Description
Quantinsti – Python for Machine Learning in Finance download, Quantinsti – Python for Machine Learning in Finance review, Quantinsti – Python for Machine Learning in Finance free
Quantinsti – Python for Machine Learning in Finance
Python for Machine Learning in Finance
This course is perfect for those looking to get started on using Python for Machine learning. Learn in a step-by-step fashion to create a Machine Learning algorithm for trading. Evaluate the performance of the machine learning algorithm and perform backtest, paper trading and live trading with Quantra’s integrated learning.
LIVE TRADING
Backtest, analyse the strategy returns and drawdowns, paper trade and live trade machine learning strategy
Describe machine learning and its applications in finance
List and implement common tasks in machine learning such as feature creation, training, forecasting, and evaluation in a step-by-step fashion
Explain and implement accuracy, f1-score, recall and confusion matrix and R-squared
Implement the train-test split for time series data
SKILLS COVERED
Machine Learning
Train-test split
Training an ML model
Forecasting
Evaluation
Stats & Maths
R-Squared
Accuracy
Recall
F1-Score
Python
Numpy
Pandas
Matplotlib
Sklearn
COURSE FEATURES
Interactive Coding Practice
Trade and Learn Together
PREREQUISITES
No experience is required except for a very basic understanding of financial markets such as how to place orders with your broker. You should be curious to explore the application of machine learning in finance. You can do this course even if you have never coded before. However, to replicate the sample trading strategy shared in the course, you might need to display some code reading and interpretation skills.
SYLLABUS
Introduction
Machine learning has myriad applications in various industries, including finance. In this section, you will acquaint yourself with the course structure, and the various teaching tools used in the course: videos, quizzes, and strategy codes. The interactive methods used help you to not only understand the concepts, but also how to implement the strategies.
Course Introduction 2m 38s
Course Structure Flow Diagram 10m
Quantra Features 3m 48s
Machine Learning Overview
In this section, you will understand how machine learning is used to solve problems which cannot be solved by traditional computer algorithms. You will also see its applications in different finance domains.
What is Machine Learning? 2m 48s
Application of Machine Learning in Finance 3m 9s
ML Based Investment Advice 2m
Preference for Machine Learning 2m
Additional Reading 10m
Introduction to Python
This section will help you update your knowledge of Python with simple exercises on implementing functions, and manipulating dataframes using Numpy and Pandas libraries. The Quantra environment ensures that you don’t have to install anything for the Jupyter notebooks to function.
Need for Python 3m 7s
Preference for Python 2m
Functionality of Python 2m
How to Use Jupyter Notebook? 1m 54s
Print Statement 5m
My First Jupyter Notebook 10m
Getting Started with Interactive Exercises 5m
Operations and Functions in Python 10m
Divide Two Numbers 5m
Pandas Dataframe 2m 22s
Function Call 5m
DataFrame Axis Label 2m
DataFrame and Basic Functionality 10m
DataFrame Syntax 2m
Dropping/Deleting Columns 2m
Create Pandas DataFrame 5m
DataFrame Indexing 2m
Print Columns 2m
Access Elements of a DataFrame 5m
Add New Column to a DataFrame 5m
Set Column as Index 5m
Add Values of a Column 5m
Additional Reading 10m
Financial Market Data and Visualisation
An important component of a successful strategy is the data set used. In this section, you will learn how to import the correct data from various web resources, so that you can work on your own unique strategy.
Importing Data 1m 44s
Correct Syntax for Importing Stock Data 2m
Importing Time Series Data 10m
Import Data from Yahoo! Finance 5m
Data Visualisation 10m
Plot Line Graph 5m
Plot Bar Graph 5m
Additional Reading 10m
Frequently Asked Questions 10m
Machine Learning Tasks
Before you start using the machine learning model, you have to train it first. In this section, you will go through the steps in creating a machine learning algorithm and how its performance can be calculated.
Machine Learning Tasks 3m 28s
Order of Machine Learning Tasks 2m
Judging Performance of ML Model 2m
Target Variable and Features
A target variable is something that a machine learning algorithm tries to predict. And in order to do that, it requires input, which are called features. This section will explain the concept of target and features through examples. You will also learn how to create target and features for an ML algorithm.
Target Variable 1m 59s
Choose the Target Variable 2m
Pre Reading Material 10m
Features 4m 29s
Use of Features 2m
Characteristics of Features 2m
Features of an ML Model 2m
Target and Features 10m
Create a Target Variable 5m
Calculate RSI 5m
Calculate EMA 5m
Check for Stationarity 5m
Correlated Features 5m
Additional Reading 10m
Machine Learning Algorithms
There are various types of machine learning algorithms. In this section, you will get an overview of each type of algorithm. You will also gain a basic understanding of which model to use for a particular problem statement.
Machine Learning Algorithms 10m
Model for Predicting Direction 2m
Model for Predicting Price 2m
Model for Computing News Sentiment Score 2m
Train-Test Split
The train-test split is the technique of splitting the data into two parts. One part is used to train the ML model, and the other part is used to evaluate how well the model is making the predictions. You will learn the correct way of dividing time-series data for the train-test split.
Train-Test Split 10m
Perform the Train-test Split 5m
Training & Forecasting
The model is trained on the training data and then it is used to make forecasts. In this section, you will learn how to use the training and testing data with a machine learning model. For illustration, we implement a Random Forest classification model in Python and use it to make predictions.
Model Training and Forecasting 10m
Training a Model 5m
Make a Prediction 5m
Metrics to Evaluate Classifier
Backtesting is used to separate good strategies from bad ones. In this section, you will learn how to analyse the performance of your strategy on the historical data through backtesting. You will also learn to develop and backtest a trading strategy in Python. You will further calculate certain metrics like strategy returns, annualised returns and Sharpe ratio.
Evaluating Classifier Model Effectiveness 4m 27s
Accuracy of ML Model 2m
Interpretation of Accuracy 2m
Meaning of Confusion Matrix 2m
Interpreting Confusion Matrix 2m
Predicting Wrong Values 2m
False Positives in Confusion Matrix 2m
Beyond Accuracy 4m 2s
Description of Precision 2m
Predicting Correct Signals 2m
Description of Recall 2m
Calculation of Precision 2m
Calculation of Recall 2m
Calculation of f1 Score 2m
Inference of Performance Metrics 2m
Metrics to Evaluate a Classifier 10m
Confusion Matrix 5m
Classification Report 5m
Additional Reading 10m
Introduction to Backtesting
Backtesting is used to separate good strategies from bad ones. In this section, you will learn how to analyse the performance of your strategy on the historical data through backtesting.
What is Backtesting? 2m 22s
Backtesting Technique 2m
Does Past Reflect Future? 2m
How to do Backtesting? 2m 24s
Steps in Backtesting 2m
Evaluate the Performance of Backtesting 2m
Need for Backtesting 2m
Drawbacks of Backtesting 2m
Strategy Backtesting 10m
Strategy Returns 5m
Annualised Returns 5m
Sharpe Ratio 5m
Additional Reading 10m
Live Trading on Blueshift
This section will walk you through the steps involved in taking your trading strategy live. You will learn about backtesting and live trading platform, Blueshift. You will learn about code structure, various functions used to create a strategy and finally, paper or live trade on Blueshift.
Section Overview 2m 21s
Live Trading Overview 2m 41s
Vectorised vs Event Driven 2m
Process in Live Trading 2m
Real-Time Data Source 2m
Blueshift Code Structure 2m 57s
Important API Methods 10m
Schedule Strategy Logic 2m
Fetch Historical Data 2m
Place Orders 2m
Backtest and Live Trade on Blueshift 4m 5s
Additional Reading 10m
Blueshift Data FAQs 10m
Live Trading Template
This section includes a template of a trading strategy that can be used on Blueshift. The live trading strategy template is based on the strategy discussed in the course. You can tweak the code by changing securities or the strategy parameters. You can also analyse the strategy performance in more detail.
Paper/Live Trading Random Forest Strategy 10m
FAQs for Live Trading on Blueshift 10m
Metrics to Evaluate a Regressor
Along with the classifier model, you can also use the regression model to predict the target variables. In this section, you will be given a brief on linear regression and also how to analyse its performance by using the goodness of fit metric.
Goodness of Fit 4m 10s
Why Goodness of Fit 2m
Error of a Good Model 2m
R-squared Value 2m
Residual Plot 2m
Pattern in Residual Plot 2m
High R-squared Value 2m
R-Squared 10m
Calculate R-squared 5m
Limitations of R-squared 2m
Assumptions for Linear Regression 10m
Highest R-squared 2m
Linearity 2m
Not an Assumption for Linear Regression 2m
Autocorrelation 2m
Residuals 2m
Run Codes Locally on Your Machine
Learn to install the Python environment in your local machine.
Python Installation Overview 2m 18s
Flow Diagram 10m
Install Anaconda on Windows 10m
Install Anaconda on Mac 10m
Know your Current Environment 2m
Troubleshooting Anaconda Installation Problems 10m
Creating a Python Environment 10m
Changing Environments 2m
Quantra Environment 2m
Troubleshooting Tips For Setting Up Environment 10m
How to Run Files in Downloadable Section? 10m
Troubleshooting For Running Files in Downloadable Section 10m
Course Summary
In this section, you will go through the different concepts you learnt throughout the course. You will also be able to download all the strategy notebooks as a zip file. You can use these notebooks and modify their contents to create your own unique strategy.
Course Summary 3m 16s
Next Steps 10m
Python Data and Codes 2m
ABOUT AUTHOR
QuantInsti®
QuantInsti is the world’s leading algorithmic and quantitative trading research & training institute with registered users in 190+ countries and territories. An initiative by founders of iRage, one of India’s top HFT firms, QuantInsti has been helping its users grow in this domain through its learning & financial applications based ecosystem for 10+ years.
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