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Would you like to inspect the original subtitles? These are the user uploaded subtitles that are being translated: 1 00:00:11,650 --> 00:00:14,110 Hi, everyone, and welcome in this new video. 2 00:00:14,650 --> 00:00:20,440 In this video, we will create a first moving averages. 3 00:00:21,430 --> 00:00:29,890 So first, we need to insert why finance then import from this surreal libraries and run this sale, 4 00:00:29,890 --> 00:00:35,980 which allow those of you who work in the dark modes to have the growth in a dark mode. 5 00:00:38,230 --> 00:00:43,780 Then I have just copy paste, the preprocessing function for all that data, which come from MetaTrader 6 00:00:43,780 --> 00:00:50,740 five and for those which come with from we finance. 7 00:00:51,070 --> 00:00:51,370 OK. 8 00:00:52,600 --> 00:01:01,060 In this chapter, we will work with the white finance data and at the end of this chapter, we will 9 00:01:01,060 --> 00:01:03,850 try to create the same strategy on. 10 00:01:05,250 --> 00:01:13,080 The meter of flight data to see if there is a difference in the profits of the strategy or not. 11 00:01:14,550 --> 00:01:18,300 So I am already creates this data frame. 12 00:01:18,450 --> 00:01:25,710 And then we need to create a simple moving averages to create it. 13 00:01:26,070 --> 00:01:27,810 It's very, very is it? 14 00:01:29,260 --> 00:01:31,880 So first, we need to create the estimate. 15 00:01:32,290 --> 00:01:39,810 Thus, it means that this assembly will be the movie simple moving average. 16 00:01:41,340 --> 00:01:44,130 With the little. 17 00:01:46,170 --> 00:01:51,120 They OK, the the little ruling, if you want. 18 00:01:54,590 --> 00:02:04,160 So we need to take the first price then to apply the warning function and then we use the mean function. 19 00:02:05,150 --> 00:02:11,990 OK, so we already have seen all of it in the. 20 00:02:14,310 --> 00:02:16,370 Chatter by them for total science. 21 00:02:16,550 --> 00:02:24,420 OK, so you feel not comfortable, I will advise you to check again this year, but here. 22 00:02:25,830 --> 00:02:30,070 It's very simple, we take the cross price, we apply the learning function. 23 00:02:30,250 --> 00:02:38,220 OK, I will show you what this function does again and then we apply the meat. 24 00:02:38,520 --> 00:02:39,900 OK, so if I. 25 00:02:42,870 --> 00:02:44,160 Show you the dataframe. 26 00:02:44,610 --> 00:02:49,290 I will just take, for example, three day four of the. 27 00:02:49,890 --> 00:02:50,190 OK. 28 00:02:50,760 --> 00:02:56,490 We take the three points here and we do the average. 29 00:02:56,790 --> 00:02:59,640 Then we put the value here. 30 00:03:00,750 --> 00:03:09,090 Then we take this three prices, and we do we make the average and we put it here. 31 00:03:09,390 --> 00:03:18,360 Then we take this three values, we make the average and we put it here again and again and again. 32 00:03:18,600 --> 00:03:18,950 OK. 33 00:03:19,890 --> 00:03:23,190 This is how retreat the moving average. 34 00:03:28,610 --> 00:03:36,830 So now we need to create the estimated slope, and I will choose 16 days. 35 00:03:38,440 --> 00:03:46,390 All the day for the women, for the first assignment and the slow smile. 36 00:03:47,840 --> 00:03:49,580 Oh, very important. 37 00:03:49,730 --> 00:03:54,710 So you can try to optimize then using, for example. 38 00:03:56,730 --> 00:04:07,590 The same method as that we have used in the course Python for algorithmic trading technical analysis 39 00:04:07,590 --> 00:04:08,340 strategies. 40 00:04:08,580 --> 00:04:08,900 OK. 41 00:04:09,180 --> 00:04:17,640 But here is not the purpose of this discourse is really to give you the basics knowledge. 42 00:04:18,000 --> 00:04:18,390 OK? 43 00:04:18,730 --> 00:04:24,930 Notes to show you how to optimize and do very complex things. 44 00:04:25,050 --> 00:04:25,440 OK. 45 00:04:25,620 --> 00:04:25,950 But. 46 00:04:27,620 --> 00:04:30,680 If you want to go deeper, you can take this course. 47 00:04:32,560 --> 00:04:43,300 Then we will just plug the results to see if we compute well or not what we want. 48 00:05:04,580 --> 00:05:12,620 So here it's not really readable, so we will take only one here, for example, 2020. 49 00:05:21,650 --> 00:05:29,750 So, Hugh, as we can see, we have politely compute or assignment and in the next video. 50 00:05:30,840 --> 00:05:38,430 I will explain you the strategy to which use the moving averages. 4568