"
548 | ],
549 | "text/plain": [
550 | " Open High Low Close Volume\n",
551 | "Date \n",
552 | "2021-02-26 30.93 30.93 30.93 30.93 ----\n",
553 | "2021-02-25 32.56 32.56 32.56 32.56 ----\n",
554 | "2021-02-24 32.21 32.21 32.21 32.21 ----\n",
555 | "2021-02-23 32.41 32.41 32.41 32.41 ----\n",
556 | "2021-02-22 33.12 33.12 33.12 33.12 ----\n",
557 | "2021-02-19 32.89 32.89 32.89 32.89 ----\n",
558 | "2021-02-18 33.21 33.21 33.21 33.21 ----\n",
559 | "2021-02-17 33.61 33.61 33.61 33.61 ----\n",
560 | "2021-02-16 33.81 33.81 33.81 33.81 ----\n",
561 | "2021-02-12 33.58 33.58 33.58 33.58 ----\n",
562 | "2021-02-11 33.19 33.19 33.19 33.19 ----\n",
563 | "2021-02-10 33.40 33.40 33.40 33.40 ----\n",
564 | "2021-02-09 33.21 33.21 33.21 33.21 ----\n",
565 | "2021-02-08 32.73 32.73 32.73 32.73 ----\n",
566 | "2021-02-05 32.24 32.24 32.24 32.24 ----\n",
567 | "2021-02-04 31.78 31.78 31.78 31.78 ----\n",
568 | "2021-02-03 31.67 31.67 31.67 31.67 ----\n",
569 | "2021-02-02 31.43 31.43 31.43 31.43 ----\n",
570 | "2021-02-01 30.40 30.40 30.40 30.40 ----\n",
571 | "2021-01-29 30.53 30.53 30.53 30.53 ----\n",
572 | "2021-01-28 30.28 30.28 30.28 30.28 ----"
573 | ]
574 | },
575 | "execution_count": 7,
576 | "metadata": {},
577 | "output_type": "execute_result"
578 | }
579 | ],
580 | "source": [
581 | "historical(isin)"
582 | ]
583 | }
584 | ],
585 | "metadata": {
586 | "kernelspec": {
587 | "display_name": "Python 3",
588 | "language": "python",
589 | "name": "python3"
590 | },
591 | "language_info": {
592 | "codemirror_mode": {
593 | "name": "ipython",
594 | "version": 3
595 | },
596 | "file_extension": ".py",
597 | "mimetype": "text/x-python",
598 | "name": "python",
599 | "nbconvert_exporter": "python",
600 | "pygments_lexer": "ipython3",
601 | "version": "3.7.7"
602 | },
603 | "latex_envs": {
604 | "LaTeX_envs_menu_present": true,
605 | "autoclose": false,
606 | "autocomplete": true,
607 | "bibliofile": "biblio.bib",
608 | "cite_by": "apalike",
609 | "current_citInitial": 1,
610 | "eqLabelWithNumbers": true,
611 | "eqNumInitial": 1,
612 | "hotkeys": {
613 | "equation": "Ctrl-E",
614 | "itemize": "Ctrl-I"
615 | },
616 | "labels_anchors": false,
617 | "latex_user_defs": false,
618 | "report_style_numbering": false,
619 | "user_envs_cfg": false
620 | },
621 | "varInspector": {
622 | "cols": {
623 | "lenName": 16,
624 | "lenType": 16,
625 | "lenVar": 40
626 | },
627 | "kernels_config": {
628 | "python": {
629 | "delete_cmd_postfix": "",
630 | "delete_cmd_prefix": "del ",
631 | "library": "var_list.py",
632 | "varRefreshCmd": "print(var_dic_list())"
633 | },
634 | "r": {
635 | "delete_cmd_postfix": ") ",
636 | "delete_cmd_prefix": "rm(",
637 | "library": "var_list.r",
638 | "varRefreshCmd": "cat(var_dic_list()) "
639 | }
640 | },
641 | "types_to_exclude": [
642 | "module",
643 | "function",
644 | "builtin_function_or_method",
645 | "instance",
646 | "_Feature"
647 | ],
648 | "window_display": false
649 | }
650 | },
651 | "nbformat": 4,
652 | "nbformat_minor": 4
653 | }
654 |
--------------------------------------------------------------------------------
/retos/Reto Pandas 1 - cotiza el fin de mes.ipynb:
--------------------------------------------------------------------------------
1 | {
2 | "cells": [
3 | {
4 | "cell_type": "markdown",
5 | "metadata": {
6 | "ExecuteTime": {
7 | "end_time": "2020-02-09T20:33:28.734309Z",
8 | "start_time": "2020-02-09T20:33:28.732194Z"
9 | }
10 | },
11 | "source": [
12 | "# Reto Pandas 1\n",
13 | "\n"
14 | ]
15 | },
16 | {
17 | "cell_type": "markdown",
18 | "metadata": {},
19 | "source": [
20 | "Texto del reto propuesto en el grupo de telegram Python para Tranding:\n",
21 | "\n",
22 | "Buenos días. Os propongo un reto / ejercicio de Pandas, para quien quiera practicar y mejorar.\n",
23 | "\n",
24 | "con el dataframe data que obtenemos con este código\n",
25 | "\n",
26 | " # Si no está instalado el módulo yfinance puedes hacerlo con \n",
27 | " # pip install yfinance\n",
28 | "\n",
29 | " import yfinance as yf\n",
30 | " data = yf.download('^GSPC')\n",
31 | "\n",
32 | "\n",
33 | "Añadir una nueva columna donde el valor sea 1 los días en que el mes al que pertenece tuvo cotización el último día natural de ese mes, y -1 si no hubo cotización, por vacaciones o fin de semanas.\n",
34 | "\n",
35 | "Así por ejemplo todos los días de cotización del mes de enero de 2020, llevarían un 1, mientras que los días de noviembre de 2019, llevarían un -1.\n",
36 | "\n",
37 | "Hay varías formas de conseguir esto, veamos cual nos gusta más. Obviamente se puede hacer sin bucles usando pandas 😁😉"
38 | ]
39 | },
40 | {
41 | "cell_type": "code",
42 | "execution_count": null,
43 | "metadata": {},
44 | "outputs": [],
45 | "source": []
46 | },
47 | {
48 | "cell_type": "markdown",
49 | "metadata": {
50 | "ExecuteTime": {
51 | "end_time": "2020-02-09T20:35:40.098319Z",
52 | "start_time": "2020-02-09T20:35:40.096450Z"
53 | }
54 | },
55 | "source": [
56 | "## Código base"
57 | ]
58 | },
59 | {
60 | "cell_type": "code",
61 | "execution_count": 5,
62 | "metadata": {
63 | "ExecuteTime": {
64 | "end_time": "2020-02-09T20:21:19.401445Z",
65 | "start_time": "2020-02-09T20:21:18.754667Z"
66 | }
67 | },
68 | "outputs": [
69 | {
70 | "name": "stdout",
71 | "output_type": "stream",
72 | "text": [
73 | "[*********************100%***********************] 1 of 1 completed\n"
74 | ]
75 | }
76 | ],
77 | "source": [
78 | "# Si no está instalado el módulo yfinance puedes hacerlo con \n",
79 | "# pip install yfinance\n",
80 | "\n",
81 | "import yfinance as yf\n",
82 | "import pandas as pd\n",
83 | "data_base = yf.download('^GSPC')"
84 | ]
85 | },
86 | {
87 | "cell_type": "code",
88 | "execution_count": null,
89 | "metadata": {},
90 | "outputs": [],
91 | "source": []
92 | },
93 | {
94 | "cell_type": "markdown",
95 | "metadata": {},
96 | "source": [
97 | "# Propuestas :"
98 | ]
99 | },
100 | {
101 | "cell_type": "markdown",
102 | "metadata": {},
103 | "source": [
104 | "## Pau @pauet7 - 1ª "
105 | ]
106 | },
107 | {
108 | "cell_type": "code",
109 | "execution_count": 2,
110 | "metadata": {
111 | "ExecuteTime": {
112 | "end_time": "2020-02-09T20:20:32.655909Z",
113 | "start_time": "2020-02-09T20:20:32.650578Z"
114 | }
115 | },
116 | "outputs": [],
117 | "source": [
118 | "import calendar\n",
119 | "import datetime\n",
120 | "data = data_base.copy()"
121 | ]
122 | },
123 | {
124 | "cell_type": "code",
125 | "execution_count": 3,
126 | "metadata": {
127 | "ExecuteTime": {
128 | "end_time": "2020-02-09T20:20:38.590228Z",
129 | "start_time": "2020-02-09T20:20:33.469115Z"
130 | }
131 | },
132 | "outputs": [
133 | {
134 | "name": "stdout",
135 | "output_type": "stream",
136 | "text": [
137 | "643 ms ± 19.1 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
138 | ]
139 | }
140 | ],
141 | "source": [
142 | "%%timeit\n",
143 | "data['last_open_day'] = data.index.map(lambda x: datetime.datetime(x.year, x.month, calendar.monthrange(x.year, x.month)[1]))\n",
144 | "data['signal'] = data.apply(lambda x: 1 if x['last_open_day'] in data.index else -1, axis=1)"
145 | ]
146 | },
147 | {
148 | "cell_type": "code",
149 | "execution_count": 4,
150 | "metadata": {
151 | "ExecuteTime": {
152 | "end_time": "2020-02-09T20:20:51.790571Z",
153 | "start_time": "2020-02-09T20:20:51.780840Z"
154 | }
155 | },
156 | "outputs": [
157 | {
158 | "data": {
159 | "text/plain": [
160 | "Date\n",
161 | "1927-12-30 -1\n",
162 | "1928-01-03 1\n",
163 | "1928-01-04 1\n",
164 | "1928-01-05 1\n",
165 | "1928-01-06 1\n",
166 | " ..\n",
167 | "2020-02-03 -1\n",
168 | "2020-02-04 -1\n",
169 | "2020-02-05 -1\n",
170 | "2020-02-06 -1\n",
171 | "2020-02-07 -1\n",
172 | "Name: signal, Length: 23135, dtype: int64"
173 | ]
174 | },
175 | "execution_count": 4,
176 | "metadata": {},
177 | "output_type": "execute_result"
178 | }
179 | ],
180 | "source": [
181 | "data['signal']"
182 | ]
183 | },
184 | {
185 | "cell_type": "markdown",
186 | "metadata": {},
187 | "source": [
188 | "## Pau @pauet7 - 2ª"
189 | ]
190 | },
191 | {
192 | "cell_type": "code",
193 | "execution_count": 12,
194 | "metadata": {},
195 | "outputs": [],
196 | "source": [
197 | "data = data_base.copy()"
198 | ]
199 | },
200 | {
201 | "cell_type": "code",
202 | "execution_count": 8,
203 | "metadata": {
204 | "ExecuteTime": {
205 | "end_time": "2020-02-09T20:22:17.985047Z",
206 | "start_time": "2020-02-09T20:22:13.752217Z"
207 | }
208 | },
209 | "outputs": [
210 | {
211 | "name": "stdout",
212 | "output_type": "stream",
213 | "text": [
214 | "531 ms ± 19.5 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
215 | ]
216 | }
217 | ],
218 | "source": [
219 | "%%timeit\n",
220 | "data['last_open_day'] = data.index.map(lambda x: pd.to_datetime(x) + pd.tseries.offsets.MonthEnd(0))\n",
221 | "data['signal'] = data.apply(lambda x: 1 if x['last_open_day'] in data.index else -1, axis=1)"
222 | ]
223 | },
224 | {
225 | "cell_type": "code",
226 | "execution_count": 9,
227 | "metadata": {
228 | "ExecuteTime": {
229 | "end_time": "2020-02-09T20:22:32.347293Z",
230 | "start_time": "2020-02-09T20:22:32.340669Z"
231 | }
232 | },
233 | "outputs": [
234 | {
235 | "data": {
236 | "text/plain": [
237 | "Date\n",
238 | "1927-12-30 -1\n",
239 | "1928-01-03 1\n",
240 | "1928-01-04 1\n",
241 | "1928-01-05 1\n",
242 | "1928-01-06 1\n",
243 | " ..\n",
244 | "2020-02-03 -1\n",
245 | "2020-02-04 -1\n",
246 | "2020-02-05 -1\n",
247 | "2020-02-06 -1\n",
248 | "2020-02-07 -1\n",
249 | "Name: signal, Length: 23135, dtype: int64"
250 | ]
251 | },
252 | "execution_count": 9,
253 | "metadata": {},
254 | "output_type": "execute_result"
255 | }
256 | ],
257 | "source": [
258 | "data['signal']"
259 | ]
260 | },
261 | {
262 | "cell_type": "code",
263 | "execution_count": 7,
264 | "metadata": {},
265 | "outputs": [],
266 | "source": [
267 | "data = data_base.copy()"
268 | ]
269 | },
270 | {
271 | "cell_type": "code",
272 | "execution_count": 19,
273 | "metadata": {},
274 | "outputs": [
275 | {
276 | "name": "stdout",
277 | "output_type": "stream",
278 | "text": [
279 | "543 ms ± 17.6 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
280 | ]
281 | }
282 | ],
283 | "source": [
284 | "%%timeit \n",
285 | "data['last_open_day'] = data.index.map(lambda x: pd.to_datetime(x) + pd.tseries.offsets.MonthEnd(0))\n",
286 | "data['signal'] = data.apply(lambda x: 1 if x['last_open_day'] in data.index else -1, axis=1)"
287 | ]
288 | },
289 | {
290 | "cell_type": "code",
291 | "execution_count": null,
292 | "metadata": {},
293 | "outputs": [],
294 | "source": []
295 | },
296 | {
297 | "cell_type": "markdown",
298 | "metadata": {},
299 | "source": [
300 | "## Antonio A @init_S - 1ª"
301 | ]
302 | },
303 | {
304 | "cell_type": "code",
305 | "execution_count": 10,
306 | "metadata": {
307 | "ExecuteTime": {
308 | "end_time": "2020-02-09T20:24:01.976288Z",
309 | "start_time": "2020-02-09T20:24:01.973453Z"
310 | }
311 | },
312 | "outputs": [],
313 | "source": [
314 | "data = data_base.copy()"
315 | ]
316 | },
317 | {
318 | "cell_type": "code",
319 | "execution_count": 11,
320 | "metadata": {
321 | "ExecuteTime": {
322 | "end_time": "2020-02-09T20:25:30.966127Z",
323 | "start_time": "2020-02-09T20:24:10.678474Z"
324 | }
325 | },
326 | "outputs": [],
327 | "source": [
328 | "data.loc[:,'rounded_time'] = data.index.to_period('M')\n",
329 | "data.loc[:, 'signal'] = data['rounded_time'].apply(lambda x: 1 if x in data[data.index.is_month_end].index.to_period('M') else -1)"
330 | ]
331 | },
332 | {
333 | "cell_type": "code",
334 | "execution_count": 15,
335 | "metadata": {
336 | "ExecuteTime": {
337 | "end_time": "2020-02-09T20:25:59.672111Z",
338 | "start_time": "2020-02-09T20:25:59.667521Z"
339 | }
340 | },
341 | "outputs": [
342 | {
343 | "data": {
344 | "text/plain": [
345 | "Date\n",
346 | "1927-12-30 -1\n",
347 | "1928-01-03 1\n",
348 | "1928-01-04 1\n",
349 | "1928-01-05 1\n",
350 | "1928-01-06 1\n",
351 | " ..\n",
352 | "2020-02-03 -1\n",
353 | "2020-02-04 -1\n",
354 | "2020-02-05 -1\n",
355 | "2020-02-06 -1\n",
356 | "2020-02-07 -1\n",
357 | "Name: signal, Length: 23135, dtype: int64"
358 | ]
359 | },
360 | "execution_count": 15,
361 | "metadata": {},
362 | "output_type": "execute_result"
363 | }
364 | ],
365 | "source": [
366 | "data['signal']"
367 | ]
368 | },
369 | {
370 | "cell_type": "code",
371 | "execution_count": null,
372 | "metadata": {},
373 | "outputs": [],
374 | "source": []
375 | },
376 | {
377 | "cell_type": "markdown",
378 | "metadata": {},
379 | "source": [
380 | "## Antonio A @init_S - 2ª"
381 | ]
382 | },
383 | {
384 | "cell_type": "code",
385 | "execution_count": 13,
386 | "metadata": {
387 | "ExecuteTime": {
388 | "end_time": "2020-02-09T20:25:48.631668Z",
389 | "start_time": "2020-02-09T20:25:48.628615Z"
390 | }
391 | },
392 | "outputs": [],
393 | "source": [
394 | "import numpy as np\n",
395 | "data = data_base.copy()"
396 | ]
397 | },
398 | {
399 | "cell_type": "code",
400 | "execution_count": 14,
401 | "metadata": {
402 | "ExecuteTime": {
403 | "end_time": "2020-02-09T20:25:54.548875Z",
404 | "start_time": "2020-02-09T20:25:49.541486Z"
405 | }
406 | },
407 | "outputs": [
408 | {
409 | "name": "stdout",
410 | "output_type": "stream",
411 | "text": [
412 | "61.9 ms ± 535 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
413 | ]
414 | }
415 | ],
416 | "source": [
417 | "%%timeit \n",
418 | "data.loc[:,'rounded_time'] = data.index.to_period('M')\n",
419 | "data.loc[:, 'signal'] = np.where(data['rounded_time'].isin(data[data.index.is_month_end].index.to_period('M')), 1, -1)"
420 | ]
421 | },
422 | {
423 | "cell_type": "code",
424 | "execution_count": 16,
425 | "metadata": {
426 | "ExecuteTime": {
427 | "end_time": "2020-02-09T20:26:04.188417Z",
428 | "start_time": "2020-02-09T20:26:04.182226Z"
429 | }
430 | },
431 | "outputs": [
432 | {
433 | "data": {
434 | "text/plain": [
435 | "Date\n",
436 | "1927-12-30 -1\n",
437 | "1928-01-03 1\n",
438 | "1928-01-04 1\n",
439 | "1928-01-05 1\n",
440 | "1928-01-06 1\n",
441 | " ..\n",
442 | "2020-02-03 -1\n",
443 | "2020-02-04 -1\n",
444 | "2020-02-05 -1\n",
445 | "2020-02-06 -1\n",
446 | "2020-02-07 -1\n",
447 | "Name: signal, Length: 23135, dtype: int64"
448 | ]
449 | },
450 | "execution_count": 16,
451 | "metadata": {},
452 | "output_type": "execute_result"
453 | }
454 | ],
455 | "source": [
456 | "data['signal']"
457 | ]
458 | },
459 | {
460 | "cell_type": "code",
461 | "execution_count": null,
462 | "metadata": {},
463 | "outputs": [],
464 | "source": []
465 | },
466 | {
467 | "cell_type": "markdown",
468 | "metadata": {},
469 | "source": [
470 | "## Dario @Bukosabino - 1ª"
471 | ]
472 | },
473 | {
474 | "cell_type": "code",
475 | "execution_count": 17,
476 | "metadata": {
477 | "ExecuteTime": {
478 | "end_time": "2020-02-09T20:26:17.038291Z",
479 | "start_time": "2020-02-09T20:26:17.035469Z"
480 | }
481 | },
482 | "outputs": [],
483 | "source": [
484 | "data = data_base.copy()"
485 | ]
486 | },
487 | {
488 | "cell_type": "code",
489 | "execution_count": 18,
490 | "metadata": {
491 | "ExecuteTime": {
492 | "end_time": "2020-02-09T20:26:26.443033Z",
493 | "start_time": "2020-02-09T20:26:18.949813Z"
494 | }
495 | },
496 | "outputs": [
497 | {
498 | "name": "stdout",
499 | "output_type": "stream",
500 | "text": [
501 | "9.29 ms ± 268 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
502 | ]
503 | }
504 | ],
505 | "source": [
506 | "%%timeit\n",
507 | "last_day_month_market = data.groupby([data.index.year, data.index.month]).tail(1).index\n",
508 | "data['signal']=pd.DataFrame(index=last_day_month_market, \n",
509 | " data=last_day_month_market.is_month_end.astype(int)).reindex(data.index).fillna(method='backfill')[0]\n",
510 | "data['signal']=np.where(data['signal']==0,-1,data['signal'])"
511 | ]
512 | },
513 | {
514 | "cell_type": "code",
515 | "execution_count": 19,
516 | "metadata": {
517 | "ExecuteTime": {
518 | "end_time": "2020-02-09T20:26:26.450311Z",
519 | "start_time": "2020-02-09T20:26:26.445226Z"
520 | }
521 | },
522 | "outputs": [
523 | {
524 | "data": {
525 | "text/plain": [
526 | "Date\n",
527 | "1927-12-30 -1.0\n",
528 | "1928-01-03 1.0\n",
529 | "1928-01-04 1.0\n",
530 | "1928-01-05 1.0\n",
531 | "1928-01-06 1.0\n",
532 | " ... \n",
533 | "2020-02-03 -1.0\n",
534 | "2020-02-04 -1.0\n",
535 | "2020-02-05 -1.0\n",
536 | "2020-02-06 -1.0\n",
537 | "2020-02-07 -1.0\n",
538 | "Name: signal, Length: 23135, dtype: float64"
539 | ]
540 | },
541 | "execution_count": 19,
542 | "metadata": {},
543 | "output_type": "execute_result"
544 | }
545 | ],
546 | "source": [
547 | "data['signal']"
548 | ]
549 | },
550 | {
551 | "cell_type": "code",
552 | "execution_count": null,
553 | "metadata": {},
554 | "outputs": [],
555 | "source": []
556 | },
557 | {
558 | "cell_type": "markdown",
559 | "metadata": {},
560 | "source": [
561 | "## Dario @Bukosabino - 2ª"
562 | ]
563 | },
564 | {
565 | "cell_type": "code",
566 | "execution_count": 20,
567 | "metadata": {
568 | "ExecuteTime": {
569 | "end_time": "2020-02-09T20:26:33.767729Z",
570 | "start_time": "2020-02-09T20:26:33.764875Z"
571 | }
572 | },
573 | "outputs": [],
574 | "source": [
575 | "data = data_base.copy()"
576 | ]
577 | },
578 | {
579 | "cell_type": "code",
580 | "execution_count": 21,
581 | "metadata": {
582 | "ExecuteTime": {
583 | "end_time": "2020-02-09T20:26:44.604543Z",
584 | "start_time": "2020-02-09T20:26:36.435229Z"
585 | }
586 | },
587 | "outputs": [
588 | {
589 | "name": "stdout",
590 | "output_type": "stream",
591 | "text": [
592 | "10.2 ms ± 752 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
593 | ]
594 | }
595 | ],
596 | "source": [
597 | "%%timeit\n",
598 | "last_day_month_market = data.groupby([data.index.year, data.index.month]).tail(1).index\n",
599 | "data['signal']=pd.Series(index=last_day_month_market, data=last_day_month_market.is_month_end.astype(int)).reindex(data.index).fillna(method='backfill').values\n",
600 | "data['signal']=np.where(data['signal']==0,-1,data['signal'])"
601 | ]
602 | },
603 | {
604 | "cell_type": "code",
605 | "execution_count": 24,
606 | "metadata": {
607 | "ExecuteTime": {
608 | "end_time": "2020-02-09T20:27:27.145210Z",
609 | "start_time": "2020-02-09T20:27:27.140927Z"
610 | }
611 | },
612 | "outputs": [
613 | {
614 | "data": {
615 | "text/plain": [
616 | "Date\n",
617 | "1927-12-30 -1\n",
618 | "1928-01-03 1\n",
619 | "1928-01-04 1\n",
620 | "1928-01-05 1\n",
621 | "1928-01-06 1\n",
622 | " ..\n",
623 | "2020-02-03 -1\n",
624 | "2020-02-04 -1\n",
625 | "2020-02-05 -1\n",
626 | "2020-02-06 -1\n",
627 | "2020-02-07 -1\n",
628 | "Name: signal, Length: 23135, dtype: int64"
629 | ]
630 | },
631 | "execution_count": 24,
632 | "metadata": {},
633 | "output_type": "execute_result"
634 | }
635 | ],
636 | "source": [
637 | "data['signal']"
638 | ]
639 | },
640 | {
641 | "cell_type": "code",
642 | "execution_count": null,
643 | "metadata": {},
644 | "outputs": [],
645 | "source": []
646 | },
647 | {
648 | "cell_type": "markdown",
649 | "metadata": {},
650 | "source": [
651 | "## Paduel @paduel - 1ª"
652 | ]
653 | },
654 | {
655 | "cell_type": "code",
656 | "execution_count": 22,
657 | "metadata": {
658 | "ExecuteTime": {
659 | "end_time": "2020-02-09T20:26:58.533431Z",
660 | "start_time": "2020-02-09T20:26:58.530895Z"
661 | }
662 | },
663 | "outputs": [],
664 | "source": [
665 | "data = data_base.copy()"
666 | ]
667 | },
668 | {
669 | "cell_type": "code",
670 | "execution_count": 25,
671 | "metadata": {
672 | "ExecuteTime": {
673 | "end_time": "2020-02-09T20:27:48.770455Z",
674 | "start_time": "2020-02-09T20:27:37.970327Z"
675 | }
676 | },
677 | "outputs": [
678 | {
679 | "name": "stdout",
680 | "output_type": "stream",
681 | "text": [
682 | "13.5 ms ± 1.35 ms per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
683 | ]
684 | }
685 | ],
686 | "source": [
687 | "%%timeit\n",
688 | "data['signal'] = data.index.to_frame().Date.groupby([data.index.year, data.index.month]).transform('last').dt.is_month_end.mul(2).sub(1)"
689 | ]
690 | },
691 | {
692 | "cell_type": "code",
693 | "execution_count": 306,
694 | "metadata": {},
695 | "outputs": [
696 | {
697 | "data": {
698 | "text/plain": [
699 | "Date\n",
700 | "1927-12-30 -1\n",
701 | "1928-01-03 1\n",
702 | "1928-01-04 1\n",
703 | "1928-01-05 1\n",
704 | "1928-01-06 1\n",
705 | " ..\n",
706 | "2020-02-03 -1\n",
707 | "2020-02-04 -1\n",
708 | "2020-02-05 -1\n",
709 | "2020-02-06 -1\n",
710 | "2020-02-07 -1\n",
711 | "Name: signal, Length: 23135, dtype: int64"
712 | ]
713 | },
714 | "execution_count": 306,
715 | "metadata": {},
716 | "output_type": "execute_result"
717 | }
718 | ],
719 | "source": [
720 | "data['signal']"
721 | ]
722 | },
723 | {
724 | "cell_type": "code",
725 | "execution_count": null,
726 | "metadata": {},
727 | "outputs": [],
728 | "source": []
729 | },
730 | {
731 | "cell_type": "markdown",
732 | "metadata": {},
733 | "source": [
734 | "## Paduel @paduel - 2ª"
735 | ]
736 | },
737 | {
738 | "cell_type": "code",
739 | "execution_count": 28,
740 | "metadata": {
741 | "ExecuteTime": {
742 | "end_time": "2020-02-09T20:29:34.499749Z",
743 | "start_time": "2020-02-09T20:29:34.496910Z"
744 | }
745 | },
746 | "outputs": [],
747 | "source": [
748 | "data = data_base.copy()"
749 | ]
750 | },
751 | {
752 | "cell_type": "code",
753 | "execution_count": 29,
754 | "metadata": {
755 | "ExecuteTime": {
756 | "end_time": "2020-02-09T20:29:42.366203Z",
757 | "start_time": "2020-02-09T20:29:35.085596Z"
758 | }
759 | },
760 | "outputs": [
761 | {
762 | "name": "stdout",
763 | "output_type": "stream",
764 | "text": [
765 | "8.97 ms ± 162 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
766 | ]
767 | }
768 | ],
769 | "source": [
770 | "%%timeit\n",
771 | "_d = data.index.to_frame().groupby([data.index.year, data.index.month]).last().squeeze()\n",
772 | "data['signal'] = pd.Series(_d.dt.is_month_end.mul(2).sub(1).values, index=_d.values).reindex(data.index).bfill()"
773 | ]
774 | },
775 | {
776 | "cell_type": "code",
777 | "execution_count": 30,
778 | "metadata": {
779 | "ExecuteTime": {
780 | "end_time": "2020-02-09T20:29:42.372847Z",
781 | "start_time": "2020-02-09T20:29:42.367971Z"
782 | }
783 | },
784 | "outputs": [
785 | {
786 | "data": {
787 | "text/plain": [
788 | "Date\n",
789 | "1927-12-30 -1.0\n",
790 | "1928-01-03 1.0\n",
791 | "1928-01-04 1.0\n",
792 | "1928-01-05 1.0\n",
793 | "1928-01-06 1.0\n",
794 | " ... \n",
795 | "2020-02-03 -1.0\n",
796 | "2020-02-04 -1.0\n",
797 | "2020-02-05 -1.0\n",
798 | "2020-02-06 -1.0\n",
799 | "2020-02-07 -1.0\n",
800 | "Name: signal, Length: 23135, dtype: float64"
801 | ]
802 | },
803 | "execution_count": 30,
804 | "metadata": {},
805 | "output_type": "execute_result"
806 | }
807 | ],
808 | "source": [
809 | "data['signal']"
810 | ]
811 | },
812 | {
813 | "cell_type": "code",
814 | "execution_count": null,
815 | "metadata": {},
816 | "outputs": [],
817 | "source": []
818 | },
819 | {
820 | "cell_type": "code",
821 | "execution_count": null,
822 | "metadata": {},
823 | "outputs": [],
824 | "source": []
825 | },
826 | {
827 | "cell_type": "markdown",
828 | "metadata": {
829 | "ExecuteTime": {
830 | "end_time": "2020-02-09T20:30:55.805524Z",
831 | "start_time": "2020-02-09T20:30:55.797010Z"
832 | }
833 | },
834 | "source": [
835 | "# Bonus"
836 | ]
837 | },
838 | {
839 | "cell_type": "markdown",
840 | "metadata": {},
841 | "source": [
842 | "Todos los métodos anteriores dan un -1 a los días del mes en curso cuando aún no tenemos datos para saber si el último día habrá o no cotización. Por lo que habría que añadir un código que corrigiera eso.\n",
843 | "A continuación una propuesta one liner que evita ese problema."
844 | ]
845 | },
846 | {
847 | "cell_type": "markdown",
848 | "metadata": {},
849 | "source": [
850 | "## Paduel @paduel 3º"
851 | ]
852 | },
853 | {
854 | "cell_type": "code",
855 | "execution_count": 31,
856 | "metadata": {
857 | "ExecuteTime": {
858 | "end_time": "2020-02-09T20:30:31.457873Z",
859 | "start_time": "2020-02-09T20:30:31.455272Z"
860 | }
861 | },
862 | "outputs": [],
863 | "source": [
864 | "data = data_base.copy()"
865 | ]
866 | },
867 | {
868 | "cell_type": "code",
869 | "execution_count": 36,
870 | "metadata": {
871 | "ExecuteTime": {
872 | "end_time": "2020-02-09T20:31:04.042758Z",
873 | "start_time": "2020-02-09T20:31:02.219288Z"
874 | }
875 | },
876 | "outputs": [
877 | {
878 | "name": "stdout",
879 | "output_type": "stream",
880 | "text": [
881 | "22.6 ms ± 1.74 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
882 | ]
883 | }
884 | ],
885 | "source": [
886 | "%%timeit\n",
887 | "data['signal'] = data.Close.reindex(pd.date_range(data.index[0], data.index[-1], freq='M')).gt(0).replace(False,-1).reindex(pd.date_range(data.index[0], data.index[-1], freq='D')).bfill().reindex(data.index)"
888 | ]
889 | },
890 | {
891 | "cell_type": "code",
892 | "execution_count": 34,
893 | "metadata": {
894 | "ExecuteTime": {
895 | "end_time": "2020-02-09T20:30:55.794640Z",
896 | "start_time": "2020-02-09T20:30:55.788006Z"
897 | }
898 | },
899 | "outputs": [
900 | {
901 | "data": {
902 | "text/plain": [
903 | "Date\n",
904 | "1927-12-30 -1.0\n",
905 | "1928-01-03 1.0\n",
906 | "1928-01-04 1.0\n",
907 | "1928-01-05 1.0\n",
908 | "1928-01-06 1.0\n",
909 | " ... \n",
910 | "2020-02-03 NaN\n",
911 | "2020-02-04 NaN\n",
912 | "2020-02-05 NaN\n",
913 | "2020-02-06 NaN\n",
914 | "2020-02-07 NaN\n",
915 | "Name: signal, Length: 23135, dtype: float64"
916 | ]
917 | },
918 | "execution_count": 34,
919 | "metadata": {},
920 | "output_type": "execute_result"
921 | }
922 | ],
923 | "source": [
924 | "data['signal']"
925 | ]
926 | },
927 | {
928 | "cell_type": "code",
929 | "execution_count": 357,
930 | "metadata": {},
931 | "outputs": [
932 | {
933 | "data": {
934 | "text/plain": [
935 | "Date\n",
936 | "2020-01-27 1.0\n",
937 | "2020-01-28 1.0\n",
938 | "2020-01-29 1.0\n",
939 | "2020-01-30 1.0\n",
940 | "2020-01-31 1.0\n",
941 | "2020-02-03 NaN\n",
942 | "2020-02-04 NaN\n",
943 | "2020-02-05 NaN\n",
944 | "2020-02-06 NaN\n",
945 | "2020-02-07 NaN\n",
946 | "Name: signal, dtype: float64"
947 | ]
948 | },
949 | "execution_count": 357,
950 | "metadata": {},
951 | "output_type": "execute_result"
952 | }
953 | ],
954 | "source": [
955 | "data['signal'].tail(10)"
956 | ]
957 | },
958 | {
959 | "cell_type": "code",
960 | "execution_count": null,
961 | "metadata": {},
962 | "outputs": [],
963 | "source": []
964 | }
965 | ],
966 | "metadata": {
967 | "hide_input": false,
968 | "kernelspec": {
969 | "display_name": "python372",
970 | "language": "python",
971 | "name": "python372"
972 | },
973 | "language_info": {
974 | "codemirror_mode": {
975 | "name": "ipython",
976 | "version": 3
977 | },
978 | "file_extension": ".py",
979 | "mimetype": "text/x-python",
980 | "name": "python",
981 | "nbconvert_exporter": "python",
982 | "pygments_lexer": "ipython3",
983 | "version": "3.7.2"
984 | },
985 | "latex_envs": {
986 | "LaTeX_envs_menu_present": true,
987 | "autoclose": false,
988 | "autocomplete": true,
989 | "bibliofile": "biblio.bib",
990 | "cite_by": "apalike",
991 | "current_citInitial": 1,
992 | "eqLabelWithNumbers": true,
993 | "eqNumInitial": 1,
994 | "hotkeys": {
995 | "equation": "Ctrl-E",
996 | "itemize": "Ctrl-I"
997 | },
998 | "labels_anchors": false,
999 | "latex_user_defs": false,
1000 | "report_style_numbering": false,
1001 | "user_envs_cfg": false
1002 | },
1003 | "toc": {
1004 | "base_numbering": 1,
1005 | "nav_menu": {},
1006 | "number_sections": true,
1007 | "sideBar": true,
1008 | "skip_h1_title": false,
1009 | "title_cell": "Tabla de Contenido",
1010 | "title_sidebar": "Contenidos",
1011 | "toc_cell": false,
1012 | "toc_position": {},
1013 | "toc_section_display": true,
1014 | "toc_window_display": false
1015 | }
1016 | },
1017 | "nbformat": 4,
1018 | "nbformat_minor": 2
1019 | }
1020 |
--------------------------------------------------------------------------------
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674 | .
675 |
--------------------------------------------------------------------------------
/Ejemplos/Filtro Super Bandpass de Jonh Ehlers.ipynb:
--------------------------------------------------------------------------------
1 | {
2 | "cells": [
3 | {
4 | "cell_type": "code",
5 | "execution_count": 1,
6 | "id": "medium-scoop",
7 | "metadata": {},
8 | "outputs": [],
9 | "source": [
10 | "from yfinance import download\n",
11 | "import matplotlib.pyplot as plt\n",
12 | "import pandas as pd\n",
13 | "import numpy as np"
14 | ]
15 | },
16 | {
17 | "cell_type": "markdown",
18 | "id": "threaded-department",
19 | "metadata": {},
20 | "source": [
21 | "## Filtro Super Bandpass de Jonh Ehlers\n",
22 | "\n",
23 | "Referencia: http://traders.com/Documentation/FEEDbk_docs/2016/07/TradersTips.html"
24 | ]
25 | },
26 | {
27 | "cell_type": "code",
28 | "execution_count": 2,
29 | "id": "least-novel",
30 | "metadata": {},
31 | "outputs": [],
32 | "source": [
33 | "def super_bandpass(data_base, flen = 40, slen = 60, column='Close'):\n",
34 | " '''Función que devuelve un dataframe con el filtro super bandpass calculado'''\n",
35 | " \n",
36 | " data = data_base.copy()\n",
37 | " \n",
38 | " a1= 5/flen\n",
39 | " a2= 5/slen\n",
40 | "\n",
41 | " pb = [data[column].iloc[:2].mean()] * 2\n",
42 | " # pb = [0, 0]\n",
43 | " for f in range(2, data.shape[0]):\n",
44 | " pb_n = (a1 - a2) * data[column].iloc[f] + (a2*(1 - a1) - a1 * (1 - a2))* data[column].iloc[f - 1] + ((1 - a1) + (1 - a2))*(pb[f-1])- (1 - a1)* (1 - a2)*(pb[f-2])\n",
45 | " pb += [pb_n]\n",
46 | " data['PB'] = pb\n",
47 | "\n",
48 | " data['RMS+'] = data['PB'].pow(2).rolling(50).sum().div(50).pow(.5)\n",
49 | " data['RMS-'] = -data['RMS+']\n",
50 | "\n",
51 | " data['Sell Signal'] = data['PB'].lt(data['RMS+']) & data['PB'].shift().gt(data['RMS+'].shift())\n",
52 | " data['Buy Signal'] = data['PB'].gt(data['RMS-']) & data['PB'].shift().lt(data['RMS-'].shift())\n",
53 | " \n",
54 | " return data"
55 | ]
56 | },
57 | {
58 | "cell_type": "code",
59 | "execution_count": 3,
60 | "id": "combined-sleeve",
61 | "metadata": {},
62 | "outputs": [],
63 | "source": [
64 | "def plot_super_bandpass(data):\n",
65 | " '''Función para graficar el filtro super bandapass'''\n",
66 | " fig, axs = plt.subplots(2, 1, sharex=True, gridspec_kw={'height_ratios': [2, 1]})\n",
67 | " axs[1].fill_between(data.index, data['RMS+'], data['RMS-'], color='g', alpha=.2)\n",
68 | " data['Close'].plot(ax=axs[0])\n",
69 | " data['Sell Signal'].mul(data['Close']).replace({0: np.nan}).plot(marker='v', color='r', ax=axs[0])\n",
70 | " data['Buy Signal'].mul(data['Close']).replace({0: np.nan}).plot(marker='^', color='g', ax=axs[0])\n",
71 | " data['PB'].plot(color='orange', figsize=(18, 12), ax=axs[1])\n",
72 | " data['Sell Signal'].mul(data['PB']).replace({0: np.nan}).plot(marker='v', color='r', ax=axs[1])\n",
73 | " data['Buy Signal'].mul(data['PB']).replace({0: np.nan}).plot(marker='^', color='g', ax=axs[1])"
74 | ]
75 | },
76 | {
77 | "cell_type": "code",
78 | "execution_count": 4,
79 | "id": "leading-composition",
80 | "metadata": {
81 | "scrolled": true
82 | },
83 | "outputs": [
84 | {
85 | "name": "stdout",
86 | "output_type": "stream",
87 | "text": [
88 | "[*********************100%***********************] 1 of 1 completed\n"
89 | ]
90 | }
91 | ],
92 | "source": [
93 | "symbol = 'MMM'\n",
94 | "data = download(symbol, auto_adjust=True)"
95 | ]
96 | },
97 | {
98 | "cell_type": "code",
99 | "execution_count": 5,
100 | "id": "fatty-interference",
101 | "metadata": {},
102 | "outputs": [
103 | {
104 | "data": {
105 | "text/html": [
106 | "