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Aug 8 14 tweets 4 min read Read on X
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
1. Exit losers quickly. Go all-in on winners.

While many spread their bets thin, Druckenmiller focuses intensely.

With a strong belief, he amplifies positions.

If mistaken? He bails without hesitation.

He commits to opportunities, not attachments.
2. Entry point trumps the idea itself.

Accuracy alone won't cut it.

You need precision in timing.

His wisdom: “Superior returns come from safeguarding capital and swinging for fences... not consistent singles.”

This isn't passive growth.

It's calculated boldness.
3. Steer clear of popular opinions.

When it's common knowledge, the edge is gone.

Druckenmiller's top wins came from unique perspectives...

...backed by bold execution on scale.

The toughest moves often yield the best rewards.
4. Big-picture economics first.

Unlike stock pickers, Druckenmiller starts with the macro view. He examines:

• Rate movements
• Money supply dynamics
• Policy shifts
• Global risks

Only after that does he hunt for positions.
5. “Markets breathe on liquidity.”

Druckenmiller's core advice: “Avoid battling the flow of money.”

He argues capital availability shapes prices more than basics.

Abundant funds → inflated assets.

Scarce funds → shrinking values.
Monitor capital trends, beyond quarterly numbers.

We've seen it play out (especially in recent cycles):

2020: Flood of cash → soaring markets.

2022: Squeeze on funds → widespread drops.
6. Turn setbacks into lessons.

He doesn't dodge errors... He dissects them.

“Any major slip-up stemmed from overcommitment to a view... without trimming losses promptly.”

Failures aren't foes.

Stubbornness is.
7. When everything lines up, stake it all.

Back in 1992, Druckenmiller teamed with Soros to challenge England's central bank.

They pocketed $1B shorting the pound.

How?

Flawless analysis met ideal conditions.

“Spot the flawless opportunity, then strike decisively.”
Can Drukenmillers' "Algorithm" be codified?

Yes - in Python.

Want to learn how? Image
🚨 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
P.S. - Want Algorithmic Trading with Python tutorials every Sunday?

Register here to join our Sunday Quant Scientist Newsletter (it's free): learn.quantscience.io/quant-scientis…

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More from @quantscience_

Aug 9
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
Read 9 tweets
Aug 4
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
2. Stock Price Data (Train/Test)

The dashed vertical line represents the separation between training and test data.

GS is shown but will use 72 assets.

Daily prices for each asset. Image
Read 9 tweets
Aug 2
7 small steps to start with algorithmic trading:

1. Start with Python
2. Learn to use VSCode
3. Take a pandas tutorial
4. Then a plotly tutorial
5. Make a portfolio with riskfolio
6. Make a backtest with vectorbt
7. Analyze performance with vectorbt

You can do this! Image
🚨 Python Algo Trading Workshop on Thursday: 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
Read 4 tweets
Jul 29
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
2. Value Effect

"A low book-to-market value (B/M < 1), i.e., low equity and relatively high market cap, implies that investors are paying more for a company than its net assets are worth."

Don't overpay - Overpaying tends to drive underperformance. Image
Read 9 tweets
Jul 27
JP Morgan's Python training.

Available 100% for free: Image
Here's the link on GitHub: github.com/jpmorganchase/…
🚨 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
Read 5 tweets
Jul 25
🚨 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
2. Goals:

GS Quant is designed to accelerate the development of quantitative trading strategies and risk management solutions, crafted over 25 years of experience navigating global markets. Image
Read 7 tweets

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