NextGen-Trader is an agent-based AI trading and market-analysis project designed to simulate how specialized analytical agents can work together to study markets, evaluate opportunities, and generate structured trade insights.
The system is organized around a modular workflow where different agents handle different parts of the decision process, such as fundamentals, sentiment, valuation, technical analysis, and risk-aware synthesis. The goal is to create a more explainable and extensible research pipeline for quantitative and AI-assisted trading experiments.
This repository is based on an existing open-source codebase and has been customized, extended, and reworked for portfolio presentation and experimentation.
| Multi-Agent Design | Structured Analysis | Modular Python Code |
|---|---|---|
| Separate agents for specialized market perspectives | Combines multiple signals into one decision flow | Easy to extend, test, and customize |
- Agentic workflow: multiple components collaborate through a coordinated graph-based process.
- Research-oriented: supports experimentation with market data, sentiment, and valuation logic.
- Readable architecture: code is arranged for clarity, debugging, and extension.
- Portfolio-friendly: professional structure suitable for GitHub presentation.
Example workflow showing how multiple analytical components interact.
- Multi-agent decision flow for market research
- Sentiment-aware signal generation
- Valuation and fundamentals analysis
- Technical analysis support
- Risk-aware final recommendation layer
- Backtesting / simulation-oriented structure
- Clean separation between analysis modules and orchestration logic
The project is organized around a workflow that can be thought of in four broad stages:
- Input gathering — collect market, company, or sentiment-related data.
- Specialist analysis — individual agents analyze the data from different perspectives.
- Synthesis — a coordinating component combines the outputs into a final view.
- Decision output — the system produces a structured recommendation or summary.
This design makes it easier to add new research agents, replace models, or plug in new data sources without rewriting the full application.
| Category | Tools |
|---|---|
| Language | Python |
| Workflow Orchestration | LangGraph |
| LLM / Agent Tooling | LangChain-style agent architecture |
| Data / Analysis | Pandas, NumPy, scientific Python ecosystem |
| Visualization | Matplotlib / workflow graph visuals |
| Environment | venv, .env |
Update this section if your local setup includes additional dependencies.
NextGen-Trader/
├── SourceCode/
│ ├── agents/
│ ├── graph/
│ ├── utils/
│ ├── Image.png
│ └── analyst_workflow_graph.png
├── README.md
├── requirements.txt
├── test.py
├── ultimate_test.py
└── .gitignore
- Python 3.9 or higher
- Git
- A virtual environment tool such as
venv - Any required API keys for your data providers or model providers
git clone https://github.com/iamdev0707/NextGen-Trader.git
cd NextGen-Trader
python -m venv venvActivate the virtual environment:
# Windows
venv\Scripts\activate
# macOS/Linux
source venv/bin/activateInstall dependencies:
pip install -r requirements.txtCreate a .env file in the project root and add the required keys.
OPENAI_API_KEY=your_api_key_here
FINANCIAL_DATASETS_API_KEY=your_api_key_hereAdd any additional provider keys required by your local code.
Run the main application from the project root:
python SourceCode/maain.pyIf your entry file has a different name, update this command accordingly.
The workflow generally follows this pattern:
- collect inputs
- run specialist analysis
- merge signals
- produce a final output
This repo is a good base for future improvements such as:
- better agent routing
- stronger risk controls
- richer dashboards
- live market data integration
- explainable recommendation summaries
- improved backtesting and reporting
- Improve project documentation
- Add cleaner configuration management
- Expand agent coverage and signal quality
- Improve result visualization
- Add a polished frontend dashboard
- Support paper-trading or simulation mode
This project builds on an existing codebase and retains the spirit of the original work while being adapted for learning, experimentation, and portfolio use.
If you publish or share the repository, keep the relevant original credit intact where required by the upstream license or project terms.
Contributions, ideas, and improvements are welcome.
- Fork the repository
- Create a new branch
- Make your changes
- Open a pull request
This project is released under the license specified in the repository. If no license has been added yet, choose one before publishing publicly.
NextGen-Trader — modular AI research for modern market analysis.