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These articles are intended to guide a delevoper, analyst, or an individual investor with some programming experience on how to use Python and Google Colab to get a financial data, analyse it, and test the investment hypothesis at scale
The Colab code is used in the blog PythonInvest. Please refer to the Website or posts on Medium to get the most detailed explanation.
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MEDIUM POSTS
- 1️⃣ Part 1: Python Environment
- 2️⃣ Part 2: Exploring Finance APIs
- 3️⃣ Part 3: Sentiment Analysis of Financial News
- 4️⃣ Part 4: Scraping Earnings Per Share (EPS)
- 5️⃣ Part 5: Developing a Short Term Investment Strategy Based on Earnings-Per-Share(EPS) Data
- 6️⃣ Part 6: Boom of IPOs in 2020
- 7️⃣ Part 7: Comparing IPOs from late 2020 vs. early 2021
- 8️⃣ Part 8: Practical Portfolio Optimisation
- 9️⃣ Part 9: Macroeconomic Indicators Affecting Stock Market
- 1️⃣0️⃣ Part 10: Passive Investing with ETFs in Europe
- 1️⃣1️⃣ Part 11: 2023 Update - Passive Investing with ETFs in Europe
- 1️⃣2️⃣ Part 12: All Weather Portfolio (of ETFs) with Crypto
- 1️⃣3️⃣ Part 13: Leveraging OpenAI's API for Financial News Summarization
FOLDERS
colab_notebooks All notebooks with the code are published in the colab_notebooks folder. Please copy them and try to run. I'm happy to hear any idea to improve the code or just that you find this work useful.
static Some colab notebooks have dumps of graphs to the JSON format (Plotly), which is inserted to the website.
yt_videos_colabs Sometimes there is a redacted version (nomally, easier and smaller) of a main colab from an article, that I use in videos on the PythonInvest YouTube channel.
Cheers,
PythonInvest team