EvoTraders

Visit the EvoTraders website EvoTraders is an open-source financial trading agent framework that builds a trading system capable of continuous learning and evolution in real markets through multi-agent collaboration and memory systems.

Before trading with real funds, please conduct thorough testing and risk assessment. Past performance does not guarantee future returns. Investment involves risks — please make decisions with caution.

Core Features

Multi-Agent Collaborative Trading A team of 6 members — including 4 specialized analyst roles (fundamentals, technical, sentiment, valuation), a portfolio manager, and a risk management agent — collaborating to make decisions like a real trading team. Continuous Learning and Evolution Based on the ReMe memory framework, agents reflect and summarize after each trade, preserving experience across rounds and forming unique investment methodologies. Through this design, agents gradually develop their own trading styles and decision preferences rather than producing one-time random inferences. Real-Time Market Trading Supports real-time market data integration with both backtesting mode and live trading mode, allowing agents to learn and make decisions amid real market fluctuations. Visualized Trading Information Observe agents’ analysis processes, communication records, and decision evolution in real-time, with complete tracking of return curves and analyst performance.

Quick Start

Installation

# Clone repository
git clone https://github.com/agentscope-ai/agentscope-samples
cd agentscope-samples/EvoTraders

# Install dependencies (recommended: uv)
uv pip install -e .
# or: pip install -e .

# Configure environment variables
cp env.template .env

Edit the .env file and fill in your API keys. The following variables are required:

# Finance data API
# At minimum, FINANCIAL_DATASETS_API_KEY is required (FIN_DATA_SOURCE=financial_datasets)
# Recommended: add FINNHUB_API_KEY (FIN_DATA_SOURCE=finnhub)
# Live mode requires FINNHUB_API_KEY
FIN_DATA_SOURCE=          # finnhub or financial_datasets
FINANCIAL_DATASETS_API_KEY=   # Required
FINNHUB_API_KEY=              # Optional

# LLM API for agents
OPENAI_API_KEY=
OPENAI_BASE_URL=
MODEL_NAME=qwen3-max-preview

# LLM & embedding API for memory
MEMORY_API_KEY=

Running

Backtest Mode:

evotraders backtest --start 2025-11-01 --end 2025-12-01
evotraders backtest --start 2025-11-01 --end 2025-12-01 --enable-memory  # with memory

If you don’t have market data API keys and just want to try the backtest demo, download the offline dataset:

wget "https://agentscope-open.oss-cn-beijing.aliyuncs.com/ret_data.zip"
unzip ret_data.zip -d backend/data

The archive includes basic stock price data so you can run the backtest demo out of the box. Live Trading:

evotraders live                    # Run immediately (default)
evotraders live --enable-memory    # Run with memory enabled
evotraders live --mock             # Mock mode for testing
evotraders live -t 22:30           # Run daily at 22:30 local time (auto-converts to NYSE timezone)

CLI Help:

evotraders --help           # Global CLI help
evotraders backtest --help  # Backtest mode parameters
evotraders live --help      # Live/mock mode parameters

Launch the Visualization Interface:

# Ensure npm is installed; then run:
evotraders frontend   # Connects to port 8765 by default
                      # Modify ./frontend/env.local to change the port

Visit http://localhost:5173/ to open the trading room. Select a date and click Run or Replay to observe the decision-making process.

System Architecture

Agent Design

Analyst Team

Role	Focus
Fundamentals Analyst	Financial health, profitability, growth quality
Technical Analyst	Price trends, technical indicators, momentum analysis
Sentiment Analyst	Market sentiment, news sentiment, insider trading
Valuation Analyst	DCF, residual income, EV/EBITDA

Decision Layer

Portfolio Manager — Integrates analysis signals from all analysts, executes communication strategies, and combines analyst/team historical performance with recent investment memories and long-term experience to make final decisions.
Risk Management — Monitors real-time price and volatility, enforces position limits, and issues multi-layer risk warnings.

Decision Process

Real-time Market Data → Independent Analysis → Intelligent Communication (1v1 / 1vN / NvN)
    → Decision Execution → Performance Evaluation → Learning & Evolution (Memory Update)

Each trading day progresses through five stages:

Analysis

Each agent independently analyzes the market using their respective tools and historical experience.

Communication

Agents exchange views through private chats, broadcasts, and group meetings.

Decision

The portfolio manager makes a comprehensive judgment and issues final trades.

Evaluation

Performance Charts — Track portfolio return curves against benchmark strategies (equal-weighted, market-cap weighted, momentum).
Analyst Rankings — View win rates across bull and bear markets to identify top contributors.
Statistics — Detailed position and trading history for in-depth analysis.

Review

Agents reflect on decisions and summarize experiences based on actual returns, storing insights in the ReMe memory framework for continuous improvement.

Module Support

Module	Project
Agent Framework	AgentScope
Memory System	ReMe
LLM Support	OpenAI, DeepSeek, Qwen, Moonshot, Zhipu AI, and more

Custom Configuration

Custom Analyst Roles

Step 1. Register the role in ./backend/agents/prompts/analyst/personas.yaml:

comprehensive_analyst:
  name: "Comprehensive Analyst"
  focus:
    - ...
  preferred_tools:   # Select flexibly based on situation
  description: |
    As a comprehensive analyst ...

Step 2. Add the role definition in ./backend/config/constants.py:

ANALYST_TYPES = {
    "comprehensive_analyst": {
        "display_name": "Comprehensive Analyst",
        "agent_id": "comprehensive_analyst",
        "description": "Uses LLM to intelligently select analysis tools, performs comprehensive analysis",
        "order": 15
    }
}

Step 3. (Optional) Register the role in the frontend configuration at ./frontend/src/config/constants.js:

export const AGENTS = [
  {
    id: "comprehensive_analyst",
    name: "Comprehensive Analyst",
    role: "Comprehensive Analyst",
    avatar: `${ASSET_BASE_URL}/...`,
    colors: { bg: '#F9FDFF', text: '#1565C0', accent: '#1565C0' }
  }
]

Custom Models

Configure the model used by each agent in your .env file:

AGENT_SENTIMENT_ANALYST_MODEL_NAME=qwen3-max-preview
AGENT_FUNDAMENTALS_ANALYST_MODEL_NAME=deepseek-chat
AGENT_TECHNICAL_ANALYST_MODEL_NAME=glm-4-plus
AGENT_VALUATION_ANALYST_MODEL_NAME=moonshot-v1-32k

Get Started

Tutorial

Out-of-box Agents

Basic Concepts

Building Blocks

Observe & Evaluate

Tune Agent

Deploy & Serve

Others

Core Features

Quick Start

Installation

Running

System Architecture

Agent Design

Decision Process

Module Support

Custom Configuration

Custom Analyst Roles

Custom Models

​Core Features

​Quick Start

​Installation

​Running

​System Architecture

​Agent Design

​Decision Process

​Module Support

​Custom Configuration

​Custom Analyst Roles

​Custom Models

Core Features

Quick Start

Installation

Running

System Architecture

Agent Design

Decision Process

Module Support

Custom Configuration

Custom Analyst Roles

Custom Models