![]() ![]() The purpose of this blog is to provide insight into the 200-day moving average primarily as a risk management tool and explore the historical context of the behavior of the S&P 500 when it closes both above and below its own 200-day moving average. Some will posit that this indicator is an outdated relic of the past, harking from the days when charts were drawn by hand and numbers rounded to provide quicker calculation, while others elevate the indicator in isolation as the holy grail of all trading systems. Its uses are limited by the skill and experience of its user rather than its own merit. However, it’s just a tool like anything else at the end of the day. Roughly equivalent to ten months of trading, this measure of long-term trend has found uses in everything from trading to risk management. It is one of many different types of moving averages and has an easily calculable formula.The 200-day moving average is arguably the most widely cited Technical Analysis indicator among financial media journalists, investment analysts, and portfolio managers alike. The Exponential Moving Average (EMA) is a moving average and technical indicator that reflects and projects the most recent data and information from the market to a trader and relies on a base of historical data. In addition, the EMA relies on historical data as its basis for operating and because news, events, and other information can change rapidly the indicator can misinterpret this information by weighting the current prices higher than when the event actually occurred. LimitationsĪlthough using the Exponential Moving Average has a lot of advantages when analyzing market trends, it is also uncertain whether or not the use of most recent data points truly affects technical and market analysis. Therefore, price movement and trend reversals or changes are closely monitored, allowing for the EMA to react quicker than other moving averages. The EMA is unique because it places more emphasis on the most recent data. It is difficult to modify the moving average to work in your favor at times, often having the preferred time to enter or exit the market pass before the moving average even shows changes in the trend or price movement for that matter.Īll of this is true, however, the EMA strives to make this easier for traders. It is important to identify and realize, however, their shortcomings, as all moving averages tend to suffer from recurring lag. ![]() ![]() Moving averages can be very useful for traders using technical analysis for profit. Similarly, the 50- and 200-day moving averages are most common for analyzing long-term trends. Look into Moving Average Convergence Divergence (MACD) for more information. This is because they are used to create specific indicators. It is most common for traders to quote and utilize 12- and 26-day EMAs in the short-term. ![]() Short-term averages, on the other hand, is a different story when analyzing Exponential Moving Average data. Analyzing these points and data streams correctly will help the trader determine when they should buy, sell, or switch investments from bearish to bullish or vice versa. An experienced trader will know to look both at the line the EMA projects, as well as the rate of change that comes from each bar as it moves to the next data point. It’s best to use the EMA when for trending markets, as it shows uptrends and downtrends when a market is strong and weak, respectively. For example, by choosing 10-day and 200-day moving averages, a trader is able to determine more from the results in a long-term trade, than a trader who is only analyzing one EMA length. It is common to use more than one EMA length at once, to provide more in-depth and focused data. Additionally, the EMA tries to amplify the importance that the most recent data points play in a calculation. Similar to other moving averages, the EMA is a technical indicator that produces buy and sell signals based on data that shows evidence of divergence and crossovers from general and historical averages. The Exponential Moving Average is equal to the closing price multiplied by the multiplier, plus the EMA of the previous day and then multiplied by 1 minus the multiplier.ĮMA = Closing price x multiplier + EMA (previous day) x (1-multiplier) Takeaways To calculate the EMA, follow this simple formula. The more a trader increases the smoothing factor value, the more influence the most recent data will have on the moving average. This value gives more credibility to the most recent data points available. Although there are many options to choose from when considering the smoothing factor, most opt for a value of 2. ![]()
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