All about the “Jay’s Options” service that we are honored to host.
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All About Jay’s Options
by Jay Urbain, Ph.D
This service uses proprietary options market analysis to identify key price levels in US equity indices and single-stock names. We also provide regular educational option and stock trade ideas including risk management techniques.
As a member of our community (you can join here) you will receive:
- Daily charts, tables, and summary views on major indices and stocks. Currently we focus on US equity indices (eg. SPY, QQQ, DIA, IWM), major tech stocks (eg. AAPL, MSFT, NVDA, etc), sector ETFs (eg. XLK, SMH) and others.
- Regular educational trade ideas using our metrics to trade options and stocks.
- Backtesting and continual refinement and improvement of our methods.
Sign up now to secure your price of just $796/yr, or $96/mo. You'll keep your price for as long as you remain a subscriber.
Want To Know How We Do It?
My background is computer science. Many years as a professor, scientist, and consultant. My basic area is applied data science and machine learning. Most of my work is in the medical field, so the work I do and the models I create need to be statistically sound and data driven. I try to bring the same rigor to analyzing option and market data to identify metrics, strategies and opportunities where we can have a statistical or positional edge trading stocks and options. With this service, I hope to share this information and create a collaborative and open learning environment where our strategies and methods are continually improved and adapted to new market regimes.
Implied Distribution
Unlike many trading indicators, option pricing, volatility, and positioning is forward looking. It captures the collective wisdom and sentiment of traders willing to put down hard earned cash to express their belief. Option pricing models are not perfect, but they’re theoretically sound. For example, we can generate the implied distribution of stock prices for any given expiration date (Fig. 1). The distribution indicates that there is a 68% chance the stock price will be between $389.47 and $427.29 (+/- 1 standard deviation) by the expiration date. I have back-tested this on several indices and the number is close to 70-71% (likely due to the variance risk premium, more later). This is a statistical edge you can use to help build a trading strategy along with technical indicators and fundamentals. In fact, regularly selling 30-DTE (days to expiration) Iron Condors with short strikes outside this range is a reliable income strategy. The more accurately we can predict this range, the more precisely we can set strike prices, and the more profitable our strategies become.
Figure 1. Implied Distribution of QQQ
Realized volatility prediction
Fig. 2 shows a QQQ option implied volatility (IV) surface. The values for IV are output from an option pricing model given the price of an option for a particular strike price, the expiration, the risk free rate, and dividends if any. We can use these values to predict the expected range of the underlying. And we do, however, option pricing models typically do not capture liquidity (supply/demand), exogenous events like earnings, Fed speak, and company announcements.
Fig. 2. QQQ 1/9/2024 Expiration Put Options Volatility Surface
To address this, we’ve built several machine learning models that predict future volatility, ATR (average true range), and returns. With these volatility models we can more accurately predict the short term expected range of the underlying. And more accurately identifying the expected range is key to successful trading. We can also use these models to compare our model’s predicted volatility to the implied volatility of any option contract. This allows us to better identify options that are underpriced, over priced, or fairly priced. This consequently drives the type of option strategy we would use, i.e., short volatility, long volatility, or neutral. Concretely, this provides us with an additional statistical edge.
Most traders and websites use IV Rank or IV Percentile, however these metrics tell you if an option is historically underpriced or overpriced. It doesn’t tell you if it is correctly priced for the current market regime.
Below are plots representing our 1-day forward realized volatility prediction. The model has been very accurate. If we use a 1.5 the predicted reliability, the model has accuracy that exceeds 96%.
Fig 3. Expected range volatility model
Fig 4. Expected range volatility model - zoom view
Market Maker Option Positioning
Delta-gamma hedging is an options strategy that combines both delta and gamma hedges to mitigate the risk of changes in the underlying asset as the underlying asset moves. Delta tells a trader how much an option's price is expected to change with a one-dollar change in the underlying price. Gamma refers to the rate of change of an option's delta with respect to the change in the price of an underlying stock’s price. Essentially, gamma reflects the fractional amount of change in the amount of hedging required to maintain a delta neutral hedging strategy.
Market makers use gamma, vanna (change in an options price with respect to the change in volatility) and charm (change in an options price with respect to an options expiration date) to maintain a delta neutral position. Market makers make money through the bid-ask spread and want to avoid any directional risk. Over the last several years, there’s been a dramatic increase in option trading and structured products. Structured products can be large mutual funds selling call options to help pay for downside put option protection. These large option trades can create large hedging flows from market makers who must buy or sell the equivalent of the underlying to stay delta neutral (directionaly neutral). These hedging flows can create support or resistance, a pinning effect, or an acceleration (gamma squeeze) at heavily optioned strike prices. By calculating the gamma, vanna, and charm weighted open interest exposure at each strike price, we can use these strike prices in our trading strategy. Fig. 4 shows the daily open interest weighted gamma, vanna, charm, and delta exposure for QQQ. Option strike prices with large hedging exposure are clearly visible.
Fig. 4. QQQ Gamma, Vanna, Charm, and Delta weighted open interest
Integrating statistical and positional indicators with other technical indicators increases the utility of these methods. Fig 5 shows net gamma weighted open interest exposure as a bar chart (net calls are green, net puts are red), volume a price profile as a blue bar chart, and expected range as dashed lines over the price action for QQQ. The idea is to provide a quick view to identify confluences between indicators.
Fig. 5. Integrated view
Market maker hedging exposure can significantly affect stock prices on key monthly and quarterly options expiration dates. Fig. 6 below shows options expirations by date.
Fig. 6. Options Expiration
Fig. 7. Shows a daily tabular summary of these metrics.
Summary of our services
- Daily charts, tables, and narrative summaries for major indices and stocks. Indices and stocks to focus on will be driven by the community. Currently we focus on SPY, QQQ, DIA, IWM, major tech stocks and ETFs, and Cestrian Research followed stocks.
- Regular educational trade ideas using our metrics to trade options and stocks.
- Backtesting and honest evaluations of our methods.
- Continual improvement in existing methods and exploration of new methods.
Reference (incomplete):
Historical Performance of Put-Writing Strategies (2019), https://cdn.cboe.com/resources/education/research_publications/PutWriteCBOE19_v14_by_Prof_Oleg_Bondarenko_as_of_June_14.pdf
Generating Income and Managing Risk: Cash-Secured Put Writing in a Low Equity Return Environment, Stephen Solaka, CBOE. (2023)