![]() ![]() ![]() Those trend origins may offer high reward:risk ratio opportunities. When the price breaks both moving averages, the long- and the short-term trend direction is about to change. The screenshot below also includes the long-term Daily 50-period moving average (blue) and the 1H 50-period moving average (green). ![]() The most important principle is that once you have chosen a moving average setting, you don’t change it again for the next 100 to 200 trades.Īgain, don’t stress about winning every single trade and learn to let winners run and cut losses short. First, take a look at the chart below wherein three exponential moving averages (EMAs) are plotted: the 10-day EMA (representing the short-term trend), the 50-day EMA (representing the medium-term trend), and the 200-day EMA (representing the longer-term trend). The 50-period MA is generally considered a medium-term moving average and it works well for various use cases. In my trading, I settled for a 50 period moving average. This leads to inconsistent trading results and a lot of frustration. Of course, some traders like to use the weighted (WMA) or the exponential moving averages (EMA), but most of the time and most traders use the simple 50, 100 and 200-period moving averages on their charts. I have seen countless traders that constantly jump around different moving average settings. Generally, though, the most popular calculation for the 50, 100 and 200 period moving averages is the simple moving average (SMA). Many people go crazy when it comes to the period setting of their moving average. The Exponential Moving Average (EMA) was designed to fix the problem with the excessive lag the SMA suffers from. Therefore, trying to align the long-term and short-term trend direction may lead to smoother trading results. Thus, trading such a trending move may be much harder. The downtrend on the left shows significantly more volatility and the price action is not as clear. In the screenshot below the uptrend on the left moves much smoother without a lot of volatility – those moves are generally easier to trade. In an overall long-term uptrend, short-term bullish trends may be much easier to trade because it’s in line with the big picture direction. Thus, the price is in an overall long-term uptrend. Over the whole screenshot, the price is above the daily 50-period moving average. The timeframe of the screenshot is the 1H and the Daily moving average helps us understand the overall trend direction. In the screenshot below I plotted a 50-period moving average from the Daily chart (blue line). When it comes to using moving averages, there are endless ways for how you can go about it.Īnother of using moving averages is as guidance to understand the higher-timeframe perspective. However, the professionals accept that their trading system will not have a high winrate and instead focus on letting winners run and cutting losses short. Amateur traders try to avoid losses at all costs. Hence, for this reason, traders prefer the use of the EMA over the SMA. This helps the trader to take quicker trading decisions. So which one is better With moving averages in general, the longer the time period, the slower it is to react to price movement. Most amateur traders will go broke because they try to achieve a winrate of 90% or 95%. EMA is quicker to react to the current market price because EMA gives more importance to the most recent data points. This is THE most important principle when it comes to using any trading strategy successfully. Thus, obsessing about which type of moving average is better is a waste of time – especially once we get into the other points shortly. I plotted the 50 period EMA and the 50 period SMA on the chart below and you can see right away that the two moving averages are mostly very close together. In this article, and in the video above, I provide the most important tips when it comes to using moving averages the right way.įirst, it’s important to realize that the difference between the EMA and the SMA is not significant. The function is self-explanatory as it merely reproduces the EMA function presented above.I get often asked about the best moving average and how to use moving averages the right way. In python language, we can define a function that calculates the EMA as follows: def ema(Data, alpha, window, what, whereSMA, whereEMA): # alpha is the smoothing factor # window is the lookback period # what is the column that needs to have its average calculated # where is where to put the exponential moving average alpha = alpha / (window + 1.0) beta = 1 - alpha # First value is a simple SMA Data = np.mean(Data) # Calculating first EMA Data = (Data * alpha) + (Data * beta) # Calculating the rest of EMA for i in range(window + 1, len(Data)): try: Data = (Data * alpha) + (Data * beta) except Inde圎rror: pass return Data Note, that if we increase the smoothing factor (also known as alpha) then, the more recent observations will have more weight. ![]()
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