Trading Strategy Algorithm: Regression	

In this course, we will try to create a trading strategy to create a market-beating strategy using regression techniques in Python. In addition, we will try to create a prediction with greater accuracy. This course is the start for implementation using machine learning algorithms for trading purposes.

After completion of this program, participants will be better able to:
* Understand the various types of regression technique
* Acquire data resources, pre-process and do cross-validation 
* Train the model and testing the performance
* Learn type of errors, diagnosis bias and variance"	


Certificate of completion (supplied by PasarTrainer)

Training Material

A multiple-choice test to assess the participants' proficiency level prior to enrolling in the training program.


Pre-Test 

* Scikit Learn
* Resize and Remove Data
* Hyperparameter
* Import Data and Libraries
* Create Input, Indicators and Output
* Scale, Imputer and Pipeline
* Cross Validation, Grid Search 


This module explains an overview of data generation and data preprocessing techniques


Module 1. Data Generation and Data Preprocessing

* Linear Regression
* Error, Residual, Assumptions 
* Cost Function, Gradient Descent
* Best Fit and Prediction


This module explain an overview of regression technique with its assumptions


Module 2. Regression

* Bias , Variance
* Overfitting, Underfitting
* Regularization


This module learn the concept of underfitting and overfitting


Module 3. Bias and Variance 

* Prediction and Model assessment
* Analyze predicted variables
* Strategy Analytics
* Performance Analytics
* Signal Generation


This module asssess the model and its prediction and how to build a trading algorithm


Module 4. Prediction and Algorithm

* Use Python to generate trading signal and prediction and assess its performance
