The Trading Score

The Trading Score is a quantitative signal that evaluates the technical and behavioral properties of a token on the Solana blockchain to determine its current market health and execution viability. It is one of three core outputs of xFractal’s Scoring Engine, and plays a critical role in guiding user research, agentic reasoning, execution filtering, and predictive modeling.

Unlike price-based indicators or raw liquidity metrics, the Trading Score synthesizes multiple dimensions of on-chain data—including volume dynamics, volatility patterns, trading behaviors, and liquidity stability—into a single standardized signal. This score enables real-time assessment of whether a token is technically tradable under current market conditions, and how it compares to other assets within the Solana ecosystem.

Objectives and Function

The Trading Score was designed to address four primary needs:

Execution Feasibility: Determine if a token can be traded efficiently, with minimal slippage or liquidity risk.
Market Momentum: Assess whether a token is experiencing organic volume growth, directional bias, or exhaustion.
Microstructure Health: Evaluate transaction behavior, liquidity lock status, and order book symmetry.
Behavioral Filtering: Identify abnormal trading patterns such as inorganic activity, transaction clustering, or sniper behavior.

These insights are critical in the context of high-frequency trading environments like Solana, where price trajectories can change within minutes and liquidity conditions shift rapidly.

Data Sources and Ingestion

The Trading Score aggregates real-time on-chain data through several indexed and non-indexed data pipelines. Key providers and endpoints include:

Mobula, Moralis, Helius: Real-time token metadata, transactional history, and pool-level liquidity data.
Birdeye, Dexscreener, Raydium, Pumpfun, Jupiter: Market volume, trading pairs, routing paths, price impact, and liquidity distribution.
Internal Indexers and Session-Scoped Monitors: Wallet clustering, LP provider tracking, sniper detection, and slippage risk assessment.

Data ingestion occurs through a hybrid architecture using websockets, RPC endpoints, and REST APIs to ensure complete coverage and redundancy.

Example of $ALCH's Trading Score Dashboard:

PreviousThe Hype Score NextThe Safety Score

Last updated 3 days ago

The Trading Score

Objectives and Function

The Trading Score was designed to address four primary needs:

Execution Feasibility: Determine if a token can be traded efficiently, with minimal slippage or liquidity risk.
Market Momentum: Assess whether a token is experiencing organic volume growth, directional bias, or exhaustion.
Microstructure Health: Evaluate transaction behavior, liquidity lock status, and order book symmetry.
Behavioral Filtering: Identify abnormal trading patterns such as inorganic activity, transaction clustering, or sniper behavior.

These insights are critical in the context of high-frequency trading environments like Solana, where price trajectories can change within minutes and liquidity conditions shift rapidly.

Data Sources and Ingestion

The Trading Score aggregates real-time on-chain data through several indexed and non-indexed data pipelines. Key providers and endpoints include:

Mobula, Moralis, Helius: Real-time token metadata, transactional history, and pool-level liquidity data.
Birdeye, Dexscreener, Raydium, Pumpfun, Jupiter: Market volume, trading pairs, routing paths, price impact, and liquidity distribution.
Internal Indexers and Session-Scoped Monitors: Wallet clustering, LP provider tracking, sniper detection, and slippage risk assessment.

Data ingestion occurs through a hybrid architecture using websockets, RPC endpoints, and REST APIs to ensure complete coverage and redundancy.

Example of $ALCH's Trading Score Dashboard:

PreviousThe Hype Score NextThe Safety Score

Last updated 3 days ago