Recent Projects
Data Analysis
Quant Finance and Python
As someone with a keen interest in the financial markets, I have always been on the lookout for ways to automate my trading strategies. To truly understand the various macro-economic factors impacting price movements, it is essential to obtain and analyze vast amounts of data to make informed decisions. This led me to experiment with different tools and software, and I eventually settled on using Python as my primary programming language. Initially, I started out using Amibroker software and its proprietary programming language called AFL. However, I quickly realized that Python offered a more robust and flexible solution for my needs. I love to write scripts in Python to help manipulate large amounts of data, and I primarily use Pandas and Jupyter Notebook for this purpose.
This Python and Django-based application showcases advanced data analysis capabilities through its robust handling of selected macroeconomic indicators.
Utilizing Pandas Datareader to seamlessly integrate with the FRED API, the application ensures the acquisition of high-quality and appropriately formatted datasets. The data is validated for accuracy, ensuring the reliability of the analysis. The application employs comprehensive technical analysis tools, applying relevant algorithmic calculations to process and prepare the data for final results. These results are then utilized in various visualizations and techniques designed to aid users in making informed investing decisions.
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This application has one primary function: to display the current trend direction of financial instruments included in Stooq's database.
By leveraging the power of FastAPI and advanced data analysis techniques, it provides users with valuable insights into market trends, aiding in informed investing decisions. The data is sourced using pandas-datareader, ensuring the acquisition of high-quality, appropriately formatted datasets from Stooq's extensive database of historical stock prices. Once the historical data is accessed, the application calculates moving averages using three lookback periods: 50-day, 100-day, and 200-day. These moving averages serve as crucial indicators of the trend direction for each instrument.
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This is a Python application powered by Django and its Object-Relational Mapper (ORM) to provide convenient access to stored data, enabling users to view different trading strategies and related financial markets.
Employs sophisticated algorithms to process and analyze financial data, preparing final results for insightful visualizations and aiding in informed investing decisions. Django's ORM allows users to query and manipulate the database in a pythonic fashion, facilitating seamless data management. After completing registration, subscribers can log in to create their own investment strategies. Deletion of records from the database requires users to be added to a special group and granted permissions by the website administrator, ensuring data integrity and security.
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