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Data Analysis in Finance
Welcome
Getting Started
1. Why Python and finance?
2. Python set-up
2.2. Using Github and Github Codespaces
2.3. Local Installation
2.4. Jupyter notebooks
2.5. VS Code
2.6. Google Colab
2.7. Markdown
2.8. Packages
2.9. Code style, PEP8, and linting
2.10. Using ChatGPT
The Basics
3. CompSci 101: Types, control, and numpy arrays
3.1. The Basics
3.2. Numpy and arrays
4. Working with data
4.2. Importing data
4.3. pandas
4.4. Cleaning our data
4.5. Exploratory data analysis (EDA)
4.6. Merging and reshaping data
4.7. Using SQL in Python
4.8. polars: A fast, fancy pandas alternative
5. Data visualization
5.1. seaborn
5.2. matplotlib
5.3. plotly
6. Using APIs for Data Imports
7. Financial time series
Applications
8. Essential portfolio math
9. Portfolio optimization
10. Unsupervised Learning
11. Factor models
12. Regression topics
13. Logit models
14. Decision Trees
15. SVM
16. Risk management
17. Monte Carlo and portfolios
18. Option basics
19. Reinforcement Learning
20. Trading Strategies and the BT Package
Binder
.ipynb
.pdf
Reinforcement Learning
19.
Reinforcement Learning
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