Python programs tailored specifically for the world of financial markets, investment analysis, and trading. Whether you're a beginner or an experienced trader, you'll find a variety of tools and resources to help you in your financial endeavors.
To use the programs in this repository, you'll need Python and a few libraries installed. After having python installed you can install the required libraries as follows:
pip install -r requirements.txtClone this repository to your local machine:
git clone https://github.com/gabrielpalassi/FinancialMarketPython.git
Navigate to the repository folder:
cd FinancialMarketPython
Now you're ready to use the programs!
python name-of-program.py
Here's an overview of the tools available in this repository (further explanations are available when running the programs):
This program downloads historical data for a given portfolio and calculates its cumulative returns.
Plot the efficient frontier and optimize a portfolio of stocks for highest sharpe ratio, highest return for a given risk or lowest risk for a given return.
Plot the drawdown graph and find out the maximum drawdown of individual assets or a portfolio.
Calculate the Value at Risk (VaR) of individual assets or a portfolio at different confidence levels.
This program provides historical data from the Brazilian Central Bank. It allows users to access and analyze various economic and financial indicators (interest rates, inflation rates and exchange rates).
This program provides market expectations data gathered by the Brazilian Central Bank. It allows users to access and analyze various economic and financial indicators (interest rates, inflation rates and exchange rates).
The Last Month Performance Method Backtest program is designed to evaluate the performance of an investment strategy based on the returns of assets in the last month. This model invests in IBOV if it outperformed CDI last month, and vice-versa.
The Moving Average Method Backtest program is a tool for assessing the performance of an investment strategy that relies on a moving average. This model invests in IBOV if the previous month's closing value was higher than the moving average. In CDI if not.
We welcome contributions to this repository. If you have ideas for new programs, bug fixes, or improvements, please open an issue or submit a pull request.
If you have any questions, suggestions, or feedback, feel free to reach out to me at my e-mail.