Riding the options trend

Riding the options trend

18 March 2021
By William Fisher, Senior Quantitative Developer

We have recently witnessed a new phenomenon where, for some markets, the notional volume of options traded largely exceeds that of the underlying stocks. Historically puts were bought more frequently to hedge portfolios of stocks. Now, it is more common to see calls bought by speculators, especially amongst retail investors. The resulting volume increase has put options on the agenda for many investment managers.

Quants are facing a number of challenges, often data related, when it comes to expanding their existing trading strategies into the options market. A fund with a global macro strategy, for example, might currently trade FX and interest rate futures but finds that pivoting to an options strategy is incompatible with its current infrastructure.

We have identified the top five obstacles quants have to overcome when expanding into options:

1. Data
A robust options quant strategy will most likely be based on volatility data, which is one of the most expensive data sets in the market. Volatility data can easily cost hundreds of thousands of dollars in one-off and recurring license costs. And this is just for the raw data.

2. Data onboarding

The quant team will need to spend valuable time, often several weeks, if not months, cleansing and normalising new data sets.

3. Analytics

Once a fund has acquired and normalised the volatility data, it can’t be analysed within the existing futures framework as it requires sophisticated analytics to turn into options prices.

4. Backtesting

Options trading strategies can’t be backtested through existing infrastructure, such as those used for futures strategies, for example. The backtesting engine will require additional quantitative analytics and knowledge of market conventions to allow for accurate backtests.

5. Trade execution
Executing options trades needs a customised integration with your order and execution management system. SIGTech comes with a Strategy Runner that integrates directly with your OEMS, providing real-time feedback into your strategies and PnL.

SIGTech significantly reduces your time to market and eliminates the expensive upfront costs of acquiring, cleansing and onboarding options data, when compared to the build-out of an internal solution. Strategy development is streamlined via existing building blocks and our backtesting engine achieves consistent and trustworthy results by factoring in real-life trading costs and market structures.

Once your strategy is built and backtested you can seamlessly move into execution without additional and lengthy configuration. An additional advantage that SIGTech offers users is a real-time feedback loop from execution to backtests, to inform and update your strategy in the research environment.


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Available options data

As part of our platform, we offer the following options volatility data, among others, ready to be used for signal detection and backtesting:

  • G10 FX Options, OTC with Vol Surfaces

  • S&P Index Options

  • Exchange Traded Options on 10 Year US Treasuries

  • FTSE Index Options

  • UK Gilts Options

  • Canadian 10 Year (CGB) Options

  • Gold and Crude Oil Options

Below is an example of a rolling short straddle strategy built on the SIGTech platform. This strategy illustrates a put and a call option sold at 50% delta and 3 month maturity, rolling every month. We can backtest this strategy against multiple indices, showing that against the S&P 500 Index the strategy is consistently profitable, but not against the Nikkei 225 or the Euro Stoxx 50, where it would pay to be an option buyer rather than a seller. Could the underlying reason be that option volumes are so much higher in the US market and therefore presenting an opportunity for fund managers?

rolling short straddle options strategy

There are many other opportunities in the options market for investors to explore. A platform like SIGTech makes it easy to identify signals, just by customising a few lines of code in the example below.

Python code quantitative options trading strategy

Get in touch if you would like to test some of your options strategy ideas!