ml-finance-python
python scripts for finance machine learning
git clone https://9o.is/git/ml-finance-python.git
notebook.ipynb
(158833B)
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9 "# Alpha Vertex: PreCog 100/500 Securities\n",
10 "In this notebook, we'll take a look at the Alpha Vertex *Top 100/500 Securities PreCog* dataset, available on [Quantopian](https://www.quantopian.com/store). This dataset spans 2010 through the current day. PreCog uses machine learning models to forecast stock returns at multiple horizons.\n",
11 "\n",
12 "The *100* dataset contains 5 day predicted log returns for the top 100 securities by market cap. The *500* dataset contains 5 day predicted log returns for the top 500 securities by market cap.\n",
13 "\n",
14 "Update time: Daily data will be updated close to midnight for the previous day. So on the 27th, you will have data with an asof_date of the 26th.\n",
15 "\n",
16 "## Notebook Contents\n",
17 "There are two ways to access the data and you'll find both of them listed below. Just click on the section you'd like to read through.\n",
18 "\n",
19 "- <a href=\"#interactive\"><strong>Interactive overview</strong></a>: This is only available on Research and uses blaze to give you access to large amounts of data. Recommended for exploration and plotting.\n",
20 "- <a href=\"#pipeline\"><strong>Pipeline overview</strong></a>: Data is made available through pipeline which is available in both the Research & Backtesting environments. Recommended for factor development and moving back & forth between research/backtesting.\n",
21 "\n",
22 "### Free samples and limits\n",
23 "The result of any expression is limited to 10,000 rows to protect against runaway memory usage. To be clear, you have access to all the data server side. We are limiting the size of the responses back from Blaze.\n",
24 "\n",
25 "There is a *free* version of this dataset as well as a paid one. The free sample includes data until 2 months prior to the current date.\n",
26 "\n",
27 "To access the most up-to-date values for this data set for trading a live algorithm (as with other partner sets), you need to purchase access to the full set.\n",
28 "\n",
29 "<a></a></a>\n",
30 "\n",
31 "# Interactive Overview\n",
32 "### Accessing the data with Blaze and Interactive on Research\n",
33 "Partner datasets are available on Quantopian Research through an API service known as [Blaze](http://blaze.pydata.org). Blaze provides the Quantopian user with a convenient interface to access very large datasets, in an interactive, generic manner.\n",
34 "\n",
35 "Blaze provides an important function for accessing these datasets. Some of these sets are many millions of records. Bringing that data directly into Quantopian Research directly just is not viable. So Blaze allows us to provide a simple querying interface and shift the burden over to the server side.\n",
36 "\n",
37 "It is common to use Blaze to perform a reduction expression on your dataset so that you don't have to pull the whole dataset into memory. You can convert the result of a blaze expression to a Pandas data structure (e.g. a [DataFrame](http://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html)) and perform further computation, manipulation, and visualization on that structure.\n",
38 "\n",
39 "Helpful links:\n",
40 "\n",
41 "* [Query building for Blaze](http://blaze.readthedocs.io/en/latest/queries.html)\n",
42 "* [Pandas-to-Blaze dictionary](http://blaze.readthedocs.io/en/latest/rosetta-pandas.html)\n",
43 "* [SQL-to-Blaze dictionary](http://blaze.readthedocs.io/en/latest/rosetta-sql.html).\n",
44 "\n",
45 "Once you have a Blaze expression that reduces the dataset to less than 10,000 rows, you can convert it to a Pandas DataFrames using:\n",
46 "\n",
47 "> `from odo import odo`\n",
48 "> `odo(expr, pandas.DataFrame)`\n",
49 "\n",
50 "#### To see how to create a factor using this data, search for the `Pipeline Overview` section of this notebook or head straight to <a href=\"#pipeline\">Pipeline Overview</a>."
51 ]
52 },
53 {
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61 "# import the free sample of the dataset\n",
62 "from quantopian.interactive.data.alpha_vertex import (\n",
63 " # Top 100 Securities\n",
64 " precog_top_100 as dataset_100,\n",
65 " # Top 500 Securities\n",
66 " precog_top_500 as dataset_500\n",
67 ")\n",
68 "\n",
69 "# import data operations\n",
70 "from odo import odo\n",
71 "# import other libraries we will use\n",
72 "import pandas as pd\n",
73 "import matplotlib.pyplot as plt"
74 ]
75 },
76 {
77 "cell_type": "code",
78 "execution_count": 2,
79 "metadata": {
80 "collapsed": false
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86 "Timestamp('2017-03-06 00:00:00')"
87 ],
88 "text/plain": [
89 "Timestamp('2017-03-06 00:00:00')"
90 ]
91 },
92 "execution_count": 2,
93 "metadata": {},
94 "output_type": "execute_result"
95 }
96 ],
97 "source": [
98 "# Let's use blaze to understand the data a bit using Blaze dshape()\n",
99 "dataset_500.asof_date.max()"
100 ]
101 },
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103 "cell_type": "code",
104 "execution_count": 3,
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112 "915545"
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115 "915545"
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123 "source": [
124 "# And how many rows are there?\n",
125 "# N.B. we're using a Blaze function to do this, not len()\n",
126 "dataset_500.count()"
127 ]
128 },
129 {
130 "cell_type": "code",
131 "execution_count": 4,
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139 "<div>\n",
140 "<table border=\"1\" class=\"dataframe\">\n",
141 " <thead>\n",
142 " <tr style=\"text-align: right;\">\n",
143 " <th></th>\n",
144 " <th>symbol</th>\n",
145 " <th>name</th>\n",
146 " <th>sid</th>\n",
147 " <th>predicted_five_day_log_return</th>\n",
148 " <th>asof_date</th>\n",
149 " <th>timestamp</th>\n",
150 " </tr>\n",
151 " </thead>\n",
152 " <tbody>\n",
153 " <tr>\n",
154 " <th>0</th>\n",
155 " <td>AA</td>\n",
156 " <td>ALCOA INC</td>\n",
157 " <td>2</td>\n",
158 " <td>0.064</td>\n",
159 " <td>2010-01-04</td>\n",
160 " <td>2010-01-05</td>\n",
161 " </tr>\n",
162 " <tr>\n",
163 " <th>1</th>\n",
164 " <td>AAPL</td>\n",
165 " <td>APPLE INC</td>\n",
166 " <td>24</td>\n",
167 " <td>0.000</td>\n",
168 " <td>2010-01-04</td>\n",
169 " <td>2010-01-05</td>\n",
170 " </tr>\n",
171 " <tr>\n",
172 " <th>2</th>\n",
173 " <td>ABT</td>\n",
174 " <td>ABBOTT LABORATORIES</td>\n",
175 " <td>62</td>\n",
176 " <td>-0.001</td>\n",
177 " <td>2010-01-04</td>\n",
178 " <td>2010-01-05</td>\n",
179 " </tr>\n",
180 " <tr>\n",
181 " <th>3</th>\n",
182 " <td>ABX</td>\n",
183 " <td>BARRICK GOLD CORP</td>\n",
184 " <td>64</td>\n",
185 " <td>0.013</td>\n",
186 " <td>2010-01-04</td>\n",
187 " <td>2010-01-05</td>\n",
188 " </tr>\n",
189 " <tr>\n",
190 " <th>4</th>\n",
191 " <td>ADSK</td>\n",
192 " <td>AUTODESK INC</td>\n",
193 " <td>67</td>\n",
194 " <td>-0.040</td>\n",
195 " <td>2010-01-04</td>\n",
196 " <td>2010-01-05</td>\n",
197 " </tr>\n",
198 " <tr>\n",
199 " <th>5</th>\n",
200 " <td>TAP</td>\n",
201 " <td>MOLSON COORS BREWING CO</td>\n",
202 " <td>76</td>\n",
203 " <td>0.012</td>\n",
204 " <td>2010-01-04</td>\n",
205 " <td>2010-01-05</td>\n",
206 " </tr>\n",
207 " <tr>\n",
208 " <th>6</th>\n",
209 " <td>ADBE</td>\n",
210 " <td>ADOBE SYSTEMS INC</td>\n",
211 " <td>114</td>\n",
212 " <td>-0.013</td>\n",
213 " <td>2010-01-04</td>\n",
214 " <td>2010-01-05</td>\n",
215 " </tr>\n",
216 " <tr>\n",
217 " <th>7</th>\n",
218 " <td>ADI</td>\n",
219 " <td>ANALOG DEVICES INC</td>\n",
220 " <td>122</td>\n",
221 " <td>-0.023</td>\n",
222 " <td>2010-01-04</td>\n",
223 " <td>2010-01-05</td>\n",
224 " </tr>\n",
225 " <tr>\n",
226 " <th>8</th>\n",
227 " <td>ADM</td>\n",
228 " <td>ARCHER-DANIELS-MIDLAND CO</td>\n",
229 " <td>128</td>\n",
230 " <td>-0.020</td>\n",
231 " <td>2010-01-04</td>\n",
232 " <td>2010-01-05</td>\n",
233 " </tr>\n",
234 " <tr>\n",
235 " <th>9</th>\n",
236 " <td>AEP</td>\n",
237 " <td>AMERICAN ELECTRIC POWER CO INC</td>\n",
238 " <td>161</td>\n",
239 " <td>0.013</td>\n",
240 " <td>2010-01-04</td>\n",
241 " <td>2010-01-05</td>\n",
242 " </tr>\n",
243 " <tr>\n",
244 " <th>10</th>\n",
245 " <td>AES</td>\n",
246 " <td>AES CORP / VA</td>\n",
247 " <td>166</td>\n",
248 " <td>-0.042</td>\n",
249 " <td>2010-01-04</td>\n",
250 " <td>2010-01-05</td>\n",
251 " </tr>\n",
252 " </tbody>\n",
253 "</table>\n",
254 "</div>"
255 ],
256 "text/plain": [
257 " symbol name sid predicted_five_day_log_return \\\n",
258 "0 AA ALCOA INC 2 0.064 \n",
259 "1 AAPL APPLE INC 24 0.000 \n",
260 "2 ABT ABBOTT LABORATORIES 62 -0.001 \n",
261 "3 ABX BARRICK GOLD CORP 64 0.013 \n",
262 "4 ADSK AUTODESK INC 67 -0.040 \n",
263 "5 TAP MOLSON COORS BREWING CO 76 0.012 \n",
264 "6 ADBE ADOBE SYSTEMS INC 114 -0.013 \n",
265 "7 ADI ANALOG DEVICES INC 122 -0.023 \n",
266 "8 ADM ARCHER-DANIELS-MIDLAND CO 128 -0.020 \n",
267 "9 AEP AMERICAN ELECTRIC POWER CO INC 161 0.013 \n",
268 "10 AES AES CORP / VA 166 -0.042 \n",
269 "\n",
270 " asof_date timestamp \n",
271 "0 2010-01-04 2010-01-05 \n",
272 "1 2010-01-04 2010-01-05 \n",
273 "2 2010-01-04 2010-01-05 \n",
274 "3 2010-01-04 2010-01-05 \n",
275 "4 2010-01-04 2010-01-05 \n",
276 "5 2010-01-04 2010-01-05 \n",
277 "6 2010-01-04 2010-01-05 \n",
278 "7 2010-01-04 2010-01-05 \n",
279 "8 2010-01-04 2010-01-05 \n",
280 "9 2010-01-04 2010-01-05 \n",
281 "10 2010-01-04 2010-01-05 "
282 ]
283 },
284 "execution_count": 4,
285 "metadata": {},
286 "output_type": "execute_result"
287 }
288 ],
289 "source": [
290 "# Let's see what the data looks like. We'll grab the first few rows.\n",
291 "dataset_500.peek()"
292 ]
293 },
294 {
295 "cell_type": "markdown",
296 "metadata": {},
297 "source": [
298 "Let's go over the columns:\n",
299 "\n",
300 "- **symbol**: The ticker symbol of the company.\n",
301 "- **sid**: The security ID (see [here](https://www.quantopian.com/tutorials/getting-started#lesson3) for explanation).\n",
302 "- **name**: The name of the company.\n",
303 "- **asof_date**: The date to which this data applies/actual report date\n",
304 "- **timestamp**: This is our timestamp on when we registered the data.\n",
305 "- **predicted_five_day_log_return**: The predicted log return for the security over the next 5 days\n",
306 "\n",
307 "Fields like `timestamp` and `sid` are standardized across all Quantopian Store Datasets, so the datasets are easy to combine. The `sid` field is also standardized across all Quantopian equity databases."
308 ]
309 },
310 {
311 "cell_type": "markdown",
312 "metadata": {},
313 "source": [
314 "Now that we understand the data a bit better, let's get the `predicted_five_day_log_return` data for Apple (sid 24) and visualize it with a chart."
315 ]
316 },
317 {
318 "cell_type": "code",
319 "execution_count": 5,
320 "metadata": {
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327 "<div>\n",
328 "<table border=\"1\" class=\"dataframe\">\n",
329 " <thead>\n",
330 " <tr style=\"text-align: right;\">\n",
331 " <th></th>\n",
332 " <th>symbol</th>\n",
333 " <th>name</th>\n",
334 " <th>sid</th>\n",
335 " <th>predicted_five_day_log_return</th>\n",
336 " <th>asof_date</th>\n",
337 " <th>timestamp</th>\n",
338 " </tr>\n",
339 " </thead>\n",
340 " <tbody>\n",
341 " <tr>\n",
342 " <th>0</th>\n",
343 " <td>AAPL</td>\n",
344 " <td>APPLE INC</td>\n",
345 " <td>24</td>\n",
346 " <td>0.000</td>\n",
347 " <td>2010-01-04</td>\n",
348 " <td>2010-01-05</td>\n",
349 " </tr>\n",
350 " <tr>\n",
351 " <th>1</th>\n",
352 " <td>AAPL</td>\n",
353 " <td>APPLE INC</td>\n",
354 " <td>24</td>\n",
355 " <td>0.003</td>\n",
356 " <td>2010-01-05</td>\n",
357 " <td>2010-01-06</td>\n",
358 " </tr>\n",
359 " <tr>\n",
360 " <th>2</th>\n",
361 " <td>AAPL</td>\n",
362 " <td>APPLE INC</td>\n",
363 " <td>24</td>\n",
364 " <td>0.016</td>\n",
365 " <td>2010-01-06</td>\n",
366 " <td>2010-01-07</td>\n",
367 " </tr>\n",
368 " <tr>\n",
369 " <th>3</th>\n",
370 " <td>AAPL</td>\n",
371 " <td>APPLE INC</td>\n",
372 " <td>24</td>\n",
373 " <td>-0.022</td>\n",
374 " <td>2010-01-07</td>\n",
375 " <td>2010-01-08</td>\n",
376 " </tr>\n",
377 " <tr>\n",
378 " <th>4</th>\n",
379 " <td>AAPL</td>\n",
380 " <td>APPLE INC</td>\n",
381 " <td>24</td>\n",
382 " <td>0.011</td>\n",
383 " <td>2010-01-08</td>\n",
384 " <td>2010-01-09</td>\n",
385 " </tr>\n",
386 " </tbody>\n",
387 "</table>\n",
388 "</div>"
389 ],
390 "text/plain": [
391 " symbol name sid predicted_five_day_log_return asof_date timestamp\n",
392 "0 AAPL APPLE INC 24 0.000 2010-01-04 2010-01-05\n",
393 "1 AAPL APPLE INC 24 0.003 2010-01-05 2010-01-06\n",
394 "2 AAPL APPLE INC 24 0.016 2010-01-06 2010-01-07\n",
395 "3 AAPL APPLE INC 24 -0.022 2010-01-07 2010-01-08\n",
396 "4 AAPL APPLE INC 24 0.011 2010-01-08 2010-01-09"
397 ]
398 },
399 "execution_count": 5,
400 "metadata": {},
401 "output_type": "execute_result"
402 }
403 ],
404 "source": [
405 "# We start by defining a Blaze expression that gets the rows where symbol == AAPL.\n",
406 "aapl_data = dataset_500[dataset_500.symbol == 'AAPL']\n",
407 "\n",
408 "# We then convert the Blaze expression to a pandas DataFrame, which is populated\n",
409 "# with the data resulting from our Blaze expression.\n",
410 "aapl_df = odo(aapl_data, pd.DataFrame)\n",
411 "\n",
412 "# Display the first few rows of the DataFrame.\n",
413 "aapl_df.head()"
414 ]
415 },
416 {
417 "cell_type": "code",
418 "execution_count": 6,
419 "metadata": {
420 "collapsed": false
421 },
422 "outputs": [],
423 "source": [
424 "# For plotting purposes, set the index of the DataFrame to the asof_date.\n",
425 "aapl_df.set_index('asof_date', inplace=True)"
426 ]
427 },
428 {
429 "cell_type": "code",
430 "execution_count": 7,
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BexGlVnjqXTAKb+GxjlO6tNV3FRUFiydJ0GBdcSrRlfOGiW0dfcjnySuDII41SOGJAabw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\nc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Md/dn0dx0+PYv6sLssD9KOnH8WOAwdwztoNaJlyGrj3OBYtXIjeXoV7P9HYCzzyBBBX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\nRERE5FkMeIiIiIiIyLP+P+XsXZ+s0ev6AAAAAElFTkSuQmCC\n",
448 "text/plain": [
449 "<matplotlib.figure.Figure at 0x7f37358cea90>"
450 ]
451 },
452 "metadata": {},
453 "output_type": "display_data"
454 }
455 ],
456 "source": [
457 "# Plot the predicted 5-day log return data.\n",
458 "aapl_df['predicted_five_day_log_return'].plot()"
459 ]
460 },
461 {
462 "cell_type": "markdown",
463 "metadata": {},
464 "source": [
465 "<a></a></a>\n",
466 "\n",
467 "# Pipeline Overview\n",
468 "[Pipeline](https://www.quantopian.com/tutorials/pipeline) is a tool that can be used to define computations called factors, filters, or classifiers. These computations can be used in an algorithm to dynamically select securities, compute portfolio weights, compute risk factors, and more. \n",
469 "\n",
470 "In research, pipeline is mostly used to explore these computations.\n",
471 "\n",
472 "The only method for accessing partner data within an algorithm on Quantopian is in a pipeline. Before moving to [the IDE](https://www.quantopian.com/algorithms) to work on an algorithm, it's a good idea to define your pipeline in research, so that you can iterate on an idea and analyze the output.\n",
473 "\n",
474 "To start, we need to import the following:"
475 ]
476 },
477 {
478 "cell_type": "code",
479 "execution_count": 8,
480 "metadata": {
481 "collapsed": true
482 },
483 "outputs": [],
484 "source": [
485 "from quantopian.pipeline import Pipeline\n",
486 "from quantopian.research import run_pipeline"
487 ]
488 },
489 {
490 "cell_type": "markdown",
491 "metadata": {},
492 "source": [
493 "To access partner data in pipeline, you must import the dataset. If the data is in a format that's difficult to use (e.g. event-based datasets), the data is sometimes available via built-in factors or filters. There are no such built-ins for the Alpha Vertex dataset as the prediction data is in a nice, usable format.\n",
494 "\n",
495 "Let's import the pipeline version of the Alpha Vertex dataset:"
496 ]
497 },
498 {
499 "cell_type": "code",
500 "execution_count": 9,
501 "metadata": {
502 "collapsed": false
503 },
504 "outputs": [],
505 "source": [
506 "# These imports can be found in the store panel for each dataset\n",
507 "# (https://www.quantopian.com/data). Note that not all store datasets\n",
508 "# can be used in pipeline yet.\n",
509 "from quantopian.pipeline.data.alpha_vertex import (\n",
510 " # Top 100 Securities\n",
511 " precog_top_100 as dataset_100,\n",
512 " # Top 500 Securities|\n",
513 " precog_top_500 as dataset_500\n",
514 ")"
515 ]
516 },
517 {
518 "cell_type": "markdown",
519 "metadata": {},
520 "source": [
521 "Now that we've imported the data, let's take a look at which fields are available for each dataset, along with their datatypes."
522 ]
523 },
524 {
525 "cell_type": "code",
526 "execution_count": 10,
527 "metadata": {
528 "collapsed": false
529 },
530 "outputs": [
531 {
532 "name": "stdout",
533 "output_type": "stream",
534 "text": [
535 "Here are the list of available fields per dataset:\n",
536 "---------------------------------------------------\n",
537 "\n",
538 "Dataset: precog_top_500\n",
539 "\n",
540 "Fields:\n",
541 "name - object\n",
542 "predicted_five_day_log_return - float64\n",
543 "asof_date - datetime64[ns]\n",
544 "symbol - object\n",
545 "\n",
546 "\n",
547 "---------------------------------------------------\n",
548 "\n"
549 ]
550 }
551 ],
552 "source": [
553 "print \"Here are the list of available fields per dataset:\"\n",
554 "print \"---------------------------------------------------\\n\"\n",
555 "\n",
556 "def _print_fields(dataset):\n",
557 " print \"Dataset: %s\\n\" % dataset.__name__\n",
558 " print \"Fields:\"\n",
559 " for field in list(dataset.columns):\n",
560 " print \"%s - %s\" % (field.name, field.dtype)\n",
561 " print \"\\n\"\n",
562 "\n",
563 "_print_fields(dataset_500)\n",
564 "\n",
565 "\n",
566 "print \"---------------------------------------------------\\n\""
567 ]
568 },
569 {
570 "cell_type": "markdown",
571 "metadata": {},
572 "source": [
573 "Now that we know what fields we have access to, let's define a [pipeline](https://www.quantopian.com/tutorials/pipeline) that gets the latest predicted five day log return for the PreCog 500 dataset for stocks in the [Q1500US](https://www.quantopian.com/tutorials/pipeline#lesson11)."
574 ]
575 },
576 {
577 "cell_type": "code",
578 "execution_count": 11,
579 "metadata": {
580 "collapsed": true
581 },
582 "outputs": [],
583 "source": [
584 "# Import the Q1500US pipeline filter.\n",
585 "from quantopian.pipeline.filters.morningstar import Q1500US"
586 ]
587 },
588 {
589 "cell_type": "code",
590 "execution_count": 12,
591 "metadata": {
592 "collapsed": false,
593 "scrolled": false
594 },
595 "outputs": [],
596 "source": [
597 "# We only want to get the signal for stocks in the Q1500US that have a non-null\n",
598 "# latest predicted_five_day_log_return.\n",
599 "universe = (Q1500US() & dataset_500.predicted_five_day_log_return.latest.notnull())\n",
600 "\n",
601 "# Define our pipeline to return the latest prediction for the stocks in `universe`.\n",
602 "pipe = Pipeline(columns= {\n",
603 " 'prediction': dataset_500.predicted_five_day_log_return.latest,\n",
604 " },\n",
605 " screen=universe)\n",
606 "\n",
607 "# Run our pipeline (this gets the data).\n",
608 "pipe_output = run_pipeline(pipe, start_date='2014-01-01', end_date='2017-01-01')"
609 ]
610 },
611 {
612 "cell_type": "code",
613 "execution_count": 13,
614 "metadata": {
615 "collapsed": false
616 },
617 "outputs": [
618 {
619 "data": {
620 "text/html": [
621 "<div>\n",
622 "<table border=\"1\" class=\"dataframe\">\n",
623 " <thead>\n",
624 " <tr style=\"text-align: right;\">\n",
625 " <th></th>\n",
626 " <th></th>\n",
627 " <th>prediction</th>\n",
628 " </tr>\n",
629 " </thead>\n",
630 " <tbody>\n",
631 " <tr>\n",
632 " <th rowspan=\"5\" valign=\"top\">2014-01-02 00:00:00+00:00</th>\n",
633 " <th>Equity(2 [ARNC])</th>\n",
634 " <td>-0.028</td>\n",
635 " </tr>\n",
636 " <tr>\n",
637 " <th>Equity(24 [AAPL])</th>\n",
638 " <td>-0.014</td>\n",
639 " </tr>\n",
640 " <tr>\n",
641 " <th>Equity(62 [ABT])</th>\n",
642 " <td>-0.004</td>\n",
643 " </tr>\n",
644 " <tr>\n",
645 " <th>Equity(67 [ADSK])</th>\n",
646 " <td>0.022</td>\n",
647 " </tr>\n",
648 " <tr>\n",
649 " <th>Equity(76 [TAP])</th>\n",
650 " <td>0.022</td>\n",
651 " </tr>\n",
652 " </tbody>\n",
653 "</table>\n",
654 "</div>"
655 ],
656 "text/plain": [
657 " prediction\n",
658 "2014-01-02 00:00:00+00:00 Equity(2 [ARNC]) -0.028\n",
659 " Equity(24 [AAPL]) -0.014\n",
660 " Equity(62 [ABT]) -0.004\n",
661 " Equity(67 [ADSK]) 0.022\n",
662 " Equity(76 [TAP]) 0.022"
663 ]
664 },
665 "execution_count": 13,
666 "metadata": {},
667 "output_type": "execute_result"
668 }
669 ],
670 "source": [
671 "# The result is a pandas DataFrame with a MultiIndex.\n",
672 "pipe_output.head()"
673 ]
674 },
675 {
676 "cell_type": "code",
677 "execution_count": 14,
678 "metadata": {
679 "collapsed": false
680 },
681 "outputs": [
682 {
683 "data": {
684 "text/plain": [
685 "<matplotlib.axes._subplots.AxesSubplot at 0x7f3714fb9f10>"
686 ]
687 },
688 "execution_count": 14,
689 "metadata": {},
690 "output_type": "execute_result"
691 },
692 {
693 "data": {
694 "image/png": 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8FIntM5NoOphZmUk6zJhrcRJUfDLN53EqGI7G1wYBuYwwAwAAco7VDQAkqa5mYN3MiKlm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\nw2QysWbNGhwdHXnwwQcZMmQIzz77LPHx8aSnp2MymRg6dCijRo3CaDQ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695 "text/plain": [
696 "<matplotlib.figure.Figure at 0x7f3714fb9250>"
697 ]
698 },
699 "metadata": {},
700 "output_type": "display_data"
701 }
702 ],
703 "source": [
704 "# Let's see how many securities we have a prediction for each day.\n",
705 "pipe_output.groupby(pipe_output.index.get_level_values(0)).count().plot()"
706 ]
707 },
708 {
709 "cell_type": "markdown",
710 "metadata": {
711 "collapsed": false
712 },
713 "source": [
714 "The set of ~500 stocks in the PreCog top 500 is derived from market cap at the beginning of each year, which we can see above!"
715 ]
716 },
717 {
718 "cell_type": "markdown",
719 "metadata": {},
720 "source": [
721 "Now, you can to try writing an algorithm using this pipeline. The [final lesson in the Pipeline Tutorial](https://www.quantopian.com/tutorials/pipeline#lesson12) gives an example of moving from research to the IDE.\n",
722 "\n",
723 "There is also an example algorithm using the PreCog 500 that can be found [here](https://www.quantopian.com/posts/alpha-vertex-precog-dataset)."
724 ]
725 }
726 ],
727 "metadata": {
728 "kernelspec": {
729 "display_name": "Python 2",
730 "language": "python",
731 "name": "python2"
732 },
733 "language_info": {
734 "codemirror_mode": {
735 "name": "ipython",
736 "version": 2
737 },
738 "file_extension": ".py",
739 "mimetype": "text/x-python",
740 "name": "python",
741 "nbconvert_exporter": "python",
742 "pygments_lexer": "ipython2",
743 "version": "2.7.12"
744 }
745 },
746 "nbformat": 4,
747 "nbformat_minor": 0
748 }