This is what you need to know: (a thread 🧵) Image 1. GS Quant:

GS Quant is a Python toolkit for quantitative finance, created on top of one of the world’s most powerful risk transfer platforms. Image
Jul 25 • 9 tweets • 3 min read
🚨BREAKING: A new Python library for algorithmic trading.

Introducing TensorTrade: An open-source Python framework for trading using Reinforcement Learning (AI) Image TensorTrade is an open source Python framework for building, training, evaluating, and deploying robust trading algorithms using reinforcement learning leveraging:

- numpy
- pandas
- gym
- keras
- tensorflow
Jul 24 • 11 tweets • 3 min read
🚨 BREAKING: I just stumbled upon this Machine Learning Python library for Algorithmic Trading that looks insane.

It's called AlphaPy.

This is what it does: Image AlphaPy is a machine learning framework for both speculators and data scientists.

It is written in Python with the scikit-learn and pandas libraries, as well as many other helpful libraries for feature engineering and visualization.

Here's some of what it does:
Jul 19 • 10 tweets • 3 min read
FinViz: A financial Visualization and Screening Tool

Available in Python.

This is how: Image Meet finvizfinance:

A Python package that collects financial information from the FinViz website.

- Stock Charts
- Fundamentals
- Technicals
- Insider Information
- News
- Forex
- Crypto
Jul 15 • 17 tweets • 5 min read
12 Python libraries for free market data everyone should know: Image yfinance

Data for stocks (historic, intraday, fundamental), FX, crypto, and options. Uses Yahoo Finance so any data available through Yahoo is available through yfinance.

github.com/ranaroussi/yfi…
Jul 14 • 13 tweets • 4 min read
Stock Prediction AI: Using Machine Learning and Deep Learning to predict stock price movements in Python.

The Python code is 100% free on GitHub.

Let's dive in (bookmark this): Image 1. The Python Machine Learning and Deep Learning Libraries:

- mxnet
- gluon
- sklearn
- xgboost Image
Jul 12 • 10 tweets • 3 min read
🚨BREAKING: A new Python library for algorithmic trading.

Introducing TensorTrade: An open-source Python framework for trading using Reinforcement Learning (AI) Image TensorTrade is an open source Python framework for building, training, evaluating, and deploying robust trading algorithms using reinforcement learning leveraging:

- numpy
- pandas
- gym
- keras
- tensorflow
Jul 11 • 9 tweets • 2 min read
In investing, your track record is everything.

In 2 minutes, I'll uncover the secrets hedge funds use to track their portfolio performance: 🧵 Image There are 3 main areas that smart investors care about:

1. Profits (Returns)
2. Risks
3. Drawdowns

Let's break them down using the snapshot:
Jul 10 • 10 tweets • 3 min read
How to create a Black-Litterman portfolio in Python.

A thread: 🧵 Image 1. What is Black-Litterman?

Black-Litterman starts with market equilibrium returns (from CAPM) and lets you add your views (e.g., “Tesla will outperform”).

It balances both to create optimal weights. No overfitting, just math.
Jul 6 • 10 tweets • 3 min read
This guy made a real-world AI Hedge Fund Team in Python.

Then he made it available for everyone for free.

Here's how he did it (and how you can too). Image @virattt is doing something incredible.

He's using AI to replicate a hedge fund.

And he's open-sourced it for the world to learn.
Jul 4 • 18 tweets • 5 min read
Quants use principal component analysis to find alpha.

Blackrock uses it to manage $100s of billions in factor funds.

Northfield uses it to earn $10s of millions selling factors to investors.

Here’s how it’s done.

In a few lines of Python: Image By reading this thread, you’ll be able to:

1. Get stock data
2. Fit a PCA model
3. Visualize the components
4. Isolate the alpha factors

But first, a quick primer on PCA if you’re unfamiliar:
Jul 4 • 11 tweets • 3 min read
🚨 BREAKING: I just stumbled upon this Machine Learning Python library for Algorithmic Trading that looks insane.

It's called AlphaPy.

This is what it does: Image AlphaPy is a machine learning framework for both speculators and data scientists.

It is written in Python with the scikit-learn and pandas libraries, as well as many other helpful libraries for feature engineering and visualization.

Here's some of what it does:
Jul 1 • 10 tweets • 3 min read
A 37-page research paper reveals why stocks misbehave (and how hedge funds profit):

Investor Sentiment

Underreact & overreact for 4.2%+ alpha.

Here’s how: 🧵 Image 1. What Is Investor Sentiment?

Stocks underreact to single news (e.g., earnings) & overreact to trends.

A 1998 study shows it creates predictable returns. Image
Jun 29 • 9 tweets • 3 min read
How to use MACD for algorithmic trading Machine Learning.

Let's dive in. 🧵 Image MACD (Moving Average Convergence Divergence) is most commonly used in Technical Trading.

But, it can be used as part of a factor model.

Let's see how. Image
Jun 28 • 9 tweets • 3 min read
A 23-page research paper reveals the number 1 method Hedge Funds use to beat the market:

Time Series Momentum

This is how: 🧵 Image 1. What Is Time Series Momentum?

Time Series Momentum (TSMOM) bets on trends continuing. If a stock’s up, buy more; if down, sell. A 2011 study of 58 assets proved it works! Image
Jun 27 • 15 tweets • 3 min read
How to create your own "mini" hedge fund with algorithmic trading and Python

A thread 🧵 Image 1. What is a Hedge Fund

Hedge funds pool money from wealthy individuals or institutions to seek higher, risk-adjusted returns across multiple markets.
Jun 22 • 6 tweets • 3 min read
Mind-blowing tear sheets for your trading strategies.

1 line of Python code: Image QuantStats performs portfolio profiling for analytics and risk metrics.

Grab it here: github.com/ranaroussi/qua…Image
Jun 18 • 9 tweets • 3 min read
How to use MACD for algorithmic trading Machine Learning.

Let's dive in. 🧵 Image MACD (Moving Average Convergence Divergence) is most commonly used in Technical Trading.

But, it can be used as part of a factor model.

Let's see how. Image
Jun 16 • 10 tweets • 4 min read
How to make a simple algorithmic trading strategy with a 472% return using Python.

A thread. 🧵 Image This strategy takes advantage of "flow effects", which is how certain points in time influence the value of an asset.

This strategy uses a simple temporal shift to determine when trades should exit relative to their entry for monthly boundary conditions. Image
Jun 14 • 11 tweets • 3 min read
🚨 BREAKING: I just stumbled upon this Machine Learning Python library for Algorithmic Trading that looks insane.

It's called AlphaPy.

This is what it does: Image AlphaPy is a machine learning framework for both speculators and data scientists.

It is written in Python with the scikit-learn and pandas libraries, as well as many other helpful libraries for feature engineering and visualization.

Here's some of what it does:
Jun 13 • 18 tweets • 6 min read
Quants use principal component analysis to find alpha.

Blackrock uses it to manage $100s of billions in factor funds.

Northfield uses it to earn $10s of millions selling factors to investors.

Here’s how it’s done.

In a few lines of Python: Image By reading this thread, you’ll be able to:

1. Get stock data
2. Fit a PCA model
3. Visualize the components
4. Isolate the alpha factors

But first, a quick primer on PCA if you’re unfamiliar: