Human-Approved Crypto DCA Using Indicators and AI Sentiment

Summary

This document outlines an automated crypto dollar-cost-averaging workflow that runs hourly. It gathers daily market candles to calculate MACD, RSI, ATR, and OBV, and retrieves recent crypto news for an AI model to assess short- and long-term sentiment. The model uses these inputs to choose among four actions: increase the investment substantially, increase it moderately, make a standard investment, or pause. A user reviews each proposed action before any spot market purchase is placed.

The workflow also records fills or declined proposals, tracks balances and account value, and displays decision history. A Telegram channel is described for querying account and trade information. The material explains a process rather than providing strategy performance evidence, and it gives no backtest results. Its reliance on model-generated sentiment and user approvals means that decision quality, consistency, and execution timing are not established by the description; the indicators and action categories alone do not specify a fully reproducible sizing rule.

Key ideas

• The workflow combines daily-candle MACD, RSI, ATR, and OBV readings with AI analysis of crypto news sentiment.
• It runs hourly and selects among three investment levels or a pause decision.
• A human must approve a proposed purchase before the system submits a spot market order.
• The system records decisions and execution details while tracking balances, account value, and performance since initialization.
• The document describes an operating workflow but supplies no backtest or evidence that its decisions are profitable.

Tags

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.