Python Programming

  1. Introduction to the basics of computer programming

    • Setup and buildup of familiarity of the platform to be used

  2. Python objects, variables and naming - basics

    • Input and output handling

    • Types of objects - strings, ints, floats, etc.

    • Operations on each type of objects

    • Meta Characters And Operators

  3. Python scripting

  4. Iterations and iterating tools

    • Lists, Tuples, Dictionaries

  5. Simple algorithms and looping mechanisms/Control Statements

  6. Numerical computations and basics of accuracy

    • Limitations of numerical computing

  7. Creation of functions

  8. Environments

  9. Global and local characteristics of variables

  10. Objects and classes - revisited in the context of functions

  11. Working with Series And DataFrame

  12. Testing and Debugging

  13. Error handling

    • Assertions and exceptions

  14. Efficiency in programming and OOP

    • Pitfalls to avoid

    • Best practices

  15. Additional Concepts

  16. Case Study(Creation of a Strategy)

  17. Supporting Files and Details For Hands-On

Python for Financial Analysis & Algorithmic Trading

coming soon

Machine Learning

  1. Introduction to machine learning

    1. Intuition behind machine learning

    2. Supervised and unsupervised learning

    3. Matrix operations and their significance

  2. Linear regression with one variable

    1. Cost and reward functions

    2. Gradient descent and its Intuition

    3. Matrix operations for linear regression

  3. Linear regression with multiple variables

    1. Features and their significance

    2. Learning rate and its significance

      1. Adjusting learning rate on the fly

    3. Matrix operations for polynomial regression

  4. Classification / Logistic regression

    1. Cost functions and their derivations

    2. Optimization and gradient descent

    3. Binary and multi-class classification

  5. Regularization

    1. Regularization types

    2. Regularization in linear regression

    3. Regularization in logistic regression

  6. Preprocessing and data manipulation

    1. Feature engineering

    2. Feature extraction

    3. Dimensionality reduction

  7. Machine learning algorithms

    1. Support vector machines

    2. Stochastic gradient descent

    3. Nearest Neighbors

    4. Decision tress

    5. Ensemble methods

    6. Random forests

    7. Other methods

  8. Neural Networks

    1. Intuition behind neural networks

    2. Types of neural networks

    3. Perceptrons

    4. Other network types (eg: RNN, RL)

Project Work

1. Automating Trading Strategies

2. Integrating Financial Models

3. Developing Backtesting UI

4. Scraping data for Stock Options or Index Options.

5. Predicting Stock Trends

6. Forecasting Indices.

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