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Alpha-1 (A1) Score

The Alpha-1 (A1) Score is a proprietary metric developed by AlphaArc to assess the relevance and importance of wallets, tokens, and transactions within the Solana blockchain ecosystem. It serves as a quantitative measure that highlights patterns, trends, and behaviors with significant authentic activity and analytical or actionable value.

Problem​

The A1 Score was designed to solve a critical problem in blockchain analysis: separating authentic market activity from manipulation. Wash trading, bot-driven metrics, and vanity statistics like inflated buy/sell counts and fake volume are often used in market manipulation schemes and rug pulls.

These deceptive practices obscure genuine signals, making it difficult for both humans and AI agents to discern meaningful trends.

AlphaArc’s approach to the A1 Score introduces a data-driven way to evaluate market activity without relying solely on human intuition or threat actor-controlled metrics. By creating a “market state” that reflects the interests of real participants, the A1 Score filters out noise and highlights truly relevant activity.

Authenticity​

The challenge becomes clear when you consider the limitations of AI agents:

Agents don’t inherently understand memes, vibes, or market nuances. They can’t rely on threat actor-controlled metrics either - like buy/sell counts or transaction volume, which are often manipulated by bots.

While some approaches use trust scores (e.g., if someone recommends a coin and its price rises, the recommender gains trust), this shifts the hard problem of token vetting entirely to human judgment.

Dataset Construction​

  1. Building the Market State:
    • Start by identifying all token holders for a given project.
    • For each holder, retrieve all other tokens they own.
    • Rank those tokens by popularity.
    • Get the holders of the most popular tokens, recursively expanding the audience.
  2. Refining the Audience:
    • By repeating this process across the top 1,000 tokens, a comprehensive market audience is built.
    • The top 300,000 wallets—those holding a significant share of these tokens—form the core market audience.
  3. Key Benefits:
    • Filters Out:
      • Bots (e.g., fresh wallets created for manipulation) or participants without genuine interest in popular tokens.
      • Inexperienced traders who might act on hype rather than understanding.
      • Noise from insignificant or short-term trends.
    • Includes:
      • Influential participants (e.g., seasoned traders and market leaders).
      • Patterns tied to broader market interest rather than isolated schemes.

Applying the A1 Score​

Once the market state is established, the A1 Score evaluates tokens and trading activity within this context:

  1. Authentic Interest:
    • Checks how many participants from the market audience are holding or trading a given token.
    • Tokens with significant engagement from this audience receive higher scores.
  2. Confidence in Patterns:
    • The A1 Score incorporates confidence metrics and a real-time market state to determine relevance.
    • Popularity scores are balanced with deeper analysis, such as:
    • Mint and freeze authority.
    • Swap data, including price, volume, and unique buyers.
  3. Agent Integration:

Agents, such as CatCafe, use the A1 Score to surface patterns for human review. Notable findings are presented as insights or notifications (e.g., tweets or alerts).

Why It Works​

The recursive approach behind the A1 Score achieves several key goals:

  • It creates an unbiased, data-driven representation of market interest.
  • It resists manipulation by excluding accounts and metrics likely influenced by wash trading or shilling.
  • It includes only seasoned traders and influencers, ensuring the focus remains on tokens with genuine market traction.

Example in Action​

Consider a new token launch:

  1. The A1 Score evaluates the token by analyzing how many wallets from the known market audience are interacting with it.
  2. If influential or seasoned traders show interest, the token’s A1 Score rises, signaling its relevance.
  3. The agent then analyzes audit data, swap metrics, and trading behavior to provide actionable insights.

Example Output (e.g., Tweet by CatCafe Agent): “Token XYZ is gaining traction among key market participants. Price up 15%, with significant activity from high-ranking wallets.”

Conclusion The A1 Score bridges the gap between human intuition and data-driven analysis. By constructing a robust market state and filtering out manipulation, it provides agents with a way to prioritize authentic activity. This ensures that the insights generated by AlphaArc’s platform are both relevant and actionable, empowering users to navigate Solana’s dynamic ecosystem with confidence.