Trade Signal Automation System
Scaled daily trading signals 5× while reducing latency to under 1 minute.
# Description
At Finosauras, the trading team was manually generating and sending 5-7 signals per day through Telegram channels. This manual process was slow, error-prone, and couldn't scale. I built an end-to-end automation system that increased signal throughput 5× (to 25+ signals/day) while reducing delivery latency from ~10 minutes to under 1 minute.
# Tech Stack
- Node.js + Express for the automation backend
- MongoDB for tracking signal history and delivery status
- Telegram Bot API for multi-channel posting
- OpenAI GPT-3.5 for intelligent message formatting
- Cron jobs for scheduled signals + webhooks for real-time triggers
# Problem
The existing workflow required analysts to manually format trade signals, validate data, and post to multiple Telegram channels. This created bottlenecks during high-volume trading periods, delayed time-sensitive signals, and limited the company's ability to serve more customers. Errors in manual formatting also led to occasional confusion among subscribers.
# Solution
I designed and built a Node.js automation pipeline that: (1) Integrated with the trading team's internal API to pull raw signal data in real-time, (2) Used OpenAI's API to intelligently format signals into standardized, human-readable messages with proper formatting and emojis, (3) Automated posting to multiple Telegram channels via the Telegram Bot API with smart rate limiting, (4) Implemented MongoDB-based tracking for delivery status, retries, and analytics, (5) Set up cron jobs for scheduled signal batches and real-time webhooks for urgent signals.
# Results
The system scaled daily signal output from 5-7 to 25+ without adding headcount. Signal delivery latency dropped from ~10 minutes to under 60 seconds. Manual errors were eliminated entirely, and the analytics dashboard I built gave leadership real-time visibility into signal performance across all channels.