├── ARIMA
    ├── ARIMA model.ipynb
    └── Electric_Production.csv
├── CAPM
    └── CAPM.ipynb
├── Car stock analysis
    └── Car stock analysis.ipynb
├── ETS model
    ├── ETS model.ipynb
    └── sales.csv
├── EWMA
    ├── EWMA.ipynb
    └── sales.csv
├── Financial sources of data
    └── Financial_data_sources.ipynb
├── Pandas time series stock data
    ├── Pandas time series stock data.ipynb
    └── stocks.csv
├── Portfolio management
    ├── Portfolio allocation.ipynb
    └── Portfolio optimization.ipynb
├── README.md
└── Statsmodels for time series data
    └── Statsmodel for time series data.ipynb


/ARIMA/Electric_Production.csv:
--------------------------------------------------------------------------------
  1 | DATE,IPG2211A2N
  2 | 1/1/1985,72.5052
  3 | 2/1/1985,70.672
  4 | 3/1/1985,62.4502
  5 | 4/1/1985,57.4714
  6 | 5/1/1985,55.3151
  7 | 6/1/1985,58.0904
  8 | 7/1/1985,62.6202
  9 | 8/1/1985,63.2485
 10 | 9/1/1985,60.5846
 11 | 10/1/1985,56.3154
 12 | 11/1/1985,58.0005
 13 | 12/1/1985,68.7145
 14 | 1/1/1986,73.3057
 15 | 2/1/1986,67.9869
 16 | 3/1/1986,62.2221
 17 | 4/1/1986,57.0329
 18 | 5/1/1986,55.8137
 19 | 6/1/1986,59.9005
 20 | 7/1/1986,65.7655
 21 | 8/1/1986,64.4816
 22 | 9/1/1986,61.0005
 23 | 10/1/1986,57.5322
 24 | 11/1/1986,59.3417
 25 | 12/1/1986,68.1354
 26 | 1/1/1987,73.8152
 27 | 2/1/1987,70.062
 28 | 3/1/1987,65.61
 29 | 4/1/1987,60.1586
 30 | 5/1/1987,58.8734
 31 | 6/1/1987,63.8918
 32 | 7/1/1987,68.8694
 33 | 8/1/1987,70.0669
 34 | 9/1/1987,64.1151
 35 | 10/1/1987,60.3789
 36 | 11/1/1987,62.4643
 37 | 12/1/1987,70.5777
 38 | 1/1/1988,79.8703
 39 | 2/1/1988,76.1622
 40 | 3/1/1988,70.2928
 41 | 4/1/1988,63.2384
 42 | 5/1/1988,61.4065
 43 | 6/1/1988,67.1097
 44 | 7/1/1988,72.9816
 45 | 8/1/1988,75.7655
 46 | 9/1/1988,67.5152
 47 | 10/1/1988,63.2832
 48 | 11/1/1988,65.1078
 49 | 12/1/1988,73.8631
 50 | 1/1/1989,77.9188
 51 | 2/1/1989,76.6822
 52 | 3/1/1989,73.3523
 53 | 4/1/1989,65.1081
 54 | 5/1/1989,63.6892
 55 | 6/1/1989,68.4722
 56 | 7/1/1989,74.0301
 57 | 8/1/1989,75.0448
 58 | 9/1/1989,69.3053
 59 | 10/1/1989,65.8735
 60 | 11/1/1989,69.0706
 61 | 12/1/1989,84.1949
 62 | 1/1/1990,84.3598
 63 | 2/1/1990,77.1726
 64 | 3/1/1990,73.1964
 65 | 4/1/1990,67.2781
 66 | 5/1/1990,65.8218
 67 | 6/1/1990,71.4654
 68 | 7/1/1990,76.614
 69 | 8/1/1990,77.1052
 70 | 9/1/1990,73.061
 71 | 10/1/1990,67.4365
 72 | 11/1/1990,68.5665
 73 | 12/1/1990,77.6839
 74 | 1/1/1991,86.0214
 75 | 2/1/1991,77.5573
 76 | 3/1/1991,73.365
 77 | 4/1/1991,67.15
 78 | 5/1/1991,68.8162
 79 | 6/1/1991,74.8448
 80 | 7/1/1991,80.0928
 81 | 8/1/1991,79.1606
 82 | 9/1/1991,73.5743
 83 | 10/1/1991,68.7538
 84 | 11/1/1991,72.5166
 85 | 12/1/1991,79.4894
 86 | 1/1/1992,85.2855
 87 | 2/1/1992,80.1643
 88 | 3/1/1992,74.5275
 89 | 4/1/1992,69.6441
 90 | 5/1/1992,67.1784
 91 | 6/1/1992,71.2078
 92 | 7/1/1992,77.5081
 93 | 8/1/1992,76.5374
 94 | 9/1/1992,72.3541
 95 | 10/1/1992,69.0286
 96 | 11/1/1992,73.4992
 97 | 12/1/1992,84.5159
 98 | 1/1/1993,87.9464
 99 | 2/1/1993,84.5561
100 | 3/1/1993,79.4747
101 | 4/1/1993,71.0578
102 | 5/1/1993,67.6762
103 | 6/1/1993,74.3297
104 | 7/1/1993,82.1048
105 | 8/1/1993,82.0605
106 | 9/1/1993,74.6031
107 | 10/1/1993,69.681
108 | 11/1/1993,74.4292
109 | 12/1/1993,84.2284
110 | 1/1/1994,94.1386
111 | 2/1/1994,87.1607
112 | 3/1/1994,79.2456
113 | 4/1/1994,70.9749
114 | 5/1/1994,69.3844
115 | 6/1/1994,77.9831
116 | 7/1/1994,83.277
117 | 8/1/1994,81.8872
118 | 9/1/1994,75.6826
119 | 10/1/1994,71.2661
120 | 11/1/1994,75.2458
121 | 12/1/1994,84.8147
122 | 1/1/1995,92.4532
123 | 2/1/1995,87.4033
124 | 3/1/1995,81.2661
125 | 4/1/1995,73.8167
126 | 5/1/1995,73.2682
127 | 6/1/1995,78.3026
128 | 7/1/1995,85.9841
129 | 8/1/1995,89.5467
130 | 9/1/1995,78.5035
131 | 10/1/1995,73.7066
132 | 11/1/1995,79.6543
133 | 12/1/1995,90.8251
134 | 1/1/1996,98.9732
135 | 2/1/1996,92.8883
136 | 3/1/1996,86.9356
137 | 4/1/1996,77.2214
138 | 5/1/1996,76.6826
139 | 6/1/1996,81.9306
140 | 7/1/1996,85.9606
141 | 8/1/1996,86.5562
142 | 9/1/1996,79.1919
143 | 10/1/1996,74.6891
144 | 11/1/1996,81.074
145 | 12/1/1996,90.4855
146 | 1/1/1997,98.4613
147 | 2/1/1997,89.7795
148 | 3/1/1997,83.0125
149 | 4/1/1997,76.1476
150 | 5/1/1997,73.8471
151 | 6/1/1997,79.7645
152 | 7/1/1997,88.4519
153 | 8/1/1997,87.7828
154 | 9/1/1997,81.9386
155 | 10/1/1997,77.5027
156 | 11/1/1997,82.0448
157 | 12/1/1997,92.101
158 | 1/1/1998,94.792
159 | 2/1/1998,87.82
160 | 3/1/1998,86.5549
161 | 4/1/1998,76.7521
162 | 5/1/1998,78.0303
163 | 6/1/1998,86.4579
164 | 7/1/1998,93.8379
165 | 8/1/1998,93.531
166 | 9/1/1998,87.5414
167 | 10/1/1998,80.0924
168 | 11/1/1998,81.4349
169 | 12/1/1998,91.6841
170 | 1/1/1999,102.1348
171 | 2/1/1999,91.1829
172 | 3/1/1999,90.7381
173 | 4/1/1999,80.5176
174 | 5/1/1999,79.3887
175 | 6/1/1999,87.8431
176 | 7/1/1999,97.4903
177 | 8/1/1999,96.4157
178 | 9/1/1999,87.2248
179 | 10/1/1999,80.6409
180 | 11/1/1999,82.2025
181 | 12/1/1999,94.5113
182 | 1/1/2000,102.2301
183 | 2/1/2000,94.2989
184 | 3/1/2000,88.0927
185 | 4/1/2000,81.4425
186 | 5/1/2000,84.4552
187 | 6/1/2000,91.0406
188 | 7/1/2000,95.9957
189 | 8/1/2000,99.3704
190 | 9/1/2000,90.9178
191 | 10/1/2000,83.1408
192 | 11/1/2000,88.041
193 | 12/1/2000,102.4558
194 | 1/1/2001,109.1081
195 | 2/1/2001,97.1717
196 | 3/1/2001,92.8283
197 | 4/1/2001,82.915
198 | 5/1/2001,82.5465
199 | 6/1/2001,90.3955
200 | 7/1/2001,96.074
201 | 8/1/2001,99.5534
202 | 9/1/2001,88.281
203 | 10/1/2001,82.686
204 | 11/1/2001,82.9319
205 | 12/1/2001,93.0381
206 | 1/1/2002,102.9955
207 | 2/1/2002,95.2075
208 | 3/1/2002,93.2556
209 | 4/1/2002,85.795
210 | 5/1/2002,85.2351
211 | 6/1/2002,93.1896
212 | 7/1/2002,102.393
213 | 8/1/2002,101.6293
214 | 9/1/2002,93.3089
215 | 10/1/2002,86.9002
216 | 11/1/2002,88.5749
217 | 12/1/2002,100.8003
218 | 1/1/2003,110.1807
219 | 2/1/2003,103.8413
220 | 3/1/2003,94.5532
221 | 4/1/2003,85.062
222 | 5/1/2003,85.4653
223 | 6/1/2003,91.0761
224 | 7/1/2003,102.22
225 | 8/1/2003,104.4682
226 | 9/1/2003,92.9135
227 | 10/1/2003,86.5047
228 | 11/1/2003,88.5735
229 | 12/1/2003,103.5428
230 | 1/1/2004,113.7226
231 | 2/1/2004,106.159
232 | 3/1/2004,95.4029
233 | 4/1/2004,86.7233
234 | 5/1/2004,89.0302
235 | 6/1/2004,95.5045
236 | 7/1/2004,101.7948
237 | 8/1/2004,100.2025
238 | 9/1/2004,94.024
239 | 10/1/2004,87.5262
240 | 11/1/2004,89.6144
241 | 12/1/2004,105.7263
242 | 1/1/2005,111.1614
243 | 2/1/2005,101.7795
244 | 3/1/2005,98.9565
245 | 4/1/2005,86.4776
246 | 5/1/2005,87.2234
247 | 6/1/2005,99.5076
248 | 7/1/2005,108.3501
249 | 8/1/2005,109.4862
250 | 9/1/2005,99.1155
251 | 10/1/2005,89.7567
252 | 11/1/2005,90.4587
253 | 12/1/2005,108.2257
254 | 1/1/2006,104.4724
255 | 2/1/2006,101.5196
256 | 3/1/2006,98.4017
257 | 4/1/2006,87.5093
258 | 5/1/2006,90.0222
259 | 6/1/2006,100.5244
260 | 7/1/2006,110.9503
261 | 8/1/2006,111.5192
262 | 9/1/2006,95.7632
263 | 10/1/2006,90.3738
264 | 11/1/2006,92.3566
265 | 12/1/2006,103.066
266 | 1/1/2007,112.0576
267 | 2/1/2007,111.8399
268 | 3/1/2007,99.1925
269 | 4/1/2007,90.8177
270 | 5/1/2007,92.0587
271 | 6/1/2007,100.9676
272 | 7/1/2007,107.5686
273 | 8/1/2007,114.1036
274 | 9/1/2007,101.5316
275 | 10/1/2007,93.0068
276 | 11/1/2007,93.9126
277 | 12/1/2007,106.7528
278 | 1/1/2008,114.8331
279 | 2/1/2008,108.2353
280 | 3/1/2008,100.4386
281 | 4/1/2008,90.9944
282 | 5/1/2008,91.2348
283 | 6/1/2008,103.9581
284 | 7/1/2008,110.7631
285 | 8/1/2008,107.5665
286 | 9/1/2008,97.7183
287 | 10/1/2008,90.9979
288 | 11/1/2008,93.8057
289 | 12/1/2008,109.4221
290 | 1/1/2009,116.8316
291 | 2/1/2009,104.4202
292 | 3/1/2009,97.8529
293 | 4/1/2009,88.1973
294 | 5/1/2009,87.5366
295 | 6/1/2009,97.2387
296 | 7/1/2009,103.9086
297 | 8/1/2009,105.7486
298 | 9/1/2009,94.8823
299 | 10/1/2009,89.2977
300 | 11/1/2009,89.3585
301 | 12/1/2009,110.6844
302 | 1/1/2010,119.0166
303 | 2/1/2010,110.533
304 | 3/1/2010,98.2672
305 | 4/1/2010,86.3
306 | 5/1/2010,90.8364
307 | 6/1/2010,104.3538
308 | 7/1/2010,112.8066
309 | 8/1/2010,112.9014
310 | 9/1/2010,100.1209
311 | 10/1/2010,88.9251
312 | 11/1/2010,92.775
313 | 12/1/2010,114.3266
314 | 1/1/2011,119.488
315 | 2/1/2011,107.3753
316 | 3/1/2011,99.1028
317 | 4/1/2011,89.3583
318 | 5/1/2011,90.0698
319 | 6/1/2011,102.8204
320 | 7/1/2011,114.7068
321 | 8/1/2011,113.5958
322 | 9/1/2011,99.4712
323 | 10/1/2011,90.3566
324 | 11/1/2011,93.8095
325 | 12/1/2011,107.3312
326 | 1/1/2012,111.9646
327 | 2/1/2012,103.3679
328 | 3/1/2012,93.5772
329 | 4/1/2012,87.5566
330 | 5/1/2012,92.7603
331 | 6/1/2012,101.14
332 | 7/1/2012,113.0357
333 | 8/1/2012,109.8601
334 | 9/1/2012,96.7431
335 | 10/1/2012,90.3805
336 | 11/1/2012,94.3417
337 | 12/1/2012,105.2722
338 | 1/1/2013,115.501
339 | 2/1/2013,106.734
340 | 3/1/2013,102.9948
341 | 4/1/2013,91.0092
342 | 5/1/2013,90.9634
343 | 6/1/2013,100.6957
344 | 7/1/2013,110.148
345 | 8/1/2013,108.1756
346 | 9/1/2013,99.2809
347 | 10/1/2013,91.7871
348 | 11/1/2013,97.2853
349 | 12/1/2013,113.4732
350 | 1/1/2014,124.2549
351 | 2/1/2014,112.8811
352 | 3/1/2014,104.7631
353 | 4/1/2014,90.2867
354 | 5/1/2014,92.134
355 | 6/1/2014,101.878
356 | 7/1/2014,108.5497
357 | 8/1/2014,108.194
358 | 9/1/2014,100.4172
359 | 10/1/2014,92.3837
360 | 11/1/2014,99.7033
361 | 12/1/2014,109.3477
362 | 1/1/2015,120.2696
363 | 2/1/2015,116.3788
364 | 3/1/2015,104.4706
365 | 4/1/2015,89.7461
366 | 5/1/2015,91.093
367 | 6/1/2015,102.6495
368 | 7/1/2015,111.6354
369 | 8/1/2015,110.5925
370 | 9/1/2015,101.9204
371 | 10/1/2015,91.5959
372 | 11/1/2015,93.0628
373 | 12/1/2015,103.2203
374 | 1/1/2016,117.0837
375 | 2/1/2016,106.6688
376 | 3/1/2016,95.3548
377 | 4/1/2016,89.3254
378 | 5/1/2016,90.7369
379 | 6/1/2016,104.0375
380 | 7/1/2016,114.5397
381 | 8/1/2016,115.5159
382 | 9/1/2016,102.7637
383 | 10/1/2016,91.4867
384 | 11/1/2016,92.89
385 | 12/1/2016,112.7694
386 | 1/1/2017,114.8505
387 | 2/1/2017,99.4901
388 | 3/1/2017,101.0396
389 | 4/1/2017,88.353
390 | 5/1/2017,92.0805
391 | 6/1/2017,102.1532
392 | 7/1/2017,112.1538
393 | 8/1/2017,108.9312
394 | 9/1/2017,98.6154
395 | 10/1/2017,93.6137
396 | 11/1/2017,97.3359
397 | 12/1/2017,114.7212
398 | 1/1/2018,129.4048
399 | 


--------------------------------------------------------------------------------
/ETS model/sales.csv:
--------------------------------------------------------------------------------
 1 | "Month","Sales"
 2 | "2001-01",266.0
 3 | "2001-02",145.9
 4 | "2001-03",183.1
 5 | "2001-04",119.3
 6 | "2001-05",180.3
 7 | "2001-06",168.5
 8 | "2001-07",231.8
 9 | "2001-08",224.5
10 | "2001-09",192.8
11 | "2001-10",122.9
12 | "2001-11",336.5
13 | "2001-12",185.9
14 | "2002-01",194.3
15 | "2002-02",149.5
16 | "2002-03",210.1
17 | "2002-04",273.3
18 | "2002-05",191.4
19 | "2002-06",287.0
20 | "2002-07",226.0
21 | "2002-08",303.6
22 | "2002-09",289.9
23 | "2002-10",421.6
24 | "2002-11",264.5
25 | "2002-12",342.3
26 | "2003-01",339.7
27 | "2003-02",440.4
28 | "2003-03",315.9
29 | "2003-04",439.3
30 | "2003-05",401.3
31 | "2003-06",437.4
32 | "2003-07",575.5
33 | "2003-08",407.6
34 | "2003-09",682.0
35 | "2003-10",475.3
36 | "2003-11",581.3
37 | "2003-12",646.9


--------------------------------------------------------------------------------
/EWMA/EWMA.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "# EWMA"
  8 |    ]
  9 |   },
 10 |   {
 11 |    "cell_type": "markdown",
 12 |    "metadata": {},
 13 |    "source": [
 14 |     "### Importing packages\n",
 15 |     "The basic packages like numpy and pandas are imported for dealing with data. To help with plotting, the matplotlib package is imported."
 16 |    ]
 17 |   },
 18 |   {
 19 |    "cell_type": "code",
 20 |    "execution_count": 1,
 21 |    "metadata": {},
 22 |    "outputs": [],
 23 |    "source": [
 24 |     "import numpy as np\n",
 25 |     "import pandas as pd\n",
 26 |     "import matplotlib.pyplot as plt\n",
 27 |     "%matplotlib inline"
 28 |    ]
 29 |   },
 30 |   {
 31 |    "cell_type": "markdown",
 32 |    "metadata": {},
 33 |    "source": [
 34 |     "### Analyze data\n",
 35 |     "The dataset used will be sales data. It is a univariate dataset which has the month and sales column. The data is read and 'Month' column is mentioned as the index column."
 36 |    ]
 37 |   },
 38 |   {
 39 |    "cell_type": "code",
 40 |    "execution_count": 2,
 41 |    "metadata": {},
 42 |    "outputs": [],
 43 |    "source": [
 44 |     "df = pd.read_csv('sales.csv',index_col='Month')"
 45 |    ]
 46 |   },
 47 |   {
 48 |    "cell_type": "code",
 49 |    "execution_count": 3,
 50 |    "metadata": {},
 51 |    "outputs": [
 52 |     {
 53 |      "data": {
 54 |       "text/html": [
 55 |        "<div>\n",
 56 |        "<style scoped>\n",
 57 |        "    .dataframe tbody tr th:only-of-type {\n",
 58 |        "        vertical-align: middle;\n",
 59 |        "    }\n",
 60 |        "\n",
 61 |        "    .dataframe tbody tr th {\n",
 62 |        "        vertical-align: top;\n",
 63 |        "    }\n",
 64 |        "\n",
 65 |        "    .dataframe thead th {\n",
 66 |        "        text-align: right;\n",
 67 |        "    }\n",
 68 |        "</style>\n",
 69 |        "<table border=\"1\" class=\"dataframe\">\n",
 70 |        "  <thead>\n",
 71 |        "    <tr style=\"text-align: right;\">\n",
 72 |        "      <th></th>\n",
 73 |        "      <th>Sales</th>\n",
 74 |        "    </tr>\n",
 75 |        "    <tr>\n",
 76 |        "      <th>Month</th>\n",
 77 |        "      <th></th>\n",
 78 |        "    </tr>\n",
 79 |        "  </thead>\n",
 80 |        "  <tbody>\n",
 81 |        "    <tr>\n",
 82 |        "      <th>2001-01</th>\n",
 83 |        "      <td>266.0</td>\n",
 84 |        "    </tr>\n",
 85 |        "    <tr>\n",
 86 |        "      <th>2001-02</th>\n",
 87 |        "      <td>145.9</td>\n",
 88 |        "    </tr>\n",
 89 |        "    <tr>\n",
 90 |        "      <th>2001-03</th>\n",
 91 |        "      <td>183.1</td>\n",
 92 |        "    </tr>\n",
 93 |        "    <tr>\n",
 94 |        "      <th>2001-04</th>\n",
 95 |        "      <td>119.3</td>\n",
 96 |        "    </tr>\n",
 97 |        "    <tr>\n",
 98 |        "      <th>2001-05</th>\n",
 99 |        "      <td>180.3</td>\n",
100 |        "    </tr>\n",
101 |        "  </tbody>\n",
102 |        "</table>\n",
103 |        "</div>"
104 |       ],
105 |       "text/plain": [
106 |        "         Sales\n",
107 |        "Month         \n",
108 |        "2001-01  266.0\n",
109 |        "2001-02  145.9\n",
110 |        "2001-03  183.1\n",
111 |        "2001-04  119.3\n",
112 |        "2001-05  180.3"
113 |       ]
114 |      },
115 |      "execution_count": 3,
116 |      "metadata": {},
117 |      "output_type": "execute_result"
118 |     }
119 |    ],
120 |    "source": [
121 |     "df.head()"
122 |    ]
123 |   },
124 |   {
125 |    "cell_type": "markdown",
126 |    "metadata": {},
127 |    "source": [
128 |     "Now the 'Month' column is changed to a datetime index so as to help with the further process."
129 |    ]
130 |   },
131 |   {
132 |    "cell_type": "code",
133 |    "execution_count": 4,
134 |    "metadata": {},
135 |    "outputs": [
136 |     {
137 |      "data": {
138 |       "text/html": [
139 |        "<div>\n",
140 |        "<style scoped>\n",
141 |        "    .dataframe tbody tr th:only-of-type {\n",
142 |        "        vertical-align: middle;\n",
143 |        "    }\n",
144 |        "\n",
145 |        "    .dataframe tbody tr th {\n",
146 |        "        vertical-align: top;\n",
147 |        "    }\n",
148 |        "\n",
149 |        "    .dataframe thead th {\n",
150 |        "        text-align: right;\n",
151 |        "    }\n",
152 |        "</style>\n",
153 |        "<table border=\"1\" class=\"dataframe\">\n",
154 |        "  <thead>\n",
155 |        "    <tr style=\"text-align: right;\">\n",
156 |        "      <th></th>\n",
157 |        "      <th>Sales</th>\n",
158 |        "    </tr>\n",
159 |        "    <tr>\n",
160 |        "      <th>Month</th>\n",
161 |        "      <th></th>\n",
162 |        "    </tr>\n",
163 |        "  </thead>\n",
164 |        "  <tbody>\n",
165 |        "    <tr>\n",
166 |        "      <th>2001-01-01</th>\n",
167 |        "      <td>266.0</td>\n",
168 |        "    </tr>\n",
169 |        "    <tr>\n",
170 |        "      <th>2001-02-01</th>\n",
171 |        "      <td>145.9</td>\n",
172 |        "    </tr>\n",
173 |        "    <tr>\n",
174 |        "      <th>2001-03-01</th>\n",
175 |        "      <td>183.1</td>\n",
176 |        "    </tr>\n",
177 |        "    <tr>\n",
178 |        "      <th>2001-04-01</th>\n",
179 |        "      <td>119.3</td>\n",
180 |        "    </tr>\n",
181 |        "    <tr>\n",
182 |        "      <th>2001-05-01</th>\n",
183 |        "      <td>180.3</td>\n",
184 |        "    </tr>\n",
185 |        "  </tbody>\n",
186 |        "</table>\n",
187 |        "</div>"
188 |       ],
189 |       "text/plain": [
190 |        "            Sales\n",
191 |        "Month            \n",
192 |        "2001-01-01  266.0\n",
193 |        "2001-02-01  145.9\n",
194 |        "2001-03-01  183.1\n",
195 |        "2001-04-01  119.3\n",
196 |        "2001-05-01  180.3"
197 |       ]
198 |      },
199 |      "execution_count": 4,
200 |      "metadata": {},
201 |      "output_type": "execute_result"
202 |     }
203 |    ],
204 |    "source": [
205 |     "df.index = pd.to_datetime(df.index)\n",
206 |     "df.head()"
207 |    ]
208 |   },
209 |   {
210 |    "cell_type": "markdown",
211 |    "metadata": {},
212 |    "source": [
213 |     "### Creating SMA\n",
214 |     "The SMA can be created by adding an extra column and using the 'rolling' function and mention the window as 6 for 6 months and 12 for 12 months."
215 |    ]
216 |   },
217 |   {
218 |    "cell_type": "code",
219 |    "execution_count": 5,
220 |    "metadata": {},
221 |    "outputs": [
222 |     {
223 |      "data": {
224 |       "text/html": [
225 |        "<div>\n",
226 |        "<style scoped>\n",
227 |        "    .dataframe tbody tr th:only-of-type {\n",
228 |        "        vertical-align: middle;\n",
229 |        "    }\n",
230 |        "\n",
231 |        "    .dataframe tbody tr th {\n",
232 |        "        vertical-align: top;\n",
233 |        "    }\n",
234 |        "\n",
235 |        "    .dataframe thead th {\n",
236 |        "        text-align: right;\n",
237 |        "    }\n",
238 |        "</style>\n",
239 |        "<table border=\"1\" class=\"dataframe\">\n",
240 |        "  <thead>\n",
241 |        "    <tr style=\"text-align: right;\">\n",
242 |        "      <th></th>\n",
243 |        "      <th>Sales</th>\n",
244 |        "      <th>6SMA</th>\n",
245 |        "    </tr>\n",
246 |        "    <tr>\n",
247 |        "      <th>Month</th>\n",
248 |        "      <th></th>\n",
249 |        "      <th></th>\n",
250 |        "    </tr>\n",
251 |        "  </thead>\n",
252 |        "  <tbody>\n",
253 |        "    <tr>\n",
254 |        "      <th>2001-01-01</th>\n",
255 |        "      <td>266.0</td>\n",
256 |        "      <td>NaN</td>\n",
257 |        "    </tr>\n",
258 |        "    <tr>\n",
259 |        "      <th>2001-02-01</th>\n",
260 |        "      <td>145.9</td>\n",
261 |        "      <td>NaN</td>\n",
262 |        "    </tr>\n",
263 |        "    <tr>\n",
264 |        "      <th>2001-03-01</th>\n",
265 |        "      <td>183.1</td>\n",
266 |        "      <td>NaN</td>\n",
267 |        "    </tr>\n",
268 |        "    <tr>\n",
269 |        "      <th>2001-04-01</th>\n",
270 |        "      <td>119.3</td>\n",
271 |        "      <td>NaN</td>\n",
272 |        "    </tr>\n",
273 |        "    <tr>\n",
274 |        "      <th>2001-05-01</th>\n",
275 |        "      <td>180.3</td>\n",
276 |        "      <td>NaN</td>\n",
277 |        "    </tr>\n",
278 |        "  </tbody>\n",
279 |        "</table>\n",
280 |        "</div>"
281 |       ],
282 |       "text/plain": [
283 |        "            Sales  6SMA\n",
284 |        "Month                  \n",
285 |        "2001-01-01  266.0   NaN\n",
286 |        "2001-02-01  145.9   NaN\n",
287 |        "2001-03-01  183.1   NaN\n",
288 |        "2001-04-01  119.3   NaN\n",
289 |        "2001-05-01  180.3   NaN"
290 |       ]
291 |      },
292 |      "execution_count": 5,
293 |      "metadata": {},
294 |      "output_type": "execute_result"
295 |     }
296 |    ],
297 |    "source": [
298 |     "df['6SMA'] = df['Sales'].rolling(window=6).mean()\n",
299 |     "df.head()"
300 |    ]
301 |   },
302 |   {
303 |    "cell_type": "code",
304 |    "execution_count": 6,
305 |    "metadata": {},
306 |    "outputs": [
307 |     {
308 |      "data": {
309 |       "text/html": [
310 |        "<div>\n",
311 |        "<style scoped>\n",
312 |        "    .dataframe tbody tr th:only-of-type {\n",
313 |        "        vertical-align: middle;\n",
314 |        "    }\n",
315 |        "\n",
316 |        "    .dataframe tbody tr th {\n",
317 |        "        vertical-align: top;\n",
318 |        "    }\n",
319 |        "\n",
320 |        "    .dataframe thead th {\n",
321 |        "        text-align: right;\n",
322 |        "    }\n",
323 |        "</style>\n",
324 |        "<table border=\"1\" class=\"dataframe\">\n",
325 |        "  <thead>\n",
326 |        "    <tr style=\"text-align: right;\">\n",
327 |        "      <th></th>\n",
328 |        "      <th>Sales</th>\n",
329 |        "      <th>6SMA</th>\n",
330 |        "      <th>12SMA</th>\n",
331 |        "    </tr>\n",
332 |        "    <tr>\n",
333 |        "      <th>Month</th>\n",
334 |        "      <th></th>\n",
335 |        "      <th></th>\n",
336 |        "      <th></th>\n",
337 |        "    </tr>\n",
338 |        "  </thead>\n",
339 |        "  <tbody>\n",
340 |        "    <tr>\n",
341 |        "      <th>2001-01-01</th>\n",
342 |        "      <td>266.0</td>\n",
343 |        "      <td>NaN</td>\n",
344 |        "      <td>NaN</td>\n",
345 |        "    </tr>\n",
346 |        "    <tr>\n",
347 |        "      <th>2001-02-01</th>\n",
348 |        "      <td>145.9</td>\n",
349 |        "      <td>NaN</td>\n",
350 |        "      <td>NaN</td>\n",
351 |        "    </tr>\n",
352 |        "    <tr>\n",
353 |        "      <th>2001-03-01</th>\n",
354 |        "      <td>183.1</td>\n",
355 |        "      <td>NaN</td>\n",
356 |        "      <td>NaN</td>\n",
357 |        "    </tr>\n",
358 |        "    <tr>\n",
359 |        "      <th>2001-04-01</th>\n",
360 |        "      <td>119.3</td>\n",
361 |        "      <td>NaN</td>\n",
362 |        "      <td>NaN</td>\n",
363 |        "    </tr>\n",
364 |        "    <tr>\n",
365 |        "      <th>2001-05-01</th>\n",
366 |        "      <td>180.3</td>\n",
367 |        "      <td>NaN</td>\n",
368 |        "      <td>NaN</td>\n",
369 |        "    </tr>\n",
370 |        "  </tbody>\n",
371 |        "</table>\n",
372 |        "</div>"
373 |       ],
374 |       "text/plain": [
375 |        "            Sales  6SMA  12SMA\n",
376 |        "Month                         \n",
377 |        "2001-01-01  266.0   NaN    NaN\n",
378 |        "2001-02-01  145.9   NaN    NaN\n",
379 |        "2001-03-01  183.1   NaN    NaN\n",
380 |        "2001-04-01  119.3   NaN    NaN\n",
381 |        "2001-05-01  180.3   NaN    NaN"
382 |       ]
383 |      },
384 |      "execution_count": 6,
385 |      "metadata": {},
386 |      "output_type": "execute_result"
387 |     }
388 |    ],
389 |    "source": [
390 |     "df['12SMA'] = df['Sales'].rolling(window=12).mean()\n",
391 |     "df.head()"
392 |    ]
393 |   },
394 |   {
395 |    "cell_type": "code",
396 |    "execution_count": 7,
397 |    "metadata": {},
398 |    "outputs": [
399 |     {
400 |      "data": {
401 |       "text/plain": [
402 |        "<AxesSubplot:xlabel='Month'>"
403 |       ]
404 |      },
405 |      "execution_count": 7,
406 |      "metadata": {},
407 |      "output_type": "execute_result"
408 |     },
409 |     {
410 |      "data": {
411 |       "image/png": 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\n",
412 |       "text/plain": [
413 |        "<Figure size 432x288 with 1 Axes>"
414 |       ]
415 |      },
416 |      "metadata": {
417 |       "needs_background": "light"
418 |      },
419 |      "output_type": "display_data"
420 |     }
421 |    ],
422 |    "source": [
423 |     "df.plot()"
424 |    ]
425 |   },
426 |   {
427 |    "cell_type": "markdown",
428 |    "metadata": {},
429 |    "source": [
430 |     "Take a note that, here while plotting SMA there is a lag initially. When the EWMA is plotted, this lag won't be there."
431 |    ]
432 |   },
433 |   {
434 |    "cell_type": "markdown",
435 |    "metadata": {},
436 |    "source": [
437 |     "### Creating EWMA\n",
438 |     "The 'ewm' function is called to find the EWMA. The 'span' argument takes in the period like 12 for 12 months. "
439 |    ]
440 |   },
441 |   {
442 |    "cell_type": "code",
443 |    "execution_count": 8,
444 |    "metadata": {},
445 |    "outputs": [
446 |     {
447 |      "data": {
448 |       "text/plain": [
449 |        "<AxesSubplot:xlabel='Month'>"
450 |       ]
451 |      },
452 |      "execution_count": 8,
453 |      "metadata": {},
454 |      "output_type": "execute_result"
455 |     },
456 |     {
457 |      "data": {
458 |       "image/png": 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\n",
459 |       "text/plain": [
460 |        "<Figure size 432x288 with 1 Axes>"
461 |       ]
462 |      },
463 |      "metadata": {
464 |       "needs_background": "light"
465 |      },
466 |      "output_type": "display_data"
467 |     }
468 |    ],
469 |    "source": [
470 |     "df['6EWMA'] = df['Sales'].ewm(span=12).mean()\n",
471 |     "df[['Sales','6EWMA']].plot()"
472 |    ]
473 |   },
474 |   {
475 |    "cell_type": "code",
476 |    "execution_count": null,
477 |    "metadata": {},
478 |    "outputs": [],
479 |    "source": []
480 |   }
481 |  ],
482 |  "metadata": {
483 |   "kernelspec": {
484 |    "display_name": "Python 3",
485 |    "language": "python",
486 |    "name": "python3"
487 |   },
488 |   "language_info": {
489 |    "codemirror_mode": {
490 |     "name": "ipython",
491 |     "version": 3
492 |    },
493 |    "file_extension": ".py",
494 |    "mimetype": "text/x-python",
495 |    "name": "python",
496 |    "nbconvert_exporter": "python",
497 |    "pygments_lexer": "ipython3",
498 |    "version": "3.7.7"
499 |   }
500 |  },
501 |  "nbformat": 4,
502 |  "nbformat_minor": 4
503 | }
504 | 


--------------------------------------------------------------------------------
/EWMA/sales.csv:
--------------------------------------------------------------------------------
 1 | "Month","Sales"
 2 | "2001-01",266.0
 3 | "2001-02",145.9
 4 | "2001-03",183.1
 5 | "2001-04",119.3
 6 | "2001-05",180.3
 7 | "2001-06",168.5
 8 | "2001-07",231.8
 9 | "2001-08",224.5
10 | "2001-09",192.8
11 | "2001-10",122.9
12 | "2001-11",336.5
13 | "2001-12",185.9
14 | "2002-01",194.3
15 | "2002-02",149.5
16 | "2002-03",210.1
17 | "2002-04",273.3
18 | "2002-05",191.4
19 | "2002-06",287.0
20 | "2002-07",226.0
21 | "2002-08",303.6
22 | "2002-09",289.9
23 | "2002-10",421.6
24 | "2002-11",264.5
25 | "2002-12",342.3
26 | "2003-01",339.7
27 | "2003-02",440.4
28 | "2003-03",315.9
29 | "2003-04",439.3
30 | "2003-05",401.3
31 | "2003-06",437.4
32 | "2003-07",575.5
33 | "2003-08",407.6
34 | "2003-09",682.0
35 | "2003-10",475.3
36 | "2003-11",581.3
37 | "2003-12",646.9


--------------------------------------------------------------------------------
/Financial sources of data/Financial_data_sources.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "# Pandas - datareader"
  8 |    ]
  9 |   },
 10 |   {
 11 |    "cell_type": "markdown",
 12 |    "metadata": {},
 13 |    "source": [
 14 |     "### Installing pandas-datareader"
 15 |    ]
 16 |   },
 17 |   {
 18 |    "cell_type": "code",
 19 |    "execution_count": 1,
 20 |    "metadata": {},
 21 |    "outputs": [],
 22 |    "source": [
 23 |     "#!pip install pandas-datareader"
 24 |    ]
 25 |   },
 26 |   {
 27 |    "cell_type": "markdown",
 28 |    "metadata": {},
 29 |    "source": [
 30 |     "### Importing the package\n",
 31 |     "The pandas_datareader package is imported for use. Along with it, the datetime library is also imported. This library will help us pass datetime objects. "
 32 |    ]
 33 |   },
 34 |   {
 35 |    "cell_type": "code",
 36 |    "execution_count": 2,
 37 |    "metadata": {},
 38 |    "outputs": [],
 39 |    "source": [
 40 |     "import pandas_datareader.data as web\n",
 41 |     "import datetime"
 42 |    ]
 43 |   },
 44 |   {
 45 |    "cell_type": "markdown",
 46 |    "metadata": {},
 47 |    "source": [
 48 |     "### Setting the start and end date\n",
 49 |     "Now we will set the start and end date so as to extract the data from this interval."
 50 |    ]
 51 |   },
 52 |   {
 53 |    "cell_type": "code",
 54 |    "execution_count": 3,
 55 |    "metadata": {},
 56 |    "outputs": [],
 57 |    "source": [
 58 |     "start = datetime.datetime(2018,1,1)\n",
 59 |     "end = datetime.datetime(2020,1,1)"
 60 |    ]
 61 |   },
 62 |   {
 63 |    "cell_type": "markdown",
 64 |    "metadata": {},
 65 |    "source": [
 66 |     "### Creating the stocks dataframe\n",
 67 |     "Now we will declare a variable that we want as a dataframe. Then we will call the 'DataReader' method. This method takes the values name, data_source, start date, end date.\n",
 68 |     "\n",
 69 |     "So here we will consider the Microsoft data and the name will be written as 'MSFT', the source will be yahoo and the start and end dates are as declared above."
 70 |    ]
 71 |   },
 72 |   {
 73 |    "cell_type": "code",
 74 |    "execution_count": 9,
 75 |    "metadata": {},
 76 |    "outputs": [],
 77 |    "source": [
 78 |     "data = web.DataReader('MSFT','yahoo',start,end)"
 79 |    ]
 80 |   },
 81 |   {
 82 |    "cell_type": "code",
 83 |    "execution_count": 10,
 84 |    "metadata": {},
 85 |    "outputs": [
 86 |     {
 87 |      "data": {
 88 |       "text/html": [
 89 |        "<div>\n",
 90 |        "<style scoped>\n",
 91 |        "    .dataframe tbody tr th:only-of-type {\n",
 92 |        "        vertical-align: middle;\n",
 93 |        "    }\n",
 94 |        "\n",
 95 |        "    .dataframe tbody tr th {\n",
 96 |        "        vertical-align: top;\n",
 97 |        "    }\n",
 98 |        "\n",
 99 |        "    .dataframe thead th {\n",
100 |        "        text-align: right;\n",
101 |        "    }\n",
102 |        "</style>\n",
103 |        "<table border=\"1\" class=\"dataframe\">\n",
104 |        "  <thead>\n",
105 |        "    <tr style=\"text-align: right;\">\n",
106 |        "      <th></th>\n",
107 |        "      <th>High</th>\n",
108 |        "      <th>Low</th>\n",
109 |        "      <th>Open</th>\n",
110 |        "      <th>Close</th>\n",
111 |        "      <th>Volume</th>\n",
112 |        "      <th>Adj Close</th>\n",
113 |        "    </tr>\n",
114 |        "    <tr>\n",
115 |        "      <th>Date</th>\n",
116 |        "      <th></th>\n",
117 |        "      <th></th>\n",
118 |        "      <th></th>\n",
119 |        "      <th></th>\n",
120 |        "      <th></th>\n",
121 |        "      <th></th>\n",
122 |        "    </tr>\n",
123 |        "  </thead>\n",
124 |        "  <tbody>\n",
125 |        "    <tr>\n",
126 |        "      <th>2018-01-02</th>\n",
127 |        "      <td>86.309998</td>\n",
128 |        "      <td>85.500000</td>\n",
129 |        "      <td>86.129997</td>\n",
130 |        "      <td>85.949997</td>\n",
131 |        "      <td>22483800.0</td>\n",
132 |        "      <td>82.383636</td>\n",
133 |        "    </tr>\n",
134 |        "    <tr>\n",
135 |        "      <th>2018-01-03</th>\n",
136 |        "      <td>86.510002</td>\n",
137 |        "      <td>85.970001</td>\n",
138 |        "      <td>86.059998</td>\n",
139 |        "      <td>86.349998</td>\n",
140 |        "      <td>26061400.0</td>\n",
141 |        "      <td>82.767044</td>\n",
142 |        "    </tr>\n",
143 |        "    <tr>\n",
144 |        "      <th>2018-01-04</th>\n",
145 |        "      <td>87.660004</td>\n",
146 |        "      <td>86.570000</td>\n",
147 |        "      <td>86.589996</td>\n",
148 |        "      <td>87.110001</td>\n",
149 |        "      <td>21912000.0</td>\n",
150 |        "      <td>83.495522</td>\n",
151 |        "    </tr>\n",
152 |        "    <tr>\n",
153 |        "      <th>2018-01-05</th>\n",
154 |        "      <td>88.410004</td>\n",
155 |        "      <td>87.430000</td>\n",
156 |        "      <td>87.660004</td>\n",
157 |        "      <td>88.190002</td>\n",
158 |        "      <td>23407100.0</td>\n",
159 |        "      <td>84.530701</td>\n",
160 |        "    </tr>\n",
161 |        "    <tr>\n",
162 |        "      <th>2018-01-08</th>\n",
163 |        "      <td>88.580002</td>\n",
164 |        "      <td>87.599998</td>\n",
165 |        "      <td>88.199997</td>\n",
166 |        "      <td>88.279999</td>\n",
167 |        "      <td>22113000.0</td>\n",
168 |        "      <td>84.616966</td>\n",
169 |        "    </tr>\n",
170 |        "  </tbody>\n",
171 |        "</table>\n",
172 |        "</div>"
173 |       ],
174 |       "text/plain": [
175 |        "                 High        Low       Open      Close      Volume  Adj Close\n",
176 |        "Date                                                                         \n",
177 |        "2018-01-02  86.309998  85.500000  86.129997  85.949997  22483800.0  82.383636\n",
178 |        "2018-01-03  86.510002  85.970001  86.059998  86.349998  26061400.0  82.767044\n",
179 |        "2018-01-04  87.660004  86.570000  86.589996  87.110001  21912000.0  83.495522\n",
180 |        "2018-01-05  88.410004  87.430000  87.660004  88.190002  23407100.0  84.530701\n",
181 |        "2018-01-08  88.580002  87.599998  88.199997  88.279999  22113000.0  84.616966"
182 |       ]
183 |      },
184 |      "execution_count": 10,
185 |      "metadata": {},
186 |      "output_type": "execute_result"
187 |     }
188 |    ],
189 |    "source": [
190 |     "data.head()"
191 |    ]
192 |   },
193 |   {
194 |    "cell_type": "markdown",
195 |    "metadata": {},
196 |    "source": [
197 |     "# Quandl\n",
198 |     "\n",
199 |     "Quandl has provision to use premium services which are paid and also the basic services which are free. To avail the services for free. Go to the Quandl website, and click on the 'Core Financial data' option. It is free to use and available for everyone. In the filter, by clicking on the 'free' option you can view the free data sources. Then you can either download the data in various formats like JSON, CSV and XML or you can also use the different API available for Python, R, MATLAB, etc. We will be using the Python API directly."
200 |    ]
201 |   },
202 |   {
203 |    "cell_type": "markdown",
204 |    "metadata": {},
205 |    "source": [
206 |     "### Installing Quandl"
207 |    ]
208 |   },
209 |   {
210 |    "cell_type": "code",
211 |    "execution_count": 14,
212 |    "metadata": {},
213 |    "outputs": [],
214 |    "source": [
215 |     "#pip install quandl"
216 |    ]
217 |   },
218 |   {
219 |    "cell_type": "markdown",
220 |    "metadata": {},
221 |    "source": [
222 |     "### Importing necessary packages\n",
223 |     "The quandl package is imported."
224 |    ]
225 |   },
226 |   {
227 |    "cell_type": "code",
228 |    "execution_count": 15,
229 |    "metadata": {},
230 |    "outputs": [],
231 |    "source": [
232 |     "import quandl"
233 |    ]
234 |   },
235 |   {
236 |    "cell_type": "markdown",
237 |    "metadata": {},
238 |    "source": [
239 |     "### Extracting the data\n",
240 |     "The data can be obtained by using the 'get' method. This method extracts a single time series. There is another method 'get_table' which extracts the entire database. \n",
241 |     "\n",
242 |     "The format for Quandl's database usage is: DATABASE_CODE/DATASET_CODE. The quandl codes can be found in the website for the respective datasets.\n",
243 |     "\n",
244 |     "We will try extracting the petroleum prices from the US department."
245 |    ]
246 |   },
247 |   {
248 |    "cell_type": "code",
249 |    "execution_count": 28,
250 |    "metadata": {},
251 |    "outputs": [],
252 |    "source": [
253 |     "data1 = quandl.get('EIA/PET_RWTC_D')"
254 |    ]
255 |   },
256 |   {
257 |    "cell_type": "code",
258 |    "execution_count": 29,
259 |    "metadata": {},
260 |    "outputs": [
261 |     {
262 |      "data": {
263 |       "text/html": [
264 |        "<div>\n",
265 |        "<style scoped>\n",
266 |        "    .dataframe tbody tr th:only-of-type {\n",
267 |        "        vertical-align: middle;\n",
268 |        "    }\n",
269 |        "\n",
270 |        "    .dataframe tbody tr th {\n",
271 |        "        vertical-align: top;\n",
272 |        "    }\n",
273 |        "\n",
274 |        "    .dataframe thead th {\n",
275 |        "        text-align: right;\n",
276 |        "    }\n",
277 |        "</style>\n",
278 |        "<table border=\"1\" class=\"dataframe\">\n",
279 |        "  <thead>\n",
280 |        "    <tr style=\"text-align: right;\">\n",
281 |        "      <th></th>\n",
282 |        "      <th>Value</th>\n",
283 |        "    </tr>\n",
284 |        "    <tr>\n",
285 |        "      <th>Date</th>\n",
286 |        "      <th></th>\n",
287 |        "    </tr>\n",
288 |        "  </thead>\n",
289 |        "  <tbody>\n",
290 |        "    <tr>\n",
291 |        "      <th>1986-01-02</th>\n",
292 |        "      <td>25.56</td>\n",
293 |        "    </tr>\n",
294 |        "    <tr>\n",
295 |        "      <th>1986-01-03</th>\n",
296 |        "      <td>26.00</td>\n",
297 |        "    </tr>\n",
298 |        "    <tr>\n",
299 |        "      <th>1986-01-06</th>\n",
300 |        "      <td>26.53</td>\n",
301 |        "    </tr>\n",
302 |        "    <tr>\n",
303 |        "      <th>1986-01-07</th>\n",
304 |        "      <td>25.85</td>\n",
305 |        "    </tr>\n",
306 |        "    <tr>\n",
307 |        "      <th>1986-01-08</th>\n",
308 |        "      <td>25.87</td>\n",
309 |        "    </tr>\n",
310 |        "  </tbody>\n",
311 |        "</table>\n",
312 |        "</div>"
313 |       ],
314 |       "text/plain": [
315 |        "            Value\n",
316 |        "Date             \n",
317 |        "1986-01-02  25.56\n",
318 |        "1986-01-03  26.00\n",
319 |        "1986-01-06  26.53\n",
320 |        "1986-01-07  25.85\n",
321 |        "1986-01-08  25.87"
322 |       ]
323 |      },
324 |      "execution_count": 29,
325 |      "metadata": {},
326 |      "output_type": "execute_result"
327 |     }
328 |    ],
329 |    "source": [
330 |     "data1.head()"
331 |    ]
332 |   },
333 |   {
334 |    "cell_type": "markdown",
335 |    "metadata": {},
336 |    "source": [
337 |     "### Plotting the data"
338 |    ]
339 |   },
340 |   {
341 |    "cell_type": "code",
342 |    "execution_count": 25,
343 |    "metadata": {},
344 |    "outputs": [
345 |     {
346 |      "data": {
347 |       "text/plain": [
348 |        "<AxesSubplot:xlabel='Date'>"
349 |       ]
350 |      },
351 |      "execution_count": 25,
352 |      "metadata": {},
353 |      "output_type": "execute_result"
354 |     },
355 |     {
356 |      "data": {
357 |       "image/png": 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\n",
358 |       "text/plain": [
359 |        "<Figure size 432x288 with 1 Axes>"
360 |       ]
361 |      },
362 |      "metadata": {
363 |       "needs_background": "light"
364 |      },
365 |      "output_type": "display_data"
366 |     }
367 |    ],
368 |    "source": [
369 |     "import matplotlib.pyplot as plt\n",
370 |     "%matplotlib inline\n",
371 |     "\n",
372 |     "data1.plot()"
373 |    ]
374 |   },
375 |   {
376 |    "cell_type": "markdown",
377 |    "metadata": {},
378 |    "source": [
379 |     "Let's look into another data like facebook."
380 |    ]
381 |   },
382 |   {
383 |    "cell_type": "code",
384 |    "execution_count": 30,
385 |    "metadata": {},
386 |    "outputs": [],
387 |    "source": [
388 |     "data2 = quandl.get('WIKI/FB')"
389 |    ]
390 |   },
391 |   {
392 |    "cell_type": "code",
393 |    "execution_count": 31,
394 |    "metadata": {},
395 |    "outputs": [
396 |     {
397 |      "data": {
398 |       "text/html": [
399 |        "<div>\n",
400 |        "<style scoped>\n",
401 |        "    .dataframe tbody tr th:only-of-type {\n",
402 |        "        vertical-align: middle;\n",
403 |        "    }\n",
404 |        "\n",
405 |        "    .dataframe tbody tr th {\n",
406 |        "        vertical-align: top;\n",
407 |        "    }\n",
408 |        "\n",
409 |        "    .dataframe thead th {\n",
410 |        "        text-align: right;\n",
411 |        "    }\n",
412 |        "</style>\n",
413 |        "<table border=\"1\" class=\"dataframe\">\n",
414 |        "  <thead>\n",
415 |        "    <tr style=\"text-align: right;\">\n",
416 |        "      <th></th>\n",
417 |        "      <th>Open</th>\n",
418 |        "      <th>High</th>\n",
419 |        "      <th>Low</th>\n",
420 |        "      <th>Close</th>\n",
421 |        "      <th>Volume</th>\n",
422 |        "      <th>Ex-Dividend</th>\n",
423 |        "      <th>Split Ratio</th>\n",
424 |        "      <th>Adj. Open</th>\n",
425 |        "      <th>Adj. High</th>\n",
426 |        "      <th>Adj. Low</th>\n",
427 |        "      <th>Adj. Close</th>\n",
428 |        "      <th>Adj. Volume</th>\n",
429 |        "    </tr>\n",
430 |        "    <tr>\n",
431 |        "      <th>Date</th>\n",
432 |        "      <th></th>\n",
433 |        "      <th></th>\n",
434 |        "      <th></th>\n",
435 |        "      <th></th>\n",
436 |        "      <th></th>\n",
437 |        "      <th></th>\n",
438 |        "      <th></th>\n",
439 |        "      <th></th>\n",
440 |        "      <th></th>\n",
441 |        "      <th></th>\n",
442 |        "      <th></th>\n",
443 |        "      <th></th>\n",
444 |        "    </tr>\n",
445 |        "  </thead>\n",
446 |        "  <tbody>\n",
447 |        "    <tr>\n",
448 |        "      <th>2012-05-18</th>\n",
449 |        "      <td>42.05</td>\n",
450 |        "      <td>45.00</td>\n",
451 |        "      <td>38.00</td>\n",
452 |        "      <td>38.2318</td>\n",
453 |        "      <td>573576400.0</td>\n",
454 |        "      <td>0.0</td>\n",
455 |        "      <td>1.0</td>\n",
456 |        "      <td>42.05</td>\n",
457 |        "      <td>45.00</td>\n",
458 |        "      <td>38.00</td>\n",
459 |        "      <td>38.2318</td>\n",
460 |        "      <td>573576400.0</td>\n",
461 |        "    </tr>\n",
462 |        "    <tr>\n",
463 |        "      <th>2012-05-21</th>\n",
464 |        "      <td>36.53</td>\n",
465 |        "      <td>36.66</td>\n",
466 |        "      <td>33.00</td>\n",
467 |        "      <td>34.0300</td>\n",
468 |        "      <td>168192700.0</td>\n",
469 |        "      <td>0.0</td>\n",
470 |        "      <td>1.0</td>\n",
471 |        "      <td>36.53</td>\n",
472 |        "      <td>36.66</td>\n",
473 |        "      <td>33.00</td>\n",
474 |        "      <td>34.0300</td>\n",
475 |        "      <td>168192700.0</td>\n",
476 |        "    </tr>\n",
477 |        "    <tr>\n",
478 |        "      <th>2012-05-22</th>\n",
479 |        "      <td>32.61</td>\n",
480 |        "      <td>33.59</td>\n",
481 |        "      <td>30.94</td>\n",
482 |        "      <td>31.0000</td>\n",
483 |        "      <td>101786600.0</td>\n",
484 |        "      <td>0.0</td>\n",
485 |        "      <td>1.0</td>\n",
486 |        "      <td>32.61</td>\n",
487 |        "      <td>33.59</td>\n",
488 |        "      <td>30.94</td>\n",
489 |        "      <td>31.0000</td>\n",
490 |        "      <td>101786600.0</td>\n",
491 |        "    </tr>\n",
492 |        "    <tr>\n",
493 |        "      <th>2012-05-23</th>\n",
494 |        "      <td>31.37</td>\n",
495 |        "      <td>32.50</td>\n",
496 |        "      <td>31.36</td>\n",
497 |        "      <td>32.0000</td>\n",
498 |        "      <td>73600000.0</td>\n",
499 |        "      <td>0.0</td>\n",
500 |        "      <td>1.0</td>\n",
501 |        "      <td>31.37</td>\n",
502 |        "      <td>32.50</td>\n",
503 |        "      <td>31.36</td>\n",
504 |        "      <td>32.0000</td>\n",
505 |        "      <td>73600000.0</td>\n",
506 |        "    </tr>\n",
507 |        "    <tr>\n",
508 |        "      <th>2012-05-24</th>\n",
509 |        "      <td>32.95</td>\n",
510 |        "      <td>33.21</td>\n",
511 |        "      <td>31.77</td>\n",
512 |        "      <td>33.0300</td>\n",
513 |        "      <td>50237200.0</td>\n",
514 |        "      <td>0.0</td>\n",
515 |        "      <td>1.0</td>\n",
516 |        "      <td>32.95</td>\n",
517 |        "      <td>33.21</td>\n",
518 |        "      <td>31.77</td>\n",
519 |        "      <td>33.0300</td>\n",
520 |        "      <td>50237200.0</td>\n",
521 |        "    </tr>\n",
522 |        "  </tbody>\n",
523 |        "</table>\n",
524 |        "</div>"
525 |       ],
526 |       "text/plain": [
527 |        "             Open   High    Low    Close       Volume  Ex-Dividend  \\\n",
528 |        "Date                                                                 \n",
529 |        "2012-05-18  42.05  45.00  38.00  38.2318  573576400.0          0.0   \n",
530 |        "2012-05-21  36.53  36.66  33.00  34.0300  168192700.0          0.0   \n",
531 |        "2012-05-22  32.61  33.59  30.94  31.0000  101786600.0          0.0   \n",
532 |        "2012-05-23  31.37  32.50  31.36  32.0000   73600000.0          0.0   \n",
533 |        "2012-05-24  32.95  33.21  31.77  33.0300   50237200.0          0.0   \n",
534 |        "\n",
535 |        "            Split Ratio  Adj. Open  Adj. High  Adj. Low  Adj. Close  \\\n",
536 |        "Date                                                                  \n",
537 |        "2012-05-18          1.0      42.05      45.00     38.00     38.2318   \n",
538 |        "2012-05-21          1.0      36.53      36.66     33.00     34.0300   \n",
539 |        "2012-05-22          1.0      32.61      33.59     30.94     31.0000   \n",
540 |        "2012-05-23          1.0      31.37      32.50     31.36     32.0000   \n",
541 |        "2012-05-24          1.0      32.95      33.21     31.77     33.0300   \n",
542 |        "\n",
543 |        "            Adj. Volume  \n",
544 |        "Date                     \n",
545 |        "2012-05-18  573576400.0  \n",
546 |        "2012-05-21  168192700.0  \n",
547 |        "2012-05-22  101786600.0  \n",
548 |        "2012-05-23   73600000.0  \n",
549 |        "2012-05-24   50237200.0  "
550 |       ]
551 |      },
552 |      "execution_count": 31,
553 |      "metadata": {},
554 |      "output_type": "execute_result"
555 |     }
556 |    ],
557 |    "source": [
558 |     "data2.head()"
559 |    ]
560 |   },
561 |   {
562 |    "cell_type": "markdown",
563 |    "metadata": {},
564 |    "source": [
565 |     "Incase you do not want all these columns. The column number can be mentioned accordingly."
566 |    ]
567 |   },
568 |   {
569 |    "cell_type": "code",
570 |    "execution_count": 32,
571 |    "metadata": {},
572 |    "outputs": [
573 |     {
574 |      "data": {
575 |       "text/html": [
576 |        "<div>\n",
577 |        "<style scoped>\n",
578 |        "    .dataframe tbody tr th:only-of-type {\n",
579 |        "        vertical-align: middle;\n",
580 |        "    }\n",
581 |        "\n",
582 |        "    .dataframe tbody tr th {\n",
583 |        "        vertical-align: top;\n",
584 |        "    }\n",
585 |        "\n",
586 |        "    .dataframe thead th {\n",
587 |        "        text-align: right;\n",
588 |        "    }\n",
589 |        "</style>\n",
590 |        "<table border=\"1\" class=\"dataframe\">\n",
591 |        "  <thead>\n",
592 |        "    <tr style=\"text-align: right;\">\n",
593 |        "      <th></th>\n",
594 |        "      <th>Open</th>\n",
595 |        "    </tr>\n",
596 |        "    <tr>\n",
597 |        "      <th>Date</th>\n",
598 |        "      <th></th>\n",
599 |        "    </tr>\n",
600 |        "  </thead>\n",
601 |        "  <tbody>\n",
602 |        "    <tr>\n",
603 |        "      <th>2012-05-18</th>\n",
604 |        "      <td>42.05</td>\n",
605 |        "    </tr>\n",
606 |        "    <tr>\n",
607 |        "      <th>2012-05-21</th>\n",
608 |        "      <td>36.53</td>\n",
609 |        "    </tr>\n",
610 |        "    <tr>\n",
611 |        "      <th>2012-05-22</th>\n",
612 |        "      <td>32.61</td>\n",
613 |        "    </tr>\n",
614 |        "    <tr>\n",
615 |        "      <th>2012-05-23</th>\n",
616 |        "      <td>31.37</td>\n",
617 |        "    </tr>\n",
618 |        "    <tr>\n",
619 |        "      <th>2012-05-24</th>\n",
620 |        "      <td>32.95</td>\n",
621 |        "    </tr>\n",
622 |        "  </tbody>\n",
623 |        "</table>\n",
624 |        "</div>"
625 |       ],
626 |       "text/plain": [
627 |        "             Open\n",
628 |        "Date             \n",
629 |        "2012-05-18  42.05\n",
630 |        "2012-05-21  36.53\n",
631 |        "2012-05-22  32.61\n",
632 |        "2012-05-23  31.37\n",
633 |        "2012-05-24  32.95"
634 |       ]
635 |      },
636 |      "execution_count": 32,
637 |      "metadata": {},
638 |      "output_type": "execute_result"
639 |     }
640 |    ],
641 |    "source": [
642 |     "data2 = quandl.get('WIKI/FB.1')\n",
643 |     "data2.head()"
644 |    ]
645 |   },
646 |   {
647 |    "cell_type": "code",
648 |    "execution_count": null,
649 |    "metadata": {},
650 |    "outputs": [],
651 |    "source": []
652 |   }
653 |  ],
654 |  "metadata": {
655 |   "kernelspec": {
656 |    "display_name": "Python 3",
657 |    "language": "python",
658 |    "name": "python3"
659 |   },
660 |   "language_info": {
661 |    "codemirror_mode": {
662 |     "name": "ipython",
663 |     "version": 3
664 |    },
665 |    "file_extension": ".py",
666 |    "mimetype": "text/x-python",
667 |    "name": "python",
668 |    "nbconvert_exporter": "python",
669 |    "pygments_lexer": "ipython3",
670 |    "version": "3.7.7"
671 |   }
672 |  },
673 |  "nbformat": 4,
674 |  "nbformat_minor": 4
675 | }
676 | 


--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
 1 | # Financial analysis and trading using Python
 2 | 
 3 | ## 1) Financial sources of data:
 4 | See folder with notebooks [here](https://github.com/jayashree8/Finance_Trading_In_Python/tree/main/Financial%20sources%20of%20data)
 5 | 
 6 | Find article [here](https://jayashree8.medium.com/how-to-get-financial-data-using-python-7a508f25fc39)
 7 | 
 8 | ## 2) Pandas for stock data analysis:
 9 | See folder with notebooks [here](https://github.com/jayashree8/Finance_Trading_In_Python/tree/main/Pandas%20time%20series%20stock%20data)
10 | 
11 | Find article [here](https://jayashree8.medium.com/how-to-deal-with-time-series-stock-data-using-pandas-3cceb1839721)
12 | 
13 | ## 3) Car Stock analysis:
14 | See folder with notebooks [here](https://github.com/jayashree8/Finance_Trading_In_Python/tree/main/Car%20stock%20analysis)
15 | 
16 | Find article [here](https://jayashree8.medium.com/toyota-v-s-bmw-v-s-tesla-stock-analysis-using-python-3762caa1713a)
17 | 
18 | ## 4) Statsmodels for time series data:
19 | See folder with notebooks [here](https://github.com/jayashree8/Finance_Trading_In_Python/tree/main/Statsmodels%20for%20time%20series%20data)
20 | 
21 | Find article [here](https://jayashree8.medium.com/statsmodels-for-time-series-data-72ddab409fdc)
22 | 
23 | ## 5) ETS model:
24 | See folder with notebooks [here](https://github.com/jayashree8/Finance_Trading_In_Python/tree/main/ETS%20model)
25 | 
26 | Find article [here](https://jayashree8.medium.com/time-series-decomposition-ets-model-using-python-4d2cd04bab77)
27 | 
28 | ## 6) EWMA:
29 | See folder with notebooks [here](https://github.com/jayashree8/Finance_Trading_In_Python/tree/main/EWMA)
30 | 
31 | Find article [here](https://jayashree8.medium.com/how-to-implement-ewma-plots-using-python-8a158e5bab48)
32 | 
33 | ## 7) ARIMA:
34 | See folder with notebooks [here](https://github.com/jayashree8/Finance_Trading_In_Python/tree/main/ARIMA)
35 | 
36 | Find article [here](https://jayashree8.medium.com/electricity-production-forecasting-using-arima-model-in-python-d3bf38dc3517)
37 | 
38 | ## 8) Portfolio management:
39 | See folder with notebooks [here](https://github.com/jayashree8/Finance_Trading_In_Python/tree/main/Portfolio%20management)
40 | 
41 | Find article [here](https://jayashree8.medium.com/portfolio-management-using-python-part-1-portfolio-allocation-df0fe9147ab)
42 | 
43 | Find article [here](https://jayashree8.medium.com/portfolio-management-using-python-portfolio-optimization-8a90dd2a21d)
44 | 
45 | ## 9) CAPM - Capital Asset Pricing Model:
46 | See folder with notebooks [here](https://github.com/jayashree8/Finance_Trading_In_Python/tree/main/CAPM)
47 | 
48 | Find article [here](https://jayashree8.medium.com/capital-assets-pricing-model-capm-using-python-285a95f40d4d)


--------------------------------------------------------------------------------
/Statsmodels for time series data/Statsmodel for time series data.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "# Statsmodels for time series data\n",
  8 |     "\n",
  9 |     "In python, a very widely used library named statsmodel is used when dealing with time series data. It is based on the statistical programming language R. This module helps in analyzing data, perform statistical functions and also create statistical models. It also has functions to plot. "
 10 |    ]
 11 |   },
 12 |   {
 13 |    "cell_type": "markdown",
 14 |    "metadata": {},
 15 |    "source": [
 16 |     "### Importing packages\n",
 17 |     "\n",
 18 |     "The basic packages like numpy and pandas to help deal with data are imported along with matplotlib to help with plottings."
 19 |    ]
 20 |   },
 21 |   {
 22 |    "cell_type": "code",
 23 |    "execution_count": 1,
 24 |    "metadata": {},
 25 |    "outputs": [],
 26 |    "source": [
 27 |     "import pandas as pd\n",
 28 |     "import numpy as np\n",
 29 |     "import matplotlib.pyplot as plt\n",
 30 |     "%matplotlib inline"
 31 |    ]
 32 |   },
 33 |   {
 34 |    "cell_type": "markdown",
 35 |    "metadata": {},
 36 |    "source": [
 37 |     "Then the statsmodel is also imported."
 38 |    ]
 39 |   },
 40 |   {
 41 |    "cell_type": "code",
 42 |    "execution_count": 2,
 43 |    "metadata": {},
 44 |    "outputs": [],
 45 |    "source": [
 46 |     "import statsmodels.api as sm"
 47 |    ]
 48 |   },
 49 |   {
 50 |    "cell_type": "markdown",
 51 |    "metadata": {},
 52 |    "source": [
 53 |     "### Obtain data\n",
 54 |     "The statsmodels has a provision to obtain dataset. There are various datasets and the one that will be used is the macrodata since it is a time series data. Using the load_pandas() method, the data will be loaded."
 55 |    ]
 56 |   },
 57 |   {
 58 |    "cell_type": "code",
 59 |    "execution_count": 3,
 60 |    "metadata": {},
 61 |    "outputs": [],
 62 |    "source": [
 63 |     "data = sm.datasets.macrodata.load_pandas().data"
 64 |    ]
 65 |   },
 66 |   {
 67 |    "cell_type": "code",
 68 |    "execution_count": 4,
 69 |    "metadata": {},
 70 |    "outputs": [
 71 |     {
 72 |      "data": {
 73 |       "text/html": [
 74 |        "<div>\n",
 75 |        "<style scoped>\n",
 76 |        "    .dataframe tbody tr th:only-of-type {\n",
 77 |        "        vertical-align: middle;\n",
 78 |        "    }\n",
 79 |        "\n",
 80 |        "    .dataframe tbody tr th {\n",
 81 |        "        vertical-align: top;\n",
 82 |        "    }\n",
 83 |        "\n",
 84 |        "    .dataframe thead th {\n",
 85 |        "        text-align: right;\n",
 86 |        "    }\n",
 87 |        "</style>\n",
 88 |        "<table border=\"1\" class=\"dataframe\">\n",
 89 |        "  <thead>\n",
 90 |        "    <tr style=\"text-align: right;\">\n",
 91 |        "      <th></th>\n",
 92 |        "      <th>year</th>\n",
 93 |        "      <th>quarter</th>\n",
 94 |        "      <th>realgdp</th>\n",
 95 |        "      <th>realcons</th>\n",
 96 |        "      <th>realinv</th>\n",
 97 |        "      <th>realgovt</th>\n",
 98 |        "      <th>realdpi</th>\n",
 99 |        "      <th>cpi</th>\n",
100 |        "      <th>m1</th>\n",
101 |        "      <th>tbilrate</th>\n",
102 |        "      <th>unemp</th>\n",
103 |        "      <th>pop</th>\n",
104 |        "      <th>infl</th>\n",
105 |        "      <th>realint</th>\n",
106 |        "    </tr>\n",
107 |        "  </thead>\n",
108 |        "  <tbody>\n",
109 |        "    <tr>\n",
110 |        "      <th>0</th>\n",
111 |        "      <td>1959.0</td>\n",
112 |        "      <td>1.0</td>\n",
113 |        "      <td>2710.349</td>\n",
114 |        "      <td>1707.4</td>\n",
115 |        "      <td>286.898</td>\n",
116 |        "      <td>470.045</td>\n",
117 |        "      <td>1886.9</td>\n",
118 |        "      <td>28.98</td>\n",
119 |        "      <td>139.7</td>\n",
120 |        "      <td>2.82</td>\n",
121 |        "      <td>5.8</td>\n",
122 |        "      <td>177.146</td>\n",
123 |        "      <td>0.00</td>\n",
124 |        "      <td>0.00</td>\n",
125 |        "    </tr>\n",
126 |        "    <tr>\n",
127 |        "      <th>1</th>\n",
128 |        "      <td>1959.0</td>\n",
129 |        "      <td>2.0</td>\n",
130 |        "      <td>2778.801</td>\n",
131 |        "      <td>1733.7</td>\n",
132 |        "      <td>310.859</td>\n",
133 |        "      <td>481.301</td>\n",
134 |        "      <td>1919.7</td>\n",
135 |        "      <td>29.15</td>\n",
136 |        "      <td>141.7</td>\n",
137 |        "      <td>3.08</td>\n",
138 |        "      <td>5.1</td>\n",
139 |        "      <td>177.830</td>\n",
140 |        "      <td>2.34</td>\n",
141 |        "      <td>0.74</td>\n",
142 |        "    </tr>\n",
143 |        "    <tr>\n",
144 |        "      <th>2</th>\n",
145 |        "      <td>1959.0</td>\n",
146 |        "      <td>3.0</td>\n",
147 |        "      <td>2775.488</td>\n",
148 |        "      <td>1751.8</td>\n",
149 |        "      <td>289.226</td>\n",
150 |        "      <td>491.260</td>\n",
151 |        "      <td>1916.4</td>\n",
152 |        "      <td>29.35</td>\n",
153 |        "      <td>140.5</td>\n",
154 |        "      <td>3.82</td>\n",
155 |        "      <td>5.3</td>\n",
156 |        "      <td>178.657</td>\n",
157 |        "      <td>2.74</td>\n",
158 |        "      <td>1.09</td>\n",
159 |        "    </tr>\n",
160 |        "    <tr>\n",
161 |        "      <th>3</th>\n",
162 |        "      <td>1959.0</td>\n",
163 |        "      <td>4.0</td>\n",
164 |        "      <td>2785.204</td>\n",
165 |        "      <td>1753.7</td>\n",
166 |        "      <td>299.356</td>\n",
167 |        "      <td>484.052</td>\n",
168 |        "      <td>1931.3</td>\n",
169 |        "      <td>29.37</td>\n",
170 |        "      <td>140.0</td>\n",
171 |        "      <td>4.33</td>\n",
172 |        "      <td>5.6</td>\n",
173 |        "      <td>179.386</td>\n",
174 |        "      <td>0.27</td>\n",
175 |        "      <td>4.06</td>\n",
176 |        "    </tr>\n",
177 |        "    <tr>\n",
178 |        "      <th>4</th>\n",
179 |        "      <td>1960.0</td>\n",
180 |        "      <td>1.0</td>\n",
181 |        "      <td>2847.699</td>\n",
182 |        "      <td>1770.5</td>\n",
183 |        "      <td>331.722</td>\n",
184 |        "      <td>462.199</td>\n",
185 |        "      <td>1955.5</td>\n",
186 |        "      <td>29.54</td>\n",
187 |        "      <td>139.6</td>\n",
188 |        "      <td>3.50</td>\n",
189 |        "      <td>5.2</td>\n",
190 |        "      <td>180.007</td>\n",
191 |        "      <td>2.31</td>\n",
192 |        "      <td>1.19</td>\n",
193 |        "    </tr>\n",
194 |        "  </tbody>\n",
195 |        "</table>\n",
196 |        "</div>"
197 |       ],
198 |       "text/plain": [
199 |        "     year  quarter   realgdp  realcons  realinv  realgovt  realdpi    cpi  \\\n",
200 |        "0  1959.0      1.0  2710.349    1707.4  286.898   470.045   1886.9  28.98   \n",
201 |        "1  1959.0      2.0  2778.801    1733.7  310.859   481.301   1919.7  29.15   \n",
202 |        "2  1959.0      3.0  2775.488    1751.8  289.226   491.260   1916.4  29.35   \n",
203 |        "3  1959.0      4.0  2785.204    1753.7  299.356   484.052   1931.3  29.37   \n",
204 |        "4  1960.0      1.0  2847.699    1770.5  331.722   462.199   1955.5  29.54   \n",
205 |        "\n",
206 |        "      m1  tbilrate  unemp      pop  infl  realint  \n",
207 |        "0  139.7      2.82    5.8  177.146  0.00     0.00  \n",
208 |        "1  141.7      3.08    5.1  177.830  2.34     0.74  \n",
209 |        "2  140.5      3.82    5.3  178.657  2.74     1.09  \n",
210 |        "3  140.0      4.33    5.6  179.386  0.27     4.06  \n",
211 |        "4  139.6      3.50    5.2  180.007  2.31     1.19  "
212 |       ]
213 |      },
214 |      "execution_count": 4,
215 |      "metadata": {},
216 |      "output_type": "execute_result"
217 |     }
218 |    ],
219 |    "source": [
220 |     "data.head()"
221 |    ]
222 |   },
223 |   {
224 |    "cell_type": "code",
225 |    "execution_count": 5,
226 |    "metadata": {},
227 |    "outputs": [
228 |     {
229 |      "data": {
230 |       "text/html": [
231 |        "<div>\n",
232 |        "<style scoped>\n",
233 |        "    .dataframe tbody tr th:only-of-type {\n",
234 |        "        vertical-align: middle;\n",
235 |        "    }\n",
236 |        "\n",
237 |        "    .dataframe tbody tr th {\n",
238 |        "        vertical-align: top;\n",
239 |        "    }\n",
240 |        "\n",
241 |        "    .dataframe thead th {\n",
242 |        "        text-align: right;\n",
243 |        "    }\n",
244 |        "</style>\n",
245 |        "<table border=\"1\" class=\"dataframe\">\n",
246 |        "  <thead>\n",
247 |        "    <tr style=\"text-align: right;\">\n",
248 |        "      <th></th>\n",
249 |        "      <th>year</th>\n",
250 |        "      <th>quarter</th>\n",
251 |        "      <th>realgdp</th>\n",
252 |        "      <th>realcons</th>\n",
253 |        "      <th>realinv</th>\n",
254 |        "      <th>realgovt</th>\n",
255 |        "      <th>realdpi</th>\n",
256 |        "      <th>cpi</th>\n",
257 |        "      <th>m1</th>\n",
258 |        "      <th>tbilrate</th>\n",
259 |        "      <th>unemp</th>\n",
260 |        "      <th>pop</th>\n",
261 |        "      <th>infl</th>\n",
262 |        "      <th>realint</th>\n",
263 |        "    </tr>\n",
264 |        "  </thead>\n",
265 |        "  <tbody>\n",
266 |        "    <tr>\n",
267 |        "      <th>198</th>\n",
268 |        "      <td>2008.0</td>\n",
269 |        "      <td>3.0</td>\n",
270 |        "      <td>13324.600</td>\n",
271 |        "      <td>9267.7</td>\n",
272 |        "      <td>1990.693</td>\n",
273 |        "      <td>991.551</td>\n",
274 |        "      <td>9838.3</td>\n",
275 |        "      <td>216.889</td>\n",
276 |        "      <td>1474.7</td>\n",
277 |        "      <td>1.17</td>\n",
278 |        "      <td>6.0</td>\n",
279 |        "      <td>305.270</td>\n",
280 |        "      <td>-3.16</td>\n",
281 |        "      <td>4.33</td>\n",
282 |        "    </tr>\n",
283 |        "    <tr>\n",
284 |        "      <th>199</th>\n",
285 |        "      <td>2008.0</td>\n",
286 |        "      <td>4.0</td>\n",
287 |        "      <td>13141.920</td>\n",
288 |        "      <td>9195.3</td>\n",
289 |        "      <td>1857.661</td>\n",
290 |        "      <td>1007.273</td>\n",
291 |        "      <td>9920.4</td>\n",
292 |        "      <td>212.174</td>\n",
293 |        "      <td>1576.5</td>\n",
294 |        "      <td>0.12</td>\n",
295 |        "      <td>6.9</td>\n",
296 |        "      <td>305.952</td>\n",
297 |        "      <td>-8.79</td>\n",
298 |        "      <td>8.91</td>\n",
299 |        "    </tr>\n",
300 |        "    <tr>\n",
301 |        "      <th>200</th>\n",
302 |        "      <td>2009.0</td>\n",
303 |        "      <td>1.0</td>\n",
304 |        "      <td>12925.410</td>\n",
305 |        "      <td>9209.2</td>\n",
306 |        "      <td>1558.494</td>\n",
307 |        "      <td>996.287</td>\n",
308 |        "      <td>9926.4</td>\n",
309 |        "      <td>212.671</td>\n",
310 |        "      <td>1592.8</td>\n",
311 |        "      <td>0.22</td>\n",
312 |        "      <td>8.1</td>\n",
313 |        "      <td>306.547</td>\n",
314 |        "      <td>0.94</td>\n",
315 |        "      <td>-0.71</td>\n",
316 |        "    </tr>\n",
317 |        "    <tr>\n",
318 |        "      <th>201</th>\n",
319 |        "      <td>2009.0</td>\n",
320 |        "      <td>2.0</td>\n",
321 |        "      <td>12901.504</td>\n",
322 |        "      <td>9189.0</td>\n",
323 |        "      <td>1456.678</td>\n",
324 |        "      <td>1023.528</td>\n",
325 |        "      <td>10077.5</td>\n",
326 |        "      <td>214.469</td>\n",
327 |        "      <td>1653.6</td>\n",
328 |        "      <td>0.18</td>\n",
329 |        "      <td>9.2</td>\n",
330 |        "      <td>307.226</td>\n",
331 |        "      <td>3.37</td>\n",
332 |        "      <td>-3.19</td>\n",
333 |        "    </tr>\n",
334 |        "    <tr>\n",
335 |        "      <th>202</th>\n",
336 |        "      <td>2009.0</td>\n",
337 |        "      <td>3.0</td>\n",
338 |        "      <td>12990.341</td>\n",
339 |        "      <td>9256.0</td>\n",
340 |        "      <td>1486.398</td>\n",
341 |        "      <td>1044.088</td>\n",
342 |        "      <td>10040.6</td>\n",
343 |        "      <td>216.385</td>\n",
344 |        "      <td>1673.9</td>\n",
345 |        "      <td>0.12</td>\n",
346 |        "      <td>9.6</td>\n",
347 |        "      <td>308.013</td>\n",
348 |        "      <td>3.56</td>\n",
349 |        "      <td>-3.44</td>\n",
350 |        "    </tr>\n",
351 |        "  </tbody>\n",
352 |        "</table>\n",
353 |        "</div>"
354 |       ],
355 |       "text/plain": [
356 |        "       year  quarter    realgdp  realcons   realinv  realgovt  realdpi  \\\n",
357 |        "198  2008.0      3.0  13324.600    9267.7  1990.693   991.551   9838.3   \n",
358 |        "199  2008.0      4.0  13141.920    9195.3  1857.661  1007.273   9920.4   \n",
359 |        "200  2009.0      1.0  12925.410    9209.2  1558.494   996.287   9926.4   \n",
360 |        "201  2009.0      2.0  12901.504    9189.0  1456.678  1023.528  10077.5   \n",
361 |        "202  2009.0      3.0  12990.341    9256.0  1486.398  1044.088  10040.6   \n",
362 |        "\n",
363 |        "         cpi      m1  tbilrate  unemp      pop  infl  realint  \n",
364 |        "198  216.889  1474.7      1.17    6.0  305.270 -3.16     4.33  \n",
365 |        "199  212.174  1576.5      0.12    6.9  305.952 -8.79     8.91  \n",
366 |        "200  212.671  1592.8      0.22    8.1  306.547  0.94    -0.71  \n",
367 |        "201  214.469  1653.6      0.18    9.2  307.226  3.37    -3.19  \n",
368 |        "202  216.385  1673.9      0.12    9.6  308.013  3.56    -3.44  "
369 |       ]
370 |      },
371 |      "execution_count": 5,
372 |      "metadata": {},
373 |      "output_type": "execute_result"
374 |     }
375 |    ],
376 |    "source": [
377 |     "data.tail()"
378 |    ]
379 |   },
380 |   {
381 |    "cell_type": "markdown",
382 |    "metadata": {},
383 |    "source": [
384 |     "To understand what the column headings mean, we can print the details using the NOTE attribute."
385 |    ]
386 |   },
387 |   {
388 |    "cell_type": "code",
389 |    "execution_count": 6,
390 |    "metadata": {},
391 |    "outputs": [
392 |     {
393 |      "name": "stdout",
394 |      "output_type": "stream",
395 |      "text": [
396 |       "::\n",
397 |       "    Number of Observations - 203\n",
398 |       "\n",
399 |       "    Number of Variables - 14\n",
400 |       "\n",
401 |       "    Variable name definitions::\n",
402 |       "\n",
403 |       "        year      - 1959q1 - 2009q3\n",
404 |       "        quarter   - 1-4\n",
405 |       "        realgdp   - Real gross domestic product (Bil. of chained 2005 US$,\n",
406 |       "                    seasonally adjusted annual rate)\n",
407 |       "        realcons  - Real personal consumption expenditures (Bil. of chained\n",
408 |       "                    2005 US$, seasonally adjusted annual rate)\n",
409 |       "        realinv   - Real gross private domestic investment (Bil. of chained\n",
410 |       "                    2005 US$, seasonally adjusted annual rate)\n",
411 |       "        realgovt  - Real federal consumption expenditures & gross investment\n",
412 |       "                    (Bil. of chained 2005 US$, seasonally adjusted annual rate)\n",
413 |       "        realdpi   - Real private disposable income (Bil. of chained 2005\n",
414 |       "                    US$, seasonally adjusted annual rate)\n",
415 |       "        cpi       - End of the quarter consumer price index for all urban\n",
416 |       "                    consumers: all items (1982-84 = 100, seasonally adjusted).\n",
417 |       "        m1        - End of the quarter M1 nominal money stock (Seasonally\n",
418 |       "                    adjusted)\n",
419 |       "        tbilrate  - Quarterly monthly average of the monthly 3-month\n",
420 |       "                    treasury bill: secondary market rate\n",
421 |       "        unemp     - Seasonally adjusted unemployment rate (%)\n",
422 |       "        pop       - End of the quarter total population: all ages incl. armed\n",
423 |       "                    forces over seas\n",
424 |       "        infl      - Inflation rate (ln(cpi_{t}/cpi_{t-1}) * 400)\n",
425 |       "        realint   - Real interest rate (tbilrate - infl)\n",
426 |       "\n"
427 |      ]
428 |     }
429 |    ],
430 |    "source": [
431 |     "print(sm.datasets.macrodata.NOTE)"
432 |    ]
433 |   },
434 |   {
435 |    "cell_type": "markdown",
436 |    "metadata": {},
437 |    "source": [
438 |     "Now to work with time series, it is important to have the year column as the index. So accordingly it is changed by using the time series analysis (tsa) module of statsmodels. It has a method called dates_from_range where the range can be mentioned. We take the start to be 1959 year of first quarter (Q1) and end to be 2009 of third quarter (Q3). Using pandas an index will be created of this. "
439 |    ]
440 |   },
441 |   {
442 |    "cell_type": "code",
443 |    "execution_count": 7,
444 |    "metadata": {},
445 |    "outputs": [],
446 |    "source": [
447 |     "idx = pd.Index(sm.tsa.datetools.dates_from_range('1959Q1','2009Q3'))"
448 |    ]
449 |   },
450 |   {
451 |    "cell_type": "markdown",
452 |    "metadata": {},
453 |    "source": [
454 |     "Now that the index is created, we can assign it to the dataframe."
455 |    ]
456 |   },
457 |   {
458 |    "cell_type": "code",
459 |    "execution_count": 8,
460 |    "metadata": {},
461 |    "outputs": [
462 |     {
463 |      "data": {
464 |       "text/html": [
465 |        "<div>\n",
466 |        "<style scoped>\n",
467 |        "    .dataframe tbody tr th:only-of-type {\n",
468 |        "        vertical-align: middle;\n",
469 |        "    }\n",
470 |        "\n",
471 |        "    .dataframe tbody tr th {\n",
472 |        "        vertical-align: top;\n",
473 |        "    }\n",
474 |        "\n",
475 |        "    .dataframe thead th {\n",
476 |        "        text-align: right;\n",
477 |        "    }\n",
478 |        "</style>\n",
479 |        "<table border=\"1\" class=\"dataframe\">\n",
480 |        "  <thead>\n",
481 |        "    <tr style=\"text-align: right;\">\n",
482 |        "      <th></th>\n",
483 |        "      <th>year</th>\n",
484 |        "      <th>quarter</th>\n",
485 |        "      <th>realgdp</th>\n",
486 |        "      <th>realcons</th>\n",
487 |        "      <th>realinv</th>\n",
488 |        "      <th>realgovt</th>\n",
489 |        "      <th>realdpi</th>\n",
490 |        "      <th>cpi</th>\n",
491 |        "      <th>m1</th>\n",
492 |        "      <th>tbilrate</th>\n",
493 |        "      <th>unemp</th>\n",
494 |        "      <th>pop</th>\n",
495 |        "      <th>infl</th>\n",
496 |        "      <th>realint</th>\n",
497 |        "    </tr>\n",
498 |        "  </thead>\n",
499 |        "  <tbody>\n",
500 |        "    <tr>\n",
501 |        "      <th>1959-03-31</th>\n",
502 |        "      <td>1959.0</td>\n",
503 |        "      <td>1.0</td>\n",
504 |        "      <td>2710.349</td>\n",
505 |        "      <td>1707.4</td>\n",
506 |        "      <td>286.898</td>\n",
507 |        "      <td>470.045</td>\n",
508 |        "      <td>1886.9</td>\n",
509 |        "      <td>28.98</td>\n",
510 |        "      <td>139.7</td>\n",
511 |        "      <td>2.82</td>\n",
512 |        "      <td>5.8</td>\n",
513 |        "      <td>177.146</td>\n",
514 |        "      <td>0.00</td>\n",
515 |        "      <td>0.00</td>\n",
516 |        "    </tr>\n",
517 |        "    <tr>\n",
518 |        "      <th>1959-06-30</th>\n",
519 |        "      <td>1959.0</td>\n",
520 |        "      <td>2.0</td>\n",
521 |        "      <td>2778.801</td>\n",
522 |        "      <td>1733.7</td>\n",
523 |        "      <td>310.859</td>\n",
524 |        "      <td>481.301</td>\n",
525 |        "      <td>1919.7</td>\n",
526 |        "      <td>29.15</td>\n",
527 |        "      <td>141.7</td>\n",
528 |        "      <td>3.08</td>\n",
529 |        "      <td>5.1</td>\n",
530 |        "      <td>177.830</td>\n",
531 |        "      <td>2.34</td>\n",
532 |        "      <td>0.74</td>\n",
533 |        "    </tr>\n",
534 |        "    <tr>\n",
535 |        "      <th>1959-09-30</th>\n",
536 |        "      <td>1959.0</td>\n",
537 |        "      <td>3.0</td>\n",
538 |        "      <td>2775.488</td>\n",
539 |        "      <td>1751.8</td>\n",
540 |        "      <td>289.226</td>\n",
541 |        "      <td>491.260</td>\n",
542 |        "      <td>1916.4</td>\n",
543 |        "      <td>29.35</td>\n",
544 |        "      <td>140.5</td>\n",
545 |        "      <td>3.82</td>\n",
546 |        "      <td>5.3</td>\n",
547 |        "      <td>178.657</td>\n",
548 |        "      <td>2.74</td>\n",
549 |        "      <td>1.09</td>\n",
550 |        "    </tr>\n",
551 |        "    <tr>\n",
552 |        "      <th>1959-12-31</th>\n",
553 |        "      <td>1959.0</td>\n",
554 |        "      <td>4.0</td>\n",
555 |        "      <td>2785.204</td>\n",
556 |        "      <td>1753.7</td>\n",
557 |        "      <td>299.356</td>\n",
558 |        "      <td>484.052</td>\n",
559 |        "      <td>1931.3</td>\n",
560 |        "      <td>29.37</td>\n",
561 |        "      <td>140.0</td>\n",
562 |        "      <td>4.33</td>\n",
563 |        "      <td>5.6</td>\n",
564 |        "      <td>179.386</td>\n",
565 |        "      <td>0.27</td>\n",
566 |        "      <td>4.06</td>\n",
567 |        "    </tr>\n",
568 |        "    <tr>\n",
569 |        "      <th>1960-03-31</th>\n",
570 |        "      <td>1960.0</td>\n",
571 |        "      <td>1.0</td>\n",
572 |        "      <td>2847.699</td>\n",
573 |        "      <td>1770.5</td>\n",
574 |        "      <td>331.722</td>\n",
575 |        "      <td>462.199</td>\n",
576 |        "      <td>1955.5</td>\n",
577 |        "      <td>29.54</td>\n",
578 |        "      <td>139.6</td>\n",
579 |        "      <td>3.50</td>\n",
580 |        "      <td>5.2</td>\n",
581 |        "      <td>180.007</td>\n",
582 |        "      <td>2.31</td>\n",
583 |        "      <td>1.19</td>\n",
584 |        "    </tr>\n",
585 |        "  </tbody>\n",
586 |        "</table>\n",
587 |        "</div>"
588 |       ],
589 |       "text/plain": [
590 |        "              year  quarter   realgdp  realcons  realinv  realgovt  realdpi  \\\n",
591 |        "1959-03-31  1959.0      1.0  2710.349    1707.4  286.898   470.045   1886.9   \n",
592 |        "1959-06-30  1959.0      2.0  2778.801    1733.7  310.859   481.301   1919.7   \n",
593 |        "1959-09-30  1959.0      3.0  2775.488    1751.8  289.226   491.260   1916.4   \n",
594 |        "1959-12-31  1959.0      4.0  2785.204    1753.7  299.356   484.052   1931.3   \n",
595 |        "1960-03-31  1960.0      1.0  2847.699    1770.5  331.722   462.199   1955.5   \n",
596 |        "\n",
597 |        "              cpi     m1  tbilrate  unemp      pop  infl  realint  \n",
598 |        "1959-03-31  28.98  139.7      2.82    5.8  177.146  0.00     0.00  \n",
599 |        "1959-06-30  29.15  141.7      3.08    5.1  177.830  2.34     0.74  \n",
600 |        "1959-09-30  29.35  140.5      3.82    5.3  178.657  2.74     1.09  \n",
601 |        "1959-12-31  29.37  140.0      4.33    5.6  179.386  0.27     4.06  \n",
602 |        "1960-03-31  29.54  139.6      3.50    5.2  180.007  2.31     1.19  "
603 |       ]
604 |      },
605 |      "execution_count": 8,
606 |      "metadata": {},
607 |      "output_type": "execute_result"
608 |     }
609 |    ],
610 |    "source": [
611 |     "data.index = idx\n",
612 |     "data.head()"
613 |    ]
614 |   },
615 |   {
616 |    "cell_type": "markdown",
617 |    "metadata": {},
618 |    "source": [
619 |     "### Visualization\n",
620 |     "\n",
621 |     "Linear plot of the DPI is plotted to see the trend."
622 |    ]
623 |   },
624 |   {
625 |    "cell_type": "code",
626 |    "execution_count": 9,
627 |    "metadata": {},
628 |    "outputs": [
629 |     {
630 |      "data": {
631 |       "text/plain": [
632 |        "<AxesSubplot:>"
633 |       ]
634 |      },
635 |      "execution_count": 9,
636 |      "metadata": {},
637 |      "output_type": "execute_result"
638 |     },
639 |     {
640 |      "data": {
641 |       "image/png": 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\n",
642 |       "text/plain": [
643 |        "<Figure size 432x288 with 1 Axes>"
644 |       ]
645 |      },
646 |      "metadata": {
647 |       "needs_background": "light"
648 |      },
649 |      "output_type": "display_data"
650 |     }
651 |    ],
652 |    "source": [
653 |     "data['realdpi'].plot()"
654 |    ]
655 |   },
656 |   {
657 |    "cell_type": "markdown",
658 |    "metadata": {},
659 |    "source": [
660 |     "The statsmodel can be useful in getting the estimated trend. A filter is used which is called as Hodrick-Prescott filter. This filter distinguishes a time series data into a trend and a cyclic component. When this filter is applied it return a tuple which consists of the estimated cycle and the trend."
661 |    ]
662 |   },
663 |   {
664 |    "cell_type": "code",
665 |    "execution_count": 10,
666 |    "metadata": {},
667 |    "outputs": [
668 |     {
669 |      "data": {
670 |       "text/plain": [
671 |        "(1959-03-31     32.611738\n",
672 |        " 1959-06-30     45.961546\n",
673 |        " 1959-09-30     23.190972\n",
674 |        " 1959-12-31     18.550907\n",
675 |        " 1960-03-31     23.077748\n",
676 |        "                  ...    \n",
677 |        " 2008-09-30   -128.596455\n",
678 |        " 2008-12-31    -87.557288\n",
679 |        " 2009-03-31   -122.358968\n",
680 |        " 2009-06-30    -11.941350\n",
681 |        " 2009-09-30    -89.467814\n",
682 |        " Name: realdpi_cycle, Length: 203, dtype: float64,\n",
683 |        " 1959-03-31     1854.288262\n",
684 |        " 1959-06-30     1873.738454\n",
685 |        " 1959-09-30     1893.209028\n",
686 |        " 1959-12-31     1912.749093\n",
687 |        " 1960-03-31     1932.422252\n",
688 |        "                   ...     \n",
689 |        " 2008-09-30     9966.896455\n",
690 |        " 2008-12-31    10007.957288\n",
691 |        " 2009-03-31    10048.758968\n",
692 |        " 2009-06-30    10089.441350\n",
693 |        " 2009-09-30    10130.067814\n",
694 |        " Name: realdpi_trend, Length: 203, dtype: float64)"
695 |       ]
696 |      },
697 |      "execution_count": 10,
698 |      "metadata": {},
699 |      "output_type": "execute_result"
700 |     }
701 |    ],
702 |    "source": [
703 |     "dpi = sm.tsa.filters.hpfilter(data['realdpi'])\n",
704 |     "dpi"
705 |    ]
706 |   },
707 |   {
708 |    "cell_type": "markdown",
709 |    "metadata": {},
710 |    "source": [
711 |     "Now using the tuple unpacking, the trend is extracted and then plotted."
712 |    ]
713 |   },
714 |   {
715 |    "cell_type": "code",
716 |    "execution_count": 11,
717 |    "metadata": {},
718 |    "outputs": [],
719 |    "source": [
720 |     "dpi_cycle,dpi_trend = sm.tsa.filters.hpfilter(data['realdpi'])"
721 |    ]
722 |   },
723 |   {
724 |    "cell_type": "code",
725 |    "execution_count": 12,
726 |    "metadata": {},
727 |    "outputs": [],
728 |    "source": [
729 |     "data['trend'] = dpi_trend"
730 |    ]
731 |   },
732 |   {
733 |    "cell_type": "code",
734 |    "execution_count": 13,
735 |    "metadata": {},
736 |    "outputs": [
737 |     {
738 |      "data": {
739 |       "text/plain": [
740 |        "<AxesSubplot:>"
741 |       ]
742 |      },
743 |      "execution_count": 13,
744 |      "metadata": {},
745 |      "output_type": "execute_result"
746 |     },
747 |     {
748 |      "data": {
749 |       "image/png": 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\n",
750 |       "text/plain": [
751 |        "<Figure size 432x288 with 1 Axes>"
752 |       ]
753 |      },
754 |      "metadata": {
755 |       "needs_background": "light"
756 |      },
757 |      "output_type": "display_data"
758 |     }
759 |    ],
760 |    "source": [
761 |     "data[['realdpi','trend']].plot()"
762 |    ]
763 |   },
764 |   {
765 |    "cell_type": "markdown",
766 |    "metadata": {},
767 |    "source": [
768 |     "Let's zoom in to get a better idea of the plot."
769 |    ]
770 |   },
771 |   {
772 |    "cell_type": "code",
773 |    "execution_count": 16,
774 |    "metadata": {},
775 |    "outputs": [
776 |     {
777 |      "data": {
778 |       "text/plain": [
779 |        "<AxesSubplot:>"
780 |       ]
781 |      },
782 |      "execution_count": 16,
783 |      "metadata": {},
784 |      "output_type": "execute_result"
785 |     },
786 |     {
787 |      "data": {
788 |       "image/png": 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\n",
789 |       "text/plain": [
790 |        "<Figure size 432x288 with 1 Axes>"
791 |       ]
792 |      },
793 |      "metadata": {
794 |       "needs_background": "light"
795 |      },
796 |      "output_type": "display_data"
797 |     }
798 |    ],
799 |    "source": [
800 |     "data[['realdpi','trend']]['2005-01-01':].plot()"
801 |    ]
802 |   },
803 |   {
804 |    "cell_type": "code",
805 |    "execution_count": null,
806 |    "metadata": {},
807 |    "outputs": [],
808 |    "source": []
809 |   }
810 |  ],
811 |  "metadata": {
812 |   "kernelspec": {
813 |    "display_name": "Python 3",
814 |    "language": "python",
815 |    "name": "python3"
816 |   },
817 |   "language_info": {
818 |    "codemirror_mode": {
819 |     "name": "ipython",
820 |     "version": 3
821 |    },
822 |    "file_extension": ".py",
823 |    "mimetype": "text/x-python",
824 |    "name": "python",
825 |    "nbconvert_exporter": "python",
826 |    "pygments_lexer": "ipython3",
827 |    "version": "3.7.7"
828 |   }
829 |  },
830 |  "nbformat": 4,
831 |  "nbformat_minor": 4
832 | }
833 | 


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