How to become a Quant Scientist:
Math:
• Calculus
• Linear algebra
Stats:
• Probability
• Time series analysis
Python:
• Pandas
• Zipline Reloaded
Markets:
• Economics
• Microstructure
#DataScience
meets
#Quant
#Finance
:
Can we use Anomaly detection to identify buy/sell patterns?
Anomaly detection for stocks in 4 steps.
A thread with
#Python
code 🧵
Python is truly remarkable for
#Quant
#Finance
.
This is the output of VectorBT - a new backtesting framework that is lightning fast.
If you want to learn more, here's our 7 step process 🧵
Python for Finance: A beginner-friendly intro to Python for financial analysis.
Great for the aspiring algorithmic trader.
Python code is available 100% for free:
How to use anomaly detection for Algorithmic Trading in Python.
Just dropped our NEW article AND full code.
Plus it uses our NEW pytimetk package for anomaly detection.
Article:
#Quant
#Science
#DataScience
#Trading
Signal processing with Fast Fourier Transform is a powerful tool for financial analysis.
Here are 4 examples how you can use FFT in Quantitative Analysis. 🧵
BloombergGPT
A large language model for finance.
• Closed source
• $5 million to train
• Uses 1.3 million GPU hours
Enter FinGPT:
An open source large language model for finance.
• Open source
• $300 to train
• Uses 80 GPU hours
Get the code on GitHub:
Average True Range (ATR) is an essential technical indicator for volatility signals.
In 3 minutes, I'll teach you what took 3 months to learn.
Let's dive in. 🧵
#Quant
#Trading
#Algorithms
#Python
Tidy Finance with Python Book. A primer on:
1. Financial Data
2. Asset Pricing
3. Modeling & Machine Learning
4. Portfolio Optimization
Available (for free):
Python blows my mind for finance.
Case in point: Making trading signals and algorithmic trading features with Pytimetk.
Check this out (step 4 is the best). 🧵
Machine learning steps for quant finance.
The most important part of creating a predictive investment strategy is setting up the data properly.
In this thread, I'll share 3 months of research on developing predictive ML models in finance.
A thread 🧵
🛑Myth: You need a $20,000/year license to Bloomberg Terminal to trade like a Hedge Fund.
✅Reality: Python and InteractiveBrokers (both are free)
Ready to build Algorithmic Trading Strategies (that actually work)?
👉Join us at our launch party here:
Algorithmic Trading in Python: From Trade Strategy to Responsible Execution
This is a new training.
We are doing it 100% free.
And we'll share how we trade responsibly with Python.
Register here:
#Python
is insane for
#Quant
#Finance
.
I just stumbled across this treasure trove of 150+ quantitative finance programs in Python.
100% free.
T/Y
@pyquantnews
👉Link:
Trading is an unfair game. Hedge funds have better tools.
It's time to level the playing field.
Tomorrow, July 31st, we will reveal 18 months of development on a complete, hedge-fund in a box.
In
#Python
.
PyGWalker
Turn your pandas DataFrame into a Tableau dashboard.
• Launch from Jupyter Notebook
• Tableau-like visualizations
• Run in Kaggle and Colab
Tableau is $800 per year.
PyGWalker is $0.
Get the code on GitHub:
The Relative Strength Index (RSI) is a key indicator of overbought and oversold conditions.
In 2 minutes, I'll share 2 months of research on RSI.
Let's dive in. 🧵
#Quant
#Trading
#Algorithms
#AlgoTrading
Algorithmic Trading in Python: From Trade Strategy to Responsible Execution
This is a new training.
We are doing it 100% free.
And we'll share how we trade responsibly with Python.
Register here:
Machine learning steps for quant finance.
The most important part of creating a predictive investment strategy is setting up the data properly.
In this thread, I'll share 3 months of research on developing predictive ML models in finance.
A thread 🧵
Filtering noise is the most challenging part of algorithmic trading.
Here are our 10 best filtering strategies.
Let's break each down. 🧵
#Quant
#Science
#Trading
#Algorithms
Myth:
AI bots are taking over the markets.
Truth:
Most shops still run Excel '97.
(But *some* shops are using machine learning)
Here are the 10 ways to use machine learning in trading (with the Python library):
Rumor has it that Renaissance Technologies (RenTech) uses Fourier Transforms to extract signals from noisy stock data.
At 11AM EST today, we'll share how to do signal processing with Fourier Transforms in Python.
👉 Join here:
How to use anomaly detection for Algorithmic Trading in Python.
Just dropped our NEW article AND full code.
Plus it uses our NEW pytimetk package for anomaly detection.
Article:
#Quant
#Science
#DataScience
#Trading