Machine Learning for Options Trading
No Cost EMI available
- Live Trading
- Learning Track
- Prerequisites
- Syllabus
- About author
- Testimonials
- Faqs
Live Trading
- Predict the price of options using the features used by options pricing models
- Forecast the direction of the underlying asset using decision tree classifier and backtest an option strategy based on forecast
- Use probability of prediction, random forest model, voting classifier model and blending approach to forecast the prices
- Enter and exit from a short straddle strategy based on the prediction of next day’s implied volatility
- Select the option strategy to trade using a completely automated approach of defining strategy universe, feature creation, training ML model and analysing the performance
Skills Covered
Strategies
- Spread trading with decision trees
- Spread trading with ensemble classifier
- Straddle with implied volatility forecast
- ML predicted options strategy
Concepts & Trading
- Options pricing
- Regression and classification models for options trading
- Volatility forecasting
- Strategy returns prediction
Python
- Pandas, Numpy
- Sklearn
- TensorFlow
- Keras
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
To start with the course, you need to have a basic understanding of machine learning and options trading. You should also be familiar with options trading related terminologies such as calls, puts, implied volatility, strike price, spot price, payoff, expiry dates, the underlying asset, and futures. Basic knowledge of supervised algorithms such as regression and classification models is required. Hand-on experience with options trading would be an added advantage.
Syllabus
- IntroductionThis course will serve as a step-by-step guide where you will learn to apply cutting-edge machine learning techniques to trade options strategies. The interactive methods used in this course will assist you in not only understanding the concepts but also answering all questions about systematic options 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.
Introduction4m 14s
Course Structure3m 42s
Course Structure Flow Diagram2m
Quantra Features and Guidance4m 9s
- FAQs and OverviewIn this section, we cover frequently asked questions about the course content and provide an overview document that explains the concepts covered in the initial few sections of the course which covers the use of ML to predict stock movement and execute a bull call spread strategy.
Frequently Asked Questions2m
Part 1: Overview3m 4s
- Vertical SpreadsVertical spread trading strategies are designed based on the direction-based movement of the underlying asset. This section covers the fundamentals of vertical spreads. Different types of vertical spread trading strategies such as bull call spread, and bear put spread are explained in detail. In addition to this, you will also learn to define the problem statement for the implementation of machine learning algorithms for trading vertical spreads.Vertical Spreads Options Strategies2m
Bull Call Spread2m
Setup Bull Call Spread2m
Bull Call Spread Payoff2m
Setup Bear Put Spread2m
Bear Put Spread Payoff2m
Market Analysis for Spread Trading2m
- Features to Predict the UnderlyingIn this section, we will cover topics such as defining predictor and target variables, the calculation and use of historical returns and technical indicators for forecasting the direction of price movements in the SPY ETF. We'll also explore the importance of stationary data and ML algorithms in the process.Features to Predict the Underlying3m 56s
Objective of the Decision Tree Classifier5m
Benefit of Historical Returns5m
Use of Technical Indicators5m
ML Algorithms and Stationary Data5m
How to Use Jupyter Notebook?2m 5s
Predictor and Target Variables5m
Calculate Returns Over Multiple Time Periods5m
Calculate the NATR5m
Define the Target Variable5m
- Forecast Direction of Underlying with Decision Tree ClassifierIn this section, we will apply the concepts from the previous section to make the predictions. Topics include data splitting, initialising ML parameters, and training the Decision Tree Classifier model. We'll guide you through the process of initialising and using the decision classifier to predict the direction of the underlying asset's price movement.
Forecasting Direction of the Underlying with ML3m
Data Splitting5m
Evaluating Performance5m
Parameter max_depth5m
Decision Tree Classifier to Forecast the Underlying5m
Initialise the Decision Tree Classifier5m
Train the Decision Tree Classifier Model5m
Additional Reading on Forecasting with Decision Tree Classifier2m
- Metrics to Evaluate a ClassifierLearn how to evaluate the performance of a classifier using the classification report and confusion matrix. Understand how to interpret these metrics and use them to study the performance of your ML model.
Evaluating Classifier Model Effectiveness4m 27s
Accuracy of ML Model2m
Interpretation of Accuracy2m
Meaning of Confusion Matrix2m
Interpreting Confusion Matrix2m
Predicting Wrong Values2m
False Positives in Confusion Matrix2m
Beyond Accuracy4m 2s
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
How to Use Interactive Exercises?5m
Confusion Matrix5m
Classification Report5m
Additional Reading10m
Test on Forecasting the Underlying with ML12m
- Options Data: Sourcing and StoringIn this section, you will learn about the data that is necessary for options trading. You will also learn how to source and store this data in a pickle file. In the end, you will be provided with a few sources to procure options data as well as the underlying asset’s data.Options Data4m 15s
Option Price Data2m
Data Derived from Options Data2m
Need for Dividend Data2m
Need for Underlying Data2m
Sourcing Options Data2m
Options Data Storing5m
EOM Contracts5m
Python Libraries5m
Working With Pickle File5m
Additional Reading on Data Vendors10m
- Options Trading with Decision Trees ClassifierIn this section, we will cover how to use predictions of the underlying to deploy the bull call spread strategy. We will also discuss steps to set up the bull call spread using Python, addition of strategy parameters such as stop loss and take profit for better risk management, backtesting the strategy on historical options data and finally, gauging the performance of the strategy by calculating and plotting the cumulative returns.Strategy Logic for Backtesting Bull Call Spread Strategy2m
Set Up the Call Spread Strategy5m
ATM Strike Price5m
Backtesting Options Spread Strategy5m
- Trade Level AnalyticsAnalysing certain metrics will help you understand whether your strategy is working. Trade level analytics represents how a strategy has been performing over a given period. In this section, you will be learning how to calculate and interpret a few widely used analytics such as the number of winning trades, number of losing trades, average profit or loss per trade, etc.Trade Level Analytics I5m
Trade Level Analytics II4m 21s
Define Win Trades2m
Calculate Win/Loss Rate2m
Calculate Average PnL Per Trade2m
Identify the Correct Strategy-I2m
Identify the Correct Strategy-II2m
Limitations of Win Trade2m
Calculate Average Trade Duration2m
Interpret the Profit Factor2m
Calculate the Profit Factor2m
Trade Level Analytics of ML Based Spread Trading Strategy10m
Average PnL Per Trade5m
Limitations of Profit Factor2m
Average Trade Duration5m
Analyse the Strategy Performance2m
Test on Trading Bull Call Spread with ML10m
- Improving the ML ModelOnce the ML model is created, your journey does not stop there. Discover ideas and methods to extract maximum performance from your machine learning model without falling prey to biases.
Improving Your Decision Tree Strategy3m 3s
Way to Improve ML Model5m
Methods to Improve ML-model Based Strategy5m
Role of Probability in Improvement of ML Model5m
- Hyperparameter Tuning and Cross-Validation MethodsIn this section, you will discover ways to improve the model by objectively finding the ideal set of hyperparameters. Further, discover ways to tune hyperparameters by splitting the data into multiple mini datasets by using cross-validationHyperparameter Tuning3m 40s
Choice of Best Hyperparameters5m
Increase of Accuracy and Hyperparameter Tuning5m
Hyperparameter Tuning with Choice of Range5m
Knowledge of Range and Hyperparameter Tuning5m
Total Number of Hyperparameters Combinations5m
Cross Validation4m 39s
Usage of Cross-Validation5m
Reason for Creation of Test Dataset5m
Steps in Cross-Validation5m
Order of Time Period in Dataset5m
Size of Datasets in Cross-Validation5m
Split Data in Blocked Cross-Validation5m
Cross-Validation Techniques5m
Advantage of Blocked Cross-Validation5m
Additional Reading2m
- Probability Levels For Improving ML ModelCertain ML models predict the final output based on which target class has the highest probability of occurrence. You can set a threshold for the ML model to predict the output for a particular target class, increasing the confidence level on the predictions.
Using Probability Levels For Improving Model2m
Difference Between Probability and Probability of Occurrence5m
Correct Usage of Predicting Probability Method5m
Effect of Probability Levels on Model Performance5m
Predicted Output After Set Probability Level5m
Control of Probability Levels in Model5m
Calculation of Probability of Occurrence5m
Challenges in Usage of Predicting Probability Method5m
Implementation of Probability Level in ML Model5m
Inference of Class Probabilities5m
Set Probability Level5m
Additional Reading2m
Test on Techniques to Improve ML Model Performance10m
- Ensemble ClassifiersEnsemble 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.Random Forest Classifier Model3m 57s
Need of Random Forest Classifier Model5m
Inclusion of ML Models in Random Forest Model5m
Use of Train Data in Random Forest Classifier Model5m
Reason for Inclusion of Multiple Decision Trees in Random Forest5m
Voting Classifier Model2m 57s
Voting Classifier and ML Models5m
Hard Voting Classifier Model5m
Soft Voting Classifier Model5m
Advantage of Voting Classifier Model5m
Voting Classifier Model5m
Implement Hard Voting Classifier Model5m
Implement Soft Voting Classifier Model5m
Additional Reading2m
- Blending ModelsBlending model is a type of ensemble model which consists of using the predicted output of multiple ML models to train a meta model and predicting the final output. You will learn how to build a blending model and analyse its performance.Blending Machine Learning Models4m 5s
Limitation of Voting Classifier Model5m
Naming Convention for Blending Model5m
Purpose of Meta Model5m
Need of Predicted Output of Base Model5m
Improvement in Performance from Blending Model5m
Initial Step to Blend Machine Learning Model5m
Training of Base Models5m
Training of Meta Model5m
Difference Between Base and Meta Model5m
Ensemble Model for Prediction5m
Comparison of Base and Meta Model's Performance5m
Disadvantage of Meta Model5m
Blending of Machine Learning Models5m
Usage of Blender Model for Prediction5m
Test on Ensemble Models10m
- Parametric Vs Nonparametric ModelsAfter completing this section you will be able to define parametric and nonparametric models. You will also be able to differentiate between the two and understand which of the two models is better when it comes to predicting options prices.
Parametric and Nonparametric Models4m 6s
Parametric and Nonparametric Model Characteristics5m
Machine Learning Models5m
Nonparametric Model5m
Options Pricing Model5m
Additional Reading on Parametric and Nonparametric Models2m
- Options Pricing: Feature EngineeringIn this section, we will discuss a few input parameters used for predicting options prices and find out where you can get them. You will also learn how to manipulate and transform the sourced data which will then be used to train a model.
Features for Options Pricing3m 42s
Target Variable5m
Moneyness5m
Years to expiry5m
Size of the dataset5m
Input Features5m
Features for Options Pricing5m
Moneyness5m
Merge the Data5m
- ML for Options PricingWith all the assumptions of parametric models such as the Black-Scholes model, have you ever thought about how an ML model can be used to predict options? In this section, we will take you through the process of predicting options prices using regression models such as Artificial Neural Network, Decision Trees, Random Forest, and Lasso. We will also create a function which can be used to predict prices using any of the models mentioned here. Finally, we will plot and compare the prediction error of these models for all contracts on the basis of moneyness.
Predicting Options Prices2m
Hidden Layers5m
Shuffle the Data5m
Activation Function5m
Predicting Options Prices5m
Split the Data5m
Compute the Prediction Error5m
Interpret the Plot5m
Polyfit5m
Options Pricing with Multiple Models5m
Prediction Error5m
Plot for Multiple Models5m
Additional Reading on Options Pricing2m
Test on Options Pricing10m
- Implied Volatility ConceptsCertain strategies profit from fluctuations in the underlying security. And, for these strategies, forecasting the degree of movement based on market participants' expectations becomes important. You will learn about implied volatility and how to calculate it in this section.
Implied Volatility1m 28s
Measurement of Volatility2m
Implied Volatility Inference2m
Properties of Implied Volatility2m
Additional Reading for Black-Scholes Model2m
Additional Reading for Implied Volatility2m
Implied Volatility Calculation5m
Calculate Implied Volatility5m
- Forecasting Implied VolatilityYou have learned about the concept of implied volatility. To make it more interesting let us apply some machine learning concepts that we have learned previously to forecast the implied volatility values.
Forecasting Implied Volatility5m 24s
Model Creation Process5m
Non-Stationary Features5m
Data Quality Issue5m
Data Preprocessing5m
Call LTP5m
Machine Learning Models5m
Additional Reading for Random Forest Model2m
Forecasting IV5m
Actual Vs Predicted IV Graph5m
Feature Engineering5m
Calculate the ADX indicator5m
- Trading Options Using Forecasted IV ValuesYou have predicted the IV values in the previous section. Let us now backtest an options strategy that takes trades based on the predicted IV values.Backtesting Short Straddle2m 12s
Market Conditions5m
Risk Management5m
Backtest Short Straddle Strategy5m
Exit Price5m
Exit Conditions - Short Straddle5m
Entry Condition5m
Exit Condition5m
Limitations - Forecasted IV Values5m
Test on Trading Implied Volatility10m
- Need for ML to Predict Option Strategy to TradeThere are plenty of options strategies such as straddle, strangle, bull call spread and iron condor. In this section, we will learn why you should use ML model to determine which strategy to trade on any given day.Need of ML for Strategy Prediction1m 37s
Need for Application of Machine Learning5m
Selection of an Options Strategy5m
- Defining the Best Option Strategy to TradeIn this section, we will define a problem statement to create a machine learning model for predicting the best options strategy to deploy on the next trading day. This section also covers the process of creating the target variable for the problem statement.Creating the Target Variable3m 18s
Creating the Target Variable - Strategy Design5m
Target Variable for the ML Model5m
List of Strategies5m
Create the Combinations of Positions5m
Filter the Strategies - 15m
Filter the Strategies - 25m
Creating the Target Variable Using Strategy Returns5m
- Input Features for Predicting the Best Options StrategyCreating relevant input features is an important step in approaching a problem using machine learning. In this section, we will create input features for the problem statement of creating a machine learning model to predict the options strategy to deploy.Selecting The Input Features1m 46s
Input Features for the Machine Learning Model5m
Input Features of Underlying Assets5m
Options Greeks as Features5m
Creating the Input Features5m
Feature to Study Momentum of the Underlying5m
Feature to Study Volatility of the Underlying5m
Normalise the Upper Bollinger Band5m
- Model Design and Backtesting the PerformanceThis section covers the process of selecting the suitable machine learning model for predicting the best options strategy to deploy. You will learn to apply deep learning by designing a Long Short-Term Memory (LSTM) artificial neural network using the input features and target variable defined in the previous section. In addition to this, you will also learn to backtest the signals generated by the LSTM model and analyse the performance of trades taken based on the signals generated.Selecting the ML Model to Deploy2m 2s
Multi-Class Classification Problems5m
Learning Rate for LSTM5m
Machine Learning Model Design5m
Backtest the Predicted Strategies5m
Trade Level Analytics of Predicted Strategies5m
Strategy Analytics of Predicted Strategies5m
Test on ML Model to Predict Strategy14m
- Challenges in Live TradingThis section talks about the challenges faced during saving the model and data, and retraining the model. It demonstrates the simulation of trading using machine learning model.Live Trading Challenges5m 50s
Pickle Parameter2m
Dump Command2m
Serialization2m
Save The Model2m
Save The Data2m
Challenges In Retraining The Model3m 25s
Retrain A Model2m
How To Perform Simulation3m 47s
Simulation Of Trading2m
Data Leakage2m
Save Train and Simulate ML Model5m
- Run Codes Locally on Your MachineIn this section, you will learn to install the Python environment on your local machine. You will also learn about some common problems while installing python and how to troubleshoot them.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 apply the knowledge you have gained in the course. You will pick up a capstone project where you will create a machine learning model that predicts the probability of returns (positive/negative) of an options trading strategy for the next trading day.Getting Started2m
Problem Statement2m
Code Template and Data Files2m
Capstone Project Model Solution5m
Capstone Solution Downloadable2m
- 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 and Live TradingTo 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 File2m
- Additional Applications of ML for Options TradingSo far, you have learnt that machine learning models can be used to price the options, predict the underlying asset, forecast the implied volatility, and also to predict the options strategy to deploy. In this section you will learn the additional applications of machine learning for options trading.Further Applications of ML for Options Trading2m
Applications of ML for Options Trading5m
Sentiment Data for Options Trading5m
- SummaryIn this section, we will summarise all the concepts that you’ve learnt throughout the course. You will also find a zip file containing all the notebooks and data files used in the course.Course Summary4m 17s
Summary and Next Steps2m
Python Codes and Data2m
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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.
- Does machine learning work for options trading?
Machine learning is proving to be a useful tool for pattern recognition and analysis of data on a scale which is difficult to process by humans alone. Once you understand the end objective and express it in the form of a logic which can be applied in a machine learning framework, you will be able to formulate a solution in no time.
When you look at options trading, your objective is simple, apply an options trading strategy which can generate positive returns and beat the benchmark as well. Thus, the framework designed in the course does just that. First, you will use the ML model to predict whether the underlying asset is going to rise or not. Accordingly, you will select and set up the appropriate options strategy. But it does not end here. You can also deploy a ML-based strategy which will help you select the right options strategy and further analyse the strategy’s performance as well. In short, you can use machine learning to deploy an options trading strategy.
- Which machine learning algorithm is best for options trading?
Unfortunately, there is no one size fits all strategy which can deliver consistent profits irrespective of the market scenario. However, depending on various factors, you can harness different kinds of machine learning algorithms to make sure that your strategy is performing well. The best algorithm will depend on the specific details of the trading strategy and the characteristics of the options data. However, some commonly used algorithms in quantitative trading include: Random Forest, Gradient Boosting Machines, Support Vector Machines, Long Short-Term Memory (LSTM) neural networks, Recurrent Neural Networks (RNN)
Further, you can use a machine learning algorithm to combine multiple machine learning models to deliver better results. It's important to note that a good algorithm is not everything, the feature engineering and the quality of data are also important factors. Also note that it's important to backtest the strategy before applying it to live trading.