WEBVTT 00:01.150 --> 00:06.250 In this video you will learn one of the most frequently used applications for moving averages and the 00:06.250 --> 00:09.690 finance and investment industry for decades. 00:09.700 --> 00:16.240 Financial traders and in particular traders who rely on technical analysis rather than fundamental analysis 00:16.570 --> 00:22.970 rely on so-called momentum strategies with a simple moving averages and you will see in a minute. 00:22.990 --> 00:25.020 The intuition behind the strategy. 00:25.300 --> 00:30.990 But first of all we still have imparted the S&P 500 closed data. 00:31.000 --> 00:39.420 So let's have a look at the first five data points and the last five data points and let's create a 00:39.420 --> 00:47.250 brand new column called A simple moving average is 50 with um the simple moving average of the close 00:47.250 --> 00:50.040 price with a window of 50. 00:50.460 --> 00:56.520 So we're using the other rolling method on the S&P 500 data frame with a window of 50. 00:56.520 --> 01:04.800 And we also need 50 periods to calculate the simple moving average and we change the mean method because 01:04.830 --> 01:08.180 we want to calculate moving averages. 01:08.340 --> 01:14.670 So let's do this here and let's have a look here at our data frame and we can see here that we have 01:14.760 --> 01:18.550 successfully added the column simple moving averages. 01:18.690 --> 01:25.090 50 and it's actually no surprise that for the first forty nine timestamps we have here any anywhere 01:25.130 --> 01:30.090 use because for these times stands we do not have 50 data points. 01:30.090 --> 01:34.140 So we have only nominal values in those columns starting from the 15th. 01:34.150 --> 01:38.190 The time stamp until the very last time stamp here. 01:38.190 --> 01:42.530 So here we can see the very last day of 2018. 01:42.630 --> 01:49.650 So here we have the price for the S&P 500 and this is actually the simple moving average for the last 01:49.650 --> 01:57.740 the preceding 50 days and now we can also plot the S&P 500 data frame with our two columns. 01:57.780 --> 02:02.450 So the actual close price and uh the simply moving average is 50. 02:02.880 --> 02:09.890 And of course you can do this here with the plot method so let's have a look here so we can see here 02:09.890 --> 02:13.670 in blue are the close price and in green. 02:13.700 --> 02:16.310 The simple moving average is 50. 02:16.910 --> 02:22.350 And we can see here that uh with uh moving averages the kind of smoothing. 02:22.370 --> 02:22.540 Yeah. 02:22.540 --> 02:23.330 Our data. 02:24.170 --> 02:31.010 So here at each and every timestamp we have actually the average for the preceding 50 timestamps. 02:31.070 --> 02:35.000 So therefore we have kind of a smooth and curve. 02:35.030 --> 02:41.000 So now let's move on and also create a column for the simple moving average 200. 02:41.030 --> 02:48.360 So here we have a rolling window of 200 timestamps and again we can do this year with the rolling method 02:48.390 --> 02:55.710 and past 200 to the windows perimeter and we want to calculate the simple moving average with the mean 02:55.710 --> 03:00.190 method so let's have a look here. 03:00.210 --> 03:06.870 Here we have the last five timestamps or for the simple moving average is two hundredths or the first 03:06.870 --> 03:12.870 one hundred ninety nine timestamps in our data frame that we have actually and a values party and the 03:12.870 --> 03:13.490 very end. 03:13.490 --> 03:15.710 Of course we have values. 03:15.780 --> 03:19.100 For the simple moving average 50 and 200. 03:19.200 --> 03:22.060 And let's also call you at the info method. 03:22.740 --> 03:29.010 And we can see here that in the close column we have two thousand five hundred sixty nine values and 03:29.010 --> 03:35.260 consequently for the SMI 50 year we have forty nine fewer data points and for the estimated two hundred 03:35.280 --> 03:38.910 we have one hundred and ninety nine fewer data points. 03:38.920 --> 03:41.770 And now let's also plot all the three columns here. 03:41.790 --> 03:43.790 So let's do this. 03:43.800 --> 03:50.070 So here we have uh the close column the S M A 50 and the ESA 200 column. 03:50.070 --> 03:56.460 And we can also see here that the S M A 50 is that's forty nine timestamps later and the S and may one 03:56.450 --> 03:59.370 hundred ninety nine timestamps later. 03:59.560 --> 04:01.790 And now let's only plot the estimate curve. 04:01.810 --> 04:06.960 So we're actually only plotting the last two columns here. 04:06.960 --> 04:11.310 So here we f and blue are the simple moving average is 50 and green. 04:11.310 --> 04:18.660 The simple moving average is 200 and it's no surprise that the estimate 200 is even more smooth than 04:18.690 --> 04:24.030 the SMI 50 and we could also say that the shorter term estimate. 04:24.030 --> 04:31.880 So the S and may 50 is actually reacting faster to price changes than the estimated 200. 04:31.920 --> 04:38.790 So for example here the blue line starts to decrease sharply and actually the Green Line takes some 04:38.790 --> 04:41.970 more time to actually also decrease. 04:41.970 --> 04:45.750 And this is actually the basis for a very simple trading strategy. 04:45.750 --> 04:49.860 So we have two simple moving averages with different windows. 04:49.860 --> 04:56.800 So we have one longer term window so you two hundred and one shorter term window 50. 04:57.030 --> 05:04.200 And the idea is that actually the trader invests when the shorter term S.M. A is above the longer term 05:04.230 --> 05:10.050 as an A and you should divest or even go short when the opposite holds true. 05:10.050 --> 05:15.710 So in this example here whenever the blue line is above uh the green line then we should invest into 05:15.720 --> 05:17.530 the S&P 500. 05:17.670 --> 05:25.170 So it's here for example or here or in this period and whenever the blue line is uh below the green 05:25.170 --> 05:25.980 line. 05:25.980 --> 05:34.230 So for example here or here or here then we should divest from the S&P 500 or even go short. 05:34.380 --> 05:41.370 And the rationale behind this is that the shorter assume a captures the most recent trends or the momentum 05:41.850 --> 05:46.710 and that the most recent trend will persist also in the near future. 05:47.370 --> 05:49.650 So for example let's have a look here. 05:49.680 --> 05:52.050 So here initially our blue line. 05:52.050 --> 06:00.240 So the s may 50 is below the green line and in this case we are not invested in the S&P 500 or even 06:00.240 --> 06:02.640 we are short the S&P 500. 06:02.820 --> 06:11.190 But then the S&P 500 starts to increase and also the asset may 50 increase US uh faster than the S&P 06:11.220 --> 06:12.660 200. 06:12.690 --> 06:17.130 So the most recent trend or the most recent momentum is positive. 06:17.280 --> 06:24.150 And therefore when both lines are crossing here and the blue line goes above the green line then we 06:24.150 --> 06:26.830 should invest in the S&P 500. 06:27.030 --> 06:31.140 And this is in this case here for a very long time period. 06:31.560 --> 06:40.980 So we should invest here in the year 2012 and then we should divest into a 16 and then for a short term 06:40.980 --> 06:41.430 period. 06:41.430 --> 06:42.390 We are not investors. 06:42.450 --> 06:50.470 And then from to 16 to to 18 or to 19 we sought to again be invested in the S&P 500. 06:50.520 --> 06:58.560 So this is the rationale behind this trading strategy and this is actually based on simple moving averages 06:58.740 --> 07:02.940 and the opposite of a momentum strategy would be a contrarian strategy. 07:03.030 --> 07:07.650 So we are finished with this video and I hope to see you also in the next one by.