Advanced Momentum Trading: Machine Learning Strategies
843 Learners
12 Hours
This course covers the application of machine learning for enhancing momentum trading strategies. Explain how to use ML classification to predict momentum and craft a time-series momentum strategy. Learn how to select relevant features and improve model accuracy through hyperparameter tuning. Explore the world of cross-sectional momentum using ML-based clustering techniques. Get hands-on experience with live trading templates and capstone projects.
Level
Advanced
Author
QuantInsti®
Price Lifetime Access Limited Time Offer
₹10750/-₹42999/-
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- Live Trading
- Learning Track
- Prerequisites
- Syllabus
- About author
- Testimonials
- Faqs
Live Trading
- Implement a classifier model to predict momentum and build an ML-based time-series momentum strategy.
- Select momentum-relevant features, evaluate the model’s performance, and learn how to improve it with the help of hyperparameter tuning.
- Use the clustering technique for stock selection for cross-sectional portfolio creation
- Practice paper trading and live trading with the strategies you create.
- Analyse Performance with key metrics and trade-level analytics
Skills Covered
Momentum Trading
- Time-series momentum with dynamic threshold
- XGBoost for predicting momentum
- Cross-sectional momentum portfolio
- Rank stocks based on clusters
Concepts & Trading
- Feature selection
- Feature reduction with Encoder
- Model Evaluation
- Hyperparameter Tuning
- Performance analysis
Python
- Pandas
- NumPy
- Sklearn
- XGBoost
- Matplotlib
learning track 8
This course is a part of the Learning Track: Advanced Algorithmic Trading Strategies
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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
You may want to familiarise yourself with momentum trading strategies. To get a grasp of these concepts you can enroll in the “Momentum Trading Strategies” course on Quantra. You should also be familiar with working with datasets. And don't forget some Python know-how! If you want to quickly understand the relevant skills in Python and how it can be used in machine learning, you can check the course, “Python for Machine Learning in Finance”. Alternatively, you can also check out the free course “Introduction to Machine Learning for Trading” to understand the use of machine learning for trading.
Syllabus
- IntroductionThis section serves as a preview of the course and introduces the course contents. The interactive methods used in this course will assist you in not only understanding the concepts but also answering all questions regarding the use of machine learning algorithms for momentum trading. This section explains the course structure as well as the various teaching tools used in the course, such as videos, quizzes, coding exercises, and the capstone project. It also covers a few course-related frequently asked questions.
Revisiting MomentumMomentum trading has been a widely tried and tested strategy, but what is it that drives momentum? In this section, we'll delve into what fuels momentum in the stock market and why it's a worthwhile approach to trading. Additionally, we'll provide a brief precap of the key concepts covered in the initial part of the course to ensure a solid foundation as we move forward.Part Overview: I2m
Momentum Trading2m
Identify the Momentum Phenomenon5m
Mutual Funds Redemption Request5m
Nature of Momentum5m
Events and Announcements5m
Momentum in Commodities5m
- Getting Data: Single AssetIn this section of the course, you will learn how to fetch the data of a single asset. This data will then be used in the upcoming sections of the course to implement and backtest momentum trading strategies.Uninterrupted Learning Journey with Quantra2m
Getting Data: Single Asset5m
Daily Price Data of Stocks5m
Data Adjusted for Corporate Actions2m
- Time-Series MomentumIn this section, you will be taken through the traditional time-series momentum strategy. The strategy uses a fixed or a “static” threshold to generate buy and sell signals. After completing this section, you will be able to explain the rationale behind the time-series momentum strategy and you will also be able to implement the traditional time-series momentum strategy using Python.Time-Series Momentum Strategy2m
Time-Series Positions5m
Past Performance in Time-Series5m
Identify the Correct Order of Steps5m
Predictor of Future Returns5m
Trading Decision Based on Returns2m
Essential Points for Momentum Trading2m
Reason for Underperformance of Strategy2m
Time-Series Momentum Strategy5m
Calculate Yearly Returns5m
Trading Rules5m
Strategy Logic5m
Hold Days5m
Additional Reading on Time-Series Momentum2m
- Live Trading on BlueshiftThis 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 Overview2m 19s
Live Trading Overview2m 41s
Vectorised vs Event Driven2m
Process in Live Trading2m
Real-Time Data Source2m
Blueshift Code Structure2m 57s
Important API Methods10m
Schedule Strategy Logic2m
Fetch Historical Data2m
Place Orders2m
Backtest and Live Trade on Blueshift4m 5s
Additional Reading10m
Blueshift Data FAQs10m
- Live Trading TemplateTo make sure that you can use your learning from the course in the live markets, a live trading template has been created which can be used to paper trade and analyse its performance. This template can be used as a starting point to create your very own unique trading strategy.Paper/Live Trading TSMOM Strategy2m
FAQs for Live Trading on Blueshift5m
- Transaction Costs and SlippageThe journey towards building a good strategy idea is incomplete without considering the transaction costs and slippages. In simple words, transaction costs encompass brokerage, commission, etc. Slippage is the difference between the expected and executed price. Learn these concepts and understand how to incorporate them into your strategy code.Transaction Costs and Slippage2m
Calculation of Transaction Cost2m
Calculation of Slippage2m
Verify Transaction Costs5m
Slippage5m
Estimating Slippage5m
Implementation of Transaction Costs and Slippage10m
Additional Reading10m
- Performance AnalysisTo understand whether your strategy is working, you need to analyse certain metrics. In this section, you will learn how to evaluate the trade level metrics to depict how well the strategy has performed over a certain period of time, as well as evaluate the performance of your strategy based on returns, risk and both.Generate Trade Sheet5m
Trade Level Analytics5m
Average Profit Per Winning Trade5m
Win Percentage5m
Average Trade Duration5m
Additional Reading on Trade Level Analytics2m
Performance Metrics5m
CAGR5m
Sharpe Ratio5m
Maximum Drawdown5m
Additional Reading on Performance Metrics10m
- Time-Series Momentum: Dynamic ThresholdStatic thresholds for a time-series momentum strategy might not always be a suitable choice. This section covers the need for a dynamic threshold in the time-series momentum strategy and also the implementation of a dynamic threshold in the TSMOM strategy.Time-Series Momentum With Dynamic Threshold2m
Drawback of Static Threshold5m
Dynamic Threshold5m
Trading Decisions5m
Positive and Negative Thresholds5m
TSMOM Strategy with Dynamic Threshold5m
Best Strategy5m
Additional Reading on Dynamic Threshold TSMOM Strategies2m
Test on Time-Series Momentum Strategy10m
- ML for Momentum TradingTraditionally, momentum trading has relied on conventional methods for identifying trends and generating signals. However, in this age of machine learning, there's a growing need to explore its potential in enhancing the accuracy and efficiency of momentum trading. This section will take you through all the ways through which ML can be useful in momentum trading.Part Overview - II2m
Using ML for Momentum Trading2m
Select the Advantage5m
Changing Market Conditions5m
Scaling Momentum Strategies5m
Variable in Momentum Trading5m
Relevance of Features5m
Assigning Weights to Variables5m
Adjustments Based on Market Conditions5m
FAQs2m
Additional Reading on the Need for ML in Momentum Trading2m
Test on ML for Momentum10m
- Capturing Momentum Using MLThis section talks about the different types of ML models and the rationale behind choosing a classifier model to predict the momentum. It also takes you through the workings of the XGBoost classification model in detail.Pre-Reading Document on Decision Trees2m
Different Types of ML Models2m
Predicting TSMOM5m
Classification Model in Momentum Trading5m
Predicting Continuous Responses5m
Not a Classification Model5m
XGBoost Classifier Model2m
Composition of XGBoost Model5m
Working of XGBoost Model5m
Combining Weak Learners5m
Incorrectly Predicted Data Points5m
Stopping Criteria5m
Additional Reading on XGBoost Model2m
- Implementation of XGBoost ClassifierA target variable is something that a machine learning algorithm tries to predict. And in order to do that, it requires input variables, which are called features. Once you have these, the next step is to split the dataset into training and testing sets and then finally train the model and make predictions. This section covers all these steps in detail, along with practical implementation in Python.Target Variable2m
Choose the Target Variable5m
Features2m
Use of Features5m
Characteristics of Features5m
Creating Features and Target Variable5m
Create the Target Variable5m
Calculate RSI5m
Splitting and Scaling the Data5m
Scaling the Data5m
Scaling Timeline5m
XGBoost Model5m
Training a Model5m
FAQs on XGBoost Classifier2m
- Metrics to Evaluate a ClassifierUntil now, we have analysed the strategy based on performance metrics, like Sharpe ratio, drawdown, CAGR, etc. But how do we analyse the performance of the classifier? We can do that by checking the accuracy, recall and other metrics specific to the classifier. We will learn more about these classification metrics in this section.Evaluating Classifier Model Effectiveness2m
Accuracy of ML Model2m
Interpretation of Accuracy2m
Meaning of Confusion Matrix2m
Interpreting Confusion Matrix2m
Predicting Wrong Values2m
False Positives in Confusion Matrix2m
Beyond Accuracy2m
Description of Precision2m
Predicting Correct Signals2m
Description of Recall2m
Calculation of Precision2m
Calculation of Recall2m
Calculation of f1 Score2m
Inference of Performance Metrics2m
Metrics to Evaluate a Classifier5m
Classification Report5m
Additional Reading10m
- Selecting the Right FeaturesIn machine learning, you have to strike a delicate balance between the number of features used to create a model, and the processing capabilities of the model itself. It would be great to have thousands of features but it will lead to complexity and higher computation time for the model. In this section, you will try to reduce the features while keeping an eye on the information lost as well.Part Overview2m
Selecting the Right Features2m
Importance of Feature Selection5m
Calculation of Feature Importance5m
XGBoost and Feature Importance5m
Decision to Remove Features5m
Decision on Removal of Features5m
Visualising Feature Importance with XGBoost5m
Create an Instance of XGBClassifier5m
Train the Model Using the Training Data5m
Calculate Feature Importance5m
Summary2m
- Reducing Features While Minimising Information LossRanking Features in terms of their predictive power and then selecting the top features is one method to reduce features. However, this leads to information loss from the discarded features. Is there another method to reduce the features but to preserve as much information as possible? You will find out in this section.Reducing Features While Minimising Information Loss2m
Concern Regarding Feature Selection5m
Suggestion to Minimise Information Loss5m
Purpose of Autoencoder5m
- Application of Encoder for Multiple FeaturesAn encoder takes multiple features and converts them into fewer features. In this section, you will understand how a neural network is used to create an encoder and preserve as much information as possible.Application of Encoder in Reducing Features2m
Purpose of an Encoder5m
Implementation of Encoder Using Neural Network5m
Calculation of Product of Weight and Input Feature5m
Final Encoder Values5m
Encoder Approach to Reduce Features5m
Define the Input layer5m
Define the Encoding Layer5m
FAQs2m
- Application of Encoder and Decoder for Reducing FeaturesAssume that an encoder model converted 126 to 8 features. How can you examine if these 8 features contain the information of the original 126 features? In this section, you will use a decoder component as well so that you can check if the reduced features are a true representation of the original features. You will go through the concept and implement the encoder and decoder model in practice.
Application of Encoder and Decoder to Reduce Features2m
Issue in Using Only Encoder5m
Verification of Encoded Value5m
Purpose of Training the Encoder5m
Reduction of Features in Encoding and Decoding5m
Expansion of Features5m
Encoder-Decoder Approach to Reduce Features5m
Training the Autoencoder Model5m
Momentum Trading Strategy Using Encoder Features5m
Additional Reading2m
Summary2m
Test on Reduction of Features16m
- Evaluating the ML Model with Cross-ValidationThis section explores using cross-validation techniques to assess the performance of your model. Through practical demonstrations, you'll learn to evaluate the accuracy of the ML model. You will learn the step-by-step process of carrying out cross-validation with the help of Python.Overview: Cross Validation and Hyperparameter Tuning2m
Cross Validation2m
FAQs on Cross Validation2m
Correct Use of Cross Validation2m
Purpose of Test Data5m
Test Dataset5m
Cross Validation Function5m
Larger K-Value in Cross Validation2m
Estimation of ML Model's Performance5m
Rotation Estimation5m
Standard Deviation of Cross Validation Scores5m
K-Fold Cross Validation5m
Additional Reading on Cross Validation2m
- Optimising the ML Model through Hyperparameter TuningThis section takes you through the process of optimising the ML model by fine-tuning its hyperparameters. Hyperparameters play a critical role in determining the performance of the model, and tuning them effectively can enhance its accuracy and generalisation ability. You will also learn various techniques such as Randomized Search and Grid Search for hyperparameter tuning. By the end of this section, you'll be equipped with the skills to fine-tune your ML model effectively for improved results - all with the help of Python.Hyperparameters2m
Process for Best Hyperparameters5m
Search Process for Hyperparameters5m
Hyperparameter Tuning2m
Identify the Technique5m
Alternative to Grid Search5m
Randomized Search vs Grid Search5m
Randomized Search Effectiveness5m
Identify the Process5m
Hyperparameter Tuning5m
Call the GridSearchCV Method5m
FAQs on Hyperparameter Tuning2m
Additional Reading on Hyperparameter Tuning2m
- Incorporation of Multiple ML ModelsMultiple ML models, or ensemble models, typically use more than one ML model to produce the final predicted output. In this section, you will go through different types of ensemble models and their performance.Part Overview2m
Incorporation of Multiple ML Models2m
Voting Classifier and ML Models5m
Hard Voting Classifier Model5m
Soft Voting Classifier Model5m
Advantage of Voting Classifier Model5m
Hard Voting Classifier Model5m
Implement Hard Voting Classifier Model5m
Soft Voting Classifier Model5m
Implement Soft Voting Classifier Model5m
Additional Reading2m
- Capstone Project 1In this section, you will create a voting classifier machine learning model based trading strategy.Getting Started2m
Problem Statement2m
Capstone Project Model Solution2m
- Validating SignalsThis section focuses on validating momentum trading signals generated by the ML model. It acknowledges that certain aspects of the ML model's learning are beyond our control. Therefore, ensuring that the ML model generates only momentum signals becomes challenging. This segment explores a strategy that can help us validate and refine ML-generated signals.Validating Signals2m
Upward Trend Prediction5m
Ensuring Momentum Based Signals5m
Identify the Position5m
Select the Best Course of Action5m
Criterion for Short Position5m
Validating Signals Strategy5m
Merge the DataFrames5m
Generate Signals5m
FAQs on Validating Signals2m
Test on Cross Validation, Hyperparameter Tuning, Multiple Models and Signal Validation12m
- Getting Data: Multiple AssetsIn this section of the course you will learn how to fetch the data of multiple assets. This data will then be used in the upcoming sections of the course to build a cross-sectional momentum portfolio.Getting Data: Multiple Assets5m
Multi-Class Shares5m
- Revisiting Cross Sectional MomentumIn this section, you will learn the fundamentals of creating a cross-sectional momentum portfolio such as filtering stocks based on average daily turnover and ranking them based on the historical performance.Part Overview on Cross-Sectional Momentum2m
Introduction to Cross-Sectional Momentum2m
Types of Momentum5m
Momentum in Returns5m
Which Lookback and Holding Period?5m
Understanding Cross-Sectional Momentum Strategy5m
Short-Term Momentum Analysis5m
Long-Term Momentum Research5m
Cross-Sectional Momentum Strategy2m
Factor to Filter Stocks5m
Steps for Cross-Sectional Momentum Strategy5m
Long-Only Strategy5m
Impact of High Number of Stocks5m
Modify the CSMOM Portfolio5m
- Filter and Rank the StocksIn this section, you will implement the filtering and ranking of stocks to create a cross-sectional momentum portfolio in Python.Filter Stocks for a CSMOM Portfolio5m
Calculate the Average Daily Turnover5m
Filter the Stocks Based on Average Turnover5m
Rank Stocks for a CSMOM Portfolio5m
Calculate the Returns in Lookback5m
Rank the Stocks Based on Lookback5m
Additional Reading2m
FAQs on Cross-Sectional Momentum (CSMOM) Strategy2m
- Implement Cross-Sectional Momentum StrategyIn this section, you will learn to implement the cross-sectional momentum strategy in Python once the stocks are filtered and ranked. You will not stop here, you will also learn to calculate and study the performance of the cross-sectional momentum portfolio.Strategy Flow Diagram2m
Create a CSMOM Portfolio5m
Generate Signals for Long-Only Portfolio5m
Generate Signals for Long-Short Portfolio5m
Performance Analysis of a CSMOM Portfolio5m
Stock Returns in the Holding Period5m
Daily Returns of Assets in Portfolio5m
Calculate Daily Portfolio Returns5m
- Capstone Project 2In this section, you will apply the knowledge you have gained in the course. You will pick up a capstone project where you will create a long-only and short-only cross sectional momentum portfolios and compare their performance.Problem Statement2m
Capstone Solution2m
- ML-Based CSMOM TradingIn this section, you will learn the need to apply machine learning for the cross-sectional portfolio creation. This section also explains the steps required to apply machine learning.Machine Learning For Cross-Sectional Momentum2m
Advantage of ML for Trading CSMOM5m
Purpose of Using ML5m
Categorisation of Stocks5m
Steps to Apply ML for CSMOM Portfolio2m
Order of Steps to Apply ML for CSMOM Trading5m
Defining Features5m
Type of ML Model5m
Position for Best Performing Stocks5m
FAQs on ML-Based CSMOM Trading2m
- Features to Create ML-Based CSMOM PortfolioIn this section, you will learn to select the features for the machine learning model to create a cross-sectional momentum portfolio.Features to Create ML-Based CSMOM Portfolio2m
Cross-Sectional Momentum Strategy5m
Factors5m
Constructing the Features5m
Momentum in Assets5m
ML for CSMOM Portfolio5m
Next Steps5m
- Select the ML ModelIn this section, we will discuss the reason behind selecting the unsupervised learning algorithm to create an ML-based cross-sectional momentum portfolio.How to Select the ML Model?2m
Grouping Stocks5m
Hierarchical Clustering5m
Advantage of Clustering5m
Partitioning a Dataset5m
Advantage of Hierarchical Clustering5m
Test on ML-Based CSMOM Portfolio10m
- What is Hierarchical Clustering?In this section, you will learn the intuition behind the hierarchical clustering process and how grouping is done using this method.What is Hierarchical Clustering?2m
Clustering2m
Correct Hierarchy2m
Grouping Items5m
Advantage of Hierarchical Clustering5m
Hierarchical Structure5m
Additional Reading10m
- Apply Hierarchical Clustering to Create CSMOM PortfolioSo far, you learnt the steps involved in implementing machine learning for creating a cross-sectional momentum portfolio. In this section, you will learn the practical implementation of hierarchical clustering to create CSMOM portfolio.Hierarchical Clustering to Create CSMOM Portfolio2m
Understanding Momentum Clustering5m
Criteria for Momentum Clustering5m
Clustering Method in the Course5m
Methods of Stock Clustering5m
Hierarchical Clustering Diagram5m
Visualise the Clusters Using a Dendrogram5m
Calculate Monthly Returns of Assets5m
Create Linkage Matrix for Clustering5m
Visualise the Dendrogram5m
FAQs on Hierarchical Clustering for CSMOM Portfolio2m
- Challenges in ClusteringIn order to apply hierarchical clustering to select stocks for a cross-sectional momentum portfolio, the clustering process needs to be customised to avoid challenges. In this section, you will learn about the challenges involved in clustering stocks to create a portfolio and methods to resolve them.Challenges in Clustering2m
Challenge of Hierarchical Clustering5m
Bias in Portfolio Performance5m
Issue of Single Stock Cluster5m
Limiting Stocks per Cluster5m
Customise the Clustering Process5m
Ways to Customise the Clustering Process5m
Limit the Number of Clusters5m
Limit the Number of Stocks in a Cluster5m
- Create the CSMOM Portfolio after ClusteringAfter customising the clustering process and creating the cluster of stocks, the cross-sectional momentum portfolio can be created using the strategy rules. In this section, you will learn all about the strategy parameters and the process of selecting the parameters to create an ML-based cross-sectional momentum portfolio.Create CSMOM Portfolio After Clustering2m
Strategy Parameters for Cross-Sectional Momentum Portfolio5m
Intermediate-Term Momentum for Momentum Strategy5m
Portfolio Rebalancing in Cross-Sectional Momentum5m
Position Sizing across the Portfolio5m
Long and Short Positions in Cross-Sectional Momentum5m
Create a CSMOM Portfolio Using Clusters5m
Monthly Returns of Clusters5m
Performance of Clusters in Lookback Period5m
Calculate the Performance of a Portfolio5m
Hierarchical Clustering for CSMOM5m
Creating the Linkage Matrix5m
Rebalancing a CSMOM Portfolio5m
FAQs on CSMOM Portfolio2m
Test on CSMOM Portfolio after Clustering10m
- Capstone Project 3In this section, you will apply a machine learning based momentum strategy using all the concepts you have studied so far in the course.Problem Statement2m
Capstone Project Model Solution2m
- Run Codes Locally on Your MachineLearn to install the Python environment in your local machine.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
- Live Trading on IBridgePyIn this section, you would go through the different processes and API methods to build your own trading strategy for the live markets, and take it live as well.Section Overview2m 2s
Live Trading Overview2m 41s
Vectorised vs Event Driven2m
Process in Live Trading2m
Real-Time Data Source2m
Code Structure2m 15s
API Methods10m
Schedule Strategy Logic2m
Fetch Historical Data2m
Place Orders2m
IBridgePy Course Link10m
Additional Reading10m
Frequently Asked Questions10m
- Paper/Live Trading TemplateTo make sure that you can use your learning from the course in the live markets, a live trading template has been created which can be used to paper trade and analyse its performance. This template can be used as a starting point to create your very own unique trading strategy.Template Documentation10m
Template Code Files2m
- SummaryIn this section, you will summarise the key concepts covered in the course and you will also go through the next steps that you should take after completing it.Course Summary2m
Summary and Next Steps2m
Python Codes and Data2m
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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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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.
- How can ML be used in momentum trading?
Machine learning (ML) can be used in momentum trading to analyse historical market data and identify patterns or trends that may not be apparent through traditional methods. ML algorithms can predict future price movements, generate trading signals, and optimise trading strategies based on the identified patterns.
- What advantages do ML approaches offer in momentum trading compared to traditional methods?
ML approaches offer several advantages in momentum trading, including the ability to analyse large datasets quickly, identify complex patterns, and adapt to changing market conditions.
- What types of data inputs are used in the ML approach for momentum trading?
The ML approach for momentum trading utilises various types of data inputs, including historical price data, trading volumes, technical indicators (e.g., moving averages, RSI), etc. These inputs help ML algorithms in identifying patterns and making predictions.
- Are there specific limitations or risks associated with ML for momentum trading?
While ML offers significant advantages, there are also specific limitations and risks associated with its use in momentum trading. These include overfitting, where the model performs well on historical data but fails to generalise to new data.
- . How adaptable are these ML strategies to changing market conditions?
ML strategies in momentum trading can be relatively adaptable to changing market conditions compared to traditional methods. ML algorithms can learn from new data and adjust trading strategies accordingly, allowing them to capture evolving market trends and dynamics. However, continuous monitoring and periodic updates to the model are necessary to ensure its effectiveness in evolving market environments.
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