Unsupervised Learning in Trading
- Live Trading
- Learning Track
- Prerequisites
- Syllabus
- About author
- Testimonials
- Faqs
Live Trading
- Creating, backtesting and paper trading a trading strategy using a clustering algorithm
- Creating a list of pairs that are suitable for pairs trading strategy
- Intuitively and mathematically describe the working of principal component analysis (PCA)
- Detailed difference between supervised learning algorithms and unsupervised learning algorithms
- Describe, implement and list the differences between the workings of k-means clustering algorithm and DBSCAN clustering algorithm
- Concepts such as euclidean distance, WCSS and interpret elbow curve
- Curse of dimensionality and various ways to overcome the curse of dimensionality
- Applications of unsupervised learning
Skills Covered
Python
- Numpy
- Pandas
- Sklearn
- Matplotlib
- Itertools
Machine Learning
- K-Means Clustering
- DBSCAN Clustering
- Curse of dimensionality
- Principal Components Analysis
- Hyperparameter selection
Statistics and Maths
- Euclidean distance
- WCSS or inertia
- Eigenvalues and eigenvectors
- Cointegration
- Skewness & hit ratio
learning track 5
This course is a part of the Learning Track: Artificial Intelligence in Trading Advanced
Course Fees
Full Learning Track
These courses are specially curated to help you with end-to-end learning of the subject.
Course Features
CommunityFaculty Support on Community
- Interactive Coding Exercises
Interactive Coding Practice
- Capstone Project
Capstone Project Using Real Market Data
- Trade & Learn Together
Trade and Learn Together
- Get Certified
Get Certified
Prerequisites
A general understanding of trading in the financial markets such as how to place orders to buy and sell is helpful. Basic knowledge of the pandas dataframe and matplotlib would be beneficial to easily work with the codes and trading strategies covered in this course. To learn how to use Python, check out our Free course "Python for Machine Learning in Finance".
Syllabus
- Introduction to the CourseUnsupervised learning has the ability to uncover hidden patterns in the dataset and can provide unique insights in your data. 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 Introduction4m 29s
Course Structure3m 15s
Course Structure Flow Diagram10m
Quantra Features and Guidance4m 9s
- Introduction to Unsupervised LearningUnsupervised learning can be applied when you are not sure about the end outcome that you are looking for. It can be used to divide the data in smaller groups when the labels are not available. This section will give you an overview of what unsupervised learning is along with an insight into one of its applications, dimensionality reduction.
Introduction to Unsupervised Learning2m 43s
Need for Unsupervised Learning I2m
Need for Unsupervised Learning II2m
An Application of Unsupervised Learning2m 56s
Dimensionality Reduction2m
Features after Dimensionality Reduction2m
Need for Dimensionality Reduction2m
Facts about Unsupervised Learning2m
- ClusteringClustering is a technique to divide the data into smaller groups with similar data points called clusters. This section explains how the outputs are generated in clustering. You will also understand how clustering is different from classification.Clustering2m 48s
What is Clustering?2m
Criteria for Grouping2m
Number of Data Points in a Cluster2m
Output of Clustering Algorithm2m
Properties of a Cluster1m 45s
Optimal Cluster2m
Properties of a Cluster2m
Classification Vs Clustering2m
Labels in the Dataset2m
Aim of Clustering2m
Select the Algorithm I2m
Select the Algorithm II2m
Limitations of Clustering2m
- K-Means ClusteringYou will be introduced to your very first clustering algorithm, the k-means. You will take a deep dive into how k-means works by developing an intuitive as well as a mathematical understanding on how the algorithm finds hidden patterns in the data.
What is K-Means Clustering?3m 29s
Optimised Centroid2m
Points in a Cluster2m
Mathematics behind K-Means Clustering5m 22s
Euclidean Distance2m
Choosing the Cluster2m
Mean Distance2m
Additional Reading10m
- K-Means for Financial DataThis section will introduce you to the application of k-means on financial data. You will find hidden patterns on the price series for Apple stock using the relative strength index and the average directional index. The implementation for this will be carried out in Python.K-Means on Financial Data2m 31s
Number of Clusters2m
Features2m
Wrong Clusters2m
Distance from Centroid2m
Using Jupyter Notebook1m 54s
Applying K-Means to Create Clusters10m
Calculate Percentage Change5m
Calculate Volatility5m
Initialise K-Means5m
Fit and Predict the Model5m
Additional Reading10m
- Scaling the DataYou want to introduce multiple features to your model, say for instance, RSI, ADX and volatility. Can you directly pass the features to the model? The model won’t appreciate that! This section will introduce you to the importance of scaling your data before passing it to the k-means model.Scaling the Data2m 33s
Distance Problem2m
Scaling Requirement2m
Range for Min-Max Scaling2m
Subtraction in Min-Max Scaling2m
Min-Max Scaling Calculation2m
Scaled Clusters2m
Scaling Technique5m
Scaling the Data10m
Min-Max Scaler5m
Additional Reading10m
- Feature SelectionRemember the time you used simple moving averages, bollinger bands, MACD and 5 other indicators to take that trade? This feature selection section will introduce and guide you in selecting your input features and what features are considered good.Feature Selection3m 6s
Selecting Features2m
Appropriate Features2m
Stationary Series2m
Correlated Series2m
Correlated Features2m
Choosing Threshold Value2m
Discarding Features2m
Feature Selection10m
Calculate Test Statistic5m
ADF test2m
Create a Correlation Matrix5m
Pairs Above the Threshold Correlation Value5m
Drop Columns5m
Additional Reading10m
- Selecting Clusters for K-MeansK-means asks you to pass the number of groups you would like to discover with unique hidden patterns. You can find eight groups or ten groups or any number as per your choice. The section answers the question whether there is an optimal number of groups that can be created.
Choosing the Number of Clusters4m 20s
Calculate WCSS for 2 Clusters2m
Penalising in WCSS2m
Minimise WCSS - I2m
Minimise WCSS - II2m
Change in WCSS2m
Optimum Clusters2m
Steps in WCSS2m
Choosing the Number of Clusters10m
Calculate WCSS5m
WCSS for Multiple Cluster Numbers5m
Additional Reading10m
- Analysing Clusters: Hit RatioYou have identified the hidden patterns and created the clusters. What do you do next? You generate trading signals! You will learn to create the very first trading strategy using the k-means algorithm and perform a comprehensive backtest.Cluster Analysis with Hit Ratio2m
Trading Signals2m
Average Future Returns2m
Future Returns2m
Threshold Value for Hit Ratio2m
Train-Test Split2m
Cluster Analysis with Hit Ratio10m
Strategy Analytics for Hit Ratio10m
Creating Train and Test Dataset5m
Analytics for a Single Cluster5m
Calculating Hit Ratio5m
Map Clusters to Trading Signals5m
Strategy Returns5m
Forecast the Cluster2m
Map the Trading Signal2m
Additional Reading10m
- Analysing Clusters: SkewnessCould there be an alternative approach to creating a trading strategy with k-means? This section will walk you through your second strategy using skewness. The strategy will aim to capture the tail events in Apple.Cluster Analysis with Skewness4m 40s
Why Skewness?2m
Calculate Skewness2m
Direction Based on Skewness2m
Skewed Distributions2m
Cluster Analysis with Skewness10m
Calculating Skewness with Python5m
Plot Returns Distribution5m
Trade Direction2m
Additional Reading10m
- Putting It All TogetherThis section will put together all of your learnings from the previous sections. You will have a workflow that you can follow to develop any trading strategy using k-means for any asset class.Strategising with K-Means1m 59s
Sequence of Steps2m
Putting It All Together10m
Additional Reading10m
- Curse of DimensionalityDoes adding more data equal more information and ultimately offer more insights? In this section, you will realise that this isn’t entirely true. You will also go about learning different ways to reduce the dataset features effectively.
Impact of Adding Features to Clustering2m 35s
Definition of Curse of Dimensionality2m
Relation Between Features and Distance Between Points2m
Issue With Cluster Equal to Data Points2m
Overcoming Curse of Dimensionality2m 5s
Criteria for Eliminating Features2m
Comparing Features for Elimination2m
Relation Between Features2m
Additional Reading10m
- Introduction to Principal Component AnalysisReducing features while minimising information loss is the ace up the sleeve of principal component analysis (PCA). You will see how PCA reduces dimensions and still keeps most of the information content.
Principal Component Analysis3m 7s
Reducing Dimensions by Finding Best Line2m
Maximum Limit of PCA Algorithm2m
Choosing the Best Line2m
PCA and Information Loss2m
Mathematical Explanation of PCA2m 22s
Matrix Multiplication2m
Preservation of Maximum Information2m
Features and Eigenvalues2m
Variance and Covariance10m
Interpretation of Variance2m
Calculate the Covariance Matrix5m
Variance of ADX2m
Covariance of ADX and EMA2m
High Covariance2m
Additional Reading10m
- Maths Behind Principal Component AnalysisIn this section, you will get under the hood of the principal component analysis and see how it uses eigenvalues and eigenvectors to reduce the dimensions effectively. You will also work on a data sample and see it in action.Basics of Matrices and Eigenvalues3m 14s
Definition of Variance Matrix2m
Properties of an Identity Matrix2m
Lambda and Eigenvectors2m
Determinant of a Matrix2m
Significance of Eigenvalues2m
Dimensions and Eigenvalues2m
Eigenvectors2m 53s
Maximum Variance and Eigenvalues2m
Selection of Eigenvalues2m
Eigenvalues and Explained Variance2m
Selecting the Correct Eigenvalue2m
Transforming Dimensions Using Eigenvectors2m
Maths Behind PCA10m
First Principal Component2m
Calculate the Eigenvectors5m
PCA Example10m
Project the Data-points in 1 Dimension5m
Calculate the Principal Components5m
Additional Reading10m
- Principal Component AnalysisPrincipal components help us in dimensionality reduction. You will work on a dataset and figure out how you can choose the number of principal components, depending on the threshold of the information contained in the dataset you would like to keep.Choosing Number of Principal Components4m 17s
Percentage of Principal Components2m
Optimum Number of Principal Components2m
Choosing the Number of Features in PCA10m
Explain 80% of the Variance5m
Explained Variance Vs Number of Features2m
Additional Reading10m
- Application of Unsupervised Learning for Pairs TradingIn simple terms, pairs trading implies that you select a pair of stocks for trading, where you go long on one and short the other. This is a market neutral trading strategy. In this section, you will see how unsupervised learning can help you identify pairs from a large number of stocks dataset.Application of Unsupervised Learning for Pairs Trading10m
Selection of Pairs From Cluster2m
Steps to Find Pairs2m
Definition of Pairs Trading2m
Test of Stationarity2m
Pairs Using Unsupervised Learning2m
Feature Selection for Pairs2m
Feature Engineering for Pairs Trading5m
Good Features2m
Need to Apply PCA2m
Need to Standardise the Data2m
Standardise the Data5m
Creating Clusters2m
- DBSCANDBSCAN is a density-based clustering technique that deals with the noise in the dataset. You will create and visualise clusters on a toy dataset using the DBSCAN algorithm in this section. You will also see how this technique is different from k-means clustering.Intuition of Density-Based Clustering3m 54s
Parameters of DBSCAN2m
Identify the Core Point2m
Identify the Noise2m
Number of Boundary Points2m
Number of Points in Neighbourhood2m
Clusters using DBSCAN2m
Limitations of DBSCAN2m
K-Means Vs DBSCAN10m
Create Clusters Using DBSCAN5m
Selecting the Parameters for DBSCAN10m
Additional Reading10m
- Pairs Trading using Clustering AlgorithmsIn this section, you will continue to work on creating pairs of stocks for pairs trading strategy. You will apply DBSCAN on the reduced data to create clusters. From the clusters, you will select pairs of stock and test for cointegration.Create Pairs using DBSCAN10m
Reduce Data for Visualisation Using TSNE5m
The Grey Points2m
Select and View Stocks in a Cluster5m
Number of Pairs2m
Form Pairs from a Cluster5m
Cointegration Test10m
Create the Portfolio5m
Test for Cointegration5m
ADF Test2m
Additional Reading10m
- Run Codes Locally on Your MachineLearn to install the Python environment in your local machine.Uninterrupted Learning Journey with Quantra2m
Python Installation Overview1m 59s
Flow Diagram10m
Install Anaconda on Windows10m
Install Anaconda on Mac10m
Know your Current Environment2m
Troubleshooting Anaconda Installation Problems10m
Creating a Python Environment10m
Changing Environments2m
Quantra Environment2m
Troubleshooting Tips For Setting Up Environment10m
How to Run Files in Downloadable Section?10m
Troubleshooting For Running Files in Downloadable Section10m
- Capstone ProjectIn this section, you will undertake a capstone project where you apply the k-means algorithm on the stock of your choice. This project helps you to practice and apply the concepts learnt in this course.Capstone Project: Getting Started10m
Problem Statement10m
Frequently Asked Questions10m
Code Template and Data Files2m
Capstone Project Model Solution10m
Capstone Solution Downloadable2m
- Automate Trading Strategy Using IBridgePyThe section will provide you with a ready-made template that can be used for live trading after tweaking the parameters to your discretion.Additional Reading10m
Sample Strategy to Run on Interactive Brokers2m
- Course SummaryIn 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 Summary3m 50s
Python Data and Codes2m
Registered Successfully!
You will receive webinar joining details on your registered email
Would you like to start learning immediately?
about author
Why quantra®?
- More in Less Time
Gain more in less time
- Expert Faculty
Get taught by practitioners
- Self-paced
Learn at your own pace
- Data & Strategy Models
Get data & strategy models to practice on your own
Faqs
- When will I have access to the course content, including videos and strategies?
You will gain access to the entire course content including videos and strategies, as soon as you complete the payment and successfully enroll in the course.
- Will I get a certificate at the completion of the course?
Yes, you will be awarded with a certification from QuantInsti after successfully completing the online learning units.
- Are there any webinars, live or classroom sessions available in the course?
No, there are no live or classroom sessions in the course. You can ask your queries on community and get responses from fellow learners and faculty members.
- Is there any support available after I purchase the course?
Yes, you can ask your queries related to the course on the community: https://quantra.quantinsti.com/community
- What are the system requirements to do this course?
Fast-speed internet connection and a browser application are required for this course. For best experience, use Chrome.
- What is the admission criteria?
There is no admission criterion. You are recommended to go through the prerequisites section and be aware of skill sets gained and required to learn most from the course.
- Is there a refund available?
We respect your time, and hence, we offer concise but effective short-term courses created under professional guidance. We try to offer the most value within the shortest time. There are a few courses on Quantra which are free of cost. Please check the price of the course before enrolling in it. Once a purchase is made, we offer complete course content. For paid courses, we follow a 'no refund' policy.
- Is the course downloadable?
Some of the course material is downloadable such as Python notebooks with strategy codes. We also guide you how to use these codes on your own system to practice further.
- Can the python strategies provided in the course be immediately used for trading?
We focus on teaching these quantitative and machine learning techniques and how learners can use them for developing their own strategies. You may or may not be able to directly use them in your own system. Please do note that we are not advising or offering any trading/investment services. The strategies are used for learning & understanding purposes and we don't take any responsibility for the performance or any profit or losses that using these techniques results in.
- I want to develop my own algorithmic trading strategy. Can I use a Quantra course notebook for the same?
Quantra environment is a zero-installation solution to get beginners to start off with coding in Python. While learning you won't have to download or install anything! However, if you wish to later implement the learning on your system, you can definitely do that. All the notebooks in the Quantra portal are available for download at the end of each course and they can be run in the local system just the same as they run in the portal. The user can modify/tweak/rework all such code files as per his need. We encourage you to implement different concepts learnt from different learning tracks into your trading strategy to make it more suited to the real-world scenario.
- If I plug in the Quantra code to my trading system, am I sure to make money?
No. We provide you guidance on how to create strategy using different techniques and indicators, but no strategy is plug and play. A lot of effort is required to backtest any strategy, after which we fine-tune the strategy parameters and see the performance on paper trading before we finally implement the live execution of trades.
- What does "lifetime access" mean?
Lifetime access means that once you enroll in the course, you will have unlimited access to all course materials, including videos, resources, readings, and other learning materials for as long as the course remains available online. There are no time limits or expiration dates on your access, allowing you to learn at your own pace and revisit the content whenever you need it, even after you've completed the course. It's important to note that "lifetime" refers to the lifetime of the course itself—if the platform or course is discontinued for any reason, we will inform you in advance. This will allow you enough time to download or access any course materials you need for future use.