What you’ll learn

  • How to use Numpy, Pandas, and matplotlib to manipulate, analyze, and visualize financial data
  • Understand the Time Value of Money applications and project selection
  • Make use of Monte Carlo method to simulate portfolio ending values, value options, and calculate Value at Risk
  • Understand complex financial terminology and methodology in simple ways
  • Featuring a premiere on Ensemble Learning with Bagging & Boosting
  • How to apply your skills to real world cryptocurrency trading such as Bitcoin and Ethereum
  • Building high-frequency trading robots
  • Implementing backtesting econometrics for trading strategies evaluation
  • Get hands-on with financial forecasting using machine learning with Python, Keras, scikit-learn, and pandas

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

The Course Overview
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
Test your knowledge

 

AI for Finance

The Course Overview
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
Summary and What’s Next?
Getting and Preparing Financial Fraud Data
Creating, Training, and Testing XGBoost Model
Summary and What’s Next?
Getting and Preparing the Stock Prices Data
Building the LSTM Model with Keras
Training and Testing the Model
Summary and What’s Next?
Test your knowledge

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