Amadeus

An AI-driven trading bot for fresh Solana memecoins. It discovers new pairs from on-chain data, scores them against declarative policies, and executes paper or live trades through a deterministic pipeline — with backtesting, per-policy analytics, and optional LLM agents that propose strategy changes for human review.

Node.js 24 · TypeScript · PostgreSQL · Redis · React · MCP

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⚠️ Read this first

This software places real trades with real money. It can and will lose funds.

• Experimental and unaudited. Written for one person’s use, published as-is. There is no warranty of any kind, and no guarantee it does what this README claims.
• Memecoin trading is close to worst-case. Tokens minted minutes ago, thin liquidity, rug pulls, honeypots, and sandwich attacks are the normal operating environment, not edge cases. Most strategies lose money net of fees and slippage.
• Not financial advice. Nothing here is a recommendation to trade anything. You are solely responsible for any funds you put at risk and for complying with the law wherever you are.
• Live trading is off by default and stays off until you set two separate environment variables. Start on paper. Then backtest. Then paper again.
• It needs your private key to sign transactions. Read Security before you put a key anywhere near this.

If you are looking for a turnkey profitable bot, this is not that. It is a framework for expressing and testing strategies, where the strategy is yours and so is the outcome.

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Contents

• How it works
• Quickstart
• Configuration
• Running the stack
• Workers
• Policies, accounts, deployments
• Security
• Known limitations
• MCP server
• UI
• Operational scripts
• Development
• License

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How it works

Data flows one way through a chain of independent workers, each reading what the previous one wrote to Postgres:

  pumpswap-pairs ──────────> pairs              new pools, from a program log subscription
  pumpswap-vault-swap ─────> pair_swap_events   buys/sells, from vault account subscriptions
  chain-snapshots ─────────> pair_snapshots     price, liquidity, volume, holders, authorities
  scoring ─────────────────> pair_scores        policy filters + weighted features -> a signal
  execution ───────────────> trade_decisions    entries, after risk and limit checks
  exit ────────────────────> trades             closes on stop / target / time / rug / collapse

Alongside that: policy-analytics rolls events into hourly buckets, backtest replays history against a policy, and account-balance keeps live wallet balances fresh.

Everything observable is a table, so every decision is auditable after the fact — why a