Algo.Py

dashboard-screenshot
interface
interface
interface
interface

Inspiration

W’ve always been fascinated by financial markets and algorithmic trading. We wanted to create a framework that allows anyone from hobbyist traders to aspiring quant developers to design, backtest, and deploy trading strategies efficiently. The inspiration came from the need to experiment with strategies quickly while keeping everything modular and reproducible.

What it does

Algo.Py is a Python-based algorithmic trading framework. It helps you:

Design trading strategies with Python
Backtest them against historical market data
Execute strategies using APIs like Binance, Alpha Vantage, Zerodha, and Interactive Brokers
Visualize trading performance through interactive dashboards built with Streamlit and Plotly

How we built it

We used Python as the core language, leveraging libraries such as Pandas and NumPy for data processing, TA-Lib for technical analysis, and scikit-learn, TensorFlow, and PyTorch for any AI/ML-based strategies. Jupyter Notebooks allow experimentation, while Docker ensures environment reproducibility. CI/CD is integrated with GitHub Actions to automate testing and deployments.

Challenges we ran into

One challenge was handling real-time market data efficiently while keeping the system modular. Integrating multiple broker APIs with different formats and rate limits was also tricky. Ensuring backtests are accurate and scalable took several iterations.

Accomplishments that we're proud of

We built a full-stack framework that is fully functional for algorithmic trading. It’s modular enough for users to plug in custom strategies or data sources. The dashboard and visualization components make it easy to analyze strategies without diving into code.

What we learned

We learned how to handle complex financial data, implement modular trading pipelines, and integrate multiple APIs seamlessly. We also gained practical experience in backtesting strategies and deploying reproducible environments with Docker and GitHub Actions.

What's next for Algo.Py

Add more AI-driven strategy templates
Improve performance and latency for real-time trading
Expand support for more broker APIs
Build an integrated recommendation system for strategy optimization

Built With

alpha-vantage-api
binance-api
docker
docker-compose
github-actions
interactive-brokers-api
jupyter-notebook
matplotlib
numpy
pandas
plotly
python
pytorch
scikit-learn
streamlit
ta-lib
tensorflow
timescaledb
zerodha-api

Submitted to

AlgoFest Hackathon

Created by

My responsibility was to take care of hooking up the APIs and making sure the deployment worked smoothly. I connected Binance, Alpha Vantage, Zerodha, and Interactive Brokers so the strategies could run live and in backtests without issues. I also set up Docker and GitHub Actions to keep everything consistent. Took some trial and error, but I learned a ton about deploying real Python apps.

Basir Bina
I handled the dashboard and visual stuff using Streamlit and Plotly. My job was to make the charts show trades, performance, and signals in a way anyone could understand. Honestly, it was my first time doing something like this for a trading project, and it was super cool to actually see the numbers move and the data come alive.

Eric Davich
I focused on the strategy design and backtesting modules. I implemented the core Python algorithms for simulating trades and calculating performance metrics. It was my first time designing a modular trading framework, and I learned how to structure code for flexibility while ensuring accurate backtests.

I now understand the end-to-end workflow of algorithmic trading, from strategy logic to backtesting and result visualization. I also improved my skills in Python data processing with Pandas and NumPy.

Farzana Abdulzada

Updates

Farzana Abdulzada started this project — Aug 31, 2025 05:54 AM EDT

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