Using OHLCV Candle Data to Calculate Common Technical Indicators
Summary
The document explains how an AI-assisted analysis workflow can use exchange candle data to calculate moving averages, RSI, MACD, and Bollinger Bands. It identifies symbol, timeframe, open, high, low, close, volume, and timestamp as useful fields, while noting that most of these indicators primarily use closing prices. The proposed process is to retrieve candles, validate ordering and completeness, calculate indicators with a library or analytics service, and return values with their timeframes and settings before interpreting them.
The article frames MCP as a possible data-access layer rather than assuming it computes indicators itself. It describes the underlying inputs at a high level: averages use price observations, RSI compares gains with losses, MACD uses exponential averages, and Bollinger Bands combine an average with a standard-deviation range. It provides no worked calculations or performance evidence. Results can be misleading when candles are stale or inconsistent, timeframes disagree, or an agent overstates weak signals; indicator readings are decision support and should be considered alongside risk controls.
Key ideas
- MCP can retrieve OHLCV candles for a separate indicator-calculation step.
- Data validation should check timestamps, missing or duplicated candles, and numeric fields.
- Moving averages, RSI, MACD, and Bollinger Bands derive from price history using different calculations.
- Indicator readings can conflict across timeframes and can produce false signals.
- AI explanations should include calculation context and should not replace risk management.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.