You are viewing delayed data with limited symbols. Subscribe now to access all features.

Platform Guide

Common Language & Variables

Ex-Earn

“Ex-Earn” refers to implied or realized volatilities with the impact of earnings removed.
For realized volatility (RV), this means excluding the trading days immediately after the earnings announcement.
For implied volatility (IV), it involves estimating the implied earnings move embedded in the term structure and adjusting all expirations by removing that component using root time scaling.

Slope

Any variable named “slope” refers to the slope of the volatility skew—typically the 30-day skew unless otherwise specified.

Contango

The term contango refers to the slope of the volatility term structure, with negative values indicating backwardation (i.e., negative contango).

Forward Volatility

Forward volatility represents the implied volatility between two option expirations. It reflects the market’s expectation of volatility for the period following the earlier expiration—i.e., once the front option expires, the forward vol estimates the expected volatility over the remaining time until the second expiration.

Forward Factor

The forward factor compares near-term implied volatility to the forward volatility between that expiration and a longer-dated one. It helps identify whether short-term options are priced rich or cheap relative to expected future volatility.

Dashboard Charts

Volatility Dashboard

IV/RV Percentile vs. VRP Scatter Plot

This scatter plot illustrates the relationship between the current and previous Volatility Risk Premium (VRP) and the current and previous implied or realized volatility percentiles (IV or RV). Each point represents a snapshot in time, showing how changes in volatility percentiles relate to changes in VRP. This chart visualizes the relationship between the Volatility Risk Premium and the percentile rank of either implied or realized volatility. Each point shows the VRP at a given percentile of volatility, helping to explore how VRP behaves across different volatility regimes. Use this chart to identify whether certain percentile zones tend to correspond with elevated, neutral, or negative VRP — but keep in mind that patterns may vary across assets and timeframes. A low or high volatility percentile does not always result in a high or low VRP; the relationship is dynamic and can be influenced by market context, macro events, or structural risk pricing. This chart helps you understand how volatility conditions may influence the opportunity (or risk) in selling or buying volatility.

Strategy Backtest

This chart lets you explore how different strategies would have performed historically on this asset. This chart shows the historical performance of selected option strategies, helping you evaluate whether volatility is being consistently over- or underpriced by the market. You can backtest long or short calls, puts, straddles, or directional butterflies to assess edge across different market conditions. Long volatility strategies (long puts, calls, or straddles) tend to perform well when the market underestimates future volatility. Short volatility strategies (short straddles, short puts/calls) benefit when implied volatility is overpriced relative to realized moves — especially when VRP is elevated. This chart can also reveal directional imbalances: for example, if puts are consistently overpriced while calls are fairly priced or even underpriced, there may be tail hedging activity or bearish skew to exploit. In some cases, only one side of the distribution (e.g., downside) is mispriced, and directional butterflies or single-leg trades can help capture that edge. Use this chart to identify patterns in volatility mispricing and tailor your strategy accordingly.

Variance Risk Premium (VRP) Timeseries

This chart displays the historical variance risk premium (VRP) for the selected asset, highlighting periods where implied volatility diverged from realized volatility. This chart shows the historical evolution of the Variance Risk Premium (VRP), which measures the difference between implied and realized volatility over time. Positive VRP suggests the market is pricing in more volatility than is realized — a potential edge for volatility sellers. Negative VRP indicates the market underestimated realized volatility — often associated with sharp moves or event-driven risk. Importantly, low current VRP does not always mean low future VRP. In some environments, low VRP readings precede sustained periods of high VRP, making short volatility trades profitable even when entry VRP is modest. This chart helps identify whether the market tends to consistently overprice or underprice volatility, offering insight into when selling or buying volatility might have an edge — even if it goes against intuition.

Volatility Cone

This chart visualizes the volatility cone for the selected asset, showing the historical range (min, max, median, 25th and 75th percentiles) of implied or realized volatility over different time horizons, along with the current level for context. This chart shows how implied or realized volatility has behaved across different time horizons, using historical percentiles to form a “cone” of expected volatility ranges. The cone provides context for whether current IV or RV is high or low compared to historical norms. When IV is well above historical realized ranges, it may signal overpricing of options, potentially creating opportunities for volatility selling strategies. When IV is unusually low or below typical realized levels, it may indicate underpricing of risk, favoring long volatility positions. Similarly, comparing RV to its historical distribution can highlight how current market movement compares to past behavior. Use this chart to spot extremes in volatility pricing and assess whether current market conditions present an opportunity to fade or follow implied volatility expectations.

Multi-Expiry Skew

This chart shows normalized option skew across multiple time frames (10D to 1Y), normalized for call delta. The left side represents puts (ITM calls = OTM puts), and the right side represents calls, allowing for a clear comparison of relative pricing across the volatility surface. This chart displays option skew across multiple expiries, normalized by delta (with 100-delta on the left, representing puts, and 0-delta on the right, representing calls). It shows how the market is pricing relative risk across the strike spectrum — with steeper skew often indicating higher perceived tail risk on one side (e.g. downside protection demand). Comparing skew shapes across expiries helps identify where risk is being priced in — short-term vs. longer-term. Divergence in skew across timeframes can signal relative value opportunities, such as trading one expiry’s skew against another (calendar skew trades). This chart is especially useful for spotting asymmetry in sentiment or hedging behavior, such as persistent demand for puts or underpricing of calls. Use it to assess directional bias, tail pricing, and multi-expiry dislocations in the volatility surface.

Term Structure

This chart displays the term structure of implied and realized volatility across expirations ranging from 10 days to 1 year, helping visualize how volatility expectations evolve over time. This chart shows the term structure of both Implied Volatility (IV) and Realized Volatility (RV) across different time horizons, typically ranging from 10 days to 1 year. It helps contextualize how volatility is being priced by the market (IV) compared to what has actually occurred (RV) over those same time frames. When IV is consistently above RV across the curve, it may indicate volatility overpricing, favoring short vol strategies. When RV exceeds IV, especially in shorter tenors, it may suggest the market is underestimating realized movement, favoring long vol. Changes in the shape of the curve (e.g., upward sloping, flat, or inverted) can reflect shifts in market expectations, uncertainty, or upcoming events. Use this chart to assess where along the curve volatility is potentially mispriced, and to guide tenor selection for volatility trades.

Skew Timeseries

This chart tracks the evolution of option skew over time, with separate lines for call, put, and risk reversal (RR) skew. Call and put skew are calculated as ATM minus OTM volatility—more negative values indicate steeper skew. RR skew is measured as OTM put volatility minus OTM call volatility, highlighting relative demand between downside and upside protection. This chart tracks how option skew has evolved over time, with separate lines for call skew, put skew, and risk reversal (RR) skew: Call and put skew are measured as ATM minus OTM volatility — more negative values indicate steeper skew, reflecting higher demand for out-of-the-money options. Risk reversal (RR) skew, calculated as OTM put volatility minus OTM call volatility, shows the relative pricing of downside vs. upside risk. Use this chart to identify shifts in market sentiment or hedging behavior — for example, increasing negative RR skew may suggest rising demand for downside protection, while a flattening or positive RR may signal growing interest in upside exposure or reduced fear. It can also help spot persistent skew regimes or sudden shifts that may indicate upcoming volatility, directional bias, or trading opportunities.

Term Structure Slope Timeseries

This chart shows the historical slope of the volatility term structure between selected expiration pairs (e.g., 10D–30D, 30D–60D, 30D–90D), highlighting how short- and long-term volatility expectations have shifted over time. This chart tracks the historical slope of the volatility term structure between selected expiration pairs (e.g., 10D–30D, 30D–60D, 30D–90D). A positive slope indicates that longer-dated implied volatility is higher than shorter-dated IV — a typical upward-sloping curve reflecting rising uncertainty over time. A negative slope suggests short-term IV is elevated relative to longer-dated IV, often due to event-driven risk (e.g., earnings, macro data, geopolitical tension). Shifts in the slope over time can signal changes in market expectations, risk timing, or volatility regime transitions. Use this chart to identify periods of curve steepening or inversion, which may offer opportunities for calendar spreads, hedging decisions, or understanding how volatility risk is being priced across time.

Option Volume & PC Ratio

This chart displays option trading activity over time, including total volume and the put/call open interest ratio, providing insight into market sentiment and positioning. This chart shows option trading activity over time, combining total options volume with the put/call open interest (OI) ratio to help assess market sentiment and positioning. Volume spikes may indicate increased speculation, hedging, or reactions to events. The put/call OI ratio reflects sentiment: – A high ratio (>1) suggests elevated demand for puts relative to calls, often signaling bearish or hedging sentiment. – A low ratio (<1) indicates greater interest in calls, often associated with bullish positioning or risk appetite. Watching how this ratio moves alongside volume helps identify shifts in sentiment, crowded positioning, or potential contrarian signals when extremes are reached.

Forward Factors

This chart shows forward factor values across various expiration pairs as bars, highlighting how near-term implied volatility compares to forward volatility over subsequent periods. Higher bars indicate short-term vol is elevated relative to forward expectations, while lower bars suggest it's discounted. This chart displays forward factor values across different expiration pairs, visualized as bars, to help assess relative implied volatility between short- and longer-dated options. A higher forward factor means near-term implied volatility is elevated relative to forward-dated volatility, which may reflect event risk or front-loaded uncertainty. A lower forward factor indicates near-term volatility is discounted compared to future periods, suggesting calmer short-term expectations. This chart is especially useful for identifying calendar spread opportunities—buying or selling volatility based on perceived mispricing between expiries.

Forward Factor / Forward Volatility Timeseries

This chart tracks the time series of forward factors and corresponding forward volatilities between selected expiration pairs. It provides insight into how short-term implied volatility compares to future expected volatility over time, helping identify shifts in market sentiment and volatility regime changes. This chart shows how forward factors and their associated forward volatilities have evolved over time between selected expiration pairs. The forward factor tracks the ratio of short-term to forward-dated implied volatility, highlighting how near-term volatility is priced relative to future expectations. Rising forward factors suggest growing near-term concern or event risk, while falling values indicate reduced short-term volatility pricing relative to the forward curve. Forward volatility provides context by showing the absolute level of expected volatility between the expiries. Together, these trends help identify shifts in sentiment, volatility regime changes, and potential calendar trading opportunities based on how volatility expectations evolve over time.

Spot Ivol Correlation

This scatter plot visualizes the relationship between spot price changes and changes in implied volatility, with a line of best fit showing the overall correlation. It helps assess whether volatility tends to rise or fall with the underlying asset, indicating skew dynamics and market sentiment. This scatter plot shows the relationship between spot price changes and changes in implied volatility, with a trend line representing the overall spot/volatility correlation. A negative correlation (typical in equity indices) suggests that volatility rises as the asset falls, reflecting demand for downside protection and a put-skewed surface. A positive correlation indicates volatility tends to rise with price increases, often seen in commodities or assets with call skew or upside panic behavior. Understanding this dynamic is crucial when placing delta-neutral strategies, as spot/vol correlation influences how option deltas behave as spot moves: In a negatively correlated environment, put deltas become more sensitive (increase) as IV rises on a down move — pushing your trade off-neutral. Knowing this helps you adjust strikes to stay closer to true delta neutrality under expected vol dynamics. Use this chart to anticipate skew behavior, inform strike selection, and manage exposure more precisely in volatility-sensitive trades.

Relative Value Dashboard

Relative Term Structures

This chart compares the volatility term structures of two assets across multiple expirations, displaying each curve individually along with the computed difference. It highlights relative richness or cheapness in implied volatility between the two assets over time. This chart compares the volatility term structures of two assets across multiple expirations, plotting each asset’s implied volatility curve along with the difference between them. It reveals where one asset’s implied volatility is richer or cheaper than the other at specific maturities. A positive spread indicates that Asset A has higher implied volatility than Asset B at that tenor, suggesting potential overpricing. A negative spread implies Asset B’s volatility is higher, pointing to relative richness on that side. This chart is useful for identifying relative value opportunities, such as pair calendars or vol arbitrage strategies across assets or related ETFs.

Relative Skews

This chart compares the option skew of two assets for a selected expiration, showing each asset’s skew curve across deltas and the difference between them. It highlights relative differences in upside vs downside pricing, useful for identifying divergence in sentiment or hedging demand. This chart compares the option skew curves of two assets for a selected expiration, plotting each asset’s skew across strike deltas along with the difference between them. It highlights where one asset shows greater demand for downside or upside protection relative to the other. Differences in skew shape or steepness can reveal divergent market sentiment, hedging pressure, or tail risk pricing between the two assets. Use this chart to spot relative sentiment shifts, volatility surface dislocations, or to construct relative value trades targeting skew divergence.

IV Scatter

This scatter plot compares the implied volatility of two assets, with each point representing a matched observation. The chart includes quantile regression lines for the median, 25th, and 75th percentiles, providing a more complete view of the relationship across the distribution. This helps identify not only the central tendency but also asymmetries or shifts in the IV relationship under different market conditions. This scatter plot compares the implied volatility (IV) of two assets, with each point representing a simultaneous observation. It includes quantile regression lines (median, 25th, and 75th percentiles) to show how the IV relationship behaves across different parts of the distribution. The median line reflects the typical relationship between the two assets’ IVs. The 25th and 75th percentile lines highlight how the relationship shifts under different volatility regimes, revealing asymmetries or non-linear behavior. Deviations from these trendlines can signal when one asset’s IV is relatively overextended or underpriced, offering potential mean-reversion or relative value trading opportunities. Use this chart to: Spot divergences that may revert over time. Identify stretched volatility conditions in one asset relative to another. Build volatility spread trades (e.g., long vol in one asset, short in the other) based on dislocations in their historical IV relationship.

RV Scatter

This scatter plot compares the realized volatility of two assets, with each point representing a matched observation. The chart includes quantile regression lines for the median, 25th, and 75th percentiles, providing a more complete view of the relationship across the distribution. This helps identify not only the central tendency but also asymmetries or shifts in the RV relationship under different market conditions. This scatter plot compares the realized volatility (RV) of two assets over matching time windows, with each point representing a simultaneous observation. The chart includes quantile regression lines for the median, 25th, and 75th percentiles, offering a detailed view of their historical volatility relationship. The median line shows the typical RV relationship between the two assets. The 25th and 75th percentile lines capture how this relationship shifts in different market environments, revealing potential asymmetries or nonlinear dynamics. When one asset’s RV deviates significantly from the typical range relative to the other, it may signal a temporary dislocation or structural divergence. This chart can be used to: Identify relative volatility dislocations that may correct over time. Understand how each asset reacts to different market regimes. Build relative value trades based on realized vol divergence (e.g., pairs trading with vol targeting or risk parity adjustments).

VRP Scatter

This scatter plot compares the variance risk premium (VRP) between two assets, with each point representing a matched observation. Quantile regression lines for the median, 25th, and 75th percentiles are included to show how the relationship behaves across different parts of the distribution, helping identify relative richness, dispersion, and regime-dependent dynamics in implied vs realized volatility pricing. This scatter plot compares the variance risk premium (VRP = IV² – RV²) between two assets, with each point representing a simultaneous observation. It includes quantile regression lines (median, 25th, and 75th percentiles) to show how the relationship behaves across the full distribution. The median line reflects the typical relative pricing of volatility risk between the two assets. The 25th and 75th percentile lines highlight how this relationship varies across different volatility regimes, capturing asymmetries, dispersion, or tail behavior. Deviations from these bands may indicate when one asset's VRP is relatively rich or cheap, offering insight into mispricing of implied vs. realized volatility. Use this chart to: Identify relative VRP dislocations and trading opportunities. Monitor how market stress or complacency impacts each asset differently. Inform volatility spread, dispersion, or mean-reversion trades based on regime-dependent behavior in volatility risk pricing.

Comparison Analysis (Custom)

This chart lets you plot any two variables against each other over time, using either their ratio or difference. The time series includes a mean and standard deviation bands to highlight deviations from typical behavior. A histogram is also provided to visualize the distribution and assess normality, helping identify anomalies, mean-reversion potential, or structural shifts. This chart lets you plot any two variables (e.g., skew, IV, RV, VRP) for a pair of assets, using either their ratio or difference over time. The time series view includes a rolling mean and standard deviation bands, helping you assess when the relationship deviates from its typical range. The accompanying histogram visualizes the distribution, allowing you to evaluate normality, skewness, and the frequency of outliers. This chart can be used to: Identify anomalies or temporary dislocations between related metrics. Spot mean-reversion opportunities when the spread or ratio moves beyond historical norms. Detect structural shifts in relationships that may reflect changes in market behavior or sentiment. It’s a flexible tool for uncovering relative value trades or monitoring the stability of statistical relationships across a wide range of volatility, skew, or pricing metrics.

Return Correlation

This scatter plot shows the relationship between the returns of two assets, with each point representing a matched return observation. A correlation line is overlaid to highlight the strength and direction of their return correlation, helping identify co-movement or regime shifts. This scatter plot shows the relationship between the returns of two assets, with each point representing a matched return observation. A correlation line is overlaid to indicate the strength and direction of their return relationship. A strong positive correlation suggests the assets tend to move together, supporting relative value volatility strategies like pairs trading or spread trading. A negative correlation may indicate hedging potential or divergent behavior useful in market-neutral positioning. Observing changes in the correlation over time can signal regime shifts, breakdowns in relationships, or emerging opportunities. Use this chart to establish return-based relationships that inform relative value trade setups, risk management, or diversification decisions.

Rolling Return Correlation

This chart displays the rolling correlation of returns between two assets over time. Users can customize both the return period (e.g., daily, weekly) and the rolling window length. It helps track how asset relationships evolve, revealing shifts in co-movement, diversification effectiveness, or changing market regimes. This chart shows the rolling correlation of returns between two assets over time. Users can adjust the return interval (e.g., daily, weekly) and the rolling window to suit different time horizons and strategies. It helps visualize how the relationship between asset returns evolves, highlighting periods of stronger or weaker co-movement. Sudden shifts or sustained changes in correlation can indicate regime changes, structural breaks, or changes in market sentiment. Use this chart to monitor pair stability, guide relative value trade timing, or refine portfolio hedging and allocation strategies.

Directional Dashboard

Stock Chart

This chart shows the stock’s price action over time, overlaid with the 200-day moving average. The moving average helps identify long-term trends, support/resistance levels, and potential momentum shifts. This chart displays the stock’s price action over time, overlaid with its 200-day moving average (200DMA) to highlight long-term trends. The 200DMA acts as a key indicator of overall market sentiment, often used to identify bullish or bearish regimes. When price is above the 200DMA, it suggests upward momentum and potential support; when below, it may signal downward pressure or resistance. Crossovers and sustained deviations from the 200DMA can indicate trend shifts, helping inform timing for entries, exits, or position adjustments. Use this chart to assess the stock’s trend strength, identify technical support/resistance, and monitor for momentum reversals.

Cross-Sectional Momentum

This chart displays the cross-sectional momentum rank of the selected ticker, shown as a decile (1 to 10) relative to its peers—stocks are ranked against other stocks, and ETFs against other ETFs. Higher deciles indicate stronger recent performance compared to the group, helping identify leaders and laggards within each asset class. This chart shows the momentum rank of the selected ticker as a decile (1 to 10) relative to its peer group— stocks are ranked against other stocks, and ETFs against other ETFs. A higher decile (8–10) indicates the asset has shown strong recent performance compared to its peers, potentially signaling leadership or trend strength. A lower decile (1–3) suggests underperformance, highlighting laggards that may be avoided or considered for mean-reversion trades. Use this chart to identify relative momentum within an asset class, support rotation strategies, or monitor changes in leadership dynamics over time.

Timeseries Momentum

This chart shows the time series momentum of the selected asset, measuring its own past return over a defined period. Positive values indicate upward momentum, while negative values reflect downward trends, helping assess recent performance in isolation. This chart displays the time series momentum of the selected asset, calculated based on its own historical return over a defined lookback period. Positive values indicate the asset has experienced upward momentum, suggesting recent strength and potential trend continuation. Negative values reflect downward momentum, signaling recent weakness or a bearish trend. This chart helps assess the asset’s recent performance in isolation, supporting trend-following strategies, entry timing, or momentum-based filtering in strategy design.

Relative Momentum

This chart measures the relative momentum of the selected ticker by comparing its return over a defined period to that of the broad market (SPY). Values above 1 indicate the asset is outperforming SPY, while values below 1 suggest underperformance. This chart measures the asset’s relative momentum by comparing its return over a defined period to that of the broad market (SPY). A value above 1 indicates the asset is outperforming SPY, suggesting relative strength. A value below 1 signals underperformance relative to the market benchmark. Use this chart to identify leaders and laggards versus the index, support relative strength strategies, or time rotations into outperforming assets.

Sector Momentum

This chart shows the time series momentum of a specific sector, reflecting its own return over a selected period. Positive values indicate upward momentum within the sector, while negative values suggest recent weakness or downward trends. This chart displays the time series momentum of a specific sector, based on its own return over a defined period. Positive values indicate the sector has upward momentum, signaling strength and potential trend continuation. Negative values reflect downward momentum, suggesting recent underperformance or weakness within the sector. Use this chart to identify sector trends or support sector rotation strategies.

Turn Over

This chart shows the stock’s turnover, calculated as trading volume divided by shares outstanding. This chart shows the turnover ratio, calculated as trading volume divided by shares outstanding, which measures how actively a stock is being traded relative to its size. Higher values indicate greater liquidity, strong investor interest, and potentially heightened short-term sentiment. Lower values may signal reduced attention, thin trading, or weaker conviction from market participants. Research has shown that high turnover is often associated with continued momentum, as increased trading activity can reflect institutional positioning, retail interest, or news-driven moves that carry forward in the short term. Use this chart to identify high-conviction moves, monitor liquidity conditions, and support momentum-based trade selection.

Proximity to 52 Week High

This chart shows how far the current stock price is from its 52-week high, expressed as a percentage. It helps gauge strength or weakness relative to the asset’s yearly peak—values near 0% indicate the stock is trading close to its high, while larger values suggest it's further below. This chart shows the percentage distance between the current stock price and its 52-week high. Values near 0% indicate the stock is trading close to its yearly peak, often a sign of strong positive momentum and potential leadership. Larger percentages reflect that the stock is trading well below its high, which may signal weakness, underperformance, or recovery potential. Stocks closer to their 52-week highs have been shown to exhibit stronger forward momentum, making this a useful signal for trend-following or relative strength strategies.

Risk Neutral and Historical Distributions

This chart compares two distributions of expected returns: one derived from the current option skew using the risk-neutral density, and the other from historical spot return simulations under the current volatility regime. The comparison highlights differences between market-implied expectations and historically observed behavior. This chart compares two distributions of expected returns: The risk-neutral distribution, derived from current option skew, reflects the market’s implied expectations. The historical distribution is based on simulated spot returns under the current realized volatility regime, capturing what has occurred in similar past environments. By comparing these, you can identify where market-implied expectations deviate from historical norms: If the risk-neutral tail (e.g., downside) is significantly more pronounced than the historical distribution, it may signal overpriced tail risk—a potential opportunity to sell directional volatility (e.g., puts). Conversely, if the market is underpricing one side, it may indicate cheap directional vol, presenting a case for long vol exposure. Use this chart to guide directional volatility trades, especially when assessing tail skew, sentiment extremes, or structural mispricings in index or single-stock options.

PEAD

The Post-Earnings Announcement Drift (PEAD) Score ranges from -2 to 2, indicating the strength and direction of price momentum following earnings. A score of 2 signals strong positive drift, -2 indicates strong negative drift, and 0 suggests a neutral or weak reaction, helping identify potential continuation trades after earnings events. This chart displays the Post-Earnings Announcement Drift (PEAD) Score, which ranges from -2 to 2, measuring the strength and direction of price momentum following an earnings release. A score of 2 indicates strong positive drift, suggesting the stock tends to continue rising after earnings beats. A score of -2 reflects strong negative drift, pointing to potential continued weakness after earnings misses. A score near 0 implies a neutral or inconsistent post-earnings reaction. Use this chart to identify stocks with a history of momentum continuation after earnings, supporting post-earnings long or short setups based on the direction and strength of the drift.

Skew Signal

The Skew Score ranges from 0 to 100, measuring the steepness and recent changes in option skew. A score near 100 indicates a very bullish skew profile, while a score near 0 reflects a strongly bearish skew, helping gauge market sentiment and positioning across the volatility surface. The Skew Score ranges from 0 to 100 and quantifies both the steepness and recent changes in the option skew curve. A score near 100 indicates a bullish skew profile, often driven by increased demand for calls or reduced downside hedging, signaling optimistic market sentiment. A score near 0 reflects a bearish skew, typically associated with elevated put demand and downside protection buying, suggesting risk-off sentiment. This signal helps gauge positioning across the volatility surface, aiding in the timing of directional trades, skew-based strategies, or identifying sentiment extremes that may precede reversals.

Earnings Dashboard

Implied vs Realized Moves

This chart compares historical implied moves (based on pre-earnings option pricing) with the actual realized moves following earnings. It helps identify whether earnings moves have historically been overpriced or underpriced by the options market. This chart compares the historical implied moves (derived from pre-earnings option pricing) to the actual realized price moves following earnings announcements. If implied moves consistently exceed realized moves, the options market has overpriced earnings volatility, suggesting a potential edge in short volatility strategies (e.g., selling straddles or strangles). If realized moves often exceed implied moves, the market has underpriced earnings risk, favoring long volatility strategies (e.g., buying options ahead of earnings). Use this chart to assess whether it has historically been more profitable to buy or sell options around earnings, helping guide volatility positioning and risk management.

Cumulative Returns

This chart shows the cumulative returns of long straddle, call, and put strategies initiated the day before earnings and closed the day after. It helps evaluate the historical profitability of directional and volatility-based trades around earnings events. This chart tracks the cumulative returns of long straddle, long call, and long put strategies entered the day before earnings and exited the day after. If returns are consistently negative, it suggests that options tend to be overpriced into earnings, making short volatility strategies (e.g., selling straddles or single legs) potentially more profitable. If one side consistently outperforms (e.g., calls or puts), it may indicate a directional bias—allowing for strategies like selling the losing side and buying the winning side. A positive trend in straddle returns suggests that realized moves tend to exceed implied expectations, favoring long volatility trades. Use this chart to assess the historical edge around earnings events and determine whether to favor volatility selling, directional plays, or long vol strategies.

Pre vs Post IV

This chart displays implied volatility levels before and after earnings announcements, highlighting the magnitude of the typical IV crush that occurs as event uncertainty is resolved. It helps assess how much option premiums deflate post-earnings. This chart shows implied volatility (IV) levels before and after earnings announcements, capturing the typical IV crush that occurs once the event risk is resolved. A sharp drop in IV post-earnings reflects how quickly option premiums deflate, often eroding the value of long option positions regardless of direction. The magnitude of the IV crush helps assess the risk/reward of holding options through earnings, particularly for straddles, strangles, or long directional plays. Use this chart to evaluate how aggressively the market prices in earnings uncertainty, and whether it may be more advantageous to sell options into elevated IV or avoid holding premium-rich positions through the announcement.

Implied Move Timeseries

This chart tracks the implied move around earnings over time, showing how market expectations for post-earnings price movement have evolved. This chart tracks the implied move priced into options ahead of earnings over the past quarter, reflecting how the market’s expectations for post-earnings volatility have changed over time. Rising implied moves suggest increasing uncertainty or anticipated impact from upcoming earnings, often due to changing fundamentals or macro conditions. Falling implied moves indicate reduced expected volatility, possibly reflecting more predictable results or lower investor interest. Use this chart to assess shifts in sentiment, gauge whether the current implied move is elevated or muted relative to recent history, and time volatility trades around earnings accordingly.

Seasonal Move Boxplot

This boxplot visualizes the distribution of realized price moves following earnings, grouped by calendar quarter. It highlights how post-earnings volatility varies seasonally showing medians and interquartile ranges for each quarter. This chart shows a boxplot of realized price moves following earnings, grouped by calendar quarter, highlighting how post-earnings volatility varies seasonally. Each box represents the distribution of realized moves for that quarter, with the median and interquartile range. By comparing these historical realized moves to current implied moves, you can assess the degree of mispricing—whether the market is overestimating or underestimating earnings volatility for a given quarter. Use this chart to spot seasonal patterns in earnings reactions and evaluate whether the current implied move aligns with typical realized behavior, helping guide volatility trading decisions.

Backtest / Research

Position Construction Methodology

Comprehensive Pre-Built Position Library

We provide an extensive library of pre-built option positions covering diverse strategy types and Days-To-Expiration (DTE), designed to target all market scenarios—bullish, neutral, and bearish. Each position is initiated precisely at or within 5% of its advertised DTE when exact DTE isn't available, ensuring consistency and realistic timing.

Powerful Cross-Sectional Backtesting

Unlike other platforms that limit backtests to single tickers, we offer powerful no-code cross-sectional backtesting across the entire universe of tickers. This approach significantly increases statistical validity by using a substantially larger dataset. However, for targeted analysis, you can easily customize your backtests to include a single ticker or any specific combination of tickers you prefer.

Realistic Trade Management and Exit Strategies

Currently, all our pre-built positions are held to expiration. While we acknowledge that traders often prefer take-profit or stop-loss exits based on signals or other indicators, implementing such dynamic exit rules on a large cross-sectional dataset would require extensive computational resources. Our development team is actively working on adding customizable exit conditions. Existing subscribers will gain early access to this highly anticipated feature at no additional cost as soon as it becomes available. Nevertheless, holding positions to expiration often serves as an excellent proxy for measuring actual returns and evaluating signal effectiveness. It frequently offers a more realistic reflection of true performance compared to optimized or data-mined exit points. Real-world trading ultimately involves personal discretion; our platform empowers you to objectively assess predictive signals and apply your intuitive judgment to manage positions effectively, closing early if your initial trade rationale becomes invalid.

Unmatched Historical Data Without Survivorship Bias

We offer comprehensive historical data from 2007 (over 18 years) captured as near end-of-day snapshots (taken 15 minutes before market close). Critically, our dataset includes all delisted tickers that previously had options, eliminating survivorship bias entirely. As far as we are aware, no other retail platform provides such extensive and unbiased historical data.

Institutional-Level Backtesting Capabilities

Our advanced backtesting environment and rich data infrastructure offer capabilities comparable to institutional trading desks. By leveraging our platform, you gain professional-grade tools for rigorously validating and refining your trade ideas, ultimately enhancing your decision-making and profitability potential.

Liquidity

Realistic Fill Price Modeling

Options are rarely filled at the mid-price. Our proprietary model estimates realistic fill prices by accounting for stock and option volume, bid-ask spread width, and historical trade data—specifically, how far trades typically deviate from quoted mids. The model also incorporates liquidity, recognizing that further OTM options are harder to fill near mid. For example, if an option is quoted $1.00 x $2.00, a realistic fill might be $1.60–$1.80 when buying, or $1.20–$1.40 when selling. Our model has been rigorously tested and leans conservative—ensuring backtests reflect real-world trading friction, not theoretical fills.

Capacity Estimation and Liquidity Constraints

We calculate a capacity metric that estimates the maximum number of contracts you could trade without materially impacting the price. This is derived from stock and option volume, spread width, and actual trade behavior. Liquidity is a key component—OTM and illiquid options carry lower capacity, which limits scalability. For smaller or less liquid stocks, this constraint becomes significant, making it harder to capitalize on mispricings at scale. Our model accurately reflects this real-world limitation so your strategy backtests aren’t just profitable on paper—they're executable in practice.

Conservative Commission Modeling

We use a worst-case commission model based on Interactive Brokers' fee structure to ensure realistic backtesting.

Premium < $0.05 → $0.25 per contract
$0.05 ≤ Premium < $0.10 → $0.50 per contract
Premium ≥ $0.10 → $0.65 per contract This conservative approach means your actual trading commissions will likely be lower than those shown—helping prevent overestimation of strategy performance.

Realistic Margin Estimation

We estimate margin requirements using REG-T margin rules, following Interactive Brokers' standard for options. Unlike many platforms that ignore margin entirely, we provide detailed margin data—enabling you to evaluate not just raw returns, but return on margin. This is critical for identifying the most margin-efficient strategies and making informed, capital-efficient trading decisions.

Signal Research

Explore and Customize Nearly 300 Signals

Our platform gives you access to nearly 300 pre-built signals covering a wide range of factors—including underlying asset characteristics, broad market indicators, implied and realized volatility metrics, relative value measures, and much more. This comprehensive signal library empowers you to dive deep into what actually drives returns, helping you isolate the predictive signals that matter most. With our robust analytics tools, you can visualize relationships through scatter plots (signal vs. return), analyze return distributions by signal decile, inspect histograms of signal, and perform detailed regression analysis. Whether you're testing one factor or multiple, you’ll gain real, actionable insights into signal behavior and performance. But we’re not stopping there. Our development team is building a powerful signal construction engine that will let you mix and match existing signals—applying transformations like log, exponential, square root, and combining them through arithmetic operations (+, −, ×, ÷). This will unlock the ability to create an almost infinite number of custom signals tailored to your specific strategy or thesis. Ultimately, this functionality will allow you to build your own alpha-generating signal library—one that adapts to different market conditions and evolves with your trading intuition.

Model Building

Rule Based

The rule-based model allows you to manually define parameter thresholds for trade entry. This approach is ideal if you have strong domain knowledge or predefined criteria for trade setups. However, in most cases, statistical models—such as regressions—tend to outperform manual rules by identifying optimal entry points automatically through data-driven methods.

Linear Regression

This model fits a linear regression to the selected input features and provides a predicted return for each potential trade. Two configurations are available:

Threshold-Based Entry: Specify a minimum predicted return required to enter a position.
Intercept Control: Choose whether to fit the model’s intercept or fix it at zero, depending on your assumptions about the base return. This model is useful for identifying directional edges based on continuous return forecast.

Logistic Regression

The logistic regression model fits a binary classifier to the selected features and estimates the probability of a trade exceeding a specified return threshold. Key settings include:

Minimum Return (Training Class Threshold): Define the return level that distinguishes a "positive" trade. By default, this is set to 0, meaning the model classifies trades with returns > 0 as positive.
Entry Probability Threshold: Set the minimum predicted probability required to enter a trade.
Intercept Control: Choose whether to fit or fix the model’s intercept. This model is ideal for classifying trade setups as favorable or unfavorable based on the likelihood of exceeding a target return.

Backtest

This platform empowers you to deeply analyze your trading strategies through two powerful tools: traditional backtesting. With traditional backtesting, you can run your strategy through historical data to generate a single deterministic outcome—ideal for understanding how your signals would have performed over a fixed historical period. To ensure your strategy is truly delivering alpha, we also provide a benchmark strategy—buy-and-hold—so you can compare your performance side by side. Knowing that you're consistently outperforming passive or alternative strategies gives you the confidence to stick with your system through real-world volatility. Start testing with clarity. Trade with conviction.

Common Backtest Problems

No Trades Taken

Typically this will mean either your entry dataset was empty (meaning your model selected zero trades) or you are sizing too small and therefore no trades are taken.