Hands-on Python for Finance
•The Course Overview
•Installing the Anaconda Platform
•Launching the Python Environment
•String and Number Objects
•Python Lists
•Python Dictionaries (Dicts)
•Repetition in Python (For Loops)
•Branching Logic in Python (If Blocks)
•Introduction to Functions in Python
•Introduction to NumPy Arrays
•NumPy – A Deeper Dive
•Pandas – Part I
•Pandas – Part II
•Introduction to Scipy.stats
•Matplotlib – Part I
•Matplotlib – Part II
•Present Value of a Stream of Cash Flows
•Future Value of Single and Multiple Cash Flows
•Net Present Value of a Project
•Internal Rate of Return
•Introduction to Amortization
•Creating an Amortization Application
•Opening and Reading a .CSV File
•Getting and Evaluating Data
•Moving Average Forecasting
•Forecasting with Single Exponential Smoothing
•Creating and Testing a Simple Trading System
•Valuing Securities with Pricing Models
•Finding Correlations Between Securities
•Linear Regression
•Calculating Beta and Expected Return
•Constructing Portfolios Along the Efficient Frontier
•Introduction to Monte Carlo
•Monte Carlo Simulation
•Using Monte Carlo Technique to Calculate Value at Risk
•Putting It All Together – Monte Simulation Application
•Test your knowledge
•Machine Learning for Algorithmic Trading Bots with Python
•Introduction to Financial Machine Learning and Algorithmic Trading
•Setting up the Environment
•Project Skeleton Overview
•Fetching and Understanding the Dataset
•Build the Conventional Buy and Hold Strategy
•Evaluate the Strategy’s Performance
•Intuition behind Random Forests Algorithm
•Build and Implement Random Forests Algorithm
•Plug-in Random Forests Implementation into Your Bot
•Evaluate Random Forest’s Performance
•Introducing Online Algorithms
•Getting Statistical Correlation
•Implement Exploit Correlation Strategy
•Evaluate the Strategy
•Ensemble Learning Theory
•Implementing GBoosting Using Python
•Evaluating the Model Performance
•Introduction to Scalpers Trading Strategy
•Implement Scalpers Trading Strategy
•Evaluate Scalpers Trading Strategy
•Introducing Value at Risk Backtest
•Implement Value at Risk Backtest
•Value at Risk with Machine Learning
•Implement VaR Using SVR
•Conclusion and Next steps
•AI for Finance
•What’s Financial Forecasting and Why It’s Important?
•Installing Pandas, Scikit-Learn, Keras, and TensorFlow
•Summary
•Getting and Preparing the Currency Exchange Data
•Building the MLP Model with Keras
•Training and Testing the Model
•Summary and What’s Next?
•Getting and Preparing the Loan Approval Data
•Creating, Training, Testing, and Using a GradientBoostingClassfier Model
•Getting and Preparing Financial Fraud Data
•Creating, Training, and Testing XGBoost Model
•Getting and Preparing the Stock Prices Data
•Building the LSTM Model with Keras
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