1/ 🧵 Tracking your #trading #performance is important! But let's face it, it's a pain in the ass. Learning #Python changed the way I trade and it's made tracking my portfolio a breeze.
👇 Below are important Python libraries and tips to start your journey:
👇 Below are important Python libraries and tips to start your journey:
2/ First things first, you'll need to gather your #trading #data. Use a tool like `pandas` @pandas_dev to import and organize your portfolio information.
3/ Once your data is organized, it's time to start analyzing it. Use `matplotlib` to create visualizations of your portfolio performance over time. matplotlib.org
4/ Next, you can use `pandas` to calculate key metrics like returns, risk, and Sharpe ratio to get a better understanding of your portfolio's performance. You can even run simulations under different market conditions. pandas.pydata.org
5/ Now that you have a solid understanding of your portfolio's performance, you can use machine learning techniques with `scikit-learn` to predict future returns and make more informed trading decisions. pypi.org/project/scikit…
6/ With all of these tools at your disposal, tracking your trading portfolio with Python has never been more accessible. Relying on third party portfolio tracking services can be costly, unsecure, and won't connect you to your data in the same way as learning Python will.
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