Building a Multi-Timeframe Crypto Analysis and News Workflow
Summary
The document outlines an automated workflow for combining cryptocurrency market data and news into recurring trading reports. It collects candlesticks across 15-minute, hourly, and daily intervals, standardizes and merges them, and retrieves recent articles for an AI model to classify short-term and long-term sentiment. A second analysis step is prompted to consider support and resistance, trend direction, and market context, then produce spot and leveraged trade recommendations. The report is divided into messages for distribution through Telegram.
The examples show how to connect data collection, sentiment scoring, multi-timeframe analysis, and delivery in a visual workflow system. They provide implementation details, but no measured accuracy, trading performance, or evidence that the generated recommendations are profitable. The sentiment prompt and analysis instructions depend on an AI model, while the document does not describe validation, data quality checks, position sizing, or risk controls. Its proposed extension to automated execution would therefore need independent testing and safeguards before use.
Key ideas
- Combine market candles from multiple timeframes into a consistent input for analysis.
- Use recent news headlines and descriptions to generate separate short-term and long-term sentiment assessments.
- Prompt an AI system to consider technical levels, trend direction, and sentiment when preparing recommendations.
- Split generated reports into sections before distributing them through a messaging channel.
- The workflow describes information processing, but does not establish recommendation accuracy or profitability.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.