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An intelligent trading bot for the crypto futures market that extracts signals from Telegram channels using LLMs and executes trades automatically on brokers.
We designed and implemented an AI-powered trading bot for the cryptocurrency futures market. The system integrates with Telegram channels, extracts trading signals using Large Language Models (LLMs), and automatically manages buy/sell positions with integrated broker APIs.
The goal was to automate the manual process of monitoring multiple channels, interpreting trading strategies, and executing trades in real time — reducing human error and maximizing reaction speed.
Unstructured Data: Telegram signals often vary in format, with different notations for stop-loss, take-profit, and leverage.
Real-Time Requirements: The bot needed to parse messages instantly and trigger trades with minimal latency.
Broker Integration: Ensuring reliable API communication with crypto exchanges/brokers under heavy load.
Risk & Security: Handling funds securely, applying stop-losses, and protecting against bad signals or API failures.
Our solution combined NLP, automation, and trading infrastructure:
Signal Processing with LLMs: We trained and tuned a custom pipeline to parse Telegram messages, normalize trade signals, and extract structured data (symbol, entry price, stop-loss, take-profit, leverage).
Trade Execution Engine: The bot integrated directly with broker APIs to place orders, monitor positions, and manage risk (stop-loss, trailing stop, etc.).
Monitoring & Logging: A dashboard for tracking trades, signal accuracy, and performance in real time.
Fail-Safes: Secure key management, fallback handling for malformed signals, and risk limits to prevent catastrophic losses.
The AI-driven trading bot achieved:
🚀 Faster execution than human traders, often reacting within seconds to signals.
🎯 Accurate parsing of inconsistent message formats thanks to LLM processing.
📊 Performance insights, with logged trades enabling backtesting and signal source comparison.
🔒 Improved reliability and safety, with automated risk management built in.
This project showcased Hippogriff’s ability to combine AI/LLMs, automation, and financial systems engineering to create cutting-edge fintech solutions.
