KiteFlux

Web application in action
iOS Application in action

Inspiration

We trade options on a daily basis, but one day I experienced a significant loss because I fell asleep after taking an options position. Even though I had predetermined my stop loss and target, most brokerage platforms lack a feature to set these parameters for options positions based on Equity and Index.

Surprisingly, 48% of traders are involved in options trading, yet 90% of them end up with a net loss for various reasons, including this one. Additionally, it's extremely challenging for a person to monitor the market continuously for 6 hours and make rational decisions without getting influenced by emotions.

That's why we decided to create KiteFlux.

What it does

KiteFlux enables traders to set stop-loss and target levels for options positions based on the index and equity prices. Additionally, it automatically exits the options position once it reaches either the stop-loss or target, thereby helping traders overcome their emotional biases.

How we built it

KiteFlux is a comprehensive iOS, Android, and web application developed using Flutter, with Dart as the primary programming language. Our platform integrates seamlessly with Zerodha's dashboard through the KiteConnect API, enabling users to interact with real-time market data.

Our approach revolves around empowering traders with the ability to set stop-loss and target levels for options positions based on index and equity prices. Leveraging Zerodha's API, we fetch real-time data from the live market, providing users with up-to-the-minute insights.

The cornerstone of our solution lies in an algorithm optimized for lightning-fast execution, completing operations in just 500 milliseconds. This means that every half-second, we retrieve real-time data and compare it against the user-defined stop-loss and target levels. If the criteria are met, our system automatically executes an API call to the Zerodha dashboard, swiftly closing the ongoing position. This process, accomplished in less than a second, surpasses human capability in speed and accuracy.

To keep users informed, we implemented push notifications using Flutter's notification package. Users receive instant notifications whenever their orders are executed, ensuring they stay up-to-date with their trading activities.

Furthermore, each trade is logged in the Firebase Realtime Database, facilitating seamless record-keeping and analysis. We plan to leverage this data to train our AI model, currently in development. Once deployed, the AI model will provide users with future trading suggestions, enhancing their decision-making process with predictive insights.

Challenges we ran into

Our primary challenges in building KiteFlux revolved around understanding the complexities of the KiteConnect API documentation and managing the code base across iOS, Android, and web platforms.

Accomplishments that we're proud of

Our most significant achievement lies in successfully testing KiteFlux in the live market environment and preparing it for release on both the Play Store and Apple Store as a Minimum Viable Product (MVP).