ConvexValue vs Quant Data: Configurable Research and Exposure Workflows

The choice starts with the workspace you want to maintain

ConvexValue and Quant Data both offer options-flow and exposure research. ConvexValue’s defining feature is the way its terminal is composed: modules become panes, commands describe their settings, and compatible views can follow a shared symbol. Quant Data emphasizes connected dashboards and a documented API for querying options and equity analytics.

That is a difference in working method rather than a simple contrast between advanced and basic tools. A visual researcher may prefer a ConvexValue layout that keeps a scanner, transaction view and term curve visible together. An analyst building repeatable interval studies may give more weight to Quant Data’s endpoint definitions and aggregation rules.

The most useful evaluation is a complete research task. Choose a symbol, expiration horizon and required output, then see how much configuration is needed to produce and reproduce it. A dense screen is valuable when each pane answers part of that task.

At a glance

Table of contents

ConvexValue makes the layout an explicit part of the process

The official terminal overview describes a desktop application for macOS, Windows and Linux, with individual modules also accessible in a browser. A command selects a module and configures its view; control panels provide the same settings without requiring command memorization.

This matters when a research setup needs to be repeated. A compact command records more of the configuration than a screenshot alone, while saved workspaces keep related panes together. The initial investment is deciding which views belong side by side and which parameters should remain fixed.

The global-symbol guide explains a specific linking mechanism: symbol-aware commands using the shared symbol follow the selected underlying. Clicking a stock in the flow scanner can change that global selection. The linking scope can apply to the current dashboard or across the workspace.

However, not every module has identical symbol behavior. ConvexValue’s time-and-sales module uses its own roots filter. A useful first check is therefore whether the intended pane actually follows the global selection or requires an independent filter. That avoids interpreting a coherent-looking layout that is showing different underlyings.

Flow ratios and dollar series answer different questions

ConvexValue’s parameter reference defines its premium flow ratio as call-buy plus put-sell value divided by put-buy plus call-sell value. The volume version substitutes contract counts. Both are ratios between opposing groups, so their neutral reference is 1.

A ratio of 2 means the numerator is twice the denominator under that definition. It does not establish twice the directional exposure, and a small denominator can create a dramatic reading on limited activity. The scanner becomes more useful when a ratio is examined beside premium, volume and the relevant contract universe.

Quant Data’s Net Drift instead returns call and put premium and volume for selected time buckets. A cumulative curve is formed from those interval observations. The question is how activity develops through time, not simply which stock has the largest ratio at one moment.

These tools can complement each other without being numerically comparable. ConvexValue can identify an underlying worth examining through relative activity. Quant Data can organize an aligned expiration cohort into a time-series study. First specify the units and selected trade set; matching bullish-looking colors is not a substitute for that alignment.

Trace an aggregate back to contracts

ConvexValue’s time-and-sales documentation lists individual option trades with the contract, execution price, contemporaneous bid and ask, size, premium, aggressor classification and Greeks. Filters can select underlyings, size, side and other fields.

This supports an explicit drill-down: a stock rises in the scanner, then the tape shows which contracts and executions contributed to the observation. Its parameter reference also distinguishes Greeks captured at the trade moment from fields describing a contract’s current state. Summed traded sensitivity and a current-chain exposure snapshot should therefore remain separate research objects.

Quant Data’s consolidated-flow endpoint organizes blocks, splits and sweeps, with comprising executions available when requested. This is useful when the question concerns the relationship between a grouped event and the individual prints behind it.

One view lists individual transactions; another groups eligible executions for readability. Compare underlying activity by contract and time window rather than row counts. Neither an aggressor label nor grouping alone establishes the trader’s full strategy or remaining position.

A dedicated hedging map differs from a Greek snapshot

ConvexValue’s map module is explicitly SPX-only. It estimates how options-related hedging pressure varies across price and time through the next close. The grid combines a hypothetical price path dimension with the passage of time; its colors describe the model’s estimated pressure at those combinations.

That is a more specific object than a generic gamma histogram. The model is useful when the question is how estimated pressure could change if SPX reaches a price later in the session. Broader stock-options coverage elsewhere in the terminal does not make the map available for every stock.

Quant Data’s Exposure By Strike endpoint organizes a selected ticker’s Greek-weighted exposure by expiration and strike. It requires the Greek mode and representation, with delta, gamma, vanna and charm supported. The representation can be raw, per one-dollar move or per one-percent move.

A map’s color and an exposure bar’s height cannot be compared as if they shared a unit. First establish whether the study needs a modeled price-time surface or a pinned exposure snapshot. For the latter, retain the snapshot time, expiration set, Greek and scale so a later change has an interpretable cause.

Volatility deserves its own pane and horizon

ConvexValue’s term-structure module compares IV across expirations and can place the current curve beside prior-day curves. A local rise around one expiration can prompt investigation of an event within that horizon.

Quant Data also provides volatility research around its flow and exposure tools. The important workflow question is whether the activity being examined belongs to the same expiration interval as the volatility observation. An all-expiration flow total and a front-expiration IV move may describe different participants and events.

Consider an earnings event. Increased call activity beyond earnings can coexist with elevated event-expiration IV and little activity in the nearest contract. The aggregate bullish interpretation becomes more specific only after the expiration distribution is understood. That distinction is often more useful than adding another market-wide sentiment total.

A worked example: a scanner candidate with a misleading headline ratio

Suppose a stock appears near the top of a call-oriented activity scan. You want to know whether the observation reflects meaningful near-term participation or a small denominator and a longer-dated trade.

In ConvexValue, include both the selected ratio and absolute activity columns. Select the underlying, confirm that linked symbol-aware panes update, then filter time and sales to that stock. Identify the expirations and transactions producing the ranking. Open term structure to see how those dates relate to the current and prior curves.

In Quant Data, request or display consolidated flow for the same symbol and expiration cohort. Add Net Drift with a matching session window. Compare the relevant exposure snapshot only after selecting its Greek and representation. This creates a record of the activity through time and the structural context at a particular moment.

The useful conclusion might be that the headline ratio was dominated by longer-dated contracts, while the nearest expiration had little comparable activity. That is a narrower and more testable observation than calling the entire stock bullish. Either platform can improve the research when it makes the underlying composition easier to inspect.

Updating and history need a reproducible definition

ConvexValue describes live analytics, but modules expose different fields: transactions, current quotes, Greek updates and prior-session observations. The parameter reference distinguishes timestamps for relevant events. A workspace should preserve those distinctions rather than treating everything visible on one screen as simultaneous.

Quant Data’s API gives an explicit way to choose sessions, intervals and exposure snapshots. Net Drift adds an implementation detail that matters for research: the latest bucket remains mutable while trades enter it. When fetching again, recompute the cumulative series from the buckets rather than appending a revised bucket a second time.

A fair comparison therefore checks the selected window and the data’s observation time, not just how often the screen redraws. For historical work, verify available lookback and the particular fields needed before designing a study around them.

Permissions, subscription structure and learning investment

ConvexValue’s terminal offering advertises its modules together, with monthly and annual billing choices. The useful access question is whether the intended module covers the instrument and history required. Desktop operating-system compatibility and the difference between a full workspace and a single browser module should also be tested.

Quant Data markets dashboard access and a separate API product. The latter documents endpoint access, request capacity and historical data for programmatic workflows. Subscriber classification and personal-use terms are part of the purchase decision; dashboard access should not be assumed to supply the credentials or allowances required by a custom application.

The learning costs follow those designs. ConvexValue rewards understanding parameter names, pane composition and symbol-link behavior, even though controls are available for users who prefer clicking. Quant Data rewards understanding data schemas, filters and units, particularly when the same research must be reproduced outside the dashboard.

A combined subscription is most sensible when the terminal supplies a visual process you repeatedly use and the API supplies a separate, well-defined dataset. Paying for two interfaces to watch the same unfiltered tape adds less value than completing one coherent workflow.

Frequently asked questions

Does ConvexValue’s SPX map work for an individual stock?

No. Its map documentation explicitly limits it to SPX. Other chain and flow modules have broader coverage. Choose the module by the analytical question rather than assuming every tool shares the terminal’s full symbol universe.

Is a ConvexValue flow ratio of 1 equivalent to no trading?

No. It is the neutral balance between the numerator and denominator groups. Both sides can be very active. Inspect absolute premium or contract volume alongside the ratio.

Will every ConvexValue pane change when I select a stock?

Only the views configured to follow the global symbol do so. Check the supported modules and the command settings. The tape’s roots filter deserves a separate check when moving between underlyings.

Should I choose Quant Data mainly for its API?

Choose it for a specific endpoint-driven task: historical flow queries, aligned premium buckets or pinned exposure snapshots. Confirm the required fields, filters, history and permissions first. A purely visual routine may be evaluated through the dashboard without building an API workflow.

Related product guides

• ConvexValue: terminal modules and linked research
• Quant Data: flow, Net Drift and exposure

Official sources

• ConvexValue terminal, desktop access and membership
• ConvexValue parameter definitions and flow ratios
• ConvexValue time and sales
• ConvexValue global-symbol behavior
• ConvexValue term structure
• ConvexValue SPX hedging map
• Quant Data consolidated flow
• Quant Data Net Drift aggregation
• Quant Data exposure modes and snapshot selection
• Quant Data API access and terms