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Quant Science Profile picture
@quantscience_
Jan 25 • 8 tweets • 3 min read • Read on X
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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
2. The Data Behind the Strategy

The TSMOM paper analyzed equities, currencies & more. T-stats showed consistent profits across 1-month lookbacks! Image
3. Coding TSMOM with Python

Code TSMOM in Python:
- Use yfinance to get data
- Then momentum = price[-1] / price[-20] - 1.

Positive? Buy
Negative? Sell Image
4. Real-World Performance

TSMOM outperforms passive investing.

We're using a modified version of TSMOM in our Hedge Fund.

One backtest shows 3500% return vs 450% S&P500. Image
We are using TSMOM in our hedge fund.

And we'd like to share exactly how it works.

Want to see how we built our hedge fund in Python?

Then join us for our free training:
🚨 FREE 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
That's a wrap! Over the next 24 days, I'm sharing my top 24 algorithmic trading concepts to help you get started.

If you enjoyed this thread:

1. Follow me @quantscience_ for more of these
2. RT the tweet below to share this thread with your audience

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More from @quantscience_

Quant Science Profile picture
Feb 12
No one made more money in trading than Jim Simons.

Not Buffett. Not Soros. Not Dalio.

His hedge fund was so powerful that he shut it down to outsiders.

$100 in 1988 grew to $400M in 30 years.

Here’s how a mathematician became the world’s greatest trader: Image
Jim Simons was no ordinary investor.

Before conquering Wall Street, he was:

• A math professor at MIT and Harvard
• A Cold War code-breaker for the NSA
• A pioneer in quantum computing

But none of this satisfied him.

"Some people like to paint, others like to cook. I like to make money."Image
At 40 years old, Simons left his prestigious academic career to start a hedge fund.

He had no finance experience. No Wall Street background.

His secret weapon? Mathematics. Image
Read 17 tweets
Quant Science Profile picture
Feb 10
A strategy with a Sharpe of 0.94 (and 513.8% cumulative return): Image
A strategy only Goldman Sachs can trade?

Nope.

It's a VIX-based futures strategy that demolishes the benchmark just like the pros.

Here's how:
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:
Read 14 tweets
Quant Science Profile picture
Feb 6
How to make your own algorithmic trading system in Python (a complete roadmap): Image
Step 1: Learn Python

- Pandas: Data Analysis
- Scikit Learn: Machine Learning
- SQLAlchemy: Databases
Step 2: Set up your financial database

Financial Data:
- Price: Yahoo Finance (free)
- Fundamentals: Financial Modeling Prep (paid)

Database:
- DuckDB (free) Image
Read 10 tweets
Quant Science Profile picture
Feb 2
Automate your trading strategies in Python

How to build your first trading bot:

(a thread) Image
1. What is a trading bot?

A trading bot is a software program that automates buying and selling financial assets like stocks and cryptocurrencies based on pre-defined strategies and rules.

These automated systems can manage portfolios without human intervention, operating 24/7.
2. Let's make a Bitcoin Trading bot

We'll use investing-algorithm-framework in Python Image
Read 12 tweets
Quant Science Profile picture
Feb 1
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
The signals for when to go short, when to cover shorts, when to go long, and when to close longs are all linked to these recurring monthly cycles.

This periodic "flow" of signals—month-in, month-out—creates a systematic pattern. Image
Read 11 tweets
Quant Science Profile picture
Feb 1
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…
pandas-datareader

pandas-datareader used to be part of the pandas project. Now an independent project. Includes data for stocks, FX, economic indicators, Fama-French factors, and many others.

pandas-datareader.readthedocs.io/en/latest/
Read 17 tweets

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