Repository overview | Agent Framework | Agent Framework patterns | Terminal dashboard | AutoGen (legacy) | Semantic Kernel (legacy) | ETF research (legacy) | Archive guide
This project shows how to use Microsoft Agent Framework for stock-market research. Agents download past prices, write and execute a technical-analysis signal script, test the resulting strategy, and save the results.
chart-cli renders a live watchlist, price chart, and Agent Framework workflow output in the terminal. Press / for the command prompt: /ask answers questions about the symbols on screen, /backtest has an agent write and test a strategy from a plain-language request, /period sets the window, /model switches the chat backend, and /help lists everything. In the screenshot below, AGENT:RULES means the workflow is using its transparent rule-based path because no chat provider was connected.
Run this from PowerShell:
cd chart-cli
pnpm install
.\scripts\test-cli.ps1The script verifies the Node UI and Python Agent Framework protocol, then opens
the interactive dashboard in the same terminal. It starts the workflow process
used by the dashboard; do not start a separate Python backend. Use
.\scripts\test-cli.ps1 -CheckOnly for verification without opening the UI.
See the terminal dashboard guide for the panels, keys, commands, periods, and provider setup.
| Area | Purpose | Use |
|---|---|---|
| Agent Framework workflow | Main workflow. | Start here. |
| Agent Framework patterns | Examples for the investment domain. | Explore features one at a time. |
| Real-time terminal dashboard | Live watchlist and charts driven by an Agent Framework workflow, with /ask, /backtest, /period, and /model commands. |
Watch signals update in a terminal. |
| AutoGen stock-research agents | Group-chat agents propose strategies, generate signals, backtest, and report results. | Legacy reference. |
| Semantic Kernel research workflow | Plugin-based agents fetch prices, execute signal code, backtest, and plot results. | Legacy reference. |
| ETF strategy research tools | Standalone scripts explore momentum, volatility targeting, drawdown controls, and benchmarks. | Legacy research. |
The Agent Framework patterns are the only pattern showcase in this repository. The 30 examples show Microsoft Agent Framework features for investment research. Semantic Kernel and AutoGen do not have separate pattern libraries here.
Python 3.13 and uv. To run the main Agent Framework example, you also need an Azure AI Foundry project with a chat model and access through the Azure CLI.
uv sync
cp .env.example .env # set Azure AI Foundry endpoint and model deployment
az login # authenticate the Azure CLI credential used by the workflow
uv run python -m agent_framework.mainOn PowerShell, use Copy-Item .env.example .env to copy the settings file. Then fill in these values in .env:
| Variable | Purpose |
|---|---|
AZURE_AI_PROJECT_ENDPOINT |
Address of the Azure AI Foundry project used by the main workflow. |
AZURE_AI_MODEL_DEPLOYMENT_NAME |
Name of the chat model deployed in that project. |
INVESTMENT_TICKER |
Stock symbol to study. The default is MSFT. |
INVESTMENT_START_DATE, INVESTMENT_END_DATE |
First and last dates for the past-price data. |
INVESTMENT_INITIAL_CAPITAL |
Pretend starting amount for the backtest. |
See the full backtest workbook, generated signal script, and validated signals.
| Path | Contents |
|---|---|
| agent_framework | The main Microsoft Agent Framework application. |
| agent_framework_patterns | Thirty small Agent Framework examples for investment research. |
| chart-cli | Real-time terminal dashboard with a companion Agent Framework workflow. |
| tests | Offline tests for Agent Framework patterns and the REPL contracts. |
| output | Agent Framework charts, metrics, and example pattern responses. |
| docs | Agent Framework, TUI, archive, and ETF research guides, linked from this single README entry point. |
| .old | Archived frameworks, comparison tests and independent architecture guides, standalone ETF tools, and non-Agent-Framework sample outputs. See the archive guide. |
The Agent Framework pattern tests run without Azure credentials or live market-data services:
uv run ruff check agent_framework agent_framework_patterns tests
uv run pytest tests -q- These results are for learning and research, not financial advice or a real trading system.
- The research workflows execute model-authored Python to create signals. Their validation is not a security sandbox; run them only in an isolated development environment without credentials or production data.
- Good results from the past do not mean the same strategy will work in the future.
- The examples use public market data and simple rules. They leave out trading fees, price changes that happen while a trade is being made, taxes, careful handling of stock splits and dividends, and checks for an individual investor's needs.
- Review AI responses, connected tools, and data licences before using this project outside a learning or research setting.
MIT