├── .ipynb_checkpoints
    ├── ANN_CNN-checkpoint.ipynb
    ├── ANN_forecasting-checkpoint.ipynb
    ├── ARIMA-checkpoint.ipynb
    └── Project-checkpoint.ipynb
├── ANN_CNN.ipynb
├── ARIMA.ipynb
├── LSTM.ipynb
├── README.md
├── _config.yml
├── price.csv
└── result
    └── result.png


/LSTM.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "### Data Science Project: LSTM"
  8 |    ]
  9 |   },
 10 |   {
 11 |    "cell_type": "markdown",
 12 |    "metadata": {},
 13 |    "source": [
 14 |     "#### Setting up system"
 15 |    ]
 16 |   },
 17 |   {
 18 |    "cell_type": "code",
 19 |    "execution_count": 1,
 20 |    "metadata": {},
 21 |    "outputs": [],
 22 |    "source": [
 23 |     "import numpy\n",
 24 |     "import pandas\n",
 25 |     "import matplotlib.pyplot as plt\n",
 26 |     "import math\n",
 27 |     "from keras.models import Sequential\n",
 28 |     "from keras.layers import Dense\n",
 29 |     "from keras.layers import LSTM\n",
 30 |     "from sklearn.preprocessing import MinMaxScaler\n",
 31 |     "from sklearn.metrics import mean_squared_error"
 32 |    ]
 33 |   },
 34 |   {
 35 |    "cell_type": "code",
 36 |    "execution_count": 2,
 37 |    "metadata": {},
 38 |    "outputs": [],
 39 |    "source": [
 40 |     "# fix random seed for reproducibility\n",
 41 |     "numpy.random.seed(7)"
 42 |    ]
 43 |   },
 44 |   {
 45 |    "cell_type": "markdown",
 46 |    "metadata": {},
 47 |    "source": [
 48 |     "#### Loading and pre-processing data"
 49 |    ]
 50 |   },
 51 |   {
 52 |    "cell_type": "code",
 53 |    "execution_count": 7,
 54 |    "metadata": {},
 55 |    "outputs": [
 56 |     {
 57 |      "data": {
 58 |       "image/png": 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\n",
 59 |       "text/plain": [
 60 |        "<Figure size 648x288 with 1 Axes>"
 61 |       ]
 62 |      },
 63 |      "metadata": {
 64 |       "needs_background": "light"
 65 |      },
 66 |      "output_type": "display_data"
 67 |     }
 68 |    ],
 69 |    "source": [
 70 |     "# load the dataset\n",
 71 |     "dataframe = pandas.read_csv('price.csv', usecols=[2], engine='python')\n",
 72 |     "my_xticks = pandas.read_csv('price.csv', usecols=[1], engine='python')\n",
 73 |     "\n",
 74 |     "dataset = dataframe.values\n",
 75 |     "dataset = dataset.astype('float32')\n",
 76 |     "my_xticks = my_xticks.values\n",
 77 |     "my_xticks = my_xticks.astype('str')\n",
 78 |     "\n",
 79 |     "#print(plt.rcParams.get('figure.figsize'))\n",
 80 |     "fig_size = plt.rcParams[\"figure.figsize\"]\n",
 81 |     "fig_size[0] = 9\n",
 82 |     "fig_size[1] = 4\n",
 83 |     "plt.rcParams[\"figure.figsize\"] = fig_size\n",
 84 |     "\n",
 85 |     "plt.plot(dataset,label='Real data',lw=2)\n",
 86 |     "\n",
 87 |     "x = numpy.linspace(0,504,15)\n",
 88 |     "x = x.astype(int)\n",
 89 |     "my_xticks = [my_xticks[y] for y in x]\n",
 90 |     "plt.xticks(x, my_xticks)\n",
 91 |     "\n",
 92 |     "plt.xlabel('Month',fontsize=15)\n",
 93 |     "plt.ylabel('Average Price - $',fontsize=15)\n",
 94 |     "plt.title('Monthly Average Electricity Price in USA',fontsize=15)\n",
 95 |     "plt.grid(b=None, which='major', axis='both')\n",
 96 |     "plt.xlim([0,520])\n",
 97 |     "plt.xticks(fontsize=8)\n",
 98 |     "plt.yticks(fontsize=15)\n",
 99 |     "\n",
100 |     "plt.savefig('data.png', dpi=600)\n",
101 |     "plt.show()"
102 |    ]
103 |   },
104 |   {
105 |    "cell_type": "code",
106 |    "execution_count": 4,
107 |    "metadata": {},
108 |    "outputs": [],
109 |    "source": [
110 |     "# normalize the dataset\n",
111 |     "scaler = MinMaxScaler(feature_range=(0, 1))\n",
112 |     "dataset = scaler.fit_transform(dataset)"
113 |    ]
114 |   },
115 |   {
116 |    "cell_type": "code",
117 |    "execution_count": 5,
118 |    "metadata": {},
119 |    "outputs": [],
120 |    "source": [
121 |     "# split into train and test sets\n",
122 |     "train_size = int(len(dataset) * 0.794)\n",
123 |     "test_size = len(dataset) - train_size\n",
124 |     "train, test = dataset[0:train_size,:], dataset[train_size:len(dataset),:]"
125 |    ]
126 |   },
127 |   {
128 |    "cell_type": "code",
129 |    "execution_count": 6,
130 |    "metadata": {},
131 |    "outputs": [],
132 |    "source": [
133 |     "# convert an array of values into a dataset matrix\n",
134 |     "def create_dataset(dataset, look_back=1):\n",
135 |     "\tdataX, dataY = [], []\n",
136 |     "\tfor i in range(len(dataset)-look_back-1):\n",
137 |     "\t\ta = dataset[i:(i+look_back), 0]\n",
138 |     "\t\tdataX.append(a)\n",
139 |     "\t\tdataY.append(dataset[i + look_back, 0])\n",
140 |     "\treturn numpy.array(dataX), numpy.array(dataY)"
141 |    ]
142 |   },
143 |   {
144 |    "cell_type": "code",
145 |    "execution_count": 7,
146 |    "metadata": {},
147 |    "outputs": [],
148 |    "source": [
149 |     "# reshape into X=t and Y=t+1\n",
150 |     "look_back = 1\n",
151 |     "trainX, trainY = create_dataset(train, look_back)\n",
152 |     "testX, testY = create_dataset(test, look_back)"
153 |    ]
154 |   },
155 |   {
156 |    "cell_type": "code",
157 |    "execution_count": 8,
158 |    "metadata": {},
159 |    "outputs": [],
160 |    "source": [
161 |     "# reshape input to be [samples, time steps, features]\n",
162 |     "trainX = numpy.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1]))\n",
163 |     "testX = numpy.reshape(testX, (testX.shape[0], 1, testX.shape[1]))"
164 |    ]
165 |   },
166 |   {
167 |    "cell_type": "markdown",
168 |    "metadata": {},
169 |    "source": [
170 |     "#### Creating, training and testing the RNN/LSTM network"
171 |    ]
172 |   },
173 |   {
174 |    "cell_type": "code",
175 |    "execution_count": 9,
176 |    "metadata": {},
177 |    "outputs": [
178 |     {
179 |      "name": "stdout",
180 |      "output_type": "stream",
181 |      "text": [
182 |       "Epoch 1/10\n",
183 |       "418/418 - 1s - loss: 0.1118\n",
184 |       "Epoch 2/10\n",
185 |       "418/418 - 1s - loss: 0.0345\n",
186 |       "Epoch 3/10\n",
187 |       "418/418 - 1s - loss: 0.0209\n",
188 |       "Epoch 4/10\n",
189 |       "418/418 - 1s - loss: 0.0098\n",
190 |       "Epoch 5/10\n",
191 |       "418/418 - 1s - loss: 0.0040\n",
192 |       "Epoch 6/10\n",
193 |       "418/418 - 1s - loss: 0.0022\n",
194 |       "Epoch 7/10\n",
195 |       "418/418 - 1s - loss: 0.0020\n",
196 |       "Epoch 8/10\n",
197 |       "418/418 - 1s - loss: 0.0021\n",
198 |       "Epoch 9/10\n",
199 |       "418/418 - 1s - loss: 0.0020\n",
200 |       "Epoch 10/10\n",
201 |       "418/418 - 1s - loss: 0.0020\n"
202 |      ]
203 |     }
204 |    ],
205 |    "source": [
206 |     "# create and fit the LSTM network\n",
207 |     "model = Sequential()\n",
208 |     "model.add(LSTM(4, input_shape=(1, look_back)))\n",
209 |     "model.add(Dense(1))\n",
210 |     "model.compile(loss='mean_squared_error', optimizer='adam')\n",
211 |     "history=model.fit(trainX, trainY, epochs=10, batch_size=1, verbose=2)"
212 |    ]
213 |   },
214 |   {
215 |    "cell_type": "code",
216 |    "execution_count": 10,
217 |    "metadata": {},
218 |    "outputs": [],
219 |    "source": [
220 |     "# make predictions\n",
221 |     "trainPredict = model.predict(trainX)\n",
222 |     "testPredict = model.predict(testX)"
223 |    ]
224 |   },
225 |   {
226 |    "cell_type": "markdown",
227 |    "metadata": {},
228 |    "source": [
229 |     "#### Results visualization"
230 |    ]
231 |   },
232 |   {
233 |    "cell_type": "code",
234 |    "execution_count": 11,
235 |    "metadata": {},
236 |    "outputs": [
237 |     {
238 |      "name": "stdout",
239 |      "output_type": "stream",
240 |      "text": [
241 |       "Train Score: 0.3936 RMSE\n",
242 |       "Test Score: 0.2496 RMSE\n"
243 |      ]
244 |     }
245 |    ],
246 |    "source": [
247 |     "# invert predictions\n",
248 |     "trainPredict = scaler.inverse_transform(trainPredict)\n",
249 |     "trainY = scaler.inverse_transform([trainY])\n",
250 |     "\n",
251 |     "testPredict = scaler.inverse_transform(testPredict)\n",
252 |     "testY = scaler.inverse_transform([testY])\n",
253 |     "\n",
254 |     "# calculate root mean squared error\n",
255 |     "#trainScore = math.sqrt(mean_squared_error(trainY[0][0:-1], trainPredict[1:,0]))\n",
256 |     "#print('Train Score: %.4f RMSE' % (trainScore))\n",
257 |     "#testScore = math.sqrt(mean_squared_error(testY[0][0:-1],testPredict[1:,0]))\n",
258 |     "#print('Test Score: %.4f RMSE' % (testScore))\n",
259 |     "\n",
260 |     "# calculate root mean squared error\n",
261 |     "trainScore = math.sqrt(mean_squared_error(trainY[0], trainPredict))\n",
262 |     "print('Train Score: %.4f RMSE' % (trainScore))\n",
263 |     "testScore = math.sqrt(mean_squared_error(testY[0],testPredict))\n",
264 |     "print('Test Score: %.4f RMSE' % (testScore))"
265 |    ]
266 |   },
267 |   {
268 |    "cell_type": "code",
269 |    "execution_count": 12,
270 |    "metadata": {
271 |     "scrolled": true
272 |    },
273 |    "outputs": [
274 |     {
275 |      "data": {
276 |       "image/png": 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\n",
277 |       "text/plain": [
278 |        "<Figure size 504x360 with 1 Axes>"
279 |       ]
280 |      },
281 |      "metadata": {
282 |       "needs_background": "light"
283 |      },
284 |      "output_type": "display_data"
285 |     }
286 |    ],
287 |    "source": [
288 |     "# shift train predictions for plotting\n",
289 |     "trainPredictPlot = numpy.empty_like(dataset)\n",
290 |     "trainPredictPlot[:, :] = numpy.nan\n",
291 |     "trainPredictPlot[look_back:len(trainPredict)+look_back, :] = trainPredict\n",
292 |     "\n",
293 |     "# shift test predictions for plotting\n",
294 |     "testPredictPlot = numpy.empty_like(dataset)\n",
295 |     "testPredictPlot[:, :] = numpy.nan\n",
296 |     "testPredictPlot[len(trainPredict)+(look_back*2)+1:len(dataset)-1, :] = testPredict\n",
297 |     "\n",
298 |     "# plot baseline and predictions\n",
299 |     "\n",
300 |     "#print(plt.rcParams.get('figure.figsize'))\n",
301 |     "fig_size = plt.rcParams[\"figure.figsize\"]\n",
302 |     "fig_size[0] = 7\n",
303 |     "fig_size[1] = 5\n",
304 |     "plt.rcParams[\"figure.figsize\"] = fig_size\n",
305 |     "\n",
306 |     "plt.plot(scaler.inverse_transform(dataset),label='Real data',color='red',lw=2)\n",
307 |     "#plt.plot(trainPredictPlot,label='Train Data',color='green')\n",
308 |     "plt.plot(testPredictPlot,label='Test Data',color='blue',lw=2)\n",
309 |     "\n",
310 |     "#plt.xticks(x, my_xticks)\n",
311 |     "\n",
312 |     "plt.xlabel('Data Points',fontsize =15)\n",
313 |     "plt.ylabel('Cost - $',fontsize =15)\n",
314 |     "plt.title('Monthly Average Electricity Price in USA',fontsize =15)\n",
315 |     "plt.grid(b=None, which='major', axis='both')\n",
316 |     "plt.legend()\n",
317 |     "plt.xlim([0,520])\n",
318 |     "plt.xticks(fontsize=15)\n",
319 |     "plt.yticks(fontsize=15)\n",
320 |     "\n",
321 |     "plt.savefig('LSTM.png', dpi=600)\n",
322 |     "plt.show()"
323 |    ]
324 |   },
325 |   {
326 |    "cell_type": "code",
327 |    "execution_count": 16,
328 |    "metadata": {},
329 |    "outputs": [
330 |     {
331 |      "data": {
332 |       "image/png": 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\n",
333 |       "text/plain": [
334 |        "<Figure size 504x360 with 1 Axes>"
335 |       ]
336 |      },
337 |      "metadata": {
338 |       "needs_background": "light"
339 |      },
340 |      "output_type": "display_data"
341 |     }
342 |    ],
343 |    "source": [
344 |     "# plot baseline and predictions\n",
345 |     "\n",
346 |     "#print(plt.rcParams.get('figure.figsize'))\n",
347 |     "fig_size = plt.rcParams[\"figure.figsize\"]\n",
348 |     "fig_size[0] = 7\n",
349 |     "fig_size[1] = 5\n",
350 |     "plt.rcParams[\"figure.figsize\"] = fig_size\n",
351 |     "\n",
352 |     "OriginalPlot = scaler.inverse_transform(dataset)\n",
353 |     "\n",
354 |     "OriginalPlot_new = OriginalPlot[420:525,:]\n",
355 |     "trainPredictPlot_new = trainPredictPlot[420:525,:]\n",
356 |     "testPredictPlot_new = testPredictPlot[420:525,:]\n",
357 |     "\n",
358 |     "plt.plot(range(420,525),OriginalPlot_new,label='Real data',color='red',lw=2)\n",
359 |     "#plt.plot(trainPredictPlot_new,label='Train Data')\n",
360 |     "plt.plot(range(420,525),testPredictPlot_new,label='Test Data',color='blue',lw=2)\n",
361 |     "\n",
362 |     "#plt.xticks(x, my_xticks)\n",
363 |     "\n",
364 |     "plt.xlabel('Data Points',fontsize =15)\n",
365 |     "plt.ylabel('Cost - $',fontsize =15)\n",
366 |     "plt.title('Monthly Average Electricity Price in USA',fontsize =15)\n",
367 |     "plt.grid(b=None, which='major', axis='both')\n",
368 |     "plt.legend()\n",
369 |     "plt.xlim([420,500])\n",
370 |     "plt.xticks(fontsize=15)\n",
371 |     "plt.yticks(fontsize=15)\n",
372 |     "\n",
373 |     "plt.savefig('LSTM_Zoom.png', dpi=600)\n",
374 |     "plt.show()"
375 |    ]
376 |   },
377 |   {
378 |    "cell_type": "code",
379 |    "execution_count": 14,
380 |    "metadata": {},
381 |    "outputs": [
382 |     {
383 |      "data": {
384 |       "image/png": 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\n",
385 |       "text/plain": [
386 |        "<Figure size 504x360 with 1 Axes>"
387 |       ]
388 |      },
389 |      "metadata": {
390 |       "needs_background": "light"
391 |      },
392 |      "output_type": "display_data"
393 |     }
394 |    ],
395 |    "source": [
396 |     "labels = [\"loss\"]\n",
397 |     "for lab in labels:\n",
398 |     "    plt.plot(history.history[lab],color='red',lw=2)\n",
399 |     "\n",
400 |     "#print(plt.rcParams.get('figure.figsize'))\n",
401 |     "fig_size = plt.rcParams[\"figure.figsize\"]\n",
402 |     "fig_size[0] = 7\n",
403 |     "fig_size[1] = 5\n",
404 |     "plt.rcParams[\"figure.figsize\"] = fig_size\n",
405 |     "\n",
406 |     "plt.yscale(\"log\")\n",
407 |     "plt.xlabel('Epochs',fontsize =15)\n",
408 |     "plt.ylabel('Log(Train Loss)',fontsize =15)\n",
409 |     "plt.title('Model-Fitting Loss-Plot',fontsize =15)\n",
410 |     "plt.grid(b=None, which='major', axis='both')\n",
411 |     "\n",
412 |     "plt.xlim([0,9])\n",
413 |     "plt.xticks(fontsize=15)\n",
414 |     "plt.yticks(fontsize=15)\n",
415 |     "\n",
416 |     "plt.savefig('Loss.png', dpi=600)\n",
417 |     "plt.show()"
418 |    ]
419 |   },
420 |   {
421 |    "cell_type": "code",
422 |    "execution_count": 15,
423 |    "metadata": {},
424 |    "outputs": [],
425 |    "source": [
426 |     "import csv\n",
427 |     "with open('data.csv', 'w',newline='') as csvfile:  \n",
428 |     "    # creating a csv writer object  \n",
429 |     "    csvwriter = csv.writer(csvfile)\n",
430 |     "        \n",
431 |     "    # writing the data rows\n",
432 |     "    csvwriter.writerows(testPredictPlot) "
433 |    ]
434 |   },
435 |   {
436 |    "cell_type": "code",
437 |    "execution_count": null,
438 |    "metadata": {},
439 |    "outputs": [],
440 |    "source": []
441 |   }
442 |  ],
443 |  "metadata": {
444 |   "kernelspec": {
445 |    "display_name": "Python 3",
446 |    "language": "python",
447 |    "name": "python3"
448 |   },
449 |   "language_info": {
450 |    "codemirror_mode": {
451 |     "name": "ipython",
452 |     "version": 3
453 |    },
454 |    "file_extension": ".py",
455 |    "mimetype": "text/x-python",
456 |    "name": "python",
457 |    "nbconvert_exporter": "python",
458 |    "pygments_lexer": "ipython3",
459 |    "version": "3.7.9"
460 |   }
461 |  },
462 |  "nbformat": 4,
463 |  "nbformat_minor": 4
464 | }
465 | 


--------------------------------------------------------------------------------
/README.md:
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 1 | # Comparison of Machine Learning and Deep Learning Tools for Residential Electricity Cost Forecasting
 2 | Each of the files access training data from the file 'price.csv'. The data has already been normalized using _minmax_ scalar so you do not need to normalize it. 
 3 | 
 4 | # Result
 5 | ![Image](https://raw.githubusercontent.com/abodh/Electricity-cost-forecasting-using-machine-learning-and-deep-learning-models/master/result/result.png)
 6 | 
 7 | # Dependencies
 8 | Please make sure that you install and import the following packages
 9 | 1. `numpy`
10 | 2. `matplotlib`
11 | 3. `pandas`
12 | 4. `torch` (you need cudatoolkit if you are running it on GPUs)
13 | 5. `tensorflow`
14 | 6. `pdmarima`
15 | 
16 | # Authors
17 | This work is completed as a class project for the course CPT 575 Data Science by the following people:
18 | 1. Abodh Poudyal
19 | 2. Habib Wajid
20 | 3. Saugat Ghimire
21 | 


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/_config.yml:
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1 | theme: jekyll-theme-minimal


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/price.csv:
--------------------------------------------------------------------------------
  1 | ,,cost,load,fuel cost,cost_norm,load_norm,del_load_norm,fuel_norm,weather,,,,
  2 | 1,Jul-76,17.84,54029.33,4.32,0.73,0.1,0.1,0.02,1,,,min_cost,11.39
  3 | 2,Aug-76,16.83,57706.13,4.33,0.62,0.13,0.03,0.02,0,,,max_cost,20.23
  4 | 3,Sep-76,17.2,53771.2,4.3,0.66,0.1,-0.03,0.01,0,,,min_energy,41825
  5 | 4,Oct-76,17.56,44995.88,4.28,0.7,0.03,-0.07,0.01,0,,,max_energy,166890.83
  6 | 5,Nov-76,17.05,46986.38,4.27,0.64,0.04,0.01,0.01,1,,,min_fuel,4.03
  7 | 6,Dec-76,16.07,57111.76,4.25,0.53,0.12,0.08,0.01,1,,,max_fuel,22.89
  8 | 7,Jan-77,15.99,65757.53,4.28,0.52,0.19,0.07,0.01,1,,,,
  9 | 8,Feb-77,16.27,61838.61,4.32,0.55,0.16,-0.03,0.02,1,,,,
 10 | 9,Mar-77,17.5,51172.85,4.33,0.69,0.07,-0.09,0.02,0,,,LEGEND:,
 11 | 10,Apr-77,17.81,44667.74,4.31,0.73,0.02,-0.05,0.02,0,,,* cost: avg residential electricity cost (cents/MWH) ,
 12 | 11,May-77,18.19,41825,4.36,0.77,0,-0.02,0.02,1,,,* load: avg electrical energy consumption (Million Kw hrs),
 13 | 12,Jun-77,18.1,48728.71,4.37,0.76,0.06,0.06,0.02,1,,,* fuel cost: overall avg fuel cost ($/gallon),
 14 | 13,Jul-77,18.01,61377.5,4.33,0.75,0.16,0.1,0.02,1,,,"* includes: crude oil, gasoline, diesel, heat oil, natural gases",
 15 | 14,Aug-77,18.77,62692.04,4.34,0.84,0.17,0.01,0.02,0,,,* weather: summer and winter -> 1 otherwise 0,
 16 | 15,Sep-77,18.29,57629.73,4.3,0.78,0.13,-0.04,0.01,0,,,"Jan,Feb,Nov,Dec (winter) and May, June, July (summer)",
 17 | 16,Oct-77,18.2,49021.33,4.28,0.77,0.06,-0.07,0.01,0,,,,
 18 | 17,Nov-77,17.66,45241.4,4.25,0.71,0.03,-0.03,0.01,1,,,,
 19 | 18,Dec-77,16.74,55286.54,4.25,0.6,0.11,0.08,0.01,1,,,,
 20 | 19,Jan-78,16.22,65906,4.13,0.55,0.19,0.08,0.01,1,,,,
 21 | 20,Feb-78,16.14,64579,4.1,0.54,0.18,-0.01,0,1,,,,
 22 | 21,Mar-78,16.86,58781,4.09,0.62,0.14,-0.04,0,0,,,,
 23 | 22,Apr-78,17.54,47396,4.05,0.7,0.04,-0.1,0,0,,,,
 24 | 23,May-78,18.19,44012,4.04,0.77,0.02,-0.02,0,1,,,,
 25 | 24,Jun-78,18.05,50832,4.05,0.75,0.07,0.05,0,1,,,,
 26 | 25,Jul-78,17.91,61747,4.06,0.74,0.16,0.09,0,1,,,,
 27 | 26,Aug-78,17.8,63843,4.06,0.73,0.18,0.02,0,0,,,,
 28 | 27,Sep-78,17.64,61984,4.05,0.71,0.16,-0.02,0,0,,,,
 29 | 28,Oct-78,17.48,51108,4.03,0.69,0.07,-0.09,0,0,,,,
 30 | 29,Nov-78,16.99,47220,6.1,0.63,0.04,-0.03,0.11,1,,,,
 31 | 30,Dec-78,16.13,57058,6.16,0.54,0.12,0.08,0.11,1,,,,
 32 | 31,Jan-79,15.6,69939,8.54,0.48,0.22,0.1,0.24,1,,,,
 33 | 32,Feb-79,15.45,67842,8.72,0.46,0.21,-0.01,0.25,1,,,,
 34 | 33,Mar-79,16.04,59314,8.95,0.53,0.14,-0.07,0.26,0,,,,
 35 | 34,Apr-79,16.62,50079,9.31,0.59,0.07,-0.07,0.28,0,,,,
 36 | 35,May-79,17.16,45730,9.8,0.65,0.03,-0.04,0.31,1,,,,
 37 | 36,Jun-79,17.69,49556,10.57,0.71,0.06,0.03,0.35,1,,,,
 38 | 37,Jul-79,17.5,58606,11.09,0.69,0.13,0.07,0.37,1,,,,
 39 | 38,Aug-79,17.33,64808,11.49,0.67,0.18,0.05,0.4,0,,,,
 40 | 39,Sep-79,17.52,59703,11.77,0.69,0.14,-0.04,0.41,0,,,,
 41 | 40,Oct-79,17.33,49505,11.78,0.67,0.06,-0.08,0.41,0,,,,
 42 | 41,Nov-79,16.46,49617,11.96,0.57,0.06,0,0.42,1,,,,
 43 | 42,Dec-79,15.93,58120,12.27,0.51,0.13,0.07,0.44,1,,,,
 44 | 43,Jan-80,15.71,65841,12.66,0.49,0.19,0.06,0.46,1,,,,
 45 | 44,Feb-80,15.51,64514,13.12,0.47,0.18,-0.01,0.48,1,,,,
 46 | 45,Mar-80,15.95,60497,13.35,0.52,0.15,-0.03,0.49,0,,,,
 47 | 46,Apr-80,16.43,51749,13.28,0.57,0.08,-0.07,0.49,0,,,,
 48 | 47,May-80,17.23,45699,13.22,0.66,0.03,-0.05,0.49,1,,,,
 49 | 48,Jun-80,17.7,52267,13.15,0.71,0.08,0.05,0.48,1,,,,
 50 | 49,Jul-80,17.99,68611,13.12,0.75,0.21,0.13,0.48,1,,,,
 51 | 50,Aug-80,17.86,75020,12.99,0.73,0.27,0.06,0.48,0,,,,
 52 | 51,Sep-80,17.71,67969,12.86,0.72,0.21,-0.06,0.47,0,,,,
 53 | 52,Oct-80,17.54,54014,12.67,0.7,0.1,-0.11,0.46,0,,,,
 54 | 53,Nov-80,17.05,50539,12.71,0.64,0.07,-0.03,0.46,1,,,,
 55 | 54,Dec-80,16.59,60775,12.89,0.59,0.15,0.08,0.47,1,,,,
 56 | 55,Jan-81,16.14,74087.02,15.08,0.54,0.26,0.11,0.59,1,,,,
 57 | 56,Feb-81,16.29,66359.16,15.68,0.55,0.2,-0.06,0.62,1,,,,
 58 | 57,Mar-81,17.07,57660.33,15.83,0.64,0.13,-0.07,0.63,0,,,,
 59 | 58,Apr-81,17.55,50913.73,15.69,0.7,0.07,-0.06,0.62,0,,,,
 60 | 59,May-81,18.31,48347.74,15.52,0.78,0.05,-0.02,0.61,1,,,,
 61 | 60,Jun-81,18.72,56165.25,15.27,0.83,0.11,0.06,0.6,1,,,,
 62 | 61,Jul-81,18.8,69989.93,14.95,0.84,0.23,0.12,0.58,1,,,,
 63 | 62,Aug-81,18.66,70298.9,14.81,0.82,0.23,0,0.57,0,,,,
 64 | 63,Sep-81,18.48,61098.02,14.67,0.8,0.15,-0.08,0.56,0,,,,
 65 | 64,Oct-81,18.42,52988.68,14.6,0.8,0.09,-0.06,0.56,0,,,,
 66 | 65,Nov-81,17.79,51965,14.61,0.72,0.08,-0.01,0.56,1,,,,
 67 | 66,Dec-81,17.45,62391.28,14.6,0.69,0.16,0.08,0.56,1,,,,
 68 | 67,Jan-82,17.12,76263.76,14.44,0.65,0.28,0.12,0.55,1,,,,
 69 | 68,Feb-82,17.62,69127.77,14.26,0.7,0.22,-0.06,0.54,1,,,,
 70 | 69,Mar-82,18.17,60498.09,13.83,0.77,0.15,-0.07,0.52,0,,,,
 71 | 70,Apr-82,18.39,54917.77,13.41,0.79,0.1,-0.05,0.5,0,,,,
 72 | 71,May-82,18.76,49091.65,13.5,0.83,0.06,-0.04,0.5,1,,,,
 73 | 72,Jun-82,19.08,54082.56,13.8,0.87,0.1,0.04,0.52,1,,,,
 74 | 73,Jul-82,19.25,65703.81,13.76,0.89,0.19,0.09,0.52,1,,,,
 75 | 74,Aug-82,19.21,69905.83,13.63,0.88,0.22,0.03,0.51,0,,,,
 76 | 75,Sep-82,19.21,63052.99,13.66,0.88,0.17,-0.05,0.51,0,,,,
 77 | 76,Oct-82,19.13,52637.85,13.79,0.88,0.09,-0.08,0.52,0,,,,
 78 | 77,Nov-82,18.35,52135.61,13.89,0.79,0.08,-0.01,0.52,1,,,,
 79 | 78,Dec-82,17.88,62102.08,13.73,0.73,0.16,0.08,0.51,1,,,,
 80 | 79,Jan-83,17.84,69966.68,13.41,0.73,0.23,0.07,0.5,1,,,,
 81 | 80,Feb-83,17.82,65038.69,13.1,0.73,0.19,-0.04,0.48,1,,,,
 82 | 81,Mar-83,18.34,58911.75,12.65,0.79,0.14,-0.05,0.46,0,,,,
 83 | 82,Apr-83,18.21,56283.96,12.91,0.77,0.12,-0.02,0.47,0,,,,
 84 | 83,May-83,18.92,49669.17,13.08,0.85,0.06,-0.06,0.48,1,,,,
 85 | 84,Jun-83,19.41,54138.01,13.14,0.91,0.1,0.04,0.48,1,,,,
 86 | 85,Jul-83,19.59,69964.83,13.05,0.93,0.23,0.13,0.48,1,,,,
 87 | 86,Aug-83,19.53,78374.35,13.06,0.92,0.29,0.06,0.48,0,,,,
 88 | 87,Sep-83,19.73,73197.28,13.01,0.94,0.25,-0.04,0.48,0,,,,
 89 | 88,Oct-83,19.4,55373.8,13.08,0.91,0.11,-0.14,0.48,0,,,,
 90 | 89,Nov-83,18.82,53703.56,12.81,0.84,0.09,-0.02,0.47,1,,,,
 91 | 90,Dec-83,18,66326.16,12.6,0.75,0.2,0.11,0.45,1,,,,
 92 | 91,Jan-84,17.36,83556.44,12.57,0.68,0.33,0.13,0.45,1,,,,
 93 | 92,Feb-84,17.79,70036.46,12.77,0.72,0.23,-0.1,0.46,1,,,,
 94 | 93,Mar-84,18.24,63855.26,12.55,0.77,0.18,-0.05,0.45,0,,,,
 95 | 94,Apr-84,18.42,56550.06,12.54,0.8,0.12,-0.06,0.45,0,,,,
 96 | 95,May-84,19.14,53687.24,12.64,0.88,0.09,-0.03,0.46,1,,,,
 97 | 96,Jun-84,19.86,60143.17,12.72,0.96,0.15,0.06,0.46,1,,,,
 98 | 97,Jul-84,20.03,71242.37,12.67,0.98,0.24,0.09,0.46,1,,,,
 99 | 98,Aug-84,20.23,73367.33,12.71,1,0.25,0.01,0.46,0,,,,
100 | 99,Sep-84,20.17,67667.18,12.65,0.99,0.21,-0.04,0.46,0,,,,
101 | 100,Oct-84,19.84,56140.25,12.51,0.96,0.11,-0.1,0.45,0,,,,
102 | 101,Nov-84,18.82,56720.55,12.29,0.84,0.12,0.01,0.44,1,,,,
103 | 102,Dec-84,18.04,67125.36,12.07,0.75,0.2,0.08,0.43,1,,,,
104 | 103,Jan-85,18,77530.26,11.84,0.75,0.29,0.09,0.41,1,,,,
105 | 104,Feb-85,17.66,78302.71,11.64,0.71,0.29,0,0.4,1,,,,
106 | 105,Mar-85,18.31,64219.97,11.71,0.78,0.18,-0.11,0.41,0,,,,
107 | 106,Apr-85,18.76,56234.7,11.94,0.83,0.12,-0.06,0.42,0,,,,
108 | 107,May-85,19.45,53039.27,12.13,0.91,0.09,-0.03,0.43,1,,,,
109 | 108,Jun-85,19.89,60878.73,12.13,0.96,0.15,0.06,0.43,1,,,,
110 | 109,Jul-85,19.85,71231.59,12.01,0.96,0.24,0.09,0.42,1,,,,
111 | 110,Aug-85,19.81,73968.62,11.97,0.95,0.26,0.02,0.42,0,,,,
112 | 111,Sep-85,19.77,71329.27,11.96,0.95,0.24,-0.02,0.42,0,,,,
113 | 112,Oct-85,19.46,57730.43,11.84,0.91,0.13,-0.11,0.41,0,,,,
114 | 113,Nov-85,18.42,57006.61,11.9,0.79,0.12,-0.01,0.42,1,,,,
115 | 114,Dec-85,17.62,72461.69,11.71,0.7,0.24,0.12,0.41,1,,,,
116 | 115,Jan-86,16.41,82899.67,11.39,0.57,0.33,0.09,0.39,1,,,,
117 | 116,Feb-86,16.97,71072.22,10.25,0.63,0.23,-0.1,0.33,1,,,,
118 | 117,Mar-86,17.25,65431.44,9.34,0.66,0.19,-0.04,0.28,0,,,,
119 | 118,Apr-86,17.79,56745.73,8.94,0.72,0.12,-0.07,0.26,0,,,,
120 | 119,May-86,17.91,54360.15,8.96,0.74,0.1,-0.02,0.26,1,,,,
121 | 120,Jun-86,18.37,64097.88,8.95,0.79,0.18,0.08,0.26,1,,,,
122 | 121,Jul-86,18.45,80505.5,8.48,0.8,0.31,0.13,0.24,1,,,,
123 | 122,Aug-86,18.31,80564.74,8.46,0.78,0.31,0,0.23,0,,,,
124 | 123,Sep-86,18.27,68662.42,8.54,0.78,0.21,-0.1,0.24,0,,,,
125 | 124,Oct-86,17.65,62984.78,8.27,0.71,0.17,-0.04,0.22,0,,,,
126 | 125,Nov-86,17.47,58691.57,8.07,0.69,0.13,-0.04,0.21,1,,,,
127 | 126,Dec-86,16.49,73072.24,8.02,0.58,0.25,0.12,0.21,1,,,,
128 | 127,Jan-87,16.22,82209.16,8.49,0.55,0.32,0.07,0.24,1,,,,
129 | 128,Feb-87,16.21,73503.45,8.7,0.54,0.25,-0.07,0.25,1,,,,
130 | 129,Mar-87,16.59,67433,8.69,0.59,0.2,-0.05,0.25,0,,,,
131 | 130,Apr-87,16.79,60070.34,8.8,0.61,0.15,-0.05,0.25,0,,,,
132 | 131,May-87,17.23,58553.7,8.99,0.66,0.13,-0.02,0.26,1,,,,
133 | 132,Jun-87,17.92,68923.48,9.21,0.74,0.22,0.09,0.27,1,,,,
134 | 133,Jul-87,17.87,83829.45,9.37,0.73,0.34,0.12,0.28,1,,,,
135 | 134,Aug-87,17.7,88242.63,9.45,0.71,0.37,0.03,0.29,0,,,,
136 | 135,Sep-87,17.41,73508.1,9.33,0.68,0.25,-0.12,0.28,0,,,,
137 | 136,Oct-87,17.3,60905.1,9.06,0.67,0.15,-0.1,0.27,0,,,,
138 | 137,Nov-87,16.69,60064.04,8.96,0.6,0.15,0,0.26,1,,,,
139 | 138,Dec-87,15.99,73167.83,8.74,0.52,0.25,0.1,0.25,1,,,,
140 | 139,Jan-88,15.55,89582.03,8.49,0.47,0.38,0.13,0.24,1,,,,
141 | 140,Feb-88,15.68,80299.02,8.38,0.49,0.31,-0.07,0.23,1,,,,
142 | 141,Mar-88,15.98,71465.58,8.31,0.52,0.24,-0.07,0.23,0,,,,
143 | 142,Apr-88,16.24,61440.53,8.45,0.55,0.16,-0.08,0.23,0,,,,
144 | 143,May-88,16.82,57617.13,8.66,0.61,0.13,-0.03,0.25,1,,,,
145 | 144,Jun-88,17.32,68831.72,8.71,0.67,0.22,0.09,0.25,1,,,,
146 | 145,Jul-88,17.38,87079.54,8.63,0.68,0.36,0.14,0.24,1,,,,
147 | 146,Aug-88,17.37,94285.1,8.63,0.68,0.42,0.06,0.24,0,,,,
148 | 147,Sep-88,17.1,77594.95,8.48,0.65,0.29,-0.13,0.24,0,,,,
149 | 148,Oct-88,16.74,63813.79,8.05,0.61,0.18,-0.11,0.21,0,,,,
150 | 149,Nov-88,16.17,63681.6,7.9,0.54,0.17,-0.01,0.2,1,,,,
151 | 150,Dec-88,15.72,77175.16,7.96,0.49,0.28,0.11,0.21,1,,,,
152 | 151,Jan-89,15.42,85220.81,8.18,0.46,0.35,0.07,0.22,1,,,,
153 | 152,Feb-89,15.39,78291.68,8.21,0.45,0.29,-0.06,0.22,1,,,,
154 | 153,Mar-89,15.45,77347.33,8.34,0.46,0.28,-0.01,0.23,0,,,,
155 | 154,Apr-89,15.93,64808.54,8.8,0.51,0.18,-0.1,0.25,0,,,,
156 | 155,May-89,16.27,61212.29,8.89,0.55,0.16,-0.02,0.26,1,,,,
157 | 156,Jun-89,16.85,71797.89,8.88,0.62,0.24,0.08,0.26,1,,,,
158 | 157,Jul-89,16.96,85741.93,8.85,0.63,0.35,0.11,0.26,1,,,,
159 | 158,Aug-89,16.98,86290.42,8.75,0.63,0.36,0.01,0.25,0,,,,
160 | 159,Sep-89,16.75,78859.57,8.71,0.61,0.3,-0.06,0.25,0,,,,
161 | 160,Oct-89,16.36,65247.76,8.62,0.56,0.19,-0.11,0.24,0,,,,
162 | 161,Nov-89,15.57,64955.32,8.47,0.47,0.18,-0.01,0.24,1,,,,
163 | 162,Dec-89,15.01,85751.1,8.73,0.41,0.35,0.17,0.25,1,,,,
164 | 163,Jan-90,14.68,95453.21,9.58,0.37,0.43,0.08,0.29,1,,,,
165 | 164,Feb-90,15.25,74548.2,8.78,0.44,0.26,-0.17,0.25,1,,,,
166 | 165,Mar-90,15.37,71945.24,8.51,0.45,0.24,-0.02,0.24,0,,,,
167 | 166,Apr-90,15.57,65227.84,8.4,0.47,0.19,-0.05,0.23,0,,,,
168 | 167,May-90,16.11,62904.32,8.42,0.53,0.17,-0.02,0.23,1,,,,
169 | 168,Jun-90,16.3,73864.28,8.38,0.55,0.26,0.09,0.23,1,,,,
170 | 169,Jul-90,16.38,90839.78,8.49,0.56,0.39,0.13,0.24,1,,,,
171 | 170,Aug-90,16.36,88500.32,9.67,0.56,0.37,-0.02,0.3,0,,,,
172 | 171,Sep-90,16.09,86164.08,10.6,0.53,0.35,-0.02,0.35,0,,,,
173 | 172,Oct-90,15.75,69602.16,11.16,0.49,0.22,-0.13,0.38,0,,,,
174 | 173,Nov-90,15.25,66464.97,10.78,0.44,0.2,-0.02,0.36,1,,,,
175 | 174,Dec-90,14.8,78504.3,10.3,0.39,0.29,0.09,0.33,1,,,,
176 | 175,Jan-91,14.36,93909.29,9.63,0.34,0.42,0.13,0.3,1,,,,
177 | 176,Feb-91,14.72,79477.86,8.95,0.38,0.3,-0.12,0.26,1,,,,
178 | 177,Mar-91,15.07,73893.84,8.48,0.42,0.26,-0.04,0.24,0,,,,
179 | 178,Apr-91,15.42,65914.97,8.48,0.46,0.19,-0.07,0.24,0,,,,
180 | 179,May-91,15.67,67282.63,8.54,0.48,0.2,0.01,0.24,1,,,,
181 | 180,Jun-91,15.99,80914.28,8.71,0.52,0.31,0.11,0.25,1,,,,
182 | 181,Jul-91,16.08,94501.8,8.57,0.53,0.42,0.11,0.24,1,,,,
183 | 182,Aug-91,16.09,92895.01,8.72,0.53,0.41,-0.01,0.25,0,,,,
184 | 183,Sep-91,15.96,84485.35,8.66,0.52,0.34,-0.07,0.25,0,,,,
185 | 184,Oct-91,15.83,69249.49,8.55,0.5,0.22,-0.12,0.24,0,,,,
186 | 185,Nov-91,15.06,70937.29,8.49,0.42,0.23,0.01,0.24,1,,,,
187 | 186,Dec-91,14.73,81955.56,8.23,0.38,0.32,0.09,0.22,1,,,,
188 | 187,Jan-92,14.53,91495.33,7.92,0.36,0.4,0.08,0.21,1,,,,
189 | 188,Feb-92,14.65,82188.78,7.83,0.37,0.32,-0.08,0.2,1,,,,
190 | 189,Mar-92,15.03,73783.95,7.79,0.41,0.26,-0.06,0.2,0,,,,
191 | 190,Apr-92,15.05,68460.97,7.94,0.41,0.21,-0.05,0.21,0,,,,
192 | 191,May-92,15.69,64793.26,8.28,0.49,0.18,-0.03,0.23,1,,,,
193 | 192,Jun-92,16.08,70888.95,8.58,0.53,0.23,0.05,0.24,1,,,,
194 | 193,Jul-92,15.9,88689.55,8.64,0.51,0.37,0.14,0.24,1,,,,
195 | 194,Aug-92,15.92,88430.25,8.59,0.51,0.37,0,0.24,0,,,,
196 | 195,Sep-92,15.93,79560.94,8.52,0.51,0.3,-0.07,0.24,0,,,,
197 | 196,Oct-92,15.58,69979.89,8.4,0.47,0.23,-0.07,0.23,0,,,,
198 | 197,Nov-92,14.97,70111.59,8.2,0.4,0.23,0,0.22,1,,,,
199 | 198,Dec-92,14.42,87555.33,7.93,0.34,0.37,0.14,0.21,1,,,,
200 | 199,Jan-93,14.15,93777.5,7.83,0.31,0.42,0.05,0.2,1,,,,
201 | 200,Feb-93,14.23,83409.79,7.82,0.32,0.33,-0.09,0.2,1,,,,
202 | 201,Mar-93,14.21,83056.62,7.86,0.32,0.33,0,0.2,0,,,,
203 | 202,Apr-93,14.76,69696.72,7.98,0.38,0.22,-0.11,0.21,0,,,,
204 | 203,May-93,15.49,63877.45,8.13,0.46,0.18,-0.04,0.22,1,,,,
205 | 204,Jun-93,15.81,76585.89,8.18,0.5,0.28,0.1,0.22,1,,,,
206 | 205,Jul-93,15.77,101066.69,8.15,0.5,0.47,0.19,0.22,1,,,,
207 | 206,Aug-93,15.73,102222.44,8.08,0.49,0.48,0.01,0.22,0,,,,
208 | 207,Sep-93,15.82,88920.15,8.02,0.5,0.38,-0.1,0.21,0,,,,
209 | 208,Oct-93,15.7,71760.12,8.08,0.49,0.24,-0.14,0.21,0,,,,
210 | 209,Nov-93,14.68,72716.59,7.75,0.37,0.25,0.01,0.2,1,,,,
211 | 210,Dec-93,14.11,87690.86,7.37,0.31,0.37,0.12,0.18,1,,,,
212 | 211,Jan-94,13.83,103777.52,7.31,0.28,0.5,0.13,0.17,1,,,,
213 | 212,Feb-94,13.97,89669.87,7.49,0.29,0.38,-0.12,0.18,1,,,,
214 | 213,Mar-94,14.35,79920.66,7.51,0.34,0.3,-0.08,0.18,0,,,,
215 | 214,Apr-94,14.73,69502.06,7.61,0.38,0.22,-0.08,0.19,0,,,,
216 | 215,May-94,15.11,67169.8,7.7,0.42,0.2,-0.02,0.19,1,,,,
217 | 216,Jun-94,15.49,84091.08,7.96,0.46,0.34,0.14,0.21,1,,,,
218 | 217,Jul-94,15.49,103601.8,8.1,0.46,0.49,0.15,0.22,1,,,,
219 | 218,Aug-94,15.52,96743,8.17,0.47,0.44,-0.05,0.22,0,,,,
220 | 219,Sep-94,15.45,85348.79,8.01,0.46,0.35,-0.09,0.21,0,,,,
221 | 220,Oct-94,14.97,71701.83,7.73,0.41,0.24,-0.11,0.2,0,,,,
222 | 221,Nov-94,14.46,71089.91,7.61,0.35,0.23,-0.01,0.19,1,,,,
223 | 222,Dec-94,14.03,85865.36,7.45,0.3,0.35,0.12,0.18,1,,,,
224 | 223,Jan-95,13.6,96572.74,7.39,0.25,0.44,0.09,0.18,1,,,,
225 | 224,Feb-95,13.84,86710.93,7.35,0.28,0.36,-0.08,0.18,1,,,,
226 | 225,Mar-95,14.03,79475.2,7.34,0.3,0.3,-0.06,0.18,0,,,,
227 | 226,Apr-95,14.44,68573.74,7.51,0.35,0.21,-0.09,0.18,0,,,,
228 | 227,May-95,14.62,70081.84,7.75,0.37,0.23,0.02,0.2,1,,,,
229 | 228,Jun-95,14.92,84217.84,7.91,0.4,0.34,0.11,0.21,1,,,,
230 | 229,Jul-95,15.03,104020.58,7.82,0.41,0.5,0.16,0.2,1,,,,
231 | 230,Aug-95,14.97,114903.4,7.82,0.4,0.58,0.08,0.2,0,,,,
232 | 231,Sep-95,14.59,93900.12,7.72,0.36,0.42,-0.16,0.2,0,,,,
233 | 232,Oct-95,14.69,74704.41,7.38,0.37,0.26,-0.16,0.18,0,,,,
234 | 233,Nov-95,14.01,76926.51,7.13,0.3,0.28,0.02,0.16,1,,,,
235 | 234,Dec-95,13.58,92414.17,7.22,0.25,0.4,0.12,0.17,1,,,,
236 | 235,Jan-96,13.06,108620.45,7.44,0.19,0.53,0.13,0.18,1,,,,
237 | 236,Feb-96,13.14,96117.55,7.46,0.2,0.43,-0.1,0.18,1,,,,
238 | 237,Mar-96,13.56,87040.09,7.72,0.25,0.36,-0.07,0.2,0,,,,
239 | 238,Apr-96,13.76,74614.67,8.21,0.27,0.26,-0.1,0.22,0,,,,
240 | 239,May-96,14.23,74538.77,8.31,0.32,0.26,0,0.23,1,,,,
241 | 240,Jun-96,14.39,90946.83,8.21,0.34,0.39,0.13,0.22,1,,,,
242 | 241,Jul-96,14.5,106125.47,8.24,0.35,0.51,0.12,0.22,1,,,,
243 | 242,Aug-96,14.69,105557.33,8.29,0.37,0.51,0,0.23,0,,,,
244 | 243,Sep-96,14.53,91585.44,8.33,0.36,0.4,-0.11,0.23,0,,,,
245 | 244,Oct-96,14.29,75379.29,8.38,0.33,0.27,-0.13,0.23,0,,,,
246 | 245,Nov-96,13.55,78254.99,8.28,0.24,0.29,0.02,0.23,1,,,,
247 | 246,Dec-96,13.09,93730.88,8.33,0.19,0.42,0.13,0.23,1,,,,
248 | 247,Jan-97,12.87,106136.91,8.36,0.17,0.51,0.09,0.23,1,,,,
249 | 248,Feb-97,13.03,90250.61,8.23,0.19,0.39,-0.12,0.22,1,,,,
250 | 249,Mar-97,13.44,81421.38,7.9,0.23,0.32,-0.07,0.21,0,,,,
251 | 250,Apr-97,13.66,72742.69,7.75,0.26,0.25,-0.07,0.2,0,,,,
252 | 251,May-97,14.1,70778.29,7.8,0.31,0.23,-0.02,0.2,1,,,,
253 | 252,Jun-97,14.5,83584.68,7.96,0.35,0.33,0.1,0.21,1,,,,
254 | 253,Jul-97,14.2,109331.11,7.92,0.32,0.54,0.21,0.21,1,,,,
255 | 254,Aug-97,14.27,106969.52,8.05,0.33,0.52,-0.02,0.21,0,,,,
256 | 255,Sep-97,14.15,94801.49,7.99,0.31,0.42,-0.1,0.21,0,,,,
257 | 256,Oct-97,13.87,84121.86,7.76,0.28,0.34,-0.08,0.2,0,,,,
258 | 257,Nov-97,13.3,79993.51,7.54,0.22,0.31,-0.03,0.19,1,,,,
259 | 258,Dec-97,12.94,95748.04,7.21,0.18,0.43,0.12,0.17,1,,,,
260 | 259,Jan-98,12.66,102540.72,7.03,0.14,0.49,0.06,0.16,1,,,,
261 | 260,Feb-98,12.83,86555.7,6.85,0.16,0.36,-0.13,0.15,1,,,,
262 | 261,Mar-98,12.89,85985.93,6.67,0.17,0.35,-0.01,0.14,0,,,,
263 | 262,Apr-98,13.23,74195.03,6.79,0.21,0.26,-0.09,0.15,0,,,,
264 | 263,May-98,13.61,77518.21,7,0.25,0.29,0.03,0.16,1,,,,
265 | 264,Jun-98,13.66,98444.51,7.04,0.26,0.45,0.16,0.16,1,,,,
266 | 265,Jul-98,13.71,121472.25,6.95,0.26,0.64,0.19,0.15,1,,,,
267 | 266,Aug-98,13.67,120267.59,6.98,0.26,0.63,-0.01,0.16,0,,,,
268 | 267,Sep-98,13.44,106640.88,6.95,0.23,0.52,-0.11,0.16,0,,,,
269 | 268,Oct-98,13.12,86822.93,6.67,0.2,0.36,-0.16,0.14,0,,,,
270 | 269,Nov-98,12.77,77017.88,6.35,0.16,0.28,-0.08,0.12,1,,,,
271 | 270,Dec-98,12.56,92647.5,6.05,0.13,0.41,0.13,0.11,1,,,,
272 | 271,Jan-99,12,111219.42,5.99,0.07,0.55,0.14,0.1,1,,,,
273 | 272,Feb-99,12.54,86705.02,6.01,0.13,0.36,-0.19,0.1,1,,,,
274 | 273,Mar-99,12.5,89449.81,6.18,0.13,0.38,0.02,0.11,0,,,,
275 | 274,Apr-99,12.71,77284.99,6.72,0.15,0.28,-0.1,0.14,0,,,,
276 | 275,May-99,12.99,77151.74,6.91,0.18,0.28,0,0.15,1,,,,
277 | 276,Jun-99,13.24,95915.32,7.08,0.21,0.43,0.15,0.16,1,,,,
278 | 277,Jul-99,13.28,123125.53,7.42,0.21,0.65,0.22,0.18,1,,,,
279 | 278,Aug-99,13.14,123959.8,7.73,0.2,0.66,0.01,0.2,0,,,,
280 | 279,Sep-99,12.99,104054.56,7.89,0.18,0.5,-0.16,0.2,0,,,,
281 | 280,Oct-99,12.98,82605.2,7.71,0.18,0.33,-0.17,0.2,0,,,,
282 | 281,Nov-99,12.52,78288.26,7.8,0.13,0.29,-0.04,0.2,1,,,,
283 | 282,Dec-99,12.26,95163.43,7.88,0.1,0.43,0.14,0.2,1,,,,
284 | 283,Jan-00,11.79,109492.09,8.14,0.05,0.54,0.11,0.22,1,,,,
285 | 284,Feb-00,11.82,98445.86,9.17,0.05,0.45,-0.09,0.27,1,,,,
286 | 285,Mar-00,12.33,84645.02,9.05,0.11,0.34,-0.11,0.27,0,,,,
287 | 286,Apr-00,12.43,76228.45,8.71,0.12,0.28,-0.06,0.25,0,,,,
288 | 287,May-00,12.7,83366.31,8.99,0.15,0.33,0.05,0.26,1,,,,
289 | 288,Jun-00,12.96,103976.43,9.48,0.18,0.5,0.17,0.29,1,,,,
290 | 289,Jul-00,13,119475.31,9.48,0.18,0.62,0.12,0.29,1,,,,
291 | 290,Aug-00,13.03,123769.36,9.43,0.19,0.66,0.04,0.29,0,,,,
292 | 291,Sep-00,12.78,108546.42,10.02,0.16,0.53,-0.13,0.32,0,,,,
293 | 292,Oct-00,12.73,86831.64,9.88,0.15,0.36,-0.17,0.31,0,,,,
294 | 293,Nov-00,12.2,84516.25,9.69,0.09,0.34,-0.02,0.3,1,,,,
295 | 294,Dec-00,11.68,113153.36,9.38,0.03,0.57,0.23,0.28,1,,,,
296 | 295,1-Jan,11.48,127080.03,9.53,0.01,0.68,0.11,0.29,1,,,,
297 | 296,1-Feb,11.91,99875.59,9.44,0.06,0.46,-0.22,0.29,1,,,,
298 | 297,1-Mar,12.32,92816.13,8.98,0.1,0.41,-0.05,0.26,0,,,,
299 | 298,1-Apr,12.5,82438.26,9.23,0.13,0.32,-0.09,0.28,0,,,,
300 | 299,1-May,12.98,81755.58,9.73,0.18,0.32,0,0.3,1,,,,
301 | 300,1-Jun,13.31,99409.92,9.59,0.22,0.46,0.14,0.29,1,,,,
302 | 301,1-Jul,13.27,120695.09,8.96,0.21,0.63,0.17,0.26,1,,,,
303 | 302,1-Aug,13.24,129189.8,8.92,0.21,0.7,0.07,0.26,0,,,,
304 | 303,1-Sep,13.06,105956.42,9.07,0.19,0.51,-0.19,0.27,0,,,,
305 | 304,1-Oct,12.98,85424.56,7.99,0.18,0.35,-0.16,0.21,0,,,,
306 | 305,1-Nov,12.45,80798.47,7.45,0.12,0.31,-0.04,0.18,1,,,,
307 | 306,1-Dec,12.18,96166.74,6.95,0.09,0.43,0.12,0.15,1,,,,
308 | 307,2-Jan,11.84,116891.8,7,0.05,0.6,0.17,0.16,1,,,,
309 | 308,2-Feb,11.99,96592.93,7,0.07,0.44,-0.16,0.16,1,,,,
310 | 309,2-Mar,11.93,95318.67,7.42,0.06,0.43,-0.01,0.18,0,,,,
311 | 310,2-Apr,12.17,85407.61,7.94,0.09,0.35,-0.08,0.21,0,,,,
312 | 311,2-May,12.55,87318.54,8.11,0.13,0.36,0.01,0.22,1,,,,
313 | 312,2-Jun,12.67,107170,8.19,0.14,0.52,0.16,0.22,1,,,,
314 | 313,2-Jul,12.77,133694.8,8.39,0.16,0.73,0.21,0.23,1,,,,
315 | 314,2-Aug,12.59,134332.47,8.47,0.14,0.74,0.01,0.24,0,,,,
316 | 315,2-Sep,12.39,115833.03,8.65,0.11,0.59,-0.15,0.24,0,,,,
317 | 316,2-Oct,12.19,94531.34,8.46,0.09,0.42,-0.17,0.23,0,,,,
318 | 317,2-Nov,11.94,88822.3,8.18,0.06,0.38,-0.04,0.22,1,,,,
319 | 318,2-Dec,11.59,109266.38,8.27,0.02,0.54,0.16,0.22,1,,,,
320 | 319,3-Jan,11.42,124272.79,8.79,0,0.66,0.12,0.25,1,,,,
321 | 320,3-Feb,11.39,110937.37,9.69,0,0.55,-0.11,0.3,1,,,,
322 | 321,3-Mar,11.84,99561.31,10.16,0.05,0.46,-0.09,0.33,0,,,,
323 | 322,3-Apr,12.55,83448.24,9.33,0.13,0.33,-0.13,0.28,0,,,,
324 | 323,3-May,12.81,87804.37,9.07,0.16,0.37,0.04,0.27,1,,,,
325 | 324,3-Jun,13.17,101285.79,9.23,0.2,0.48,0.11,0.28,1,,,,
326 | 325,3-Jul,13.07,130673.86,9.36,0.19,0.71,0.23,0.28,1,,,,
327 | 326,3-Aug,13.03,134467.8,9.58,0.19,0.74,0.03,0.29,0,,,,
328 | 327,3-Sep,12.56,113848.98,9.41,0.13,0.58,-0.16,0.29,0,,,,
329 | 328,3-Oct,12.48,89780.86,9.04,0.12,0.38,-0.2,0.27,0,,,,
330 | 329,3-Nov,12.29,86658.68,8.86,0.1,0.36,-0.02,0.26,1,,,,
331 | 330,3-Dec,11.66,113083.85,8.83,0.03,0.57,0.21,0.25,1,,,,
332 | 331,4-Jan,11.53,127121.38,9.3,0.02,0.68,0.11,0.28,1,,,,
333 | 332,4-Feb,11.63,112464.06,9.54,0.03,0.56,-0.12,0.29,1,,,,
334 | 333,4-Mar,12.01,98947.21,9.77,0.07,0.46,-0.1,0.3,0,,,,
335 | 334,4-Apr,12.42,85376.87,10,0.12,0.35,-0.11,0.32,0,,,,
336 | 335,4-May,12.56,90598.21,10.62,0.13,0.39,0.04,0.35,1,,,,
337 | 336,4-Jun,12.82,112335.13,10.71,0.16,0.56,0.17,0.35,1,,,,
338 | 337,4-Jul,12.9,129305.35,10.82,0.17,0.7,0.14,0.36,1,,,,
339 | 338,4-Aug,13.09,126423.34,11.15,0.19,0.68,-0.02,0.38,0,,,,
340 | 339,4-Sep,12.9,112337.69,11.26,0.17,0.56,-0.12,0.38,0,,,,
341 | 340,4-Oct,12.36,93466.17,11.83,0.11,0.41,-0.15,0.41,0,,,,
342 | 341,4-Nov,12.18,89649.74,11.65,0.09,0.38,-0.03,0.4,1,,,,
343 | 342,4-Dec,11.67,113956.43,10.93,0.03,0.58,0.2,0.37,1,,,,
344 | 343,5-Jan,11.56,125287.86,10.88,0.02,0.67,0.09,0.36,1,,,,
345 | 344,5-Feb,11.84,106666.91,11.24,0.05,0.52,-0.15,0.38,1,,,,
346 | 345,5-Mar,11.96,104065.2,12.03,0.06,0.5,-0.02,0.42,0,,,,
347 | 346,5-Apr,12.4,86749.16,12.54,0.11,0.36,-0.14,0.45,0,,,,
348 | 347,5-May,12.86,87384.11,12.29,0.17,0.36,0,0.44,1,,,,
349 | 348,5-Jun,13.15,116627.44,12.81,0.2,0.6,0.24,0.47,1,,,,
350 | 349,5-Jul,13.04,144476.34,13.48,0.19,0.82,0.22,0.5,1,,,,
351 | 350,5-Aug,13.17,146904.78,14.27,0.2,0.84,0.02,0.54,0,,,,
352 | 351,5-Sep,13,126515.64,15.64,0.18,0.68,-0.16,0.62,0,,,,
353 | 352,5-Oct,12.74,102685.88,15.65,0.15,0.49,-0.19,0.62,0,,,,
354 | 353,5-Nov,12.82,91686.57,13.91,0.16,0.4,-0.09,0.52,1,,,,
355 | 354,5-Dec,12.17,120177.22,13.42,0.09,0.63,0.23,0.5,1,,,,
356 | 355,6-Jan,12.49,120418.85,13.77,0.12,0.63,0,0.52,1,,,,
357 | 356,6-Feb,12.81,104511.06,13.47,0.16,0.5,-0.13,0.5,1,,,,
358 | 357,6-Mar,12.88,104955.19,13.69,0.17,0.5,0,0.51,0,,,,
359 | 358,6-Apr,13.4,89374.1,14.62,0.23,0.38,-0.12,0.56,0,,,,
360 | 359,6-May,13.74,93999.95,15.33,0.27,0.42,0.04,0.6,1,,,,
361 | 360,6-Jun,14.02,118815.31,15.35,0.3,0.62,0.2,0.6,1,,,,
362 | 361,6-Jul,14.08,147338.33,15.72,0.3,0.84,0.22,0.62,1,,,,
363 | 362,6-Aug,13.99,150064.43,15.85,0.29,0.87,0.03,0.63,0,,,,
364 | 363,6-Sep,14.06,116072.16,14.57,0.3,0.59,-0.28,0.56,0,,,,
365 | 364,6-Oct,13.66,96246.21,13.02,0.26,0.44,-0.15,0.48,0,,,,
366 | 365,6-Nov,13.14,94842.85,12.99,0.2,0.42,-0.02,0.48,1,,,,
367 | 366,6-Dec,12.63,114881.6,13.31,0.14,0.58,0.16,0.49,1,,,,
368 | 367,7-Jan,12.89,125286.24,12.69,0.17,0.67,0.09,0.46,1,,,,
369 | 368,7-Feb,12.62,121464.25,12.88,0.14,0.64,-0.03,0.47,1,,,,
370 | 369,7-Mar,13.04,105694.76,13.7,0.19,0.51,-0.13,0.51,0,,,,
371 | 370,7-Apr,13.46,90282.05,14.5,0.23,0.39,-0.12,0.56,0,,,,
372 | 371,7-May,13.58,96388.85,15.05,0.25,0.44,0.05,0.58,1,,,,
373 | 372,7-Jun,13.95,117417.6,15.29,0.29,0.6,0.16,0.6,1,,,,
374 | 373,7-Jul,13.9,139027.03,15.54,0.28,0.78,0.18,0.61,1,,,,
375 | 374,7-Aug,13.9,150101.49,15.25,0.28,0.87,0.09,0.59,0,,,,
376 | 375,7-Sep,13.7,129512.02,15.41,0.26,0.7,-0.17,0.6,0,,,,
377 | 376,7-Oct,13.48,103753.92,15.58,0.24,0.5,-0.2,0.61,0,,,,
378 | 377,7-Nov,13.23,95904.83,16.6,0.21,0.43,-0.07,0.67,1,,,,
379 | 378,7-Dec,12.74,117407.96,16.32,0.15,0.6,0.17,0.65,1,,,,
380 | 379,8-Jan,12.46,133368.44,16.39,0.12,0.73,0.13,0.66,1,,,,
381 | 380,8-Feb,12.45,118553.65,16.55,0.12,0.61,-0.12,0.66,1,,,,
382 | 381,8-Mar,12.76,107162.48,18.18,0.16,0.52,-0.09,0.75,0,,,,
383 | 382,8-Apr,13.32,92165.89,19.31,0.22,0.4,-0.12,0.81,0,,,,
384 | 383,8-May,13.81,92245.63,21,0.27,0.4,0,0.9,1,,,,
385 | 384,8-Jun,14.11,121375.88,22.6,0.31,0.64,0.24,0.98,1,,,,
386 | 385,8-Jul,14.37,143179.15,22.89,0.34,0.81,0.17,1,1,,,,
387 | 386,8-Aug,14.41,138459.92,21.03,0.34,0.77,-0.04,0.9,0,,,,
388 | 387,8-Sep,14.2,117078.14,19.57,0.32,0.6,-0.17,0.82,0,,,,
389 | 388,8-Oct,14.19,96110.59,16.73,0.32,0.43,-0.17,0.67,0,,,,
390 | 389,8-Nov,13.97,95766.49,13.5,0.29,0.43,0,0.5,1,,,,
391 | 390,8-Dec,13.39,125195.49,11.51,0.23,0.67,0.24,0.4,1,,,,
392 | 391,9-Jan,13.51,136228.78,11.24,0.24,0.75,0.08,0.38,1,,,,
393 | 392,9-Feb,13.7,115212.23,11.18,0.26,0.59,-0.16,0.38,1,,,,
394 | 393,9-Mar,13.84,106501.36,11.12,0.28,0.52,-0.07,0.38,0,,,,
395 | 394,9-Apr,14.09,91631.16,11.48,0.31,0.4,-0.12,0.39,0,,,,
396 | 395,9-May,14.42,94312.18,12.12,0.34,0.42,0.02,0.43,1,,,,
397 | 396,9-Jun,14.33,114395.19,13.47,0.33,0.58,0.16,0.5,1,,,,
398 | 397,9-Jul,14.39,137497.96,13.44,0.34,0.76,0.18,0.5,1,,,,
399 | 398,9-Aug,14.45,138222.68,13.94,0.35,0.77,0.01,0.53,0,,,,
400 | 399,9-Sep,14.44,115163.38,13.67,0.35,0.59,-0.18,0.51,0,,,,
401 | 400,9-Oct,14.03,98552.02,13.39,0.3,0.45,-0.14,0.5,0,,,,
402 | 401,9-Nov,13.51,93027.98,13.85,0.24,0.41,-0.04,0.52,1,,,,
403 | 402,9-Dec,13.07,124013.23,13.51,0.19,0.66,0.25,0.5,1,,,,
404 | 403,10-Jan,12.57,147499.88,14.05,0.13,0.84,0.18,0.53,1,,,,
405 | 404,10-Feb,13.07,122840.2,13.8,0.19,0.65,-0.19,0.52,1,,,,
406 | 405,10-Mar,13.33,111789.99,14.26,0.22,0.56,-0.09,0.54,0,,,,
407 | 406,10-Apr,14.04,88046.44,14.86,0.3,0.37,-0.19,0.57,0,,,,
408 | 407,10-May,14.29,94843.01,14.72,0.33,0.42,0.05,0.57,1,,,,
409 | 408,10-Jun,14.29,127495.97,14.65,0.33,0.69,0.27,0.56,1,,,,
410 | 409,10-Jul,14.42,154688.71,14.8,0.34,0.9,0.21,0.57,1,,,,
411 | 410,10-Aug,14.39,154053.43,14.93,0.34,0.9,0,0.58,0,,,,
412 | 411,10-Sep,14.27,124582.73,14.7,0.33,0.66,-0.24,0.57,0,,,,
413 | 412,10-Oct,14.12,96688.03,14.77,0.31,0.44,-0.22,0.57,0,,,,
414 | 413,10-Nov,13.79,93166.14,14.72,0.27,0.41,-0.03,0.57,1,,,,
415 | 414,10-Dec,13.08,130013.88,15.14,0.19,0.71,0.3,0.59,1,,,,
416 | 415,11-Jan,12.81,145061.69,15.68,0.16,0.83,0.12,0.62,1,,,,
417 | 416,11-Feb,12.99,120109.97,16.37,0.18,0.63,-0.2,0.65,1,,,,
418 | 417,11-Mar,13.46,104922.12,17.68,0.23,0.5,-0.13,0.72,0,,,,
419 | 418,11-Apr,13.58,93701.64,18.66,0.25,0.41,-0.09,0.78,0,,,,
420 | 419,11-May,13.83,97689.1,18.68,0.28,0.45,0.04,0.78,1,,,,
421 | 420,11-Jun,13.88,125982.79,18.45,0.28,0.67,0.22,0.76,1,,,,
422 | 421,11-Jul,13.98,154728.92,18.42,0.29,0.9,0.23,0.76,1,,,,
423 | 422,11-Aug,13.94,153738.73,18.17,0.29,0.89,-0.01,0.75,0,,,,
424 | 423,11-Sep,14,122719.5,17.94,0.3,0.65,-0.24,0.74,0,,,,
425 | 424,11-Oct,13.89,94585.12,17.28,0.28,0.42,-0.23,0.7,0,,,,
426 | 425,11-Nov,13.52,93220.5,17.25,0.24,0.41,-0.01,0.7,1,,,,
427 | 426,11-Dec,13.08,116341.02,16.78,0.19,0.6,0.19,0.68,1,,,,
428 | 427,12-Jan,13.05,125881.48,16.82,0.19,0.67,0.07,0.68,1,,,,
429 | 428,12-Feb,13.14,107975.36,17.33,0.2,0.53,-0.14,0.7,1,,,,
430 | 429,12-Mar,13.33,99361.69,18.14,0.22,0.46,-0.07,0.75,0,,,,
431 | 430,12-Apr,13.56,88103.21,18.11,0.25,0.37,-0.09,0.75,0,,,,
432 | 431,12-May,13.56,100895.12,17.79,0.25,0.47,0.1,0.73,1,,,,
433 | 432,12-Jun,13.79,122934.13,17,0.27,0.65,0.18,0.69,1,,,,
434 | 433,12-Jul,13.69,154579.07,16.98,0.26,0.9,0.25,0.69,1,,,,
435 | 434,12-Aug,13.8,147941.04,17.9,0.27,0.85,-0.05,0.74,0,,,,
436 | 435,12-Sep,13.88,118831.34,18.26,0.28,0.62,-0.23,0.75,0,,,,
437 | 436,12-Oct,13.54,96669.43,17.61,0.24,0.44,-0.18,0.72,0,,,,
438 | 437,12-Nov,13.25,97155.23,16.87,0.21,0.44,0,0.68,1,,,,
439 | 438,12-Dec,13.1,114187.6,16.52,0.19,0.58,0.14,0.66,1,,,,
440 | 439,13-Jan,12.89,131784.84,16.45,0.17,0.72,0.14,0.66,1,,,,
441 | 440,13-Feb,13.02,113113.89,17.17,0.18,0.57,-0.15,0.7,1,,,,
442 | 441,13-Mar,13.03,112096.73,17.13,0.19,0.56,-0.01,0.69,0,,,,
443 | 442,13-Apr,13.42,95540.85,16.75,0.23,0.43,-0.13,0.67,0,,,,
444 | 443,13-May,13.94,95191.6,16.99,0.29,0.43,0,0.69,1,,,,
445 | 444,13-Jun,14.06,117982.11,17.23,0.3,0.61,0.18,0.7,1,,,,
446 | 445,13-Jul,14.16,143854.75,17.53,0.31,0.82,0.21,0.72,1,,,,
447 | 446,13-Aug,13.99,138065.07,17.65,0.29,0.77,-0.05,0.72,0,,,,
448 | 447,13-Sep,13.96,121419.22,17.58,0.29,0.64,-0.13,0.72,0,,,,
449 | 448,13-Oct,13.79,98893.02,16.62,0.27,0.46,-0.18,0.67,0,,,,
450 | 449,13-Nov,13.47,97904.02,15.88,0.24,0.45,-0.01,0.63,1,,,,
451 | 450,13-Dec,13.02,128966.06,15.89,0.18,0.7,0.25,0.63,1,,,,
452 | 451,14-Jan,12.91,146511.44,16.05,0.17,0.84,0.14,0.64,1,,,,
453 | 452,14-Feb,13.21,128475.36,16.61,0.21,0.69,-0.15,0.67,1,,,,
454 | 453,14-Mar,13.53,114232.8,16.82,0.24,0.58,-0.11,0.68,0,,,,
455 | 454,14-Apr,13.57,92289.72,16.92,0.25,0.4,-0.18,0.68,0,,,,
456 | 455,14-May,14.14,95726.77,17.16,0.31,0.43,0.03,0.7,1,,,,
457 | 456,14-Jun,14.27,118049.04,17.49,0.33,0.61,0.18,0.71,1,,,,
458 | 457,14-Jul,14.37,137027.97,17.46,0.34,0.76,0.15,0.71,1,,,,
459 | 458,14-Aug,14.32,135829.8,17.1,0.33,0.75,-0.01,0.69,0,,,,
460 | 459,14-Sep,14.22,120741.35,16.62,0.32,0.63,-0.12,0.67,0,,,,
461 | 460,14-Oct,13.83,98037.68,15.47,0.28,0.45,-0.18,0.61,0,,,,
462 | 461,14-Nov,13.73,99485.77,14.38,0.26,0.46,0.01,0.55,1,,,,
463 | 462,14-Dec,13.43,120800.62,13.02,0.23,0.63,0.17,0.48,1,,,,
464 | 463,15-Jan,13.44,137764.86,11.39,0.23,0.77,0.14,0.39,1,,,,
465 | 464,15-Feb,13.61,123837.72,11.38,0.25,0.66,-0.11,0.39,1,,,,
466 | 465,15-Mar,13.62,117167.11,11.87,0.25,0.6,-0.06,0.42,0,,,,
467 | 466,15-Apr,13.93,90199.19,11.73,0.29,0.39,-0.21,0.41,0,,,,
468 | 467,15-May,14.22,95160.53,12.61,0.32,0.43,0.04,0.45,1,,,,
469 | 468,15-Jun,14.17,120299.75,12.98,0.31,0.63,0.2,0.47,1,,,,
470 | 469,15-Jul,14.17,146038.29,12.77,0.31,0.83,0.2,0.46,1,,,,
471 | 470,15-Aug,14.14,144515.15,12,0.31,0.82,-0.01,0.42,0,,,,
472 | 471,15-Sep,14.3,125416.67,11.43,0.33,0.67,-0.15,0.39,0,,,,
473 | 472,15-Oct,13.95,99349.05,10.79,0.29,0.46,-0.21,0.36,0,,,,
474 | 473,15-Nov,13.92,92677.75,10.07,0.29,0.41,-0.05,0.32,1,,,,
475 | 474,15-Dec,13.51,111670.44,9.31,0.24,0.56,0.15,0.28,1,,,,
476 | 475,16-Jan,13.14,130971.85,8.57,0.2,0.71,0.15,0.24,1,,,,
477 | 476,16-Feb,13.32,115959.43,8.16,0.22,0.59,-0.12,0.22,1,,,,
478 | 477,16-Mar,13.76,100226.58,8.77,0.27,0.47,-0.12,0.25,0,,,,
479 | 478,16-Apr,13.57,88244.34,9.17,0.25,0.37,-0.1,0.27,0,,,,
480 | 479,16-May,13.93,94198.03,10.02,0.29,0.42,0.05,0.32,1,,,,
481 | 480,16-Jun,13.82,125211.24,10.77,0.27,0.67,0.25,0.36,1,,,,
482 | 481,16-Jul,13.77,154409.33,10.83,0.27,0.9,0.23,0.36,1,,,,
483 | 482,16-Aug,13.96,156441.52,10.76,0.29,0.92,0.02,0.36,0,,,,
484 | 483,16-Sep,13.92,129362.93,10.76,0.29,0.7,-0.22,0.36,0,,,,
485 | 484,16-Oct,13.44,101507.97,10.67,0.23,0.48,-0.22,0.35,0,,,,
486 | 485,16-Nov,13.74,93244.09,10.03,0.27,0.41,-0.07,0.32,1,,,,
487 | 486,16-Dec,13.14,121280.86,10.18,0.2,0.64,0.23,0.33,1,,,,
488 | 487,17-Jan,13.06,129212.5,10.49,0.19,0.7,0.06,0.34,1,,,,
489 | 488,17-Feb,13.66,100968.24,10.53,0.26,0.47,-0.23,0.34,1,,,,
490 | 489,17-Mar,13.79,103095.52,10.43,0.27,0.49,0.02,0.34,0,,,,
491 | 490,17-Apr,13.59,90724.5,10.77,0.25,0.39,-0.1,0.36,0,,,,
492 | 491,17-May,13.97,98281.16,10.93,0.29,0.45,0.06,0.37,1,,,,
493 | 492,17-Jun,14.09,122543.17,11.04,0.31,0.65,0.2,0.37,1,,,,
494 | 493,17-Jul,13.96,149900.48,11.17,0.29,0.86,0.21,0.38,1,,,,
495 | 494,17-Aug,13.97,142007.17,11.52,0.29,0.8,-0.06,0.4,0,,,,
496 | 495,17-Sep,14.05,118778.78,12.03,0.3,0.62,-0.18,0.42,0,,,,
497 | 496,17-Oct,13.53,102811.04,11.45,0.24,0.49,-0.13,0.39,0,,,,
498 | 497,17-Nov,13.64,98320.57,11.38,0.25,0.45,-0.04,0.39,1,,,,
499 | 498,17-Dec,13.1,122004.62,11.25,0.19,0.64,0.19,0.38,1,,,,
500 | 499,18-Jan,12.8,148917.38,11.62,0.16,0.86,0.22,0.4,1,,,,
501 | 500,18-Feb,13.2,113751.28,11.66,0.2,0.58,-0.28,0.4,1,,,,
502 | 501,18-Mar,13.56,107218.43,11.56,0.25,0.52,-0.06,0.4,0,,,,
503 | 502,18-Apr,13.43,95453.62,12.02,0.23,0.43,-0.09,0.42,0,,,,
504 | 503,18-May,13.65,103848,13.16,0.26,0.5,0.07,0.48,1,,,,
505 | 504,18-Jun,13.52,129912.9,13.56,0.24,0.7,0.2,0.51,1,,,,
506 | 505,18-Jul,13.61,153566.05,13.76,0.25,0.89,0.19,0.52,1,,,,
507 | 506,18-Aug,13.72,153496.49,13.79,0.26,0.89,0,0.52,0,,,,
508 | 507,18-Sep,13.44,128909.79,13.71,0.23,0.7,-0.19,0.51,0,,,,
509 | 508,18-Oct,13.25,107048.75,13.26,0.21,0.52,-0.18,0.49,0,,,,
510 | 509,18-Nov,13.31,103789.96,12.16,0.22,0.5,-0.02,0.43,1,,,,
511 | 510,18-Dec,12.83,123180.4,11.09,0.16,0.65,0.15,0.37,1,,,,
512 | 511,19-Jan,12.87,133317.55,10.94,0.17,0.73,0.08,0.37,1,,,,
513 | 512,19-Feb,13.1,116608,11.27,0.19,0.6,-0.13,0.38,1,,,,
514 | 513,19-Mar,13.17,112605.42,11.65,0.2,0.57,-0.03,0.4,0,,,,
515 | 514,19-Apr,13.55,90383.82,12.3,0.24,0.39,-0.18,0.44,0,,,,
516 | 515,19-May,13.6,100331.07,12.57,0.25,0.47,0.08,0.45,1,,,,
517 | 516,19-Jun,13.6,120116.17,12.47,0.25,0.63,0.16,0.45,1,,,,
518 | 517,19-Jul,13.5,153748.89,12.72,0.24,0.89,0.26,0.46,1,,,,
519 | 518,19-Aug,13.53,150083.06,12.48,0.24,0.87,-0.02,0.45,0,,,,
520 | 519,19-Sep,13.37,131566.73,12.42,0.22,0.72,-0.15,0.44,0,,,,
521 | 520,19-Oct,12.98,107997.21,11.81,0.18,0.53,-0.19,0.41,0,,,,
522 | 521,19-Nov,13.17,102452.92,11.34,0.2,0.48,-0.05,0.39,1,,,,
523 | 522,19-Dec,12.79,121078.08,11.34,0.16,0.63,0.15,0.39,1,,,,
524 | 523,20-Jan,12.88,124414.14,11.28,0.17,0.66,0.03,0.38,1,,,,
525 | 524,20-Feb,12.93,111931.39,10.58,0.17,0.56,-0.1,0.35,1,,,,
526 | 525,20-Mar,13.23,104006.32,9.46,0.21,0.5,-0.06,0.29,0,,,,
527 | 526,20-Apr,13.53,97464.58,8.35,0.24,0.44,-0.06,0.23,0,,,,
528 | 527,20-May,13.4,105411.88,8.45,0.23,0.51,0.07,0.23,1,,,,
529 | 528,20-Jun,13.46,131242.46,9.59,0.23,0.71,0.2,0.29,1,,,,
530 | 529,20-Jul,13.36,166890.83,10.11,0.22,1,0.29,0.32,1,,,,
531 | 530,20-Aug,13.36,158821.42,10.25,0.22,0.94,-0.06,0.33,0,,,,
532 | 


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