An intelligent multi-agent system leveraging Large Language Models for automated Counter-Strike 2 market analysis and trading decisions
CSGOTrading presents a novel multi-agent framework that applies Large Language Models (LLMs) to the domain of virtual item trading in Counter-Strike 2 (CS2) markets. Our system employs a hierarchical agent architecture built on LangGraph, incorporating specialized analyst agents (technical, sentiment, liquidity, and event-driven) coordinated by a meta-planner agent. The portfolio manager agent synthesizes multi-modal market signals to generate optimal trading decisions while accounting for transaction costs and risk constraints.
Key contributions:
- Multi-Agent Architecture: Modular design with specialized analyst agents for different market aspects
- Dynamic Agent Selection: Meta-planner agent adaptively selects relevant analysts based on market conditions
- Multi-Source Data Integration: Seamless aggregation of CS2 market data, Steam news, and Reddit sentiment
- Configurable Workflow: Support for both agentic workflows and direct LLM analysis modes
- Risk-Aware Portfolio Management: Sophisticated position sizing with transaction cost modeling
| Agent | Function | Input | Output |
|---|---|---|---|
| Planner | Selects relevant analysts based on market context | Ticker, available analysts | List of selected analysts |
| Technical Analyst | Analyzes price patterns and trends | Historical price data | Technical signals (BUY/SELL/HOLD) |
| Sentiment Analyst | Processes community sentiment | Reddit posts, Steam news | Sentiment score and direction |
| Liquidity Analyst | Evaluates market depth and volume | Order book, trading volume | Liquidity assessment |
| Event Analyst | Identifies market-moving events | News, updates | Event impact analysis |
| Portfolio Manager | Executes risk-aware trading decisions | Analyst signals, portfolio state | Trading actions with position sizes |
- Technical Analysis: Price action, trend detection, support/resistance levels
- Sentiment Analysis: NLP-based sentiment extraction from Reddit and Steam communities
- Liquidity Analysis: Market depth and volume-based assessments
- Event-Driven Analysis: Impact evaluation of game updates and news
- Meta-Planner: Dynamically selects optimal analyst combination for each ticker
- Modular Design: Easily extensible agent registry system
- Flexible Workflows: Support for both agentic and direct LLM modes
- Risk Control: Maximum position ratio constraints and drawdown protection
- Transaction Costs: Realistic modeling of 2% trading fees
- Position Sizing: Intelligent allocation across multiple assets
- State Persistence: Complete portfolio history tracking in database
- Database Support: SQLite for local development
- Multi-Provider LLM: OpenAI, Anthropic, DeepSeek, Ollama, etc.
- Extensive Configuration: 50+ pre-configured experiment setups
- Comprehensive Logging: Detailed agent execution and decision tracking
- Python 3.8 or higher
- pip package manager
- (Optional) SQLite for local database
- Clone the repository
git clone https://github.com/IatomicreactorI/CSGOTrading.git
cd CSGOTrading- Create virtual environment
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate- Install dependencies
pip install -r requirements.txt- Configure environment variables
cp .env.example .env
# Edit .env and add your API keys:
# - OPENAI_API_KEY (for OpenAI models)
# - ANTHROPIC_API_KEY (for Claude models)
# - DEEPSEEK_API_KEY (for DeepSeek models)- Initialize database
python database/cs2_sqlite_setup.py- Fetch historical data
Before running experiments, you can pre-fetch historical data to avoid API rate limits during backtesting:
Fetch Reddit data (past 1 year):
# From project root directory
python -m apis.reddit.fetch_reddit_data
This script fetches Reddit posts from the past year and saves them to apis/reddit/reddit_data.csv. No parameters required.
Fetch Steam news data:
# From project root directory
python -m apis.steam.fetch_steam_data \
--config config/Direct-cd.yaml \
--start-date 2025-09-25 \
--end-date 2025-11-15 \
--limit 15
Parameters:
--config: Path to config YAML file (must containexp_nameandtickers)--start-date: Start date in YYYY-MM-DD format (required)--end-date: End date in YYYY-MM-DD format, inclusive (required)--limit: Maximum news items per ticker per day (default: 15, optional)--output: Output CSV file path (default:<script_dir>/steam_data.csv, optional)
Fetch CS2 market data:
# From project root directory
python -m apis.cs2market.fetch_cs2_data
This script fetches current price data for candidate items from Steam Community Market and saves to apis/cs2market/cs2_data.csv. No parameters required. The script will automatically retry failed items up to 3 times.
Note: These fetch scripts can be run anytime to update the historical data. The main experiment workflow will use these CSV files to avoid making API calls during backtesting.
Run a single-day experiment with default configuration:
python run.py --config TS-ds.yaml --start-date 2025-09-25 --end-date 2025-09-25Run multi-day backtesting:
python run.py \
--config TS-ds.yaml \
--start-date 2025-09-25 \
--end-date 2025-10-27The system supports multiple workflow configurations:
- Direct: Direct LLM analysis without analyst agents
- T: Technical analyst only
- TS: Technical + Sentiment analysts
- TSL: Technical + Sentiment + Liquidity
- TSLE: All analysts (Technical + Sentiment + Liquidity + Event)
- TSrL: Technical + Reverse Sentiment + Liquidity
Each configuration can be combined with different LLM providers:
-ds: DeepSeek-gm: Gemini-gt: GPT-cd: Claude-km: Kimi-qw: Qwen
Example: TSLE-cd.yaml uses all analysts with Claude 3.5 Sonnet.
# View all information of specified experiment
python view.py TS-ds
# View portfolios
python view.py TS-ds portfolios
# View latest positions
python view.py TS-ds positions
# View daily portfolios and export CSV
python view.py TS-ds daily
# View portfolios of specified date
python view.py TS-ds daily 2025-09-26
# Export thinking process JSON file
python view.py TS-ds thinking
# View data summary
python view.py TS-ds summary
# List all experiments
python view.py list# Clear experiment data
python clear.py --config-name TS-dsexp_name: "TS-ds" # Experiment name
cashflow: 10000 # Initial capital
tickers: # Assets to trade
- "AK-47 | Redline (Field-Tested)"
- "AWP | Asiimov (Field-Tested)"
llm: # LLM configuration
provider: "deepseek"
model: "deepseek-chat"
planner_mode: true # Enable meta-planner
workflow_analysts: # Available analysts
- technical
- sentiment
enable_transaction_fee: true # Include trading costsLocal SQLite (default):
python run.py --config TS-ds.yamlCSGOTrading/
├── agents/ # Agent implementations
│ ├── planner.py # Meta-planner agent
│ ├── portfolio_manager.py # Portfolio management
│ ├── registry.py # Agent registry
│ └── analysts/ # Specialized analysts
│ ├── technical.py
│ ├── sentiment.py
│ ├── sentiment_reverse.py
│ ├── liquidity.py
│ └── event.py
├── apis/ # Data source integrations
│ ├── cs2market/ # CS2 market data
│ ├── steam/ # Steam news API
│ ├── reddit/ # Reddit sentiment API
│ ├── router.py # API router
│ └── common_model.py # Common data models
├── database/ # Database layer
│ ├── interface.py # Abstract interface
│ ├── cs2_sqlite_helper.py
│ └── cs2_sqlite_setup.py
├── graph/ # LangGraph workflow
│ ├── workflow.py # Main workflow
│ ├── schema.py # State definitions
│ └── constants.py
├── llm/ # LLM integration
│ ├── inference.py # LLM calls
│ ├── provider.py # Provider configs
│ └── prompt.py # Prompt templates
├── config/ # Experiment configurations
├── util/ # Utilities
│ ├── config.py
│ ├── cs2_db_helper.py
│ └── logger.py
├── figs/ # Figures and images
├── run.py # Main execution script
├── view.py # Results visualization
├── clear.py # Data cleanup
├── requirements.txt
├── .env.example # Environment variables template
├── LICENSE
└── README.md
- Create analyst implementation in
agents/analysts/:
from graph.constants import AgentKey
from llm.inference import agent_call
def custom_analyst(ticker: str, llm_config, analyst_signal):
# Your analysis logic here
return {
"action": "BUY",
"confidence": 0.8,
"justification": "Analysis reasoning"
}- Register in
agents/registry.py:
AgentRegistry.register(
AgentKey.CUSTOM,
custom_analyst,
"Custom analyst description"
)- Add to workflow configuration:
workflow_analysts:
- technical
- sentiment
- custom # Your new analystAdd provider configuration in llm/provider.py:
@dataclass
class ProviderConfig:
name: str
model_class: Any
requires_api_key: bool = True
env_key: str = "CUSTOM_API_KEY"
base_url: str = None
# Register provider
Provider.add_provider("custom", ProviderConfig(...))We welcome contributions from the community! Please follow these guidelines:
- Fork the repository and create a feature branch
- Follow PEP 8 coding standards
- Add tests for new functionality
- Update documentation for API changes
- Submit a pull request with a clear description
# Install development dependencies
pip install -r requirements-dev.txt
# Run tests
pytest tests/
# Run linting
flake8 .
black .This project is licensed under the MIT License - see the LICENSE file for details.
- LangChain & LangGraph: Foundation for agent orchestration
- OpenAI, Anthropic, DeepSeek: LLM providers
- CS2 Community: Market data and insights
- Contributors: All community contributors
- Reinforcement Learning Integration: Train agents with RL for adaptive strategies
- Real-time Trading: Live market integration and execution
- Advanced Risk Models: VaR, CVaR, and Kelly criterion
- Multi-Asset Correlation: Cross-asset analysis and hedging
- Web Dashboard: Interactive visualization and monitoring
- Distributed Execution: Multi-process and cloud deployment
- More Data Sources: Integration with additional market APIs
- v0.1.0 (2026-01-05): Initial release with core multi-agent framework
- v0.0.1 (2025-12): Private beta testing
⭐ Star this repository if you find it useful! ⭐
Made with ❤️ by the CSGOTrading Team