TradeCompass

a powerful tool for real-time market analysis and trading insights. It helps traders explore stock trends, assess market conditions, and evaluate risks using advanced financial models and predictive algorithms.

Features

• Real-Time Market Analysis: Access up-to-date information on stock trends and market conditions.

• Risk Evaluation: Assess risk using volatility measures, Value at Risk (VaR), and other financial metrics.

• Portfolio Optimization: Optimize your portfolio based on expected returns and risk assessments.

• Monte Carlo Simulations: Simulate future stock prices to understand potential outcomes and plan strategies.

• Black-Scholes Options Pricing: Evaluate options pricing to make informed trading decisions.

• Stock Comparisons: Compare stocks or indices side-by-side to identify better investment opportunities.

Requirements

To run TradeCompass, you need to have the following Python packages installed:

• Python 3.7+

• yfinance: Fetches live stock data and historical prices, giving you the data needed to make informed decisions.

• pandas: Helps in handling and analyzing data easily and efficiently.

• requests: Makes HTTP requests to get data from the web, such as financial or economic information.

• BeautifulSoup: Extracts data from web pages, useful for getting economic indicators.

• numpy: Handles numerical operations, such as calculating returns and assessing risk.

• statsmodels: Provides tools for statistical modeling and hypothesis testing.

• scikit-learn: Used for machine learning tasks like linear regression and data preprocessing.

• scipy: Supports mathematical functions for optimization and advanced calculations.

• matplotlib: Creates visualizations, such as charts of stock trends and results from Monte Carlo simulations.

You can install these dependencies using the following command:

pip install -r requirements.txt

Explanation

TradeCompass uses a combination of financial models and predictive algorithms to analyze stock market data. Key methods include:

• Monte Carlo Simulations to predict future stock prices based on historical data.

• Black-Scholes Options Pricing for evaluating fair value of options contracts.

• Portfolio Optimization techniques to balance risk and returns.

The goal is to provide actionable insights that help traders make informed decisions in real-time.

Installation

To get started with TradeCompass, clone the repository and install the required packages:

1- Ensure Python 3.7 or higher is installed on your machine. You can download Python from python.org.

2- Clone the repository:

git clone github.com/komyl/TradeCompass.git

3- Navigate to the project directory:

cd TradeCompass

4- Install the required dependencies:

pip install -r requirements.txt

Usage

To run TradeCompass, simply execute the main script:

1- Run the script:

python tradecompass.py

2-Follow the prompts to analyze stocks, compare indices, evaluate risk, and more.

Example

Here is an example of using TradeCompass to compare two stocks:

python tradecompass.py

• Choose the compare option when prompted.

• Enter the stock symbols you want to compare (e.g., AAPL and MSFT).

• The program will provide a detailed comparison of the stock trends, risks, and other relevant metrics.

Important Notes

• Data Accuracy: The data provided by TradeCompass depends on the availability and accuracy of data from third-party services like Yahoo Finance. Use the information at your own discretion.

• Financial Disclaimer: TradeCompass is intended for educational purposes. The analysis and predictions provided are not financial advice. Always consult a professional financial advisor before making any trading decisions.

• API Limits: The data-fetching functions rely on public APIs that may have usage limits. Frequent requests could lead to temporary blocking of API access.

Contributing

We welcome contributions to TradeCompass! Since this is a personal project, any new ideas or improvements are greatly appreciated. Feel free to open an issue or submit a pull request. Please make sure your code follows the established style guidelines and includes appropriate tests to ensure quality.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Authors

This project was developed by Komeyl Kalhorinia. You can reach me at [[email protected]] for any inquiries or contributions.

Made with ❤️ by Komeyl Kalhorinia