How to run:

Installation process for the agents and AgentCore:

Inside the main folder run:

python3 -m venv venv && source venv/bin/activate
pip install -r requirements.txt

Create a dot env file .env with the following content and add your bucket:

S3_CHART_BUCKET=s3://add_your_own_bucket_here

Installation process for the frontend

Inside the front2 folder:

python3 -m venv venv && source venv/bin/activate
pip install -r requirements.txt

Running AgentCore locally:

inside the main folder

source venv/bin/activate
python main.py

Running the frontend locally:

In the folder front1 run the following commands:

npm run dev

Open http://127.0.0.1:3000 with your browser to see the result.

Building

Build the application for production:

npm run build

Running the frontend locally: [Deprecated]

inside the main folder

source venv/bin/activate
python app.py

What to expect:

1. We have data from 25 Stocks (AMZN, Netflix, MSFT, GOOGL, Costco)
2. The agents can fetch news for any stock (mode than the 25)
3. The agents can run technical analysis on any of the 25 stocks.
4. The agents can calculate the returns of any of the 25 stocks for up to 3 months
5. Questions at this moment have to involve a stock or list of stocks:

• Example: Compare the returns of Amazon, Apple and Google Whats the correlation between Apple and Amazon?

The next step is to hook the whole universe of stocks and PySpark to have the agent running complex calcudlations.

Tasks:

1. Adding PySpark and support using the aws_agents/parquet_agent.py
2. Adding FDC3 support for the front-end

Security See CONTRIBUTING for more information.

License This library is licensed under the MIT-0 License. See the LICENSE file.