Latest Twitter Threads by @quantscience_ on Thread Reader App

Mar 31 • 8 tweets • 3 min read

🚨BREAKING: Microsoft open-sourced an AI Quant investment platform in Python

This is what you need to know:

(a thread)

1. What is Qlib?

Qlib is an open-source, AI-oriented quantitative investment platform that aims to realize the potential, empower research, and create value using AI technologies in quantitative investment, from exploring ideas to implementing production.

Mar 27 • 17 tweets • 5 min read

12 Python libraries for free market data everyone should know:

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…

Mar 26 • 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: 🧵

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!

Mar 18 • 11 tweets • 3 min read

A Bloomberg Terminal costs $30,000 a year.

Here's how to build 90% of it for free.

Wall Street doesn't advertise this. But every function that matters has a free alternative in Python.

Here's the DIY version (with Python):

━━━━━━━━━━━━━━━━━
🔹 REAL-TIME PRICES & CHARTS
━━━━━━━━━━━━━━━━━
Bloomberg: GP
Python: yfinance + Plotly

yfinance pulls live and historical price data for any ticker in seconds. Plotly turns it into interactive charts. Add your own indicators, overlays, and alerts. No subscription needed.

Mar 18 • 9 tweets • 3 min read

This strategy has delivered 18% annual returns since 1926.

No black box. No complex ML model.

Just breakouts, trailing stops, and volatility sizing applied to industry portfolios.

Here's how it works (and link to the 37 page PDF):

─────────────────
🔹 THE ENTRIES
─────────────────
A long position triggers when an industry breaks above either:
• A Donchian Channel (20-day high, 40-day lower band)
• A Keltner Channel (20-day EMA ± 1.4× ATR, 40-day lower band)

The asymmetric lookback keeps you invested during sustained trends.
No short positions. When nothing is trending, capital sits in T-bills.

Mar 17 • 9 tweets • 3 min read

Most traders skip the math.

Then wonder why their backtests fail in live trading.

You don't need a math degree. But you need the right foundations.

Here's the complete math & stats roadmap for trading:

─────────────────
🔹 STATISTICS & PROBABILITY
─────────────────
• Sample Size & Law of Large Numbers — a 10-trade backtest proves nothing. You need hundreds before trusting your edge.
• Expected Value — the only number that actually matters: (Win% × Avg Win) - (Loss% × Avg Loss)
• Standard Deviation — becomes volatility when applied to returns. Every position sizing formula runs through this.
• Correlation — 3 momentum strategies on correlated assets isn't diversification. It's 3x the risk.
• Conditional Probability — your strategy's win rate changes by market regime. Know when it works.

Mar 5 • 8 tweets • 3 min read

🚨BREAKING: Microsoft open-sourced an AI Quant investment platform in Python

This is what you need to know:

(a thread)

Mar 3 • 12 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):

1. The Python Machine Learning and Deep Learning Libraries:

- mxnet
- gluon
- sklearn
- xgboost

Mar 2 • 12 tweets • 3 min read

Stop trading in Excel like it’s 2009.

Start using Python + ChatGPT.

Here's 7 small steps:

1. Pick ONE strategy rule (keep it boring)

Start with something simple like moving averages or a 12-1 momentum.

Your goal is execution and learning, not “printing money.”

Feb 28 • 11 tweets • 4 min read

Build an End-to-End Python Algorithmic Trading System (complete roadmap + skills + tools)

Bookmark this.

1) Foundations (1–2 weeks)

Learn these:

1. Python basics → data/ML
2. Pandas (data wrangling)
3. Scikit-learn (ML)
4. SQLAlchemy (DB access)

Feb 26 • 16 tweets • 3 min read

Top 10 Algorithmic Trading Strategies (and how they work) 🧵

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.

Feb 26 • 10 tweets • 3 min read

Python is wild for finance.

You can get FinViz in Python for free (this is how):

(a thread)

1. What is finvizfinance?

finvizfinance is a package that collects financial information from FinViz website. It has:

- Stock charts, fundamental & technical information
- Insider information
- Stock news
- Forex charts
- Crypto charts

Here's some examples of what you can do:

Feb 24 • 16 tweets • 3 min read

How to create your own "mini" hedge fund with algorithmic trading and Python

A thread 🧵

1. What is a Hedge Fund

Hedge funds pool money from wealthy individuals or institutions to seek higher, risk-adjusted returns across multiple markets.

Feb 23 • 9 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): 🧵

1. What is TradingAgents

TradingAgents is a multi-agent trading framework that mirrors the dynamics of real-world hedge funds.

Feb 23 • 10 tweets • 3 min read

How to make a simple algorithmic trading strategy with a 472% return using Python.

A thread. 🧵

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.

Feb 22 • 18 tweets • 5 min read

12 Python libraries for free market data everyone should know:

Feb 22 • 5 tweets • 2 min read

*Using Mathematics to Make Money* by legend Jim Simons.

8 page PDF free for download:

The main ingredients of RenTec's success:

• Building and continuously improving investment models
• Fostering collaboration among the company’s workforce
• Hiring scientists who have decided they want to make money

Here's the link:

papers.ssrn.com/sol3/papers.cf…

Feb 19 • 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: 🧵

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!

Feb 17 • 11 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)

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

Feb 12 • 17 tweets • 5 min read

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:

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."

Feb 10 • 14 tweets • 4 min read

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

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:

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