One market. Different exposures.
See the distinction.
Compare volatility across underlyings, follow downside skew through time, then examine what happened inside a single session. Each exhibit starts with actual OptionSpace data.
How did volatility change
across these underlyings?
The same maturity and end-of-day convention make the three histories comparable. Inspect quality-filtered observations, with the resolved snapshot retained in the data.
See the Python example
histories = [
client.tenor_history(
symbol, 30, "2025-01-02", "2025-06-30",
snapshot="EOD", fields=["atf_iv", "skew_25d"]
) for symbol in ["SPY", "QQQ", "NVDA"]
]
charts.history_chart(histories, metric="atf_iv").show()The level of volatility
is only part of the picture.
Use the same observations to compare 25-delta put-minus-call skew. The chart reads stored skew directly; changing the view makes no additional data request.
See the Python example
charts.history_chart(histories, metric="skew_25d").show()Separate the event.
Then test a trade.
See volatility with and without earnings, compare eight reports, follow their five-minute run-ups, and inspect a put-spread simulation with fills, fees and daily risk.
Explore the NVDA earnings study →Historical research demonstration. Source: stored OptionSpace surface-tenor data. Charts show volatility in percent and skew in volatility points. End of day means the last available entitled snapshot at or before the session close. Constant maturity is not a fixed listed contract. Missing or excluded observations remain gaps.
Figures are generated from recorded API results; loading this page makes no data API request.