├── README.md ├── W1_Visualizing_and_Munging_Stock_Data ├── Build+a+simple+trading+strategy.ipynb ├── Create+new+features+and+columns+in+DataFrame.ipynb ├── DataFrame.ipynb ├── Import+data.ipynb ├── MA.png └── MS2016.png ├── W2_Random_variables_and_distribution ├── Frequency+and+Distribution.ipynb ├── Models+of+Stock+Return.ipynb └── Outcomes+and+Random+Variables.ipynb ├── W3_Sampling_and_Inference ├── Association+between+two+random+variables.ipynb ├── Confidence+Interval.ipynb ├── Hypothesis+testing.ipynb ├── Population+and+Sample.ipynb ├── Variation+of+Sample.ipynb └── p-value.pdf ├── W4_Linear_Regression_Models_for_Financial_Analysis ├── Association+between+two+random+variables.ipynb ├── Diagnostic+of+models.ipynb ├── Evaluating+strategy+built+from+Regression+model.ipynb ├── Multiple+linear+regression+model.ipynb └── Simple+linear+regression+model.ipynb └── data ├── apple.csv ├── facebook.csv ├── housing.csv ├── ibm1.csv ├── indice ├── ALLOrdinary.csv ├── CAC40.csv ├── DAXI.csv ├── DJI.csv ├── HSI.csv ├── Nikkei225.csv ├── SP500.csv ├── SPY.csv ├── indicepanel.csv └── nasdaq_composite.csv ├── microsoft.csv └── tsla.csv /README.md: -------------------------------------------------------------------------------- 1 | # HKUST Python and Statistics for Financial Analysis 2 | The Hong Kong University of Science and Technology course "Python and Statistics for Financial Analysis" by Prof. Xuhu Wan on Coursera (Feb. 2023). 3 | 4 | 5 | ![image](https://user-images.githubusercontent.com/59873708/219880842-629df982-ed90-4330-b2ff-5ac0e21c128e.png) 6 | 7 | 8 | ### Syllabus 9 | 10 | Week 1 - Visualizing and Munging Stock Data 11 | 12 | Why do investment banks and consumer banks use Python to build quantitative models to predict returns and evaluate risks? 13 | What makes Python one of the most popular tools for financial analysis? You are going to learn basic python to import, 14 | manipulate and visualize stock data in this module. As Python is highly readable and simple enough, you can build one of 15 | the most popular trading models - Trend following strategy by the end of this module! 16 | 17 | 1.0 Module Introduction 18 | 1.1 Packages for Data Analysis 19 | 1.2 Importing data 20 | 1.3 Basics of Dataframe 21 | 1.4 Generate new variables in Dataframe 22 | 1.5 Trading Strategy 23 | 24 | 25 | Week 2 - Random variables and distribution 26 | In the previous module, we built a simple trading strategy base on Moving Average 10 and 50, which are 27 | "random variables" in statistics. In this module, we are going to explore basic concepts of random variables. 28 | By understanding the frequency and distribution of random variables, we extend further to the discussion of 29 | probability. In the later part of the module, we apply the probability concept in measuring the risk of 30 | investing a stock by looking at the distribution of log daily return using python. Learners are expected 31 | to have basic knowledge of probability before taking this module. 32 | 33 | 2.0 Module Introduction 34 | 2.1 Outcomes and Random Variables 35 | 2.2 Frequency and Distributions 36 | 2.3 Models of Distribution 37 | 38 | 39 | Week 3 - Sampling and Inference 40 | In financial analysis, we always infer the real mean return of stocks, or equity funds, based on the historical data 41 | of a couple years. This situation is in line with a core part of statistics - Statistical Inference - which we also 42 | base on sample data to infer the population of a target variable.In this module, you are going to understand the basic 43 | concept of statistical inference such as population, samples and random sampling. In the second part of the module, we 44 | shall estimate the range of mean return of a stock using a concept called confidence interval, after we understand the 45 | distribution of sample mean.We will also testify the claim of investment return using another statistical concept - 46 | hypothesis testing. 47 | 48 | 3.0 Introduction 49 | 3.1 Population and Sample 50 | 3.2 Variation of Sample 51 | 3.3 Confidence Interval 52 | 3.4 Hypothesis Testing 53 | 54 | 55 | Week 4 - Linear Regression Models for Financial Analysis 56 | In this module, we will explore the most often used prediction method - linear regression. From learning the association 57 | of random variables to simple and multiple linear regression model, we finally come to the most interesting part of this 58 | course: we will build a model using multiple indices from the global markets and predict the price change of an ETF of 59 | S&P500. In addition to building a stock trading model, it is also great fun to test the performance of your own models, 60 | which I will also show you how to evaluate them! 61 | 62 | 4.0 Introduction 63 | 4.1 Association of random variables 64 | 4.2 Simple linear regression model 65 | 4.3 Diagnostic of linear regression model 66 | 4.4 Multiple linear regression model 67 | 4.5 Evaluate the strategy 68 | 69 | 70 | ### Instructor 71 | 72 | Xuhu Wan 73 | Associate Professor - The Hong Kong University of Science and Technology 74 | 75 | Xuhu Wan is an Associate Professor of Statistics in Department of Information Systems, Business Statistics and Operation Management at HKUST. He received his Ph.D. in Financial Mathematics from the University of Southern California in 2005. His research interest lies primarily in the area of dynamic contract theory and information design. The second area of interest is parallel and distributed computing and low-latency programming. 76 | 77 | 78 | ### Offered by 79 | 80 | # The Hong Kong University of Science and Technology 81 | 82 | HKUST - A dynamic, international research university, in relentless pursuit of excellence, leading the advance of science and technology, and educating the new generation of front-runners for Asia and the world. 83 | -------------------------------------------------------------------------------- /W1_Visualizing_and_Munging_Stock_Data/MA.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PeterSchuld/HKUST-Python_and_Statistics_for_Financial_Analysis/778cfd135b07daabd4cc4efbcccf98735f900aac/W1_Visualizing_and_Munging_Stock_Data/MA.png -------------------------------------------------------------------------------- /W1_Visualizing_and_Munging_Stock_Data/MS2016.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PeterSchuld/HKUST-Python_and_Statistics_for_Financial_Analysis/778cfd135b07daabd4cc4efbcccf98735f900aac/W1_Visualizing_and_Munging_Stock_Data/MS2016.png -------------------------------------------------------------------------------- /W2_Random_variables_and_distribution/Frequency+and+Distribution.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Frequency and Distribution" 8 | ] 9 | }, 10 | { 11 | "cell_type": "code", 12 | "execution_count": 1, 13 | "metadata": { 14 | "collapsed": true 15 | }, 16 | "outputs": [], 17 | "source": [ 18 | "import pandas as pd\n", 19 | "import matplotlib.pyplot as plt\n", 20 | "%matplotlib inline" 21 | ] 22 | }, 23 | { 24 | "cell_type": "code", 25 | "execution_count": 2, 26 | "metadata": { 27 | "collapsed": true 28 | }, 29 | "outputs": [], 30 | "source": [ 31 | "# To recall, this is the code to mimic the roll dice game for 50 times\n", 32 | "\n", 33 | "die = pd.DataFrame([1, 2, 3, 4, 5, 6])\n", 34 | "trial = 50\n", 35 | "results = [die.sample(2, replace=True).sum().loc[0] for i in range(trial)]" 36 | ] 37 | }, 38 | { 39 | "cell_type": "code", 40 | "execution_count": 4, 41 | "metadata": {}, 42 | "outputs": [ 43 | { 44 | "name": "stdout", 45 | "output_type": "stream", 46 | "text": [ 47 | "4 3\n", 48 | "5 4\n", 49 | "6 15\n", 50 | "7 9\n", 51 | "8 6\n", 52 | "9 5\n", 53 | "10 2\n", 54 | "11 2\n", 55 | "12 4\n", 56 | "Name: 0, dtype: int64\n" 57 | ] 58 | } 59 | ], 60 | "source": [ 61 | "# This is the code for summarizing the results of sum of faces by frequency\n", 62 | "\n", 63 | "freq = pd.DataFrame(results)[0].value_counts()\n", 64 | "sort_freq = freq.sort_index()\n", 65 | "print(sort_freq)" 66 | ] 67 | }, 68 | { 69 | "cell_type": "code", 70 | "execution_count": 7, 71 | "metadata": {}, 72 | "outputs": [ 73 | { 74 | "data": { 75 | "text/plain": [ 76 | "" 77 | ] 78 | }, 79 | "execution_count": 7, 80 | "metadata": {}, 81 | "output_type": "execute_result" 82 | }, 83 | { 84 | "data": { 85 | "image/png": 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86 | "text/plain": [ 87 | "" 88 | ] 89 | }, 90 | "metadata": {}, 91 | "output_type": "display_data" 92 | } 93 | ], 94 | "source": [ 95 | "#plot the bar chart base on the result\n", 96 | "\n", 97 | "sort_freq.plot(kind='bar', color='blue', figsize=(15, 8))" 98 | ] 99 | }, 100 | { 101 | "cell_type": "markdown", 102 | "metadata": {}, 103 | "source": [ 104 | "## Relative Frequency" 105 | ] 106 | }, 107 | { 108 | "cell_type": "code", 109 | "execution_count": 8, 110 | "metadata": {}, 111 | "outputs": [ 112 | { 113 | "data": { 114 | "text/plain": [ 115 | "" 116 | ] 117 | }, 118 | "execution_count": 8, 119 | "metadata": {}, 120 | "output_type": "execute_result" 121 | }, 122 | { 123 | "data": { 124 | "image/png": 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7kSQfTHLoLMwNAACw5+1f4piLkzy46f6xbFxxW8Z251689aCquiHJDUly8ODBJR8aANhJ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\nhhJ0AAAAQ/1fos9lZRDIv/gAAAAASUVORK5CYII=\n", 125 | "text/plain": [ 126 | "" 127 | ] 128 | }, 129 | "metadata": {}, 130 | "output_type": "display_data" 131 | } 132 | ], 133 | "source": [ 134 | "# Using relative frequency, we can rescale the frequency so that we can compare results from different number of trials\n", 135 | "relative_freq = sort_freq/trial\n", 136 | "relative_freq.plot(kind='bar', color='blue', figsize=(15, 8))" 137 | ] 138 | }, 139 | { 140 | "cell_type": "code", 141 | "execution_count": 10, 142 | "metadata": {}, 143 | "outputs": [ 144 | { 145 | "data": { 146 | "text/plain": [ 147 | "" 148 | ] 149 | }, 150 | "execution_count": 10, 151 | "metadata": {}, 152 | "output_type": "execute_result" 153 | }, 154 | { 155 | "data": { 156 | "image/png": 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AAIMSdAAAAIMSdAAAAIMSdAAAAIMSdAAAAIMSdAAAAIMSdAAAAIMSdAAAAIMSdAAAAIMS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\nKMmbk7gS8Lkeqqq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157 | "text/plain": [ 158 | "" 159 | ] 160 | }, 161 | "metadata": {}, 162 | "output_type": "display_data" 163 | } 164 | ], 165 | "source": [ 166 | "# Let us try to increase the number of trials to 10000, and see what will happen...\n", 167 | "trial = 20000\n", 168 | "results = [die.sample(2, replace=True).sum().loc[0] for i in range(trial)]\n", 169 | "freq = pd.DataFrame(results)[0].value_counts()\n", 170 | "sort_freq = freq.sort_index()\n", 171 | "relative_freq = sort_freq/trial\n", 172 | "relative_freq.plot(kind='bar', color='blue', figsize=(15, 8))" 173 | ] 174 | }, 175 | { 176 | "cell_type": "markdown", 177 | "metadata": {}, 178 | "source": [ 179 | "### We can see that with more trials, the result looks more and more stable, and this is very close to a probability distribution. Try increasing the number of \"trial\" further (but it may take some time for Jupyter Notebook to output the result)" 180 | ] 181 | }, 182 | { 183 | "cell_type": "markdown", 184 | "metadata": {}, 185 | "source": [ 186 | "## Expectation and Variance of a distribution" 187 | ] 188 | }, 189 | { 190 | "cell_type": "code", 191 | "execution_count": 11, 192 | "metadata": {}, 193 | "outputs": [ 194 | { 195 | "data": { 196 | "text/html": [ 197 | "
\n", 198 | "\n", 199 | " \n", 200 | " \n", 201 | " \n", 202 | " \n", 203 | " \n", 204 | " \n", 205 | " \n", 206 | " \n", 207 | " \n", 208 | " \n", 209 | " \n", 210 | " \n", 211 | " \n", 212 | " \n", 213 | " \n", 214 | " \n", 215 | " \n", 216 | " \n", 217 | " \n", 218 | " \n", 219 | " \n", 220 | " \n", 221 | " \n", 222 | " \n", 223 | " \n", 224 | " \n", 225 | " \n", 226 | " \n", 227 | " \n", 228 | " \n", 229 | " \n", 230 | " \n", 231 | " \n", 232 | " \n", 233 | " \n", 234 | " \n", 235 | " \n", 236 | " \n", 237 | " \n", 238 | " \n", 239 | " \n", 240 | " \n", 241 | " \n", 242 | " \n", 243 | " \n", 244 | " \n", 245 | " \n", 246 | " \n", 247 | " \n", 248 | " \n", 249 | " \n", 250 | " \n", 251 | "
Prob
20.027778
30.055556
40.083333
50.111111
60.138889
70.166667
80.138889
90.111111
100.083333
110.055556
120.027778
\n", 252 | "
" 253 | ], 254 | "text/plain": [ 255 | " Prob\n", 256 | "2 0.027778\n", 257 | "3 0.055556\n", 258 | "4 0.083333\n", 259 | "5 0.111111\n", 260 | "6 0.138889\n", 261 | "7 0.166667\n", 262 | "8 0.138889\n", 263 | "9 0.111111\n", 264 | "10 0.083333\n", 265 | "11 0.055556\n", 266 | "12 0.027778" 267 | ] 268 | }, 269 | "execution_count": 11, 270 | "metadata": {}, 271 | "output_type": "execute_result" 272 | } 273 | ], 274 | "source": [ 275 | "# assume that we have fair dice, which means all faces will be shown with equal probability\n", 276 | "# then we can say we know the 'Distribtuion' of the random variable - sum_of_dice\n", 277 | "\n", 278 | "X_distri = pd.DataFrame(index=[2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12])\n", 279 | "X_distri['Prob'] = [1, 2, 3, 4, 5, 6, 5, 4, 3, 2, 1]\n", 280 | "X_distri['Prob'] = X_distri['Prob']/36\n", 281 | "X_distri" 282 | ] 283 | }, 284 | { 285 | "cell_type": "code", 286 | "execution_count": 12, 287 | "metadata": { 288 | "collapsed": true 289 | }, 290 | "outputs": [], 291 | "source": [ 292 | "mean = pd.Series(X_distri.index * X_distri['Prob']).sum()\n", 293 | "var = pd.Series(((X_distri.index - mean)**2)*X_distri['Prob']).sum()" 294 | ] 295 | }, 296 | { 297 | "cell_type": "code", 298 | "execution_count": 13, 299 | "metadata": {}, 300 | "outputs": [ 301 | { 302 | "name": "stdout", 303 | "output_type": "stream", 304 | "text": [ 305 | "7.0 5.83333333333\n" 306 | ] 307 | } 308 | ], 309 | "source": [ 310 | "#Output the mean and variance of the distribution. Mean and variance can be used to describe a distribution\n", 311 | "print(mean, var)" 312 | ] 313 | }, 314 | { 315 | "cell_type": "markdown", 316 | "metadata": {}, 317 | "source": [ 318 | "## Empirical mean and variance" 319 | ] 320 | }, 321 | { 322 | "cell_type": "code", 323 | "execution_count": 14, 324 | "metadata": { 325 | "collapsed": true 326 | }, 327 | "outputs": [], 328 | "source": [ 329 | "# if we calculate mean and variance of outcomes (with high enough number of trials, eg 20000)...\n", 330 | "trial = 20000\n", 331 | "results = [die.sample(2, replace=True).sum().loc[0] for i in range(trial)]" 332 | ] 333 | }, 334 | { 335 | "cell_type": "code", 336 | "execution_count": 15, 337 | "metadata": {}, 338 | "outputs": [ 339 | { 340 | "name": "stdout", 341 | "output_type": "stream", 342 | "text": [ 343 | "7.0007 5.75298715936\n" 344 | ] 345 | } 346 | ], 347 | "source": [ 348 | "#print the mean and variance of the 20000 trials\n", 349 | "results = pd.Series(results)\n", 350 | "print(results.mean(), results.var())" 351 | ] 352 | }, 353 | { 354 | "cell_type": "code", 355 | "execution_count": null, 356 | "metadata": { 357 | "collapsed": true 358 | }, 359 | "outputs": [], 360 | "source": [] 361 | } 362 | ], 363 | "metadata": { 364 | "kernelspec": { 365 | "display_name": "Python 3", 366 | "language": "python", 367 | "name": "python3" 368 | }, 369 | "language_info": { 370 | "codemirror_mode": { 371 | "name": "ipython", 372 | "version": 3 373 | }, 374 | "file_extension": ".py", 375 | "mimetype": "text/x-python", 376 | "name": "python", 377 | "nbconvert_exporter": "python", 378 | "pygments_lexer": "ipython3", 379 | "version": "3.6.2" 380 | } 381 | }, 382 | "nbformat": 4, 383 | "nbformat_minor": 2 384 | } 385 | -------------------------------------------------------------------------------- /W2_Random_variables_and_distribution/Models+of+Stock+Return.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "## Models of Stock Return" 8 | ] 9 | }, 10 | { 11 | "cell_type": "code", 12 | "execution_count": 1, 13 | "metadata": { 14 | "collapsed": true 15 | }, 16 | "outputs": [], 17 | "source": [ 18 | "import pandas as pd\n", 19 | "import numpy as np\n", 20 | "import matplotlib.pyplot as plt\n", 21 | "% matplotlib inline" 22 | ] 23 | }, 24 | { 25 | "cell_type": "code", 26 | "execution_count": 2, 27 | "metadata": {}, 28 | "outputs": [ 29 | { 30 | "data": { 31 | "text/html": [ 32 | "
\n", 33 | "\n", 34 | " \n", 35 | " \n", 36 | " \n", 37 | " \n", 38 | " \n", 39 | " \n", 40 | " \n", 41 | " \n", 42 | " \n", 43 | " \n", 44 | " \n", 45 | " \n", 46 | " \n", 47 | " \n", 48 | " \n", 49 | " \n", 50 | " \n", 51 | " \n", 52 | " \n", 53 | " \n", 54 | " \n", 55 | " \n", 56 | " \n", 57 | " \n", 58 | " \n", 59 | " \n", 60 | " \n", 61 | " \n", 62 | " \n", 63 | " \n", 64 | " \n", 65 | " \n", 66 | " \n", 67 | " \n", 68 | " \n", 69 | " \n", 70 | " \n", 71 | " \n", 72 | " \n", 73 | " \n", 74 | " \n", 75 | " \n", 76 | " \n", 77 | " \n", 78 | " \n", 79 | " \n", 80 | " \n", 81 | " \n", 82 | " \n", 83 | " \n", 84 | " \n", 85 | " \n", 86 | " \n", 87 | " \n", 88 | " \n", 89 | " \n", 90 | " \n", 91 | " \n", 92 | " \n", 93 | " \n", 94 | " \n", 95 | " \n", 96 | " \n", 97 | " \n", 98 | " \n", 99 | " \n", 100 | " \n", 101 | "
OpenHighLowCloseAdj CloseVolume
Date
2014-12-3146.73000047.43999946.45000146.45000142.84876321552500
2015-01-0246.66000047.41999846.54000146.75999843.13473127913900
2015-01-0546.36999946.73000046.25000046.33000242.73806839673900
2015-01-0646.38000146.75000045.54000145.65000242.11078336447900
2015-01-0745.98000046.45999945.49000246.23000042.64581729114100
\n", 102 | "
" 103 | ], 104 | "text/plain": [ 105 | " Open High Low Close Adj Close Volume\n", 106 | "Date \n", 107 | "2014-12-31 46.730000 47.439999 46.450001 46.450001 42.848763 21552500\n", 108 | "2015-01-02 46.660000 47.419998 46.540001 46.759998 43.134731 27913900\n", 109 | "2015-01-05 46.369999 46.730000 46.250000 46.330002 42.738068 39673900\n", 110 | "2015-01-06 46.380001 46.750000 45.540001 45.650002 42.110783 36447900\n", 111 | "2015-01-07 45.980000 46.459999 45.490002 46.230000 42.645817 29114100" 112 | ] 113 | }, 114 | "execution_count": 2, 115 | "metadata": {}, 116 | "output_type": "execute_result" 117 | } 118 | ], 119 | "source": [ 120 | "ms = pd.DataFrame.from_csv('../data/microsoft.csv')\n", 121 | "ms.head()" 122 | ] 123 | }, 124 | { 125 | "cell_type": "markdown", 126 | "metadata": {}, 127 | "source": [ 128 | "## Distribution of Log return" 129 | ] 130 | }, 131 | { 132 | "cell_type": "code", 133 | "execution_count": 3, 134 | "metadata": { 135 | "collapsed": true 136 | }, 137 | "outputs": [], 138 | "source": [ 139 | "# let play around with ms data by calculating the log daily return\n", 140 | "ms['LogReturn'] = np.log(ms['Close']).shift(-1) - np.log(ms['Close'])" 141 | ] 142 | }, 143 | { 144 | "cell_type": "code", 145 | "execution_count": 4, 146 | "metadata": {}, 147 | "outputs": [ 148 | { 149 | "data": { 150 | "image/png": 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151 | "text/plain": [ 152 | "" 153 | ] 154 | }, 155 | "metadata": {}, 156 | "output_type": "display_data" 157 | } 158 | ], 159 | "source": [ 160 | "# Plot a histogram to show the distribution of log return of Microsoft's stock. \n", 161 | "# You can see it is very close to a normal distribution\n", 162 | "from scipy.stats import norm\n", 163 | "mu = ms['LogReturn'].mean()\n", 164 | "sigma = ms['LogReturn'].std(ddof=1)\n", 165 | "\n", 166 | "density = pd.DataFrame()\n", 167 | "density['x'] = np.arange(ms['LogReturn'].min()-0.01, ms['LogReturn'].max()+0.01, 0.001)\n", 168 | "density['pdf'] = norm.pdf(density['x'], mu, sigma)\n", 169 | "\n", 170 | "ms['LogReturn'].hist(bins=50, figsize=(15, 8))\n", 171 | "plt.plot(density['x'], density['pdf'], color='red')\n", 172 | "plt.show()" 173 | ] 174 | }, 175 | { 176 | "cell_type": "markdown", 177 | "metadata": {}, 178 | "source": [ 179 | "## Calculate the probability of the stock price will drop over a certain percentage in a day" 180 | ] 181 | }, 182 | { 183 | "cell_type": "code", 184 | "execution_count": 5, 185 | "metadata": {}, 186 | "outputs": [ 187 | { 188 | "name": "stdout", 189 | "output_type": "stream", 190 | "text": [ 191 | "The Probability is 0.000171184826087\n" 192 | ] 193 | } 194 | ], 195 | "source": [ 196 | "# probability that the stock price of microsoft will drop over 5% in a day\n", 197 | "prob_return1 = norm.cdf(-0.05, mu, sigma)\n", 198 | "print('The Probability is ', prob_return1)" 199 | ] 200 | }, 201 | { 202 | "cell_type": "code", 203 | "execution_count": 6, 204 | "metadata": {}, 205 | "outputs": [ 206 | { 207 | "name": "stdout", 208 | "output_type": "stream", 209 | "text": [ 210 | "The Probability is 6.05677563486e-13\n" 211 | ] 212 | } 213 | ], 214 | "source": [ 215 | "# Now is your turn, calculate the probability that the stock price of microsoft will drop over 10% in a day\n", 216 | "prob_return1 = norm.cdf(-0.1,mu,sigma)\n", 217 | "print('The Probability is ', prob_return1)" 218 | ] 219 | }, 220 | { 221 | "cell_type": "markdown", 222 | "metadata": {}, 223 | "source": [ 224 | "**Expected Output: ** The Probability is 6.05677563486e-13" 225 | ] 226 | }, 227 | { 228 | "cell_type": "markdown", 229 | "metadata": {}, 230 | "source": [ 231 | "## Calculate the probability of the stock price will drop over a certain percentage in a year" 232 | ] 233 | }, 234 | { 235 | "cell_type": "code", 236 | "execution_count": 7, 237 | "metadata": {}, 238 | "outputs": [ 239 | { 240 | "name": "stdout", 241 | "output_type": "stream", 242 | "text": [ 243 | "The probability of dropping over 40% in 220 days is 0.00291236331333\n" 244 | ] 245 | } 246 | ], 247 | "source": [ 248 | "# drop over 40% in 220 days\n", 249 | "mu220 = 220*mu\n", 250 | "sigma220 = (220**0.5) * sigma\n", 251 | "print('The probability of dropping over 40% in 220 days is ', norm.cdf(-0.4, mu220, sigma220))" 252 | ] 253 | }, 254 | { 255 | "cell_type": "code", 256 | "execution_count": 8, 257 | "metadata": {}, 258 | "outputs": [ 259 | { 260 | "name": "stdout", 261 | "output_type": "stream", 262 | "text": [ 263 | "The probability of dropping over 20% in 220 days is 0.0353523772749\n" 264 | ] 265 | } 266 | ], 267 | "source": [ 268 | "# drop over 20% in 220 days\n", 269 | "mu220 = 220*mu\n", 270 | "sigma220 = (220**0.5) * sigma\n", 271 | "drop20 = norm.cdf(-0.2, mu220, sigma220)\n", 272 | "print('The probability of dropping over 20% in 220 days is ', drop20)" 273 | ] 274 | }, 275 | { 276 | "cell_type": "markdown", 277 | "metadata": {}, 278 | "source": [ 279 | "**Expected Output: ** The probability of dropping over 20% in 220 days is 0.0353523772749" 280 | ] 281 | }, 282 | { 283 | "cell_type": "markdown", 284 | "metadata": {}, 285 | "source": [ 286 | "## Calculate Value at risk (VaR)" 287 | ] 288 | }, 289 | { 290 | "cell_type": "code", 291 | "execution_count": 9, 292 | "metadata": {}, 293 | "outputs": [ 294 | { 295 | "name": "stdout", 296 | "output_type": "stream", 297 | "text": [ 298 | "Single day value at risk -0.0225233624071\n" 299 | ] 300 | } 301 | ], 302 | "source": [ 303 | "# Value at risk(VaR)\n", 304 | "VaR = norm.ppf(0.05, mu, sigma)\n", 305 | "print('Single day value at risk ', VaR)" 306 | ] 307 | }, 308 | { 309 | "cell_type": "code", 310 | "execution_count": 10, 311 | "metadata": {}, 312 | "outputs": [ 313 | { 314 | "name": "stdout", 315 | "output_type": "stream", 316 | "text": [ 317 | "5% quantile -0.0225233624071\n", 318 | "95% quantile 0.0241638253793\n" 319 | ] 320 | } 321 | ], 322 | "source": [ 323 | "# Quatile \n", 324 | "# 5% quantile\n", 325 | "print('5% quantile ', norm.ppf(0.05, mu, sigma))\n", 326 | "# 95% quantile\n", 327 | "print('95% quantile ', norm.ppf(0.95, mu, sigma))" 328 | ] 329 | }, 330 | { 331 | "cell_type": "code", 332 | "execution_count": 12, 333 | "metadata": {}, 334 | "outputs": [ 335 | { 336 | "name": "stdout", 337 | "output_type": "stream", 338 | "text": [ 339 | "25% quantile -0.00875205783841\n", 340 | "75% quantile 0.0103925208107\n" 341 | ] 342 | } 343 | ], 344 | "source": [ 345 | "# This is your turn to calcuate the 25% and 75% Quantile of the return\n", 346 | "# 25% quantile\n", 347 | "q25 = norm.ppf(0.25, mu, sigma)\n", 348 | "print('25% quantile ', q25)\n", 349 | "# 75% quantile\n", 350 | "q75 = norm.ppf(0.75, mu, sigma)\n", 351 | "print('75% quantile ', q75)" 352 | ] 353 | }, 354 | { 355 | "cell_type": "markdown", 356 | "metadata": {}, 357 | "source": [ 358 | "**Expected Output: ** 25% quantile -0.00875205783841\n", 359 | "75% quantile 0.0103925208107" 360 | ] 361 | } 362 | ], 363 | "metadata": { 364 | "kernelspec": { 365 | "display_name": "Python 3", 366 | "language": "python", 367 | "name": "python3" 368 | }, 369 | "language_info": { 370 | "codemirror_mode": { 371 | "name": "ipython", 372 | "version": 3 373 | }, 374 | "file_extension": ".py", 375 | "mimetype": "text/x-python", 376 | "name": "python", 377 | "nbconvert_exporter": "python", 378 | "pygments_lexer": "ipython3", 379 | "version": "3.6.2" 380 | } 381 | }, 382 | "nbformat": 4, 383 | "nbformat_minor": 2 384 | } 385 | -------------------------------------------------------------------------------- /W2_Random_variables_and_distribution/Outcomes+and+Random+Variables.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "## Outcomes and Variables" 8 | ] 9 | }, 10 | { 11 | "cell_type": "code", 12 | "execution_count": 1, 13 | "metadata": { 14 | "collapsed": true 15 | }, 16 | "outputs": [], 17 | "source": [ 18 | "#import numpy and pandas package\n", 19 | "import numpy as np\n", 20 | "import pandas as pd" 21 | ] 22 | }, 23 | { 24 | "cell_type": "markdown", 25 | "metadata": {}, 26 | "source": [ 27 | "### Mimic the roll dice game" 28 | ] 29 | }, 30 | { 31 | "cell_type": "code", 32 | "execution_count": 2, 33 | "metadata": {}, 34 | "outputs": [ 35 | { 36 | "name": "stdout", 37 | "output_type": "stream", 38 | "text": [ 39 | "Sum of dice is 5\n" 40 | ] 41 | } 42 | ], 43 | "source": [ 44 | "# roll two dice for multiple times\n", 45 | "die = pd.DataFrame([1, 2, 3, 4, 5, 6])\n", 46 | "sum_of_dice = die.sample(2, replace=True).sum().loc[0]\n", 47 | "print('Sum of dice is', sum_of_dice) \n", 48 | "\n", 49 | "# you may get different outcomes as we now mimic the result of rolling 2 dice, but the range must be limited between 2 and 12. " 50 | ] 51 | }, 52 | { 53 | "cell_type": "code", 54 | "execution_count": 9, 55 | "metadata": {}, 56 | "outputs": [], 57 | "source": [ 58 | "# It is your turn! let's replace the none with the code of rolling three dice, instead of two\n", 59 | "\n", 60 | "np.random.seed(1) # This is for checking answer, do NOT modify this line of code\n", 61 | "\n", 62 | "#Modify the code, replace the None\n", 63 | "sum_of_three_dice = die.sample(3,replace=True).sum().loc[0]" 64 | ] 65 | }, 66 | { 67 | "cell_type": "code", 68 | "execution_count": 10, 69 | "metadata": {}, 70 | "outputs": [ 71 | { 72 | "name": "stdout", 73 | "output_type": "stream", 74 | "text": [ 75 | "Sum of three dice is 15\n" 76 | ] 77 | } 78 | ], 79 | "source": [ 80 | "print('Sum of three dice is', sum_of_three_dice)" 81 | ] 82 | }, 83 | { 84 | "cell_type": "markdown", 85 | "metadata": {}, 86 | "source": [ 87 | "**Expected output: ** Sum of three dice is 15" 88 | ] 89 | }, 90 | { 91 | "cell_type": "markdown", 92 | "metadata": {}, 93 | "source": [ 94 | "### Mimic the roll dice game for multiple times" 95 | ] 96 | }, 97 | { 98 | "cell_type": "code", 99 | "execution_count": 11, 100 | "metadata": { 101 | "collapsed": true 102 | }, 103 | "outputs": [], 104 | "source": [ 105 | "# The following code mimics the roll dice game for 50 times. And the results are all stored into \"Result\"\n", 106 | "# Lets try and get the results of 50 sum of faces.\n", 107 | "\n", 108 | "trial = 50\n", 109 | "result = [die.sample(2, replace=True).sum().loc[0] for i in range(trial)]" 110 | ] 111 | }, 112 | { 113 | "cell_type": "code", 114 | "execution_count": 12, 115 | "metadata": {}, 116 | "outputs": [ 117 | { 118 | "name": "stdout", 119 | "output_type": "stream", 120 | "text": [ 121 | "[3, 10, 2, 7, 11, 5, 11, 8, 9, 8]\n" 122 | ] 123 | } 124 | ], 125 | "source": [ 126 | "#print the first 10 results\n", 127 | "print(result[:10])" 128 | ] 129 | }, 130 | { 131 | "cell_type": "code", 132 | "execution_count": null, 133 | "metadata": { 134 | "collapsed": true 135 | }, 136 | "outputs": [], 137 | "source": [] 138 | } 139 | ], 140 | "metadata": { 141 | "kernelspec": { 142 | "display_name": "Python 3", 143 | "language": "python", 144 | "name": "python3" 145 | }, 146 | "language_info": { 147 | "codemirror_mode": { 148 | "name": "ipython", 149 | "version": 3 150 | }, 151 | "file_extension": ".py", 152 | "mimetype": "text/x-python", 153 | "name": "python", 154 | "nbconvert_exporter": "python", 155 | "pygments_lexer": "ipython3", 156 | "version": "3.6.2" 157 | } 158 | }, 159 | "nbformat": 4, 160 | "nbformat_minor": 2 161 | } 162 | -------------------------------------------------------------------------------- /W3_Sampling_and_Inference/Confidence+Interval.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Confidence Interval" 8 | ] 9 | }, 10 | { 11 | "cell_type": "code", 12 | "execution_count": 1, 13 | "metadata": { 14 | "collapsed": true 15 | }, 16 | "outputs": [], 17 | "source": [ 18 | "import pandas as pd\n", 19 | "import numpy as np\n", 20 | "from scipy.stats import norm" 21 | ] 22 | }, 23 | { 24 | "cell_type": "code", 25 | "execution_count": 2, 26 | "metadata": {}, 27 | "outputs": [ 28 | { 29 | "data": { 30 | "text/html": [ 31 | "
\n", 32 | "\n", 33 | " \n", 34 | " \n", 35 | " \n", 36 | " \n", 37 | " \n", 38 | " \n", 39 | " \n", 40 | " \n", 41 | " \n", 42 | " \n", 43 | " \n", 44 | " \n", 45 | " \n", 46 | " \n", 47 | " \n", 48 | " \n", 49 | " \n", 50 | " \n", 51 | " \n", 52 | " \n", 53 | " \n", 54 | " \n", 55 | " \n", 56 | " \n", 57 | " \n", 58 | " \n", 59 | " \n", 60 | " \n", 61 | " \n", 62 | " \n", 63 | " \n", 64 | " \n", 65 | " \n", 66 | " \n", 67 | " \n", 68 | " \n", 69 | " \n", 70 | " \n", 71 | " \n", 72 | " \n", 73 | " \n", 74 | " \n", 75 | " \n", 76 | " \n", 77 | " \n", 78 | " \n", 79 | " \n", 80 | " \n", 81 | " \n", 82 | " \n", 83 | " \n", 84 | " \n", 85 | " \n", 86 | " \n", 87 | " \n", 88 | " \n", 89 | " \n", 90 | " \n", 91 | " \n", 92 | " \n", 93 | " \n", 94 | " \n", 95 | " \n", 96 | " \n", 97 | " \n", 98 | " \n", 99 | " \n", 100 | "
OpenHighLowCloseAdj CloseVolume
Date
2014-12-3146.73000047.43999946.45000146.45000142.84876321552500
2015-01-0246.66000047.41999846.54000146.75999843.13473127913900
2015-01-0546.36999946.73000046.25000046.33000242.73806839673900
2015-01-0646.38000146.75000045.54000145.65000242.11078336447900
2015-01-0745.98000046.45999945.49000246.23000042.64581729114100
\n", 101 | "
" 102 | ], 103 | "text/plain": [ 104 | " Open High Low Close Adj Close Volume\n", 105 | "Date \n", 106 | "2014-12-31 46.730000 47.439999 46.450001 46.450001 42.848763 21552500\n", 107 | "2015-01-02 46.660000 47.419998 46.540001 46.759998 43.134731 27913900\n", 108 | "2015-01-05 46.369999 46.730000 46.250000 46.330002 42.738068 39673900\n", 109 | "2015-01-06 46.380001 46.750000 45.540001 45.650002 42.110783 36447900\n", 110 | "2015-01-07 45.980000 46.459999 45.490002 46.230000 42.645817 29114100" 111 | ] 112 | }, 113 | "execution_count": 2, 114 | "metadata": {}, 115 | "output_type": "execute_result" 116 | } 117 | ], 118 | "source": [ 119 | "ms = pd.DataFrame.from_csv('../data/microsoft.csv')\n", 120 | "ms.head()" 121 | ] 122 | }, 123 | { 124 | "cell_type": "markdown", 125 | "metadata": {}, 126 | "source": [ 127 | "## Estimate the average stock return with 90% Confidence Interval" 128 | ] 129 | }, 130 | { 131 | "cell_type": "code", 132 | "execution_count": 3, 133 | "metadata": { 134 | "collapsed": true 135 | }, 136 | "outputs": [], 137 | "source": [ 138 | "# we will use log return for average stock return of Microsoft\n", 139 | "\n", 140 | "ms['logReturn'] = np.log(ms['Close'].shift(-1)) - np.log(ms['Close'])" 141 | ] 142 | }, 143 | { 144 | "cell_type": "code", 145 | "execution_count": 20, 146 | "metadata": { 147 | "collapsed": true 148 | }, 149 | "outputs": [], 150 | "source": [ 151 | "# Lets build 90% confidence interval for log return\n", 152 | "sample_size = ms['logReturn'].shape[0]\n", 153 | "sample_mean = ms['logReturn'].mean()\n", 154 | "sample_std = ms['logReturn'].std(ddof=1) / sample_size**0.5\n", 155 | "\n", 156 | "# left and right quantile\n", 157 | "z_left = norm.ppf(0.05)\n", 158 | "z_right = norm.ppf(0.95)\n", 159 | "\n", 160 | "# upper and lower bound\n", 161 | "interval_left = sample_mean + z_left*sample_std\n", 162 | "interval_right = sample_mean + z_right*sample_std" 163 | ] 164 | }, 165 | { 166 | "cell_type": "code", 167 | "execution_count": 22, 168 | "metadata": {}, 169 | "outputs": [ 170 | { 171 | "name": "stdout", 172 | "output_type": "stream", 173 | "text": [ 174 | "90% confidence interval is (-1.5603253899378836e-05, 0.001656066226145423)\n" 175 | ] 176 | } 177 | ], 178 | "source": [ 179 | "# 90% confidence interval tells you that there will be 90% chance that the average stock return lies between \"interval_left\"\n", 180 | "# and \"interval_right\".\n", 181 | "\n", 182 | "print('90% confidence interval is ', (interval_left, interval_right))" 183 | ] 184 | }, 185 | { 186 | "cell_type": "markdown", 187 | "metadata": {}, 188 | "source": [ 189 | "** Expected output: ** 90% confidence interval is (-1.5603253899378836e-05, 0.001656066226145423)" 190 | ] 191 | } 192 | ], 193 | "metadata": { 194 | "kernelspec": { 195 | "display_name": "Python 3", 196 | "language": "python", 197 | "name": "python3" 198 | }, 199 | "language_info": { 200 | "codemirror_mode": { 201 | "name": "ipython", 202 | "version": 3 203 | }, 204 | "file_extension": ".py", 205 | "mimetype": "text/x-python", 206 | "name": "python", 207 | "nbconvert_exporter": "python", 208 | "pygments_lexer": "ipython3", 209 | "version": "3.6.2" 210 | } 211 | }, 212 | "nbformat": 4, 213 | "nbformat_minor": 2 214 | } 215 | -------------------------------------------------------------------------------- /W3_Sampling_and_Inference/Population+and+Sample.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "## Population and Sample" 8 | ] 9 | }, 10 | { 11 | "cell_type": "code", 12 | "execution_count": 46, 13 | "metadata": { 14 | "collapsed": true 15 | }, 16 | "outputs": [], 17 | "source": [ 18 | "import pandas as pd\n", 19 | "import numpy as np" 20 | ] 21 | }, 22 | { 23 | "cell_type": "code", 24 | "execution_count": 47, 25 | "metadata": { 26 | "collapsed": true 27 | }, 28 | "outputs": [], 29 | "source": [ 30 | "# Create a Population DataFrame with 10 data \n", 31 | "\n", 32 | "data = pd.DataFrame()\n", 33 | "data['Population'] = [47, 48, 85, 20, 19, 13, 72, 16, 50, 60]" 34 | ] 35 | }, 36 | { 37 | "cell_type": "markdown", 38 | "metadata": {}, 39 | "source": [ 40 | "You may get different results from sampling." 41 | ] 42 | }, 43 | { 44 | "cell_type": "code", 45 | "execution_count": 48, 46 | "metadata": {}, 47 | "outputs": [ 48 | { 49 | "name": "stdout", 50 | "output_type": "stream", 51 | "text": [ 52 | "3 20\n", 53 | "3 20\n", 54 | "6 72\n", 55 | "1 48\n", 56 | "7 16\n", 57 | "Name: Population, dtype: int64\n" 58 | ] 59 | } 60 | ], 61 | "source": [ 62 | "# Draw sample with replacement, size=5 from Population\n", 63 | "\n", 64 | "a_sample_with_replacement = data['Population'].sample(5, replace=True)\n", 65 | "print(a_sample_with_replacement)" 66 | ] 67 | }, 68 | { 69 | "cell_type": "code", 70 | "execution_count": 49, 71 | "metadata": {}, 72 | "outputs": [ 73 | { 74 | "name": "stdout", 75 | "output_type": "stream", 76 | "text": [ 77 | "5 13\n", 78 | "1 48\n", 79 | "7 16\n", 80 | "3 20\n", 81 | "0 47\n", 82 | "Name: Population, dtype: int64\n" 83 | ] 84 | } 85 | ], 86 | "source": [ 87 | "# Draw sample without replacement, size=5 from Population\n", 88 | "\n", 89 | "a_sample_without_replacement = data['Population'].sample(5, replace=False)\n", 90 | "print(a_sample_without_replacement)" 91 | ] 92 | }, 93 | { 94 | "cell_type": "markdown", 95 | "metadata": {}, 96 | "source": [ 97 | "# Parameters and Statistics" 98 | ] 99 | }, 100 | { 101 | "cell_type": "code", 102 | "execution_count": 50, 103 | "metadata": {}, 104 | "outputs": [ 105 | { 106 | "name": "stdout", 107 | "output_type": "stream", 108 | "text": [ 109 | "Population mean is 43.0\n", 110 | "Population variance is 571.8\n" 111 | ] 112 | } 113 | ], 114 | "source": [ 115 | "# Calculate mean and variance\n", 116 | "population_mean = data['Population'].mean()\n", 117 | "population_var = data['Population'].var(ddof=0)\n", 118 | "print('Population mean is ', population_mean)\n", 119 | "print('Population variance is', population_var)" 120 | ] 121 | }, 122 | { 123 | "cell_type": "markdown", 124 | "metadata": {}, 125 | "source": [ 126 | "**Expected Output: ** Population mean is 43.0\n", 127 | "Population variance is 571.8\n" 128 | ] 129 | }, 130 | { 131 | "cell_type": "markdown", 132 | "metadata": {}, 133 | "source": [ 134 | "You may get different result from sampling." 135 | ] 136 | }, 137 | { 138 | "cell_type": "code", 139 | "execution_count": 51, 140 | "metadata": {}, 141 | "outputs": [ 142 | { 143 | "name": "stdout", 144 | "output_type": "stream", 145 | "text": [ 146 | "Sample mean is 31.8\n", 147 | "Sample variance is 275.288888889\n" 148 | ] 149 | } 150 | ], 151 | "source": [ 152 | "# Calculate sample mean and sample standard deviation, size =10\n", 153 | "# You will get different mean and varince every time when you excecute the below code\n", 154 | "\n", 155 | "a_sample = data['Population'].sample(10, replace=True)\n", 156 | "sample_mean = a_sample.mean()\n", 157 | "sample_var = a_sample.var()\n", 158 | "print('Sample mean is ', sample_mean)\n", 159 | "print('Sample variance is', sample_var)" 160 | ] 161 | }, 162 | { 163 | "cell_type": "markdown", 164 | "metadata": {}, 165 | "source": [ 166 | "# Average of an unbiased estimator" 167 | ] 168 | }, 169 | { 170 | "cell_type": "code", 171 | "execution_count": 52, 172 | "metadata": { 173 | "collapsed": true 174 | }, 175 | "outputs": [], 176 | "source": [ 177 | "sample_length = 500\n", 178 | "sample_variance_collection0=[data['Population'].sample(50, replace=True).var(ddof=0) for i in range(sample_length)]\n", 179 | "sample_variance_collection1=[data['Population'].sample(50, replace=True).var(ddof=1) for i in range(sample_length)]" 180 | ] 181 | }, 182 | { 183 | "cell_type": "code", 184 | "execution_count": 53, 185 | "metadata": {}, 186 | "outputs": [ 187 | { 188 | "name": "stdout", 189 | "output_type": "stream", 190 | "text": [ 191 | "Population variance is 571.8\n", 192 | "Average of sample variance with n is 559.477552\n", 193 | "Average of sample variance with n-1 is 571.595501224\n" 194 | ] 195 | } 196 | ], 197 | "source": [ 198 | "print('Population variance is ', data['Population'].var(ddof=0))\n", 199 | "print('Average of sample variance with n is ', pd.DataFrame(sample_variance_collection0)[0].mean())\n", 200 | "print('Average of sample variance with n-1 is ', pd.DataFrame(sample_variance_collection1)[0].mean())" 201 | ] 202 | }, 203 | { 204 | "cell_type": "markdown", 205 | "metadata": {}, 206 | "source": [ 207 | "### The sample variance with n-1 is closer to the population variance !" 208 | ] 209 | }, 210 | { 211 | "cell_type": "code", 212 | "execution_count": null, 213 | "metadata": { 214 | "collapsed": true 215 | }, 216 | "outputs": [], 217 | "source": [] 218 | } 219 | ], 220 | "metadata": { 221 | "kernelspec": { 222 | "display_name": "Python 3", 223 | "language": "python", 224 | "name": "python3" 225 | }, 226 | "language_info": { 227 | "codemirror_mode": { 228 | "name": "ipython", 229 | "version": 3 230 | }, 231 | "file_extension": ".py", 232 | "mimetype": "text/x-python", 233 | "name": "python", 234 | "nbconvert_exporter": "python", 235 | "pygments_lexer": "ipython3", 236 | "version": "3.6.2" 237 | } 238 | }, 239 | "nbformat": 4, 240 | "nbformat_minor": 2 241 | } 242 | -------------------------------------------------------------------------------- /W3_Sampling_and_Inference/Variation+of+Sample.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Variation of Sample" 8 | ] 9 | }, 10 | { 11 | "cell_type": "code", 12 | "execution_count": 1, 13 | "metadata": { 14 | "collapsed": true 15 | }, 16 | "outputs": [], 17 | "source": [ 18 | "import pandas as pd\n", 19 | "import numpy as np\n", 20 | "from scipy.stats import norm\n", 21 | "%matplotlib inline" 22 | ] 23 | }, 24 | { 25 | "cell_type": "code", 26 | "execution_count": 2, 27 | "metadata": {}, 28 | "outputs": [ 29 | { 30 | "name": "stdout", 31 | "output_type": "stream", 32 | "text": [ 33 | "sample mean is 9.30288716438\n", 34 | "sample SD is 6.3145162193\n" 35 | ] 36 | } 37 | ], 38 | "source": [ 39 | "# Sample mean and SD keep changing, but always within a certain range\n", 40 | "Fstsample = pd.DataFrame(np.random.normal(10, 5, size=30))\n", 41 | "print('sample mean is ', Fstsample[0].mean())\n", 42 | "print('sample SD is ', Fstsample[0].std(ddof=1))" 43 | ] 44 | }, 45 | { 46 | "cell_type": "markdown", 47 | "metadata": {}, 48 | "source": [ 49 | "## Empirical Distribution of mean" 50 | ] 51 | }, 52 | { 53 | "cell_type": "code", 54 | "execution_count": 4, 55 | "metadata": { 56 | "collapsed": true 57 | }, 58 | "outputs": [], 59 | "source": [ 60 | "meanlist = []\n", 61 | "for t in range(10000):\n", 62 | " sample = pd.DataFrame(np.random.normal(10, 5, size=30))\n", 63 | " meanlist.append(sample[0].mean())" 64 | ] 65 | }, 66 | { 67 | "cell_type": "code", 68 | "execution_count": 9, 69 | "metadata": { 70 | "collapsed": true 71 | }, 72 | "outputs": [], 73 | "source": [ 74 | "collection = pd.DataFrame()\n", 75 | "collection['meanlist'] = meanlist" 76 | ] 77 | }, 78 | { 79 | "cell_type": "code", 80 | "execution_count": 10, 81 | "metadata": {}, 82 | "outputs": [ 83 | { 84 | "data": { 85 | "text/plain": [ 86 | "" 87 | ] 88 | }, 89 | "execution_count": 10, 90 | "metadata": {}, 91 | "output_type": "execute_result" 92 | }, 93 | { 94 | "data": { 95 | "image/png": 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A0JjQBgAA0JjQBgAA0JjQBgAA0JjQBgAA0JjQBgAA0JjQBgAA0JjQBgAA0Ni+qQsA2G0O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\ncMhNdwAAAABJRU5ErkJggg==\n", 96 | "text/plain": [ 97 | "" 98 | ] 99 | }, 100 | "metadata": {}, 101 | "output_type": "display_data" 102 | } 103 | ], 104 | "source": [ 105 | "collection['meanlist'].hist(bins=100, normed=1,figsize=(15,8))" 106 | ] 107 | }, 108 | { 109 | "cell_type": "markdown", 110 | "metadata": {}, 111 | "source": [ 112 | "## Sampling from arbritary distribution" 113 | ] 114 | }, 115 | { 116 | "cell_type": "code", 117 | "execution_count": 11, 118 | "metadata": {}, 119 | "outputs": [ 120 | { 121 | "data": { 122 | "text/plain": [ 123 | "array([[]], dtype=object)" 124 | ] 125 | }, 126 | "execution_count": 11, 127 | "metadata": {}, 128 | "output_type": "execute_result" 129 | }, 130 | { 131 | "data": { 132 | "image/png": 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133 | "text/plain": [ 134 | "" 135 | ] 136 | }, 137 | "metadata": {}, 138 | "output_type": "display_data" 139 | } 140 | ], 141 | "source": [ 142 | "# See what central limit theorem tells you...the sample size is larger enough, \n", 143 | "# the distribution of sample mean is approximately normal\n", 144 | "# apop is not normal, but try to change the sample size from 100 to a larger number. The distribution of sample mean of apop \n", 145 | "# becomes normal.\n", 146 | "sample_size = 100\n", 147 | "samplemeanlist = []\n", 148 | "apop = pd.DataFrame([1, 0, 1, 0, 1])\n", 149 | "for t in range(10000):\n", 150 | " sample = apop[0].sample(sample_size, replace=True) # small sample size\n", 151 | " samplemeanlist.append(sample.mean())\n", 152 | "\n", 153 | "acollec = pd.DataFrame()\n", 154 | "acollec['meanlist'] = samplemeanlist\n", 155 | "acollec.hist(bins=100, normed=1,figsize=(15,8))" 156 | ] 157 | }, 158 | { 159 | "cell_type": "code", 160 | "execution_count": null, 161 | "metadata": { 162 | "collapsed": true 163 | }, 164 | "outputs": [], 165 | "source": [] 166 | } 167 | ], 168 | "metadata": { 169 | "kernelspec": { 170 | "display_name": "Python 3", 171 | "language": "python", 172 | "name": "python3" 173 | }, 174 | "language_info": { 175 | "codemirror_mode": { 176 | "name": "ipython", 177 | "version": 3 178 | }, 179 | "file_extension": ".py", 180 | "mimetype": "text/x-python", 181 | "name": "python", 182 | "nbconvert_exporter": "python", 183 | "pygments_lexer": "ipython3", 184 | "version": "3.6.2" 185 | } 186 | }, 187 | "nbformat": 4, 188 | "nbformat_minor": 2 189 | } 190 | -------------------------------------------------------------------------------- /W3_Sampling_and_Inference/p-value.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PeterSchuld/HKUST-Python_and_Statistics_for_Financial_Analysis/778cfd135b07daabd4cc4efbcccf98735f900aac/W3_Sampling_and_Inference/p-value.pdf -------------------------------------------------------------------------------- /data/housing.csv: -------------------------------------------------------------------------------- 1 | ,LSTAT,INDUS,NOX,RM,MEDV 2 | 0,4.98,2.31,0.538,6.575,24.0 3 | 1,9.14,7.07,0.469,6.421,21.6 4 | 2,4.03,7.07,0.469,7.185,34.7 5 | 3,2.94,2.18,0.458,6.998,33.4 6 | 4,5.33,2.18,0.458,7.147,36.2 7 | 5,5.21,2.18,0.458,6.43,28.7 8 | 6,12.43,7.87,0.524,6.012,22.9 9 | 7,19.15,7.87,0.524,6.172,27.1 10 | 8,29.93,7.87,0.524,5.631,16.5 11 | 9,17.1,7.87,0.524,6.004,18.9 12 | 10,20.45,7.87,0.524,6.377,15.0 13 | 11,13.27,7.87,0.524,6.009,18.9 14 | 12,15.71,7.87,0.524,5.889,21.7 15 | 13,8.26,8.14,0.538,5.949,20.4 16 | 14,10.26,8.14,0.538,6.096,18.2 17 | 15,8.47,8.14,0.538,5.834,19.9 18 | 16,6.58,8.14,0.538,5.935,23.1 19 | 17,14.67,8.14,0.538,5.99,17.5 20 | 18,11.69,8.14,0.538,5.456,20.2 21 | 19,11.28,8.14,0.538,5.727,18.2 22 | 20,21.02,8.14,0.538,5.57,13.6 23 | 21,13.83,8.14,0.538,5.965,19.6 24 | 22,18.72,8.14,0.538,6.142,15.2 25 | 23,19.88,8.14,0.538,5.813,14.5 26 | 24,16.3,8.14,0.538,5.924,15.6 27 | 25,16.51,8.14,0.538,5.599,13.9 28 | 26,14.81,8.14,0.538,5.813,16.6 29 | 27,17.28,8.14,0.538,6.047,14.8 30 | 28,12.8,8.14,0.538,6.495,18.4 31 | 29,11.98,8.14,0.538,6.674,21.0 32 | 30,22.6,8.14,0.538,5.713,12.7 33 | 31,13.04,8.14,0.538,6.072,14.5 34 | 32,27.71,8.14,0.538,5.95,13.2 35 | 33,18.35,8.14,0.538,5.701,13.1 36 | 34,20.34,8.14,0.538,6.096,13.5 37 | 35,9.68,5.96,0.499,5.933,18.9 38 | 36,11.41,5.96,0.499,5.841,20.0 39 | 37,8.77,5.96,0.499,5.85,21.0 40 | 38,10.13,5.96,0.499,5.966,24.7 41 | 39,4.32,2.95,0.428,6.595,30.8 42 | 40,1.98,2.95,0.428,7.024,34.9 43 | 41,4.84,6.91,0.448,6.77,26.6 44 | 42,5.81,6.91,0.448,6.169,25.3 45 | 43,7.44,6.91,0.448,6.211,24.7 46 | 44,9.55,6.91,0.448,6.069,21.2 47 | 45,10.21,6.91,0.448,5.682,19.3 48 | 46,14.15,6.91,0.448,5.786,20.0 49 | 47,18.8,6.91,0.448,6.03,16.6 50 | 48,30.81,6.91,0.448,5.399,14.4 51 | 49,16.2,6.91,0.448,5.602,19.4 52 | 50,13.45,5.64,0.439,5.963,19.7 53 | 51,9.43,5.64,0.439,6.115,20.5 54 | 52,5.28,5.64,0.439,6.511,25.0 55 | 53,8.43,5.64,0.439,5.998,23.4 56 | 54,14.8,4.0,0.41,5.888,18.9 57 | 55,4.81,1.22,0.403,7.249,35.4 58 | 56,5.77,0.74,0.41,6.383,24.7 59 | 57,3.95,1.32,0.411,6.816,31.6 60 | 58,6.86,5.13,0.453,6.145,23.3 61 | 59,9.22,5.13,0.453,5.927,19.6 62 | 60,13.15,5.13,0.453,5.741,18.7 63 | 61,14.44,5.13,0.453,5.966,16.0 64 | 62,6.73,5.13,0.453,6.456,22.2 65 | 63,9.5,5.13,0.453,6.762,25.0 66 | 64,8.05,1.38,0.4161,7.104,33.0 67 | 65,4.67,3.37,0.398,6.29,23.5 68 | 66,10.24,3.37,0.398,5.787,19.4 69 | 67,8.1,6.07,0.409,5.878,22.0 70 | 68,13.09,6.07,0.409,5.594,17.4 71 | 69,8.79,6.07,0.409,5.885,20.9 72 | 70,6.72,10.81,0.413,6.417,24.2 73 | 71,9.88,10.81,0.413,5.961,21.7 74 | 72,5.52,10.81,0.413,6.065,22.8 75 | 73,7.54,10.81,0.413,6.245,23.4 76 | 74,6.78,12.83,0.437,6.273,24.1 77 | 75,8.94,12.83,0.437,6.286,21.4 78 | 76,11.97,12.83,0.437,6.279,20.0 79 | 77,10.27,12.83,0.437,6.14,20.8 80 | 78,12.34,12.83,0.437,6.232,21.2 81 | 79,9.1,12.83,0.437,5.874,20.3 82 | 80,5.29,4.86,0.426,6.727,28.0 83 | 81,7.22,4.86,0.426,6.619,23.9 84 | 82,6.72,4.86,0.426,6.302,24.8 85 | 83,7.51,4.86,0.426,6.167,22.9 86 | 84,9.62,4.49,0.449,6.389,23.9 87 | 85,6.53,4.49,0.449,6.63,26.6 88 | 86,12.86,4.49,0.449,6.015,22.5 89 | 87,8.44,4.49,0.449,6.121,22.2 90 | 88,5.5,3.41,0.489,7.007,23.6 91 | 89,5.7,3.41,0.489,7.079,28.7 92 | 90,8.81,3.41,0.489,6.417,22.6 93 | 91,8.2,3.41,0.489,6.405,22.0 94 | 92,8.16,15.04,0.464,6.442,22.9 95 | 93,6.21,15.04,0.464,6.211,25.0 96 | 94,10.59,15.04,0.464,6.249,20.6 97 | 95,6.65,2.89,0.445,6.625,28.4 98 | 96,11.34,2.89,0.445,6.163,21.4 99 | 97,4.21,2.89,0.445,8.069,38.7 100 | 98,3.57,2.89,0.445,7.82,43.8 101 | 99,6.19,2.89,0.445,7.416,33.2 102 | 100,9.42,8.56,0.52,6.727,27.5 103 | 101,7.67,8.56,0.52,6.781,26.5 104 | 102,10.63,8.56,0.52,6.405,18.6 105 | 103,13.44,8.56,0.52,6.137,19.3 106 | 104,12.33,8.56,0.52,6.167,20.1 107 | 105,16.47,8.56,0.52,5.851,19.5 108 | 106,18.66,8.56,0.52,5.836,19.5 109 | 107,14.09,8.56,0.52,6.127,20.4 110 | 108,12.27,8.56,0.52,6.474,19.8 111 | 109,15.55,8.56,0.52,6.229,19.4 112 | 110,13.0,8.56,0.52,6.195,21.7 113 | 111,10.16,10.01,0.547,6.715,22.8 114 | 112,16.21,10.01,0.547,5.913,18.8 115 | 113,17.09,10.01,0.547,6.092,18.7 116 | 114,10.45,10.01,0.547,6.254,18.5 117 | 115,15.76,10.01,0.547,5.928,18.3 118 | 116,12.04,10.01,0.547,6.176,21.2 119 | 117,10.3,10.01,0.547,6.021,19.2 120 | 118,15.37,10.01,0.547,5.872,20.4 121 | 119,13.61,10.01,0.547,5.731,19.3 122 | 120,14.37,25.65,0.581,5.87,22.0 123 | 121,14.27,25.65,0.581,6.004,20.3 124 | 122,17.93,25.65,0.581,5.961,20.5 125 | 123,25.41,25.65,0.581,5.856,17.3 126 | 124,17.58,25.65,0.581,5.879,18.8 127 | 125,14.81,25.65,0.581,5.986,21.4 128 | 126,27.26,25.65,0.581,5.613,15.7 129 | 127,17.19,21.89,0.624,5.693,16.2 130 | 128,15.39,21.89,0.624,6.431,18.0 131 | 129,18.34,21.89,0.624,5.637,14.3 132 | 130,12.6,21.89,0.624,6.458,19.2 133 | 131,12.26,21.89,0.624,6.326,19.6 134 | 132,11.12,21.89,0.624,6.372,23.0 135 | 133,15.03,21.89,0.624,5.822,18.4 136 | 134,17.31,21.89,0.624,5.757,15.6 137 | 135,16.96,21.89,0.624,6.335,18.1 138 | 136,16.9,21.89,0.624,5.942,17.4 139 | 137,14.59,21.89,0.624,6.454,17.1 140 | 138,21.32,21.89,0.624,5.857,13.3 141 | 139,18.46,21.89,0.624,6.151,17.8 142 | 140,24.16,21.89,0.624,6.174,14.0 143 | 141,34.41,21.89,0.624,5.019,14.4 144 | 142,26.82,19.58,0.871,5.403,13.4 145 | 143,26.42,19.58,0.871,5.468,15.6 146 | 144,29.29,19.58,0.871,4.903,11.8 147 | 145,27.8,19.58,0.871,6.13,13.8 148 | 146,16.65,19.58,0.871,5.628,15.6 149 | 147,29.53,19.58,0.871,4.926,14.6 150 | 148,28.32,19.58,0.871,5.186,17.8 151 | 149,21.45,19.58,0.871,5.597,15.4 152 | 150,14.1,19.58,0.871,6.122,21.5 153 | 151,13.28,19.58,0.871,5.404,19.6 154 | 152,12.12,19.58,0.871,5.012,15.3 155 | 153,15.79,19.58,0.871,5.709,19.4 156 | 154,15.12,19.58,0.871,6.129,17.0 157 | 155,15.02,19.58,0.871,6.152,15.6 158 | 156,16.14,19.58,0.871,5.272,13.1 159 | 157,4.59,19.58,0.605,6.943,41.3 160 | 158,6.43,19.58,0.605,6.066,24.3 161 | 159,7.39,19.58,0.871,6.51,23.3 162 | 160,5.5,19.58,0.605,6.25,27.0 163 | 161,1.73,19.58,0.605,7.489,50.0 164 | 162,1.92,19.58,0.605,7.802,50.0 165 | 163,3.32,19.58,0.605,8.375,50.0 166 | 164,11.64,19.58,0.605,5.854,22.7 167 | 165,9.81,19.58,0.605,6.101,25.0 168 | 166,3.7,19.58,0.605,7.929,50.0 169 | 167,12.14,19.58,0.605,5.877,23.8 170 | 168,11.1,19.58,0.605,6.319,23.8 171 | 169,11.32,19.58,0.605,6.402,22.3 172 | 170,14.43,19.58,0.605,5.875,17.4 173 | 171,12.03,19.58,0.605,5.88,19.1 174 | 172,14.69,4.05,0.51,5.572,23.1 175 | 173,9.04,4.05,0.51,6.416,23.6 176 | 174,9.64,4.05,0.51,5.859,22.6 177 | 175,5.33,4.05,0.51,6.546,29.4 178 | 176,10.11,4.05,0.51,6.02,23.2 179 | 177,6.29,4.05,0.51,6.315,24.6 180 | 178,6.92,4.05,0.51,6.86,29.9 181 | 179,5.04,2.46,0.488,6.98,37.2 182 | 180,7.56,2.46,0.488,7.765,39.8 183 | 181,9.45,2.46,0.488,6.144,36.2 184 | 182,4.82,2.46,0.488,7.155,37.9 185 | 183,5.68,2.46,0.488,6.563,32.5 186 | 184,13.98,2.46,0.488,5.604,26.4 187 | 185,13.15,2.46,0.488,6.153,29.6 188 | 186,4.45,2.46,0.488,7.831,50.0 189 | 187,6.68,3.44,0.437,6.782,32.0 190 | 188,4.56,3.44,0.437,6.556,29.8 191 | 189,5.39,3.44,0.437,7.185,34.9 192 | 190,5.1,3.44,0.437,6.951,37.0 193 | 191,4.69,3.44,0.437,6.739,30.5 194 | 192,2.87,3.44,0.437,7.178,36.4 195 | 193,5.03,2.93,0.401,6.8,31.1 196 | 194,4.38,2.93,0.401,6.604,29.1 197 | 195,2.97,0.46,0.422,7.875,50.0 198 | 196,4.08,1.52,0.404,7.287,33.3 199 | 197,8.61,1.52,0.404,7.107,30.3 200 | 198,6.62,1.52,0.404,7.274,34.6 201 | 199,4.56,1.47,0.403,6.975,34.9 202 | 200,4.45,1.47,0.403,7.135,32.9 203 | 201,7.43,2.03,0.415,6.162,24.1 204 | 202,3.11,2.03,0.415,7.61,42.3 205 | 203,3.81,2.68,0.4161,7.853,48.5 206 | 204,2.88,2.68,0.4161,8.034,50.0 207 | 205,10.87,10.59,0.489,5.891,22.6 208 | 206,10.97,10.59,0.489,6.326,24.4 209 | 207,18.06,10.59,0.489,5.783,22.5 210 | 208,14.66,10.59,0.489,6.064,24.4 211 | 209,23.09,10.59,0.489,5.344,20.0 212 | 210,17.27,10.59,0.489,5.96,21.7 213 | 211,23.98,10.59,0.489,5.404,19.3 214 | 212,16.03,10.59,0.489,5.807,22.4 215 | 213,9.38,10.59,0.489,6.375,28.1 216 | 214,29.55,10.59,0.489,5.412,23.7 217 | 215,9.47,10.59,0.489,6.182,25.0 218 | 216,13.51,13.89,0.55,5.888,23.3 219 | 217,9.69,13.89,0.55,6.642,28.7 220 | 218,17.92,13.89,0.55,5.951,21.5 221 | 219,10.5,13.89,0.55,6.373,23.0 222 | 220,9.71,6.2,0.507,6.951,26.7 223 | 221,21.46,6.2,0.507,6.164,21.7 224 | 222,9.93,6.2,0.507,6.879,27.5 225 | 223,7.6,6.2,0.507,6.618,30.1 226 | 224,4.14,6.2,0.504,8.266,44.8 227 | 225,4.63,6.2,0.504,8.725,50.0 228 | 226,3.13,6.2,0.504,8.04,37.6 229 | 227,6.36,6.2,0.504,7.163,31.6 230 | 228,3.92,6.2,0.504,7.686,46.7 231 | 229,3.76,6.2,0.504,6.552,31.5 232 | 230,11.65,6.2,0.504,5.981,24.3 233 | 231,5.25,6.2,0.504,7.412,31.7 234 | 232,2.47,6.2,0.507,8.337,41.7 235 | 233,3.95,6.2,0.507,8.247,48.3 236 | 234,8.05,6.2,0.507,6.726,29.0 237 | 235,10.88,6.2,0.507,6.086,24.0 238 | 236,9.54,6.2,0.507,6.631,25.1 239 | 237,4.73,6.2,0.507,7.358,31.5 240 | 238,6.36,4.93,0.428,6.481,23.7 241 | 239,7.37,4.93,0.428,6.606,23.3 242 | 240,11.38,4.93,0.428,6.897,22.0 243 | 241,12.4,4.93,0.428,6.095,20.1 244 | 242,11.22,4.93,0.428,6.358,22.2 245 | 243,5.19,4.93,0.428,6.393,23.7 246 | 244,12.5,5.86,0.431,5.593,17.6 247 | 245,18.46,5.86,0.431,5.605,18.5 248 | 246,9.16,5.86,0.431,6.108,24.3 249 | 247,10.15,5.86,0.431,6.226,20.5 250 | 248,9.52,5.86,0.431,6.433,24.5 251 | 249,6.56,5.86,0.431,6.718,26.2 252 | 250,5.9,5.86,0.431,6.487,24.4 253 | 251,3.59,5.86,0.431,6.438,24.8 254 | 252,3.53,5.86,0.431,6.957,29.6 255 | 253,3.54,5.86,0.431,8.259,42.8 256 | 254,6.57,3.64,0.392,6.108,21.9 257 | 255,9.25,3.64,0.392,5.876,20.9 258 | 256,3.11,3.75,0.394,7.454,44.0 259 | 257,5.12,3.97,0.647,8.704,50.0 260 | 258,7.79,3.97,0.647,7.333,36.0 261 | 259,6.9,3.97,0.647,6.842,30.1 262 | 260,9.59,3.97,0.647,7.203,33.8 263 | 261,7.26,3.97,0.647,7.52,43.1 264 | 262,5.91,3.97,0.647,8.398,48.8 265 | 263,11.25,3.97,0.647,7.327,31.0 266 | 264,8.1,3.97,0.647,7.206,36.5 267 | 265,10.45,3.97,0.647,5.56,22.8 268 | 266,14.79,3.97,0.647,7.014,30.7 269 | 267,7.44,3.97,0.575,8.297,50.0 270 | 268,3.16,3.97,0.575,7.47,43.5 271 | 269,13.65,6.96,0.464,5.92,20.7 272 | 270,13.0,6.96,0.464,5.856,21.1 273 | 271,6.59,6.96,0.464,6.24,25.2 274 | 272,7.73,6.96,0.464,6.538,24.4 275 | 273,6.58,6.96,0.464,7.691,35.2 276 | 274,3.53,6.41,0.447,6.758,32.4 277 | 275,2.98,6.41,0.447,6.854,32.0 278 | 276,6.05,6.41,0.447,7.267,33.2 279 | 277,4.16,6.41,0.447,6.826,33.1 280 | 278,7.19,6.41,0.447,6.482,29.1 281 | 279,4.85,3.33,0.4429,6.812,35.1 282 | 280,3.76,3.33,0.4429,7.82,45.4 283 | 281,4.59,3.33,0.4429,6.968,35.4 284 | 282,3.01,3.33,0.4429,7.645,46.0 285 | 283,3.16,1.21,0.401,7.923,50.0 286 | 284,7.85,2.97,0.4,7.088,32.2 287 | 285,8.23,2.25,0.389,6.453,22.0 288 | 286,12.93,1.76,0.385,6.23,20.1 289 | 287,7.14,5.32,0.405,6.209,23.2 290 | 288,7.6,5.32,0.405,6.315,22.3 291 | 289,9.51,5.32,0.405,6.565,24.8 292 | 290,3.33,4.95,0.411,6.861,28.5 293 | 291,3.56,4.95,0.411,7.148,37.3 294 | 292,4.7,4.95,0.411,6.63,27.9 295 | 293,8.58,13.92,0.437,6.127,23.9 296 | 294,10.4,13.92,0.437,6.009,21.7 297 | 295,6.27,13.92,0.437,6.678,28.6 298 | 296,7.39,13.92,0.437,6.549,27.1 299 | 297,15.84,13.92,0.437,5.79,20.3 300 | 298,4.97,2.24,0.4,6.345,22.5 301 | 299,4.74,2.24,0.4,7.041,29.0 302 | 300,6.07,2.24,0.4,6.871,24.8 303 | 301,9.5,6.09,0.433,6.59,22.0 304 | 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27 | 2015-02-06,42.68,42.790001000000004,42.150002,42.41,39.121983,34311700 28 | 2015-02-09,42.240002000000004,42.740002000000004,42.209998999999996,42.360001000000004,39.075859,31381100 29 | 2015-02-10,42.740002000000004,42.77,42.18,42.599998,39.297249,29670700 30 | 2015-02-11,42.650002,42.650002,42.209998999999996,42.380001,39.094307,38262500 31 | 2015-02-12,42.66,43.09,42.509997999999996,43.09,39.749268,33268800 32 | 2015-02-13,43.380001,43.869999,43.150002,43.869999,40.468792,40264900 33 | 2015-02-17,43.970001,44.0,43.189999,43.580002,40.487365999999994,33695700 34 | 2015-02-18,43.630001,43.700001,43.389998999999996,43.529999,40.440917999999996,27074900 35 | 2015-02-19,43.18,43.529999,43.049999,43.5,40.413047999999996,27603400 36 | 2015-02-20,43.509997999999996,43.880001,43.290001000000004,43.860001000000004,40.747498,29721100 37 | 2015-02-23,43.700001,44.189999,43.650002,44.150002,41.016921999999994,32518800 38 | 2015-02-24,44.150002,44.299999,43.919998,44.09,40.961182,25253000 39 | 2015-02-25,43.950001,44.09,43.799999,43.990002000000004,40.868279,29759800 40 | 2015-02-26,43.990002000000004,44.23,43.889998999999996,44.060001,40.933308000000004,28957300 41 | 2015-02-27,44.130001,44.200001,43.66,43.849998,40.738205,33807700 42 | 2015-03-02,43.669998,44.189999,43.549999,43.880001,40.766083,31924000 43 | 2015-03-03,43.560001,43.830002,43.09,43.279999,40.208659999999995,31748600 44 | 2015-03-04,43.009997999999996,43.209998999999996,42.880001,43.060001,40.004272,25705800 45 | 2015-03-05,43.07,43.240002000000004,42.82,43.110001000000004,40.050724,23193500 46 | 2015-03-06,43.0,43.110001000000004,42.150002,42.360001000000004,39.353951,36248800 47 | 2015-03-09,42.189999,43.130001,42.189999,42.849998,39.809166,32108000 48 | 2015-03-10,42.349998,42.709998999999996,42.029999,42.029999,39.047363,38506100 49 | 2015-03-11,42.310001,42.369999,41.84,41.98,39.000904,32215300 50 | 2015-03-12,41.330002,41.650002,40.860001000000004,41.02,38.109039,59992500 51 | 2015-03-13,40.700001,41.470001,40.610001000000004,41.380001,38.443489,58007700 52 | 2015-03-16,41.470001,41.639998999999996,41.279999,41.560001,38.610714,35273500 53 | 2015-03-17,41.369999,41.830002,41.150002,41.700001,38.740784000000005,31587200 54 | 2015-03-18,41.43,42.830002,41.330002,42.5,39.484009,43971800 55 | 2015-03-19,42.259997999999996,42.59,42.220001,42.290001000000004,39.288918,33879100 56 | 2015-03-20,42.560001,42.98,42.490002000000004,42.880001,39.837051,71904500 57 | 2015-03-23,42.880001,43.130001,42.779999,42.860001000000004,39.818462,26049000 58 | 2015-03-24,42.779999,43.169998,42.75,42.900002,39.855629,25513300 59 | 2015-03-25,42.919998,42.93,41.439999,41.459998999999996,38.517811,43469900 60 | 2015-03-26,41.220001,41.610001000000004,40.919998,41.209998999999996,38.285557,37495600 61 | 2015-03-27,41.119999,41.43,40.830002,40.970001,38.062588,33820300 62 | 2015-03-30,41.099998,41.540001000000004,40.91,40.959998999999996,38.053295,35049700 63 | 2015-03-31,40.779999,41.029999,40.540001000000004,40.66,37.774592999999996,34887200 64 | 2015-04-01,40.599998,40.759997999999996,40.310001,40.720001,37.83033,36752000 65 | 2015-04-02,40.66,40.740002000000004,40.119999,40.290001000000004,37.43084,37487500 66 | 2015-04-06,40.34,41.779999,40.18,41.549999,38.601428999999996,39223700 67 | 2015-04-07,41.610001000000004,41.91,41.310001,41.529999,38.582848,28809400 68 | 2015-04-08,41.459998999999996,41.689999,41.040001000000004,41.419998,38.480652,24753400 69 | 2015-04-09,41.25,41.619999,41.25,41.48,38.536404,25723900 70 | 2015-04-10,41.630001,41.950001,41.41,41.720001,38.759365,28022000 71 | 2015-04-13,41.400002,42.060001,41.389998999999996,41.759997999999996,38.796524,30276700 72 | 2015-04-14,41.799999,42.029999,41.389998999999996,41.650002,38.694324,24078000 73 | 2015-04-15,41.759997999999996,42.459998999999996,41.68,42.259997999999996,39.261036,27343600 74 | 2015-04-16,41.950001,42.34,41.82,42.16,39.16814,22509700 75 | 2015-04-17,41.669998,41.740002000000004,41.16,41.619999,38.666462,42387600 76 | 2015-04-20,41.73,43.169998,41.68,42.91,39.864917999999996,45738800 77 | 2015-04-21,43.0,43.150002,42.529999,42.639998999999996,39.614071,26013800 78 | 2015-04-22,42.669998,43.130001,42.549999,42.990002000000004,39.939236,25064300 79 | 2015-04-23,42.889998999999996,43.610001000000004,42.799999,43.34,40.2644,46309500 80 | 2015-04-24,45.66,48.139998999999996,45.650002,47.869999,44.472935,130933700 81 | 2015-04-27,47.23,48.130001,47.220001,48.029999,44.621574,59248200 82 | 2015-04-28,47.779999,49.209998999999996,47.700001,49.16,45.671391,60730800 83 | 2015-04-29,48.720001,49.310001,48.5,49.060001,45.578487,47804600 84 | 2015-04-30,48.700001,49.540001000000004,48.599998,48.639998999999996,45.188286,64725500 85 | 2015-05-01,48.580002,48.880001,48.400002,48.66,45.206875,38937300 86 | 2015-05-04,48.369999,48.869999,48.18,48.240002000000004,44.816669,34039500 87 | 2015-05-05,47.82,48.16,47.310001,47.599998,44.222088,50369200 88 | 2015-05-06,47.57,47.77,46.02,46.279999,42.995765999999996,52433000 89 | 2015-05-07,46.27,47.09,46.16,46.700001,43.385963000000004,32971700 90 | 2015-05-08,47.549999,47.98,47.52,47.75,44.361446,35364900 91 | 2015-05-11,47.549999,47.91,47.369999,47.369999,44.008410999999995,24609400 92 | 2015-05-12,46.849998,47.68,46.419998,47.349998,43.989826,29928300 93 | 2015-05-13,48.189999,48.32,47.57,47.630001,44.249966,34184600 94 | 2015-05-14,48.029999,48.82,48.029999,48.720001,45.262611,32980900 95 | 2015-05-15,48.869999,48.91,48.049999,48.299999,44.872414,28642700 96 | 2015-05-18,47.98,48.220001,47.610001000000004,48.009997999999996,44.602993,23631000 97 | 2015-05-19,47.560001,47.810001,47.18,47.580002,44.490788,28574800 98 | 2015-05-20,47.389998999999996,47.93,47.27,47.580002,44.490788,25047900 99 | 2015-05-21,47.279999,47.599998,47.009997999999996,47.419998,44.341179,22410700 100 | 2015-05-22,47.299999,47.349998,46.82,46.900002,43.854939,25720600 101 | 2015-05-26,46.830002,46.880001,46.189999,46.59,43.565056,29581900 102 | 2015-05-27,46.82,47.77,46.619999,47.610001000000004,44.518840999999995,27335600 103 | 2015-05-28,47.5,48.02,47.389998999999996,47.450001,44.369232000000004,19283700 104 | 2015-05-29,47.43,47.57,46.59,46.860001000000004,43.817532,35428100 105 | 2015-06-01,47.060001,47.77,46.619999,47.23,44.163509000000005,28837300 106 | 2015-06-02,46.93,47.349998,46.619999,46.919998,43.873638,21283400 107 | 2015-06-03,47.369999,47.740002000000004,46.82,46.849998,43.808182,28002200 108 | 2015-06-04,46.790001000000004,47.16,46.200001,46.360001000000004,43.349995,27745500 109 | 2015-06-05,46.310001,46.52,45.84,46.139998999999996,43.144287,25438100 110 | 2015-06-08,46.299999,46.43,45.669998,45.73,42.760902,21822300 111 | 2015-06-09,45.759997999999996,45.939999,45.459998999999996,45.650002,42.6861,24406100 112 | 2015-06-10,45.790001000000004,46.830002,45.689999,46.610001000000004,43.583767,28417400 113 | 2015-06-11,46.66,46.919998,46.130001,46.439999,43.424801,27347800 114 | 2015-06-12,46.220001,46.470001,45.900002,45.970001,42.985321,23931000 115 | 2015-06-15,45.450001,45.650002,45.02,45.48,42.527134000000004,33254500 116 | 2015-06-16,45.349998,46.240002000000004,45.299999,45.830002,42.854403999999995,27070300 117 | 2015-06-17,45.73,46.07,45.360001000000004,45.970001,42.985321,28704100 118 | 2015-06-18,46.220001,46.799999,46.169998,46.720001,43.68663,32658300 119 | 2015-06-19,46.790001000000004,46.830002,45.990002000000004,46.099998,43.10688,63837000 120 | 2015-06-22,46.330002,46.720001,46.16,46.23,43.228439,20318100 121 | 2015-06-23,46.130001,46.279999,45.619999,45.91,42.929214,25896500 122 | 2015-06-24,45.669998,46.25,45.549999,45.639998999999996,42.676743,34890900 123 | 2015-06-25,46.029999,46.060001,45.5,45.650002,42.6861,20616000 124 | 2015-06-26,45.650002,46.279999,45.029999,45.259997999999996,42.321407,49835300 125 | 2015-06-29,45.040001000000004,45.23,44.360001000000004,44.369999,41.489201,34081700 126 | 2015-06-30,44.709998999999996,44.720001,43.939999,44.150002,41.283489,35945400 127 | 2015-07-01,44.459998999999996,45.23,44.099998,44.450001,41.564007000000004,28343900 128 | 2015-07-02,44.48,44.75,44.060001,44.400002,41.517258,21752000 129 | 2015-07-06,43.959998999999996,44.48,43.950001,44.389998999999996,41.507896,23034000 130 | 2015-07-07,44.34,44.490002000000004,43.32,44.299999,41.423747999999996,36435800 131 | 2015-07-08,44.439999,44.900002,44.029999,44.240002000000004,41.367645,39785900 132 | 2015-07-09,44.75,45.220001,44.5,44.52,41.629467,32424700 133 | 2015-07-10,45.009997999999996,45.139998999999996,44.57,44.610001000000004,41.713612,25465800 134 | 2015-07-13,44.98,45.619999,44.950001,45.540001000000004,42.583237,28178300 135 | 2015-07-14,45.450001,45.959998999999996,45.310001,45.619999,42.658035,22880300 136 | 2015-07-15,45.68,45.889998999999996,45.43,45.759997999999996,42.788948,26629600 137 | 2015-07-16,46.009997999999996,46.689999,45.970001,46.66,43.630520000000004,26271700 138 | 2015-07-17,46.549999,46.779999,46.259997999999996,46.619999,43.593117,29467100 139 | 2015-07-20,46.650002,47.130001,46.439999,46.919998,43.873638,30631900 140 | 2015-07-21,46.779999,47.330002,46.48,47.279999,44.210266,42781900 141 | 2015-07-22,45.439999,46.93,45.200001,45.540001000000004,42.583237,59152400 142 | 2015-07-23,45.27,46.23,45.099998,46.110001000000004,43.11623,33934000 143 | 2015-07-24,45.91,46.32,45.799999,45.939999,42.95726,32333200 144 | 2015-07-27,45.939999,46.009997999999996,45.25,45.349998,42.405567,39701400 145 | 2015-07-28,45.580002,45.639998999999996,44.790001000000004,45.34,42.396229,34328900 146 | 2015-07-29,45.400002,46.779999,45.259997999999996,46.290001000000004,43.284546,40945900 147 | 2015-07-30,46.259997999999996,47.400002,45.93,46.880001,43.836234999999995,39777900 148 | 2015-07-31,47.290001000000004,47.369999,46.5,46.700001,43.667922999999995,31201500 149 | 2015-08-03,46.98,47.0,46.450001,46.810001,43.770782000000004,24125900 150 | 2015-08-04,46.75,47.709998999999996,46.68,47.540001000000004,44.453377,33403900 151 | 2015-08-05,47.98,48.41,47.540001000000004,47.580002,44.490788,26959700 152 | 2015-08-06,47.709998999999996,47.77,46.330002,46.619999,43.593117,27368000 153 | 2015-08-07,46.389998999999996,46.779999,46.259997999999996,46.740002000000004,43.705326,19163000 154 | 2015-08-10,46.950001,47.490002000000004,46.84,47.330002,44.257019,23079900 155 | 2015-08-11,46.82,46.939999,45.900002,46.41,43.396747999999995,28384300 156 | 2015-08-12,46.189999,46.900002,45.709998999999996,46.740002000000004,43.705326,30181400 157 | 2015-08-13,47.060001,47.099998,46.490002000000004,46.73,43.695976,22627200 158 | 2015-08-14,46.529999,47.099998,46.52,47.0,43.948440999999995,21356800 159 | 2015-08-17,46.810001,47.450001,46.57,47.32,44.247669,21099700 160 | 2015-08-18,46.84,47.43,46.700001,47.27,44.49239,23574100 161 | 2015-08-19,46.779999,47.080002,46.299999,46.610001000000004,43.871173999999996,31485500 162 | 2015-08-20,46.07,46.470001,45.66,45.66,42.977001,36238200 163 | 2015-08-21,45.299999,45.48,43.07,43.07,40.539184999999996,70053100 164 | 2015-08-24,40.450001,42.689999,39.720001,41.68,39.230865,88753700 165 | 2015-08-25,42.57,43.240002000000004,40.389998999999996,40.470001,38.091969,70616600 166 | 2015-08-26,42.009997999999996,42.84,41.060001,42.709998999999996,40.200336,63408000 167 | 2015-08-27,43.23,43.950001,42.93,43.900002,41.320408,50943200 168 | 2015-08-28,43.400002,44.150002,43.389998999999996,43.93,41.348652,28246700 169 | 2015-08-31,43.560001,43.93,43.099998,43.52,40.962753,34159100 170 | 2015-09-01,42.169998,42.59,41.66,41.82,39.362629,49688900 171 | 2015-09-02,42.360001000000004,43.380001,41.880001,43.360001000000004,40.812145,37671500 172 | 2015-09-03,43.41,43.98,43.279999,43.5,40.943913,28285200 173 | 2015-09-04,42.810001,43.040001000000004,42.200001,42.610001000000004,40.106215999999996,37138800 174 | 2015-09-08,43.299999,44.0,43.200001,43.889998999999996,41.311001,32469800 175 | 2015-09-09,44.209998999999996,44.400002,42.91,43.07,40.539184999999996,33318800 176 | 2015-09-10,43.119999,43.790001000000004,42.75,43.290001000000004,40.746258000000005,31366600 177 | 2015-09-11,43.139998999999996,43.59,42.939999,43.48,40.925087,27132500 178 | 2015-09-14,43.43,43.439999,42.860001000000004,43.040001000000004,40.510948,23656000 179 | 2015-09-15,43.189999,44.290001000000004,43.080002,43.98,41.395714,28662100 180 | 2015-09-16,43.970001,44.380001,43.84,44.299999,41.696911,23372200 181 | 2015-09-17,44.290001000000004,45.0,44.080002,44.25,41.649848999999996,32768200 182 | 2015-09-18,43.5,43.990002000000004,43.330002,43.48,40.925087,62221600 183 | 2015-09-21,43.619999,44.470001,43.599998,44.110001000000004,41.518074,26177200 184 | 2015-09-22,43.380001,44.049999,43.310001,43.900002,41.320408,28085900 185 | 2015-09-23,43.93,44.169998,43.509997999999996,43.869999,41.292171,17145200 186 | 2015-09-24,43.450001,44.130001,43.27,43.91,41.329826000000004,27905600 187 | 2015-09-25,44.48,44.73,43.759997999999996,43.939999,41.358063,29384600 188 | 2015-09-28,43.830002,44.09,43.209998999999996,43.290001000000004,40.746258000000005,27613800 189 | 2015-09-29,43.369999,43.57,43.049999,43.439999,40.887444,32763600 190 | 2015-09-30,43.880001,44.299999,43.66,44.259997999999996,41.659259999999996,34589500 191 | 2015-10-01,44.75,44.75,43.75,44.610001000000004,41.988696999999995,28657900 192 | 2015-10-02,44.27,45.57,43.919998,45.57,42.892284000000004,41839000 193 | 2015-10-05,45.75,46.889998999999996,45.700001,46.630001,43.889998999999996,34369300 194 | 2015-10-06,46.330002,47.18,46.220001,46.75,44.002945000000004,27017200 195 | 2015-10-07,47.099998,47.349998,45.950001,46.799999,44.050011,27711500 196 | 2015-10-08,46.560001,47.52,46.5,47.450001,44.661815999999995,33772700 197 | 2015-10-09,47.450001,47.540001000000004,46.919998,47.110001000000004,44.341797,28600600 198 | 2015-10-12,46.98,47.07,46.5,47.0,44.238258,19769100 199 | 2015-10-13,46.560001,47.130001,46.560001,46.889998999999996,44.13472,19987800 200 | 2015-10-14,46.650002,47.099998,46.529999,46.68,43.937053999999996,24697800 201 | 2015-10-15,47.009997999999996,47.029999,46.529999,47.009997999999996,44.247669,27189400 202 | 2015-10-16,47.02,47.540001000000004,46.900002,47.509997999999996,44.718284999999995,26450300 203 | 2015-10-19,47.419998,47.880001,47.02,47.619999,44.821822999999995,29387600 204 | 2015-10-20,47.439999,47.810001,47.02,47.77,44.963004999999995,30574000 205 | 2015-10-21,47.919998,47.990002000000004,47.110001000000004,47.200001,44.426505999999996,25144300 206 | 2015-10-22,47.529999,48.950001,47.09,48.029999,45.207737,56637100 207 | 2015-10-23,52.299999,54.07,52.25,52.869999,49.763332,135227100 208 | 2015-10-26,52.529999,54.32,52.5,54.25,51.062241,64633300 209 | 2015-10-27,53.990002000000004,54.369999,53.580002,53.689999,50.535149,50999900 210 | 2015-10-28,53.540001000000004,53.98,52.860001000000004,53.98,50.808102000000005,47000800 211 | 2015-10-29,53.540001000000004,53.830002,53.220001,53.360001000000004,50.224545,30036300 212 | 2015-10-30,53.32,53.990002000000004,52.619999,52.639998999999996,49.546844,46619800 213 | 2015-11-02,52.849998,53.360001000000004,52.619999,53.240002000000004,50.111588,30285000 214 | 2015-11-03,52.93,54.389998999999996,52.900002,54.150002,50.968117,36596900 215 | 2015-11-04,54.18,54.880001,54.060001,54.400002,51.203426,37020400 216 | 2015-11-05,54.490002000000004,54.700001,54.0,54.380001,51.184605,31468500 217 | 2015-11-06,54.09,54.98,53.959998999999996,54.919998,51.692871000000004,32851200 218 | 2015-11-09,54.549999,54.869999,53.560001,54.16,50.977528,32513100 219 | 2015-11-10,54.07,54.130001,53.27,53.509997999999996,50.365719,55283700 220 | 2015-11-11,53.700001,54.200001,53.459998999999996,53.650002,50.497498,36516300 221 | 2015-11-12,53.48,53.98,53.189999,53.32,50.186890000000005,35361100 222 | 2015-11-13,53.07,53.290001000000004,52.529999,52.84,49.735096,36848200 223 | 2015-11-16,53.080002,53.889998999999996,52.849998,53.77,50.610451,32165200 224 | 2015-11-17,53.169998,53.529999,52.849998,52.970001,50.193512,31551300 225 | 2015-11-18,53.0,53.98,52.98,53.849998,51.027382,29710000 226 | 2015-11-19,53.990002000000004,54.66,53.779999,53.939999,51.112663,28149200 227 | 2015-11-20,54.25,54.299999,53.27,54.189999,51.34956,37147600 228 | 2015-11-23,54.25,54.459998999999996,53.75,54.189999,51.34956,28235900 229 | 2015-11-24,53.919998,54.439999,53.580002,54.25,51.406422,24600000 230 | 2015-11-25,54.09,54.23,53.689999,53.689999,50.875771,21005100 231 | 2015-11-27,53.799999,54.080002,53.790001000000004,53.93,51.103188,9009100 232 | 2015-11-30,54.540001000000004,54.959998999999996,54.0,54.349998,51.501175,56241400 233 | 2015-12-01,54.41,55.23,54.299999,55.220001,52.325581,39952800 234 | 2015-12-02,55.32,55.959998999999996,55.060001,55.209998999999996,52.316093,47274900 235 | 2015-12-03,55.490002000000004,55.77,53.93,54.200001,51.359039,38627800 236 | 2015-12-04,54.119999,56.23,54.099998,55.91,52.979404,43963700 237 | 2015-12-07,55.790001000000004,55.970001,55.290001000000004,55.810001,52.884646999999994,30709800 238 | 2015-12-08,55.470001,56.099998,54.990002000000004,55.790001000000004,52.865696,32878000 239 | 2015-12-09,55.369999,55.869999,54.509997999999996,54.98,52.098156,36373200 240 | 2015-12-10,55.389998999999996,55.66,55.009997999999996,55.27,52.372955,31620700 241 | 2015-12-11,54.709998999999996,55.099998,54.009997999999996,54.060001,51.226376,39549500 242 | 2015-12-14,54.330002,55.209998999999996,53.68,55.139998999999996,52.249763,46768900 243 | 2015-12-15,55.66,55.900002,55.09,55.200001,52.306625,39843000 244 | 2015-12-16,55.540001000000004,56.25,54.759997999999996,56.130001,53.187874,37503300 245 | 2015-12-17,56.360001000000004,56.790001000000004,55.529999,55.700001,52.780415000000005,41280900 246 | 2015-12-18,55.77,56.0,54.029999,54.130001,51.292709,84684200 247 | 2015-12-21,54.880001,55.349998,54.23,54.830002,51.95602,37246300 248 | 2015-12-22,54.990002000000004,55.48,54.5,55.349998,52.448761,28300300 249 | 2015-12-23,55.700001,55.880001,55.439999,55.82,52.894127000000005,27279800 250 | 2015-12-24,55.860001000000004,55.959998999999996,55.43,55.669998,52.75198,9570000 251 | 2015-12-28,55.349998,55.950001,54.98,55.950001,53.017315,21698000 252 | 2015-12-29,56.290001000000004,56.849998,56.060001,56.549999,53.585861,27731400 253 | 2015-12-30,56.470001,56.779999,56.290001000000004,56.310001,53.35844399999999,21704500 254 | 2015-12-31,56.040001000000004,56.189999,55.419998,55.48,52.57194499999999,27334100 255 | 2016-01-04,54.32,54.799999,53.389998999999996,54.799999,51.927593,53778000 256 | 2016-01-05,54.93,55.389998999999996,54.540001000000004,55.049999,52.16449,34079700 257 | 2016-01-06,54.32,54.400002,53.639998999999996,54.049999,51.2169,39518900 258 | 2016-01-07,52.700001,53.490002000000004,52.07,52.169998,49.435444,56564900 259 | 2016-01-08,52.369999,53.279999,52.150002,52.330002,49.587063,48754000 260 | 2016-01-11,52.509997999999996,52.849998,51.459998999999996,52.299999,49.558632,36663600 261 | 2016-01-12,52.759997999999996,53.099998,52.060001,52.779999,50.01347,36095500 262 | 2016-01-13,53.799999,54.07,51.299999,51.639998999999996,48.933223999999996,66883600 263 | 2016-01-14,52.0,53.419998,51.57,53.110001000000004,50.32618,52381900 264 | 2016-01-15,51.310001,51.970001,50.34,50.990002000000004,48.317295,71820700 265 | 2016-01-19,51.48,51.68,50.060001,50.560001,47.909836,43564500 266 | 2016-01-20,49.98,51.380001,49.099998,50.790001000000004,48.127781,63273000 267 | 2016-01-21,51.0,51.580002,50.299999,50.48,47.83403,40191200 268 | 2016-01-22,51.41,52.330002,51.259997999999996,52.290001000000004,49.549152,37555800 269 | 2016-01-25,51.939999,52.650002,51.650002,51.790001000000004,49.075359000000006,34707700 270 | 2016-01-26,51.790001000000004,52.439999,51.549999,52.169998,49.435444,28699500 271 | 2016-01-27,52.009997999999996,52.200001,51.02,51.220001,48.53524,36775200 272 | 2016-01-28,51.860001000000004,52.209998999999996,51.25,52.060001,49.331215,62513800 273 | 2016-01-29,54.73,55.09,54.0,55.09,52.202389000000004,83611700 274 | 2016-02-01,54.880001,55.09,54.5,54.709998999999996,51.842304,44208500 275 | 2016-02-02,54.169998,54.259997999999996,52.650002,53.0,50.221934999999995,56313800 276 | 2016-02-03,53.25,53.389998999999996,51.259997999999996,52.16,49.425968,57559800 277 | 2016-02-04,52.099998,52.810001,51.369999,52.0,49.274361,46803400 278 | 2016-02-05,51.939999,52.0,49.560001,50.16,47.530803999999996,62009000 279 | 2016-02-08,49.549999,49.57,48.189999,49.41,46.82011,59290500 280 | 2016-02-09,49.02,50.240002000000004,48.669998,49.279999,46.696926,45822200 281 | 2016-02-10,49.889998999999996,50.389998999999996,49.52,49.709998999999996,47.104385,38237000 282 | 2016-02-11,48.68,50.110001000000004,48.509997999999996,49.689999,47.085438,48878600 283 | 2016-02-12,50.25,50.68,49.75,50.5,47.852978,34243300 284 | 2016-02-16,50.900002,51.09,50.130001,51.09,48.759646999999994,37291200 285 | 2016-02-17,51.490002000000004,52.77,51.450001,52.419998,50.02898,40789000 286 | 2016-02-18,52.330002,52.950001,52.099998,52.189999,49.809478999999996,27176000 287 | 2016-02-19,51.970001,52.279999,51.529999,51.82,49.456352,33559100 288 | 2016-02-22,52.279999,53.0,52.279999,52.650002,50.248489,25008300 289 | 2016-02-23,52.34,52.369999,50.98,51.18,48.845543,28895300 290 | 2016-02-24,50.689999,51.5,50.200001,51.360001000000004,49.017334000000005,33014500 291 | 2016-02-25,51.73,52.099998,50.610001000000004,52.099998,49.723579,26695900 292 | 2016-02-26,52.599998,52.68,51.099998,51.299999,48.960068,35975900 293 | 2016-02-29,51.349998,51.650002,50.66,50.880001,48.559227,31654000 294 | 2016-03-01,50.970001,52.59,50.919998,52.580002,50.18169,33024500 295 | 2016-03-02,52.41,52.959998999999996,52.16,52.950001,50.534809,29289900 296 | 2016-03-03,52.970001,52.970001,51.779999,52.349998,49.962177000000004,24418100 297 | 2016-03-04,52.400002,52.450001,51.709998999999996,52.029999,49.656769,33034200 298 | 2016-03-07,51.560001,51.799999,50.580002,51.029999,48.702377,38407800 299 | 2016-03-08,50.799999,52.130001,50.599998,51.650002,49.294113,33835100 300 | 2016-03-09,51.889998999999996,52.849998,51.860001000000004,52.84,50.429821000000004,28251600 301 | 2016-03-10,52.93,52.939999,51.16,52.049999,49.675858000000005,38384200 302 | 2016-03-11,53.0,53.07,52.380001,53.07,50.649334,32275700 303 | 2016-03-14,52.709998999999996,53.59,52.630001,53.169998,50.74477,24083600 304 | 2016-03-15,52.75,53.59,52.740002000000004,53.59,51.145618,21104800 305 | 2016-03-16,53.450001,54.599998,53.400002,54.349998,51.870948999999996,31691700 306 | 2016-03-17,54.209998999999996,55.0,54.0,54.66,52.166809,28223900 307 | 2016-03-18,54.919998,54.970001,53.450001,53.490002000000004,51.050182,67625500 308 | 2016-03-21,53.25,53.93,52.93,53.860001000000004,51.403304999999996,23925700 309 | 2016-03-22,53.610001000000004,54.25,53.459998999999996,54.07,51.603722,23124100 310 | 2016-03-23,54.110001000000004,54.240002000000004,53.740002000000004,53.970001,51.508286,20129000 311 | 2016-03-24,53.84,54.330002,53.73,54.209998999999996,51.737339,19950000 312 | 2016-03-28,54.209998999999996,54.290001000000004,53.330002,53.540001000000004,51.097896999999996,17025100 313 | 2016-03-29,53.66,54.860001000000004,53.450001,54.709998999999996,52.214527000000004,23375000 314 | 2016-03-30,54.93,55.639998999999996,54.900002,55.049999,52.539024,23008300 315 | 2016-03-31,54.950001,55.59,54.860001000000004,55.23,52.710804,26360500 316 | 2016-04-01,55.049999,55.610001000000004,54.57,55.57,53.03530500000001,24399200 317 | 2016-04-04,55.43,55.66,55.0,55.43,52.901688,18928800 318 | 2016-04-05,55.189999,55.299999,54.459998999999996,54.560001,52.071373,19272300 319 | 2016-04-06,54.360001000000004,55.200001,54.209998999999996,55.119999,52.605819999999994,21032100 320 | 2016-04-07,54.869999,54.91,54.23,54.459998999999996,51.975929,19225100 321 | 2016-04-08,54.669998,55.279999,54.32,54.419998,51.937748,22167200 322 | 2016-04-11,54.490002000000004,55.150002,54.299999,54.310001,51.832778999999995,21414200 323 | 2016-04-12,54.369999,54.779999,53.759997999999996,54.650002,52.157269,24574200 324 | 2016-04-13,55.119999,55.439999,54.889998999999996,55.349998,52.825340000000004,20818000 325 | 2016-04-14,55.220001,55.580002,55.07,55.360001000000004,52.834881,20875100 326 | 2016-04-15,55.299999,55.919998,55.110001000000004,55.650002,53.11166,28793800 327 | 2016-04-18,55.490002000000004,56.59,55.209998999999996,56.459998999999996,53.884712,23150300 328 | 2016-04-19,56.630001,56.77,55.68,56.389998999999996,53.817902000000004,29596800 329 | 2016-04-20,56.290001000000004,56.5,55.490002000000004,55.59,53.054393999999995,36195700 330 | 2016-04-21,55.799999,56.23,55.419998,55.779999,53.235721999999996,38909100 331 | 2016-04-22,51.91,52.43,50.77,51.779999,49.418178999999995,126834100 332 | 2016-04-25,51.779999,52.130001,51.630001,52.110001000000004,49.733128,33226900 333 | 2016-04-26,52.259997999999996,52.349998,51.09,51.439999,49.093678000000004,33532600 334 | 2016-04-27,51.48,51.5,50.549999,50.939999,48.616486,43369300 335 | 2016-04-28,50.619999,50.77,49.560001,49.900002,47.623924,43134800 336 | 2016-04-29,49.349998,50.25,49.349998,49.869999,47.595295,48411700 337 | 2016-05-02,50.0,50.75,49.779999,50.610001000000004,48.301548,33114500 338 | 2016-05-03,50.34,50.41,49.599998,49.779999,47.509399,26460200 339 | 2016-05-04,49.84,50.060001,49.459998999999996,49.869999,47.595295,24257600 340 | 2016-05-05,49.869999,50.299999,49.73,49.939999,47.662098,25390700 341 | 2016-05-06,49.919998,50.389998999999996,49.66,50.389998999999996,48.091576,24787300 342 | 2016-05-09,50.490002000000004,50.59,50.0,50.07,47.786171,17951600 343 | 2016-05-10,50.330002,51.099998,50.189999,51.02,48.692840999999994,22741500 344 | 2016-05-11,51.130001,51.779999,51.0,51.049999,48.721470000000004,24039100 345 | 2016-05-12,51.200001,51.810001,50.919998,51.509997999999996,49.160488,24102800 346 | 2016-05-13,51.439999,51.900002,51.040001000000004,51.080002,48.750107,22592300 347 | 2016-05-16,50.799999,51.959998999999996,50.75,51.830002,49.465893,20032000 348 | 2016-05-17,51.720001,51.73,50.360001000000004,50.509997999999996,48.543274,27803500 349 | 2016-05-18,50.48,51.139998999999996,50.299999,50.810001,48.831589,24907500 350 | 2016-05-19,50.470001,50.619999,49.82,50.32,48.360671999999994,23842400 351 | 2016-05-20,50.48,51.220001,50.400002,50.619999,48.648998,23905800 352 | 2016-05-23,50.599998,50.68,49.98,50.029999,48.081965999999994,25999700 353 | 2016-05-24,50.700001,51.709998999999996,50.400002,51.59,49.581226,34757900 354 | 2016-05-25,51.919998,52.490002000000004,51.790001000000004,52.119999,50.090584,24040200 355 | 2016-05-26,51.93,51.98,51.360001000000004,51.889998999999996,49.869537,24182900 356 | 2016-05-27,51.919998,52.32,51.77,52.32,50.282799,17721400 357 | 2016-05-31,52.259997999999996,53.0,52.080002,53.0,50.936321,37653100 358 | 2016-06-01,52.439999,52.950001,52.439999,52.849998,50.792159999999996,25324800 359 | 2016-06-02,52.639998999999996,52.740002000000004,51.84,52.48,50.436569,22565300 360 | 2016-06-03,52.380001,52.419998,51.599998,51.790001000000004,49.773438,23368300 361 | 2016-06-06,51.990002000000004,52.349998,51.889998999999996,52.130001,50.100196999999994,18243300 362 | 2016-06-07,52.240002000000004,52.73,52.099998,52.099998,50.071362,20866800 363 | 2016-06-08,52.02,52.439999,51.869999,52.040001000000004,50.013699,21149400 364 | 2016-06-09,52.0,52.0,51.490002000000004,51.619999,49.610054,20305700 365 | 2016-06-10,51.049999,52.049999,51.040001000000004,51.48,49.475506,25833200 366 | 2016-06-13,49.580002,50.720001,49.060001,50.139998999999996,48.187678999999996,83217800 367 | 2016-06-14,49.900002,50.099998,49.57,49.830002,47.889751000000004,42577100 368 | 2016-06-15,49.779999,50.119999,49.689999,49.689999,47.755203,33757600 369 | 2016-06-16,49.52,50.470001,49.509997999999996,50.389998999999996,48.427944000000004,31188600 370 | 2016-06-17,50.41,50.43,49.82,50.130001,48.178074,45710500 371 | 2016-06-20,50.639998999999996,50.830002,50.029999,50.07,48.120407,35607900 372 | 2016-06-21,50.200001,51.43,50.16,51.189999,49.196793,34097800 373 | 2016-06-22,51.080002,51.459998999999996,50.950001,50.990002000000004,49.004589,28816800 374 | 2016-06-23,51.279999,52.060001,51.150002,51.91,49.88876,29028800 375 | 2016-06-24,49.810001,50.939999,49.52,49.830002,47.889751000000004,133503000 376 | 2016-06-27,49.099998,49.150002,48.040001000000004,48.43,46.544270000000004,50216300 377 | 2016-06-28,48.919998,49.470001,48.669998,49.439999,47.514938,38140700 378 | 2016-06-29,49.91,50.720001,49.799999,50.540001000000004,48.572105,31304000 379 | 2016-06-30,50.720001,51.299999,50.5,51.169998,49.177578000000004,28527800 380 | 2016-07-01,51.130001,51.720001,51.07,51.16,49.167969,21400400 381 | 2016-07-05,50.830002,51.279999,50.740002000000004,51.169998,49.177578000000004,24806400 382 | 2016-07-06,50.779999,51.540001000000004,50.389998999999996,51.380001,49.379402,28167500 383 | 2016-07-07,51.419998,51.610001000000004,51.07,51.380001,49.379402,19580800 384 | 2016-07-08,51.73,52.360001000000004,51.549999,52.299999,50.26358,28391000 385 | 2016-07-11,52.5,52.830002,52.470001,52.59,50.54229,22269200 386 | 2016-07-12,52.939999,53.400002,52.790001000000004,53.209998999999996,51.138145,27317600 387 | 2016-07-13,53.560001,53.860001000000004,53.18,53.509997999999996,51.42646,25356800 388 | 2016-07-14,53.84,53.990002000000004,53.580002,53.740002000000004,51.647511,24545500 389 | 2016-07-15,53.950001,54.0,53.209998999999996,53.700001,51.609066,32024400 390 | 2016-07-18,53.700001,54.34,53.549999,53.959998999999996,51.858940000000004,31433900 391 | 2016-07-19,53.709998999999996,53.900002,52.93,53.09,51.02282,53336500 392 | 2016-07-20,56.150002,56.84,55.529999,55.91,53.733013,89893300 393 | 2016-07-21,55.98,56.23,55.759997999999996,55.799999,53.627295999999994,32776700 394 | 2016-07-22,56.080002,56.630001,55.779999,56.57,54.367309999999996,32157200 395 | 2016-07-25,56.470001,56.740002000000004,56.259997999999996,56.73,54.521083999999995,25610600 396 | 2016-07-26,56.52,57.290001000000004,56.509997999999996,56.759997999999996,54.549915000000006,28079000 397 | 2016-07-27,56.610001000000004,56.799999,56.110001000000004,56.189999,54.002106000000005,32095300 398 | 2016-07-28,56.0,56.369999,55.720001,56.209998999999996,54.021328000000004,37550400 399 | 2016-07-29,56.259997999999996,56.759997999999996,56.049999,56.68,54.47303,30558700 400 | 2016-08-01,56.599998,56.75,56.139998999999996,56.580002,54.37693,26003400 401 | 2016-08-02,56.849998,56.900002,56.310001,56.580002,54.37693,35122000 402 | 2016-08-03,56.68,57.110001000000004,56.490002000000004,56.970001,54.751743000000005,22075600 403 | 2016-08-04,56.799999,57.52,56.669998,57.389998999999996,55.155388,26466400 404 | 2016-08-05,57.650002,58.209998999999996,57.450001,57.959998999999996,55.703185999999995,29335200 405 | 2016-08-08,58.060001,58.09,57.779999,58.060001,55.799296999999996,19473500 406 | 2016-08-09,58.169998,58.5,58.02,58.200001,55.933849,16920700 407 | 2016-08-10,58.16,58.32,57.82,58.02,55.760853000000004,15756900 408 | 2016-08-11,58.029999,58.450001,58.029999,58.299999,56.029949,18133800 409 | 2016-08-12,58.029999,58.189999,57.619999,57.939999,55.68396800000001,21655200 410 | 2016-08-15,58.009997999999996,58.5,57.959998999999996,58.119999,55.856956000000004,19283900 411 | 2016-08-16,57.610001000000004,57.619999,57.27,57.439999,55.547501000000004,20523500 412 | 2016-08-17,57.540001000000004,57.68,57.23,57.560001,55.663548,18856400 413 | 2016-08-18,57.419998,57.700001,57.27,57.599998,55.702231999999995,14214300 414 | 2016-08-19,57.43,57.73,57.200001,57.619999,55.721573,17271000 415 | 2016-08-22,57.599998,57.75,57.259997999999996,57.669998,55.769923999999996,15221900 416 | 2016-08-23,57.900002,58.18,57.849998,57.889998999999996,55.982677,18732400 417 | 2016-08-24,57.799999,58.040001000000004,57.720001,57.950001,56.040699,18151500 418 | 2016-08-25,57.880001,58.290001000000004,57.779999,58.169998,56.253452,18552600 419 | 2016-08-26,58.279999,58.700001,57.689999,58.029999,56.118065,20971200 420 | 2016-08-29,58.18,58.599998,58.099998,58.099998,56.185756999999995,16217900 421 | 2016-08-30,57.98,58.189999,57.610001000000004,57.889998999999996,55.982677,16930200 422 | 2016-08-31,57.650002,57.799999,57.299999,57.459998999999996,55.566841000000004,20860300 423 | 2016-09-01,57.009997999999996,57.82,57.009997999999996,57.59,55.692554,26075400 424 | 2016-09-02,57.669998,58.189999,57.419998,57.669998,55.769923999999996,18900500 425 | 2016-09-06,57.779999,57.799999,57.209998999999996,57.610001000000004,55.711906000000006,16278400 426 | 2016-09-07,57.470001,57.84,57.41,57.66,55.760254,17493400 427 | 2016-09-08,57.630001,57.790001000000004,57.18,57.43,55.53783000000001,19972500 428 | 2016-09-09,56.790001000000004,57.52,56.209998999999996,56.209998999999996,54.358028000000004,35113900 429 | 2016-09-12,56.0,57.209998999999996,55.610001000000004,57.049999,55.170349,29303000 430 | 2016-09-13,56.5,56.650002,56.049999,56.529999,54.667483999999995,30130200 431 | 2016-09-14,56.389998999999996,56.630001,56.029999,56.259997999999996,54.406380000000006,24062500 432 | 2016-09-15,56.150002,57.349998,55.98,57.189999,55.30574,26847000 433 | 2016-09-16,57.630001,57.630001,56.75,57.25,55.363766000000005,44607000 434 | 2016-09-19,57.27,57.75,56.849998,56.93,55.054306000000004,20879200 435 | 2016-09-20,57.349998,57.349998,56.75,56.810001,54.938263,17384000 436 | 2016-09-21,57.509997999999996,57.849998,57.080002,57.759997999999996,55.856953000000004,33707300 437 | 2016-09-22,57.919998,58.0,57.630001,57.82,55.914981999999995,19822200 438 | 2016-09-23,57.869999,57.91,57.380001,57.43,55.53783000000001,19955300 439 | 2016-09-26,57.080002,57.139998999999996,56.830002,56.900002,55.02529499999999,21688700 440 | 2016-09-27,56.93,58.060001,56.68,57.950001,56.040699,28065100 441 | 2016-09-28,57.880001,58.060001,57.669998,58.029999,56.118065,20536400 442 | 2016-09-29,57.810001,58.169998,57.209998999999996,57.400002,55.50881999999999,25463500 443 | 2016-09-30,57.57,57.77,57.34,57.599998,55.702231999999995,29910800 444 | 2016-10-03,57.41,57.549999,57.060001,57.419998,55.528164000000004,19189500 445 | 2016-10-04,57.27,57.599998,56.970001,57.240002000000004,55.354088,20085900 446 | 2016-10-05,57.290001000000004,57.959998999999996,57.259997999999996,57.639998999999996,55.740913,16726400 447 | 2016-10-06,57.740002000000004,57.860001000000004,57.279999,57.740002000000004,55.837627000000005,16212600 448 | 2016-10-07,57.849998,57.98,57.419998,57.799999,55.895641000000005,20089000 449 | 2016-10-10,57.91,58.389998999999996,57.869999,58.040001000000004,56.127734999999994,18084400 450 | 2016-10-11,57.889998999999996,58.02,56.889998999999996,57.189999,55.30574,26497400 451 | 2016-10-12,57.110001000000004,57.27,56.400002,57.110001000000004,55.228374,22177500 452 | 2016-10-13,56.700001,57.299999,56.32,56.919998,55.044628,25313700 453 | 2016-10-14,57.119999,57.740002000000004,57.119999,57.419998,55.528164000000004,27402500 454 | 2016-10-17,57.360001000000004,57.459998999999996,56.869999,57.220001,55.334755,23830000 455 | 2016-10-18,57.529999,57.950001,57.41,57.66,55.760254,18631500 456 | 2016-10-19,57.470001,57.84,57.400002,57.529999,55.634537,22878400 457 | 2016-10-20,57.5,57.52,56.66,57.25,55.363766000000005,49455600 458 | 2016-10-21,60.279999,60.450001,59.490002000000004,59.66,57.694359,80032200 459 | 2016-10-24,59.939999,61.0,59.93,61.0,58.990215,54067000 460 | 2016-10-25,60.849998,61.369999,60.799999,60.990002000000004,58.980541,35137200 461 | 2016-10-26,60.810001,61.200001,60.470001,60.630001,58.632401,29911600 462 | 2016-10-27,60.610001000000004,60.830002,60.09,60.099998,58.119862,28479900 463 | 2016-10-28,60.009997999999996,60.52,59.580002,59.869999,57.897442000000005,33574700 464 | 2016-10-31,60.16,60.419998,59.919998,59.919998,57.945789000000005,26434700 465 | 2016-11-01,59.970001,60.02,59.25,59.799999,57.82975,24533000 466 | 2016-11-02,59.82,59.93,59.299999,59.43,57.471939,22147000 467 | 2016-11-03,59.529999,59.639998999999996,59.110001000000004,59.209998999999996,57.259181999999996,21600400 468 | 2016-11-04,58.650002,59.279999,58.52,58.709998999999996,56.775661,28697000 469 | 2016-11-07,59.779999,60.52,59.779999,60.419998,58.429320999999995,31664800 470 | 2016-11-08,60.549999,60.779999,60.150002,60.470001,58.477672999999996,22862000 471 | 2016-11-09,60.0,60.59,59.200001,60.169998,58.187553,49632500 472 | 2016-11-10,60.48,60.490002000000004,57.630001,58.700001,56.76599100000001,57822400 473 | 2016-11-11,58.23,59.119999,58.009997999999996,59.02,57.075447,38767800 474 | 2016-11-14,59.02,59.080002,57.279999,58.119999,56.205093000000005,40861900 475 | 2016-11-15,58.330002,59.490002000000004,58.32,58.869999,57.314983,35904100 476 | 2016-11-16,58.939999,59.66,58.810001,59.650002,58.074383,26851400 477 | 2016-11-17,60.41,60.950001,59.970001,60.639998999999996,59.038235,32132700 478 | 2016-11-18,60.779999,61.139998999999996,60.299999,60.349998,58.755894,27686300 479 | 2016-11-21,60.5,60.970001,60.419998,60.860001000000004,59.252421999999996,19652600 480 | 2016-11-22,60.98,61.259997999999996,60.810001,61.119999,59.50555,23206700 481 | 2016-11-23,61.009997999999996,61.099998,60.25,60.400002,58.804568999999994,21847200 482 | 2016-11-25,60.299999,60.529999,60.130001,60.529999,58.931132999999996,8409600 483 | 2016-11-28,60.34,61.02,60.209998999999996,60.610001000000004,59.009021999999995,20732600 484 | 2016-11-29,60.650002,61.41,60.52,61.09,59.476349,22366700 485 | 2016-11-30,60.860001000000004,61.18,60.220001,60.259997999999996,58.668266,34655400 486 | 2016-12-01,60.110001000000004,60.150002,58.939999,59.200001,57.636265,34542100 487 | 2016-12-02,59.080002,59.470001,58.799999,59.25,57.684943999999994,25515700 488 | 2016-12-05,59.700001,60.59,59.560001,60.220001,58.62933,23552700 489 | 2016-12-06,60.43,60.459998999999996,59.799999,59.950001,58.366463,19907000 490 | 2016-12-07,60.009997999999996,61.380001,59.799999,61.369999,59.748951,30809000 491 | 2016-12-08,61.299999,61.580002,60.84,61.009997999999996,59.39845699999999,21220800 492 | 2016-12-09,61.18,61.990002000000004,61.130001,61.970001,60.333103,27349400 493 | 2016-12-12,61.82,62.299999,61.720001,62.169998,60.527817000000006,20198100 494 | 2016-12-13,62.5,63.419998,62.240002000000004,62.98,61.316421999999996,35718900 495 | 2016-12-14,63.0,63.450001,62.529999,62.68,61.024345,30352700 496 | 2016-12-15,62.700001,63.150002,62.299999,62.580002,60.926991,27669900 497 | 2016-12-16,62.950001,62.950001,62.119999,62.299999,60.65438100000001,42204700 498 | 2016-12-19,62.560001,63.77,62.419998,63.619999,61.93951800000001,34338200 499 | 2016-12-20,63.689999,63.799999,63.029999,63.540001000000004,61.861633,26028400 500 | 2016-12-21,63.43,63.700001,63.119999,63.540001000000004,61.861633,17096300 501 | 2016-12-22,63.84,64.099998,63.41,63.549999,61.871365000000004,22176600 502 | 2016-12-23,63.450001,63.540001000000004,62.799999,63.240002000000004,61.569556999999996,12398000 503 | 2016-12-27,63.209998999999996,64.07,63.209998999999996,63.279999,61.60849,11763200 504 | 2016-12-28,63.400002,63.400002,62.830002,62.990002000000004,61.326156999999995,14653300 505 | 2016-12-29,62.860001000000004,63.200001,62.73,62.900002,61.238541000000005,10181600 506 | 2016-12-30,62.959998999999996,62.990002000000004,62.029999,62.139998999999996,60.498604,25579900 507 | 2017-01-03,62.790001000000004,62.84,62.130001,62.580002,60.926991,20694100 508 | 2017-01-04,62.48,62.75,62.119999,62.299999,60.65438100000001,21340000 509 | 2017-01-05,62.189999,62.66,62.029999,62.299999,60.65438100000001,24876000 510 | 2017-01-06,62.299999,63.150002,62.040001000000004,62.84,61.180119,19922900 511 | 2017-01-09,62.759997999999996,63.080002,62.540001000000004,62.639998999999996,60.98540500000001,20256600 512 | 2017-01-10,62.73,63.07,62.279999,62.619999,60.965931000000005,18593000 513 | 2017-01-11,62.610001000000004,63.23,62.43,63.189999,61.520874,21517300 514 | 2017-01-12,63.060001,63.400002,61.950001,62.610001000000004,60.956196,20968200 515 | 2017-01-13,62.619999,62.869999,62.349998,62.700001,61.04381899999999,19422300 516 | 2017-01-17,62.68,62.700001,62.029999,62.529999,60.87830699999999,20620400 517 | 2017-01-18,62.669998,62.700001,62.119999,62.5,60.849106000000006,19670100 518 | 2017-01-19,62.240002000000004,62.98,62.200001,62.299999,60.65438100000001,18451700 519 | 2017-01-20,62.669998,62.82,62.369999,62.740002000000004,61.08276,30213500 520 | 2017-01-23,62.700001,63.119999,62.57,62.959998999999996,61.296946999999996,23097600 521 | 2017-01-24,63.200001,63.740002000000004,62.939999,63.52,61.842158999999995,24672900 522 | 2017-01-25,63.950001,64.099998,63.450001,63.68,61.997929000000006,23672700 523 | 2017-01-26,64.120003,64.540001,63.549999,64.269997,62.572345999999996,43554600 524 | 2017-01-27,65.389999,65.910004,64.889999,65.779999,64.042465,44818000 525 | 2017-01-30,65.690002,65.790001,64.800003,65.129997,63.409626,31651400 526 | 2017-01-31,64.860001,65.150002,64.260002,64.650002,62.94231,25270500 527 | 2017-02-01,64.360001,64.620003,63.470001,63.580002,61.900574,39671500 528 | 2017-02-02,63.25,63.41,62.75,63.169998,61.5014,45827000 529 | 2017-02-03,63.5,63.700001,63.07,63.68,61.997929000000006,30301800 530 | 2017-02-06,63.5,63.650002,63.139998999999996,63.639998999999996,61.958992,19796400 531 | 2017-02-07,63.740002000000004,63.779999,63.23,63.43,61.754536,20277200 532 | 2017-02-08,63.57,63.810001,63.220001,63.34,61.666916,18096400 533 | 2017-02-09,63.52,64.440002,63.32,64.059998,62.367889,22644400 534 | 2017-02-10,64.25,64.300003,63.98,64.0,62.309475,18170700 535 | 2017-02-13,64.239998,64.860001,64.129997,64.720001,63.010464,22920100 536 | 2017-02-14,64.410004,64.720001,64.019997,64.57,63.245537,23065900 537 | 2017-02-15,64.5,64.57,64.160004,64.529999,63.20636,17005200 538 | 2017-02-16,64.739998,65.239998,64.440002,64.519997,63.196564,20546300 539 | 2017-02-17,64.470001,64.690002,64.300003,64.620003,63.294514,21248800 540 | 2017-02-21,64.610001,64.949997,64.449997,64.489998,63.167179000000004,20655900 541 | 2017-02-22,64.33000200000001,64.389999,64.050003,64.360001,63.03984499999999,19292700 542 | 2017-02-23,64.41999799999999,64.730003,64.190002,64.620003,63.294514,20273100 543 | 2017-02-24,64.529999,64.800003,64.139999,64.620003,63.294514,21796800 544 | 2017-02-27,64.540001,64.540001,64.050003,64.230003,62.912518000000006,15871500 545 | 2017-02-28,64.08000200000001,64.199997,63.759997999999996,63.98,62.667637,23239800 546 | 2017-03-01,64.129997,64.989998,64.019997,64.940002,63.60795600000001,26937500 547 | 2017-03-02,64.690002,64.75,63.880001,64.010002,62.697029,24539600 548 | 2017-03-03,63.990002000000004,64.279999,63.619999,64.25,62.932102,18135900 549 | 2017-03-06,63.970001,64.559998,63.810001,64.269997,62.951691000000004,18750300 550 | 2017-03-07,64.190002,64.779999,64.190002,64.400002,63.079025,18521000 551 | 2017-03-08,64.260002,65.08000200000001,64.25,64.989998,63.656918000000005,21510900 552 | 2017-03-09,65.190002,65.199997,64.480003,64.730003,63.402267,19846800 553 | 2017-03-10,65.110001,65.260002,64.75,64.93,63.598156,19538200 554 | 2017-03-13,65.010002,65.190002,64.57,64.709999,63.382668,20100000 555 | 2017-03-14,64.529999,64.550003,64.150002,64.410004,63.088825,14280200 556 | 2017-03-15,64.550003,64.91999799999999,64.25,64.75,63.421848,24833800 557 | 2017-03-16,64.75,64.760002,64.300003,64.639999,63.314102,20674300 558 | 2017-03-17,64.910004,65.239998,64.68,64.870003,63.539387,49219700 559 | 2017-03-20,64.910004,65.18,64.720001,64.93,63.598156,14598100 560 | 2017-03-21,65.190002,65.5,64.129997,64.209999,62.892925,26640500 561 | 2017-03-22,64.120003,65.139999,64.120003,65.029999,63.696102,20680000 562 | 2017-03-23,64.940002,65.239998,64.769997,64.870003,63.539387,19269200 563 | 2017-03-24,65.360001,65.449997,64.760002,64.980003,63.647133,22617100 564 | 2017-03-27,64.629997,65.220001,64.349998,65.099998,63.76466,18614700 565 | 2017-03-28,64.959999,65.470001,64.650002,65.290001,63.950768000000004,20080400 566 | 2017-03-29,65.120003,65.5,64.949997,65.470001,64.12709,13618400 567 | 2017-03-30,65.41999799999999,65.980003,65.360001,65.709999,64.36216,15122800 568 | 2017-03-31,65.650002,66.190002,65.449997,65.860001,64.509079,21040300 569 | 2017-04-03,65.809998,65.940002,65.190002,65.550003,64.205437,20400900 570 | 2017-04-04,65.389999,65.809998,65.279999,65.730003,64.381752,12997400 571 | 2017-04-05,66.300003,66.349998,65.440002,65.559998,64.215225,21448600 572 | 2017-04-06,65.599998,66.059998,65.480003,65.730003,64.381752,18103500 573 | 2017-04-07,65.849998,65.959999,65.440002,65.68,64.332779,14108500 574 | 2017-04-10,65.610001,65.82,65.360001,65.529999,64.18584399999999,17952700 575 | 2017-04-11,65.599998,65.610001,64.849998,65.480003,64.136871,18791500 576 | 2017-04-12,65.41999799999999,65.510002,65.110001,65.230003,63.892002000000005,17108500 577 | 2017-04-13,65.290001,65.860001,64.949997,64.949997,63.617741,17896500 578 | 2017-04-17,65.040001,65.489998,65.010002,65.480003,64.136871,16689300 579 | 2017-04-18,65.33000200000001,65.709999,65.160004,65.389999,64.048721,15155600 580 | 2017-04-19,65.650002,65.75,64.889999,65.040001,63.705902,26992800 581 | 2017-04-20,65.459999,65.75,65.139999,65.5,64.156456,22299500 582 | 2017-04-21,65.66999799999999,66.699997,65.449997,66.400002,65.038002,32522600 583 | 2017-04-24,67.480003,67.660004,67.099998,67.529999,66.144821,29770000 584 | 2017-04-25,67.900002,68.040001,67.599998,67.91999799999999,66.52681700000001,30242700 585 | 2017-04-26,68.08000200000001,68.309998,67.620003,67.83000200000001,66.438675,26190800 586 | 2017-04-27,68.150002,68.379997,67.58000200000001,68.269997,66.869644,34971000 587 | 2017-04-28,68.910004,69.139999,67.690002,68.459999,67.05574,39423500 588 | 2017-05-01,68.68,69.550003,68.5,69.410004,67.98625899999999,31954400 589 | 2017-05-02,69.709999,69.709999,69.129997,69.300003,67.878525,23906100 590 | 2017-05-03,69.379997,69.379997,68.709999,69.08000200000001,67.663025,28928000 591 | 2017-05-04,69.029999,69.08000200000001,68.639999,68.809998,67.398567,21749400 592 | 2017-05-05,68.900002,69.029999,68.489998,69.0,67.584671,19128800 593 | 2017-05-08,68.970001,69.050003,68.41999799999999,68.940002,67.525909,18566100 594 | 2017-05-09,68.860001,69.279999,68.68,69.040001,67.623856,22858400 595 | 2017-05-10,68.989998,69.559998,68.91999799999999,69.309998,67.888306,17977800 596 | 2017-05-11,68.360001,68.730003,68.120003,68.459999,67.05574,28789400 597 | 2017-05-12,68.610001,68.610001,68.040001,68.379997,66.977386,18714100 598 | 2017-05-15,68.139999,68.480003,67.57,68.43,67.02636,31530300 599 | 2017-05-16,68.230003,69.440002,68.160004,69.410004,68.375961,34956000 600 | 2017-05-17,68.889999,69.099998,67.43,67.480003,66.47470899999999,30548800 601 | 2017-05-18,67.400002,68.129997,67.139999,67.709999,66.701279,25201300 602 | 2017-05-19,67.5,68.099998,67.43,67.690002,66.68158000000001,26961100 603 | 2017-05-22,67.889999,68.5,67.5,68.449997,67.430252,16237600 604 | 2017-05-23,68.720001,68.75,68.379997,68.68,67.65683,15425800 605 | 2017-05-24,68.870003,68.879997,68.449997,68.769997,67.745491,14593900 606 | 2017-05-25,68.970001,69.879997,68.910004,69.620003,68.582832,21854100 607 | 2017-05-26,69.800003,70.220001,69.519997,69.959999,68.917755,19827900 608 | 2017-05-30,69.790001,70.410004,69.769997,70.410004,69.36106099999999,17072800 609 | 2017-05-31,70.529999,70.739998,69.809998,69.839996,68.799545,30436400 610 | 2017-06-01,70.239998,70.610001,69.449997,70.099998,69.055672,21603600 611 | 2017-06-02,70.440002,71.860001,70.239998,71.760002,70.69094799999999,34770300 612 | 2017-06-05,71.970001,72.889999,71.809998,72.279999,71.203201,33316800 613 | 2017-06-06,72.300003,72.620003,72.269997,72.519997,71.439613,31511100 614 | 2017-06-07,72.639999,72.769997,71.949997,72.389999,71.311562,22301800 615 | 2017-06-08,72.510002,72.519997,71.5,71.949997,70.878113,24456300 616 | 2017-06-09,72.040001,72.08000200000001,68.589996,70.32,69.2724,49187400 617 | 2017-06-12,69.25,69.940002,68.129997,69.779999,68.74044,47761700 618 | 2017-06-13,70.019997,70.82,69.959999,70.650002,69.597481,25258600 619 | 2017-06-14,70.910004,71.099998,69.43,70.269997,69.223137,25510700 620 | 2017-06-15,69.269997,70.209999,68.800003,69.900002,68.85865,26068700 621 | 2017-06-16,69.730003,70.029999,69.220001,70.0,68.957169,48345100 622 | 2017-06-19,70.5,70.940002,70.349998,70.870003,69.814201,23798300 623 | 2017-06-20,70.82,70.870003,69.870003,69.910004,68.868507,21512200 624 | 2017-06-21,70.209999,70.620003,69.940002,70.269997,69.223137,19891100 625 | 2017-06-22,70.540001,70.589996,69.709999,70.260002,69.213295,22965700 626 | 2017-06-23,70.089996,71.25,69.91999799999999,71.209999,70.149139,27617300 627 | 2017-06-26,71.400002,71.709999,70.440002,70.529999,69.479271,19607000 628 | 2017-06-27,70.110001,70.18,69.18,69.209999,68.17893199999999,25215100 629 | 2017-06-28,69.209999,69.839996,68.790001,69.800003,68.760147,25806200 630 | 2017-06-29,69.379997,69.489998,68.089996,68.489998,67.469658,28918700 631 | 2017-06-30,68.779999,69.379997,68.739998,68.93,67.90310699999999,24161100 632 | 2017-07-03,69.33000200000001,69.599998,68.019997,68.16999799999999,67.154427,16165500 633 | 2017-07-05,68.260002,69.440002,68.220001,69.08000200000001,68.05088,21176300 634 | 2017-07-06,68.269997,68.779999,68.120003,68.57,67.54847,21117600 635 | 2017-07-07,68.699997,69.839996,68.699997,69.459999,68.425209,16878300 636 | 2017-07-10,69.459999,70.25,69.199997,69.980003,68.937469,15014500 637 | 2017-07-11,70.0,70.68,69.75,69.989998,68.947319,17460000 638 | 2017-07-12,70.690002,71.279999,70.550003,71.150002,70.090034,17750900 639 | 2017-07-13,71.5,72.040001,71.309998,71.769997,70.70079,20269800 640 | 2017-07-14,72.239998,73.269997,71.959999,72.779999,71.695747,25868100 641 | 2017-07-17,72.800003,73.449997,72.720001,73.349998,72.257263,21803900 642 | 2017-07-18,73.089996,73.389999,72.660004,73.300003,72.208,26435300 643 | 2017-07-19,73.5,74.040001,73.449997,73.860001,72.759651,22416200 644 | 2017-07-20,74.18,74.300003,73.279999,74.220001,73.114296,42361000 645 | 2017-07-21,73.449997,74.290001,73.16999799999999,73.790001,72.690704,46717100 646 | 2017-07-24,73.529999,73.75,73.129997,73.599998,72.50353199999999,21394800 647 | 2017-07-25,73.800003,74.309998,73.5,74.190002,73.08474,22018700 648 | 2017-07-26,74.339996,74.379997,73.809998,74.050003,72.946831,16252200 649 | 2017-07-27,73.760002,74.41999799999999,72.32,73.160004,72.070091,36844200 650 | 2017-07-28,72.66999799999999,73.309998,72.540001,73.040001,71.951881,18306700 651 | 2017-07-31,73.300003,73.440002,72.410004,72.699997,71.616936,23600100 652 | 2017-08-01,73.099998,73.41999799999999,72.489998,72.58000200000001,71.498734,22132300 653 | 2017-08-02,72.550003,72.559998,71.440002,72.260002,71.183502,26499200 654 | 2017-08-03,72.190002,72.440002,71.849998,72.150002,71.07513399999999,18214400 655 | 2017-08-04,72.400002,73.040001,72.239998,72.68,71.59723699999999,22579000 656 | 2017-08-07,72.800003,72.900002,72.260002,72.400002,71.32141899999999,18705700 657 | 2017-08-08,72.089996,73.129997,71.75,72.790001,71.705597,22044600 658 | 2017-08-09,72.25,72.510002,72.050003,72.470001,71.390366,22213400 659 | 2017-08-10,71.900002,72.190002,71.349998,71.410004,70.346161,24734500 660 | 2017-08-11,71.610001,72.699997,71.279999,72.5,71.419914,21443700 661 | 2017-08-14,73.059998,73.720001,72.949997,73.589996,72.493675,20067300 662 | 2017-08-15,73.589996,73.589996,73.040001,73.220001,72.513489,19181400 663 | 2017-08-16,73.339996,74.099998,73.16999799999999,73.650002,72.939346,18150400 664 | 2017-08-17,73.58000200000001,73.870003,72.400002,72.400002,71.701401,22977500 665 | 2017-08-18,72.269997,72.839996,71.93,72.489998,71.790527,18761500 666 | 2017-08-21,72.470001,72.480003,71.699997,72.150002,71.453812,17734800 667 | 2017-08-22,72.349998,73.239998,72.349998,73.160004,72.454071,14343700 668 | 2017-08-23,72.959999,73.150002,72.529999,72.720001,72.01831800000001,13766500 669 | 2017-08-24,72.739998,72.860001,72.07,72.690002,71.988602,17098300 670 | 2017-08-25,72.860001,73.349998,72.480003,72.82,72.11734799999999,12794300 671 | 2017-08-28,73.059998,73.089996,72.550003,72.83000200000001,72.127258,14569700 672 | 2017-08-29,72.25,73.160004,72.050003,73.050003,72.345139,11478400 673 | 2017-08-30,73.010002,74.209999,72.83000200000001,74.010002,73.295868,16897800 674 | 2017-08-31,74.029999,74.959999,73.800003,74.769997,74.04853100000001,27652800 675 | 2017-09-01,74.709999,74.739998,73.639999,73.940002,73.226547,21736200 676 | 2017-09-05,73.339996,73.889999,72.980003,73.610001,72.899727,21556000 677 | 2017-09-06,73.739998,74.040001,73.349998,73.400002,72.69175,16535800 678 | 2017-09-07,73.68,74.599998,73.599998,74.339996,73.622681,17471200 679 | 2017-09-08,74.33000200000001,74.440002,73.839996,73.980003,73.266159,14703800 680 | 2017-09-11,74.309998,74.940002,74.309998,74.760002,74.038635,17910400 681 | 2017-09-12,74.760002,75.239998,74.370003,74.68,73.959404,14394900 682 | 2017-09-13,74.93,75.230003,74.550003,75.209999,74.484291,13380800 683 | 2017-09-14,75.0,75.489998,74.519997,74.769997,74.04853100000001,15733900 684 | 2017-09-15,74.83000200000001,75.389999,74.07,75.309998,74.583321,38578400 685 | 2017-09-18,75.230003,75.970001,75.040001,75.160004,74.434776,23307000 686 | 2017-09-19,75.209999,75.709999,75.010002,75.440002,74.712067,16093300 687 | 2017-09-20,75.349998,75.550003,74.309998,74.940002,74.21689599999999,21587900 688 | 2017-09-21,75.110001,75.239998,74.110001,74.209999,73.493935,19186100 689 | 2017-09-22,73.989998,74.510002,73.849998,74.410004,73.692009,14111400 690 | 2017-09-25,74.089996,74.25,72.91999799999999,73.260002,72.553108,24149200 691 | 2017-09-26,73.66999799999999,73.809998,72.989998,73.260002,72.553108,18019600 692 | 2017-09-27,73.550003,74.16999799999999,73.16999799999999,73.849998,73.137413,19565100 693 | 2017-09-28,73.540001,73.970001,73.309998,73.870003,73.157219,10883800 694 | 2017-09-29,73.940002,74.540001,73.879997,74.489998,73.771233,17079100 695 | 2017-10-02,74.709999,75.010002,74.300003,74.610001,73.89007600000001,15304800 696 | 2017-10-03,74.66999799999999,74.879997,74.190002,74.260002,73.54345699999999,12190400 697 | 2017-10-04,74.089996,74.720001,73.709999,74.690002,73.969307,13317700 698 | 2017-10-05,75.220001,76.120003,74.959999,75.970001,75.236954,21195300 699 | 2017-10-06,75.66999799999999,76.029999,75.540001,76.0,75.266663,13959800 700 | 2017-10-09,75.970001,76.550003,75.860001,76.290001,75.553864,11386500 701 | 2017-10-10,76.33000200000001,76.629997,76.139999,76.290001,75.553864,13944500 702 | 2017-10-11,76.360001,76.459999,75.949997,76.41999799999999,75.68261,15388900 703 | 2017-10-12,76.489998,77.290001,76.370003,77.120003,76.375862,16876500 704 | 2017-10-13,77.589996,77.870003,77.290001,77.489998,76.74227900000001,15335700 705 | 2017-10-16,77.41999799999999,77.809998,77.349998,77.650002,76.900749,12380100 706 | 2017-10-17,77.470001,77.620003,77.25,77.589996,76.841316,16824000 707 | 2017-10-18,77.66999799999999,77.849998,77.370003,77.610001,76.86113,13300700 708 | 2017-10-19,77.57,77.93,77.349998,77.910004,77.158234,15092800 709 | 2017-10-20,78.32,78.970001,78.220001,78.809998,78.049545,22866400 710 | 2017-10-23,78.989998,79.339996,78.760002,78.83000200000001,78.06935899999999,20627200 711 | 2017-10-24,78.900002,79.199997,78.459999,78.860001,78.099068,17517200 712 | 2017-10-25,78.58000200000001,79.099998,78.010002,78.629997,77.871284,20410800 713 | 2017-10-26,79.199997,79.41999799999999,78.75,78.760002,78.000038,32120700 714 | 2017-10-27,84.370003,86.199997,83.610001,83.809998,83.001305,71066700 715 | 2017-10-30,83.699997,84.33000200000001,83.110001,83.889999,83.080536,31756700 716 | 2017-10-31,84.360001,84.360001,83.110001,83.18,82.377388,27086600 717 | 2017-11-01,83.68,83.760002,82.879997,83.18,82.377388,22307400 718 | 2017-11-02,83.349998,84.459999,83.120003,84.050003,83.238991,23992900 719 | 2017-11-03,84.08000200000001,84.540001,83.400002,84.139999,83.328117,17633500 720 | 2017-11-06,84.199997,84.699997,84.08000200000001,84.470001,83.654938,19860900 721 | 2017-11-07,84.769997,84.900002,83.93,84.269997,83.456863,17939700 722 | 2017-11-08,84.139999,84.610001,83.83000200000001,84.559998,83.744064,18034200 723 | 2017-11-09,84.110001,84.269997,82.900002,84.089996,83.278603,21178400 724 | 2017-11-10,83.790001,84.099998,83.230003,83.870003,83.06073,19397800 725 | 2017-11-13,83.660004,83.940002,83.459999,83.93,83.12014,14196900 726 | 2017-11-14,83.5,84.099998,82.980003,84.050003,83.238991,18801300 727 | 2017-11-15,83.470001,83.690002,82.690002,82.980003,82.592033,19383100 728 | 2017-11-16,83.099998,83.41999799999999,82.940002,83.199997,82.810997,20962800 729 | 2017-11-17,83.120003,83.120003,82.239998,82.400002,82.01474,22079000 730 | 2017-11-20,82.400002,82.589996,82.25,82.529999,82.144127,16315000 731 | 2017-11-21,82.739998,83.839996,82.739998,83.720001,83.328568,21237500 732 | 2017-11-22,83.83000200000001,83.900002,83.040001,83.110001,82.72142,20553100 733 | 2017-11-24,83.010002,83.43,82.779999,83.260002,82.87071999999999,7425600 734 | 2017-11-27,83.309998,83.980003,83.300003,83.870003,83.477867,18265200 735 | 2017-11-28,84.07,85.059998,84.019997,84.879997,84.48313900000001,21926000 736 | 2017-11-29,84.709999,84.91999799999999,83.18,83.339996,82.95034,27381100 737 | 2017-11-30,83.510002,84.519997,83.339996,84.16999799999999,83.776459,33054600 738 | 2017-12-01,83.599998,84.809998,83.220001,84.260002,83.866043,29532100 739 | 2017-12-04,84.41999799999999,84.43,80.699997,81.08000200000001,80.700912,39094900 740 | 2017-12-05,81.339996,82.68,80.980003,81.589996,81.208519,26152300 741 | 2017-12-06,81.550003,83.139999,81.43,82.779999,82.39296,26162100 742 | 2017-12-07,82.540001,82.800003,82.0,82.489998,82.10431700000001,23184500 743 | 2017-12-08,83.629997,84.58000200000001,83.33000200000001,84.160004,83.76651,24489100 744 | 2017-12-11,84.290001,85.370003,84.120003,85.230003,84.831512,22857900 745 | 2017-12-12,85.309998,86.050003,85.08000200000001,85.58000200000001,85.17987099999999,23924100 746 | 2017-12-13,85.739998,86.0,85.16999799999999,85.349998,84.95094300000001,22062700 747 | 2017-12-14,85.43,85.870003,84.529999,84.690002,84.294037,19306000 748 | 2017-12-15,85.260002,87.089996,84.879997,86.849998,86.44393199999999,53936700 749 | 2017-12-18,87.120003,87.5,86.230003,86.379997,85.976128,22283800 750 | 2017-12-19,86.349998,86.349998,85.269997,85.83000200000001,85.428703,23524800 751 | 2017-12-20,86.199997,86.300003,84.709999,85.519997,85.120148,23674900 752 | 2017-12-21,86.050003,86.099998,85.400002,85.5,85.100243,17990700 753 | 2017-12-22,85.400002,85.629997,84.91999799999999,85.510002,85.110199,14145800 754 | 2017-12-26,85.309998,85.529999,85.029999,85.400002,85.00071,9891200 755 | 2017-12-27,85.650002,85.980003,85.220001,85.709999,85.309265,14678000 756 | 2017-12-28,85.900002,85.93,85.550003,85.720001,85.319214,10594300 757 | 2017-12-29,85.629997,86.050003,85.5,85.540001,85.14005999999999,18717400 758 | 2018-01-02,86.129997,86.309998,85.5,85.949997,85.54813399999999,22483800 759 | 2018-01-03,86.059998,86.510002,85.970001,86.349998,85.94626600000001,26061400 760 | 2018-01-04,86.589996,87.660004,86.57,87.110001,86.702721,21912000 761 | 2018-01-05,87.660004,88.410004,87.43,88.190002,87.777672,23407100 762 | 2018-01-08,88.199997,88.58000200000001,87.599998,88.279999,87.867249,22113000 763 | 2018-01-09,88.650002,88.730003,87.860001,88.220001,87.807526,19484300 764 | 2018-01-10,87.860001,88.190002,87.410004,87.82,87.40939300000001,18652200 765 | 2018-01-11,88.129997,88.129997,87.239998,88.08000200000001,87.668182,17808900 766 | 2018-01-12,88.66999799999999,89.779999,88.449997,89.599998,89.181076,24271500 767 | 2018-01-16,90.099998,90.790001,88.010002,88.349998,87.93692,36599700 768 | 2018-01-17,89.08000200000001,90.279999,88.75,90.139999,89.718552,25621200 769 | 2018-01-18,89.800003,90.66999799999999,89.660004,90.099998,89.67873399999999,24159700 770 | 2018-01-19,90.139999,90.610001,89.660004,90.0,89.579201,36875000 771 | 2018-01-22,90.0,91.620003,89.739998,91.610001,91.181679,23601600 772 | 2018-01-23,91.900002,92.300003,91.540001,91.900002,91.470322,23412800 773 | 2018-01-24,92.550003,93.43,91.58000200000001,91.82,91.390694,33277500 774 | 2018-01-25,92.470001,93.239998,91.93,92.33000200000001,91.898315,26383200 775 | 2018-01-26,93.120003,94.059998,92.58000200000001,94.059998,93.620216,29172200 776 | 2018-01-29,95.139999,95.449997,93.720001,93.91999799999999,93.480873,31569900 777 | 2018-01-30,93.300003,93.660004,92.099998,92.739998,92.306389,38635100 778 | 2018-01-31,93.75,95.400002,93.510002,95.010002,94.565781,48756300 779 | 2018-02-01,94.790001,96.07,93.58000200000001,94.260002,93.81929000000001,47227900 780 | 2018-02-02,93.639999,93.970001,91.5,91.779999,91.350883,47867800 781 | 2018-02-05,90.559998,93.239998,88.0,88.0,87.588554,51031500 782 | -------------------------------------------------------------------------------- /data/tsla.csv: -------------------------------------------------------------------------------- 1 | Symbol,Date,Close,High,Low,Open,Volume 2 | TSLA,2015-01-01,222.41,222.41,222.41,222.41,0 3 | TSLA,2015-01-02,219.31,223.25,213.26,222.63,4764443 4 | TSLA,2015-01-05,210.09,216.5,207.1626,214.5,5368477 5 | TSLA,2015-01-06,211.28,214.2,204.21,210.06,6261936 6 | TSLA,2015-01-07,210.95,214.78,209.78,213.4,2968390 7 | TSLA,2015-01-08,210.615,213.7999,210.01,212.81,3442509 8 | TSLA,2015-01-09,206.66,209.98,204.96,208.8,4668295 9 | TSLA,2015-01-12,202.21,204.47,199.25,203.05,5950280 10 | TSLA,2015-01-13,204.25,207.61,200.911,203.32,4477320 11 | TSLA,2015-01-14,192.69,195.2,185.0,185.83,11551855 12 | TSLA,2015-01-15,191.87,195.7499,190.0,194.49,5216524 13 | TSLA,2015-01-16,193.07,194.49,189.65,190.68,3603158 14 | TSLA,2015-01-19,193.07,193.07,193.07,193.07,0 15 | TSLA,2015-01-20,191.93,194.1199,187.04,193.87,4503182 16 | TSLA,2015-01-21,196.57,198.68,189.51,189.55,4153043 17 | TSLA,2015-01-22,201.62,203.24,195.2,197.0,4116905 18 | TSLA,2015-01-23,201.29,203.5,198.33,200.29,3442371 19 | TSLA,2015-01-26,206.55,208.62,201.05,201.5,3234522 20 | TSLA,2015-01-27,205.98,208.03,203.3,204.41,2781024 21 | TSLA,2015-01-28,199.37,206.368,198.42,206.25,3149606 22 | TSLA,2015-01-29,205.2,205.98,196.5,201.11,3548106 23 | TSLA,2015-01-30,203.6,207.47,203.0,203.81,3006959 24 | TSLA,2015-02-02,210.94,211.9499,203.3,204.0,4149186 25 | TSLA,2015-02-03,218.36,220.37,211.27,213.22,4826244 26 | TSLA,2015-02-04,218.55,221.479,216.8,218.29,3305377 27 | TSLA,2015-02-05,220.99,225.48,219.638,219.89,3522947 28 | TSLA,2015-02-06,217.36,223.4,216.5001,222.0,3243931 29 | TSLA,2015-02-09,217.48,217.93,211.99,215.38,3472423 30 | TSLA,2015-02-10,216.29,220.5,215.0,217.55,5390542 31 | TSLA,2015-02-11,212.8,214.74,207.28,212.2,9769102 32 | TSLA,2015-02-12,202.88,203.0882,193.28,193.57,15649607 33 | TSLA,2015-02-13,203.77,205.99,200.91,202.89,6191003 34 | TSLA,2015-02-16,203.77,203.77,203.77,203.77,0 35 | TSLA,2015-02-17,204.35,205.7,201.5,202.7,3979647 36 | TSLA,2015-02-18,204.46,206.17,202.6,204.01,2713598 37 | TSLA,2015-02-19,211.705,212.44,203.75,205.0,5154148 38 | TSLA,2015-02-20,217.11,217.6,209.81,210.74,5982089 39 | TSLA,2015-02-23,207.335,218.2,206.33,214.99,8499775 40 | TSLA,2015-02-24,204.11,207.29,201.7,207.29,6603560 41 | TSLA,2015-02-25,203.76,207.14,202.58,204.94,3909520 42 | TSLA,2015-02-26,207.19,211.09,202.22,203.95,6472855 43 | TSLA,2015-02-27,203.34,208.55,202.8,207.0,3882084 44 | TSLA,2015-03-02,197.325,203.34,195.825,202.7,7922065 45 | TSLA,2015-03-03,199.56,200.2435,195.32,196.81,4432329 46 | TSLA,2015-03-04,202.435,202.52,197.21,199.01,4221962 47 | TSLA,2015-03-05,200.63,206.19,200.15,202.85,4877015 48 | TSLA,2015-03-06,193.88,200.75,192.151,199.21,6712438 49 | TSLA,2015-03-09,190.88,194.49,188.25,194.43,6736724 50 | TSLA,2015-03-10,190.32,193.5,187.6,188.4,5579691 51 | TSLA,2015-03-11,193.74,196.18,191.01,191.15,4974871 52 | TSLA,2015-03-12,191.07,194.45,189.75,193.75,4149294 53 | TSLA,2015-03-13,188.68,191.75,187.32,188.89,5434298 54 | TSLA,2015-03-16,195.7,195.91,189.8,192.01,5628783 55 | TSLA,2015-03-17,194.73,198.71,193.94,195.28,4894052 56 | TSLA,2015-03-18,200.71,200.88,193.11,194.95,4820936 57 | TSLA,2015-03-19,195.65,204.59,194.53,201.84,8475244 58 | TSLA,2015-03-20,198.08,198.99,195.621,197.45,4269467 59 | TSLA,2015-03-23,199.63,200.5,197.47,198.48,2631626 60 | TSLA,2015-03-24,201.72,203.79,199.75,201.58,3649860 61 | TSLA,2015-03-25,194.3,198.59,192.7,198.49,5730389 62 | TSLA,2015-03-26,190.405,194.79,189.7,193.91,4127956 63 | TSLA,2015-03-27,185.0,189.2899,181.4,189.07,8604947 64 | TSLA,2015-03-30,190.57,192.25,181.8,189.07,10089516 65 | TSLA,2015-03-31,188.77,193.76,188.41,193.53,5026569 66 | TSLA,2015-04-01,187.59,192.3,186.05,188.7,3794621 67 | TSLA,2015-04-02,191.0,193.23,190.0,190.23,5010368 68 | TSLA,2015-04-03,191.0,191.0,191.0,191.0,0 69 | TSLA,2015-04-06,203.1,207.75,197.5,198.0,12455811 70 | TSLA,2015-04-07,203.25,205.06,201.14,202.5,4347864 71 | TSLA,2015-04-08,207.67,210.9,205.87,208.2,6303117 72 | TSLA,2015-04-09,210.09,210.37,206.12,208.15,3800225 73 | TSLA,2015-04-10,210.9,211.65,209.0,209.85,4067675 74 | TSLA,2015-04-13,209.78,213.0,209.05,210.435,3758230 75 | TSLA,2015-04-14,207.46,209.49,205.5,208.57,3025963 76 | TSLA,2015-04-15,207.83,209.59,206.6,207.07,1952378 77 | TSLA,2015-04-16,206.7,209.17,206.29,207.61,1659059 78 | TSLA,2015-04-17,206.79,206.88,203.5,204.99,2469926 79 | TSLA,2015-04-20,205.27,207.85,203.85,206.78,2559251 80 | TSLA,2015-04-21,209.41,210.75,204.31,205.75,3432541 81 | TSLA,2015-04-22,219.44,221.88,211.69,212.5,7863037 82 | TSLA,2015-04-23,218.6,221.48,217.1501,218.17,4411184 83 | TSLA,2015-04-24,218.425,220.8,218.01,220.5,2427843 84 | TSLA,2015-04-27,231.55,238.75,222.0,222.63,11672627 85 | TSLA,2015-04-28,230.48,235.5,228.03,234.75,6085379 86 | TSLA,2015-04-29,232.45,234.97,227.63,230.05,3936077 87 | TSLA,2015-04-30,226.05,232.89,225.17,230.35,3911857 88 | TSLA,2015-05-01,226.03,231.77,220.405,229.74,5281689 89 | TSLA,2015-05-04,230.51,234.73,227.11,228.18,4434596 90 | TSLA,2015-05-05,232.95,239.5,229.13,237.76,5796873 91 | TSLA,2015-05-06,230.43,234.47,228.2,234.07,5270933 92 | TSLA,2015-05-07,236.8,237.48,220.25,221.0,9455909 93 | TSLA,2015-05-08,236.61,238.4099,233.7,235.96,4668236 94 | TSLA,2015-05-11,239.49,242.88,235.31,236.29,5672262 95 | TSLA,2015-05-12,244.74,246.35,238.19,240.1,6363429 96 | TSLA,2015-05-13,243.18,248.3,242.25,247.61,5440165 97 | TSLA,2015-05-14,244.1,244.89,241.25,244.82,2895936 98 | TSLA,2015-05-15,248.84,249.4,242.5,243.93,4527563 99 | TSLA,2015-05-18,248.75,249.9,246.0,246.86,3353212 100 | TSLA,2015-05-19,247.14,251.0,246.15,248.34,3674231 101 | TSLA,2015-05-20,244.35,247.74,241.3721,247.12,3755569 102 | TSLA,2015-05-21,245.62,246.62,242.3574,243.17,1970643 103 | TSLA,2015-05-22,247.73,248.6,245.01,245.38,2223089 104 | TSLA,2015-05-25,247.73,247.73,247.73,247.73,0 105 | TSLA,2015-05-26,247.455,252.0,246.5,247.68,3498682 106 | TSLA,2015-05-27,247.43,249.5,245.55,248.51,3408200 107 | TSLA,2015-05-28,251.45,251.8,245.05,247.0,3647283 108 | TSLA,2015-05-29,250.8,252.8677,249.43,250.85,3789283 109 | TSLA,2015-06-01,249.45,251.6,247.47,251.26,2505057 110 | TSLA,2015-06-02,248.35,249.4,246.3,248.79,2134809 111 | TSLA,2015-06-03,248.99,250.72,247.01,248.0,1781505 112 | TSLA,2015-06-04,245.92,249.3,245.71,247.5,2453615 113 | TSLA,2015-06-05,249.14,249.7,245.68,246.16,3022026 114 | TSLA,2015-06-08,256.29,258.75,250.31,250.77,5016997 115 | TSLA,2015-06-09,256.0,257.74,254.14,255.5,2611146 116 | TSLA,2015-06-10,250.7,254.0,248.5,251.9,3454453 117 | TSLA,2015-06-11,251.41,254.37,250.43,253.25,2044058 118 | TSLA,2015-06-12,250.69,253.46,250.21,250.21,1422335 119 | TSLA,2015-06-15,250.38,251.28,246.01,249.68,2186175 120 | TSLA,2015-06-16,253.12,253.44,249.095,250.13,1984678 121 | TSLA,2015-06-17,260.41,264.36,252.02,252.17,5512920 122 | TSLA,2015-06-18,261.89,263.46,260.02,261.83,2782703 123 | TSLA,2015-06-19,262.51,263.8,260.1,262.21,2463013 124 | TSLA,2015-06-22,259.79,264.4,255.69,262.15,4561079 125 | TSLA,2015-06-23,267.67,268.0,258.57,260.31,3870818 126 | TSLA,2015-06-24,265.17,267.35,263.72,267.0,2412293 127 | TSLA,2015-06-25,268.79,271.41,265.25,266.49,2849204 128 | TSLA,2015-06-26,267.09,269.11,266.0,269.11,3838434 129 | TSLA,2015-06-29,262.02,265.95,260.7,261.95,3478909 130 | TSLA,2015-06-30,268.26,270.92,264.0,264.8,3086935 131 | TSLA,2015-07-01,269.15,272.62,267.8503,271.15,2101224 132 | TSLA,2015-07-02,280.02,282.45,273.31,280.195,7163930 133 | TSLA,2015-07-03,280.02,280.02,280.02,280.02,0 134 | TSLA,2015-07-06,279.72,281.69,276.3,278.88,4121933 135 | TSLA,2015-07-07,267.88,275.2,260.77,275.0,6105126 136 | TSLA,2015-07-08,254.96,260.8,254.31,259.32,6221077 137 | TSLA,2015-07-09,257.92,262.95,256.79,259.08,3334077 138 | TSLA,2015-07-10,259.15,263.0,257.82,262.22,2610858 139 | TSLA,2015-07-13,262.16,262.55,256.05,262.25,2960319 140 | TSLA,2015-07-14,265.65,265.9899,260.51,262.1,1907641 141 | TSLA,2015-07-15,263.14,267.49,262.08,266.74,2021621 142 | TSLA,2015-07-16,266.68,267.2,263.16,264.22,1615961 143 | TSLA,2015-07-17,274.66,275.54,268.25,272.5,5004099 144 | TSLA,2015-07-20,282.26,286.65,272.54,275.0,4978454 145 | TSLA,2015-07-21,266.77,273.5,266.55,270.05,6108686 146 | TSLA,2015-07-22,267.87,269.44,260.86,261.27,3104960 147 | TSLA,2015-07-23,267.2,269.9,265.27,269.65,2227239 148 | TSLA,2015-07-24,265.41,271.09,263.92,267.38,2836498 149 | TSLA,2015-07-27,253.01,264.4321,250.79,262.43,4694192 150 | TSLA,2015-07-28,264.82,265.4,251.8373,255.75,3895808 151 | TSLA,2015-07-29,263.82,267.89,262.0,264.27,2790095 152 | TSLA,2015-07-30,266.79,266.94,262.11,262.69,2034560 153 | TSLA,2015-07-31,266.15,269.3599,265.123,267.6,2222552 154 | TSLA,2015-08-03,259.99,266.7099,257.07,266.29,2553474 155 | TSLA,2015-08-04,266.28,266.72,258.34,260.01,2352530 156 | TSLA,2015-08-05,270.13,271.0,260.4,263.58,6214319 157 | TSLA,2015-08-06,246.13,255.0,236.12,249.54,14623754 158 | TSLA,2015-08-07,242.51,243.73,238.39,243.58,5073390 159 | TSLA,2015-08-10,241.14,242.97,236.05,238.15,4185860 160 | TSLA,2015-08-11,237.37,239.3,234.44,237.15,4264939 161 | TSLA,2015-08-12,238.17,239.77,232.74,235.0,3739157 162 | TSLA,2015-08-13,242.51,246.48,239.12,239.86,4689182 163 | TSLA,2015-08-14,243.15,247.93,241.77,247.24,4364810 164 | TSLA,2015-08-17,254.99,256.59,250.51,255.56,7176690 165 | TSLA,2015-08-18,260.72,260.95,253.5601,255.38,4195035 166 | TSLA,2015-08-19,255.25,260.65,255.02,260.33,3604282 167 | TSLA,2015-08-20,242.18,254.56,241.9,252.06,4905757 168 | TSLA,2015-08-21,230.77,243.7999,230.51,236.0,6590234 169 | TSLA,2015-08-24,218.87,231.4,195.0,202.79,9581585 170 | TSLA,2015-08-25,220.03,230.9,219.12,230.52,4327294 171 | TSLA,2015-08-26,224.84,228.0,215.51,227.93,4963042 172 | TSLA,2015-08-27,242.99,244.75,230.81,231.0,7655959 173 | TSLA,2015-08-28,248.48,251.45,241.57,241.86,5513673 174 | TSLA,2015-08-31,249.06,254.95,245.51,245.62,4700232 175 | TSLA,2015-09-01,238.63,246.0,236.97,240.34,5454765 176 | TSLA,2015-09-02,247.69,247.88,239.78,245.3,4629174 177 | TSLA,2015-09-03,245.57,252.08,245.0,252.06,4194772 178 | TSLA,2015-09-04,241.93,244.09,238.2,240.89,3689153 179 | TSLA,2015-09-07,241.93,241.93,241.93,241.93,0 180 | TSLA,2015-09-08,248.17,249.16,244.05,245.05,3138231 181 | TSLA,2015-09-09,248.91,254.25,248.303,252.05,3390788 182 | TSLA,2015-09-10,248.48,250.7231,245.33,247.23,2709024 183 | TSLA,2015-09-11,250.24,250.24,244.73,247.64,2350844 184 | TSLA,2015-09-14,253.19,254.25,249.67,251.1,2890851 185 | TSLA,2015-09-15,253.57,254.6,249.5,252.75,2933466 186 | TSLA,2015-09-16,262.25,262.88,252.88,253.04,4417081 187 | TSLA,2015-09-17,262.07,265.5,260.69,263.96,3585812 188 | TSLA,2015-09-18,260.62,263.82,257.5,257.96,3763064 189 | TSLA,2015-09-21,264.2,271.57,255.8,263.98,6120155 190 | TSLA,2015-09-22,260.94,262.65,255.87,259.03,3664353 191 | TSLA,2015-09-23,261.06,262.08,257.5838,261.95,2600778 192 | TSLA,2015-09-24,263.12,263.45,256.21,259.53,3448191 193 | TSLA,2015-09-25,256.91,266.91,256.15,266.61,3773392 194 | TSLA,2015-09-28,248.43,259.79,246.61,257.35,4901057 195 | TSLA,2015-09-29,246.65,254.73,245.46,250.46,3703154 196 | TSLA,2015-09-30,248.4,252.4,242.34,252.0,4929582 197 | TSLA,2015-10-01,239.88,248.5,237.13,247.51,4572964 198 | TSLA,2015-10-02,247.57,247.7,234.93,235.6,4423982 199 | TSLA,2015-10-05,246.15,249.84,244.13,248.84,3689865 200 | TSLA,2015-10-06,241.46,243.03,235.58,240.0,5235897 201 | TSLA,2015-10-07,231.96,237.7,229.12,236.63,6813959 202 | TSLA,2015-10-08,226.72,230.72,221.31,230.08,6133216 203 | TSLA,2015-10-09,220.69,224.37,218.36,220.93,6158370 204 | TSLA,2015-10-12,215.58,223.0,215.27,222.99,3836303 205 | TSLA,2015-10-13,219.25,222.522,211.13,213.28,5171535 206 | TSLA,2015-10-14,216.88,220.95,215.43,220.67,3104446 207 | TSLA,2015-10-15,221.31,221.73,213.7,216.43,2844233 208 | TSLA,2015-10-16,227.01,230.4805,222.87,223.04,4334493 209 | TSLA,2015-10-19,228.1,231.15,224.94,226.5,2507895 210 | TSLA,2015-10-20,213.03,228.6,202.0,227.72,14900047 211 | TSLA,2015-10-21,210.09,214.81,208.8,211.99,4183471 212 | TSLA,2015-10-22,211.72,215.75,209.4,211.56,2825159 213 | TSLA,2015-10-23,209.09,215.35,207.69,215.0,4235462 214 | TSLA,2015-10-26,215.26,215.88,210.0,211.38,3391438 215 | TSLA,2015-10-27,210.35,217.1,207.51,214.84,3519449 216 | TSLA,2015-10-28,212.96,213.45,208.3,211.31,2728593 217 | TSLA,2015-10-29,211.63,213.7481,210.64,211.75,1805032 218 | TSLA,2015-10-30,206.93,211.63,203.89,210.4,4438901 219 | TSLA,2015-11-02,213.79,215.8,207.22,208.92,3927944 220 | TSLA,2015-11-03,208.35,214.44,207.75,213.85,8332485 221 | TSLA,2015-11-04,231.63,232.74,225.2,227.0,12726366 222 | TSLA,2015-11-05,231.77,234.5843,229.19,230.58,4496843 223 | TSLA,2015-11-06,232.36,233.359,229.5,230.7,2445293 224 | TSLA,2015-11-09,225.33,232.99,224.31,232.99,3850860 225 | TSLA,2015-11-10,216.5,223.7,216.08,223.48,4617007 226 | TSLA,2015-11-11,219.08,219.48,213.63,217.77,3347806 227 | TSLA,2015-11-12,212.94,219.0,212.66,217.85,2915900 228 | TSLA,2015-11-13,207.19,212.99,206.52,212.95,3430327 229 | TSLA,2015-11-16,214.31,214.98,205.8,206.09,2925395 230 | TSLA,2015-11-17,214.0,216.0,211.4,215.2,2148679 231 | TSLA,2015-11-18,221.07,221.38,212.52,214.5,2811900 232 | TSLA,2015-11-19,221.8,226.19,220.3,220.54,2504375 233 | TSLA,2015-11-20,220.01,225.0,213.58,223.49,4400722 234 | TSLA,2015-11-23,217.75,219.18,214.6798,217.35,2526199 235 | TSLA,2015-11-24,218.25,221.0,215.0,215.37,2480293 236 | TSLA,2015-11-25,229.64,230.825,220.375,221.34,3990779 237 | TSLA,2015-11-26,229.64,229.64,229.64,229.64,0 238 | TSLA,2015-11-27,231.61,232.25,227.01,231.06,1949353 239 | TSLA,2015-11-30,230.26,234.28,229.08,231.79,2659813 240 | TSLA,2015-12-01,237.19,238.0,231.05,231.06,3733955 241 | TSLA,2015-12-02,231.99,238.6,231.23,237.0,2981468 242 | TSLA,2015-12-03,232.71,237.45,230.0,235.48,2939564 243 | TSLA,2015-12-04,230.38,233.27,227.66,232.46,2573603 244 | TSLA,2015-12-07,231.13,235.63,226.15,227.7,3144223 245 | TSLA,2015-12-08,226.72,228.8,224.2,227.52,2687636 246 | TSLA,2015-12-09,224.52,227.5,220.72,226.7,3057753 247 | TSLA,2015-12-10,227.07,228.49,223.64,224.71,2071692 248 | TSLA,2015-12-11,217.02,225.75,216.64,225.24,3268726 249 | TSLA,2015-12-14,218.58,220.92,214.87,217.51,2831518 250 | TSLA,2015-12-15,221.09,222.22,218.0,221.82,2244424 251 | TSLA,2015-12-16,234.51,234.88,220.73,222.1,5104341 252 | TSLA,2015-12-17,233.39,237.76,229.8149,233.94,3298638 253 | TSLA,2015-12-18,230.46,235.9,229.29,232.89,3014170 254 | TSLA,2015-12-21,232.56,235.83,231.08,231.69,1953174 255 | TSLA,2015-12-22,229.95,236.55,229.63,234.99,1961495 256 | TSLA,2015-12-23,229.7,233.45,228.13,232.18,1554979 257 | TSLA,2015-12-24,230.57,231.88,228.28,230.56,710277 258 | TSLA,2015-12-25,230.57,230.57,230.57,230.57,0 259 | TSLA,2015-12-28,228.95,231.98,225.54,231.49,1901304 260 | TSLA,2015-12-29,237.19,237.72,229.547,230.06,2406290 261 | TSLA,2015-12-30,238.09,243.634,235.6707,236.6,3697921 262 | TSLA,2015-12-31,240.01,243.45,238.37,238.51,2715038 263 | TSLA,2016-01-01,240.01,240.01,240.01,240.01,0 264 | TSLA,2016-01-04,223.41,231.38,219.0,230.72,6827146 265 | TSLA,2016-01-05,223.43,226.89,220.0,226.36,3186752 266 | TSLA,2016-01-06,219.04,220.05,215.98,220.0,3779128 267 | TSLA,2016-01-07,215.65,218.44,213.67,214.19,3554251 268 | TSLA,2016-01-08,211.0,220.44,210.77,217.86,3628058 269 | TSLA,2016-01-11,207.85,214.45,203.0,214.01,4091421 270 | TSLA,2016-01-12,209.97,213.7395,205.31,211.6,3091917 271 | TSLA,2016-01-13,200.31,212.65,200.0,212.01,4126416 272 | TSLA,2016-01-14,206.18,210.0,193.38,202.21,6490741 273 | TSLA,2016-01-15,204.99,205.07,197.25,198.97,5578640 274 | TSLA,2016-01-18,204.99,204.99,204.99,204.99,0 275 | TSLA,2016-01-19,204.72,210.47,200.78,208.71,4038675 276 | TSLA,2016-01-20,198.7,201.28,191.25,199.4,5838608 277 | TSLA,2016-01-21,199.97,203.23,195.02,201.55,3166159 278 | TSLA,2016-01-22,202.55,205.5,199.03,204.8005,3124055 279 | TSLA,2016-01-25,196.38,203.57,195.88,200.06,2698739 280 | TSLA,2016-01-26,193.56,197.82,188.88,196.7,4964180 281 | TSLA,2016-01-27,188.07,193.26,185.77,192.38,3617221 282 | TSLA,2016-01-28,189.7,191.28,182.41,190.79,4592754 283 | TSLA,2016-01-29,191.2,193.74,188.08,189.95,2852289 284 | TSLA,2016-02-01,196.94,199.52,182.75,188.76,5297639 285 | TSLA,2016-02-02,182.78,193.12,180.23,192.42,5773637 286 | TSLA,2016-02-03,173.48,183.94,170.18,183.59,7931362 287 | TSLA,2016-02-04,175.33,175.98,166.99,170.7,4385366 288 | TSLA,2016-02-05,162.6,173.0,157.7442,171.3,9437591 289 | TSLA,2016-02-08,147.99,157.15,146.0,157.105,9312988 290 | TSLA,2016-02-09,148.25,159.79,141.05,142.32,8651648 291 | TSLA,2016-02-10,143.67,154.97,141.74,150.5,10406513 292 | TSLA,2016-02-11,150.47,163.26,147.0,152.0,14252364 293 | TSLA,2016-02-12,151.04,157.01,143.7,155.0,7235783 294 | TSLA,2016-02-15,151.04,151.04,151.04,151.04,0 295 | TSLA,2016-02-16,155.17,162.95,154.11,158.7,5593794 296 | TSLA,2016-02-17,168.68,169.34,156.68,159.0,5825159 297 | TSLA,2016-02-18,166.77,172.95,164.77,172.42,3887574 298 | TSLA,2016-02-19,166.58,167.49,162.5,163.66,2959390 299 | TSLA,2016-02-22,177.74,178.91,169.85,170.12,5060051 300 | TSLA,2016-02-23,177.21,181.73,173.68,176.16,5984374 301 | TSLA,2016-02-24,179.0,179.5,167.84,172.75,5395609 302 | TSLA,2016-02-25,187.43,188.5192,175.2,178.65,5750741 303 | TSLA,2016-02-26,190.34,192.0,185.0,188.7,6065117 304 | TSLA,2016-02-29,191.93,196.35,189.222,192.4,4498997 305 | TSLA,2016-03-01,186.35,195.9484,182.7,194.25,6712159 306 | TSLA,2016-03-02,188.34,188.52,181.5,183.73,4862396 307 | TSLA,2016-03-03,195.74,197.42,184.22,188.28,4829018 308 | TSLA,2016-03-04,201.04,204.03,197.5001,198.0,6489058 309 | TSLA,2016-03-07,205.29,209.7,197.4,197.68,5337072 310 | TSLA,2016-03-08,202.6,207.5,202.2,203.5,4178693 311 | TSLA,2016-03-09,208.72,209.3726,202.79,204.52,3208554 312 | TSLA,2016-03-10,205.18,213.29,200.671,210.0,5192523 313 | TSLA,2016-03-11,207.5,209.42,205.33,207.93,3343077 314 | TSLA,2016-03-14,215.15,216.72,210.64,212.65,4065705 315 | TSLA,2016-03-15,218.34,218.97,211.5,214.27,3180452 316 | TSLA,2016-03-16,221.93,222.58,217.02,218.0,3516703 317 | TSLA,2016-03-17,226.38,228.5,220.0,221.47,3782891 318 | TSLA,2016-03-18,232.74,234.48,228.06,229.1,4711793 319 | TSLA,2016-03-21,238.32,239.88,235.0,235.34,5307822 320 | TSLA,2016-03-22,234.24,238.99,232.558,237.21,4315988 321 | TSLA,2016-03-23,222.58,234.73,222.03,232.37,4948841 322 | TSLA,2016-03-24,227.75,228.8877,215.0,215.78,4960900 323 | TSLA,2016-03-25,227.75,227.75,227.75,227.75,0 324 | TSLA,2016-03-28,230.26,234.81,225.0,231.61,3925685 325 | TSLA,2016-03-29,230.13,232.38,225.33,229.89,4014330 326 | TSLA,2016-03-30,226.89,235.5,226.5,235.09,4032981 327 | TSLA,2016-03-31,229.77,237.42,225.01,229.34,8012872 328 | TSLA,2016-04-01,237.59,247.9,233.25,244.825,15997509 329 | TSLA,2016-04-04,246.99,252.12,243.64,249.12,13475327 330 | TSLA,2016-04-05,255.47,256.56,240.0,240.5,9948699 331 | TSLA,2016-04-06,265.42,267.74,253.45,253.97,11705479 332 | TSLA,2016-04-07,257.2,269.34,254.51,266.45,8856171 333 | TSLA,2016-04-08,250.07,260.82,248.0201,260.5,7363935 334 | TSLA,2016-04-11,249.92,258.99,245.3,251.0,9161693 335 | TSLA,2016-04-12,247.82,251.8,243.63,249.5,5763208 336 | TSLA,2016-04-13,254.53,255.5,247.33,248.51,4925595 337 | TSLA,2016-04-14,251.86,256.839,251.0501,253.0,4132185 338 | TSLA,2016-04-15,254.51,254.6,249.12,251.31,3752366 339 | TSLA,2016-04-18,253.88,258.31,251.66,252.23,4271362 340 | TSLA,2016-04-19,247.37,254.3699,241.251,253.12,6357526 341 | TSLA,2016-04-20,249.97,253.66,241.5,246.26,5194051 342 | TSLA,2016-04-21,248.29,250.9,246.91,248.99,2783059 343 | TSLA,2016-04-22,253.75,254.0,245.71,248.89,3786327 344 | TSLA,2016-04-25,251.82,257.38,250.76,253.01,3670348 345 | TSLA,2016-04-26,253.74,255.73,249.39,252.05,3223839 346 | TSLA,2016-04-27,251.47,255.0,249.4,252.75,3205808 347 | TSLA,2016-04-28,247.71,253.43,247.44,249.85,2518990 348 | TSLA,2016-04-29,240.76,248.43,237.81,248.14,5413787 349 | TSLA,2016-05-02,241.8,243.19,234.82,241.5,3843935 350 | TSLA,2016-05-03,232.32,238.91,231.62,237.36,4302222 351 | TSLA,2016-05-04,222.56,234.46,220.4,230.29,8700459 352 | TSLA,2016-05-05,211.53,228.64,209.79,228.46,11254827 353 | TSLA,2016-05-06,214.93,216.37,208.11,210.87,5685237 354 | TSLA,2016-05-09,208.92,216.15,206.8,215.72,4776383 355 | TSLA,2016-05-10,208.69,209.47,205.0,207.55,4070617 356 | TSLA,2016-05-11,208.96,215.48,206.05,207.59,5161864 357 | TSLA,2016-05-12,207.28,211.67,203.6572,211.44,3650475 358 | TSLA,2016-05-13,207.61,211.2,206.7,207.78,2822781 359 | TSLA,2016-05-16,208.29,213.15,207.92,208.15,2949396 360 | TSLA,2016-05-17,204.66,209.8199,204.02,209.05,2843597 361 | TSLA,2016-05-18,211.17,215.31,207.75,209.15,5617519 362 | TSLA,2016-05-19,215.21,216.79,207.3,213.62,6866321 363 | TSLA,2016-05-20,220.28,220.55,216.35,216.99,9007076 364 | TSLA,2016-05-23,216.22,222.6,215.86,219.87,5102479 365 | TSLA,2016-05-24,217.91,218.74,215.18,216.6,3013843 366 | TSLA,2016-05-25,219.58,221.36,216.51,217.91,3132615 367 | TSLA,2016-05-26,225.12,225.26,219.05,220.5,4072424 368 | TSLA,2016-05-27,223.04,225.93,220.75,224.99,3650272 369 | TSLA,2016-05-30,223.04,223.04,223.04,223.04,0 370 | TSLA,2016-05-31,223.23,224.7497,221.5001,223.04,2789002 371 | TSLA,2016-06-01,219.56,222.4,216.89,221.48,2982695 372 | TSLA,2016-06-02,218.96,219.909,217.11,219.59,2032832 373 | TSLA,2016-06-03,218.99,221.94,218.01,220.0,2228970 374 | TSLA,2016-06-06,220.68,220.9,215.45,218.0,2249508 375 | TSLA,2016-06-07,232.34,234.44,221.52,222.24,6213573 376 | TSLA,2016-06-08,235.52,240.845,232.605,233.8,5971995 377 | TSLA,2016-06-09,229.36,235.33,227.06,234.98,4492075 378 | TSLA,2016-06-10,218.79,227.97,218.4217,227.39,6026603 379 | TSLA,2016-06-13,217.87,225.77,217.66,219.5,4193022 380 | TSLA,2016-06-14,214.96,222.2,212.53,218.88,3580167 381 | TSLA,2016-06-15,217.7,221.9,215.13,216.95,2908522 382 | TSLA,2016-06-16,217.93,218.04,213.5,217.42,2440259 383 | TSLA,2016-06-17,215.47,219.99,214.5,217.81,3112620 384 | TSLA,2016-06-20,219.7,223.75,218.23,219.5,3555471 385 | TSLA,2016-06-21,219.61,222.569,218.81,220.68,4529005 386 | TSLA,2016-06-22,196.66,205.95,195.75,199.47,23742414 387 | TSLA,2016-06-23,196.4,197.55,192.13,195.69,10130748 388 | TSLA,2016-06-24,193.15,195.12,189.73,190.05,7026516 389 | TSLA,2016-06-27,198.55,198.81,187.87,190.86,7220323 390 | TSLA,2016-06-28,201.79,204.05,199.41,201.89,6212422 391 | TSLA,2016-06-29,210.19,211.78,203.0,205.13,5994908 392 | TSLA,2016-06-30,212.28,213.4999,209.02,212.97,4843111 393 | TSLA,2016-07-01,216.5,218.24,206.0,206.14,5399951 394 | TSLA,2016-07-04,216.5,216.5,216.5,216.5,0 395 | TSLA,2016-07-05,213.98,214.5441,208.0,209.73,5175345 396 | TSLA,2016-07-06,214.44,215.23,209.0,210.0,4919855 397 | TSLA,2016-07-07,215.94,218.12,213.01,213.1,3612022 398 | TSLA,2016-07-08,216.78,219.81,214.5,217.8,4074785 399 | TSLA,2016-07-11,224.78,226.78,219.51,219.96,5429823 400 | TSLA,2016-07-12,224.65,227.5,223.22,224.1,4576165 401 | TSLA,2016-07-13,222.53,225.59,220.29,225.5,3567062 402 | TSLA,2016-07-14,221.53,224.94,221.05,223.12,2675834 403 | TSLA,2016-07-15,220.4,222.7499,219.64,222.52,2234247 404 | TSLA,2016-07-18,226.25,227.09,218.3,219.64,3412055 405 | TSLA,2016-07-19,225.26,229.1,224.75,225.0,3115065 406 | TSLA,2016-07-20,228.36,229.8,225.0,226.47,2568498 407 | TSLA,2016-07-21,220.5,227.847,219.1,226.0,4428651 408 | TSLA,2016-07-22,222.27,224.5,218.88,221.99,2579692 409 | TSLA,2016-07-25,230.01,231.39,221.3715,222.27,4490683 410 | TSLA,2016-07-26,229.51,230.0,225.3,227.69,3430042 411 | TSLA,2016-07-27,228.49,233.36,226.92,229.34,2889007 412 | TSLA,2016-07-28,230.61,230.76,226.6,227.95,2419059 413 | TSLA,2016-07-29,234.79,235.28,230.24,230.7,3070813 414 | TSLA,2016-08-01,230.01,236.63,229.38,235.5,4016283 415 | TSLA,2016-08-02,227.2,229.87,221.4,229.37,3934432 416 | TSLA,2016-08-03,225.79,229.699,224.21,227.37,3887759 417 | TSLA,2016-08-04,230.61,230.86,222.05,225.69,4146997 418 | TSLA,2016-08-05,230.03,232.0,227.4,230.0,3205215 419 | TSLA,2016-08-08,226.16,229.6,226.09,228.0,2263584 420 | TSLA,2016-08-09,229.08,231.5375,226.65,226.82,2207833 421 | TSLA,2016-08-10,225.65,229.87,224.62,228.24,2338301 422 | TSLA,2016-08-11,224.91,227.57,223.41,226.17,1880936 423 | TSLA,2016-08-12,225.61,226.65,224.04,225.41,1813540 424 | TSLA,2016-08-15,225.59,229.5,224.93,226.02,2034328 425 | TSLA,2016-08-16,223.61,227.19,223.4101,225.49,2267147 426 | TSLA,2016-08-17,223.24,224.83,222.8,224.33,1787127 427 | TSLA,2016-08-18,223.51,225.66,222.29,223.82,1714467 428 | TSLA,2016-08-19,225.0,225.169,222.53,223.54,1659530 429 | TSLA,2016-08-22,222.93,225.11,222.68,224.17,2065493 430 | TSLA,2016-08-23,224.84,228.49,222.8,224.32,4784418 431 | TSLA,2016-08-24,222.62,227.15,222.22,227.05,2570693 432 | TSLA,2016-08-25,220.96,223.8,220.77,223.11,1762519 433 | TSLA,2016-08-26,219.99,222.855,218.82,222.14,2238992 434 | TSLA,2016-08-29,215.2,220.4,215.0,220.15,3266334 435 | TSLA,2016-08-30,211.34,216.11,210.52,216.11,3168862 436 | TSLA,2016-08-31,212.01,212.6,208.65,210.43,3276548 437 | TSLA,2016-09-01,200.77,211.0999,200.5,209.01,7943138 438 | TSLA,2016-09-02,197.78,203.2,196.2,202.33,5977413 439 | TSLA,2016-09-05,197.78,197.78,197.78,197.78,0 440 | TSLA,2016-09-06,202.83,203.25,199.0,199.02,4390572 441 | TSLA,2016-09-07,201.71,206.4968,200.71,205.5,3640923 442 | TSLA,2016-09-08,197.36,199.89,196.36,199.55,3377946 443 | TSLA,2016-09-09,194.47,199.92,193.7,199.09,3756992 444 | TSLA,2016-09-12,198.3,201.369,194.1,195.0,3715161 445 | TSLA,2016-09-13,196.05,198.49,193.45,197.06,3589379 446 | TSLA,2016-09-14,196.41,197.9248,194.8562,195.75,2259231 447 | TSLA,2016-09-15,200.42,202.5193,196.4,196.49,3085115 448 | TSLA,2016-09-16,205.4,205.7,199.0,200.42,3107808 449 | TSLA,2016-09-19,206.34,209.43,205.0,207.0,2299498 450 | TSLA,2016-09-20,204.64,207.75,203.91,206.85,2410488 451 | TSLA,2016-09-21,205.22,207.0,201.56,206.37,2633503 452 | TSLA,2016-09-22,206.43,207.28,203.0,206.4,2382902 453 | TSLA,2016-09-23,207.45,210.18,205.67,205.99,2905229 454 | TSLA,2016-09-26,208.99,211.0,206.5,206.5,2394358 455 | TSLA,2016-09-27,205.81,209.9818,204.6093,209.65,3373180 456 | TSLA,2016-09-28,206.27,208.25,205.26,207.51,2088374 457 | TSLA,2016-09-29,200.7,207.33,200.58,205.6,2727029 458 | TSLA,2016-09-30,204.03,204.98,199.55,202.21,2586273 459 | TSLA,2016-10-03,213.7,215.6688,208.25,212.3,5999892 460 | TSLA,2016-10-04,211.41,213.32,208.82,213.1,3541481 461 | TSLA,2016-10-05,208.46,213.15,208.12,212.24,1877534 462 | TSLA,2016-10-06,201.0,204.2099,200.21,202.46,4703402 463 | TSLA,2016-10-07,196.61,201.32,195.8,201.0,3493018 464 | TSLA,2016-10-10,200.95,204.14,199.66,201.35,3316297 465 | TSLA,2016-10-11,200.1,202.2,198.31,201.85,2328422 466 | TSLA,2016-10-12,201.51,203.88,200.42,200.95,1970689 467 | TSLA,2016-10-13,200.24,200.895,197.05,200.5,2495413 468 | TSLA,2016-10-14,196.51,201.43,196.3,200.66,4269850 469 | TSLA,2016-10-17,193.96,198.39,192.0,197.05,4554080 470 | TSLA,2016-10-18,199.1,199.47,193.26,195.99,5680475 471 | TSLA,2016-10-19,203.56,206.66,198.06,199.74,6991183 472 | TSLA,2016-10-20,199.1,203.0,197.05,202.12,5072877 473 | TSLA,2016-10-21,200.09,201.57,197.41,198.6,2943402 474 | TSLA,2016-10-24,202.76,203.9452,200.25,201.0,2751562 475 | TSLA,2016-10-25,202.34,204.69,201.2,202.9,2445014 476 | TSLA,2016-10-26,202.24,203.19,200.1,201.0,5632841 477 | TSLA,2016-10-27,204.01,213.7,201.65,211.34,13093744 478 | TSLA,2016-10-28,199.97,205.32,199.83,204.0,4280141 479 | TSLA,2016-10-31,197.73,202.49,195.81,202.49,4692273 480 | TSLA,2016-11-01,190.79,198.5,188.105,198.04,7060036 481 | TSLA,2016-11-02,188.02,192.6951,187.505,190.05,4253382 482 | TSLA,2016-11-03,187.42,191.47,187.0401,189.0,2653023 483 | TSLA,2016-11-04,190.56,193.46,185.96,189.0,5146043 484 | TSLA,2016-11-07,193.21,194.29,190.05,193.59,3870112 485 | TSLA,2016-11-08,194.94,197.49,191.26,193.79,3267580 486 | TSLA,2016-11-09,190.06,192.0,183.95,186.875,8173064 487 | TSLA,2016-11-10,185.35,191.61,180.42,191.05,6750341 488 | TSLA,2016-11-11,188.56,188.88,183.0,184.24,3988504 489 | TSLA,2016-11-14,181.45,188.25,178.19,188.0,6552205 490 | TSLA,2016-11-15,183.77,186.43,182.05,182.78,3902018 491 | TSLA,2016-11-16,183.93,184.73,181.21,182.65,3434437 492 | TSLA,2016-11-17,188.66,189.49,182.1101,183.49,4887067 493 | TSLA,2016-11-18,185.02,193.0,185.0,190.65,5210347 494 | TSLA,2016-11-21,184.52,188.89,184.41,185.04,4361043 495 | TSLA,2016-11-22,191.17,191.47,183.71,185.84,5603361 496 | TSLA,2016-11-23,193.14,195.644,189.0,190.61,4891893 497 | TSLA,2016-11-24,193.14,193.14,193.14,193.14,0 498 | TSLA,2016-11-25,196.65,197.2372,193.64,193.64,2366098 499 | TSLA,2016-11-28,196.12,199.35,194.55,195.48,4529182 500 | TSLA,2016-11-29,189.57,196.73,189.5,195.56,4439256 501 | TSLA,2016-11-30,189.4,191.89,187.5,191.0,3547104 502 | TSLA,2016-12-01,181.88,188.53,181.0,188.25,5126401 503 | TSLA,2016-12-02,181.47,184.88,180.0,182.88,4042324 504 | TSLA,2016-12-05,186.8,188.89,182.51,182.51,4072238 505 | TSLA,2016-12-06,185.85,186.58,182.6825,185.52,3391622 506 | TSLA,2016-12-07,193.15,193.4,185.0,186.15,5461851 507 | TSLA,2016-12-08,192.29,192.5,189.54,192.05,3194148 508 | TSLA,2016-12-09,192.18,193.836,190.81,190.87,2722505 509 | TSLA,2016-12-12,192.43,194.42,191.04,192.8,2438876 510 | TSLA,2016-12-13,198.15,201.28,193.0,193.18,6823884 511 | TSLA,2016-12-14,198.69,203.0,196.76,198.74,4150927 512 | TSLA,2016-12-15,197.58,200.74,197.39,198.41,3219567 513 | TSLA,2016-12-16,202.49,202.59,197.6,198.08,3796889 514 | TSLA,2016-12-19,202.73,204.45,199.84,202.49,3488071 515 | TSLA,2016-12-20,208.79,209.0,202.5,203.05,4689071 516 | TSLA,2016-12-21,207.7,212.23,207.41,208.45,5207622 517 | TSLA,2016-12-22,208.45,209.99,206.5,208.22,3111108 518 | TSLA,2016-12-23,213.34,213.45,207.71,208.0,4670464 519 | TSLA,2016-12-26,213.34,213.34,213.34,213.34,0 520 | TSLA,2016-12-27,219.53,222.25,214.42,214.88,5915732 521 | TSLA,2016-12-28,219.74,223.8,217.2,221.53,3782456 522 | TSLA,2016-12-29,214.68,219.2,214.1225,218.56,4044968 523 | TSLA,2016-12-30,213.69,217.5,211.68,216.3,4642620 524 | TSLA,2017-01-02,213.69,213.69,213.69,213.69,0 525 | TSLA,2017-01-03,216.99,220.33,210.96,214.86,5923254 526 | TSLA,2017-01-04,226.99,228.0,214.31,214.75,11213471 527 | TSLA,2017-01-05,226.75,227.48,221.95,226.42,5911695 528 | TSLA,2017-01-06,229.01,230.31,225.45,226.93,5527893 529 | TSLA,2017-01-09,231.28,231.92,228.0,228.97,3979484 530 | TSLA,2017-01-10,229.87,232.0,226.89,232.0,3659955 531 | TSLA,2017-01-11,229.73,229.98,226.68,229.07,3650825 532 | TSLA,2017-01-12,229.59,230.7,225.58,229.06,3790229 533 | TSLA,2017-01-13,237.75,237.85,229.59,230.0,6092960 534 | TSLA,2017-01-16,237.75,237.75,237.75,237.75,0 535 | TSLA,2017-01-17,235.58,239.96,234.37,236.7,4617522 536 | TSLA,2017-01-18,238.36,239.71,235.58,236.65,3768967 537 | TSLA,2017-01-19,243.76,248.68,240.75,247.25,7732303 538 | TSLA,2017-01-20,244.73,246.0,243.01,245.46,4204275 539 | TSLA,2017-01-23,248.92,250.8899,245.5,245.85,6262938 540 | TSLA,2017-01-24,254.61,254.8,249.65,250.0,4965451 541 | TSLA,2017-01-25,254.47,258.46,251.8,257.31,5146361 542 | TSLA,2017-01-26,252.51,255.74,250.75,254.29,3152123 543 | TSLA,2017-01-27,252.95,253.0,248.52,251.38,3166336 544 | TSLA,2017-01-30,250.63,255.2899,247.1,252.53,3801074 545 | TSLA,2017-01-31,251.93,255.89,247.7,249.24,4116104 546 | TSLA,2017-02-01,249.24,253.2,249.05,253.05,3958829 547 | TSLA,2017-02-02,251.55,252.42,247.71,248.34,2499775 548 | TSLA,2017-02-03,251.33,252.179,249.68,251.91,2186723 549 | TSLA,2017-02-06,257.77,257.82,250.63,251.0,3562517 550 | TSLA,2017-02-07,257.48,260.0,256.42,258.19,4244775 551 | TSLA,2017-02-08,262.08,263.36,256.2,257.35,3933014 552 | TSLA,2017-02-09,269.2,271.18,266.15,266.25,7820222 553 | TSLA,2017-02-10,269.23,270.95,266.11,269.79,3619739 554 | TSLA,2017-02-13,280.6,280.7899,270.51,270.74,7029605 555 | TSLA,2017-02-14,280.98,287.39,278.61,279.03,7345224 556 | TSLA,2017-02-15,279.76,282.24,276.44,280.0,4947856 557 | TSLA,2017-02-16,268.95,280.0,268.5,277.6,7077322 558 | TSLA,2017-02-17,272.23,272.89,264.15,265.8,6257149 559 | TSLA,2017-02-20,272.23,272.23,272.23,272.23,0 560 | TSLA,2017-02-21,277.39,281.4,274.01,275.45,5676747 561 | TSLA,2017-02-22,273.51,283.45,272.6,280.31,8754975 562 | TSLA,2017-02-23,255.99,264.66,255.56,264.0,14915249 563 | TSLA,2017-02-24,257.0,258.25,250.2,252.66,8171626 564 | TSLA,2017-02-27,246.23,248.36,242.01,248.17,11460810 565 | TSLA,2017-02-28,249.99,251.0,243.9,244.19,6078145 566 | TSLA,2017-03-01,250.02,254.85,249.11,254.18,4809488 567 | TSLA,2017-03-02,250.48,253.28,248.27,249.71,3351833 568 | TSLA,2017-03-03,251.57,251.9,249.0,250.74,2929234 569 | TSLA,2017-03-06,251.21,251.7,247.51,247.91,3355500 570 | TSLA,2017-03-07,248.59,253.89,248.32,251.92,3459470 571 | TSLA,2017-03-08,246.87,250.07,245.32,247.0,3728649 572 | TSLA,2017-03-09,244.9,248.66,243.0,247.63,3879293 573 | TSLA,2017-03-10,243.69,246.5,243.0,246.21,3066272 574 | TSLA,2017-03-13,246.17,246.85,242.781,244.39,3022625 575 | TSLA,2017-03-14,258.0,258.12,246.02,246.11,7598446 576 | TSLA,2017-03-15,255.73,261.0,254.27,257.0,5330806 577 | TSLA,2017-03-16,262.05,265.75,259.06,262.4,7132153 578 | TSLA,2017-03-17,261.5,265.33,261.2,264.0,6497496 579 | TSLA,2017-03-20,261.92,264.55,258.821,260.6,3614294 580 | TSLA,2017-03-21,250.68,264.8,250.24,262.83,6908554 581 | TSLA,2017-03-22,255.01,255.07,250.51,251.56,4059297 582 | TSLA,2017-03-23,254.78,257.672,253.3,255.39,3320245 583 | TSLA,2017-03-24,263.16,263.89,255.01,255.7,5647253 584 | TSLA,2017-03-27,270.22,270.57,259.75,260.6,6230795 585 | TSLA,2017-03-28,277.45,280.68,275.0,277.02,7987604 586 | TSLA,2017-03-29,277.38,279.6,275.54,278.34,3676157 587 | TSLA,2017-03-30,277.92,282.0,277.21,278.04,4148425 588 | TSLA,2017-03-31,278.3,279.68,276.3197,278.73,3294640 589 | TSLA,2017-04-03,298.52,299.0,284.58,286.9,13888618 590 | TSLA,2017-04-04,303.7,304.81,294.53,296.89,10134556 591 | TSLA,2017-04-05,295.0,304.88,294.2,302.04,7880938 592 | TSLA,2017-04-06,298.7,301.94,294.1,296.88,5520588 593 | TSLA,2017-04-07,302.54,302.69,297.15,297.5,4579613 594 | TSLA,2017-04-10,312.39,313.7299,308.71,309.15,7664458 595 | TSLA,2017-04-11,308.71,313.47,305.5,313.38,5724577 596 | TSLA,2017-04-12,296.84,308.4481,296.32,306.34,6050682 597 | TSLA,2017-04-13,304.0,307.39,295.3,296.7,9284634 598 | TSLA,2017-04-14,304.0,304.0,304.0,304.0,0 599 | TSLA,2017-04-17,301.44,304.0,298.68,302.7,4138735 600 | TSLA,2017-04-18,300.25,300.8399,297.9,299.7,3035698 601 | TSLA,2017-04-19,305.52,306.62,302.11,302.46,3898024 602 | TSLA,2017-04-20,302.51,309.15,300.23,306.51,6149352 603 | TSLA,2017-04-21,305.6,306.4,300.42,302.0,4509756 604 | TSLA,2017-04-24,308.03,310.55,306.0215,309.22,5083505 605 | TSLA,2017-04-25,313.79,313.98,305.86,308.0,6735659 606 | TSLA,2017-04-26,310.17,314.5,309.0,312.37,4695044 607 | TSLA,2017-04-27,308.63,313.09,307.5,311.69,3468569 608 | TSLA,2017-04-28,314.07,314.8,308.0,309.83,4505478 609 | TSLA,2017-05-01,322.83,327.25,314.81,314.88,8829565 610 | TSLA,2017-05-02,318.89,327.6599,316.5601,324.0,5382777 611 | TSLA,2017-05-03,311.02,321.53,310.45,317.67,7133365 612 | TSLA,2017-05-04,295.46,307.77,290.7601,307.435,14152008 613 | TSLA,2017-05-05,308.35,308.55,296.8,298.0,8177346 614 | TSLA,2017-05-08,307.19,313.79,305.82,310.9,7006471 615 | TSLA,2017-05-09,321.26,321.99,309.1,309.38,9676537 616 | TSLA,2017-05-10,325.22,325.5,318.12,321.56,5741607 617 | TSLA,2017-05-11,323.1,326.0,319.6,323.4,4753819 618 | TSLA,2017-05-12,324.81,327.0,321.53,325.48,4121612 619 | TSLA,2017-05-15,315.88,320.2,312.53,318.38,7622004 620 | TSLA,2017-05-16,317.01,320.06,315.14,317.59,4152484 621 | TSLA,2017-05-17,306.11,314.63,305.5,314.39,6711940 622 | TSLA,2017-05-18,313.06,313.94,305.31,307.0,5653801 623 | TSLA,2017-05-19,310.83,316.5,310.2,315.5,4687572 624 | TSLA,2017-05-22,310.35,314.37,306.8,312.8,4329178 625 | TSLA,2017-05-23,303.86,310.73,303.48,310.46,4318354 626 | TSLA,2017-05-24,310.22,311.0,305.4,306.51,5041692 627 | TSLA,2017-05-25,316.83,316.97,307.81,311.02,5013963 628 | TSLA,2017-05-26,325.14,325.49,316.31,317.28,7802199 629 | TSLA,2017-05-29,325.14,325.14,325.14,325.14,0 630 | TSLA,2017-05-30,335.1,336.28,325.76,326.0,7782916 631 | TSLA,2017-05-31,341.01,342.89,335.16,337.69,9963444 632 | TSLA,2017-06-01,340.37,344.88,337.29,344.0,7607996 633 | TSLA,2017-06-02,339.85,342.88,335.93,339.77,5590239 634 | TSLA,2017-06-05,347.32,348.44,334.21,338.5,6784368 635 | TSLA,2017-06-06,352.85,359.4929,339.97,344.7,11086798 636 | TSLA,2017-06-07,359.65,360.5,355.14,356.34,9397959 637 | TSLA,2017-06-08,370.0,371.9,360.22,363.75,9061496 638 | TSLA,2017-06-09,357.32,376.87,354.8,374.42,17261435 639 | TSLA,2017-06-12,359.01,364.5,350.62,357.99,10517660 640 | TSLA,2017-06-13,375.95,376.0,366.61,367.62,11807920 641 | TSLA,2017-06-14,380.66,384.25,376.31,381.085,12818429 642 | TSLA,2017-06-15,375.34,375.46,366.49,372.5,10426469 643 | TSLA,2017-06-16,371.4,378.01,370.1,377.975,6730973 644 | TSLA,2017-06-19,369.8,376.7,367.8,375.0,6549332 645 | TSLA,2017-06-20,372.24,378.88,369.73,376.67,7438701 646 | TSLA,2017-06-21,376.4,376.99,368.02,374.35,4923210 647 | TSLA,2017-06-22,382.61,385.0,373.57,377.99,7529778 648 | TSLA,2017-06-23,383.45,386.99,379.345,382.45,6445758 649 | TSLA,2017-06-26,377.49,386.95,373.1,386.69,6604099 650 | TSLA,2017-06-27,362.37,376.4,362.02,376.4,6996399 651 | TSLA,2017-06-28,371.24,371.74,362.52,366.68,6302463 652 | TSLA,2017-06-29,360.75,371.0,354.1,370.61,8221038 653 | TSLA,2017-06-30,361.61,366.7674,359.6187,363.71,5848521 654 | TSLA,2017-07-03,352.62,371.35,351.5,370.24,6305401 655 | TSLA,2017-07-04,352.62,352.62,352.62,352.62,0 656 | TSLA,2017-07-05,327.09,347.24,326.33,347.2,17046701 657 | TSLA,2017-07-06,308.83,320.7899,306.3,317.26,19324495 658 | TSLA,2017-07-07,313.22,317.0,307.38,313.5,14176915 659 | TSLA,2017-07-10,316.05,317.94,303.13,312.9,13820889 660 | TSLA,2017-07-11,327.22,327.28,314.3,316.0,11559402 661 | TSLA,2017-07-12,329.52,333.1,324.5,330.4,10346127 662 | TSLA,2017-07-13,323.41,331.6,319.97,330.11,8594466 663 | TSLA,2017-07-14,327.78,328.42,321.22,323.19,5625211 664 | TSLA,2017-07-17,319.57,327.1,313.45,325.54,9876912 665 | TSLA,2017-07-18,328.24,329.13,315.66,317.5,6373720 666 | TSLA,2017-07-19,325.26,331.65,323.2193,328.23,6357014 667 | TSLA,2017-07-20,329.92,330.22,324.2,326.9,5166188 668 | TSLA,2017-07-21,328.4,331.2575,325.8,329.46,4901606 669 | TSLA,2017-07-24,342.52,343.399,330.01,330.24,8637082 670 | TSLA,2017-07-25,339.6,345.6,334.15,345.0,6989197 671 | TSLA,2017-07-26,343.85,345.5,338.12,340.36,4820790 672 | TSLA,2017-07-27,334.46,347.5,326.29,346.0,8302405 673 | TSLA,2017-07-28,335.07,339.6,332.51,336.89,4880414 674 | TSLA,2017-07-31,323.47,341.49,321.04,335.5,8535136 675 | TSLA,2017-08-01,319.57,324.45,316.13,323.0,8303102 676 | TSLA,2017-08-02,325.89,327.12,311.22,318.94,13091462 677 | TSLA,2017-08-03,347.09,350.0,343.15,345.33,13535033 678 | TSLA,2017-08-04,356.91,357.27,343.3,347.0,9268909 679 | TSLA,2017-08-07,355.17,359.48,352.75,357.35,6324480 680 | TSLA,2017-08-08,365.22,368.58,357.4,357.53,7449837 681 | TSLA,2017-08-09,363.53,370.0,358.95,361.0,6892096 682 | TSLA,2017-08-10,355.4,366.6504,354.66,361.6,7092858 683 | TSLA,2017-08-11,357.87,361.26,353.62,356.97,4365783 684 | TSLA,2017-08-14,363.8,367.66,362.6,364.63,4519186 685 | TSLA,2017-08-15,362.33,365.49,359.37,365.2,3085088 686 | TSLA,2017-08-16,362.91,366.5,362.52,363.0,3413773 687 | TSLA,2017-08-17,351.92,363.3,351.59,361.21,5027660 688 | TSLA,2017-08-18,347.46,354.0,345.8,352.91,5408183 689 | TSLA,2017-08-21,337.86,345.82,331.85,345.82,6495424 690 | TSLA,2017-08-22,341.35,342.24,337.3725,341.13,4321966 691 | TSLA,2017-08-23,352.77,353.49,338.3041,338.99,4954504 692 | TSLA,2017-08-24,352.93,356.66,349.74,352.52,4584687 693 | TSLA,2017-08-25,348.05,355.69,347.3,354.24,3483956 694 | TSLA,2017-08-28,345.66,347.35,339.72,347.28,3763956 695 | TSLA,2017-08-29,347.36,349.05,338.75,339.48,4073674 696 | TSLA,2017-08-30,353.18,353.47,347.0,349.67,3412943 697 | TSLA,2017-08-31,355.9,358.44,352.82,353.55,4072795 698 | TSLA,2017-09-01,355.4,357.59,353.6902,356.12,3049546 699 | TSLA,2017-09-04,355.4,355.4,355.4,355.4,0 700 | TSLA,2017-09-05,349.59,355.49,345.89,353.8,3848382 701 | TSLA,2017-09-06,344.53,350.979,341.56,349.5,4091351 702 | TSLA,2017-09-07,350.61,352.48,343.45,345.98,4239213 703 | TSLA,2017-09-08,343.4,349.78,342.3,348.99,3263508 704 | TSLA,2017-09-11,363.69,363.71,350.0,351.35,7667055 705 | TSLA,2017-09-12,362.75,368.76,360.4,364.49,5972907 706 | TSLA,2017-09-13,366.23,368.07,359.59,363.82,4185231 707 | TSLA,2017-09-14,377.64,377.96,362.63,364.33,7202524 708 | TSLA,2017-09-15,379.81,380.0,372.7,374.51,5420496 709 | TSLA,2017-09-18,385.0,389.61,377.68,380.25,7187980 710 | TSLA,2017-09-19,375.1,382.39,373.57,380.0,6451886 711 | TSLA,2017-09-20,373.91,378.249,371.07,373.0,4919052 712 | TSLA,2017-09-21,366.48,376.83,364.51,374.9,4618190 713 | TSLA,2017-09-22,351.09,369.8999,350.88,366.49,8159418 714 | TSLA,2017-09-25,344.99,357.469,342.88,353.15,7605946 715 | TSLA,2017-09-26,345.25,351.24,340.9,350.93,7156274 716 | TSLA,2017-09-27,340.97,351.489,340.5,349.9,6060330 717 | TSLA,2017-09-28,339.6,342.75,335.4,339.88,5319617 718 | TSLA,2017-09-29,341.1,344.68,338.601,341.86,5107082 719 | TSLA,2017-10-02,341.53,343.7,335.51,342.52,5286774 720 | TSLA,2017-10-03,348.14,348.55,331.28,335.9,10153596 721 | TSLA,2017-10-04,355.01,358.62,349.6,351.25,8163543 722 | TSLA,2017-10-05,355.33,357.435,351.35,356.0,4171674 723 | TSLA,2017-10-06,356.88,360.0992,352.25,353.1,4297474 724 | TSLA,2017-10-09,342.94,351.75,342.67,349.65,7493654 725 | TSLA,2017-10-10,355.59,355.63,345.5305,346.8,6978495 726 | TSLA,2017-10-11,354.6,357.6,351.15,353.89,4500831 727 | TSLA,2017-10-12,355.68,359.78,352.64,352.95,4087048 728 | TSLA,2017-10-13,355.57,358.49,353.68,356.98,3540533 729 | TSLA,2017-10-16,350.6,354.48,347.16,353.76,5375486 730 | TSLA,2017-10-17,355.75,356.22,350.07,350.91,3293345 731 | TSLA,2017-10-18,359.65,363.0,354.13,355.97,4939074 732 | TSLA,2017-10-19,351.81,357.1465,348.2,355.56,5061843 733 | TSLA,2017-10-20,345.1,354.55,344.34,352.69,4930395 734 | TSLA,2017-10-23,337.02,349.95,336.25,349.88,5747346 735 | TSLA,2017-10-24,337.34,342.8,336.16,338.8,4491672 736 | TSLA,2017-10-25,325.84,337.5,323.56,336.7,8594073 737 | TSLA,2017-10-26,326.17,330.23,323.2,327.78,5023500 738 | TSLA,2017-10-27,320.87,324.59,316.66,319.75,6979704 739 | TSLA,2017-10-30,320.08,323.78,317.25,319.18,4254378 740 | TSLA,2017-10-31,331.53,331.95,320.18,320.23,5672347 741 | TSLA,2017-11-01,321.08,332.6089,320.26,332.25,8457336 742 | TSLA,2017-11-02,299.26,308.69,292.63,300.13,19791416 743 | TSLA,2017-11-03,306.09,306.25,295.13,299.5,8893974 744 | TSLA,2017-11-06,302.78,307.5,299.01,307.0,6486009 745 | TSLA,2017-11-07,306.05,306.5,300.03,301.02,5294274 746 | TSLA,2017-11-08,304.39,306.89,301.3001,305.5,4725271 747 | TSLA,2017-11-09,302.99,304.46,296.3,302.5,5447147 748 | TSLA,2017-11-10,302.99,308.36,301.85,302.5,4625429 749 | TSLA,2017-11-13,315.4,316.8,299.11,300.13,7584944 750 | TSLA,2017-11-14,308.7,316.35,306.9,315.0,5676076 751 | TSLA,2017-11-15,311.3,312.49,301.5,306.01,5978665 752 | TSLA,2017-11-16,312.5,318.14,311.3,313.99,5822073 753 | TSLA,2017-11-17,315.05,326.67,313.15,325.67,13735139 754 | TSLA,2017-11-20,308.74,315.5,304.75,313.79,8247650 755 | TSLA,2017-11-21,317.81,318.23,308.71,310.86,7261273 756 | TSLA,2017-11-22,312.6,317.42,311.84,316.77,4917636 757 | TSLA,2017-11-23,312.6,312.6,312.6,312.6,0 758 | TSLA,2017-11-24,315.55,316.41,311.0,313.79,3244065 759 | TSLA,2017-11-27,316.81,317.34,309.51,313.25,4555894 760 | TSLA,2017-11-28,317.55,320.0,313.92,316.36,4949491 761 | TSLA,2017-11-29,307.54,318.0,301.23,317.3,8767398 762 | TSLA,2017-11-30,308.85,310.7,304.54,308.56,4351587 763 | TSLA,2017-12-01,306.53,310.32,305.05,305.44,4292868 764 | TSLA,2017-12-04,305.2,308.265,300.61,306.5,5835140 765 | TSLA,2017-12-05,303.7,308.0,301.0,302.0,4646520 766 | TSLA,2017-12-06,313.26,313.39,300.0,300.1,7195341 767 | TSLA,2017-12-07,311.24,318.6341,311.05,312.0,4780597 768 | TSLA,2017-12-08,315.13,316.98,311.26,314.6,3468458 769 | TSLA,2017-12-11,328.91,329.01,313.75,314.63,7937981 770 | TSLA,2017-12-12,341.03,341.44,330.03,330.45,8733199 771 | TSLA,2017-12-13,339.03,344.22,336.5,340.93,6221461 772 | TSLA,2017-12-14,337.89,347.44,336.9,341.01,5799916 773 | TSLA,2017-12-15,343.45,343.9,335.76,342.04,6933199 774 | TSLA,2017-12-18,338.87,346.73,337.58,344.9,5476166 775 | TSLA,2017-12-19,331.1,341.4925,330.3,340.26,6824971 776 | TSLA,2017-12-20,328.98,333.1,325.04,332.69,5953800 777 | TSLA,2017-12-21,331.66,333.74,327.21,329.59,4385222 778 | TSLA,2017-12-22,325.2,330.9214,324.82,329.51,4215807 779 | TSLA,2017-12-25,325.2,325.2,325.2,325.2,0 780 | TSLA,2017-12-26,317.29,323.94,316.58,323.83,4378413 781 | TSLA,2017-12-27,311.64,317.68,310.75,316.0,4712111 782 | TSLA,2017-12-28,315.36,315.82,309.54,311.75,4316347 783 | TSLA,2017-12-29,311.35,316.41,310.0,316.18,3777155 784 | TSLA,2018-01-01,311.35,311.35,311.35,311.35,0 785 | TSLA,2018-01-02,320.53,322.1099,311.0,312.0,4352241 786 | TSLA,2018-01-03,317.25,325.25,315.55,321.0,4521527 787 | TSLA,2018-01-04,314.62,318.55,305.68,312.87,9946304 788 | TSLA,2018-01-05,316.58,317.24,312.0,316.62,4591180 789 | TSLA,2018-01-08,336.41,337.0199,315.5,316.0,9859435 790 | TSLA,2018-01-09,333.69,338.8,327.405,335.16,7146631 791 | TSLA,2018-01-10,334.8,337.0,330.0,332.2,4309926 792 | TSLA,2018-01-11,337.95,344.8099,333.26,335.24,6645484 793 | TSLA,2018-01-12,336.22,340.41,333.67,338.63,4825059 794 | TSLA,2018-01-15,336.22,336.22,336.22,336.22,0 795 | TSLA,2018-01-16,340.06,345.0,334.8,337.54,6474251 796 | TSLA,2018-01-17,347.16,349.0,339.75,340.47,7103505 797 | TSLA,2018-01-18,344.57,352.3,343.74,345.67,5685845 798 | TSLA,2018-01-19,350.02,350.5899,342.6,345.0,4888303 799 | TSLA,2018-01-22,351.56,357.83,349.2,349.4,6210360 800 | TSLA,2018-01-23,352.79,360.5,351.0,360.0,5465414 801 | TSLA,2018-01-24,345.89,354.75,343.52,354.58,5287478 802 | TSLA,2018-01-25,337.64,349.2,336.4,348.27,6740303 803 | TSLA,2018-01-26,342.85,344.0,335.71,341.5,4539356 804 | TSLA,2018-01-29,349.53,350.85,338.28,339.85,4747149 805 | TSLA,2018-01-30,345.82,348.27,342.17,345.14,4717700 806 | TSLA,2018-01-31,354.31,356.19,345.19,347.51,6214069 807 | TSLA,2018-02-01,349.25,359.66,348.63,351.0,4197687 808 | TSLA,2018-02-02,343.75,351.95,340.51,348.44,3704836 809 | TSLA,2018-02-05,333.13,344.47,333.0,337.97,4464147 810 | --------------------------------------------------------------------------------