Forgd Market Maker Leaderboard - How it works

Executive Summary

Forgd’s Market Maker Leaderboard is built upon one of the largest structured datasets of market maker activity in the digital asset industry.

Unlike traditional market data providers that observe only public exchange activity, Forgd combines first-party market maker data transmitted through direct API integrations with independently collected exchange and blockchain data to measure both absolute market conditions and the specific contribution of individual market makers.

Today, the platform monitors nearly 500 active market maker engagements across more than 40 market makers, with daily data collection covering centralized and decentralized exchanges worldwide. This methodology enables Forgd to benchmark market makers using standardized performance metrics while preserving the confidentiality of underlying client engagements.

Methodology Overview

The Forgd methodology is built on three complementary data systems: Arctic, Torrent, and Market Maker Analytics. Each is described below.

Arctic

Arctic is Forgd’s institutional asset intelligence platform. It aggregates market data, tokenomics, fundraising history, investor allocations, emissions schedules, exchange listings, and on-chain information across approximately 1,000 blockchain projects into a normalized dataset.

Torrent

Torrent is Forgd’s real-time market microstructure engine. Through direct WebSocket connections and on-chain analytics, Torrent continuously captures:

• Full order books
• Top-of-book spreads
• L1 and L2 depth
• Buy and sell pressure
• Funding rates
• Open interest
• Liquidations

across major centralized and decentralized exchanges. This provides Forgd with an independent, normalized view of overall market behavior.

Market Maker Analytics

The third system captures engagement-specific data directly from participating market makers. For every monitored engagement, Forgd collects structured daily reporting including:

Trading Activity

• Maker fill volume
• Taker fill volume
• Top-of-book spreads
• Liquidity depth within standardized basis-point thresholds

Commercial Structure

• Engagement type
• Loan balances
• Stablecoin balances
• Option premiums
• Monthly retainers
• Interest rates
• Tranche structures
• Contract tenor

Performance Requirements

• Contractual KPI targets
• Required spreads
• Required depth
• Required uptime
• Venue coverage

Data is standardized on a per-project, per-exchange, and per-trading-pair basis, creating a consistent framework for benchmarking market maker performance.

First-Party Data Collection via API

Forgd collects market maker activity through direct API integrations with participating market makers. Each participating market maker automatically transmits structured datasets from its internal trading infrastructure to Forgd on a recurring basis. These datasets include metrics such as maker and taker fill volume, liquidity provision, top-of-book spreads, liquidity depth across standardized basis-point thresholds, engagement structure, commercial terms, and contractual performance obligations. It is important to acknowledge that this is first-party data originating from the market maker itself; however, this should not be confused with manual reporting or questionnaire-based disclosures.

Rather than relying on employees to manually enter statistics into spreadsheets or web forms, Forgd consumes machine-generated data through standardized API integrations directly from the systems market makers use to monitor their own trading activity. This significantly improves consistency, reduces operational friction, and enables standardized reporting across hundreds of active engagements.

Importantly, many of the datasets collected—including commercial terms, loan balances, maker-versus-taker attribution, and contractual KPIs—cannot be inferred from public exchange data. These datasets necessarily originate from the market makers themselves and are delivered to Forgd through these API integrations.

Independent Market Validation

Receiving first-party data via API is only one component of Forgd’s methodology. Every API submission is contextualized against independent market-wide datasets collected through Arctic and Torrent.

Using direct WebSocket connections to centralized exchanges and on-chain analytics across decentralized venues, Forgd independently measures:

• Total exchange volume
• Total order book depth
• Top-of-book spreads
• Open interest
• Funding rates
• Liquidity distribution
• Overall market behavior

By simultaneously observing both the market maker’s reported activity and the complete market environment, Forgd evaluates market maker performance relative to broader market conditions rather than treating reported metrics as standalone performance indicators.

The API explains what the market maker did. Arctic and Torrent independently measure what the market looked like.

This combination allows Forgd to calculate:

• Market maker contribution as a percentage of total liquidity
• Share of total executed volume
• Relative spread competitiveness
• Liquidity efficiency
• Consistency over time

rather than simply recording raw activity.

Detection of Market Abnormalities

Independent market telemetry also enables Forgd to identify behaviors that would not be apparent from first-party reporting alone. Examples include:

• Flash liquidity
• Spoofing behavior
• Excessive taker activity
• Sudden liquidity withdrawal
• Abnormal spread widening
• Inconsistent market participation

These observations provide additional context when evaluating engagement quality and long-term market maker performance.

Statistical Methodology

Forgd provides its market maker monitoring platform free of charge to blockchain projects. This business model enables broad industry adoption and allows the platform to aggregate nearly 500 live engagements spanning more than 40 market makers.

For public benchmarking:

• Individual projects remain anonymous
• Engagement-specific commercial information remains confidential
• Counterparty relationships remain private

Performance is instead aggregated across all tracked engagements for each market maker to produce standardized industry benchmarks.

As Forgd continues to expand adoption, the statistical robustness of the leaderboard improves through larger sample sizes, broader market coverage, and more diverse market conditions.

Why This Dataset Is Unique

Traditional market data providers can observe public markets. Market makers can observe only their own activity. Blockchain projects can observe only their own engagements. Forgd combines all three perspectives into a single analytics framework.

Machine-generated first-party data delivered through API integrations provides insight into proprietary trading activity and engagement structure, while Arctic and Torrent independently capture the broader market environment. Together, these datasets enable Forgd to evaluate not simply how markets performed, but how individual market makers contributed to those outcomes.

APPENDIX: Illustrative Example: From Raw API Data to Market Maker-Level Benchmarking

The following hypothetical example illustrates how first-party data received through a market maker API integration is normalized and transformed before contributing to Forgd’s market maker-level analytics.

For simplicity, assume a hypothetical market maker, Helix Markets, has three active engagements:

• Atlas Protocol: Binance ATLAS/USDT and Coinbase ATLAS/USD
• Nova Network: OKX NOVA/USDT, Bybit NOVA/USDT, and Bitget NOVA/USDT
• Meridian Chain: Binance MER/USDT, KuCoin MER/USDT, Gate MER/USDT, MEXC MER/USDT, and Kraken MER/USD

For each project, the market maker transmits daily trading activity on a per-exchange and per-trading-pair basis. In this simplified example, each record contains two metrics: maker fill volume and bid-side depth within -50 basis points. In production, Forgd captures additional fields and independently contextualizes the submitted information against market-wide data collected through Arctic and Torrent.

All figures below are hypothetical and included solely to illustrate the aggregation methodology. Fill volume is shown in USD millions and liquidity depth in USD thousands.

Step 1 — Raw Data Received Through the Market Maker API

At the ingestion layer, Forgd preserves the granularity of the market maker’s reporting. An engagement trading across five venues therefore generates five distinct observations for each daily reporting period rather than a single project-level statistic. Three hypothetical engagements are shown below. Each project trades on one additional venue where the market maker recorded no activity, allowing market share calculations to include the full observable market.

Engagement 1 — Atlas Protocol

Engagement 2 — Nova Network

Engagement 3 — Meridian Chain

Step 2 — Normalize and Aggregate Each Project

The next transformation removes exchange and trading-pair fragmentation for metrics intended to be evaluated at the project level. Exchange-level observations are aggregated to the project level. Absolute market maker activity and total market activity are summed independently before market share is calculated.

For example, Atlas Protocol’s August 3 submission contains:

• MM Maker Fill: $1.80M + $1.96M + $0.00M = $3.76M
• Total Market Fill: $4.20M + $4.60M + $1.80M = $10.60M
• Maker Fill Share: $3.76M ÷ $10.60M = 35.5%
• MM Depth: $194K + $246K + $0K = $440K
• Total Market Depth: $410K + $520K + $220K = $1.15M
• Depth Share: $440K ÷ $1.15M = 38.3%

The same calculation is applied to each project for each reporting period.

Atlas Protocol — Daily Project Aggregation

Nova Network — Daily Project Aggregation

Meridian Chain — Daily Project Aggregation

This transformation is important because the number of venues on which an engagement operates varies materially. Forgd retains the underlying venue-level observations for deeper analysis but also creates a standardized project-level representation of the market maker’s activity.

Step 3 — Calculate the Representative Performance of Each Engagement

For this simplified three-day example, the daily project-level observations are averaged to establish a representative daily value for each engagement.

At this stage, each engagement has been reduced to the same analytical unit regardless of whether the underlying project trades across two venues or five.

Importantly, Forgd does not simply combine every trading-pair observation across the market maker’s portfolio into one dataset. Doing so could cause an engagement with more trading pairs to disproportionately influence the market maker’s aggregate statistics. Instead, aggregation occurs first at the project level.

Step 4 — Aggregate Across the Market Maker’s Portfolio

Finally, Forgd takes a simple average across the market maker’s individual project engagements. Using the three hypothetical engagements above:

This final step gives each underlying project engagement equal weight in the market maker-level aggregation rather than allowing a project with more exchanges, trading pairs, or observations to mechanically dominate the result.

The same process can be applied across the much larger sample of engagements monitored by Forgd. Individual project identities and engagement relationships remain confidential, while standardized project-level observations can be aggregated to evaluate a market maker’s performance across its broader portfolio. This methodology is consistent with Forgd’s approach of aggregating performance across tracked engagements while preserving the confidentiality of individual projects and counterparties.