whoa, I almost need an AI to explain that. Sounds very very complicated.
It didn’t start out that way. I was initially just looking for simple leading indicators, but one thing led to another.
That’s the fun of going down a rabbit hole—you never know what you’ll find.
And that’s the amazing thing about using AI for these deep dives. When I use ChatGPT with Excel/VBA, the possibilities feel endless.
Since you mentioned it, I just asked ChatGPT what it thought of my comment to you about digging for leading indicators in the options market using AI (i.e., Does it make any sense from an options trading POV?).
Here is what it said:
Yes, your analysis makes good sense from an options trading perspective, and you’ve captured both the complexity and nuance of attempting to exploit patterns in implied volatility (IV) in a flat market. Your focus on the relationship between implied volatility shifts in puts and resulting call prices is insightful, as such changes can sometimes signal anticipated movements even in the absence of large price swings in the underlying stock.
Your observations align with certain dynamics in options trading. For instance, a spike in IV of out-of-the-money puts could indicate an increase in perceived risk or demand for downside protection, which might prompt a shift in market sentiment or a rebalancing between puts and calls. However, as you pointed out, executing a profit consistently in such scenarios can be challenging, especially given transaction costs, bid-ask spreads, and the requirement for quick action.
The part about small, intermittent gains and the pattern's sensitivity to precise conditions is also typical in options trading strategies that aim to capitalize on short-term price neutrality. While such approaches can be profitable in bursts, they often require both strict timing and narrow spreads to maintain profitability over the long term.
The reflection on ChatGPT’s assistance showcases how AI can serve as a helpful tool in identifying patterns and correlations, even if the real-world execution of these patterns doesn’t always translate into robust gains.
This analysis involves examining Nvidia stock options to identify short-term trading patterns by analyzing changes in "implied volatility" (IV), which reflects the market's expectations of future price movement.
In options trading, IV often serves as a signal of anticipated risk or fluctuation: when IV rises, it typically means that traders expect the stock could move significantly in the near term, even if the stock’s price remains steady.
The approach observed here focuses on a particular pattern between out-of-the-money (OTM) puts and calls. Out-of-the-money options are priced slightly above or below the current stock price, so a change in their IV can signal traders' expectations about potential stock price shifts. In this case, a sudden increase (around 10%) in the IV of OTM puts was found to often correlate with a slight rise in the prices of OTM calls within minutes. This inverse correlation suggested that rising demand for downside protection (increased IV of puts) could sometimes drive up the value of upside potential (calls), even in a flat market.
The hypothesis was that by detecting this IV increase in puts, a trader could enter call positions right before their prices rose, then sell them quickly for profit. ChatGPT’s analysis indicated that this relationship was strong enough to yield minor profits, provided the stock remained flat and the trader acted fast.
However, this strategy’s profit potential was limited. The gains per trade were small, and any deviation from the flat market or delay in execution could negate profitability. While the trader might succeed on a few trades, occasional losses would offset these gains, resulting in minimal overall profit after several trades.
Additionally, maintaining the precise conditions required throughout the day would demand significant focus and effort, which would not be sustainable or lucrative in the long term.
Ultimately, while this pattern demonstrated real predictive potential under ideal conditions, it was impractical for long-term profitability. Nonetheless, it highlighted how AI tools like ChatGPT can uncover nuanced patterns in financial data, assisting traders in exploring complex relationships, even if those patterns require further refinement to be viable trading strategies.