Brief Summary
This video provides a walkthrough of building a tweet tracker that automatically buys meme coins based on influencer tweets. It covers the architecture, coding, and challenges involved in creating such a system. Key points include:
- Identifying and scraping relevant tweets.
- Using an LLM to determine if a tweet is a buy signal.
- Interfacing with decentralized exchanges (DEXs) to execute trades.
- Addressing the challenges of transaction speed and reliability on the Solana blockchain.
What are we making?
The video aims to create a tweet tracker that monitors Twitter accounts for endorsements of tokens. The goal is to automatically purchase these tokens from decentralised exchanges (DEXs) immediately after a tweet, capitalising on the price surge that often follows. The strategy exploits market manipulation, with the intention of selling the tokens within a short timeframe (2-3 minutes) to secure a profit. The creator issues disclaimers, advising viewers against trading with money they cannot afford to lose and emphasising that the video is for educational purposes only. The project requires knowledge of Node.js, Solana, web3.js, DEXs, wallets, private keys, and the Twitter API. By the end of the video, viewers will learn how to interface with DEXs and automate buying and selling processes.
How $TRUMP coin and other Meme Coin work
The video discusses the recent trend of celebrities and influencers releasing or endorsing meme coins, using Donald Trump's meme coin as a prime example. Trump's tweet about his official meme coin led to significant market activity, with early investors making substantial profits. The video highlights how crypto influencers' tweets about other coins also cause immediate price spikes, presenting opportunities for quick gains. The common pattern involves a large green candle immediately after the tweet, followed by consolidation back to original levels. The goal is to follow these Twitter accounts, identify tokens they mention, and execute immediate buys with stop-loss orders. The creator reiterates the risks involved, advising against trading with unaffordable funds and suggesting building such products for others instead.
Architecture (Dumb v/s Smart way)
The video outlines two architectural approaches for building the tweet tracker: a "dumb" way and a "smart" way, mirroring how a high-frequency trading (HFT) system would operate. The "dumb" approach involves using a polling server to scrape Twitter for tweets from influencers. Due to Twitter API limitations, this requires either paying for expensive API access or using unofficial methods. Once a tweet is detected, it's sent to a large language model (LLM) to determine if it's a bullish endorsement and to extract the token address. The system then forwards a buy request to a Solana Remote Procedure Call (RPC) to execute the trade. The "smart" approach involves running a personal RPC node close to Solana validators, reducing latency and improving transaction priority.
Building the Twitter API scraper
The video addresses the challenges of using the Twitter API due to rate limits. The official API is heavily restricted, making it difficult to get real-time tweet data. The creator explores alternative methods, including unofficial APIs and creating multiple Twitter accounts to bypass rate limits. They demonstrate using RapidAPI's Twitter API to fetch user tweets, acknowledging that the response format is convoluted but manageable. The chosen API provides a decent solution for polling Twitter without immediate rate limiting, allowing the project to proceed with coding the tweet scraper.
Code - Scraper
The video transitions into the coding phase, starting with setting up a new Node.js project with TypeScript. The core logic involves creating a main function that polls Twitter every few seconds, retrieves new tweets, and forwards them to an LLM. The getTweets function is created to fetch tweets from a specified user, using the RapidAPI endpoint. The function parses the API response to extract relevant tweet information, such as content and timestamp, filtering out tweets older than a specified threshold. Environment variables are used to store API keys and other sensitive information.
Code - Sending Message to the LLM
The video focuses on implementing the getTokenFromLLM function, which sends tweet content to an LLM to determine if it's a bullish endorsement of a Solana token. The OpenAI API is used, with a system prompt to guide the LLM's response. The system prompt instructs the LLM to identify Solana token addresses within the tweet and only return the address if the tweet is a buy signal. The function is tested to ensure it correctly identifies bullish tweets and extracts token addresses, while ignoring bearish tweets.
Implementing the Swap Function
The video explains how to swap tokens on Solana using decentralized exchanges (DEXs) like Radium and Jupiter. It details the process of creating a local wallet, exporting its private key, and using the Radium API to perform on-chain swaps. The importance of slippage tolerance is discussed, along with the need to get a quote from Radium before executing a swap. The video walks through the code required to get a quote, create a swap transaction, sign it with the private key, and send it to the Solana blockchain. The challenges of transaction congestion and rate limiting are addressed, with the creator demonstrating how to use different RPC providers to improve transaction success rates.
Conclusion (Recording Stopped :/)
The video concludes with a summary of the project's learnings and potential improvements. Key takeaways include the unreliability of LLM responses, the need for manual parsing of tweets, and the importance of retry logic for swap transactions. The creator suggests spamming transactions, using different RPCs, and adding logic to sell tokens after a set period. The video ends with a demonstration of the tweet tracker in action, successfully buying a meme coin based on a tweet.
Bye Bye
The video ends.
