[Python] Trading Simulation With Moving Average
Moving average is common and well-known statistical calculation, widely used to smooth out fluctuations or short-term noise in data. It is also commonly applied in finance and trading, it serves to analyze the historical price data of an asset like stocks or foreign currencies in order to identify trends and potential trading opportunities. There are three types of moving averages: Simple Moving Average (SMA), Exponential Moving Average (EMA), and Weighted Moving Average (WMA). Through Python coding, we’ll explore how these moving averages can be harnessed for trading simulations.
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Disclaimer: This article serves as an illustrative example of Python coding within a quantitative finance context. None of the content should be construed as investment or trading advice. Investing and trading inherently involve risks, and past performance does not guarantee future results. It is utterly crucial to conduct thorough research and consider your risk tolerance before engaging in any financial activities.
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To kick off, let’s fetch historical stock data. We’ve chosen to use the Apple Inc. (AAPL) and will retrieve its market data from January 2022 to November 2023 through…