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Computes a Base-100 rebased index of token holder-address density (holders per million of market cap) across a series of observations, anchored to a chosen base row.

Calculates the correlation between token holder-address density (holders per million USD market cap) and provided benchmark values for a supplied set of token observations.

Computes a distribution histogram of token holder-address density (holders per million USD of market cap) from supplied token observations, grouped into configurable bins.

Computes a statistical distribution summary (quantiles, spread) for token holder-address density (holders per million USD market cap) across a supplied batch of token observations.

Computes IQR-based outlier flags for token holder-address density (holders per million USD of market cap) across a batch of supplied token observations.

Computes the local finite-change elasticity of token holder-address density (holders per million) given a baseline and a step size, returning a deterministic elasticity report.

Computes a one-input sensitivity table for token holder-address density (holders per million market cap) by varying either holder count or market cap across a supplied range of values.

Computes an ordered trend analysis of token holder-address density (holders per million USD of market cap) over a supplied time series of observations.

Calculates the token holder-address density (holders per million USD of market cap) given a token's holder count and market capitalization.

Generates a two-input scenario grid (stress test) for token holder-address density (holders per million market cap), varying both holder count and market cap across user-defined value ranges.

Computes a weighted mean of token holder-address density (holders per million USD market cap) across multiple token observations supplied by the caller.

Validates a CSV table of token holder observations by checking that category values fall within a user-defined allowlist of permitted categories.

Computes per-column descriptive statistics for a CSV dataset of token holder observations, validating structure and data quality up to 1,000 rows.

Validates a CSV of token holder observations and returns a report identifying exact duplicate rows, up to 1000 rows.

Validates a CSV of token holder observations and exports a report of rows that fail declared contract rules (range, category, length constraints).

Validates a CSV table of token holder observations (up to 1000 rows) and returns a located error report identifying cell-level data issues.

Validates numeric columns in a token holder observations CSV table by checking that all values fall within a specified minimum/maximum range.

Validates that all numeric columns in a token holder observations CSV conform to expected numeric types, range bounds, and allowed categories.

Validates that required cells in a token holder observations CSV are non-empty and conform to declared constraints, returning a report of violations.

Validates a CSV table of token holder observations by detecting spreadsheet formula injection flags, supporting up to 1000 rows with configurable delimiter, value bounds, and allowed category lists.

Validates a CSV table of token holder observations (up to 1000 rows) and returns a structured validation summary report.

Validates a CSV of token holder observation data and exports only the rows that pass all declared contract rules, up to 1000 rows.

Computes period-over-period changes in net token supply flow (issued minus burned) across a sequence of observations.

Computes a base-100 rebased index of net token supply flow (issued minus burned) across a series of observations, normalizing values relative to a chosen base period.

Computes and compares token net supply (issued minus burned) for two time snapshots, returning a deterministic before-and-after delta report.

Generates a distribution histogram of net token supply flow (issued minus burned) across a configurable number of bins for a supplied dataset of token observations.

Computes a statistical distribution summary of token net supply (issued minus burned) across a series of observations

Detects IQR-based statistical outliers in token net supply flow (issued minus burned) across a batch of token observations

Compares named what-if scenarios for token net supply (issued minus burned) against a baseline, returning deterministic verdicts for each scenario.

Analyzes the ordered trend of token net supply flow (issued minus burned tokens) across a time series of observations

Ranks records by net token supply flow (issued minus burned tokens) across a batch of observations

Computes a rolling mean of net token supply flow (issued minus burned) over a sliding window across a time series of token observations.

Generates a 2D scenario grid showing net token supply (issued minus burned) across combinations of issued and burned token values

Computes a weighted mean of net token supply flow (issued minus burned) across multiple token observations.

Checks on-chain data to assess the status and viability of a token launch against live consensus state

Checks whether a token has been launched or is eligible for launch on the AmanChain DEX, using live on-chain consensus state

Batch-calculates token liquidation participation scenarios for multiple positions, estimating liquidation timelines based on position size, daily volume, and participation rate.

Computes and compares token liquidation participation metrics (days-to-exit estimate) for two scenarios — a 'before' state and an 'after' state — based on caller-supplied position size, daily volume, and participation rate.

Computes a deterministic distribution summary for token liquidation participation scenarios given position size, daily volume, and participation percentage inputs.

Computes local finite-change elasticity for a token liquidation participation scenario given position size, daily volume, and participation rate inputs.

Compares named scenarios for token liquidation time estimation based on position size, daily volume, and participation rate overrides against a baseline.

Runs a one-input sensitivity table over token liquidation participation scenarios, varying one factor (position size, daily volume, or participation rate) across a range of values against a fixed baseline.

Ranks a set of token liquidation participation records by computing deterministic liquidation time scores based on position size, daily volume, and participation percentage.

Computes a sequential (waterfall) attribution of how changes in position size, daily volume, and participation rate each contribute to a shift in estimated token liquidation time.

Calculates the target gap for a token liquidation participation scenario given position size, daily volume, and participation percentage inputs.

Computes a deterministic two-dimensional scenario grid showing token liquidation time estimates across combinations of position size, daily volume, and participation percentage.

Computes a time-weighted mean of token liquidation participation metrics across multiple position/volume/participation scenarios using caller-supplied weights.

Calculates the relative percentage change in token liquidity between two adjacent time periods for one or more token observations.

Computes a base-100 rebase index for token liquidity relative change across a batch of supplied observations, anchored to a chosen base row.

Compares token liquidity relative change between two time windows (before and after) and returns a deterministic percentage-change verdict.

Computes the Pearson (or similar) correlation between token liquidity relative changes and a set of caller-supplied benchmark values, returning a deterministic bounded report.

Computes a distribution histogram of relative liquidity changes across a batch of token observations, bucketed into a configurable number of bins.

Computes a statistical distribution summary of relative liquidity changes across a batch of token observations (current vs. previous liquidity pairs).

Computes a one-input sensitivity table showing how token liquidity relative change varies as a single input parameter (current or previous liquidity) sweeps across a set of supplied values against a baseline.

Analyzes an ordered sequence of token liquidity observations to compute relative change trends across multiple time points.

Ranks a batch of token observations by their relative liquidity change (current vs previous), returning a deterministic record-level ranking report.

Computes a rolling mean of token liquidity relative change (current vs previous liquidity) over a sliding window across a series of token observations.

Calculates the relative change (percentage or ratio) in token liquidity between a previous and current liquidity value for a single computation

Calculates the target gap for token liquidity relative change given current and previous liquidity values and a user-specified target threshold.

Classifies whether the relative change in token liquidity between two periods falls within a caller-specified threshold band (lower/upper bounds).

Generates a two-input scenario grid computing token liquidity relative change across combinations of current and previous liquidity values.

Computes a weighted mean of relative liquidity changes across multiple token observations by comparing current vs. previous liquidity values.

Calculates a base-100 rebase index of token liquidity-to-market-cap ratios across a series of supplied observations, anchored to a chosen base row.

Calculates the liquidity-to-market-cap ratio for a batch of up to 200 token observations, returning a deterministic report for each row.

Computes and compares a token's liquidity-to-market-cap ratio across two time snapshots, returning a deterministic before/after verdict.

Calculates the correlation between token liquidity-to-market-cap ratios and a set of caller-supplied benchmark values, returning a deterministic statistical report.

Computes a distribution histogram of token liquidity-to-market-cap ratios from supplied token observations, bucketed into user-defined bins.

Computes a statistical distribution summary of token liquidity-to-market-cap ratios across a supplied batch of token observations.

Computes IQR-based outlier flags on token liquidity-to-market-cap ratios for a supplied batch of token observations

Computes the local finite-change elasticity of token liquidity relative to market cap given supplied token observations and an optional step percentage.

Compares named token liquidity-to-market-cap ratio scenarios against a baseline, returning a deterministic verdict for each labeled scenario.

Computes a one-input sensitivity table showing how the token liquidity-to-market-cap ratio changes as either liquidity_usd or market_cap_usd varies across a supplied range of values.

Computes an ordered trend analysis of the liquidity-to-market-cap ratio across a sequence of token observations, returning a deterministic analytical report.

Ranks a batch of token observations by their liquidity-to-market-cap ratio using a deterministic calculation engine.

Computes a rolling mean of token liquidity-to-market-cap ratios over a sliding window for a supplied sequence of token observations.

Computes a sequential input-change attribution (waterfall) report showing how changes in liquidity_usd and market_cap_usd drive changes in the token liquidity-to-market-cap ratio.

Generates a two-dimensional scenario grid comparing token liquidity (USD) against market cap (USD) across user-supplied value combinations to compute deterministic liquidity-to-cap ratios or threshold verdicts.

Computes a weighted arithmetic mean of liquidity-to-market-cap ratios across a supplied set of token observations using caller-specified weights.

Computes the net buy-count balance for a token and returns a deterministic before-and-after comparison report given two sets of buy/sell count observations.

Calculates the net buy-count balance (buy count minus sell count ratio) for a single token observation given buy and sell count inputs.

Generates a two-input scenario grid (stress test) for token net buy-count balance across combinations of buy_count and sell_count values.

Calculates price change metrics between adjacent observation periods for a batch of token price pairs

Compares two sets of token price observations (before and after) and returns a deterministic report of the price change between them.

Computes the statistical correlation between observed token price changes (current vs previous price pairs) and a supplied set of benchmark values.

Computes a distribution histogram of observed token price changes from a set of before/after price pairs, bucketed into a configurable number of bins.

Computes a statistical distribution summary of observed price changes across a batch of token price pairs (current vs previous price).

Flags statistical outliers in a batch of token price-change observations using interquartile range (IQR) analysis.

Computes the local finite-change elasticity of a token's observed price change given two price observations and a step size.

Analyzes an ordered sequence of token price-change observations to identify directional trends using a deterministic algorithm.

Ranks a supplied list of token price-change observations by their observed price change, returning a deterministic record-ranking report.

Computes a rolling mean of token price change ratios across a supplied window of current/previous price observation pairs

Performs sequential input-change (waterfall) attribution to explain how changes in token price inputs contribute to an observed price change between two states.

Calculates the observed percentage or absolute price change for a token given a current price and a previous price, returning a bounded single-calculation report.

Calculates the gap between a token's observed price change and a user-supplied target threshold, returning a deterministic verdict report.

Classifies whether a token's observed price change falls within a caller-specified lower/upper threshold range, returning a deterministic verdict for each supplied token price pair.

Generates a two-input scenario grid showing how token observed price change varies across combinations of current_price and previous_price values.

Computes a weighted mean of observed price-change ratios across multiple token price observations.

Validates calendar date fields in a CSV table of token pool observations, checking up to 1000 rows for date format and range compliance.

Validates that all category/group values in a CSV table of token pool observations belong to a declared allowlist of permitted categories.

Validates a CSV of token pool observations and returns per-column statistics such as min, max, nulls, and category distributions.