By
@Sameed
A Scratch like platform for experimenting with algorithmic stock trading strategies. The web-app also allows comprehensive back-testing of these strategies on historical EOD data.
QuantBlock is an interactive web-app that allows users to create algorithmic trading strategies using drag-and-drop blocks. It utilizes a python backend called QuantLab (I hope that explains any confusion about the name and banner). Users can then backtest thier strategy on a vast amount of stock data and obtain valuable metrics like the Sharpe Ratio.
1h 44m 14s logged
Added the final touches to the frontend. Added a feedback section, element explainer and a strategy explainer. I also added some premade strategies just as an example.
4h 10m 59s logged
I made some visual changes to the frontend. And I add the sharpe ratio which is one of the most useful financial metric for evaluating a strategy.
3h 14m 29s logged
DEVLOG 2: I added a section to create your own variable from the available indicators. You can create a variable by multiplying, dividing, adding, or subtracting any two indicators. This variable can then be used in the main condition. How it works in the backend is that the variable assignment is sent as a json tree to the backend. At the backend the json tree is parsed to calculate the value of the variable and the it is added to the main dataframe where it is accessible by the main condition.
8h 34m 25s logged
QuantBlock is a web app inspired by Scratch, built for beginners to play around with algorithmic stock trading strategies without writing any code. The web app also backtests these strategies and outputs the returns. Users can build strategies using simple drag-and-drop elements. Right now, there are 8 indicators and 2 operators, but soon more will be added. The UI is inspired by early 2000s GUIs and the Bloomberg terminal.The front-end is written using the HTML, CSS, and JavaScript stack. The front-end simply converts the user-defined strategy and sends it to a Python backend where all the processing happens. This backend then returns the final portfolio value, the percentage return, and the daily portfolio value in an array so that it can be displayed on a graph.Disclaimer: The demo is not fully functional yet.Future work:-Better Documentation-More indicators and the ability to create custom indicators-Bug Fixes
