How to Trade Options Using Machine Learning? | Quantra Classroom

In today’s classroom, we are going to merge two heavyweights, machine learning and options trading together, and evaluate the strategy performance.

Why apply machine learning in the first place? Depending on your market view, you can deploy different options trading strategies. What if you had some assistance when you were trying to predict the direction of the market? This is where machine learning can be a valuable tool in your trading arsenal. Read on to find out how!

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A Brief Refresher on Bull Call Spread

You must have a view of the market before deploying an options strategy. In case you are an optimist, you might feel that the market will go up. So you buy a call option at a strike price which is close to the underlying asset, or at the money.?

However accurate your analysis might be, you know that the underlying asset cannot move exponentially high in a short time. So you sell an out-of-the-money call option and pocket the premium. Your profit opportunity might have been reduced, but the potential loss and initial capital are also reduced. This way, you have deployed a bull call spread.?

Example Of An Options Trading Strategy Using Machine Learning

We are going to use a machine learning model to predict if this underlying asset can move up or down, and accordingly deploy a bull call spread strategy.?

Now you might be thinking, which machine learning model should I use to predict the underlying?

Here, we are going to use a decision tree machine learning model which will predict if the S&P 500 will move up or down.?

Once we have this prediction, we will decide if we should use the bull call spread strategy, or not. Let’s backtest this approach.

Steps To Backtest The Options Trading Strategy:

Step 1: Get Data

We are using SPY ETF daily data as the underlying asset and the SPX options as the options data. This data has been taken from OptionsDX but you can use any data provider of your choice.?

All the concepts covered in this article are taken from the Quantra course Machine Learning for Options Trading. You can take a Free Preview of the course and learn all these concepts in detail.

Note: The link will be accessible only after logging into quantra.quantinsti.com.        

The data providers listed in the link are for indicative purposes only and not endorsed by QuantInsti. Before subscribing to any paid data/service, please perform your own analysis and due diligence.

Step 2: Predict the Underlying Asset’s Movement

We will be using the following features to predict the underlying asset’s movement.

We can calculate the returns over the past 1 day, 5 days, 10 days, 22 days, 44 days, and 88 days. These are named as f_ret_1, f_ret_5, f_ret_22, f_ret_44, f_ret_88.

You will also use the following technical indicators: Naturalised ATR (f_natr), Relative Strength Index (f_rsi), Bollinger Bands (f_norm_upper, f_norm_lower and f_norm_middle).

In the context of forecasting the future returns of the S&P 500 index, represented by the SPY, the target variable would be the 1-day future returns of SPY.

We will classify the returns in two labels: 0 for negative returns and 1 for positive returns.

Step 3: Set Up Entry And Exit Rules

We will set the entry and signals on the basis of the forecasted values of the SPY ETF we obtained by using the decision tree classifier model.

Entry Rules: If the value of the signal is 1, it indicates that the SPY is forecasted to move upward on the next trading day. Hence, setting up a bull call spread would be a good idea to benefit from the upward movement in prices.

Exit Rules: When the signal is 0, it indicates that the SPY is expected to move downward on the upcoming trading day. Hence, you can consider closing the bull call spread position on such days.

Note: The prediction generated is for the next day. For example, if the prediction is generated as "1" on 29th September 2022 then it means that an up move is being predicted for 30th September 2022. Note that for the sake of calculations, we assume that we have bought the asset at the close of the 29th of September. This is for backtesting purposes only.        

Step 4: Backtesting

We will set up the bull call spread when entry conditions are met, exit before the expiry and update the trade in round trips. To see how this is done in Python, try the notebook in this unit of the course.

Note: The link will be accessible only after logging into quantra.quantinsti.com.        

The strategy took a total of 269 trades. The figure below shows the trades related information of the first 7 trades including the profit or loss incurred from the trade.

Step 5: Analyse Trades and Performance

In the above plot, you can see that the strategy faced a substantial drawdown during the initial period. However, the performance seemed to recover from 2019 onwards and the final value is $424. Note that backtesting results do not guarantee future performance. The presented strategy results are intended solely for educational purposes and should not be interpreted as investment advice.

You can tweak the take profit and stop loss levels to improve the performance.

Which Machine Learning Model to Use?

It depends on the end objective. For example, the decision tree model was used to predict whether the underlying asset would move up or down. Keeping the same target variable, you can use random forest, XGBoost, or voting classifier models as well. You can compare the performance of different models and then check which model is better.

What to do next??

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IMPORTANT DISCLAIMER: This article is for educational purposes only and is not a solicitation or recommendation to buy or sell any securities. Investing in financial markets involves risks and you should seek the advice of a licensed financial advisor before making any investment decisions. Your investment decisions are solely your responsibility. The information provided is based on publicly available data and our own analysis, and we do not guarantee its accuracy or completeness. By no means is this communication sent as the licensed equity analysts or financial advisors and it should not be construed as professional advice or a recommendation to buy or sell any securities or any other kind of asset.