Human-Approved Crypto DCA Using Indicators and News Sentiment
Summary
This workflow automates a dollar-cost averaging process for cryptocurrency spot purchases. On a recurring hourly cycle, it gathers market data to calculate MACD, RSI, ATR, and OBV, and retrieves recent crypto news for AI-based short- and long-term sentiment assessment. A language model combines those inputs and selects among increased, standard, or paused investment decisions according to the stated framework.
A human reviews each proposed action before execution. Approval sends a spot market buy; rejection is logged without a trade. The workflow also tracks balances, holdings, portfolio value, decisions, and cumulative return, and provides a Telegram channel for account queries and historical records. This is an operational design description, not evidence of investment performance: no backtest or measured results are given. Decisions depend on model interpretation, data and service availability, and manual response; the document does not establish that its sentiment or indicator rules produce an advantage.
Key ideas
- The workflow combines MACD, RSI, ATR, and OBV with AI analysis of cryptocurrency news.
- A language model classifies market conditions into DCA investment or pause decisions.
- A human approval step gates spot market purchases, while rejected proposals are recorded.
- Account values and decision history are tracked, with Telegram commands supporting monitoring.
- The document describes system mechanics but provides no evidence that its signals improve returns.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.