Multi-Timeframe Crypto Analysis with News Sentiment and AI Reports
Summary
This document outlines an automated cryptocurrency analysis workflow. It collects 200 candles each from 15-minute, hourly, and daily timeframes, alongside recent crypto news. A language model classifies news sentiment over short and long horizons, assigning a category, score, and rationale. A second analysis stage combines those sentiment outputs with price action, trends, and support and resistance levels to produce spot and leveraged trade suggestions with entries, stops, targets, and brief explanations.
The workflow is designed to run on a schedule and send formatted reports to a Telegram channel or group. Its described outputs are model-generated analysis, not demonstrated trading results. The document offers no validation of sentiment accuracy, forecast quality, or profitability, and it does not explain how proposed leverage or position sizes are determined. The workflow description therefore illustrates an information and reporting pipeline rather than evidence of a tested trading strategy.
Key ideas
- The workflow combines crypto candles from intraday and daily timeframes with recent news.
- A language model converts news into short-term and long-term sentiment categories and scores.
- A second model uses market data and sentiment to draft spot and leveraged trade plans.
- Reports are formatted and delivered automatically through Telegram.
- The document provides no evidence that the model outputs are accurate or profitable.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.