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?
🚨 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
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!
🚨 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
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 🧵)
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.
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.
🚨 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
GS Quant is a Python toolkit for quantitative finance, created on top of one of the world’s most powerful risk transfer platforms.
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.