How Tradytics trade ideas are built: the research behind them
By Faizan Ahmad. Updated .
Tradytics trade ideas come from one of the largest studies of retail options flow published by any platform: more than half a billion pattern and horizon combinations tested on 10.9 million options prints over 378 sessions, joined to real 1-minute stock bars and real option quotes, with the filter and exits judged on months they were not fitted on. Most of what was tested failed. Only patterns that held up became idea sources, a trained filter hides the weakest prints, and each idea shows its source's own track record instead of an AI "confidence score". The ideas are calls and puts with a take-profit plan, at /ideas.
What data went into the study?
| Input | What it was |
|---|---|
| Options prints | 10.9 million single-leg prints, with spreads, multi-leg orders and late reports left out |
| Sessions | 378, from May 2025 to October 2026 |
| Stock prices | Real 1-minute and daily bars for every name, with dividends added back into returns |
| Option prices | Real option quotes (the national best bid and offer), and settlement at the official close on expiry day |
| Combinations tested | More than half a billion pattern and horizon combinations |
Spreads were left out because a spread's legs can't be read one at a time: a call bought as half of a spread isn't a bullish bet on its own. How to read spreads in options flow explains why.
How were the patterns tested?
Two ways, because they answer different questions.
- Did the stock move? Each pattern was searched on one period, judged on a second, and scored once on a third that the search never saw. Patterns had to point the right way in all three, and a price-only control showed how much came from the flow itself rather than the stock's own setup.
- Did the contract make money? This is what an options trader actually holds. Each pattern bought the printed contract at the ask, paid fees, and was followed to its exit or to expiry on real prices. One signal per ticker per session, so a burst of repeat prints in one name can't count many times.
The filter that hides weak prints was trained only on earlier months and asked to judge the next, so every one of its calls was out of sample. Exit rules were picked the same way, a week at a time on data held out from the choice.
What failed?
Most ideas about options flow didn't survive real prices. Publishing what failed is as useful as what worked:
- Stock direction over 2 to 10 days. Patterns that looked strong did no better than a control with random signs.
- Big intraday edges. The striking ones came from the same ticker printing again and again on one day. Counted once per ticker per session, they shrank to almost nothing.
- Stock moves too small to trade with options. The stock-direction patterns that held up moved the stock a fraction of a percent, which doesn't pay for an option's spread and time decay on real quotes.
- Chasing what worked lately. Taking the patterns that worked over the last 60 or 90 sessions and trading them next did no better than random picks.
- Following a stock's own flow direction. A ticker's lean carried into its next window about as often as a coin flip.
- "Unusualness" scores. A ticker's top 1% of premium, the top 1% of size against open interest and similar rankings did no better than the base rate.
- Machine learning as a picker. Models trained on contract outcomes couldn't pick winners on average, though they were good at spotting losers. That made them a filter, not a source.
- Patterns that faded in a later replay. Late-day calls in very cheap stocks, small-cap call buying and record-size prints all looked promising in the search and were dropped after a replay on later sessions.
Earlier Tradytics work came to the same place for gamma walls, pinning, gamma direction, net flow direction and whale direction: on their own they don't predict the next move. That's why Tradytics shows them as positioning, not signals, and why following unusual options activity on its own is a weak plan.
What survived?
Six patterns held up on the contract itself and became idea sources:
| Source | What it watches for |
|---|---|
| In-the-money put buying | Puts bought at least 2% in the money in liquid, high-priced stocks |
| Repeat ETF call buying | The same in-the-money ETF call bought again across recent sessions |
| Heavy call volume | A call's volume running far above its open interest in a low-priced stock |
| Opening calls at fair volatility | New call positions bought while implied volatility sits at the stock's normal |
| Bullish premium bursts | A sudden burst of bullish premium in a stock on a strong market day |
| Market-wide call tides | Calls bought in single stocks while SPY, QQQ and IWM flow leans heavily bullish |
Each one, with its exact conditions and the take-profit plan on its ideas, is in the six sources behind Tradytics trade ideas.
What filters run before an idea is shown?
- The trained filter. A model scores every print that matches a source and hides the weakest 40%. What it learned was mostly about the contract (moneyness, calls or puts, premium size, liquidity), not about how exciting the flow looked.
- Price must confirm the side. A buy has to print at or above the ask.
- Rich options are dropped. Implied volatility above 1.3 times the stock's normal, at-the-money contracts under $0.25, and 0DTE buys in the last 30 minutes of the session lost money in every group and never become ideas.
- Tight markets on three sources. In-the-money puts, heavy call volume and opening calls also need a bid/ask spread of at most 10% of the midpoint.
Why is there no AI confidence score?
Because nobody can measure one. A language model asked "how confident are you?" returns a number that isn't tied to any record, and it reads like a probability when it isn't. Tradytics shows something you can check instead: how ideas from the same source have actually done.
How does the track-record score work?
Each idea carries its source's score, computed from resolved ideas only, never from an AI's judgment or the raw model score.
- Won means the idea made money after the spread and fees under the exit rule shown on the card.
- The score blends the source's own record with a base rate: (n x won rate + 50 x base rate) / (n + 50), where n is the source's resolved ideas over the trailing 400 sessions and the base rate is every filtered near-the-money option buy under the same exit. A young source starts near the base rate and earns its own number as ideas resolve.
- The evidence view shows the raw won rate, the share of ideas that doubled, the average result, the number of ideas and how many months were up.
- Recomputed monthly by the same replay.
Rules are frozen. Changing any condition makes it a new source, which needs its own replay before it can produce ideas.
Common questions
Are Tradytics trade ideas made by AI?
No language model picks them. The sources are fixed rules that held up in the study, a trained filter hides the weakest prints, and the score is arithmetic on each source's own resolved ideas.
How big was the Tradytics options flow study?
More than half a billion pattern and horizon combinations tested on 10.9 million options prints over 378 sessions, joined to real 1-minute bars and real option quotes, with the filter and exits judged on months they were not fitted on.
Why do the ideas use the contract's result and not the stock's?
Because the contract is what you hold. A stock can move the right way by less than the option's spread and time decay, and the trade still loses. The study counted what the option did, at real prices.
When can I see trade ideas?
Pro sees ideas as they open. Free and Plus see the previous day's. Plans are in Tradytics pricing.
How are trade ideas different from the AI Fund?
Ideas come from options flow patterns and are single calls and puts. The Tradytics AI Fund is a fundamental long/short options book run by AI agents on dated catalysts, and doesn't use flow as evidence.
Sources
- Options Price Reporting Authority (OPRA): the consolidated last sale and quotation feed from the US options exchanges, the raw material for the prints and quotes studied.
- Selina Han, "Unveiling the Sophistication: Understanding Retail Investors' Trading Behavior in the U.S. Options Market" (Cboe, May 17, 2024): Cboe's study of how retail investors trade options.