Automating Multi-Timeframe Crypto Analysis with News Sentiment and AI
Summary
This document describes a workflow that combines cryptocurrency market data and news to produce analysis reports for delivery through Telegram. It collects candlesticks across 15-minute, hourly, and daily timeframes, then asks a language model to score short- and long-term sentiment from recent news. A second model combines those sentiment outputs with the price data to generate spot and leveraged trade suggestions, including proposed entries, stops, and targets.
The workflow is presented as a modular, scheduled system built from visual nodes, with configurable trading pairs and automated message formatting. The document explains the stages and intended outputs, but provides no measured forecasting or trading results. Its suggested levels and reasoning are AI-generated analysis, not evidence of a profitable strategy; the workflow does not itself define validated signal rules or risk controls. Its claimed ease of use and analysis quality are not demonstrated, and automated leveraged recommendations require independent review.
Key ideas
- The workflow gathers candlestick data at three timeframes and recent cryptocurrency news.
- A language model converts news into short- and long-term sentiment categories and scores.
- A second model combines sentiment with price data to produce proposed spot and leveraged trades.
- Reports are formatted and automatically sent to a configured Telegram destination.
- The document offers no performance evidence or validation of the generated recommendations.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.