Machine Learning for Options Trading
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- 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
- Community
Faculty 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 14sCourse Structure3m 42sCourse Structure Flow Diagram2mQuantra Features and Guidance4m 9s
FAQs and Overview
In 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 Questions2mPart 1: Overview3m 4sVertical Spreads
Vertical 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 Strategies2mBull Call Spread2mSetup Bull Call Spread2mBull Call Spread Payoff2mSetup Bear Put Spread2mBear Put Spread Payoff2mMarket Analysis for Spread Trading2mFeatures to Predict the Underlying
In 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 56sObjective of the Decision Tree Classifier5mBenefit of Historical Returns5mUse of Technical Indicators5mML Algorithms and Stationary Data5mHow to Use Jupyter Notebook?2m 5sPredictor and Target Variables5mCalculate Returns Over Multiple Time Periods5mCalculate the NATR5mDefine the Target Variable5mForecast Direction of Underlying with Decision Tree Classifier
In 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 ML3mData Splitting5mEvaluating Performance5mParameter max_depth5mDecision Tree Classifier to Forecast the Underlying5mInitialise the Decision Tree Classifier5mTrain the Decision Tree Classifier Model5mAdditional Reading on Forecasting with Decision Tree Classifier2mMetrics to Evaluate a Classifier
Learn 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 27sAccuracy of ML Model2mInterpretation of Accuracy2mMeaning of Confusion Matrix2mInterpreting Confusion Matrix2mPredicting Wrong Values2mFalse Positives in Confusion Matrix2mBeyond Accuracy4m 2sDescription of Precision2mPredicting Correct Signals2mDescription of Recall2mCalculation of Precision2mCalculation of Recall2mCalculation of f1 Score2mInference of Performance Metrics2mMetrics to Evaluate a Classifier5mHow to Use Interactive Exercises?5mConfusion Matrix5mClassification Report5mAdditional Reading10mTest on Forecasting the Underlying with ML12mOptions Data: Sourcing and Storing
In 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 15sOption Price Data2mData Derived from Options Data2mNeed for Dividend Data2mNeed for Underlying Data2mSourcing Options Data2mOptions Data Storing5mEOM Contracts5mPython Libraries5mWorking With Pickle File5mAdditional Reading on Data Vendors10mOptions Trading with Decision Trees Classifier
In 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 Strategy2mSet Up the Call Spread Strategy5mATM Strike Price5mBacktesting Options Spread Strategy5mTrade Level Analytics
Analysing 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 I5mTrade Level Analytics II4m 21sDefine Win Trades2mCalculate Win/Loss Rate2mCalculate Average PnL Per Trade2mIdentify the Correct Strategy-I2mIdentify the Correct Strategy-II2mLimitations of Win Trade2mCalculate Average Trade Duration2mInterpret the Profit Factor2mCalculate the Profit Factor2mTrade Level Analytics of ML Based Spread Trading Strategy10mAverage PnL Per Trade5mLimitations of Profit Factor2mAverage Trade Duration5mAnalyse the Strategy Performance2mTest on Trading Bull Call Spread with ML10mImproving the ML Model
Once 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 3sWay to Improve ML Model5mMethods to Improve ML-model Based Strategy5mRole 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 40sChoice of Best Hyperparameters5mIncrease of Accuracy and Hyperparameter Tuning5mHyperparameter Tuning with Choice of Range5mKnowledge of Range and Hyperparameter Tuning5mTotal Number of Hyperparameters Combinations5mCross Validation4m 39sUsage of Cross-Validation5mReason for Creation of Test Dataset5mSteps in Cross-Validation5mOrder of Time Period in Dataset5mSize of Datasets in Cross-Validation5mSplit Data in Blocked Cross-Validation5mCross-Validation Techniques5mAdvantage of Blocked Cross-Validation5mAdditional Reading2m
Probability Levels For Improving ML Model
Certain 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 Model2mDifference Between Probability and Probability of Occurrence5mCorrect Usage of Predicting Probability Method5mEffect of Probability Levels on Model Performance5mPredicted Output After Set Probability Level5mControl of Probability Levels in Model5mCalculation of Probability of Occurrence5mChallenges in Usage of Predicting Probability Method5mImplementation of Probability Level in ML Model5mInference of Class Probabilities5mSet Probability Level5mAdditional Reading2mTest on Techniques to Improve ML Model Performance10mEnsemble Classifiers
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.Random Forest Classifier Model3m 57sNeed of Random Forest Classifier Model5mInclusion of ML Models in Random Forest Model5mUse of Train Data in Random Forest Classifier Model5mReason for Inclusion of Multiple Decision Trees in Random Forest5mVoting Classifier Model2m 57sVoting Classifier and ML Models5mHard Voting Classifier Model5mSoft Voting Classifier Model5mAdvantage of Voting Classifier Model5mVoting Classifier Model5mImplement Hard Voting Classifier Model5mImplement Soft Voting Classifier Model5mAdditional 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 5sLimitation of Voting Classifier Model5mNaming Convention for Blending Model5mPurpose of Meta Model5mNeed of Predicted Output of Base Model5mImprovement in Performance from Blending Model5mInitial Step to Blend Machine Learning Model5mTraining of Base Models5mTraining of Meta Model5mDifference Between Base and Meta Model5mEnsemble Model for Prediction5mComparison of Base and Meta Model's Performance5mDisadvantage of Meta Model5mBlending of Machine Learning Models5mUsage of Blender Model for Prediction5mTest on Ensemble Models10m
Parametric Vs Nonparametric Models
After 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 6sParametric and Nonparametric Model Characteristics5mMachine Learning Models5mNonparametric Model5mOptions Pricing Model5mAdditional Reading on Parametric and Nonparametric Models2mOptions Pricing: Feature Engineering
In 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 42sTarget Variable5mMoneyness5mYears to expiry5mSize of the dataset5mInput Features5mFeatures for Options Pricing5mMoneyness5mMerge the Data5mML for Options Pricing
With 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 Prices2mHidden Layers5mShuffle the Data5mActivation Function5mPredicting Options Prices5mSplit the Data5mCompute the Prediction Error5mInterpret the Plot5mPolyfit5mOptions Pricing with Multiple Models5mPrediction Error5mPlot for Multiple Models5mAdditional Reading on Options Pricing2mTest on Options Pricing10mImplied Volatility Concepts
Certain 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 28sMeasurement of Volatility2mImplied Volatility Inference2mProperties of Implied Volatility2mAdditional Reading for Black-Scholes Model2mAdditional Reading for Implied Volatility2mImplied Volatility Calculation5mCalculate Implied Volatility5mForecasting Implied Volatility
You 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 24sModel Creation Process5mNon-Stationary Features5mData Quality Issue5mData Preprocessing5mCall LTP5mMachine Learning Models5mAdditional Reading for Random Forest Model2mForecasting IV5mActual Vs Predicted IV Graph5mFeature Engineering5mCalculate 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 12sMarket Conditions5mRisk Management5mBacktest Short Straddle Strategy5mExit Price5mExit Conditions - Short Straddle5mEntry Condition5mExit Condition5mLimitations - Forecasted IV Values5mTest 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 37sNeed for Application of Machine Learning5mSelection 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 18sCreating the Target Variable - Strategy Design5mTarget Variable for the ML Model5mList of Strategies5mCreate the Combinations of Positions5mFilter the Strategies - 15mFilter the Strategies - 25mCreating 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 46sInput Features for the Machine Learning Model5mInput Features of Underlying Assets5mOptions Greeks as Features5mCreating the Input Features5mFeature to Study Momentum of the Underlying5mFeature to Study Volatility of the Underlying5mNormalise 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 2sMulti-Class Classification Problems5mLearning Rate for LSTM5mMachine Learning Model Design5mBacktest the Predicted Strategies5mTrade Level Analytics of Predicted Strategies5mStrategy Analytics of Predicted Strategies5mTest 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 50sPickle Parameter2mDump Command2mSerialization2mSave The Model2mSave The Data2mChallenges In Retraining The Model3m 25sRetrain A Model2mHow To Perform Simulation3m 47sSimulation Of Trading2mData Leakage2mSave 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 Quantra2mPython Installation Overview1m 59sFlow Diagram10mInstall Anaconda on Windows10mInstall Anaconda on Mac10mKnow your Current Environment2mTroubleshooting Anaconda Installation Problems10mCreating a Python Environment10mChanging Environments2mQuantra Environment2mTroubleshooting Tips for Setting Up Environment10mHow to Run Files in Downloadable Section?10mTroubleshooting 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 Started2mProblem Statement2mCode Template and Data Files2mCapstone Project Model Solution5mCapstone 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 2sLive Trading Overview2m 41sVectorised vs Event Driven2mProcess in Live Trading2mReal-Time Data Source2mCode Structure2m 15sAPI Methods10mSchedule Strategy Logic2mFetch Historical Data2mPlace Orders2mIBridgePy Course Link10mAdditional Reading10mFrequently 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 Documentation10mTemplate 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 Trading2mApplications of ML for Options Trading5mSentiment 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 17sSummary and Next Steps2mPython 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.