├── Research Summary.png ├── misc ├── LSTM graphs.pdf └── LICENSE ├── feedforward vs recurrent ├── eth-LTSM.png ├── bitcoin-LTSM.png ├── eth-feedforward.png └── bitcoin-feedforward.png ├── Catastrophes in volatile financial markets.pdf ├── LICENSE ├── README.md ├── feedforward.py ├── lstm.py └── data ├── GoogleTrends.csv ├── all_eth.csv └── all_bitcoin.csv /Research Summary.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ShrutiAppiah/crypto-forecasting-with-neuralnetworks/HEAD/Research Summary.png -------------------------------------------------------------------------------- /misc/LSTM graphs.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ShrutiAppiah/crypto-forecasting-with-neuralnetworks/HEAD/misc/LSTM graphs.pdf -------------------------------------------------------------------------------- /feedforward vs recurrent/eth-LTSM.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ShrutiAppiah/crypto-forecasting-with-neuralnetworks/HEAD/feedforward vs recurrent/eth-LTSM.png -------------------------------------------------------------------------------- /feedforward vs recurrent/bitcoin-LTSM.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ShrutiAppiah/crypto-forecasting-with-neuralnetworks/HEAD/feedforward vs recurrent/bitcoin-LTSM.png -------------------------------------------------------------------------------- /feedforward vs recurrent/eth-feedforward.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ShrutiAppiah/crypto-forecasting-with-neuralnetworks/HEAD/feedforward vs recurrent/eth-feedforward.png -------------------------------------------------------------------------------- /Catastrophes in volatile financial markets.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ShrutiAppiah/crypto-forecasting-with-neuralnetworks/HEAD/Catastrophes in volatile financial markets.pdf -------------------------------------------------------------------------------- /feedforward vs recurrent/bitcoin-feedforward.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ShrutiAppiah/crypto-forecasting-with-neuralnetworks/HEAD/feedforward vs recurrent/bitcoin-feedforward.png -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2018 Shruti Appiah 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /misc/LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2018 Shruti Appiah, Shrey Khosla, Shashank Shabhlok, Xianzhuo Yu 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Forecasting with feedforward and LSTM neural networks 2 | 3 | ## Paper & assoicated research 4 | Read the research paper. 5 | 6 | ## Highlights 7 | ### Noise can sometimes be good! 8 | - Noise helps optimizers escape saddle points and local maxima/minima 9 | 10 | ### Vanishing gradients are a problem. LSTM units save the day. 11 | - LSTM (Long Short-term Memory) neural networks are mindful of long-term dependencies. They remember things from the past just like your girlfriend does. Read more about gradient descents. 12 | 13 | ### Adam optimizers can recalculate neuron weights based on both first and second order moments 14 | - Adam optimizers combine Adaptive Gradient (AdaGrad) and Root Mean Square Propogation (RMS Prop) calculators. 15 | - In a distribution, the first-order moment is the mean. The second-order moment is the variance. 16 | - AdaGrad is great at handling sparse gradients. It calculates second-order moments based on multiple past gradients. 17 | - RMSProp is based solely on first-order moments i.e means. 18 | - Combined, the Adam Optimizer produces more sensible learning rates in each iteration. 19 | 20 | ## Overview 21 |
22 | Research Summary 23 |
24 |
25 |
26 | 27 | ## License 28 | [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT) 29 | 30 | Copyright (c) 2018 Shruti Appiah 31 | -------------------------------------------------------------------------------- /feedforward.py: -------------------------------------------------------------------------------- 1 | # Import 2 | import sys 3 | import tensorflow as tf 4 | import numpy as np 5 | import pandas as pd 6 | import matplotlib.pyplot as plt 7 | from sklearn.preprocessing import MinMaxScaler 8 | from sklearn.model_selection import train_test_split 9 | 10 | # convert an array of values into a dataset matrix 11 | def create_dataset(dataset, days_in_advance): 12 | dataX, dataY = [], [] 13 | for i in range(len(dataset)): 14 | if (i + days_in_advance < len(dataset)): 15 | dataX.append(dataset[i]) 16 | dataY.append(dataset[i + days_in_advance]) 17 | return np.asarray(dataX), np.asarray(dataY) 18 | 19 | # Import data 20 | # data = pd.read_csv('./data/all_eth.csv') 21 | data = pd.read_csv('./data/all_bitcoin.csv') 22 | # data = pd.read_csv('./data/data_stocks.csv') 23 | 24 | # Drop variables 25 | 26 | ## data_stocks: 27 | # data = data.drop(['DATE'], 1) 28 | 29 | ## all_eth && all_bitcoin 30 | data = data.drop(['Date'], 1) 31 | data = data.drop(['Open'], 1) 32 | data = data.drop(['High'], 1) 33 | data = data.drop(['Low'], 1) 34 | data = data.drop(['Volume'], 1) 35 | data = data.drop(['Market Cap'], 1) 36 | 37 | # Make data a np.array 38 | data = data.values 39 | 40 | # normalize the dataset 41 | scaler = MinMaxScaler(feature_range=(0, 1)) 42 | data = scaler.fit_transform(data) 43 | 44 | #prepare the X and Y label 45 | X,y = create_dataset(data, int(sys.argv[1])) 46 | 47 | #Take 80% of data as the training sample and 20% as testing sample 48 | X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, shuffle=False) 49 | 50 | # Neurons 51 | n_neurons_1 = 256 52 | n_neurons_2 = 128 53 | n_neurons_3 = 64 54 | n_neurons_4 = 32 55 | 56 | # Session 57 | net = tf.InteractiveSession() 58 | 59 | # Placeholder 60 | X = tf.placeholder(dtype=tf.float32) 61 | Y = tf.placeholder(dtype=tf.float32) 62 | 63 | # Initializers 64 | sigma = 1 65 | weight_initializer = tf.variance_scaling_initializer(mode="fan_avg", distribution="uniform", scale=sigma) 66 | bias_initializer = tf.zeros_initializer() 67 | 68 | # Hidden weights 69 | W_hidden_1 = tf.Variable(weight_initializer([X_train.shape[1], n_neurons_1])) 70 | bias_hidden_1 = tf.Variable(bias_initializer([n_neurons_1])) 71 | W_hidden_2 = tf.Variable(weight_initializer([n_neurons_1, n_neurons_2])) 72 | bias_hidden_2 = tf.Variable(bias_initializer([n_neurons_2])) 73 | W_hidden_3 = tf.Variable(weight_initializer([n_neurons_2, n_neurons_3])) 74 | bias_hidden_3 = tf.Variable(bias_initializer([n_neurons_3])) 75 | W_hidden_4 = tf.Variable(weight_initializer([n_neurons_3, n_neurons_4])) 76 | bias_hidden_4 = tf.Variable(bias_initializer([n_neurons_4])) 77 | 78 | # Output weights 79 | W_out = tf.Variable(weight_initializer([n_neurons_4, 1])) 80 | bias_out = tf.Variable(bias_initializer([1])) 81 | 82 | # Hidden layer 83 | hidden_1 = tf.nn.relu(tf.add(tf.matmul(X, W_hidden_1), bias_hidden_1)) 84 | hidden_2 = tf.nn.relu(tf.add(tf.matmul(hidden_1, W_hidden_2), bias_hidden_2)) 85 | hidden_3 = tf.nn.relu(tf.add(tf.matmul(hidden_2, W_hidden_3), bias_hidden_3)) 86 | hidden_4 = tf.nn.relu(tf.add(tf.matmul(hidden_3, W_hidden_4), bias_hidden_4)) 87 | 88 | # Output layer (transpose!) 89 | out = tf.transpose(tf.add(tf.matmul(hidden_4, W_out), bias_out)) 90 | 91 | # Cost function 92 | mse = tf.reduce_mean(tf.squared_difference(out, Y)) 93 | 94 | # Optimizer 95 | opt = tf.train.AdamOptimizer().minimize(mse) 96 | 97 | # Init 98 | net.run(tf.global_variables_initializer()) 99 | 100 | # Fit neural net 101 | batch_size = 10 102 | pred = [] 103 | 104 | # Run 105 | epochs = 5 106 | for e in range(epochs): 107 | # Minibatch training 108 | for i in range(0, len(y_train) // batch_size): 109 | start = i * batch_size 110 | batch_x = X_train[start:start + batch_size] 111 | batch_y = y_train[start:start + batch_size] 112 | # Run optimizer with batch 113 | net.run(opt, feed_dict={X: batch_x, Y: batch_y}) 114 | # Prediction 115 | pred = net.run(out, feed_dict={X: X_test}) 116 | 117 | pred = [ [i] for i in pred[0] ] 118 | 119 | #Inverse Transform the predicted and testing data outputs to get accuracy 120 | testPredict = scaler.inverse_transform(pred) 121 | testY = scaler.inverse_transform(y_test) 122 | 123 | acc = 0 124 | for index,element in enumerate(testY) : 125 | acc += 1 - abs((element - testPredict[index])[0])/element[0] 126 | acc /= len(testY) 127 | 128 | print("Prediction: ", testPredict[-1]) 129 | print("Actual: ", testY[-1]) 130 | print("Accuracy: ", acc * 100) 131 | 132 | # plot baseline and predictions 133 | plt.plot(testY, label="Actual Price") 134 | plt.plot(testPredict, label="Predicted Price") 135 | plt.xlabel("Day") 136 | plt.ylabel("Ethereum Price") 137 | plt.legend() 138 | plt.show() 139 | -------------------------------------------------------------------------------- /lstm.py: -------------------------------------------------------------------------------- 1 | # Import 2 | import os 3 | import sys 4 | import math 5 | import numpy as np 6 | import matplotlib.pyplot as plt 7 | from pandas import read_csv 8 | from keras.models import Sequential, load_model 9 | from keras.layers import Dense 10 | from keras.layers import LSTM 11 | from sklearn.preprocessing import MinMaxScaler 12 | from sklearn.metrics import mean_squared_error 13 | from sklearn.model_selection import train_test_split 14 | 15 | # convert an array of values into a dataset matrix 16 | def create_dataset(dataset, days_in_advance): 17 | dataX, dataY = [], [] 18 | for i in range(len(dataset)): 19 | if (i + days_in_advance < len(dataset)): 20 | dataX.append(dataset[i]) 21 | dataY.append(dataset[i + days_in_advance]) 22 | return np.asarray(dataX), np.asarray(dataY) 23 | 24 | # fix random seed for reproducibility 25 | np.random.seed(7) 26 | 27 | # load the dataset 28 | df = read_csv('./data/all_bitcoin.csv') 29 | # df = read_csv('./data/all_eth.csv') 30 | # df = read_csv('./data/data_stocks.csv') 31 | gt = read_csv('./data/GoogleTrends.csv') 32 | df = df.iloc[::-1] 33 | ## all_eth && all_bitcoin 34 | df = df.drop(['Date','Open','High','Low','Volume','Market Cap'], axis=1) 35 | ## data_stocks 36 | # df = df.drop(['DATE'], axis=1) 37 | dataset = df.values 38 | dataset = dataset.astype('float32') 39 | ## all_bitcoin 40 | gt = gt.drop(['Day','ethereum','Cryptocurrency'],axis=1) 41 | ## all_eth 42 | # gt = gt.drop(['Day','bitcoin','Cryptocurrency'],axis=1) 43 | gdataset = gt.values 44 | gdataset = gdataset.astype('float32') 45 | 46 | # normalize the dataset 47 | scaler = MinMaxScaler(feature_range=(0, 1)) 48 | dataset = scaler.fit_transform(dataset) 49 | #gdataset = scaler.fit_transform(gdataset) 50 | 51 | #prepare the X and Y label 52 | X,y = create_dataset(dataset, int(sys.argv[1])) 53 | 54 | #Take 80% of data as the training sample and 20% as testing sample 55 | trainX, testX, trainY, testY = train_test_split(X, y, test_size=0.20, shuffle=False) 56 | 57 | # reshape input to be [samples, time steps, features] 58 | trainX = np.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1])) 59 | testX = np.reshape(testX, (testX.shape[0], 1, testX.shape[1])) 60 | 61 | # create and fit the LSTM network 62 | model = Sequential() 63 | model.add(LSTM(256, input_shape=(1, 1))) 64 | model.add(Dense(1)) 65 | model.compile(loss='mean_squared_error', optimizer='adam') 66 | model.fit(trainX, trainY, epochs=5, batch_size=10, verbose=2) 67 | 68 | # save model for later use 69 | # model.save('./savedModel') 70 | # load_model 71 | # model = load_model('./bitsavedModel') 72 | 73 | # # make predictions 74 | trainPredict = model.predict(trainX) 75 | testPredict = model.predict(testX) 76 | 77 | futurePredict = model.predict(np.asarray([[testPredict[-1]]])) 78 | futurePredict = scaler.inverse_transform(futurePredict) 79 | 80 | # # invert predictions 81 | trainPredict = scaler.inverse_transform(trainPredict) 82 | trainY = scaler.inverse_transform(trainY) 83 | testPredict = scaler.inverse_transform(testPredict) 84 | testY = scaler.inverse_transform(testY) 85 | 86 | print("Price Prediction for last 5 days: ") 87 | print(testPredict[-5:]) 88 | print("Bitcoin price for tomorrow: ", futurePredict) 89 | 90 | # calculate root mean squared error 91 | trainScore = math.sqrt(mean_squared_error(trainY[:,0], trainPredict[:,0])) 92 | print('Train Score: %.2f RMSE' % (trainScore)) 93 | testScore = math.sqrt(mean_squared_error(testY[:,0], testPredict[:,0])) 94 | print('Test Score: %.2f RMSE' % (testScore)) 95 | 96 | # shift train predictions for plotting 97 | trainPredictPlot = np.empty_like(dataset) 98 | trainPredictPlot[:, :] = np.nan 99 | trainPredictPlot[1:len(trainPredict)+1, :] = trainPredict 100 | 101 | # shift test predictions for plotting 102 | testPredictPlot = np.empty_like(dataset) 103 | testPredictPlot[:, :] = np.nan 104 | testPredictPlot[len(trainPredict):len(dataset)-1, :] = testPredict 105 | 106 | print(testPredict) 107 | 108 | # calculate accuracies 109 | testAcc = 0 110 | for index,element in enumerate(testY) : 111 | testAcc += 1 - abs((element - testPredict[index])[0])/element[0] 112 | testAcc /= len(testY) 113 | 114 | trainAcc = 0 115 | for index,element in enumerate(trainY) : 116 | trainAcc += 1 - abs((element - trainPredict[index])[0])/element[0] 117 | trainAcc /= len(trainY) 118 | 119 | print("Prediction: ", testPredict[-1]) 120 | print("Actual: ", testY[-1]) 121 | print("Training Accuracy: ", trainAcc * 100) 122 | print("Testing Accuracy: ", testAcc * 100) 123 | 124 | # plot baseline and predictions 125 | plt.plot(scaler.inverse_transform(dataset),label= "Actual Price") 126 | plt.plot(trainPredictPlot,label = "Training Price") 127 | plt.plot(testPredictPlot,label="Predicted Price") 128 | plt.legend() 129 | plt.xlabel('Day') 130 | # all_bitcoin 131 | plt.ylabel('Bitcoin Price') 132 | # all_eth 133 | # plt.ylabel('Ethereum Price') 134 | # plot google trends 135 | ax2 = plt.twinx() 136 | ax2.plot(gdataset, color="purple", linestyle="dotted",label="Popularity") 137 | # all_bitcoin 138 | ax2.set_ylabel('Bitcoin Trends Popularity') 139 | # all_eth 140 | # ax2.set_ylabel('Ethereum Trends Popularity') 141 | ax2.legend(loc = "lower right") 142 | plt.show() 143 | -------------------------------------------------------------------------------- /data/GoogleTrends.csv: -------------------------------------------------------------------------------- 1 | Day,bitcoin,ethereum,Cryptocurrency 18-03-27,19.00,2.00,4.00 18-03-26,21.00,2.00,4.00 18-03-25,15.00,2.00,3.00 18-03-24,14.00,2.00,3.00 18-03-23,17.00,2.00,4.00 18-03-22,18.00,2.00,4.00 18-03-21,18.00,2.00,4.00 18-03-20,20.00,2.00,5.00 18-03-19,21.00,2.00,5.00 18-03-18,22.00,3.00,6.00 18-03-17,18.00,2.00,4.00 18-03-16,20.00,2.00,4.00 18-03-15,25.00,2.00,6.00 18-03-14,22.00,2.00,6.00 18-03-13,18.00,2.00,5.00 18-03-12,20.00,2.00,5.00 18-03-11,18.00,2.00,4.00 18-03-10,18.00,2.00,4.00 18-03-09,24.00,2.00,6.00 18-03-08,21.00,2.00,5.00 18-03-07,22.00,2.00,6.00 18-03-06,19.00,2.00,5.00 18-03-05,19.00,2.00,5.00 18-03-04,17.00,2.00,4.00 18-03-03,19.00,2.00,4.00 18-03-02,19.00,2.00,5.00 18-03-01,19.00,2.00,5.00 18-02-28,21.00,2.00,5.00 18-02-27,22.00,2.00,5.00 18-02-26,21.00,2.00,5.00 18-02-25,19.00,2.00,5.00 18-02-24,20.00,2.00,5.00 18-02-23,23.00,2.00,6.00 18-02-22,26.00,3.00,7.00 18-02-21,28.00,3.00,8.00 18-02-20,27.00,3.00,7.00 18-02-19,25.00,3.00,6.00 18-02-18,28.00,3.00,6.00 18-02-17,26.00,3.00,6.00 18-02-16,29.00,3.00,6.00 18-02-15,31.00,3.00,7.00 18-02-14,29.00,3.00,7.00 18-02-13,28.00,3.00,7.00 18-02-12,32.00,3.00,7.00 18-02-11,27.00,3.00,6.00 18-02-10,32.00,3.00,7.00 18-02-09,38.00,3.00,7.00 18-02-08,43.00,4.00,8.00 18-02-07,51.00,4.00,9.00 18-02-06,83.00,6.00,13.00 18-02-05,64.00,6.00,12.00 18-02-04,37.00,4.00,9.00 18-02-03,42.00,4.00,8.00 18-02-02,65.00,6.00,14.00 18-02-01,50.00,5.00,12.00 18-01-31,39.00,4.00,9.00 18-01-30,35.00,4.00,10.00 18-01-29,31.00,5.00,9.00 18-01-28,30.00,5.00,9.00 18-01-27,32.00,4.00,9.00 18-01-26,38.00,4.00,9.00 18-01-25,39.00,5.00,9.00 18-01-24,40.00,4.00,9.00 18-01-23,44.00,5.00,10.00 18-01-22,45.00,5.00,11.00 18-01-21,39.00,5.00,11.00 18-01-20,43.00,5.00,11.00 18-01-19,50.00,6.00,12.00 18-01-18,71.00,8.00,15.00 18-01-17,100.00,11.00,22.00 18-01-16,74.00,11.00,25.00 18-01-15,37.00,7.00,13.00 18-01-14,38.00,7.00,13.00 18-01-13,40.00,8.00,14.00 18-01-12,47.00,8.00,16.00 18-01-11,56.00,10.00,19.00 18-01-10,50.00,13.00,19.00 18-01-09,48.00,10.00,18.00 18-01-08,52.00,11.00,20.00 18-01-07,45.00,9.00,19.00 18-01-06,45.00,7.00,18.00 18-01-05,51.00,9.00,20.00 18-01-04,52.00,10.00,22.00 18-01-03,53.00,8.00,18.00 18-01-02,54.00,8.00,16.00 18-01-01,47.00,5.00,12.00 17-12-31,48.00,5.00,12.00 17-12-30,57.00,6.00,16.00 17-12-29,56.00,5.00,13.00 17-12-28,38.00,3.00,7.00 17-12-27,37.00,3.00,7.00 17-12-26,41.00,3.00,7.00 17-12-25,40.00,3.00,7.00 17-12-24,48.00,3.00,7.00 17-12-23,61.00,4.00,9.00 17-12-22,100.00,6.00,13.00 17-12-21,52.00,4.00,9.00 17-12-20,68.00,5.00,10.00 17-12-19,49.00,6.00,10.00 17-12-18,48.00,5.00,9.00 17-12-17,47.00,5.00,8.00 17-12-16,41.00,4.00,7.00 17-12-15,43.00,5.00,8.00 17-12-14,47.00,6.00,9.00 17-12-13,52.00,8.00,10.00 17-12-12,59.00,7.00,10.00 17-12-11,63.00,5.00,6.00 17-12-10,53.00,4.00,6.00 17-12-09,58.00,5.00,7.00 17-12-08,84.00,5.00,7.00 17-12-07,93.00,6.00,7.00 17-12-06,46.00,5.00,6.00 17-12-05,34.00,4.00,5.00 17-12-04,34.00,3.00,5.00 17-12-03,30.00,3.00,4.00 17-12-02,29.00,3.00,4.00 17-12-01,37.00,3.00,4.00 17-11-30,49.00,4.00,5.00 17-11-29,62.00,6.00,6.00 17-11-28,37.00,4.00,4.00 17-11-27,33.00,4.00,4.00 17-11-26,24.00,3.00,3.00 17-11-25,16.00,3.00,3.00 17-11-24,15.00,3.00,2.00 17-11-23,16.00,3.00,3.00 17-11-22,17.00,2.00,3.00 17-11-21,20.00,2.00,3.00 17-11-20,19.00,2.00,3.00 17-11-19,15.00,2.00,2.00 17-11-18,15.00,1.00,2.00 17-11-17,20.00,2.00,2.00 17-11-16,18.00,2.00,2.00 17-11-15,17.00,2.00,2.00 17-11-14,17.00,2.00,2.00 17-11-13,22.00,2.00,2.00 17-11-12,24.00,2.00,2.00 17-11-11,20.00,2.00,2.00 17-11-10,20.00,2.00,2.00 17-11-09,16.00,2.00,2.00 17-11-08,18.00,2.00,2.00 17-11-07,16.00,2.00,2.00 17-11-06,18.00,2.00,2.00 17-11-05,17.00,2.00,2.00 17-11-04,17.00,2.00,2.00 17-11-03,21.00,2.00,2.00 17-11-02,24.00,2.00,2.00 17-11-01,18.00,2.00,2.00 17-10-31,14.00,1.00,2.00 17-10-30,12.00,1.00,2.00 17-10-29,10.00,1.00,2.00 17-10-28,10.00,1.00,1.00 17-10-27,11.00,1.00,2.00 17-10-26,13.00,1.00,2.00 17-10-25,13.00,1.00,2.00 17-10-24,15.00,1.00,2.00 17-10-23,13.00,2.00,2.00 17-10-22,12.00,1.00,2.00 17-10-21,14.00,2.00,2.00 17-10-20,13.00,1.00,2.00 17-10-19,11.00,1.00,2.00 17-10-18,11.00,2.00,2.00 17-10-17,11.00,2.00,2.00 17-10-16,11.00,2.00,2.00 17-10-15,11.00,2.00,2.00 17-10-14,12.00,2.00,2.00 17-10-13,18.00,2.00,2.00 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17-04-02,18.00,3.00,2.00 17-04-01,18.00,2.00,2.00 -------------------------------------------------------------------------------- /data/all_eth.csv: -------------------------------------------------------------------------------- 1 | Date,Close,Open,High,Low,Volume,Market Cap 2 | 26-Mar-18,489.95,,,,, 3 | 25-Mar-18,524.29,,,,, 4 | 24-Mar-18,526.44,,,,, 5 | 23-Mar-18,539.62,,,,, 6 | 22-Mar-18,539.7,,,,, 7 | 21-Mar-18,561.73,,,,, 8 | 20-Mar-18,557.18,,,,, 9 | 19-Mar-18,556.73,,,,, 10 | 18-Mar-18,538.64,,,,, 11 | 17-Mar-18,552.78,,,,, 12 | 16-Mar-18,601.67,,,,, 13 | 15-Mar-18,611.3,,,,, 14 | 14-Mar-18,614.29,,,,, 15 | 13-Mar-18,690.83,,,,, 16 | 12-Mar-18,699.83,,,,, 17 | 11-Mar-18,723.34,,,,, 18 | 10-Mar-18,686.89,,,,, 19 | 9-Mar-18,728.92,,,,, 20 | 8-Mar-18,704.6,,,,, 21 | 7-Mar-18,752.83,,,,, 22 | 6-Mar-18,816.95,,,,, 23 | 5-Mar-18,853.68,,,,, 24 | 4-Mar-18,866.68,,,,, 25 | 3-Mar-18,857.23,,,,, 26 | 2-Mar-18,856.85,,,,, 27 | 1-Mar-18,872.2,,,,, 28 | 28-Feb-18,855.2,,,,, 29 | 27-Feb-18,878.27,,,,, 30 | 26-Feb-18,869.32,,,,, 31 | 25-Feb-18,844.81,,,,, 32 | 24-Feb-18,840.52,,,,, 33 | 23-Feb-18,864.19,,,,, 34 | 22-Feb-18,812.85,,,,, 35 | 21-Feb-18,849.97,,,,, 36 | 20-Feb-18,895.37,,,,, 37 | 19-Feb-18,943.87,,,,, 38 | 18-Feb-18,923.92,,,,, 39 | 17-Feb-18,974.12,,,,, 40 | 16-Feb-18,944.21,,,,, 41 | 15-Feb-18,936.98,,,,, 42 | 14-Feb-18,923.56,,,,, 43 | 13-Feb-18,845.26,,,,, 44 | 12-Feb-18,868.71,,,,, 45 | 11-Feb-18,814.66,,,,, 46 | 10-Feb-18,860.42,,,,, 47 | 9-Feb-18,883.87,,,,, 48 | 8-Feb-18,817.81,,,,, 49 | 7-Feb-18,757.07,,,,, 50 | 6-Feb-18,793.12,,,,, 51 | 5-Feb-18,697.95,,,,, 52 | 4-Feb-18,834.68,,,,, 53 | 3-Feb-18,964.02,,,,, 54 | 2-Feb-18,915.79,,,,, 55 | 1-Feb-18,1036.79,,,,, 56 | 31-Jan-18,1118.31,,,,, 57 | 30-Jan-18,1071.13,,,,, 58 | 29-Jan-18,1182.36,,,,, 59 | 28-Jan-18,1246.01,,,,, 60 | 27-Jan-18,1107.07,,,,, 61 | 26-Jan-18,1055.17,,,,, 62 | 25-Jan-18,1056.03,,,,, 63 | 24-Jan-18,1058.78,,,,, 64 | 23-Jan-18,986.23,,,,, 65 | 22-Jan-18,1003.26,,,,, 66 | 21-Jan-18,1049.58,,,,, 67 | 20-Jan-18,1155.15,,,,, 68 | 19-Jan-18,1039.1,,,,, 69 | 18-Jan-18,1036.28,,,,, 70 | 17-Jan-18,1014.25,,,,, 71 | 16-Jan-18,1053.69,,,,, 72 | 15-Jan-18,1291.92,,,,, 73 | 14-Jan-18,1366.77,,,,, 74 | 13-Jan-18,1396.42,,,,, 75 | 12-Jan-18,1273.2,,,,, 76 | 11-Jan-18,1154.93,,,,, 77 | 10-Jan-18,1255.82,,,,, 78 | 9-Jan-18,1299.74,,,,, 79 | 8-Jan-18,1148.53,,,,, 80 | 7-Jan-18,1153.17,,,,, 81 | 6-Jan-18,1041.68,,,,, 82 | 5-Jan-18,997.72,,,,, 83 | 4-Jan-18,980.92,,,,, 84 | 3-Jan-18,962.72,,,,, 85 | 2-Jan-18,884.44,,,,, 86 | 1-Jan-18,772.64,,,,, 87 | 31-Dec-17,756.73,,,,, 88 | 30-Dec-17,717.26,,,,, 89 | 29-Dec-17,753.59,,,,, 90 | 28-Dec-17,737.02,,,,, 91 | 27-Dec-17,762.84,,,,, 92 | 26-Dec-17,773.84,,,,, 93 | 25-Dec-17,765.83,,,,, 94 | 24-Dec-17,694.15,,,,, 95 | 23-Dec-17,719.39,,,,, 96 | 22-Dec-17,674.86,,,,, 97 | 21-Dec-17,821.06,,,,, 98 | 20-Dec-17,819.09,,,,, 99 | 19-Dec-17,826.82,,,,, 100 | 18-Dec-17,794.65,,,,, 101 | 17-Dec-17,719.98,,,,, 102 | 16-Dec-17,696.21,,,,, 103 | 15-Dec-17,684.45,,,,, 104 | 14-Dec-17,695.82,,,,, 105 | 13-Dec-17,702.77,,,,, 106 | 12-Dec-17,651.43,,,,, 107 | 11-Dec-17,515.14,,,,, 108 | 10-Dec-17,441.72,,,,, 109 | 9-Dec-17,473.5,,,,, 110 | 8-Dec-17,456.03,,,,, 111 | 7-Dec-17,434.41,,,,, 112 | 6-Dec-17,428.59,,,,, 113 | 5-Dec-17,463.28,,,,, 114 | 4-Dec-17,470.2,,,,, 115 | 3-Dec-17,465.85,,,,, 116 | 2-Dec-17,463.45,,,,, 117 | 1-Dec-17,466.54,,,,, 118 | 30-Nov-17,447.11,,,,, 119 | 29-Nov-17,427.52,,,,, 120 | 28-Nov-17,472.9,,,,, 121 | 27-Nov-17,480.36,,,,, 122 | 26-Nov-17,471.33,,,,, 123 | 25-Nov-17,466.28,,,,, 124 | 24-Nov-17,474.91,,,,, 125 | 23-Nov-17,410.17,,,,, 126 | 22-Nov-17,380.65,,,,, 127 | 21-Nov-17,360.4,,,,, 128 | 20-Nov-17,366.73,,,,, 129 | 19-Nov-17,354.39,,,,, 130 | 18-Nov-17,347.61,,,,, 131 | 17-Nov-17,332.39,,,,, 132 | 16-Nov-17,330.92,,,,, 133 | 15-Nov-17,333.36,,,,, 134 | 14-Nov-17,337.63,,,,, 135 | 13-Nov-17,316.72,,,,, 136 | 12-Nov-17,307.91,,,,, 137 | 11-Nov-17,314.68,,,,, 138 | 10-Nov-17,299.25,,,,, 139 | 9-Nov-17,320.88,,,,, 140 | 8-Nov-17,309.07,,,,, 141 | 7-Nov-17,294.66,,,,, 142 | 6-Nov-17,298.89,,,,, 143 | 5-Nov-17,296.26,,,,, 144 | 4-Nov-17,300.47,,,,, 145 | 3-Nov-17,305.71,,,,, 146 | 2-Nov-17,287.43,,,,, 147 | 1-Nov-17,291.69,,,,, 148 | 31-Oct-17,305.88,,,,, 149 | 30-Oct-17,307.75,,,,, 150 | 29-Oct-17,305.09,,,,, 151 | 28-Oct-17,296.3,,,,, 152 | 27-Oct-17,297.42,,,,, 153 | 26-Oct-17,296.53,,,,, 154 | 25-Oct-17,297.93,,,,, 155 | 24-Oct-17,298.33,,,,, 156 | 23-Oct-17,286.95,,,,, 157 | 22-Oct-17,295.45,,,,, 158 | 21-Oct-17,300.19,,,,, 159 | 20-Oct-17,304.01,,,,, 160 | 19-Oct-17,308.09,,,,, 161 | 18-Oct-17,314.32,,,,, 162 | 17-Oct-17,317.08,,,,, 163 | 16-Oct-17,333.38,,,,, 164 | 15-Oct-17,336.6,,,,, 165 | 14-Oct-17,339.63,,,,, 166 | 13-Oct-17,338.76,,,,, 167 | 12-Oct-17,304.14,,,,, 168 | 11-Oct-17,303.46,,,,, 169 | 10-Oct-17,299.87,,,,, 170 | 9-Oct-17,297.39,,,,, 171 | 8-Oct-17,308.61,,,,, 172 | 7-Oct-17,311.12,,,,, 173 | 6-Oct-17,308.59,,,,, 174 | 5-Oct-17,295.86,,,,, 175 | 4-Oct-17,292.66,,,,, 176 | 3-Oct-17,292.46,,,,, 177 | 2-Oct-17,297.48,,,,, 178 | 1-Oct-17,302.34,,,,, 179 | 30-Sep-17,301.47,,,,, 180 | 29-Sep-17,291.47,,,,, 181 | 28-Sep-17,299.16,,,,, 182 | 27-Sep-17,306.47,,,,, 183 | 26-Sep-17,287.44,,,,, 184 | 25-Sep-17,292.33,,,,, 185 | 24-Sep-17,282.48,,,,, 186 | 23-Sep-17,286.17,,,,, 187 | 22-Sep-17,264.31,,,,, 188 | 21-Sep-17,258.58,,,,, 189 | 20-Sep-17,283.74,,,,, 190 | 19-Sep-17,282.8,,,,, 191 | 18-Sep-17,293.5,,,,, 192 | 17-Sep-17,251.75,,,,, 193 | 16-Sep-17,246.52,,,,, 194 | 15-Sep-17,250.46,,,,, 195 | 14-Sep-17,213.91,,,,, 196 | 13-Sep-17,277.11,,,,, 197 | 12-Sep-17,291.46,,,,, 198 | 11-Sep-17,294.53,,,,, 199 | 10-Sep-17,288.75,,,,, 200 | 9-Sep-17,294.41,,,,, 201 | 8-Sep-17,296.5,,,,, 202 | 7-Sep-17,329.43,,,,, 203 | 6-Sep-17,334.34,,,,, 204 | 5-Sep-17,312.99,,,,, 205 | 4-Sep-17,295.17,,,,, 206 | 3-Sep-17,347.48,,,,, 207 | 2-Sep-17,348.98,,,,, 208 | 1-Sep-17,387.74,,,,, 209 | 31-Aug-17,383.04,,,,, 210 | 30-Aug-17,378.49,,,,, 211 | 29-Aug-17,370.67,,,,, 212 | 28-Aug-17,347.75,,,,, 213 | 27-Aug-17,347.89,,,,, 214 | 26-Aug-17,333.88,,,,, 215 | 25-Aug-17,331.92,,,,, 216 | 24-Aug-17,325.61,,,,, 217 | 23-Aug-17,317.52,,,,, 218 | 22-Aug-17,314.79,,,,, 219 | 21-Aug-17,321.59,,,,, 220 | 20-Aug-17,301.43,,,,, 221 | 19-Aug-17,297.47,,,,, 222 | 18-Aug-17,295.59,,,,, 223 | 17-Aug-17,301.46,,,,, 224 | 16-Aug-17,302.27,,,,, 225 | 15-Aug-17,289.82,,,,, 226 | 14-Aug-17,300.1,,,,, 227 | 13-Aug-17,298.06,,,,, 228 | 12-Aug-17,310.6,,,,, 229 | 11-Aug-17,308.86,,,,, 230 | 10-Aug-17,295.89,,,,, 231 | 9-Aug-17,296.03,,,,, 232 | 8-Aug-17,296.77,,,,, 233 | 7-Aug-17,269.18,,,,, 234 | 6-Aug-17,261.57,,,,, 235 | 5-Aug-17,256.51,,,,, 236 | 4-Aug-17,223.07,,,,, 237 | 3-Aug-17,225.34,,,,, 238 | 2-Aug-17,219.95,,,,, 239 | 1-Aug-17,226.77,,,,, 240 | 31-Jul-17,203.87,,,,, 241 | 30-Jul-17,197.98,,,,, 242 | 29-Jul-17,205.79,,,,, 243 | 28-Jul-17,193.12,,,,, 244 | 27-Jul-17,204.32,,,,, 245 | 26-Jul-17,203.95,,,,, 246 | 25-Jul-17,206.71,,,,, 247 | 24-Jul-17,224.71,,,,, 248 | 23-Jul-17,225.95,,,,, 249 | 22-Jul-17,229.48,,,,, 250 | 21-Jul-17,218.31,,,,, 251 | 20-Jul-17,227.27,,,,, 252 | 19-Jul-17,199.7,,,,, 253 | 18-Jul-17,234.39,,,,, 254 | 17-Jul-17,193.42,,,,, 255 | 16-Jul-17,157.36,,,,, 256 | 15-Jul-17,170.66,,,,, 257 | 14-Jul-17,199.66,,,,, 258 | 13-Jul-17,209.73,,,,, 259 | 12-Jul-17,230.77,,,,, 260 | 11-Jul-17,197.4,,,,, 261 | 10-Jul-17,215.36,,,,, 262 | 9-Jul-17,242.14,,,,, 263 | 8-Jul-17,251.7,,,,, 264 | 7-Jul-17,245.99,,,,, 265 | 6-Jul-17,270.55,,,,, 266 | 5-Jul-17,268.77,,,,, 267 | 4-Jul-17,273.3,,,,, 268 | 3-Jul-17,282.9,,,,, 269 | 2-Jul-17,287.99,,,,, 270 | 1-Jul-17,274.6,,,,, 271 | 30-Jun-17,294.92,,,,, 272 | 29-Jun-17,302.88,,,,, 273 | 28-Jun-17,327.93,,,,, 274 | 27-Jun-17,293.09,,,,, 275 | 26-Jun-17,272.69,,,,, 276 | 25-Jun-17,303.25,,,,, 277 | 24-Jun-17,323.7,,,,, 278 | 23-Jun-17,341.74,,,,, 279 | 22-Jun-17,336.37,,,,, 280 | 21-Jun-17,336.87,,,,, 281 | 20-Jun-17,359.01,,,,, 282 | 19-Jun-17,370.06,,,,, 283 | 18-Jun-17,371.46,,,,, 284 | 17-Jun-17,379.41,,,,, 285 | 16-Jun-17,370.23,,,,, 286 | 15-Jun-17,361.93,,,,, 287 | 14-Jun-17,359.05,,,,, 288 | 13-Jun-17,397.54,,,,, 289 | 12-Jun-17,401.49,,,,, 290 | 11-Jun-17,340.61,,,,, 291 | 10-Jun-17,337.67,,,,, 292 | 9-Jun-17,281.74,,,,, 293 | 8-Jun-17,261.67,,,,, 294 | 7-Jun-17,258.07,,,,, 295 | 6-Jun-17,264.47,,,,, 296 | 5-Jun-17,248.46,,,,, 297 | 4-Jun-17,245.33,,,,, 298 | 3-Jun-17,224.38,,,,, 299 | 2-Jun-17,223.78,,,,, 300 | 1-Jun-17,222.24,,,,, 301 | 31-May-17,230.67,,,,, 302 | 30-May-17,231.91,,,,, 303 | 29-May-17,194.91,,,,, 304 | 28-May-17,170.51,,,,, 305 | 27-May-17,157.76,,,,, 306 | 26-May-17,160.4,,,,, 307 | 25-May-17,174.45,,,,, 308 | 24-May-17,190.05,,,,, 309 | 23-May-17,181.95,,,,, 310 | 22-May-17,174.26,,,,, 311 | 21-May-17,157.94,,,,, 312 | 20-May-17,126.52,,,,, 313 | 19-May-17,129.53,,,,, 314 | 18-May-17,96.91,,,,, 315 | 17-May-17,89.86,,,,, 316 | 16-May-17,89.44,,,,, 317 | 15-May-17,92.41,,,,, 318 | 14-May-17,90.79,,,,, 319 | 13-May-17,90.84,,,,, 320 | 12-May-17,88.66,,,,, 321 | 11-May-17,89.88,,,,, 322 | 10-May-17,89.52,,,,, 323 | 9-May-17,91.16,,,,, 324 | 8-May-17,91.42,,,,, 325 | 7-May-17,94.01,,,,, 326 | 6-May-17,97.81,,,,, 327 | 5-May-17,94.4,,,,, 328 | 4-May-17,96.98,,,,, 329 | 3-May-17,79.72,,,,, 330 | 2-May-17,77.26,,,,, 331 | 1-May-17,76.3,,,,, 332 | 30-Apr-17,79.02,,,,, 333 | 29-Apr-17,68.38,,,,, 334 | 28-Apr-17,70.16,,,,, 335 | 27-Apr-17,62.17,,,,, 336 | 26-Apr-17,52.72,,,,, 337 | 25-Apr-17,49.89,,,,, 338 | 24-Apr-17,50.03,,,,, 339 | 23-Apr-17,48.49,,,,, 340 | 22-Apr-17,48.55,,,,, 341 | 21-Apr-17,48.22,,,,, 342 | 20-Apr-17,49.67,,,,, 343 | 19-Apr-17,48.31,,,,, 344 | 18-Apr-17,50.71,,,,, 345 | 17-Apr-17,48.3,,,,, 346 | 16-Apr-17,48.72,,,,, 347 | 15-Apr-17,49.1,,,,, 348 | 14-Apr-17,47.57,,,,, 349 | 13-Apr-17,50.22,,,,, 350 | 12-Apr-17,46.29,,,,, 351 | 11-Apr-17,43.41,,,,, 352 | 10-Apr-17,43.44,,,,, 353 | 9-Apr-17,43.27,,,,, 354 | 8-Apr-17,44.31,,,,, 355 | 7-Apr-17,42.16,,,,, 356 | 6-Apr-17,43.24,,,,, 357 | 5-Apr-17,45.3,,,,, 358 | 4-Apr-17,44.64,,,,, 359 | 3-Apr-17,44.36,,,,, 360 | 2-Apr-17,48.75,,,,, 361 | 1-Apr-17,50.7,,,,, 362 | -------------------------------------------------------------------------------- /data/all_bitcoin.csv: -------------------------------------------------------------------------------- 1 | Date,Open,High,Low,Close,Volume,Market Cap 2 | 25-Mar-18,8612.81,8682.01,8449.1,8495.78,4569880000,1.45882E+11 3 | 24-Mar-18,8901.95,8996.18,8665.7,8668.12,5664600000,1.50762E+11 4 | 23-Mar-18,8736.25,8879.62,8360.62,8879.62,5954120000,1.47941E+11 5 | 22-Mar-18,8939.44,9100.71,8564.9,8728.47,5530390000,1.51366E+11 6 | 21-Mar-18,8937.48,9177.37,8846.33,8929.28,6043130000,1.51316E+11 7 | 20-Mar-18,8619.67,9051.02,8389.89,8913.47,6361790000,1.45922E+11 8 | 19-Mar-18,8344.12,8675.87,8182.4,8630.65,6729110000,1.4124E+11 9 | 18-Mar-18,7890.52,8245.51,7397.99,8223.68,6639190000,1.33547E+11 10 | 17-Mar-18,8321.91,8346.53,7812.82,7916.88,4426150000,1.40834E+11 11 | 16-Mar-18,8322.91,8585.15,8005.31,8338.35,5289380000,1.40834E+11 12 | 15-Mar-18,8290.76,8428.35,7783.05,8300.86,6834430000,1.40275E+11 13 | 14-Mar-18,9214.65,9355.85,8068.59,8269.81,6438230000,1.55891E+11 14 | 13-Mar-18,9173.04,9470.38,8958.19,9194.85,5991140000,1.55168E+11 15 | 12-Mar-18,9602.93,9937.5,8956.43,9205.12,6457400000,1.62421E+11 16 | 11-Mar-18,8852.78,9711.89,8607.12,9578.63,6296370000,1.49716E+11 17 | 10-Mar-18,9350.59,9531.32,8828.47,8866,5386320000,1.58119E+11 18 | 09-Mar-18,9414.69,9466.35,8513.03,9337.55,8704190000,1.59185E+11 19 | 08-Mar-18,9951.44,10147.4,9335.87,9395.01,7186090000,1.68241E+11 20 | 07-Mar-18,10803.9,10929.5,9692.12,9965.57,8797910000,1.82631E+11 21 | 06-Mar-18,11500.1,11500.1,10694.3,10779.9,6832170000,1.94378E+11 22 | 05-Mar-18,11532.4,11704.1,11443.9,11573.3,6468540000,1.94903E+11 23 | 04-Mar-18,11497.4,11512.6,11136.1,11512.6,6084150000,1.94289E+11 24 | 03-Mar-18,11101.9,11528.2,11002.4,11489.7,6690570000,1.87581E+11 25 | 02-Mar-18,10977.4,11189,10850.1,11086.4,7620590000,1.85456E+11 26 | 01-Mar-18,10385,11052.3,10352.7,10951,7317280000,1.75427E+11 27 | 28-Feb-18,10687.2,11089.8,10393.1,10397.9,6936190000,1.8051E+11 28 | 27-Feb-18,10393.9,10878.5,10246.1,10725.6,6966180000,1.75536E+11 29 | 26-Feb-18,9669.43,10475,9501.73,10366.7,7287690000,1.63283E+11 30 | 25-Feb-18,9796.42,9923.22,9407.06,9664.73,5706940000,1.65407E+11 31 | 24-Feb-18,10287.7,10597.2,9546.97,9813.07,6917930000,1.73682E+11 32 | 23-Feb-18,9937.07,10487.3,9734.56,10301.1,7739500000,1.67746E+11 33 | 22-Feb-18,10660.4,11039.1,9939.09,10005,8040080000,1.79936E+11 34 | 21-Feb-18,11372.2,11418.5,10479.1,10690.4,9405340000,1.91927E+11 35 | 20-Feb-18,11231.8,11958.5,11231.8,11403.7,9926540000,1.89536E+11 36 | 19-Feb-18,10552.6,11273.8,10513.2,11225.3,7652090000,1.78055E+11 37 | 18-Feb-18,11123.4,11349.8,10326,10551.8,8744010000,1.87663E+11 38 | 17-Feb-18,10207.5,11139.5,10149.4,11112.7,8660880000,1.72191E+11 39 | 16-Feb-18,10135.7,10324.1,9824.82,10233.9,7296160000,1.7096E+11 40 | 15-Feb-18,9488.32,10234.8,9395.58,10166.4,9062540000,1.60025E+11 41 | 14-Feb-18,8599.92,9518.54,8599.92,9494.63,7909820000,1.45023E+11 42 | 13-Feb-18,8926.72,8958.47,8455.41,8598.31,5696720000,1.50516E+11 43 | 12-Feb-18,8141.43,8985.92,8141.43,8926.57,6256440000,1.37258E+11 44 | 11-Feb-18,8616.13,8616.13,7931.1,8129.97,6122190000,1.45245E+11 45 | 10-Feb-18,8720.08,9122.55,8295.47,8621.9,7780960000,1.46981E+11 46 | 09-Feb-18,8271.84,8736.98,7884.71,8736.98,6784820000,1.39412E+11 47 | 08-Feb-18,7637.86,8558.77,7637.86,8265.59,9346750000,1.28714E+11 48 | 07-Feb-18,7755.49,8509.11,7236.79,7621.3,9169280000,1.30683E+11 49 | 06-Feb-18,7051.75,7850.7,6048.26,7754,13999800000,1.1881E+11 50 | 05-Feb-18,8270.54,8364.84,6756.68,6955.27,9285290000,1.39325E+11 51 | 04-Feb-18,9175.7,9334.87,8031.22,8277.01,7073550000,1.54553E+11 52 | 03-Feb-18,8852.12,9430.75,8251.63,9174.91,7263790000,1.49085E+11 53 | 02-Feb-18,9142.28,9142.28,7796.49,8830.75,12726900000,1.53953E+11 54 | 01-Feb-18,10237.3,10288.8,8812.28,9170.54,9959400000,1.72372E+11 55 | 31-Jan-18,10108.2,10381.6,9777.42,10221.1,8041160000,1.70183E+11 56 | 30-Jan-18,11306.8,11307.2,10036.2,10106.3,8637860000,1.90339E+11 57 | 29-Jan-18,11755.5,11875.6,11179.2,11296.4,7107360000,1.97871E+11 58 | 28-Jan-18,11475.3,12040.3,11475.3,11786.3,8350360000,1.93133E+11 59 | 27-Jan-18,11174.9,11614.9,10989.2,11440.7,7583270000,1.88054E+11 60 | 26-Jan-18,11256,11656.7,10470.3,11171.4,9746200000,1.89398E+11 61 | 25-Jan-18,11421.7,11785.7,11057.4,11259.4,8873170000,1.92163E+11 62 | 24-Jan-18,10903.4,11501.4,10639.8,11359.4,9940990000,1.83419E+11 63 | 23-Jan-18,10944.5,11377.6,10129.7,10868.4,9660610000,1.84087E+11 64 | 22-Jan-18,11633.1,11966.4,10240.2,10931.4,10537400000,1.95645E+11 65 | 21-Jan-18,12889.2,12895.9,11288.2,11600.1,9935180000,2.1674E+11 66 | 20-Jan-18,11656.2,13103,11656.2,12899.2,11801700000,1.95979E+11 67 | 19-Jan-18,11429.8,11992.8,11172.1,11607.4,10740400000,1.9215E+11 68 | 18-Jan-18,11198.8,12107.3,10942.5,11474.9,15020400000,1.88242E+11 69 | 17-Jan-18,11431.1,11678,9402.29,11188.6,18830600000,1.92123E+11 70 | 16-Jan-18,13836.1,13843.1,10194.9,11490.5,18853800000,2.32517E+11 71 | 15-Jan-18,13767.3,14445.5,13641.7,13819.8,12750800000,2.31334E+11 72 | 14-Jan-18,14370.8,14511.8,13268,13772,11084100000,2.41447E+11 73 | 13-Jan-18,13952.4,14659.5,13952.4,14360.2,12763600000,2.34391E+11 74 | 12-Jan-18,13453.9,14229.9,13158.1,13980.6,12065700000,2.25986E+11 75 | 11-Jan-18,14968.2,15018.8,13105.9,13405.8,16534100000,2.51387E+11 76 | 10-Jan-18,14588.5,14973.3,13691.2,14973.3,18500800000,2.44981E+11 77 | 09-Jan-18,15123.7,15497.5,14424,14595.4,16660000000,2.53935E+11 78 | 08-Jan-18,16476.2,16537.9,14208.2,15170.1,18413900000,2.76612E+11 79 | 07-Jan-18,17527.3,17579.6,16087.7,16477.6,15866000000,2.94222E+11 80 | 06-Jan-18,17462.1,17712.4,16764.6,17527,18314600000,2.93091E+11 81 | 05-Jan-18,15477.2,17705.2,15202.8,17429.5,23840900000,2.59748E+11 82 | 04-Jan-18,15270.7,15739.7,14522.2,15599.2,21783200000,2.5625E+11 83 | 03-Jan-18,14978.2,15572.8,14844.5,15201,16871900000,2.51312E+11 84 | 02-Jan-18,13625,15444.6,13163.6,14982.1,16846600000,2.28579E+11 85 | 01-Jan-18,14112.2,14112.2,13154.7,13657.2,10291200000,2.36725E+11 86 | 31-Dec-17,12897.7,14377.4,12755.6,14156.4,12136300000,2.16326E+11 87 | 30-Dec-17,14681.9,14681.9,12350.1,12952.2,14452600000,2.46224E+11 88 | 29-Dec-17,14695.8,15279,14307,14656.2,13025500000,2.46428E+11 89 | 28-Dec-17,15864.1,15888.4,13937.3,14606.5,12336500000,2.65988E+11 90 | 27-Dec-17,16163.5,16930.9,15114.3,15838.5,12487600000,2.70976E+11 91 | 26-Dec-17,14036.6,16461.2,14028.9,16099.8,13454300000,2.35294E+11 92 | 25-Dec-17,13995.9,14593,13448.9,14026.6,10664700000,2.3459E+11 93 | 24-Dec-17,14608.2,14626,12747.7,13925.8,11572300000,2.44824E+11 94 | 23-Dec-17,13948.7,15603.2,13828.8,14699.2,13086000000,2.33748E+11 95 | 22-Dec-17,15898,15943.4,11833,13831.8,22198000000,2.66381E+11 96 | 21-Dec-17,16642.4,17567.7,15342.7,15802.9,16516600000,2.78827E+11 97 | 20-Dec-17,17760.3,17934.7,16077.7,16624.6,22149700000,2.97526E+11 98 | 19-Dec-17,19118.3,19177.8,17275.4,17776.7,16894500000,3.20242E+11 99 | 18-Dec-17,19106.4,19371,18355.9,19114.2,14839500000,3.2E+11 100 | 17-Dec-17,19475.8,20089,18974.1,19140.8,13314600000,3.26141E+11 101 | 16-Dec-17,17760.3,19716.7,17515.3,19497.4,12740600000,2.97376E+11 102 | 15-Dec-17,16601.3,18154.1,16601.3,17706.9,14310000000,2.77936E+11 103 | 14-Dec-17,16384.6,17085.8,16185.9,16564,13777400000,2.74269E+11 104 | 13-Dec-17,17500,17653.1,16039.7,16408.2,12976900000,2.929E+11 105 | 12-Dec-17,16919.8,17781.8,16571.6,17415.4,14603800000,2.83155E+11 106 | 11-Dec-17,15427.4,17513.9,15404.8,16936.8,12153900000,2.58147E+11 107 | 10-Dec-17,15168.4,15850.6,13226.6,15455.4,13433300000,2.53782E+11 108 | 09-Dec-17,16523.3,16783,13674.9,15178.2,13911300000,2.76415E+11 109 | 08-Dec-17,17802.9,18353.4,14336.9,16569.4,21136000000,2.97787E+11 110 | 07-Dec-17,14266.1,17899.7,14057.3,17899.7,17950700000,2.386E+11 111 | 06-Dec-17,11923.4,14369.1,11923.4,14291.5,12656300000,1.9939E+11 112 | 05-Dec-17,11685.7,12032,11604.6,11916.7,6895260000,1.95389E+11 113 | 04-Dec-17,11315.4,11657.2,11081.8,11657.2,6132410000,1.89172E+11 114 | 03-Dec-17,11082.7,11858.7,10862,11323.2,6608310000,1.85258E+11 115 | 02-Dec-17,10978.3,11320.2,10905.1,11074.6,5138500000,1.8349E+11 116 | 01-Dec-17,10198.6,11046.7,9694.65,10975.6,6783120000,1.70436E+11 117 | 30-Nov-17,9906.79,10801,9202.05,10233.6,8310690000,1.65537E+11 118 | 29-Nov-17,10077.4,11517.4,9601.03,9888.61,11568800000,1.68367E+11 119 | 28-Nov-17,9823.43,10125.7,9736.3,10058.8,6348820000,1.64104E+11 120 | 27-Nov-17,9352.72,9818.35,9352.72,9818.35,5653320000,1.56221E+11 121 | 26-Nov-17,8789.04,9522.93,8775.59,9330.55,5475580000,1.46789E+11 122 | 25-Nov-17,8241.71,8790.92,8191.15,8790.92,4342060000,1.37632E+11 123 | 24-Nov-17,8074.02,8374.16,7940.93,8253.69,5058610000,1.34816E+11 124 | 23-Nov-17,8232.38,8267.4,8038.77,8038.77,4225180000,1.37444E+11 125 | 22-Nov-17,8077.95,8302.26,8075.47,8253.55,3633530000,1.34851E+11 126 | 21-Nov-17,8205.74,8348.66,7762.71,8071.26,4277610000,1.36967E+11 127 | 20-Nov-17,8039.07,8336.86,7949.36,8200.64,3488450000,1.34167E+11 128 | 19-Nov-17,7766.03,8101.91,7694.1,8036.49,3149320000,1.29595E+11 129 | 18-Nov-17,7697.21,7884.99,7463.44,7790.15,3667190000,1.28425E+11 130 | 17-Nov-17,7853.57,8004.59,7561.09,7708.99,4651670000,1.31026E+11 131 | 16-Nov-17,7323.24,7967.38,7176.58,7871.69,5123810000,1.22164E+11 132 | 15-Nov-17,6634.76,7342.25,6634.76,7315.54,4200880000,1.10667E+11 133 | 14-Nov-17,6561.48,6764.98,6461.75,6635.75,3197110000,1.09434E+11 134 | 13-Nov-17,5938.25,6811.19,5844.29,6559.49,6263250000,99029000000 135 | 12-Nov-17,6295.45,6625.05,5519.01,5950.07,8957350000,1.0498E+11 136 | 11-Nov-17,6618.61,6873.15,6204.22,6357.6,4908680000,1.10362E+11 137 | 10-Nov-17,7173.73,7312,6436.87,6618.14,5208250000,1.19607E+11 138 | 09-Nov-17,7446.83,7446.83,7101.52,7143.58,3226250000,1.24146E+11 139 | 08-Nov-17,7141.38,7776.42,7114.02,7459.69,4602200000,1.19041E+11 140 | 07-Nov-17,7023.1,7253.32,7023.1,7144.38,2326340000,1.17056E+11 141 | 06-Nov-17,7403.22,7445.77,7007.31,7022.76,3111900000,1.23379E+11 142 | 05-Nov-17,7404.52,7617.48,7333.19,7407.41,2380410000,1.23388E+11 143 | 04-Nov-17,7164.48,7492.86,7031.28,7379.95,2483800000,1.19376E+11 144 | 03-Nov-17,7087.53,7461.29,7002.94,7207.76,3369860000,1.18084E+11 145 | 02-Nov-17,6777.77,7367.33,6758.72,7078.5,4653770000,1.1291E+11 146 | 01-Nov-17,6440.97,6767.31,6377.88,6767.31,2870320000,1.07287E+11 147 | 31-Oct-17,6132.02,6470.43,6103.33,6468.4,2311380000,1.0213E+11 148 | 30-Oct-17,6114.85,6214.99,6040.85,6130.53,1772150000,1.01833E+11 149 | 29-Oct-17,5754.44,6255.71,5724.58,6153.85,2859040000,95819800000 150 | 28-Oct-17,5787.82,5876.72,5689.19,5753.09,1403920000,96369600000 151 | 27-Oct-17,5899.74,5988.39,5728.82,5780.9,1710130000,98225400000 152 | 26-Oct-17,5747.95,5976.8,5721.22,5904.83,1905040000,95685100000 153 | 25-Oct-17,5524.6,5754.33,5397.88,5750.8,1966990000,91954200000 154 | 24-Oct-17,5935.52,5935.52,5504.18,5526.64,2735700000,98781600000 155 | 23-Oct-17,6006,6075.59,5732.47,5930.32,2401840000,99941600000 156 | 22-Oct-17,6036.66,6076.26,5792.34,6008.42,2034630000,1.00438E+11 157 | 21-Oct-17,5996.79,6194.88,5965.07,6031.6,2207100000,99763200000 158 | 20-Oct-17,5708.11,6060.11,5627.23,6011.45,2354430000,94947900000 159 | 19-Oct-17,5583.74,5744.35,5531.06,5708.52,1780540000,92867000000 160 | 18-Oct-17,5603.82,5603.82,5151.44,5590.69,2399270000,93190200000 161 | 17-Oct-17,5741.58,5800.35,5472.72,5605.51,1821570000,95469300000 162 | 16-Oct-17,5687.57,5776.23,5544.21,5725.59,2008070000,94559000000 163 | 15-Oct-17,5835.96,5852.48,5478.61,5678.19,1976040000,97011900000 164 | 14-Oct-17,5643.53,5837.7,5591.64,5831.79,1669030000,93803000000 165 | 13-Oct-17,5464.16,5840.3,5436.85,5647.21,3615480000,90812400000 166 | 12-Oct-17,4829.58,5446.91,4822,5446.91,2791610000,80256700000 167 | 11-Oct-17,4789.25,4873.73,4751.63,4826.48,1222280000,79578200000 168 | 10-Oct-17,4776.21,4922.17,4765.1,4781.99,1597140000,79351800000 169 | 09-Oct-17,4614.52,4878.71,4564.25,4772.02,1968740000,76656500000 170 | 08-Oct-17,4429.67,4624.14,4405.64,4610.48,1313870000,73575400000 171 | 07-Oct-17,4369.35,4443.88,4321.05,4426.89,906928000,72565100000 172 | 06-Oct-17,4324.46,4413.27,4320.53,4370.81,1069940000,71810600000 173 | 05-Oct-17,4229.88,4362.64,4164.05,4328.41,1161770000,70233700000 174 | 04-Oct-17,4319.37,4352.31,4210.42,4229.36,1116770000,71712500000 175 | 03-Oct-17,4408.46,4432.47,4258.89,4317.48,1288020000,73181300000 176 | 02-Oct-17,4395.81,4470.23,4377.46,4409.32,1431730000,72963200000 177 | 01-Oct-17,4341.05,4403.74,4269.81,4403.74,1208210000,72047300000 178 | 30-Sep-17,4166.11,4358.43,4160.86,4338.71,1207450000,69136600000 179 | 29-Sep-17,4171.62,4214.63,4039.29,4163.07,1367050000,69219200000 180 | 28-Sep-17,4197.13,4279.31,4109.7,4174.73,1712320000,69633200000 181 | 27-Sep-17,3892.94,4210.05,3884.82,4200.67,1686880000,64579200000 182 | 26-Sep-17,3928.41,3969.89,3869.9,3892.35,1043740000,65161000000 183 | 25-Sep-17,3681.58,3950.25,3681.58,3926.07,1374210000,61061100000 184 | 24-Sep-17,3796.15,3796.15,3666.9,3682.84,768015000,62954300000 185 | 23-Sep-17,3629.92,3819.21,3594.58,3792.4,928114000,60190000000 186 | 22-Sep-17,3628.02,3758.27,3553.53,3630.7,1194830000,60152300000 187 | 21-Sep-17,3901.47,3916.42,3613.63,3631.04,1411480000,64677600000 188 | 20-Sep-17,3916.36,4031.39,3857.73,3905.95,1213830000,64918500000 189 | 19-Sep-17,4073.79,4094.07,3868.87,3924.97,1563980000,67520300000 190 | 18-Sep-17,3591.09,4079.23,3591.09,4065.2,1943210000,59514100000 191 | 17-Sep-17,3606.28,3664.81,3445.64,3582.88,1239150000,59757800000 192 | 16-Sep-17,3637.75,3808.84,3487.79,3625.04,1818400000,60271600000 193 | 15-Sep-17,3166.3,3733.45,2946.62,3637.52,4148070000,52453500000 194 | 14-Sep-17,3875.37,3920.6,3153.86,3154.95,2716310000,64191600000 195 | 13-Sep-17,4131.98,4131.98,3789.92,3882.59,2219410000,68432200000 196 | 12-Sep-17,4168.88,4344.65,4085.22,4130.81,1864530000,69033400000 197 | 11-Sep-17,4122.47,4261.67,4099.4,4161.27,1557330000,68256000000 198 | 10-Sep-17,4229.34,4245.44,3951.04,4122.94,1679090000,70018100000 199 | 09-Sep-17,4229.81,4308.82,4114.11,4226.06,1386230000,70017200000 200 | 08-Sep-17,4605.16,4661,4075.18,4228.75,2700890000,76220200000 201 | 07-Sep-17,4589.14,4655.04,4491.33,4599.88,1844620000,75945000000 202 | 06-Sep-17,4376.59,4617.25,4376.59,4597.12,2172100000,72418700000 203 | 05-Sep-17,4228.29,4427.84,3998.11,4376.53,2697970000,69954400000 204 | 04-Sep-17,4591.63,4591.63,4108.4,4236.31,2987330000,75955500000 205 | 03-Sep-17,4585.27,4714.08,4417.59,4582.96,1933190000,75841700000 206 | 02-Sep-17,4901.42,4975.04,4469.24,4578.77,2722140000,81060600000 207 | 01-Sep-17,4701.76,4892.01,4678.53,4892.01,2599080000,77748400000 208 | 31-Aug-17,4555.59,4736.05,4549.4,4703.39,1944930000,75322300000 209 | 30-Aug-17,4570.36,4626.52,4471.41,4565.3,1937850000,75556600000 210 | 29-Aug-17,4389.21,4625.68,4352.13,4579.02,2486080000,72553800000 211 | 28-Aug-17,4384.45,4403.93,4224.64,4382.66,1959330000,72467900000 212 | 27-Aug-17,4345.1,4416.59,4317.29,4382.88,1537460000,71809200000 213 | 26-Aug-17,4372.06,4379.28,4269.52,4352.4,1511610000,72249100000 214 | 25-Aug-17,4332.82,4455.7,4307.35,4371.6,1727970000,71595100000 215 | 24-Aug-17,4137.6,4376.39,4130.26,4334.68,2037750000,68363900000 216 | 23-Aug-17,4089.01,4255.78,4078.41,4151.52,2369820000,67553000000 217 | 22-Aug-17,3998.35,4128.76,3674.58,4100.52,3764240000,66051000000 218 | 21-Aug-17,4090.48,4109.14,3988.6,4001.74,2800890000,67567100000 219 | 20-Aug-17,4189.31,4196.29,4069.88,4087.66,2109770000,69192700000 220 | 19-Aug-17,4137.75,4243.26,3970.55,4193.7,2975820000,68333100000 221 | 18-Aug-17,4324.34,4370.13,4015.4,4160.62,2941710000,71406500000 222 | 17-Aug-17,4384.44,4484.7,4243.71,4331.69,2553360000,72389100000 223 | 16-Aug-17,4200.34,4381.23,3994.42,4376.63,2272040000,69342700000 224 | 15-Aug-17,4326.99,4455.97,3906.18,4181.93,3258050000,71425500000 225 | 14-Aug-17,4066.1,4325.13,3989.16,4325.13,2463090000,67112300000 226 | 13-Aug-17,3880.04,4208.39,3857.8,4073.26,3159090000,64034100000 227 | 12-Aug-17,3650.63,3949.92,3613.7,3884.71,2219590000,60242100000 228 | 11-Aug-17,3373.82,3679.72,3372.12,3650.62,2021190000,55668000000 229 | 10-Aug-17,3341.84,3453.45,3319.47,3381.28,1515110000,55134700000 230 | 09-Aug-17,3420.4,3422.76,3247.67,3342.47,1468960000,56424900000 231 | 08-Aug-17,3370.22,3484.85,3345.83,3419.94,1752760000,55590300000 232 | 07-Aug-17,3212.78,3397.68,3180.89,3378.94,1482280000,52987300000 233 | 06-Aug-17,3257.61,3293.29,3155.6,3213.94,1105030000,53720900000 234 | 05-Aug-17,2897.63,3290.01,2874.83,3252.91,1945700000,47778200000 235 | 04-Aug-17,2806.93,2899.33,2743.72,2895.89,1002120000,46276200000 236 | 03-Aug-17,2709.56,2813.31,2685.14,2804.73,804797000,44666400000 237 | 02-Aug-17,2727.13,2762.53,2668.59,2710.67,1094950000,44950800000 238 | 01-Aug-17,2871.3,2921.35,2685.61,2718.26,1324670000,47321800000 239 | 31-Jul-17,2763.24,2889.62,2720.61,2875.34,860575000,45535800000 240 | 30-Jul-17,2724.39,2758.53,2644.85,2757.18,705943000,44890700000 241 | 29-Jul-17,2807.02,2808.76,2692.8,2726.45,803746000,46246700000 242 | 28-Jul-17,2679.73,2897.45,2679.73,2809.01,1380100000,44144400000 243 | 27-Jul-17,2538.71,2693.32,2529.34,2671.78,789104000,41816500000 244 | 26-Jul-17,2577.77,2610.76,2450.8,2529.45,937404000,42455000000 245 | 25-Jul-17,2757.5,2768.08,2480.96,2576.48,1460090000,45410100000 246 | 24-Jul-17,2732.7,2777.26,2699.19,2754.86,866474000,44995600000 247 | 23-Jul-17,2808.1,2832.18,2653.94,2730.4,1072840000,46231100000 248 | 22-Jul-17,2668.63,2862.42,2657.71,2810.12,1177130000,43929600000 249 | 21-Jul-17,2838.41,2838.41,2621.85,2667.76,1489450000,46719000000 250 | 20-Jul-17,2269.89,2900.7,2269.89,2817.6,2249260000,37356800000 251 | 19-Jul-17,2323.08,2397.17,2260.23,2273.43,1245100000,38227800000 252 | 18-Jul-17,2233.52,2387.61,2164.77,2318.88,1512450000,36749400000 253 | 17-Jul-17,1932.62,2230.49,1932.62,2228.41,1201760000,31795000000 254 | 16-Jul-17,1991.98,2058.77,1843.03,1929.82,1182870000,32767600000 255 | 15-Jul-17,2230.12,2231.14,1990.41,1998.86,993608000,36681300000 256 | 14-Jul-17,2360.59,2363.25,2183.22,2233.34,882503000,38823100000 257 | 13-Jul-17,2402.7,2425.22,2340.83,2357.9,835770000,39511000000 258 | 12-Jul-17,2332.77,2423.71,2275.14,2398.84,1117410000,38355900000 259 | 11-Jul-17,2385.89,2413.47,2296.81,2337.79,1329760000,39224200000 260 | 10-Jul-17,2525.25,2537.16,2321.13,2372.56,1111200000,41509000000 261 | 09-Jul-17,2572.61,2635.49,2517.59,2518.44,527856000,42283200000 262 | 08-Jul-17,2520.27,2571.34,2492.31,2571.34,733330000,41417700000 263 | 07-Jul-17,2608.59,2916.14,2498.87,2518.66,917412000,42864200000 264 | 06-Jul-17,2608.1,2616.72,2581.69,2608.56,761957000,42851400000 265 | 05-Jul-17,2602.87,2622.65,2538.55,2601.99,941566000,42760800000 266 | 04-Jul-17,2561,2631.59,2559.35,2601.64,985516000,42067900000 267 | 03-Jul-17,2498.56,2595,2480.47,2564.06,964112000,41037200000 268 | 02-Jul-17,2436.4,2514.28,2394.84,2506.47,803747000,40010500000 269 | 01-Jul-17,2492.6,2515.27,2419.23,2434.55,779914000,40928200000 270 | 30-Jun-17,2539.24,2559.25,2478.43,2480.84,860273000,41689100000 271 | 29-Jun-17,2567.56,2588.83,2510.48,2539.32,949979000,42150300000 272 | 28-Jun-17,2553.03,2603.98,2484.42,2574.79,1183870000,41906700000 273 | 27-Jun-17,2478.45,2552.45,2332.99,2552.45,1489790000,40677900000 274 | 26-Jun-17,2590.57,2615.25,2376.29,2478.45,1663280000,42514000000 275 | 25-Jun-17,2607.25,2682.26,2552.12,2589.41,1161100000,42783800000 276 | 24-Jun-17,2738.52,2757.94,2583.19,2608.72,982750000,44932900000 277 | 23-Jun-17,2707.34,2765.17,2706.37,2744.91,961319000,44415900000 278 | 22-Jun-17,2691.03,2723.74,2642.36,2705.41,1097940000,44143700000 279 | 21-Jun-17,2709.43,2772.01,2660.4,2689.1,1626580000,44440800000 280 | 20-Jun-17,2591.26,2763.45,2589.82,2721.79,1854190000,42498000000 281 | 19-Jun-17,2549.03,2662.85,2549.03,2589.6,1446840000,41800600000 282 | 18-Jun-17,2655.35,2662.1,2516.33,2548.29,1178660000,43539300000 283 | 17-Jun-17,2514.01,2685.19,2484.96,2655.88,1534510000,41217200000 284 | 16-Jun-17,2469.57,2539.92,2385.15,2518.56,1195190000,40484100000 285 | 15-Jun-17,2499.58,2534.71,2212.96,2464.58,2026260000,40971300000 286 | 14-Jun-17,2716.88,2786.83,2412.94,2506.37,1696560000,44528300000 287 | 13-Jun-17,2680.91,2789.04,2650.38,2717.02,1781200000,43934100000 288 | 12-Jun-17,2953.22,2997.26,2518.56,2659.63,2569530000,48391200000 289 | 11-Jun-17,2942.41,2996.6,2840.53,2958.11,1752400000,48208700000 290 | 10-Jun-17,2828.14,2950.99,2746.55,2947.71,2018890000,46331400000 291 | 09-Jun-17,2807.44,2901.71,2795.62,2823.81,1348950000,45987100000 292 | 08-Jun-17,2720.49,2815.3,2670.95,2805.62,1281170000,44557100000 293 | 07-Jun-17,2869.38,2869.38,2700.56,2732.16,1517710000,46989800000 294 | 06-Jun-17,2690.84,2999.91,2690.84,2863.2,2089610000,44061000000 295 | 05-Jun-17,2512.4,2686.81,2510.22,2686.81,1369310000,41133900000 296 | 04-Jun-17,2547.79,2585.89,2452.54,2511.81,1355120000,41708200000 297 | 03-Jun-17,2493.72,2581.91,2423.57,2515.35,1514950000,40817100000 298 | 02-Jun-17,2404.03,2488.55,2373.32,2488.55,1317030000,39344600000 299 | 01-Jun-17,2288.33,2448.39,2288.33,2407.88,1653180000,37446200000 300 | 31-May-17,2187.19,2311.08,2145.57,2286.41,1544830000,35786700000 301 | 30-May-17,2255.36,2301.96,2124.57,2175.47,1443970000,36897000000 302 | 29-May-17,2159.43,2307.05,2107.17,2255.61,994625000,35323500000 303 | 28-May-17,2054.08,2267.34,2054.08,2155.8,1147140000,33595900000 304 | 27-May-17,2196.27,2260.2,1855.83,2038.87,1700480000,35917100000 305 | 26-May-17,2320.89,2573.79,2071.99,2202.42,1763480000,37950600000 306 | 25-May-17,2446.24,2763.71,2285.3,2304.98,2406700000,39995400000 307 | 24-May-17,2321.37,2523.72,2321.37,2443.64,1725380000,37949200000 308 | 23-May-17,2191.56,2320.82,2178.5,2320.42,1378750000,35822600000 309 | 22-May-17,2043.19,2303.9,2017.87,2173.4,1942220000,33393600000 310 | 21-May-17,2067.03,2119.08,2037.5,2041.2,1147860000,33779400000 311 | 20-May-17,1984.24,2084.73,1974.92,2084.73,961336000,32422400000 312 | 19-May-17,1897.37,2004.52,1890.25,1987.71,1157290000,30999000000 313 | 18-May-17,1818.7,1904.48,1807.12,1888.65,894321000,29710500000 314 | 17-May-17,1726.73,1864.05,1661.91,1839.09,1064730000,28204800000 315 | 16-May-17,1741.7,1785.94,1686.54,1734.45,959045000,28446300000 316 | 15-May-17,1808.44,1812.8,1708.54,1738.43,731529000,29532600000 317 | 14-May-17,1800.86,1831.42,1776.62,1808.91,437196000,29405100000 318 | 13-May-17,1723.12,1812.99,1651.08,1804.91,579635000,28132300000 319 | 12-May-17,1845.76,1856.15,1694.01,1724.24,740984000,30131100000 320 | 11-May-17,1780.37,1873.93,1755.35,1848.57,799490000,29060600000 321 | 10-May-17,1756.52,1788.44,1719.1,1787.13,915723000,28668100000 322 | 09-May-17,1723.89,1833.49,1716.3,1755.36,1167920000,28132200000 323 | 08-May-17,1596.92,1723.35,1596.92,1723.35,1340320000,26056500000 324 | 07-May-17,1579.47,1596.72,1559.76,1596.71,1080030000,25768500000 325 | 06-May-17,1556.81,1578.8,1542.5,1578.8,582530000,25395600000 326 | 05-May-17,1540.87,1618.03,1530.31,1555.45,946036000,25133100000 327 | 04-May-17,1490.72,1608.91,1490.72,1537.67,933549000,24311900000 328 | 03-May-17,1453.78,1492.77,1447.49,1490.09,583796000,23707100000 329 | 02-May-17,1421.03,1473.9,1415.69,1452.82,477338000,23170200000 330 | 01-May-17,1348.3,1434.32,1348.3,1421.6,713624000,21981800000 331 | 30-Apr-17,1321.87,1347.91,1314.92,1347.89,413115000,21548400000 332 | 29-Apr-17,1317.84,1327.2,1315.21,1321.79,422706000,21479800000 333 | 28-Apr-17,1317.74,1331.28,1292.37,1316.48,527489000,21476000000 334 | 27-Apr-17,1281.88,1319.7,1281.3,1317.73,449197000,20889200000 335 | 26-Apr-17,1265.99,1294.83,1265.93,1281.08,329631000,20627900000 336 | 25-Apr-17,1250.45,1267.58,1249.97,1265.49,242556000,20372300000 337 | 24-Apr-17,1209.63,1250.94,1209.63,1250.15,235806000,19705400000 338 | 23-Apr-17,1231.92,1232.2,1203.94,1207.21,258951000,20066200000 339 | 22-Apr-17,1222.71,1235.56,1208.47,1231.71,249320000,19913900000 340 | 21-Apr-17,1229.42,1235.94,1215.56,1222.05,272167000,20020700000 341 | 20-Apr-17,1211.08,1240.79,1208.41,1229.08,315108000,19719900000 342 | 19-Apr-17,1212.13,1215.51,1205.08,1210.29,288061000,19734800000 343 | 18-Apr-17,1193.77,1217.57,1193.77,1211.67,270524000,19433800000 344 | 17-Apr-17,1183.25,1194.9,1172.65,1193.91,253206000,19260500000 345 | 16-Apr-17,1172.61,1187.22,1172.61,1182.94,183231000,19085100000 346 | 15-Apr-17,1167.3,1188,1164.96,1172.52,203559000,18996500000 347 | 14-Apr-17,1170.33,1190.8,1159.79,1167.54,254827000,19043800000 348 | 13-Apr-17,1201.02,1205.89,1156.44,1169.28,351969000,19541300000 349 | 12-Apr-17,1204.81,1207.14,1196.76,1200.37,288702000,19600800000 350 | 11-Apr-17,1187.46,1208.07,1187.46,1205.01,216182000,19316000000 351 | 10-Apr-17,1187.3,1190.34,1179.04,1187.13,215883000,19311200000 352 | 09-Apr-17,1176.57,1197.21,1171.86,1187.87,242343000,19134400000 353 | 08-Apr-17,1172.65,1184.98,1162.58,1175.95,209312000,19068600000 354 | 07-Apr-17,1178.94,1186.58,1163.39,1176.9,317022000,19168500000 355 | 06-Apr-17,1125.81,1188.37,1125.81,1182.68,511222000,18302600000 356 | 05-Apr-17,1134.14,1135.09,1113.63,1124.78,414784000,18435700000 357 | 04-Apr-17,1145.52,1156.44,1120.52,1133.25,436310000,18619000000 358 | 03-Apr-17,1102.95,1151.74,1102.95,1143.81,580444000,17924600000 359 | 02-Apr-17,1080.61,1107.59,1075.45,1102.17,514187000,17559400000 360 | 01-Apr-17,1071.71,1091.72,1061.09,1080.5,289634000,17413000000 361 | --------------------------------------------------------------------------------