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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.
12 subscribers
Sep 13 • 10 tweets • 3 min read
Can AI be used for quant financial analysis?

A 54-page PDF. Image Abstract:

OpenAI's new flagship model, ChatGPT-4o, released on May 13, 2024, offers enhanced natural language understanding and more coherent responses.
Sep 11 • 16 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.
Sep 11 • 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
Sep 7 • 11 tweets • 4 min read
David Tepper has generated average annual returns of 28% since 1993.

But with Python, you can do it too.

This is how to make a David Tepper algorithm with Python: 🧵 Image Here’s a “Tepper-inspired” macro risk-on / risk-off allocator in Python.

It leans on public, high-level principles often associated with David Tepper’s approach (credit spreads, macro regime, volatility)
Sep 6 • 8 tweets • 3 min read
🚨BREAKING: A new open-source multi-agent LLM trading framework in Python

It's called TradingAgents.

Here's what it does (and how to get it for FREE): 🧵 Image 1. What is TradingAgents

TradingAgents is a multi-agent trading framework that mirrors the dynamics of real-world hedge funds.
Sep 5 • 8 tweets • 3 min read
Can AI trade?

Introducing FinSphere: A real-time stock analysis agent

A 16-page PDF: Image 1. The Problem

The paper posits that LLMs struggle with 2 limitations:

1) Absence of evaluation metrics

2) Lack of stock analysis logic
Sep 3 • 4 tweets • 2 min read
Nothing beats the feeling of executing dozens of trades from Python via API.

All you need is:
1. Python
2. Interactive Brokers

Want to learn how? Image 🚨 LIVE Python Algo Trading Workshop: Learn how we built our hedge fund

• QSConnect: Build your quant research database
• QSResearch: Research and run machine learning strategies
• Omega: Automate trade execution with Python

👉 Get the system: learn.quantscience.io/become-a-pro-q…Image
Sep 2 • 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…
Sep 1 • 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
Aug 31 • 4 tweets • 2 min read
The 5 things you need to start algo trading:

1. One trading strategy
2. A $500 computer
3. A broker with an API
4. An internet connection
5. The Python Quant Scientist Stack

That's it.

Want to learn how? Image 🚨 LIVE Python Algo Trading Workshop: Learn how we built our hedge fund

• QSConnect: Build your quant research database
• QSResearch: Research and run machine learning strategies
• Omega: Automate trade execution with Python

👉 Get the system: learn.quantscience.io/become-a-pro-q…Image
Aug 30 • 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
Aug 28 • 5 tweets • 2 min read
JP Morgan's Python training.

Available 100% for free: Image Here's the link on GitHub: github.com/jpmorganchase/…Image
Aug 27 • 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
Aug 27 • 10 tweets • 4 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
Aug 26 • 8 tweets • 3 min read
🚨BREAKING: New Python Library for Algorithmic Trading with AI

It's called limex ziplime.

This is what you need to know: Image 1. What is ZipLime?

ZipLime is your AI quant for creating and backtesting trading strategies.

If you have an idea for a strategy or portfolio, just describe it, and I’ll help translate it into working code, run the backtest, and analyze the results.
Aug 17 • 8 tweets • 3 min read
The secret of hedge funds is revealed in a 41-page PDF:

This paper analyzed 464 stocks that 10X-ed over a 24-year period.

Here are the best factors that drive outperformance: (number 3 is the best 🧵) Image 1. Size Effect

"Small-cap stocks outperform medium and large companies in 11 out of 12 cases"

Smaller stocks tend to perform better, but it's not the only contributor. Image
Aug 15 • 5 tweets • 2 min read
JP Morgan's Python training.

Available 100% for free: Image Here's the link on GitHub: github.com/jpmorganchase/…Image
Aug 11 • 8 tweets • 3 min read
🚨BREAKING: Introducing QF-Lib

A new Python library for Quant Finance.

Here's everything you need to know... (a thread) 🧵 Image 1. What Is QF-Lib?

QF-Lib is a modular Python library that provides an advanced event-driven backtester and a set of high-quality tools for quantitative finance.
Aug 10 • 21 tweets • 4 min read
Look at this guy:

• Averaged 25% annual returns over 30 years
• Secret Sauce: Discounted Cash Flow

Here's how to trade just like him with algorithms in Python: Image His name is David Tepper.

David Tepper climbed from a modest Pittsburgh upbringing to Wall Street legend status, delivering ~25% annual returns over 30 years.

Let's unpack his strategy and standout trades:
Aug 9 • 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
Aug 8 • 14 tweets • 4 min read
Look at this guy.

He achieved zero losing years over 3 decades.

He delivered over 30% yearly returns by defying conventional wisdom.

Discover 7 key strategies that cemented his iconic status:
(No. 7 is sheer genius) 🧵 Image This is Stanley Drukenmiller:

• Made $1B shorting the pound
• 30% CAGR over 30 years
• 0 losing years

Here's the Drukenmiller algorithm: Image