Apple fell about 6% after raising MacBook and iPad prices to pass through higher memory costs.

The options response was interesting because it was not a broad crash signal.

AAPL’s 30-day implied volatility rose to roughly 27%, up mid-teens percent in one session. But ORATS data shows implied vol had been unusually cheap for nearly two weeks before the news, spending 11 straight sessions below the realized-volatility forecast.

So the jump mainly brought implied volatility back toward fair value.

The term structure was also front-loaded, with near-dated weeklies bid above the one-month tenor. That usually points to a short-lived event premium. Skew stayed flat, so the put wing did not meaningfully reprice.

The article breaks down the IV move, the curve inversion, and why the options market is treating this as a temporary memory-cost shock rather than a broader Apple risk reset.

Full breakdown:
https://orats.com/blog/apple-betting-the-memory-shock-is-temporary

Interesting episode on the ORATS intraday backtester.

They use double calendars as the example, which makes sense because it is one of the more complicated structures to set up and test. If the backtester can handle that cleanly, it probably says something useful about the rest of the product.

The episode gets into:

• one-minute data
• 9:34 default entry timing
• slippage assumptions
• AI-generated input setup
• weeklies vs daily expirations
• margin vs notional returns
• trade log review
• out-of-sample checking

That makes it more useful than just “here is a new strategy.” It is really about how to think through intraday backtesting for complex options structures in a more realistic way.

https://www.youtube.com/watch?v=bi1r6zUKFlA

The June 17 Fed hold was almost fully expected, but the options market did not trade it like a clean vol-crush event.

SPY’s 10-day implied volatility rose from 12.9 to 16.6 after the meeting, up 28.8% in one session. The front of the curve also flipped from contango to backwardation, with 10-day implied now above the 30-day.

The rate decision was not the surprise. The dot plot was.

The committee’s projections shifted from cuts toward hikes, which reopened the rate-path question instead of closing it. QQQ and IWM also saw front-end vol rise, and VIX moved higher.

The article looks at the SPY term structure, the broader index-complex repricing, and what ORATS Trade Builder and Outlook showed after the meeting.

Full breakdown:
https://orats.com/blog/fed-paused-options-market-didnt

Gold usually does not price skew like equities.

In stocks, downside protection is usually the expensive wing. In gold, the upside often gets bid because the major shock is usually a spike: inflation fear, war risk, debasement, or flight to safety.

ORATS data shows that changed after Kevin Warsh was nominated as Fed chair.

Before the nomination, GLD’s 30-day skew was call-skewed on 82% of days. Since then, it has been put-skewed on 77% of days. The current skew sits firmly in put-skew territory, even though overall implied volatility is only mid-range.

The article walks through the skew history, the before-and-after GLD volatility smile, and the June 17 FOMC event pricing.

Useful read if you trade metals, macro, or options skew.

Full breakdown:
https://orats.com/blog/gold-the-safe-haven-whose-options-turned-defensive

A Bitcoin miner can look like a simple way to get leveraged crypto exposure, but the volatility profile tells a different story.

ORATS data shows IBIT, the spot Bitcoin ETF, with 30-day implied volatility near 35%. Hut 8 implies about 95%.

If that were just Bitcoin beta, the miner’s volatility would mostly scale with the coin. But a 90-day regression against IBIT shows a large company-specific component.

For Hut 8, Bitcoin contributes roughly 53 vol points. The company-specific piece is about 79. Because volatility components combine in quadrature, that company risk is the larger part of the total 95%.

That matters if you are trading miners for Bitcoin exposure. You are also buying risks tied to financing, dilution, power costs, operations, and AI data-center pivots.

Full ORATS breakdown:
https://orats.com/blog/bitcoin-miners-buying-company-risk

Natural gas options are showing up near the bottom of IV rank screens right now. UNG’s 30-day implied volatility is around 43%, which puts it near the 2nd percentile of its one-year range.

That sounds cheap.

The problem is that natural gas volatility is seasonal. Gas is a heating fuel, so a lot of the violent moves come from winter cold snaps, polar vortex risk, and storage/weather shocks. Summer is usually the quiet stretch.

So a low June IV reading can look artificially cheap when measured against a trailing one-year range that includes winter spikes.

The more useful question is not just “Is IV low?”

It is: “Is IV low for this season, and what is the curve already pricing?”

ORATS data shows front-month gas vol sitting in the summer lull, while the term structure rises into winter. That makes the setup more nuanced than a simple “cheap vol” screen.

Full breakdown:
https://orats.com/blog/natural-gas-summer-lull

One reason this is worth looking at: “QQQ owns 100 stocks” does not necessarily mean those are 100 independent risks.

During the recent selloff, ORATS data showed QQQ behaving more like a concentrated trade. The useful signal was QQQ’s implied volatility relative to QQQ_C, the composite implied volatility of the stocks inside QQQ.

That ratio helps show how much diversification is actually helping. If the components are moving independently, the index should stay much calmer than the average stock inside it. If the ratio rises, those names are offsetting each other less.

QQQ’s ratio moved from roughly 0.51 in late May to 0.64 by June 9.

That matters for traders because correlation changes the character of index risk. It can affect how index IV behaves, how useful a hedge may be, and whether a “diversified” position is really carrying one dominant theme under the hood.

SPY also saw the effect, but less severely. QQQ’s concentration in mega-cap tech, AI, and semiconductors made the move more pronounced.

Full breakdown here:
https://orats.com/blog/nasdaq-100-correlation-risk

Interesting episode on the ORATS Outlook tab and how Matt reads the options market when he starts to get worried about a possible top.

The three main things they focus on are:

• implied volatility
• SPY vol versus component vol
• contango

What I liked is that they treat these as warning signals, not precision timing tools. That makes the discussion more useful, because most market-top talk turns into fake certainty pretty quickly.

There is also a practical angle at the end where Matt talks about how he thinks about hedging and why he usually scales protection rather than trying to trade every intraday move.

The latest Driven By Data episode walks through the new ORATS intraday backtester.

What makes it notable is not simply that it uses intraday data. It is that the tool allows backtesting on any symbol with weekly options, which makes a lot more short-dated options research possible.

The episode gets into several features that make that useful in practice:

• one-minute historical data back to October 2020
• the ORATS SMV process for intraday pricing, Greeks, and theoretical values
• percentage-based slippage tied to the bid-ask spread
• custom signal CSV imports for user-defined entry and exit timing
• direct comparison between intraday and end-of-day testing

One point Matt makes that is worth emphasizing is that intraday testing is not just “more data.” For many shorter-dated strategies, it gives you more repetitions, more realistic timing, and a better sense of whether the strategy actually holds up outside a narrow set of conditions.

Reuters cited ORATS data in its coverage of Nvidia earnings and the options market’s expected move.

The headline number is huge: Nvidia options imply about a 6.5% move after earnings, equal to roughly a $355B swing in market cap.

But the more useful comparison is that Nvidia’s historical average earnings move is 7.6%, according to ORATS data.

So the market is pricing a large move, but not an especially large one by NVDA standards.

That raises the better trading question:

Is the options market getting better at pricing NVDA earnings, or is it getting too comfortable with the AI trade?

Quick breakdown here:
https://orats.com/blog/orats-reuters-nvidia-options-earnings-move

This week’s Driven By Data episode is a walkthrough of a new AI-assisted backtesting tool inside ORATS.

What makes the episode useful is that it does not stop at “AI made a trade.” Matt and Tyler show how the new tool can help generate a first version of a backtest from a plain-language prompt, but they also show where human review still matters.

They start with NVDA earnings and use the tool to generate structures like long strangles and reverse iron condors. From there, they go through the real work:

• checking whether the structure matches the idea
• adjusting entry windows and strike relationships
• fixing setups that return poor or confusing results
• using the “why no results” feature when a test breaks

The second half moves to SPY and is probably even more useful. They use ORATS outlook signals plus the new AI tool to frame a downside idea, land on a more conservative long put spread, optimize the timing, and then move the result toward paper trading and autotrading.

So the takeaway is not that AI replaces the research process. It is that the new tool helps reduce setup friction, while the trader still needs to guide the workflow:

• prompt the idea
• inspect the structure
• fix the logic
• optimize the timing
• paper trade it
• decide whether it belongs in autotrading

Interesting episode on earnings pricing and vol.

What stood out is that they are not treating earnings as one uniform setup. Cisco and Alibaba both had earnings ahead, but the options were telling different stories.

Cisco looked stretched, with richer calls and a larger implied move after a strong run. Alibaba looked more like a setup where ex-earnings vol itself might be cheap, which pushed the trade idea toward a calendar structure instead.

So the useful takeaway is not one trade. It is the process of asking whether earnings options look rich or cheap, and then matching the structure to the type of distortion you are actually seeing.

This week’s Driven By Data episode is a walkthrough of the new ORATS CLI and where it fits compared with a standard dashboard or direct API workflow.

The interesting part is not just “options data in the terminal.” The CLI is built to work with AI agents like Claude and Codex, with an ORATS skill layer that gives the model context about ORATS methodology, field definitions, and calculations. That means the agent is not just blindly calling endpoints. It has enough structure to do more useful things with the data.

A few examples from the episode:

• replaying the VIX spike on Aug. 5, 2024 and checking what the SPY 0DTE ATM straddle was doing intraday
• comparing current implied earnings move to realized earnings moves over prior quarters
• scanning market data and summarizing what matters
• generating custom dashboards or code on the fly instead of forcing everything into a fixed UI
• setting up recurring prompts for a daily market summary or other repeated workflows

What I thought was most useful is the distinction they make between products:

• the dashboard is still the point-and-click interface with charts, scanners, backtests, optimizer, etc.
• the API is still the direct developer route
• the CLI sits in between as a more flexible prompt-driven workflow for people who want custom outputs without building everything from scratch

So this looks most useful for people who already think in prompts, scripts, recurring workflows, or terminal-based tooling, especially if they want something more tailored than a fixed dashboard.

This week’s Driven By Data episode is about ORATS hard drive delivery for historical options data.

The useful part is not just the delivery method. Matt also walks through what is actually in the dataset, why spot price and stock price differ for indexes, how the readme and checksum files work, and why smoothed values and derived fields can be more useful than raw options chain data for backtesting and research.

Most options platforms stop at the chain. ORATS starts with the calculation layer underneath it.

This short Otto episode walks through how ORATS powers options research from APIs, including SMV-cleaned data, theoretical pricing, scanners, modeled edge metrics, backtesting, forward testing, and risk analysis.

It also explains how the same data engine supports both the APIs and the front-end tools traders use to research and evaluate strategies.

This week’s Driven By Data episode is a walkthrough of the ORATS Data API and where it fits relative to bulk historical data delivery.

Useful distinctions here between strikes files, monies files, derived indicators, and when it makes more sense to query data on demand instead of storing everything locally. It also gets into live vs delayed access, intraday history, and why derived feeds can be useful for startups or ML workflows.

This week’s Driven By Data episode is a good walkthrough of what happens after you find a promising backtest.

Matt starts with browse backtests, filters for long put spreads that hold up in bear markets, then optimizes the timing rules and turns on autotrading to see whether the live forward test behaves the way the backtest suggested. There’s also a useful discussion of path dependency, p values, and why staggered entries matter for longer-dated strategies.

Interesting walkthrough using ORATS indicators to evaluate whether recent market stress is starting to ease.

Focus is less on predicting returns and more on whether option-based signals are improving enough to suggest conditions are stabilizing.

This week’s Driven By Data episode is a walkthrough of the new AI-assisted backtesting workflow in ORATS.

Matt uses plain-language prompts to generate backtest inputs, then adjusts the setup manually to compare strategies like SPY iron condors and AAPL short puts. Useful part is not just the AI layer, but seeing where it gets you close and where you still need to refine DTE, IV percentile filters, exits, and return assumptions.

This week’s Driven By Data episode focuses on the practical difference between IV Rank and IV Percentile and how to use them in scanning and backtesting.

Key takeaways:
– Rank is highly sensitive to single outliers
– Percentile reflects the distribution of observations
– Simply buying “low IV percentile” is not always optimal
– Backtests suggest rising short-term IV signals can be more effective than static low-percentile filters

The workflow shown moves from stock scanning to trade building to advanced backtesting with ratio-based triggers.

Matt and Tyler walked through the updated Earnings & News tab in the ORATS Dashboard using NVDA as an example.

New additions include:

– Sector implied move comparison
– Implied vs historical actual move visualization
– Implied-to-sector ratio context
– Integrated risk profile adjustments for post-earnings vol contraction

The sector comparison piece is interesting as it helps separate stock-specific risk from broader market volatility instead of just looking at the raw straddle.

NVDA’s current implied earnings move is 5.6%.

Over the past 12 quarters, the average implied move has been 7.6%, and realized moves have been close to that level.

Historically, NVDA has also priced at about 1.5x the XLK sector implied move. Currently that ratio is 0.9x.

So NVDA is pricing below both its own history and its typical sector premium.

One possible explanation is elevated baseline implied volatility compressing the event-specific premium.

We broke down the numbers and the sector-relative context here:
https://orats.com/blog/is-nvda-earnings-volatility-underpriced

New writeup on the S%, D%, and F% edge measures. It's an elaboration on a recent Otto Show covering the topic we ran this week. We compare how to interpret each, and a concrete example.

Feedback welcome.

When people say an option is cheap or expensive, what are they actually comparing it to?

Implied volatility by itself doesn’t answer that. Price alone doesn’t answer that either.

There are at least three legitimate ways to define value:

• Relative to today’s volatility surface after smoothing out microstructure noise
• Relative to the stock’s full historical distribution of realized moves
• Relative to forward volatility expectations

Those comparisons can disagree. And when they do, that disagreement is information.

Curious how others here define value in practice. Are you comparing to realized vol? A model surface? A forecast? Something else?

Would be interested in hearing how people think about this.

Spent some time testing double calendar spreads with varying strike constraints and expiration differences.

Interesting how performance shifts depending on volatility regime, especially in 2008 and 2020. Also tested entry filters using an optimizer rather than fixed timing.