Building and Backtesting a Five- and Twenty-Day Moving Average Strategy

Summary

This tutorial introduces MindGo’s strategy workflow through a simple stock trading example. It explains the roles of initialization and recurring data handling, then describes retrieving recent closing prices, calculating five-day and twenty-day averages, and placing orders according to their relationship. The example selects a single Chinese stock and uses available cash to buy when the shorter average is higher, then exits when it is lower and a position exists.

The article also outlines how to configure a daily backtest with a specified date range and starting capital, and how to enable simulated trading signals and app notifications. It provides implementation examples and platform steps, but reports no performance metrics or comparison against a benchmark. The sample checks whether one average is above or below the other rather than explicitly detecting a crossover, so it can repeatedly submit buy orders while the condition holds. It also omits transaction costs, slippage, position limits, and risk controls; its backtest and paper-trading workflow does not establish live profitability.

Key ideas

• The platform separates one-time account initialization from recurring market-data handling.
• The example calculates five-day and twenty-day averages from recent daily closing prices.
• It buys with available cash when the short average exceeds the long average and exits when it falls below.
• MindGo supports backtesting and simulated trading with notifications, but the tutorial gives no evidence of strategy performance.
• The sample omits transaction costs, slippage, and explicit risk controls.

Tags

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