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Jan 25 8 tweets 3 min read Read on X
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.

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More from @quantscience_

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
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
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
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
Jan 30
How to bootstrap your own mini hedge fund in 2026 (Learn these skills): Image
Skills to begin with (ranked in order of importance):

1. Python
2. Pandas
3. Numpy
4. Plotly
5. Scikit Learn

This is your Python foundation.

Then learn these: 👇 Image
Next, add these skills for financial data analysis in Python:

6. yfinance
7. zipline
8. vectorbt
9. pytimetk
10. IBKR

Want to learn how?

I have a free training: 👇 Image
Read 6 tweets
Jan 29
Top 10 Algorithmic Trading Strategies (and how they work) 🧵 Image
1. Pairs Trading

Trades two correlated instruments simultaneously. It goes long on one asset and short on the other to profit from deviations from their historical relationship, expecting the correlation to eventually resume.
2. Scalping

Involves making numerous small trades to capture minimal price differences over a short time. For example, tape reading is used to analyze order flow and timing, enabling scalpers to profit from very brief price fluctuations.
Read 16 tweets

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