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because of the limited API credits, i can't search in this moment.
This project was thought of to help my friends who have been consistently rugged while trading memecoins. A market that has successfully destroyed the confidence of small scale traders all over the world through market manipulation and community rugging of tokens. There is a limited number of ways to be financially independent in today's world and limiting anyone to be a scam reduces your chances of ever living the rat race. This project aims to help not just my friends but everyone trading get the upper hand for once in the highly fluctuating market of memecoins.
It gives you access to the most traded blockchain in memecoin, solana. Allowing you to access memecoins under the solana blockchain using its name, CA or ticker and get information such as market capitalization, price, volume, liquidity etc. where it stands out from conventional DEXs is its feature to allow users scan a specific memecoin and get the top 10 wallets that traded in it. Giving you access to capital inputted, how long they stayed (long term means stable and short term means rug), profit and loss realized(P/L), percentage held. It also gives you access to top 5 latest memecoin that each of the 10 wallets has been in and also similar information (this let's you detect rug patterns. Know which top wallets are collaborating, giving you decision to avoid specific memecoins or jump in and out before they rug and realise profit before it crumbles). It is the ultimate rug detector that will let you be more confident in trading solana pairs.
it was built on fastAPI, a python web framework. I also used prostgreSQL as my database to store tokens, wallets and calculated trading information. Redis was used for caching frequently requested data to stop request from hitting DB everytime. We used mainly two APIs, birdeye and helius. one was for market data, wallets and its P/L and holders (birdeye) and the other was for blockchain access and transaction parsing where required. Then it got deployed on render. And we used typescript, react, vite, tailwind CSS charting library and we deployed it on vercel.
My main challenge was working with free tier tools, such as the APIs. Changing the codes from demo mode to the live deployable code also was a struggle. And finally, deploying the backend on render was a roadblock in this project, render kept rejecting the python version 3.13.o which i had to change to 3.11.9 to bypass the continuously failing deployment.
Completing this project and it working without a problem is something i am proud of.
Learnt how to work with python more as my go to language is node.js
It reaching majority of memecoin traders and being valuable and then implementation of more blockchains.
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