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Where finance practitioners get started with Python for quant finance, algorithmic trading, and data analysis | Tweets & threads with free Python code & tools.
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Risk management 101: Asset returns are not normally distributed. That's why you need CVaR. Here's how it works: https://t.co/158xXAXtfs
In 2011, I committed my first code to GitHub. Since then, I've starred the best Python repos for quant finance on the internet. Here's the 9 best (*definitely* bookmark this):
I've traded options for 22 years. The struggles: • Expensive broker feeds for real time data • Low-quality data for historical analysis Finally, low-cost real time options data....
Turn your pandas DataFrame into a Tableau dashboard. • Uses Jupyter Notebook • Tableau-like visualizations • Run right in Kaggle and Colab Tableau is $800+ per user. PyGWalker i...
Quickly store 2,370,886 rows of historic options data: With ArcticDB It's a DataFrame database proven at petabyte-scale. Used by Man Group and Bloomberg. Grab the step-by-step...
Stanford University offers free online courses. (Free as in $0) Join the 567,958 people already enrolled in the best 5: https://t.co/Wfwilti7PM
A strategy with a Sharpe of 0.94 (and 513.8% cumulative return). A strategy only Goldman Sachs can trade? The secret behind their $8.3 billion 2022 net income? Nope. Backest a...
There are TRILLIONS of data points generated by the market daily. But we only need a few thousand. The problem? Downloading data is slow. So quants store it locally using ultra...
Use ChatGPT for sentiment analysis on stocks. The step-by-step guide with code and prompts:
One way to get started with algorithmic trading: Buying a $120,000 master's degree from MIT. The other way: YouTube. The 10 YouTube videos to get you up and running today:
The 11 Python libraries of the Quant Stack: 1. SciPy: Scientific Computing 2. IB API: Brokerage Integration 3. NumPy: Numerical Operations 4. pandas: Data Analysis 5. VectorBT: Ba...
A top rated book all quants need on their desk: Advanced Portfolio Management Here are 3 key insights you need to know: https://t.co/o46Exl47jV