TradeMaster

Problem Crypto traders constantly face information overload. New tokens launch every hour, conversations explode on X, and by the time retail investors notice, the alpha is already gone. Most traders either miss opportunities or can’t stay awake 24/7 to catch them.

Solution Our project solves this by combining real-time token data with social signals:

1. We collect live data on tokens (market cap, volume, liquidity, chain).
2. We track what people are saying on X to capture early sentiment.
3. The bot delivers alpha signals directly to users, highlighting tokens gaining traction.
4. Users can enable automatic trading so opportunities are executed even while they sleep.
5. Custom filters let each trader define their own strategy — whether based on mentions, market cap, liquidity, influencer activity, or chain preference.

Impact This means traders get a personalized alpha feed, backed by both on-chain data and social buzz, with the ability to act instantly through automated trading. Instead of chasing trends late, they stay ahead of the market.

We built the project with a focus on speed, automation, and modularity. The core stack is Node.js with TypeScript, which allowed us to structure the code cleanly while moving fast.

1. Data & Scheduling: We used cron jobs to continuously fetch both token data and social mentions on a set interval, making sure our signals stay real-time without overloading APIs.

2. On-chain & Trading: For wallet interactions and trade execution, we used ethers.js. We integrated with Uniswap v4 hooks to simulate and execute trades efficiently, which was crucial for enabling automated alpha trading.

3. Storage Layer: We used MongoDB as temporary storage for caching token metadata, influencer mentions, and user-defined filters. This lightweight approach gave us flexibility without needing heavy infrastructure.

4. RPC & Data Access: We relied on Alchemy RPC for reliable on-chain queries and trade execution. It provided the stability and speed we needed during development.

5. User Interface: For user interaction, we built a Telegram bot. This allowed us to deliver alpha signals directly into users’ chats and let them control filters or enable auto-trading from an interface they already use daily.