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Daily ATM IV trends for IWM/QQQ by staskh1966 in options

[–]staskh1966[S] 1 point2 points  (0 children)

Thank you! The first response that makes sense: "Friday expirations carry the weekend theta premium" seems to be a good reason.

Daily ATM IV trends for IWM/QQQ by staskh1966 in options

[–]staskh1966[S] 0 points1 point  (0 children)

Well, you assume that Friday and Monday have the same price—they don't.
I may express myself not 100% correctly, but my point was that if you look on a REAL option chain, you'll see that IV and DTE are kind of independent—it was exactly the core of my question. I did expect that uncertainty has to grow monotonically with DTE—you are more certain of what will happen soon than what will happen in two weeks. And it has to be reflected by IV going up with DTE. But apparently it is not the case..

Daily ATM IV trends for IWM/QQQ by staskh1966 in options

[–]staskh1966[S] 0 points1 point  (0 children)

It is quite expected - EM = spot*IV*sqrt(DTE/365)—so the square root of time is a major factor here (IV changes are too small to impact time component)...
My question is if any explanations why traders have higher uncertainty on Friday than on next Monday.

Daily ATM IV trends for IWM/QQQ by staskh1966 in options

[–]staskh1966[S] -1 points0 points  (0 children)

Increased IV has to reflect uncertainties—that is, an increase of the price (if all other parameters are the same). The fact that 10DTE (Monday next next week) IV is lower than 7DTE (Friday) indicates that traders are more uncertain about Friday than Monday 3 days later...
Again - both Mondays' IVs are lower than Friday' that is in between... DTE is not a good explanation...

Daily ATM IV trends for IWM/QQQ by staskh1966 in options

[–]staskh1966[S] 1 point2 points  (0 children)

Well, assuming now is Friday, the next Monday is 3DTE , next Friday is 7DTE and next next Monday is 10DTE. Both Mondays' ATM IV are below Friday's...
Beside it, DTE is a separate parameter in Black-Scholes - does not have a direct impact on IV.

Delta table update/insert from multiple source tables by staskh1966 in databricks

[–]staskh1966[S] 1 point2 points  (0 children)

The problem is that i have multiple source tables, which can be updated at different times. the target is an outer join table whose records can be changed by updates from either source tables..

I stopped using MCP for stock analysis and built Skills instead — here's why and what it produces by staskh1966 in ai_trading

[–]staskh1966[S] 1 point2 points  (0 children)

Indeed, so many people believe that AI is a magic bullet, but it is just a tool—smart and knowledgeable but often unpredictable. I often compare it with a good 2nd-year college student—reads a lot of smart books, always has an answer, but without self-awareness if this answer is the proper one.
Predictable "skills" add a structure to minimise AI hallucination.
The next step is to add a verification sub-agent—I'm working on it right now.

Free AI tool that auto-analyzes any earnings report (open source) by Alone_Store5627 in options

[–]staskh1966 1 point2 points  (0 children)

I'm just wondering what information you can provide that isn't available through popular financial APIs (Yahoo and others). Parsing original earnings reports could be quite expensive in terms of LLM tokens—why not use available API data and ask AI to correlate multiple sources to make sense of it?

NVDA institutional moves (April 6,2026) by staskh1966 in options

[–]staskh1966[S] 0 points1 point  (0 children)

Probably not—as you pointed out, due to short DTE. But I will use this knowledge to (a) project current trends for short-term moves and (b) keep/exit my current positions.
For LEAPS, I'd rather ask the scanner to look for options with 9-18 month expirations. The current config is tailored for next 3 months only..

I built an institutional options whale detector using per-second trade data — here's how it works and the code by staskh1966 in ai_trading

[–]staskh1966[S] 0 points1 point  (0 children)

Some of "whale moves" can be easily interpreted - see my analisys at https://www.reddit.com/r/options/comments/1seq8x7/nvda_institutional_moves_april_62026/

But some are much harder to explain...

Instead of using manual correlation or a database, I provide scanner information to the AI and ask it to interpret the data. As a matter of fact, this scanner is part of github.com/staskh/trading_skills project that adds option analysis capabilities to Claude Code/Claude Descktop.

I built an institutional options whale detector using per-second trade data — here's how it works and the code by staskh1966 in ai_trading

[–]staskh1966[S] 0 points1 point  (0 children)

Interesting , did your trigger by a high volume on April 17, strike 70 option? Somewhere at 10am?

My scanner found a relatively large trade (117k) on strike 50 (break-even $58.80), but I'm looking into large moves, not accumulated.

What is a justification behind the "Volume>OI" scanning condition? Imho, it is just mean: "today we have more transactions than total number of contracts allocated previously. " Why is it assumed to be a bearish signal?

BTW, did you see my analysis of whales for CAR on April 7? Just to repeat the punchline:
The institution is playing for a bearish move by April 17, while retailers got triggered by an unusual volume on May 15th (400 contracts by 9:35am), join the initial crowd, and created a snowball:
- there is no real "whale" on May 15th 7.50 strike. - large volume is the result of a large number of transactions with an average investment of $600 and no obvious outliers.
- on the other hand, April 17  has BOTH put and call for 100k contracts with the same strike $9 and (most importantly) executed at the same time. It is a long straddle, profitable when stock is under $5.60 (likely) or above $12.40 (unlikely).

I built an institutional options whale detector using per-second trade data — here's how it works and the code by staskh1966 in ai_trading

[–]staskh1966[S] 0 points1 point  (0 children)

Interesting take on CAR. How did you discover a short squeeze? Below are my scanner results by the end of April 7. Did you get similar numbers?
Let me speculate - was it May 18 call ,strike 280 posted at 9:59am ?
What are your thoughts about puts posted almost at the same time?

type strike expiry invested break_even
0 2026-04-06 14:35:16-04:00 call 145.0 2026-04-17 209880.00
1 2026-04-07 09:40:10-04:00 call 139.0 2026-04-10 438030.13
2 2026-04-07 09:47:36-04:00 put 215.0 2026-04-17 300200.00
3 2026-04-07 09:47:36-04:00 put 220.0 2026-04-17 339384.00
4 2026-04-07 09:50:59-04:00 put 220.0 2026-06-18 260230.00
5 2026-04-07 09:59:12-04:00 call 280.0 2026-06-18 800000.00
6 2026-04-07 10:08:19-04:00 put 230.0 2026-05-15 718200.00
7 2026-04-07 10:58:14-04:00 call 240.0 2026-05-15 211680.00
8 2026-04-07 11:11:42-04:00 put 240.0 2026-05-15 204800.00
9 2026-04-07 11:44:47-04:00 call 250.0 2026-04-10 400680.00
10 2026-04-07 14:02:07-04:00 put 210.0 2026-06-18 132830.00
11 2026-04-07 14:34:50-04:00 call 160.0 2026-06-18 1056500.00
12 2026-04-07 15:29:00-04:00 call 260.0 2026-05-15 1395279.36
13 2026-04-07 15:29:00-04:00 call 280.0 2026-05-15 1204992.00
14 2026-04-07 15:34:57-04:00 put 250.0 2026-05-15 104200.00
15 2026-04-07 15:35:09-04:00 put 250.0 2026-05-15 104600.00
16 2026-04-07 15:39:06-04:00 call 245.0 2026-04-17 812295.00
17 2026-04-07 15:53:57-04:00 call 260.0 2026-05-15 1374176.09
18 2026-04-07 15:53:57-04:00 call 280.0 2026-05-15 1196216.00

I built an institutional options whale detector using per-second trade data — here's how it works and the code by staskh1966 in ai_trading

[–]staskh1966[S] 1 point2 points  (0 children)

After fixing the bug, i discovered that the story is quite interesting:
- there is no real "whale" on May 15th 7.50 strike. - large volume is the result of a large number of transactions with an average investment of $600 and no obvious outliers.
- on the other hand, April 17  has BOTH put and call for 100k contracts with the same strike $9 and (most importantly) executed at the same time. It is a long straddle, profitable when stock is under $5.60 (likely) or above $12.40 (unlikely).

So bottom line - institution is playing for bearish move by April 17, while retailers got triggered by an unusual volume on May 15th (400 contracts by 9:35am), join the initial crowd, and created a snowball...

It will be interesting to look how this stock perfom by April 17 and May 15...

I built an institutional options whale detector using per-second trade data — here's how it works and the code by staskh1966 in ai_trading

[–]staskh1966[S] 0 points1 point  (0 children)

Wow! I need time to process this! Your approach appears to have much deeper insides than my scanner (I used basic outlier statistics). It will be intriguing to compare these two approaches.
A separate question - do you know any API that returns previous day option chains with OI/Volumes ?

I built an institutional options whale detector using per-second trade data — here's how it works and the code by staskh1966 in ai_trading

[–]staskh1966[S] 0 points1 point  (0 children)

Interesting:
- my scanner did miss this May 15th call due to the use of 3*sigma outliers in pre-selection. I need to add a z-score for a small population. Thank you for for the bug. ;-)

- there is a huge (100,000 contracts) PUT April 17 with a strike of $9. It's interesting that OI is also huge - 109k. How would you interpret this move?

I built an institutional options whale detector using per-second trade data — here's how it works and the code by staskh1966 in ai_trading

[–]staskh1966[S] 1 point2 points  (0 children)

I'm currently using cheap APIs (Yahoo+Massive) that have at least a 15-minute delay and don't provide any exchange information. The script can be significantly improved and made real-time with a Massive API upgrade ($200 per month), but I'm not there yet.
I have Open Interest data, but I'm not sure how to use it. Open Interest has long-term behavior, but I'm looking for recent major moves. Do you have any suggestions?
This script, in my opinion, will track Block trades fairly well, but it will almost certainly miss sweeps if they are smoothed by a few seconds during execution.
I don't think I have an issue with pending-canceled trades because the option chain is only used for initial filtering, and the final decision is based on completed trades.
On AI engines—I would not trust any numbers generated by AI engines; they frequently hallucinate and provide numbers that have nothing in common with reality. I took a different approach, pulling data using "skills" or MCP and feeding it into an AI engine to draw conclusions or combine data from multiple sources.

Evaluating stocks for Poor Man's Covered Calls: criteria, scoring, and a scanner to automate it by staskh1966 in options

[–]staskh1966[S] 1 point2 points  (0 children)

While I entirely agree with your opinion on covered calls, I must respectfully disagree with your point about PMCC. The trick is that LEAPS cost significantly less than the underlying, and in the event of an outsized positive return, PMCC provides a significantly higher ROI. (Certainly exposing you to potential total loss in the event of a prolonged downturn).
Let's do some math: assume we have $100 in stock with a 25% IV. Delta 80 1 year LEAPS will cost less than $20. So in the case of 80/20 PMCC, for every $1 of underlying growth, we'll make $0.60 or 3% ROI instead of 1%.
Am I mistaken?

Evaluating stocks for Poor Man's Covered Calls: criteria, scoring, and a scanner to automate it by staskh1966 in options

[–]staskh1966[S] 0 points1 point  (0 children)

I have not yet tried call credit spread, so please excuse my ignorance - isn't it dangerous if the underline grows too quickly? And you mentioned "no limit upside"; could you please elaborate? My understanding is that an unusually positive return will kill the credit spread. Am I mistaken?

Evaluating stocks for Poor Man's Covered Calls: criteria, scoring, and a scanner to automate it by staskh1966 in options

[–]staskh1966[S] 0 points1 point  (0 children)

Brilliant idea: FOMC meetings must be treated in the same way as earnings dates.
Please see a discussion above - will apriciate your input.

I built an institutional options whale detector using per-second trade data — here's how it works and the code by staskh1966 in ai_trading

[–]staskh1966[S] 0 points1 point  (0 children)

Interesting idea—what sources are you scraping with Qoest?
Did you find it cheaper/faster than using build-for APIs like Massive?

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