WEBVTT 00:02.010 --> 00:08.010 This is a short overview on this costs and a guidance how to maximize your learning experience and how 00:08.010 --> 00:14.160 to customize the costs in a way that best fits your interests and your current level of knowledge. 00:14.160 --> 00:20.060 Typically students have different skill sets and educational backgrounds so some have already good panels 00:20.100 --> 00:27.480 coding skills with no specific finance background and others are completely new to Python or us. 00:27.660 --> 00:31.700 And in the next minutes I will give you some helpful guidance how to tackle this costs. 00:31.740 --> 00:36.280 Given your specific background and I will start with the cough. 00:36.300 --> 00:37.280 This costs. 00:37.320 --> 00:44.340 So that's the part to managing financial data and it's first of all have a look at the cost dashboard. 00:45.710 --> 00:51.410 We are here on the dashboard and we can get an overview on the costs by clicking here to cost content 00:52.050 --> 00:58.730 and we are currently in the section 1 getting started and you will learn how to download and install 00:58.730 --> 01:01.160 our Python coding environment. 01:01.160 --> 01:06.770 And if you have already installed the anaconda distribution and if you have already experience with 01:06.770 --> 01:11.440 the Jupiter notebooks then you might also skip those three lectures here. 01:12.630 --> 01:20.730 And the car or the heart of this course is actually part two financial data analysis and the very first 01:20.730 --> 01:27.630 section and part 2 is a more general section where you will learn the required basics on how to work 01:27.630 --> 01:30.070 with the dates and times and pandas. 01:30.090 --> 01:35.360 After that we will start with financial data analysis and let's have a closer look about. 01:35.490 --> 01:42.200 We will cover here at the beginning of Section 2 you will learn and master your basic and essential 01:42.200 --> 01:47.210 workflows that you will frequently need when working with financial data. 01:47.210 --> 01:53.780 So for example you will learn how to impart financial data not only from Excel files and CSP files but 01:53.780 --> 01:57.770 also from a free web sources like Yahoo Finance. 01:57.770 --> 02:05.720 And then once we have imparted sorry to financial price data then we calculate the return risk and correlations 02:05.960 --> 02:13.490 and we also up sample and down sample price data and the returns from daily to weekly monthly annual 02:13.490 --> 02:20.300 and so on and we will also learn the difference between simple returns and lucky returns. 02:20.330 --> 02:30.750 All right then we will come to a more advanced analysis techniques and we will calculate Rowling's statistics 02:30.810 --> 02:37.390 so for example we will calculate simple moving averages and the rolling return and rolling risk and 02:37.390 --> 02:40.850 rolling correlation between stocks and the. 02:40.900 --> 02:42.760 We also aggregate data. 02:42.910 --> 02:51.500 So here on the right hand side you can see the S&P 500 return triangle for the last 30 years. 02:51.580 --> 02:57.010 So this is a great visual aggregation of the past performance of the S&P 500 03:00.260 --> 03:04.190 next civil right to customize the financial indexes. 03:04.190 --> 03:11.180 And even if you don't plan to create and publish your own customized index this is a really essential 03:11.180 --> 03:17.680 workflow because every time when you are creating an investment strategy and you actually back test 03:17.780 --> 03:23.720 the investment strategy to figure out how your investment strategy performed in the past. 03:23.720 --> 03:31.620 Then you will exactly need and use the workflows that we also use the event creating financial indexes. 03:31.640 --> 03:38.440 For example we have to define the constituents of our index and then how to evade the constituents. 03:38.450 --> 03:43.790 And we also have to decide whether we want to have a price return index like a total return index 03:46.520 --> 03:54.170 then we are continuing with the portfolio analysis and uh we will create many random portfolios. 03:54.170 --> 03:55.670 So you're on the right hand side. 03:55.670 --> 04:03.710 Actually each single point is a portfolio that we assimilated or created and we analyze the portfolio 04:03.710 --> 04:07.040 depending on their performance the risk and return. 04:07.040 --> 04:13.800 And we will also calculate the sharp ratio and understand why these Sharpe ratio is a so important metric. 04:13.820 --> 04:21.410 And finally we will also optimize portfolios so we will find the optimal portfolio that we can create 04:21.410 --> 04:21.860 with. 04:21.890 --> 04:24.020 For example six stocks. 04:24.230 --> 04:24.820 All right. 04:26.800 --> 04:32.630 Then we are coming to a section that is actually more theoretical so investment fundamentals. 04:32.640 --> 04:38.770 A modern portfolio theory and asset pricing models but we will do this in a very intuitive and non-farm 04:38.770 --> 04:42.310 a way by analyzing some data with pandas. 04:42.370 --> 04:48.820 And this is definitely the best way to really understand and to really get the intuition behind those 04:49.000 --> 04:50.950 theories and models. 04:50.950 --> 04:56.350 So in this section I will explain the modern portfolio theory to the Capital Asset Pricing Model or 04:56.350 --> 05:04.750 risk diversification and the metrics alpha and beta in a way that is a pretty unique and intuitive. 05:04.750 --> 05:08.530 So you will definitely not find this approach in any textbook. 05:08.530 --> 05:16.930 All right and finally in the last section of Part 2 we are creating interactive plots and price charts. 05:16.930 --> 05:23.510 So this is actually a real fun once you have learned and mastered the content of Part 2. 05:23.630 --> 05:30.650 You will have the opportunity to step into the role of a financial advisor and analyze a client's portfolio 05:31.160 --> 05:34.970 and in the end you will also be asked to give an appropriate advice. 05:34.970 --> 05:41.150 So that's the final comprehensive real world project that combines most of the topics that you have 05:41.150 --> 05:44.860 learned before and you can actually find the final project. 05:44.870 --> 05:46.730 In part 3. 05:46.760 --> 05:49.100 All right. 05:49.260 --> 05:55.890 It might be the case that you haven't worked with us before and say Mass with Excel before you can do 05:55.890 --> 06:03.930 the advanced stuff you need to learn and understand the basic functionalities love pandas and in part 06:03.930 --> 06:09.420 one of the scores you can find an extensive introduction to pandas from zero to hero. 06:09.750 --> 06:14.870 So everything you need to know before you sort of work with financial data is explained here. 06:15.480 --> 06:21.320 And once you have finished part one you probably never ever want to work with Excel again. 06:21.450 --> 06:27.120 That might be the case that you are already familiar with the basics of pandas are you even on the road 06:27.120 --> 06:29.540 and my general pandas costs. 06:29.700 --> 06:33.500 And for those students part one might be a repetition. 06:33.510 --> 06:40.350 However I highly recommend doing the coding exercises of Part 1 to check your skills in the exercises 06:40.350 --> 06:47.070 of part 1 you will analyze the listings data set that contains our current stock listings in the US 06:47.520 --> 06:53.330 and later on part two and in particular on the final project you will need to work with the listings 06:53.330 --> 06:56.830 data set so it's best to get familiar with it. 06:56.880 --> 07:05.270 In the exercises of Part 1 This course is probably one of the most practical courses on your premier. 07:05.560 --> 07:08.610 So that's the final real world project. 07:08.680 --> 07:13.960 And in addition at the end of each and every section you will have the opportunity to practice what 07:13.960 --> 07:15.620 you have learned before. 07:15.640 --> 07:22.270 In total there are 200 coding exercises and uh the really special thing is that you can choose your 07:22.270 --> 07:24.330 own level of difficulty. 07:24.700 --> 07:26.860 So let's have a look how this works. 07:27.810 --> 07:34.020 We are here in one of the coding exercises of part 1 and you can see here that you have to insert your 07:34.020 --> 07:42.090 own code or you have to fill in the gaps like here and at the very end of each exercise notebook you 07:42.090 --> 07:50.010 can find the hints section and here you can find some additional help for each and every coding exercise. 07:50.010 --> 07:53.980 So if you need some more help then you can have a look here at the hint section. 07:54.150 --> 08:01.520 And if you wonder the exercises to be more challenging then you should actually avoid the hint section. 08:01.590 --> 08:03.480 We are here in part to exercise. 08:03.510 --> 08:08.330 And here you have an additional option to adjust to the level of difficulty. 08:08.400 --> 08:15.570 So either you choose the self guided option where the question style is pretty much open and you have 08:15.570 --> 08:19.020 to find your own ways to the Final Solution. 08:19.020 --> 08:25.860 So for example here you have to import the monthly returns of the S&P 500 price return index and the 08:25.860 --> 08:33.570 Dow Jones price return index and you have to calculate and visualize the 36 month rolling correlation 08:33.570 --> 08:35.920 between those two indexes. 08:35.940 --> 08:42.960 And second you can go for the guided and instructed option where I will guide and instruct you through 08:42.960 --> 08:51.630 all major steps onto the final solution and also he has an additional option you can find at the very 08:51.630 --> 08:52.320 end. 08:52.450 --> 09:02.940 The section so here this we are here back on the cost dashboard and in part of this scores you can find 09:03.120 --> 09:10.040 some more specific and advanced lectures on working with dates and times and pandas and these lectures 09:10.230 --> 09:13.620 what general and not to finance specific. 09:13.680 --> 09:20.310 And finally if you struggle with some basic Python concepts even if you are completely new to Python 09:20.550 --> 09:27.360 you can find a crash course on Python and also on non pie here in the appendix and there you can learn 09:27.390 --> 09:30.840 all you need to know before you can start with pandas. 09:30.870 --> 09:37.350 So if you are completely new to Python then it might be best to start your journey here and it might 09:37.350 --> 09:41.520 also be the case that I will use a concept somewhere in the costs. 09:41.520 --> 09:49.620 For instance a non pi random number generator that you haven't seen before and they need some more background 09:50.010 --> 09:56.360 and it's pretty likely that you will find a more detailed explanation here in the appendix. 09:56.420 --> 10:01.320 This is this we are finished with the introduction and I hope I could give you some helpful guidance 10:01.590 --> 10:04.620 and I am looking forward to seeing you in the course by.