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Quant Science Profile picture
@quantscience_
Jul 11 • 9 tweets • 2 min read • Read on X
In investing, your track record is everything.

In 2 minutes, I'll uncover the secrets hedge funds use to track their portfolio performance: 🧵 Image
There are 3 main areas that smart investors care about:

1. Profits (Returns)
2. Risks
3. Drawdowns

Let's break them down using the snapshot:
1. Profitability Insights

Annual Return: 22.44%—strong growth!

CAGR: 23.78%—compounded gains over time.

MAR: 0% (minimum acceptable)—room to beat risk-free rates.

Significance level (5%) sets the risk benchmark.
2. Risk Measures

Std Dev: 12.41%—volatility is moderate.

VaR (18.97%) & CVaR (29.76%)—expect losses up to 18.97% (95%) or 29.76% (worst cases).

Worst Realization: 50.74%—a rare but brutal drop to watch
3. Drawdowns and Risks

Max Drawdown: 14.61%—peak loss to recover from.

Ulcer Index: 2.80%—measures drawdown stress.

Drawdown at Risk (5.21%)—likely loss duration.
Use these to stress-test your strategy
Want to learn how we do this inside our hedge fund in Python?

In our new live workshop, we'll share everything (and you can ask any question)
🚨 WORKSHOP: How I built an automated algorithmic trading system with Python.

Hedge funds have an unfair advantage: better tools & faster execution.

That ends on July 24th.

👉 Register here to learn how with Python (500 seats): 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 to learn Algorithmic Trading Strategies that actually work?

I'm hosting a live workshop. Join here: learn.quantscience.io/qs-register

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More from @quantscience_

Quant Science Profile picture
Jul 10
How to create a Black-Litterman portfolio in Python.

A thread: 🧵 Image
1. What is Black-Litterman?

Black-Litterman starts with market equilibrium returns (from CAPM) and lets you add your views (e.g., “Tesla will outperform”).

It balances both to create optimal weights. No overfitting, just math.
2. How it works

Using skfolio in Python, you:

- Compute market returns (e.g., S&P 500).
- Add views (e.g., 5% outperformance for tech).
- Blend with confidence levels.
- Optimize weights.
Read 10 tweets
Quant Science Profile picture
Jul 4
Quants use principal component analysis to find alpha.

Blackrock uses it to manage $100s of billions in factor funds.

Northfield uses it to earn $10s of millions selling factors to investors.

Here’s how it’s done.

In a few lines of Python: Image
By reading this thread, you’ll be able to:

1. Get stock data
2. Fit a PCA model
3. Visualize the components
4. Isolate the alpha factors

But first, a quick primer on PCA if you’re unfamiliar:
PCA is used in many ways including signal processing, image recognition, and of course quant finance.

PCA:

• Isolates factors that drive returns
• Explains the variance in a dataset
• Used for factor investing and risk management

Let’s dig in!
Read 18 tweets
Quant Science Profile picture
Jul 4
🚨 BREAKING: I just stumbled upon this Machine Learning Python library for Algorithmic Trading that looks insane.

It's called AlphaPy.

This is what it does: Image
AlphaPy is a machine learning framework for both speculators and data scientists.

It is written in Python with the scikit-learn and pandas libraries, as well as many other helpful libraries for feature engineering and visualization.

Here's some of what it does:
1. Run machine learning models using scikit-learn and xgboost.

2. Create models for analyzing the markets with MarketFlow.

3. Predict sporting events with SportFlow.

4. Develop trading systems and analyze portfolios using MarketFlow and Quantopian’s pyfolio.
Read 11 tweets
Quant Science Profile picture
Jul 1
A 37-page research paper reveals why stocks misbehave (and how hedge funds profit):

Investor Sentiment

Underreact & overreact for 4.2%+ alpha.

Here’s how: 🧵 Image
1. What Is Investor Sentiment?

Stocks underreact to single news (e.g., earnings) & overreact to trends.

A 1998 study shows it creates predictable returns. Image
2. The Data Behind the Strategy

Analyzed 1933-1986 U.S. stock data.

Underreaction gives 0.34 quarterly autocorrelation. Overreaction flips after 3-5 years
Read 10 tweets
Quant Science Profile picture
Jun 29
How to use MACD for algorithmic trading Machine Learning.

Let's dive in. 🧵 Image
MACD (Moving Average Convergence Divergence) is most commonly used in Technical Trading.

But, it can be used as part of a factor model.

Let's see how. Image
1. What is MACD?

MACD is a trend-following momentum indicator that shows the relationship between two moving averages of a security's price.

The MACD is calculated by subtracting the long-term exponential moving average (EMA) from the short-term EMA.
Read 9 tweets
Quant Science Profile picture
Jun 28
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

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