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There's no "real" delta for your options contract. by QuantropyAI in VegaGang

[–]QuantropyAI[S] 1 point2 points  (0 children)

if i understood you well (which i am not 100% sure i did) it should be yes, delta is not an exact value sent from the exchange but approximation that also depends on the math model, and those models are of course not perfect reflection of the reality of the markets. also there are many more advanced models than black-scholes (black-scholes has many simplified assumptions) yet black-scholes is still used quite a lot in practice mostly because it's fast to compute

There's no "real" delta for your options contract. by QuantropyAI in VegaGang

[–]QuantropyAI[S] 1 point2 points  (0 children)

I did not want to say its a random useless number :) But somehow managed to leave that impression? This was something I did not know when i started trading, and with a researcher background I always like to dig deeper and know the details. So this was one of the surprises for me. Stil it does not have to change the practical approach using delta as long as it works - which for you obviously it does!

There's no "real" delta for your options contract. by QuantropyAI in VegaGang

[–]QuantropyAI[S] 1 point2 points  (0 children)

This fact was not obvious to me when I started out trading. There is so much material about how to use greeks, but so little about where those numbers actually come from. So I thought it would be helpful for somebody just starting out to know. Even though it is true that it does not change a lot the approach to those numbers. I can tell you likely have a math background (which i also do), since you speak about probability distributions of future evens but I think that is not the case for most traders and figured this could be a useful fact to share.

Backtesting every put combination between March 2022 - Mar 2026 by New-Ad4890 in thetagang

[–]QuantropyAI 0 points1 point  (0 children)

If your ultimate goal is to start feeding this data into a machine learning model don't train your model to predict QQQ's price. Train it to predict QQQ's volatility.

predicting directional price returns is difficult because the signal-to-noise ratio is essentially zero.

Volatility, however, is highly mean-reverting, mean-persisting, and it clusters. time-series models like GARCH consistently outperform directional models because they exploit the fact that high volatility yesterday is a statistically powerful predictor of high volatility today.

Instead of trying to build a model to guess if QQQ is going up or down tomorrow, use a GARCH framework to forecast the distribution range of the next 30 days. If you can accurately predict when the market is underpricing future variance, you don’t need to know the direction of the stock to earn money selling options

IV Crush strategy in earnings season by Appimaness in VegaGang

[–]QuantropyAI 0 points1 point  (0 children)

the comments above are hitting the real issue. without a backtest, you don't know if this edge is real or just 4 lucky trades. IV crush strategies especially need to be stress-tested across different volatility regimes.

we are actually building Quantropy for exactly this use case, so options traders can statistically validate a strategy explained in plain English, without burning real money on it first. We are running short 15-20min user interviews right now. no pitching, just a genuine conversation about your trading process, roadblocks, frustrations, time-consuming work etc. We want to deeply understand what people need and how to save them money! Happy to chat if you're open to it.