All language subtitles for 3. Create a simple moving average (SMA)

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Would you like to inspect the original subtitles? These are the user uploaded subtitles that are being translated: 1 00:00:10,820 --> 00:00:12,800 Hi, everyone, and welcome in this new video. 2 00:00:13,190 --> 00:00:18,440 In this video, I will show you how to create a simple, moving average using pandas. 3 00:00:21,050 --> 00:00:31,880 So first, we're going to create a new colors in or dataframe, which is named as semi for simple moving 4 00:00:31,880 --> 00:00:39,020 average 15, because we're going to take a simple moving average on 15 days. 5 00:00:40,650 --> 00:00:48,410 Then we need to take the clothes column of our data frame. 6 00:00:49,750 --> 00:00:58,610 Then we use the rolling function to create a wall on the 15 day. 7 00:01:02,770 --> 00:01:03,130 And. 8 00:01:05,650 --> 00:01:10,360 All 15, then we want to apply the mean function. 9 00:01:13,780 --> 00:01:20,110 So we do exactly the same for the Assembly 16. 10 00:01:23,490 --> 00:01:28,110 And I will show you all the different. 11 00:01:30,210 --> 00:01:31,860 I will just create. 12 00:01:34,270 --> 00:01:40,220 A very lethal estimate to show you something extremely important. 13 00:01:44,750 --> 00:01:53,260 Have advocated the semi free state, so it's very not relevant for all strategy, but it is really just 14 00:01:53,260 --> 00:01:58,390 to show you something extremely important here. 15 00:01:58,810 --> 00:02:10,840 We need to put a shift on all indicators because if you look the estimate free days, we can see that 16 00:02:12,790 --> 00:02:19,960 this mean is the average of these three values and. 17 00:02:22,380 --> 00:02:31,560 If the cruise price, for example, is the targets, we will have an interference in our data because 18 00:02:33,060 --> 00:02:42,210 if we take this value to protect this value, so this value is already in the average. 19 00:02:42,600 --> 00:02:50,640 So theoretically, when you are going to be tested, you will have an amazing result. 20 00:02:50,670 --> 00:02:54,390 You will have a strategy very profitable. 21 00:02:54,720 --> 00:03:01,680 But in reality, it is because you predict the past using the future. 22 00:03:02,190 --> 00:03:13,980 So it is the most important thing to understand because this error is one of the must made. 23 00:03:14,520 --> 00:03:19,620 So for example, if here I put a shift. 24 00:03:24,850 --> 00:03:33,640 We can see that this value is the mean of these values. 25 00:03:34,060 --> 00:03:43,900 So there is no issue if I want to predict this value with this value because we have put a shift in 26 00:03:43,900 --> 00:03:52,900 our data and very are no interference between the SMI free days and the cruise price that we want to 27 00:03:52,900 --> 00:03:53,410 predict. 28 00:03:54,760 --> 00:04:00,610 So we are going to delete this 29 00:04:03,820 --> 00:04:04,300 columns. 30 00:04:04,510 --> 00:04:10,360 But I would devote once that you understand this notion because 31 00:04:13,540 --> 00:04:22,420 if you don't put the shift in the right place, you will have many issues in. 32 00:04:22,450 --> 00:04:26,590 You could and your anger with trading project. 33 00:04:38,570 --> 00:04:45,110 Now, let me just show you how to display this value to have a better understanding. 34 00:04:55,880 --> 00:05:02,000 To do it, we are going to use the plug function directly from pundits. 35 00:05:05,810 --> 00:05:14,360 Because it's more easy to use when you just want to display some metrics. 36 00:05:14,960 --> 00:05:25,550 Then we just use the let property to display the value in 2010 to have a better visualization. 37 00:05:44,420 --> 00:05:53,930 So as we can see, we have the cruise price here and all too simple moving average. 38 00:05:56,470 --> 00:06:02,800 So it is awful this video and in the next video, I will show you, how did you quit moving standard 39 00:06:02,800 --> 00:06:03,580 deviation? 3954

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