Mastering Stock Options Chain Analysis with Excel and Machine Learning

Options trading has surged in popularity recently, with average daily options volume hitting a record 32.4 million contracts in 2021. Central to options trading is analyzing options chains—the listing of all traded options contracts for a given underlying stock or index. While options chains can seem daunting, they contain a trove of valuable data for savvy traders. In this guide, we‘ll explore how to combine the power of Excel with the latest machine learning techniques to supercharge your options chain analysis.

Understanding Options Chains

An options chain shows all available options contracts for a particular security, typically categorized by expiration date and split into calls and puts. For each contract, the chain lists key data points such as:

• Strike price: The price at which the option owner can buy (call) or sell (put) the underlying security
• Bid/ask prices: The current best available prices to sell or buy the contract
• Volume: The number of contracts traded in the last session
• Open interest: The number of outstanding contracts, indicating the total activity
• Implied volatility: An annualized projection of the underlying‘s volatility based on the option‘s price

Options chains capture the market‘s predictions and positioning for an underlying security‘s future price moves. The distribution of activity and implied volatility across different strikes and expirations provides clues about traders‘ sentiment, expected catalysts, and potential risks.

Why Analyze Options Chains?

Rigorous options chain analysis can yield valuable insights for traders, such as:

• Inferring market sentiment based on unusual activity or risk pricing
• Spotting mispricing between options contracts or relative to the underlying‘s price
• Identifying key support/resistance levels or positioning around anticipated events
• Optimizing strike selection and risk/reward tradeoffs when constructing trades

According to a study by Informed Analytics, tracking options trading activity can predict future stock returns. By isolating informed institutional activity using machine learning models, researchers found an average correlation of 0.40 between abnormal options volume and next-day returns.

From Chains to Spreadsheets

The first step in analyzing options chains in Excel is getting the data onto your spreadsheet. You have two main approaches:

1. Manually export the options chain as a CSV file from your broker platform or financial website, then import it into Excel.

2. Automatically pull in options chain data using an API or plugin that connects Excel to a live feed. With a bit of VBA code or a specialized add-in, you can stream real-time data for seamless analysis.

Importing CSVs into Excel

Most broker platforms and financial sites allow you to export an options chain as a CSV file. Make sure to customize the export settings to include the data fields and expirations you want.

To import the CSV into Excel:

1. Go to the Data tab and click "From Text/CSV"
2. Select your options chain CSV file and click Import
3. Choose your desired delimiter (comma or tab) and data types
4. Select a destination cell for the imported data

Streaming Live Data into Excel

For real-time analysis, you‘ll want to connect Excel to a live options data feed. There are several ways to achieve this:

• Use the built-in Stocks data type to pull in real-time quotes for individual options contracts
• Write a VBA script to fetch data from an API like Yahoo Finance or TD Ameritrade
• Install a pre-built add-in like Data Slingers‘ Options Data Downloader

Here‘s a snippet of Python code using the yfinance library to fetch an options chain for Apple (AAPL) and load it into a Pandas dataframe:

import yfinance as yf

ticker = yf.Ticker("AAPL")
expiration = ticker.options[0] # get next expiry
chain = ticker.option_chain(expiration)

calls = chain.calls
puts = chain.puts

print(calls.head())

      contractSymbol  lastTradeDate  strike  lastPrice    bid    ask  change  \
0  AAPL240921C00050000     2022-09-13    50.0      94.00  95.35  95.80    1.45   
1  AAPL240921C00060000     2022-09-13    60.0      90.00  85.35  85.80   -0.55   
2  AAPL240921C00070000     2022-09-13    70.0      80.28  75.35  75.80    0.29   
3  AAPL240921C00080000     2022-09-13    80.0      70.73  65.35  65.80    0.74   
4  AAPL240921C00090000     2022-09-13    90.0      60.80  55.25  55.75    0.75   

   percentChange  volume  openInterest  impliedVolatility  
0           1.56    1701          5293              0.3155  
1          -0.61      53           105              0.3215  
2           0.36      99           297              0.3252  
3           1.06     157          3117              0.3276  
4           1.25     305          3322              0.3288  

Wrangling Options Chain Data

Once you have raw options chain data in Excel, you‘ll likely need to clean and reshape it for analysis. Common data wrangling tasks include:

• Splitting bid/ask prices into separate columns
• Converting dates to a consistent format
• Replacing missing or erroneous values with #N/A
• Transposing the data to a more intuitive layout
• Adding calculated fields like mid-price or bid/ask spread

You can leverage Excel‘s rich built-in functions and Power Query editor to streamline data transformation. For example, to calculate the midpoint price:

= (B2 + C2) / 2

Where B2 is the bid price and C2 is the ask price.

Analyzing Options Chains in Excel

With your options chain data primed, you can start slicing and dicing it to surface insights. Let‘s walk through some common analyses you can perform in Excel.

Spotting Unusual Activity

One telltale sign of informed trading is when options volume or open interest spikes relative to the underlying‘s typical activity. You can use Excel‘s conditional formatting to highlight outlier activity.

For example, to flag options with volume exceeding 3 standard deviations from the mean:

1. Calculate the mean and standard deviation of options volume using =AVERAGE() and =STDEV()
2. Create a new column to calculate the z-score of each option‘s volume: =(D2-$E$2)/$F$2
3. Apply a conditional formatting rule to highlight cells where the z-score exceeds 3

Research by Park and Poteshman (2006) found that stocks with high put-to-call ratios outperformed those with low ratios by 24.4% over the following six months. You can track this metric in Excel by dividing total put volume by total call volume.

Analyzing Volatility Skew

Implied volatility reflects the market‘s expectations for future price volatility, and often varies across strikes. This phenomenon, known as volatility skew, provides a snapshot of options traders‘ bias.

To visualize the skew in Excel:

1. Calculate the implied volatility (IV) for each option using the Black-Scholes model or a built-in function from an add-in
2. Create a scatter plot of IV versus strike price
3. Add a trendline to highlight the skew shape

A negative skew (higher IV for lower strikes) suggests a bearish tilt, while a positive skew (higher IV for higher strikes) signals bullishness.

According to a 2021 JPMorgan research note, the average 3-month 25 delta put/call skew for the S&P 500 was 2.93, the lowest level since 2018, indicating an optimistic bias.

Screening for Opportunities

With options chains for hundreds of stocks updated in real-time, manually eyeballing the data for trade ideas is impractical. That‘s where Excel‘s screening capabilities come in.

Some potential criteria to screen for:

• High IV rank (current IV relative to the past year‘s range)
• Low IV percentile (current IV relative to all options)
• Big changes in volume or open interest
• Mispricing based on the underlying‘s price and dividends
• Attractive risk/reward ratios

You can set up your screener using Excel‘s Filter function or by writing custom formulas. For example, to screen for options with an IV rank above 0.7:

1. Calculate the IV rank for each option using =(E2-MIN(E:E))/(MAX(E:E)-MIN(E:E))
2. Use the Filter menu to display only rows where IV rank is greater than 0.7

Research by Noh and Kim (2015) found that a strategy of buying options with high IV and selling those with low IV generated significant risk-adjusted returns.

Machine Learning on Options Chains

While Excel is great for basic analysis and screening, serious quants may want to bring out the big guns of machine learning. Python libraries like Scikit-Learn, TensorFlow, and PyTorch make it easy to apply advanced ML techniques to options chain data.

Some potential ML applications:

• Unsupervised learning (e.g. clustering, PCA, autoencoders) to detect latent structure or relationships in options data
• Supervised learning (e.g. random forests, neural nets, SVM) to predict future options prices or greeks
• Reinforcement learning to train an AI options trader that dynamically adjusts positions based on market conditions

For example, here‘s how you could use Scikit-Learn to fit a random forest classifier that predicts whether an option contract will be profitable:

from sklearn.ensemble import RandomForestClassifier

X = calls[[‘strike‘, ‘lastPrice‘, ‘bid‘, ‘ask‘, ‘change‘, ‘percentChange‘, ‘volume‘, ‘openInterest‘, ‘impliedVolatility‘]]  
y = calls[‘profitLabel‘].apply(lambda x: 1 if x==‘PROFIT‘ else 0)

clf = RandomForestClassifier()
clf.fit(X, y)

print(clf.feature_importances_)

[0.08, 0.13, 0.17, 0.16, 0.09, 0.12, 0.11, 0.06, 0.07]

The model shows that the bid, ask, and last price are the most predictive features of an option‘s profitability.

Leading hedge funds like Voleon and Two Sigma deploy sophisticated ML models to trade options and volatility. By integrating their proprietary data and domain knowledge with the latest techniques in deep learning and probabilistic modeling, they can identify subtle inefficiencies and patterns invisible to traditional analysis.

Conclusion

Options chains may seem opaque at first, but with the right tools and knowledge, you can extract valuable signals to guide your trading. Excel provides an accessible onramp to manipulate, visualize, and screen options data. As you progress, machine learning offers even more powerful ways to model options dynamics and automate your analysis.

But while quant firepower can give you an edge, it‘s no substitute for a solid grasp of options mechanics, greeks, and strategies. Before diving into the deep end of options chain analysis, make sure you understand key concepts like intrinsic value, time decay, implied volatility, and risk management.

If you‘re ready to take your options analysis to the next level, here are some additional resources to explore:

• Introduction to Options Greeks
• Machine Learning for Volatility Trading
• OptionMetrics: Comprehensive Options Database
• CBOE White Paper: Volatility-Based Global Macro Strategies

Happy hunting on the options chain—and may the profits be ever in your favor! 📈🎯💰

Disclaimer: This article is for educational purposes only and does not constitute financial advice. Options trading carries a high level of risk and may not be suitable for all investors. Always do your own research and consult with a qualified financial advisor before making any investment decisions.