https://github.com/dor-rondel/crypto-trading-multiagent-system
A multi-wallet, multi-chain, automated agentic crypto trading platform simulated on testnets
https://github.com/dor-rondel/crypto-trading-multiagent-system
agentkit avalanche-avax ethereum langraph solana sqlite
Last synced: about 13 hours ago
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A multi-wallet, multi-chain, automated agentic crypto trading platform simulated on testnets
- Host: GitHub
- URL: https://github.com/dor-rondel/crypto-trading-multiagent-system
- Owner: dor-rondel
- Created: 2026-06-09T08:14:35.000Z (26 days ago)
- Default Branch: main
- Last Pushed: 2026-07-01T20:01:41.000Z (3 days ago)
- Last Synced: 2026-07-01T22:05:53.532Z (3 days ago)
- Topics: agentkit, avalanche-avax, ethereum, langraph, solana, sqlite
- Language: Python
- Homepage:
- Size: 502 KB
- Stars: 1
- Watchers: 0
- Forks: 0
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
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README
# Agentic Testnet Trading System
## Overview
This project is an event-driven, agentic cryptocurrency trading simulation platform built for test networks.
The system executes simulated trading strategies against:
- **Solana Devnet** (via `solana-py`)
- **Ethereum Sepolia** (via `AgentKit/CDP`)
- **Avalanche Fuji** (via `AgentKit/CDP`)
Real market data is used to generate trading signals, while all trade execution occurs on testnets using a **three-wallet USDC strategy** to avoid risking real capital.
---
## Goals
- Evaluate AI-generated trading strategies safely
- Simulate multi-chain portfolio management
- Test autonomous trading workflows
- Support long-running blockchain operations
- Maintain reproducibility and auditability
---
## Core Technologies
### AI / Workflow
- LangGraph
- LangChain
- **Groq** (via LangChain-Groq)
- **LangSmith** (for observability)
- **Coinbase AgentKit** (for EVM execution)
### Blockchain
- **Solana Python SDK** (`solana-py`)
- **Coinbase CDP SDK**
- **web3.py** (for deterministic validations)
### Infrastructure
- Python 3.12+
- **SQLite (aiosqlite)** for persistent trade tracking
---
## Architecture
### Multi-Agent Parallel Workflow
The system uses a decoupled LangGraph architecture to separate reasoning from execution. It utilizes a parallel fan-out structure where specialized subagents provide context to a central aggregator.
#### Workflow Nodes
- **`research_spawner`**: The entry point that fans out into parallel analysis nodes.
- **Analyst Nodes (`gas`, `news`, `trend`, `performance`, `liquidity`, `correlation`, `whale`, `volatility`)**: Specialized subagents that run in parallel to analyze network fees, macro sentiment, technical indicators, portfolio PnL, pool liquidity, BTC correlations, on-chain whale movements, and market volatility, respectively.
- **`aggregator`**: Consumes reports from all specialized subagents to generate a final, high-conviction `TradePlan`.
- **`validator`**: A deterministic node that enforces balance constraints, maximum trade limits, and slippage guardrails.
- **`executor`**: Dispatches validated actions to chain-specific wallet adapters and records them in the database.
### Background Services
- **Market Watcher (`src/services/market_watcher.py`):** Aggregates price snapshots and triggers the agent loop.
- **Transaction Monitor (`src/services/transaction_monitor.py`):** A parallel service that polls the blockchain to update the status of `PENDING` trades in SQLite.
---
## Design Principles
### Stateless Non-Blocking Execution
To handle flaky testnets, the agent loop completes immediately after submitting a transaction. The `TransactionMonitor` handles the asynchronous confirmation, allowing the agent to stay responsive to new signals.
### Singleton Wallet Management
The `WalletManager` is a singleton ensuring that wallet keys and initialized providers are shared across the application, preventing redundant initialization and race conditions.
### SDK Thread Isolation
Calls to loop-heavy SDKs (like Coinbase AgentKit) are offloaded to separate threads using `asyncio.to_thread` to prevent event loop conflicts.
### Centralized Persistence
All SQL queries are centralized in `src/persistence/queries.py` and use parameterized queries to prevent injection from LLM-generated rationale strings.
---
## Wallet Management & Capital
### Three-Wallet Strategy
The system maintains three distinct wallets, one for each supported chain.
- Each wallet uses **USDC** as its base "bank" currency.
- **Gas Requirements:** Each wallet must be funded with a small amount of the chain's native testnet token (SOL, ETH, or AVAX) to cover gas fees for swaps.
- Trades are simulated by swapping USDC for the target asset and back.
### Initialization: Two-Stage Polling
1. **Stage 1: Funding Poll:** The main script polls for native/USDC balances. It will wait indefinitely until at least one wallet is funded. Instructions are provided in `WALLETS.md`.
2. **Stage 2: Market Poll:** Once funds are detected, the system enters its active loop, polling market data providers for signals to trigger the Aggregator Agent.
---
## Development
### Prerequisites
- Python 3.12+
- [uv](https://github.com/astral-sh/uv)
- `libffi-dev` (required for `cffi` build)
### Install
```bash
# Install dependencies and setup virtual environment
make install
```
### Visualization
Generate a visual map of the trading graph:
```bash
make graph
```
### Run Full System
```bash
uv run python -m src.workflows.main
```
### Run Quality Checks
```bash
# Formatter, Ruff, Pylint (10/10), Mypy, and Pytest
make check
```