# Simulations

This directory contains simulations for testing and evaluating the performance of the ADAM system and its agents. Each simulation provides a controlled environment for running experiments and measuring key performance indicators.

## Simulation Scenarios

Here are some examples of the simulation scenarios that can be run using the ADAM system:

### Credit Rating Assessment

In this scenario, the system is tasked with assessing the credit rating of a company. The simulation uses a variety of data sources, including financial statements, news articles, and analyst reports, to generate a credit rating for the company. The accuracy of the credit rating is then evaluated against the actual credit rating of the company.

### Fraud Detection

In this scenario, the system is tasked with detecting fraudulent transactions in a stream of financial data. The simulation uses a variety of machine learning models to identify suspicious transactions and flag them for review. The performance of the fraud detection system is then evaluated based on its ability to correctly identify fraudulent transactions while minimizing false positives.

### Portfolio Optimization

In this scenario, the system is tasked with optimizing an investment portfolio. The simulation uses a variety of data sources, including historical market data, analyst forecasts, and risk models, to generate an optimal portfolio that meets the investor's objectives. The performance of the portfolio is then evaluated based on its returns, risk, and other key metrics.

## Available Simulations

*   **`Credit_Rating_Assessment_Simulation.py`:** Simulates the process of assessing the credit rating of a company.
*   **`Fraud_Detection_Simulation.py`:** Simulates the detection of fraudulent transactions in financial data.
*   **`Investment_Committee_Simulation.py`:** Simulates the decision-making process of an investment committee.
*   **`Merger_Acquisition_Simulation.py`:** Simulates the process of a merger or acquisition between two companies.
*   **`Portfolio_Optimization_Simulation.py`:** Simulates the process of optimizing an investment portfolio.
*   **`Regulatory_Compliance_Simulation.py`:** Simulates the process of ensuring compliance with financial regulations.
*   **`Stress_Testing_Simulation.py`:** Simulates the performance of the system under various stress scenarios.

## Running a Simulation

To run a simulation, you can use the `scripts/run_simulations.sh` script. This script allows you to specify which simulations to run and how many times to run them.

```bash
./scripts/run_simulations.sh --simulation <simulation_name> --iterations <num_iterations>
```

For example, to run the `Credit_Rating_Assessment_Simulation` 10 times, you would use the following command:

```bash
./scripts/run_simulations.sh --simulation Credit_Rating_Assessment_Simulation --iterations 10
```

## Creating a New Simulation

To create a new simulation, follow these steps:

1.  **Create a new Python file** in this directory. The file name should be descriptive of the simulation (e.g., `my_new_simulation.py`).
2.  **Define the simulation environment.** This includes setting up the initial conditions, such as the data to be used and the agents to be involved.
3.  **Implement the simulation logic.** This includes defining the steps of the simulation and the interactions between the agents.
4.  **Define the evaluation metrics.** This includes specifying the key performance indicators that will be used to measure the performance of the system.
5.  **Add the new simulation to the `scripts/run_simulations.sh` script.** This will make the simulation available to be run from the command line.

By following these guidelines, you can help to ensure that the simulations in the ADAM system are well-designed, easy to use, and provide valuable insights into the performance of the system.