A Moving-Average Crossover Strategy Connected to OANDA Data

Summary

This article outlines a Python workflow for retrieving historical market data through OANDA, storing it locally, and evaluating a simple trading rule. It describes selecting an instrument, date range, and granularity, handling data in chunks, and saving observations in a binary store. The example strategy compares short and long rolling means calculated from minute bars. A crossover upward generates a long signal, while the implementation is described as long-only. The article then compares strategy and market returns and considers trade count and return variability.

The reported illustration covers a selected historical interval and notes that the strategy loses during some market segments. Its performance observations are not a robust validation: the text gives limited detail on costs, slippage, risk controls, parameter selection, or out-of-sample testing. The high trade count for the short sample highlights the importance of implementation costs. The post presents a basic research and backtesting workflow, while real-time streaming and automated order execution are left for a separate discussion.

Key ideas

• The workflow retrieves historical OANDA data by instrument, time range, and bar granularity, then stores it for analysis.
• The example signal compares short and long rolling averages and takes long exposure when the short average rises above the long average.
• The strategy evaluation considers cumulative returns, number of trades, and return standard deviation.
• The example shows losses in some market segments and does not establish performance after trading costs or out-of-sample validation.

Tags

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