Measuring EURUSD Trend Statistics with a Python Swing-Point Method

Summary

The document outlines a Python workflow for studying forex trends using historical data from MetaTrader 5. It identifies local highs and lows with a centered rolling window, alternates successive extremes to form uptrends and downtrends, and records each move’s dates, duration, point magnitude, and percentage change. The window size controls sensitivity: smaller windows detect more short moves, while larger ones yield fewer, longer segments. The workflow then calculates summary statistics and creates visualizations of trend distributions.

The article applies the analysis to EURUSD and discusses using observed trend characteristics to shape targets, holding periods, and position management. Its conclusion describes hourly trends as typically lasting about five days and measuring 150–180 points, and it recommends adding to positions at stated movement thresholds while limiting aggregate risk. These findings are specific to the data, market, timeframe, and identification choices used. Centered windows rely on future bars to confirm extrema, so detected turning points are not immediately available in live trading; the text also notes that local-extreme methods can miss other price structures.

Key ideas

• A centered rolling window identifies local highs and lows, which are then sequenced into trend segments.
• Window size changes how many trends are found and their typical duration and magnitude.
• The workflow summarizes trend duration, point movement, and percentage change, with separate statistics for rising and falling moves.
• The document reports EURUSD hourly trend characteristics and uses them to motivate trend-following position additions and aggregate risk limits.
• Centered-window extrema require later bars for confirmation, and results depend on the sample and trend definition.

Tags

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