├── Gold.zip ├── Basics of Financial Markets.pdf ├── LICENSE ├── TCS.NS.csv ├── ^NSEI.csv ├── Module6.md ├── Module5.md ├── README.md ├── Module4.md ├── Module3.md ├── fortis_stock_data.csv ├── ongc_stock_data.csv ├── Module1.md ├── Module 1- Pandas(Format for solution).ipynb ├── Module2.md ├── demo.csv ├── NIFTY50_Data.csv ├── GOLD.csv └── 30 stocks └── largecaps ├── dlf_stock_data.csv └── bhel_stock_data.csv /Gold.zip: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Rajatendu1/Machine-Learning-Project-for-MindTree/HEAD/Gold.zip -------------------------------------------------------------------------------- /Basics of Financial Markets.pdf: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Rajatendu1/Machine-Learning-Project-for-MindTree/HEAD/Basics of Financial Markets.pdf -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2019 Rajatendu Dey 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 | -------------------------------------------------------------------------------- /TCS.NS.csv: -------------------------------------------------------------------------------- 1 | Date,Open,High,Low,Close,Adj Close,Volume 2 | 2018-06-30,null,null,null,null,null,null 3 | 2018-07-31,1949.949951,2092.000000,1945.000000,2078.399902,2052.981445,42262355 4 | 2018-08-31,2084.800049,2211.899902,2018.150024,2183.699951,2156.993652,44640172 5 | 2018-09-30,2190.899902,2275.949951,1784.349976,1938.150024,1914.446777,68060513 6 | 2018-10-31,1943.650024,1997.000000,1784.500000,1968.250000,1948.273804,52728186 7 | 2018-11-30,1984.000000,2029.699951,1870.250000,1893.050049,1873.837158,49342879 8 | 2018-12-31,1896.000000,2019.750000,1808.000000,2014.099976,1993.658569,69895226 9 | 2019-01-31,2009.500000,2097.949951,1881.300049,1983.449951,1967.527832,62771882 10 | 2019-02-28,1995.050049,2068.949951,1958.050049,2001.650024,1985.581787,53475963 11 | 2019-03-31,2010.000000,2266.949951,2007.000000,2260.350098,2242.205078,67089639 12 | 2019-04-30,2260.350098,2260.350098,2032.250000,2196.550049,2178.917236,60480344 13 | 2019-05-31,2201.000000,2292.500000,2142.100098,2227.199951,2209.321045,46280351 14 | 2019-06-30,2235.000000,2258.800049,2104.550049,2133.350098,2133.350098,18036354 15 | 2019-07-10,0.000000,2127.850098,2071.300049,2108.199951,2108.199951,6342153 16 | -------------------------------------------------------------------------------- /^NSEI.csv: -------------------------------------------------------------------------------- 1 | Date,Open,High,Low,Close,Adj Close,Volume 2 | 2018-06-30,null,null,null,null,null,null 3 | 2018-07-31,11359.799805,11760.200195,11234.950195,11680.500000,11680.500000,4923000 4 | 2018-08-31,11751.799805,11751.799805,10850.299805,10930.450195,10930.450195,5151800 5 | 2018-09-30,10930.900391,11035.650391,10004.549805,10386.599609,10386.599609,7030800 6 | 2018-10-31,10441.700195,10922.450195,10341.900391,10876.750000,10876.750000,5318400 7 | 2018-11-30,10930.700195,10985.150391,10333.849609,10862.549805,10862.549805,6533100 8 | 2018-12-31,10868.849609,10987.450195,10583.650391,10830.950195,10830.950195,6765700 9 | 2019-01-31,10851.349609,11118.099609,10585.650391,10792.500000,10792.500000,7237500 10 | 2019-02-28,10842.650391,11630.349609,10817.000000,11623.900391,11623.900391,6651500 11 | 2019-03-31,11665.200195,11856.150391,11549.099609,11748.150391,11748.150391,6321300 12 | 2019-04-30,11725.549805,12041.150391,11108.299805,11922.799805,11922.799805,8386300 13 | 2019-05-31,11953.750000,12103.049805,11625.099609,11788.849609,11788.849609,6229700 14 | 2019-06-30,11839.900391,11981.750000,11461.450195,11555.900391,11555.900391,2670300 15 | 2019-07-10,11536.150391,11593.700195,11475.650391,11498.900391,11498.900391,0 16 | -------------------------------------------------------------------------------- /Module6.md: -------------------------------------------------------------------------------- 1 | # Clustering for Diversification analysis 2 | Clustering is a method of unsupervised learning and is a common technique for statistical data analysis used in many fields. 3 | 4 | Clustering stocks 7 | 8 | Clustering is a Machine Learning technique that involves the grouping of data points. Given a set of data points, we can use a clustering algorithm to classify each data point into a specific group. In theory, data points that are in the same group should have similar properties and/or features, while data points in different groups should have highly dissimilar properties and/or features. 9 | 10 | In financial Markets, Cluster analysis is a technique used to group sets of objects that share similar characteristics. It is common in statistics, but investors will use the approach to build a diversified portfolio. Stocks that exhibit high correlations in returns fall into one basket, those slightly less correlated in another, and so on, until each stock is placed into a category. 11 | 12 | # Problem Statements 13 | 6.1 Create a table/data frame with the closing prices of 30 different stocks, with 10 from each of the caps 14 | 15 | 6.2 Calculate average annual percentage return and volatility of all 30 stocks over a theoretical one year period 16 | 17 | 6.3 Cluster the 30 stocks according to their mean annual Volatilities and Returns using K-means clustering. Identify the optimum number of clusters using the Elbow curve method 18 | 19 | 6.4 Prepare a separate Data frame to show which stocks belong to the same cluster 20 | -------------------------------------------------------------------------------- /Module5.md: -------------------------------------------------------------------------------- 1 | # Module 5 2 | ## Modern Portfolio Theory 3 | In this module, We’ll be looking at investment portfolio optimization with python, the fundamental concept of diversification and the creation of an efficient frontier that can be used by investors to choose specific mixes of assets based on investment goals; that is, the trade off between their desired level of portfolio return vs their desired level of portfolio risk. 4 | 5 | Modern Portfolio Theory suggests that it is possible to construct an "efficient frontier" of optimal portfolios, offering the maximum possible expected return for a given level of risk. It suggests that it is not enough to look at the expected risk and return of one particular stock. By investing in more than one stock, an investor can reap the benefits of diversification, particularly a reduction in the riskiness of the portfolio. MPT quantifies the benefits of diversification, also known as not putting all of your eggs in one basket. 6 | 7 | ## Problem Statements 8 | 5.1 For your chosen stock, calculate the mean daily return and daily standard deviation of returns, and then just annualise them to get mean expected annual return and volatility of that single stock. ( annual mean = daily mean * 252 , annual stdev = daily stdev * sqrt(252) ) 9 | 10 | 5.2 Now, we need to diversify our portfolio. Build your own portfolio by choosing any 5 stocks, preferably of different sectors and different caps. Assume that all 5 have the same weightage, i.e. 20% . Now calculate the annual returns and volatility of the entire portfolio ( Hint : Don't forget to use the covariance ) 11 | 12 | 5.3 Prepare a scatter plot for differing weights of the individual stocks in the portfolio , the axes being the returns and volatility. Colour the data points based on the Sharpe Ratio ( Returns/Volatility) of that particular portfolio. 13 | 14 | 5.4 Mark the 2 portfolios where - 15 | 16 | Portfolio 1 - The Sharpe ratio is the highest 17 | 18 | Portfolio 2 - The volatility is the lowest. 19 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Machine-Learning-Project-for-MindTree 2 | This is a internship project by Career Launcher. 3 | 4 | ## Welcome! 5 | Investment Bankers . CA's . Hedge Fund / Portfolio Managers . Forex traders . Commodities Analysts. 6 | These have been historically considered to be among the most coveted professions of all time. 7 | Yet, if one fails to keep up with the demands of the day, one would find one's skills to be obsolete in this era of data analysis. 8 | Data Science has inarguably been the hottest domain of the decade, asserting its need in every single sphere of corporate life. 9 | It was not long agowhen we discovered the massive potential of incorporating ML/AI in the financial world. 10 | Now, the very idea of the two being disjointed sounds strange. 11 | Data Science has been incremental in providing powerful insights ( which people didn't even know existed ) and 12 | helped massively increase the efficiency, helping everyone from a scalp trader to a long term debt investor. 13 | Accurate predictions, unbiased analysis, powerful tools that run through millions of rows of data in the blink of an eye have transformed the industry in ways we could've never imagined. 14 | The following program is designed to both test your knowledge and to give you the feel and experience of a real world financial world - data science problem. 15 | 16 | ## Steps to complete this project:- 17 | 18 | 1) Go through the "Basics of Financial Market" pdf to understand the basic terminologies of stock market. 19 | 2) Go through the instructions in the respective modules to understand the tasks assigned for each module 20 | 3) Go through the format notebooks for writing the solutions for the respective modules in the correct format. 21 | 4) Edit the solution jupyter notebooks and add your code for the queries in the respective modules or uplaod your notebook for that module. 22 | ### Note: Only .ipynb files are supported.Other modules will be uploaded after I get the solutions for the current modules. 23 | 24 | ### Disclaimer before contributing: Only significant contributions to this project would be accepted. 25 | -------------------------------------------------------------------------------- /Module4.md: -------------------------------------------------------------------------------- 1 | # Module 4: 2 | 3 | ## Trade Call Prediction using Classification 4 | In this module, we'd be covering the concept of classification and utilize our skills to solve the following queries – (Stock Price = Close Price) 5 | Time-series classification 8 | 9 | ## Problem Statements 10 | 4.1 Import the csv file of the stock which contained the Bollinger columns as well. 11 | Create a new column 'Call' , whose entries are - 12 | 'Buy' if the stock price is below the lower Bollinger band 13 | 'Hold Buy/ Liquidate Short' if the stock price is between the lower and middle Bollinger band 14 | 'Hold Short/ Liquidate Buy' if the stock price is between the middle and upper Bollinger band 15 | 'Short' if the stock price is above the upper Bollinger band 16 | Now train a classification model with the 3 bollinger columns and the stock price as inputs and 'Calls' as output. Check the accuracy on a test set. (There are many classifier models to choose from, try each one out and compare the accuracy for each) 17 | Import another stock data and create the bollinger columns. Using the already defined model, predict the daily calls for this new stock. 18 | 19 | 4.2 Now, we'll again utilize classification to make a trade call, and measure the efficiency of our trading algorithm over the past two years. For this assignment , we will use RandomForest classifier. 20 | Import the stock data file of your choice 21 | Define 4 new columns , whose values are: 22 | % change between Open and Close price for the day 23 | % change between Low and High price for the day 24 | 5 day rolling mean of the day to day % change in Close Price 25 | 5 day rolling std of the day to day % change in Close Price 26 | Create a new column 'Action' whose values are: 27 | 1 if next day's price(Close) is greater than present day's. 28 | (-1) if next day's price(Close) is less than present day's. 29 | i.e. Action [ i ] = 1 if Close[ i+1 ] > Close[ i ] 30 | i.e. Action [ i ] = (-1) if Close[ i+1 ] < Close[ i ] 31 | Construct a classification model with the 4 new inputs and 'Action' as target 32 | Check the accuracy of this model , also , plot the net cumulative returns (in %) if we were to follow this algorithmic model 33 | -------------------------------------------------------------------------------- /Module3.md: -------------------------------------------------------------------------------- 1 | # Module 3 2 | 3 | ## Fundamental analysis using Regression 4 | 5 | This module would introduce us to the Regression related inferences to be drawn from the data. 6 | 7 | Regression is basically a statistical approach to find the relationship between variables. In machine learning, this is used to predict the outcome of an event based on the relationship between variables obtained from the data-set. More often than not, we utilize linear regression to come up with an ideal inference. 8 | linear regression image 11 | We'd be using the regression model to solve the following problems: 12 | 13 | ## Problem Statements 14 | 15 | 3.1 Import the file 'gold.csv' (you will find this in the intro section to download or in '/Data/gold.csv' if you are using the jupyter notebook), which contains the data of the last 2 years price action of Indian (MCX) gold standard. Explore the dataframe. You'd see 2 unique columns - 'Pred' and 'new'. One of the 2 columns is a linear combination of the OHLC prices with varying coefficients while the other is a polynomial function of the same inputs. Also, one of the 2 columns is partially filled. 16 | Using linear regression, find the coefficients of the inputs and using the same trained model, complete the entire column. 17 | Also, try to fit the other column as well using a new linear regression model. Check if the predictions are accurate. Mention which column is a linear function and which is polynomial. 18 | (Hint: Plotting a histogram & distplot helps in recognizing the discrepencies in prediction, if any.) 19 | CAPM CAPM Analysis and Beta Calculation using regression - 20 | CAPM(Capital Asset Pricing Model) attempts to price securities by examining the relationship that exists between expected returns and risk. 21 | Read more about CAPM. (Investopedia CAPM reference) 22 | The Beta of an asset is a measure of the sensitivity of its returns relative to a market benchmark (usually a market index). How sensitive/insensitive is the returns of an asset to the overall market returns (usually a market index like S&P 500 index). What happens when the market jumps, does the returns of the asset jump accordingly or jump somehow? 23 | Read more about Beta (Investopedia Beta reference) 24 | 25 | 3.2 Import the stock of your choosing AND the Nifty index. 26 | Using linear regression (OLS), calculate - 27 | The daily Beta value for the past 3 months. (Daily= Daily returns) 28 | The monthly Beta value. (Monthly= Monthly returns) 29 | Refrain from using the (covariance(x,y)/variance(x)) formula. 30 | Attempt the question using regression.(Regression Reference) 31 | Were the Beta values more or less than 1 ? What if it was negative ? 32 | Discuss. Include a brief writeup in the bottom of your jupyter notebook with your inferences from the Beta values and regression results 33 | 34 | 35 | -------------------------------------------------------------------------------- /fortis_stock_data.csv: -------------------------------------------------------------------------------- 1 | Date, Symbol, Series, Open Price, High Price, Low Price, Last Traded Price , Close Price, Total Traded Quantity, Turnover (in Lakhs) 2 | 12-Jul-19, FORTIS, EQ,128.4,129.15,127.7,128.6,128.9,200037,257.62 3 | 11-Jul-19, FORTIS, EQ,129.2,129.35,128,128.8,128.35,473229,607.7 4 | 10-Jul-19, FORTIS, EQ,129.35,130.15,127.2,129,128.7,562637,722.68 5 | 09-Jul-19, FORTIS, EQ,130.5,131.1,129.25,129.65,129.8,296994,386.33 6 | 08-Jul-19, FORTIS, EQ,132.1,132.65,129.75,130.45,130.55,921556,1200.9 7 | 05-Jul-19, FORTIS, EQ,133.15,133.6,132,132.6,132.7,611323,809.27 8 | 04-Jul-19, FORTIS, EQ,133.2,134.6,133.05,133.8,133.8,257284,344.08 9 | 03-Jul-19, FORTIS, EQ,132.7,133.7,130.6,133.15,133.45,359871,477.6 10 | 02-Jul-19, FORTIS, EQ,133.7,134.2,132.55,133,132.85,346087,460.79 11 | 01-Jul-19, FORTIS, EQ,130.15,133.5,130,133.05,132.85,684086,904.57 12 | 28-Jun-19, FORTIS, EQ,129.9,130.95,128.8,130,130.1,1731499,2251.9 13 | 27-Jun-19, FORTIS, EQ,129.2,130.5,128.75,129.95,129.75,480088,623.46 14 | 26-Jun-19, FORTIS, EQ,128.8,130.1,128.1,129.1,129.4,1104106,1424.71 15 | 25-Jun-19, FORTIS, EQ,129,129.4,128.1,129.05,129,968578,1247.22 16 | 24-Jun-19, FORTIS, EQ,128.2,129.95,127.5,129.6,129.25,490926,631.5 17 | 21-Jun-19, FORTIS, EQ,131.4,131.4,127.75,128,129.2,996246,1291.71 18 | 20-Jun-19, FORTIS, EQ,133,133.45,130.7,131.35,131.15,326509,429.34 19 | 19-Jun-19, FORTIS, EQ,131.05,134,130.45,133.6,132.75,805612,1064.05 20 | 18-Jun-19, FORTIS, EQ,129.55,131.8,129,131.05,131.05,698124,911.43 21 | 17-Jun-19, FORTIS, EQ,131.9,132.5,128.75,129.5,129.55,480092,624.45 22 | 14-Jun-19, FORTIS, EQ,131.2,131.75,130.35,131,131,241735,316.75 23 | 13-Jun-19, FORTIS, EQ,131,132.5,129.6,131.45,131.2,395201,517.25 24 | 12-Jun-19, FORTIS, EQ,131.1,132.4,129.3,131.25,131.15,657288,858.34 25 | 11-Jun-19, FORTIS, EQ,128,134.8,127.6,132.05,132.55,4156157,5455.2 26 | 10-Jun-19, FORTIS, EQ,123.4,129.45,122,128.1,127.25,6609709,8119.94 27 | 07-Jun-19, FORTIS, EQ,122.4,123.6,121.25,122.45,122.45,619871,759.17 28 | 06-Jun-19, FORTIS, EQ,124.3,124.4,121.85,122,122.15,759033,930.43 29 | 04-Jun-19, FORTIS, EQ,124.9,126.3,123.45,123.8,124,822997,1024.68 30 | 03-Jun-19, FORTIS, EQ,125.1,125.35,124.1,124.7,124.95,441725,551.53 31 | 31-May-19, FORTIS, EQ,126.1,126.2,124.5,125.1,125.3,457788,573.93 32 | 30-May-19, FORTIS, EQ,126.1,126.8,125.7,126,126.15,859892,1086.92 33 | 29-May-19, FORTIS, EQ,127.4,127.65,126.3,126.95,126.5,1178109,1496.08 34 | 28-May-19, FORTIS, EQ,128.85,128.9,127,127.25,127.25,673066,859.7 35 | 27-May-19, FORTIS, EQ,127.05,132,127.05,128.1,128.25,2442613,3168.02 36 | 24-May-19, FORTIS, EQ,125.95,126.15,125,125.55,125.45,690945,868.67 37 | 23-May-19, FORTIS, EQ,125.95,126.9,125,125.6,125.85,947812,1194.22 38 | 22-May-19, FORTIS, EQ,125.9,126.5,124.7,125.8,125.95,771592,969.25 39 | 21-May-19, FORTIS, EQ,126.9,128.35,125.4,125.4,125.8,1217275,1539.54 40 | 20-May-19, FORTIS, EQ,128,129.9,126.15,126.85,126.75,1439227,1830.51 41 | 17-May-19, FORTIS, EQ,127.5,127.9,126.15,127,126.9,611010,775.54 42 | 16-May-19, FORTIS, EQ,128.35,128.85,127.2,128.3,128,400373,512.15 43 | 15-May-19, FORTIS, EQ,129.5,130.65,127.95,128.05,128.45,409685,528.95 44 | 14-May-19, FORTIS, EQ,129.15,130.7,127.9,129.9,129.6,801754,1035.76 45 | 13-May-19, FORTIS, EQ,131.1,132.05,129.2,129.8,129.7,412786,538.71 46 | 10-May-19, FORTIS, EQ,133.05,133.6,131,131.5,131.9,868778,1151.55 47 | 09-May-19, FORTIS, EQ,133.6,133.75,132.3,133.05,133,959494,1275.07 48 | 08-May-19, FORTIS, EQ,135.35,135.75,133.3,133.5,133.6,377871,506.94 49 | 07-May-19, FORTIS, EQ,136,136.7,135,135.35,135.2,165465,224.93 50 | 06-May-19, FORTIS, EQ,137.15,137.75,135.6,135.85,136.35,175876,240.63 51 | 03-May-19, FORTIS, EQ,137.85,138.45,137.3,137.55,137.75,134430,185.33 52 | 02-May-19, FORTIS, EQ,138.9,138.9,137,137.7,137.85,238818,328.81 53 | 30-Apr-19, FORTIS, EQ,138.15,139.1,137,138.85,138.55,485562,670.01 54 | 26-Apr-19, FORTIS, EQ,139.4,139.7,138.5,139,139.1,308156,428.33 55 | 25-Apr-19, FORTIS, EQ,139,140.1,138.6,138.95,138.95,516366,719.85 56 | 24-Apr-19, FORTIS, EQ,139.9,139.9,139,139,139.15,444148,618.23 57 | 23-Apr-19, FORTIS, EQ,139.65,140.7,138.5,139.2,139.5,549528,766.04 58 | 22-Apr-19, FORTIS, EQ,139.6,140.35,139.2,139.5,139.55,380870,531.96 59 | 18-Apr-19, FORTIS, EQ,141.4,141.4,139,140.1,140.4,581496,814.7 60 | 16-Apr-19, FORTIS, EQ,141.9,141.9,139.85,140.75,141.15,441128,622.18 61 | 15-Apr-19, FORTIS, EQ,139.15,141.8,138.75,140.9,141.1,950881,1338.13 62 | -------------------------------------------------------------------------------- /ongc_stock_data.csv: -------------------------------------------------------------------------------- 1 | Date, Symbol, Series, Open Price, High Price, Low Price, Last Traded Price , Close Price, Total Traded Quantity, Turnover (in Lakhs) 2 | 12-Jul-19, ONGC, EQ,153,153.35,148,148.9,149.7,13015538,19603.69 3 | 11-Jul-19, ONGC, EQ,153,154.1,152,153,153.1,6576542,10069.94 4 | 10-Jul-19, ONGC, EQ,153.05,154.4,149.65,151.7,151.65,5310646,8067.62 5 | 09-Jul-19, ONGC, EQ,152.4,155.45,150.65,153,153.4,15033604,23020.49 6 | 08-Jul-19, ONGC, EQ,161.45,161.5,151.05,152.5,152.4,10273814,15844.92 7 | 05-Jul-19, ONGC, EQ,167.1,168.05,160,160.4,161.5,14509056,23536.81 8 | 04-Jul-19, ONGC, EQ,166.4,168.9,166.1,167.1,167.1,8827834,14771.33 9 | 03-Jul-19, ONGC, EQ,165.8,167.95,163.5,166.5,166.3,8253849,13703.79 10 | 02-Jul-19, ONGC, EQ,163,166.25,161.5,165.8,165.65,10975678,18007.89 11 | 01-Jul-19, ONGC, EQ,168.2,170.4,159.25,161.25,161,15160240,24735.22 12 | 28-Jun-19, ONGC, EQ,170.3,171.3,166.65,168,167.75,5325984,8992.23 13 | 27-Jun-19, ONGC, EQ,167.55,171.8,167.25,169.9,170.6,36738164,62562.27 14 | 26-Jun-19, ONGC, EQ,166.1,169.15,166.1,167.35,167.55,5978575,10029.88 15 | 25-Jun-19, ONGC, EQ,165.5,168.45,165.5,166,166.2,6697367,11169.62 16 | 24-Jun-19, ONGC, EQ,171.15,171.85,164.85,165.8,165.2,6585159,10965.99 17 | 21-Jun-19, ONGC, EQ,171.75,173.45,169.8,170.85,170.95,11402911,19537.66 18 | 20-Jun-19, ONGC, EQ,166.3,172.75,166.3,172.15,172,5793816,9864.92 19 | 19-Jun-19, ONGC, EQ,166.8,169.1,164.6,167.25,167.35,4625698,7723.53 20 | 18-Jun-19, ONGC, EQ,164,166.95,163.25,166.5,166.15,5808715,9588.41 21 | 17-Jun-19, ONGC, EQ,169.5,169.95,162.95,163.8,164.4,4603627,7666.52 22 | 14-Jun-19, ONGC, EQ,168.95,171,168.45,168.8,169.3,7153585,12154.24 23 | 13-Jun-19, ONGC, EQ,168.8,170.2,167,168.95,168.95,6678516,11262.63 24 | 12-Jun-19, ONGC, EQ,168.05,171.45,166,170.85,170.85,9478876,16128.3 25 | 11-Jun-19, ONGC, EQ,164.85,170.35,164.85,168.9,169.25,11282418,19029.82 26 | 10-Jun-19, ONGC, EQ,168.1,169,163.2,165,164.65,10866149,18055.92 27 | 07-Jun-19, ONGC, EQ,169.6,170.1,166.55,167.55,167.5,5052519,8481.12 28 | 06-Jun-19, ONGC, EQ,170.5,172.1,168.85,169.55,169.4,14152285,24081.78 29 | 04-Jun-19, ONGC, EQ,172.9,173.5,170.05,170.35,170.6,8578302,14707.39 30 | 03-Jun-19, ONGC, EQ,173,173.2,167.5,173.2,172.15,17756361,30183.5 31 | 31-May-19, ONGC, EQ,168,173.95,166.55,172,171.95,17113729,29120.79 32 | 30-May-19, ONGC, EQ,172,172,167.55,169.25,169.45,21945960,37210.16 33 | 29-May-19, ONGC, EQ,173.9,174.65,170.35,171.4,171.55,7205487,12364.36 34 | 28-May-19, ONGC, EQ,173.15,176.4,171.6,174.8,175.3,16628505,29039.38 35 | 27-May-19, ONGC, EQ,174,175.2,172.55,173.15,173.65,7461458,12963.26 36 | 24-May-19, ONGC, EQ,174,175.9,168.8,174,174.4,9263195,15927.13 37 | 23-May-19, ONGC, EQ,178.5,178.9,173,174.35,174.15,9247879,16298.28 38 | 22-May-19, ONGC, EQ,174.85,178,174.15,177,177,10347293,18301.97 39 | 21-May-19, ONGC, EQ,175.1,176.5,173.8,174.25,174.75,7135887,12468.34 40 | 20-May-19, ONGC, EQ,170,176.6,169.1,176.1,176,7103649,12384.9 41 | 17-May-19, ONGC, EQ,166.5,168,162.1,167.3,167.35,5532072,9118.47 42 | 16-May-19, ONGC, EQ,163.4,166.9,162,166.5,165.95,5197438,8520.34 43 | 15-May-19, ONGC, EQ,164.6,165.2,161.8,162.65,162.75,5412275,8830.98 44 | 14-May-19, ONGC, EQ,162,165.4,160.85,164,164.15,7268915,11827.61 45 | 13-May-19, ONGC, EQ,167.2,167.9,163,164.4,164.1,7792358,12932.58 46 | 10-May-19, ONGC, EQ,168.4,169.4,165.7,166.75,166.3,6195094,10361.73 47 | 09-May-19, ONGC, EQ,167.4,170.1,167.4,169,169.4,6869953,11604.55 48 | 08-May-19, ONGC, EQ,171.4,171.4,167.75,168.95,168.9,8603091,14549.09 49 | 07-May-19, ONGC, EQ,170.4,172.5,168.6,171.5,171.7,9042790,15415.56 50 | 06-May-19, ONGC, EQ,170,171.7,168.15,170.2,170.15,18962836,32063.33 51 | 03-May-19, ONGC, EQ,168.85,171.9,168.7,170.7,170.25,12720120,21713.1 52 | 02-May-19, ONGC, EQ,169.3,172,168.4,169.5,168.9,22056708,37386.5 53 | 30-Apr-19, ONGC, EQ,168,171.15,166.15,169.3,169.2,9844317,16575.5 54 | 26-Apr-19, ONGC, EQ,169.5,169.95,167.9,168.1,168.4,8708111,14684.21 55 | 25-Apr-19, ONGC, EQ,168.35,170,166.55,169,168.85,134264338,226440.39 56 | 24-Apr-19, ONGC, EQ,165.7,170.45,165.7,168.35,168.65,33596281,56505.88 57 | 23-Apr-19, ONGC, EQ,158,165,157.6,165,163.75,10137024,16381.56 58 | 22-Apr-19, ONGC, EQ,160.7,161.5,156.9,157.9,157.9,5543020,8786.48 59 | 18-Apr-19, ONGC, EQ,160.85,162.2,159.05,160.5,160.6,9606055,15411.25 60 | 16-Apr-19, ONGC, EQ,156.5,161,155.85,160.85,160.45,8394765,13316.91 61 | 15-Apr-19, ONGC, EQ,158.3,158.3,156.1,156.45,156.45,3902646,6119.99 62 | -------------------------------------------------------------------------------- /Module1.md: -------------------------------------------------------------------------------- 1 | # Module 1 : 2 | 3 | ## Introduction to the problem: 4 | In Module 1, you are going to get familiar with pandas, the python module which is used to process and analyse data. Processing could include removing unknown values from the data or replacing unknown values with values which make sense, maybe 0. Analysing the data could include finding out the trend of a stock price, e.g. how the stock price changes with respect to the Nifty 50 basket of stocks. 5 | 6 | Please go through the reference material suggested for module 1 before attempting the tasks in module 1. 7 | 8 | You should target to finish module 1, including the prerequisites, in 1 week 9 | 10 | ### Problem Statements: 11 | 12 | 1.1 Import the csv file of the stock you have been allotted using 'pd.read_csv()' function into a dataframe. 13 | Shares of a company can be offered in more than one category. The category of a stock is indicated in the ‘Series’ column. If the csv file has data on more than one category, the ‘Date’ column will have repeating values. To avoid repetitions in the date, remove all the rows where 'Series' column is NOT 'EQ'. 14 | Analyze and understand each column properly. 15 | You'd find the head(), tail() and describe() functions to be immensely useful for exploration. You're free to carry out any other exploration of your own. 16 | 17 | 1.2 Calculate the maximum, minimum and mean price for the last 90 days. (price=Closing Price unless stated otherwise) 18 | 19 | 1.3 Analyse the data types for each column of the dataframe. Pandas knows how to deal with dates in an intelligent manner. But to make use of Pandas functionality for dates, you need to ensure that the column is of type 'datetime64(ns)'. Change the date column from 'object' type to 'datetime64(ns)' for future convenience. See what happens if you subtract the minimum value of the date column from the maximum value. 20 | 21 | 1.4 In a separate array , calculate the monthwise VWAP (Volume Weighted Average Price ) of the stock. 22 | ( VWAP = sum(price*volume)/sum(volume) ) 23 | To know more about VWAP , visit - VWAP definition 24 | {Hint : Create a new dataframe column ‘Month’. The values for this column can be derived from the ‘Date” column by using appropriate pandas functions. Similarly, create a column ‘Year’ and initialize it. Then use the 'groupby()' function by month and year. Finally, calculate the vwap value for each month (i.e. for each group created). 25 | 26 | 1.5Write a function to calculate the average price over the last N days of the stock price data where N is a user defined parameter. Write a second function to calculate the profit/loss percentage over the last N days. 27 | Calculate the average price AND the profit/loss percentages over the course of last - 28 | 1 week, 2 weeks, 1 month, 3 months, 6 months and 1 year. 29 | {Note : Profit/Loss percentage between N days is the percentage change between the closing prices of the 2 days } 30 | 31 | 1.6 Add a column 'Day_Perc_Change' where the values are the daily change in percentages i.e. the percentage change between 2 consecutive day's closing prices. Instead of using the basic mathematical formula for computing the same, use 'pct_change()' function provided by Pandas for dataframes. You will note that the first entry of the column will have a ‘Nan’ value. Why does this happen? Either remove the first row, or set the entry to 0 before proceeding. 32 | 33 | 1.7 Add another column 'Trend' whose values are: 34 | 'Slight or No change' for 'Day_Perc_Change' in between -0.5 and 0.5 35 | 'Slight positive' for 'Day_Perc_Change' in between 0.5 and 1 36 | 'Slight negative' for 'Day_Perc_Change' in between -0.5 and -1 37 | 'Positive' for 'Day_Perc_Change' in between 1 and 3 38 | 'Negative' for 'Day_Perc_Change' in between -1 and -3 39 | 'Among top gainers' for 'Day_Perc_Change' in between 3 and 7 40 | 'Among top losers' for 'Day_Perc_Change' in between -3 and -7 41 | 'Bull run' for 'Day_Perc_Change' >7 42 | 'Bear drop' for 'Day_Perc_Change' <-7 43 | 44 | 1.8 Find the average and median values of the column 'Total Traded Quantity' for each of the types of 'Trend'. 45 | {Hint : use 'groupby()' on the 'Trend' column and then calculate the average and median values of the column 'Total Traded Quantity'} 46 | 47 | 1.9 SAVE the dataframe with the additional columns computed as a csv file week2.csv. In Module 2, you are going to get familiar with matplotlib, the python module which is used to visualize data. 48 | 49 | Follow the rules step-by-step specifically and perform the ask as per the queries to get the required result. 50 | 51 | Checking for PR.. 52 | Checking for PR SM 53 | Checking for PR.. 1 54 | Checking for PR.. 2 55 | Checking for PR.. 3 56 | -------------------------------------------------------------------------------- /Module 1- Pandas(Format for solution).ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Module 1" 8 | ] 9 | }, 10 | { 11 | "cell_type": "markdown", 12 | "metadata": {}, 13 | "source": [ 14 | " ### Welcome to the Answer notebook for Module 1 ! \n", 15 | "These notebooks have been provided to code and solve all the queries asked in the modules.\n", 16 | "\n", 17 | "This environment has all the necessary libraries pre-installed, and all the Stock, Commodities and Index data files available at the following location - \n", 18 | "\n" 19 | ] 20 | }, 21 | { 22 | "cell_type": "markdown", 23 | "metadata": {}, 24 | "source": [ 25 | "#### The problem statements and their corresponding answers are expected to be in the following format - \n", 26 | "\n" 27 | ] 28 | }, 29 | { 30 | "cell_type": "markdown", 31 | "metadata": {}, 32 | "source": [ 33 | "#--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------#" 34 | ] 35 | }, 36 | { 37 | "cell_type": "markdown", 38 | "metadata": {}, 39 | "source": [ 40 | "### Query 1.1 \n", 41 | "Import the csv file of the stock of your choosing using 'pd.read_csv()' function into a dataframe.\n", 42 | "Shares of a company can be offered in more than one category. The category of a stock is indicated in the ‘Series’ column. If the csv file has data on more than one category, the ‘Date’ column will have repeating values. To avoid repetitions in the date, remove all the rows where 'Series' column is NOT 'EQ'.\n", 43 | "Analyze and understand each column properly.\n", 44 | "You'd find the head(), tail() and describe() functions to be immensely useful for exploration. You're free to carry out any other exploration of your own." 45 | ] 46 | }, 47 | { 48 | "cell_type": "code", 49 | "execution_count": 4, 50 | "metadata": {}, 51 | "outputs": [], 52 | "source": [ 53 | "#The solution code should start right after the query statement, for example -\n", 54 | "import numpy as np \n", 55 | "import pandas as pd\n", 56 | "import warnings\n", 57 | "warnings.filterwarnings('ignore')\n", 58 | "#And solve the query" 59 | ] 60 | }, 61 | { 62 | "cell_type": "code", 63 | "execution_count": 5, 64 | "metadata": {}, 65 | "outputs": [], 66 | "source": [ 67 | "#Once the solution of the first query is written, it should immediately be followed by the next query" 68 | ] 69 | }, 70 | { 71 | "cell_type": "markdown", 72 | "metadata": {}, 73 | "source": [ 74 | "### Query 1.2\n", 75 | "Calculate the maximum, minimum and mean price for the last 90 days. (price=Closing Price unless stated otherwise)" 76 | ] 77 | }, 78 | { 79 | "cell_type": "code", 80 | "execution_count": null, 81 | "metadata": {}, 82 | "outputs": [], 83 | "source": [ 84 | "#And so on !" 85 | ] 86 | }, 87 | { 88 | "cell_type": "markdown", 89 | "metadata": {}, 90 | "source": [ 91 | "**This is the expected format of the answer notebook**" 92 | ] 93 | }, 94 | { 95 | "cell_type": "code", 96 | "execution_count": null, 97 | "metadata": {}, 98 | "outputs": [], 99 | "source": [ 100 | "#So remove the comments and start coding !" 101 | ] 102 | }, 103 | { 104 | "cell_type": "code", 105 | "execution_count": null, 106 | "metadata": {}, 107 | "outputs": [], 108 | "source": [] 109 | }, 110 | { 111 | "cell_type": "code", 112 | "execution_count": null, 113 | "metadata": {}, 114 | "outputs": [], 115 | "source": [] 116 | }, 117 | { 118 | "cell_type": "code", 119 | "execution_count": null, 120 | "metadata": {}, 121 | "outputs": [], 122 | "source": [] 123 | }, 124 | { 125 | "cell_type": "code", 126 | "execution_count": null, 127 | "metadata": {}, 128 | "outputs": [], 129 | "source": [] 130 | } 131 | ], 132 | "metadata": { 133 | "kernelspec": { 134 | "display_name": "Python 3", 135 | "language": "python", 136 | "name": "python3" 137 | }, 138 | "language_info": { 139 | "codemirror_mode": { 140 | "name": "ipython", 141 | "version": 3 142 | }, 143 | "file_extension": ".py", 144 | "mimetype": "text/x-python", 145 | "name": "python", 146 | "nbconvert_exporter": "python", 147 | "pygments_lexer": "ipython3", 148 | "version": "3.6.5" 149 | } 150 | }, 151 | "nbformat": 4, 152 | "nbformat_minor": 2 153 | } 154 | -------------------------------------------------------------------------------- /Module2.md: -------------------------------------------------------------------------------- 1 | # Module 2: 2 | ## Data visualization and Technical Analysis 3 | 'A picture speaks a thousand words' has never been truer in financial markets. Absolutely no one goes through the millions of rows of numbers, we always prefer the data in a plotted form to draw better inferences. This module would cover the plotting, basic technical indicators and our own customisation, and making our own trade calls! 4 | You should target to finish module 2, including the prerequisites, in 1-2 weeks. 5 | 6 | ## Problem Statements 7 | 2.1 Load the week2.csv file into a dataframe. What is the type of the Date column? Make sure it is of type datetime64. Convert the Date column to the index of the dataframe. 8 | Plot the closing price of each of the days for the entire time frame to get an idea of what the general outlook of the stock is. 9 | Look out for drastic changes in this stock, you have the exact date when these took place, try to fetch the news for this day of this stock 10 | This would be helpful if we are to train our model to take NLP inputs. 11 | 12 | 2.2 A stem plot is a discrete series plot, ideal for plotting daywise data. It can be plotted using the plt.stem() function. 13 | 14 | Display a stem plot of the daily change in of the stock price in percentage. This column was calculated in module 1 and should be already available in week2.csv. Observe whenever there's a large change. 15 | 2.3 Plot the daily volumes as well and compare the percentage stem plot to it. Document your analysis of the relationship between volume and daily percentage change. 16 | 17 | 2.4 We had created a Trend column in module 1. We want to see how often each Trend type occurs. This can be seen as a pie chart, with each sector representing the percentage of days each trend occurs. Plot a pie chart for all the 'Trend' to know about relative frequency of each trend. You can use the groupby function with the trend column to group all days with the same trend into a single group before plotting the pie chart. From the grouped data, create a BAR plot of average & median values of the 'Total Traded Quantity' by Trend type. 18 | 19 | 2.5 Plot the daily return (percentage) distribution as a histogram. 20 | Histogram analysis is one of the most fundamental methods of exploratory data analysis. In this case, it'd return a frequency plot of various values of percentage changes . 21 | 2.6 We next want to analyse how the behaviour of different stocks are correlated. The correlation is performed on the percentage change of the stock price instead of the stock price. 22 | 23 | Load any 5 stocks of your choice into 5 dataframes. Retain only rows for which ‘Series’ column has value ‘EQ’. Create a single dataframe which contains the ‘Closing Price’ of each stock. This dataframe should hence have five columns. Rename each column to the name of the stock that is contained in the column. Create a new dataframe which is a percentage change of the values in the previous dataframe. Drop Nan’s from this dataframe. 24 | Using seaborn, analyse the correlation between the percentage changes in the five stocks. This is extremely useful for a fund manager to design a diversified portfolio. To know more, check out these resources on correlation and diversification. 25 | 26 | 2.7 Volatility is the change in variance in the returns of a stock over a specific period of time.Do give the following documentation on volatility a read. 27 | You have already calculated the percentage changes in several stock prices. Calculate the 7 day rolling average of the percentage change of any of the stock prices, then compute the standard deviation (which is the square root of the variance) and plot the values. 28 | Note: pandas provides a rolling() function for dataframes and a std() function also which you can use. 29 | 2.8 Calculate the volatility for the Nifty index and compare the 2. This leads us to a useful indicator known as 'Beta' ( We'll be covering this in length in Module 3) 30 | 31 | 2.9 Trade Calls - Using Simple Moving Averages. Study about moving averages here. 32 | 33 | Plot the 21 day and 34 day Moving average with the average price and decide a Call ! 34 | Call should be buy whenever the smaller moving average (21) crosses over longer moving average (34) AND the call should be sell whenever smaller moving average crosses under longer moving average. 35 | One of the most widely used technical indicators. 36 | 2.10 Trade Calls - Using Bollinger Bands 37 | Plot the bollinger bands for this stock - the duration of 14 days and 2 standard deviations away from the average 38 | The bollinger bands comprise the following data points- 39 | The 14 day rolling mean of the closing price (we call it the average) 40 | Upper band which is the rolling mean + 2 standard deviations away from the average. 41 | Lower band which is the rolling mean - 2 standard deviations away from the average. 42 | Average Daily stock price. 43 | Bollinger bands are extremely reliable , with a 95% accuracy at 2 standard deviations , and especially useful in sideways moving market. 44 | Observe the bands yourself , and analyse the accuracy of all the trade signals provided by the bollinger bands. 45 | Save to a new csv file. 46 | -------------------------------------------------------------------------------- /demo.csv: -------------------------------------------------------------------------------- 1 | 2018-10-19,164.05000000000007 2 | 2018-07-19,90.64999999999998 3 | 2018-04-20,82.25 4 | 2018-02-23,71.19999999999993 5 | 2018-10-31,64.70000000000005 6 | 2018-08-31,62.65000000000009 7 | 2018-01-18,59.299999999999955 8 | 2018-01-22,54.950000000000045 9 | 2018-11-28,50.64999999999998 10 | 2018-03-05,45.5 11 | 2018-03-15,44.10000000000002 12 | 2018-10-08,42.30000000000007 13 | 2019-01-22,39.35000000000002 14 | 2018-10-01,38.549999999999955 15 | 2018-04-02,38.25 16 | 2017-11-27,37.59999999999991 17 | 2018-04-26,36.899999999999864 18 | 2018-08-27,36.75 19 | 2018-06-14,36.0 20 | 2019-01-29,35.0 21 | 2018-09-21,34.149999999999864 22 | 2017-05-25,33.94999999999999 23 | 2018-06-29,33.89999999999998 24 | 2018-04-30,33.899999999999864 25 | 2018-04-25,33.75 26 | 2018-03-06,33.44999999999993 27 | 2018-09-05,32.950000000000045 28 | 2018-03-19,32.950000000000045 29 | 2018-05-02,32.94999999999982 30 | 2018-04-19,32.5 31 | 2018-03-16,31.299999999999955 32 | 2018-08-13,30.899999999999977 33 | 2018-06-25,30.649999999999977 34 | 2018-01-08,30.350000000000023 35 | 2018-02-05,30.149999999999977 36 | 2018-08-16,30.09999999999991 37 | 2018-07-12,28.5 38 | 2018-02-21,28.350000000000023 39 | 2018-10-15,27.450000000000045 40 | 2018-10-12,27.299999999999955 41 | 2018-09-19,27.100000000000136 42 | 2019-02-18,26.899999999999977 43 | 2018-08-14,26.850000000000023 44 | 2019-01-17,26.649999999999977 45 | 2018-11-30,26.549999999999955 46 | 2019-02-11,26.5 47 | 2018-04-05,26.40000000000009 48 | 2019-03-14,26.200000000000045 49 | 2018-10-25,25.799999999999955 50 | 2018-04-06,25.75 51 | 2019-01-03,25.700000000000045 52 | 2018-08-24,25.65000000000009 53 | 2018-11-29,24.649999999999977 54 | 2018-10-11,24.5 55 | 2018-07-10,24.34999999999991 56 | 2019-01-25,24.299999999999955 57 | 2019-03-01,24.050000000000068 58 | 2018-11-01,23.75 59 | 2018-12-18,23.449999999999932 60 | 2018-05-21,23.249999999999886 61 | 2018-11-02,23.149999999999977 62 | 2017-12-22,23.050000000000068 63 | 2018-06-06,22.949999999999932 64 | 2017-12-04,22.850000000000023 65 | 2018-10-05,22.699999999999818 66 | 2018-08-01,22.600000000000023 67 | 2018-07-30,22.549999999999955 68 | 2017-07-20,22.349999999999966 69 | 2018-02-15,22.0 70 | 2018-12-13,21.850000000000023 71 | 2018-05-04,21.799999999999955 72 | 2018-02-19,21.65000000000009 73 | 2018-09-27,21.25 74 | 2019-01-14,21.149999999999977 75 | 2018-04-04,21.100000000000023 76 | 2018-04-23,20.75 77 | 2017-10-26,20.649999999999977 78 | 2019-01-07,20.350000000000023 79 | 2018-06-28,20.149999999999977 80 | 2018-04-27,19.899999999999864 81 | 2018-05-03,19.800000000000182 82 | 2019-01-04,19.799999999999955 83 | 2019-01-11,19.75 84 | 2018-11-13,19.649999999999977 85 | 2018-07-18,19.200000000000045 86 | 2019-03-19,19.200000000000045 87 | 2019-01-18,19.149999999999977 88 | 2018-09-28,19.09999999999991 89 | 2018-12-12,18.899999999999977 90 | 2019-02-19,18.699999999999932 91 | 2017-12-12,18.600000000000023 92 | 2017-11-16,18.55000000000001 93 | 2018-07-09,18.5 94 | 2018-02-02,18.450000000000045 95 | 2017-09-14,18.30000000000001 96 | 2018-03-22,18.25 97 | 2018-01-19,18.25 98 | 2018-03-21,17.949999999999932 99 | 2018-11-05,17.899999999999977 100 | 2018-04-18,17.850000000000023 101 | 2019-02-12,17.75 102 | 2018-11-09,17.699999999999932 103 | 2017-06-05,17.550000000000068 104 | 2019-01-28,17.5 105 | 2018-09-04,17.40000000000009 106 | 2019-03-05,17.200000000000045 107 | 2018-07-23,17.149999999999977 108 | 2017-12-07,17.049999999999955 109 | 2018-08-30,16.899999999999864 110 | 2018-12-03,16.800000000000068 111 | 2018-07-31,16.799999999999955 112 | 2018-04-16,16.700000000000045 113 | 2019-02-20,16.65000000000009 114 | 2018-05-08,16.549999999999955 115 | 2018-02-01,16.399999999999977 116 | 2018-12-26,16.34999999999991 117 | 2018-02-09,16.09999999999991 118 | 2018-02-08,15.849999999999909 119 | 2018-04-13,15.75 120 | 2018-02-06,15.700000000000045 121 | 2018-08-09,15.649999999999977 122 | 2018-09-10,15.299999999999955 123 | 2018-06-13,15.25 124 | 2019-03-18,15.100000000000023 125 | 2018-09-12,15.049999999999955 126 | 2018-05-23,15.000000000000114 127 | 2018-06-08,14.899999999999977 128 | 2018-05-25,14.749999999999886 129 | 2018-09-11,14.649999999999864 130 | 2018-02-22,14.550000000000068 131 | 2019-01-23,14.550000000000068 132 | 2017-10-03,14.349999999999966 133 | 2018-03-13,14.299999999999955 134 | 2018-12-05,14.200000000000045 135 | 2018-09-03,14.150000000000091 136 | 2019-01-08,14.149999999999977 137 | 2018-04-17,14.100000000000023 138 | 2018-03-08,14.099999999999909 139 | 2018-09-25,13.949999999999818 140 | 2018-07-16,13.850000000000136 141 | 2018-06-26,13.849999999999909 142 | 2019-04-26,13.549999999999955 143 | 2017-05-18,13.450000000000045 144 | 2018-05-14,13.449999999999932 145 | 2018-07-03,13.399999999999977 146 | 2017-11-13,13.350000000000023 147 | 2019-01-16,13.299999999999955 148 | 2018-09-24,13.299999999999955 149 | 2018-09-26,13.25 150 | 2018-05-09,13.25 151 | 2018-08-08,13.200000000000045 152 | 2019-01-30,13.149999999999977 153 | 2017-08-10,13.050000000000011 154 | 2018-11-27,13.0 155 | 2018-07-25,13.0 156 | 2017-12-05,12.799999999999955 157 | 2018-11-06,12.75 158 | 2017-12-27,12.649999999999977 159 | 2018-01-12,12.549999999999955 160 | 2018-01-29,12.400000000000091 161 | 2018-02-28,12.399999999999977 162 | 2017-09-27,12.300000000000011 163 | 2018-11-20,12.200000000000045 164 | 2017-08-07,12.149999999999977 165 | 2018-05-28,12.149999999999977 166 | 2018-07-11,12.049999999999955 167 | 2018-06-20,11.950000000000045 168 | 2019-03-22,11.649999999999977 169 | 2018-02-16,11.600000000000023 170 | 2017-11-22,11.550000000000068 171 | 2019-05-06,11.549999999999955 172 | 2018-08-28,11.399999999999864 173 | 2018-10-09,11.350000000000023 174 | 2018-11-19,11.300000000000068 175 | 2018-10-22,11.299999999999955 176 | 2019-01-10,11.200000000000045 177 | 2019-05-03,11.199999999999932 178 | 2018-06-12,11.099999999999909 179 | 2018-05-31,11.099999999999909 180 | 2017-06-23,11.0 181 | 2018-08-17,10.949999999999818 182 | 2018-03-26,10.850000000000023 183 | 2017-12-08,10.799999999999955 184 | 2017-06-06,10.75 185 | 2018-07-24,10.699999999999932 186 | 2018-05-22,10.699999999999932 187 | 2017-10-10,10.650000000000034 188 | 2018-06-18,10.600000000000023 189 | 2017-07-12,10.400000000000091 190 | 2017-10-27,10.149999999999977 191 | 2018-07-26,10.149999999999977 192 | 2017-10-17,10.100000000000023 193 | 2017-11-14,10.100000000000023 194 | 2017-08-14,10.100000000000023 195 | 2018-03-23,10.100000000000023 196 | 2019-02-28,10.099999999999909 197 | 2018-02-26,10.049999999999955 198 | 2018-07-13,10.049999999999955 199 | 2017-08-30,10.0 200 | 2018-10-04,10.0 201 | 2019-04-25,10.0 202 | 2018-12-06,9.850000000000023 203 | 2019-02-04,9.850000000000023 204 | 2017-06-07,9.800000000000068 205 | 2017-08-28,9.800000000000011 206 | 2019-03-26,9.75 207 | 2018-06-15,9.75 208 | 2017-10-16,9.699999999999989 209 | 2019-03-12,9.600000000000023 210 | 2018-04-03,9.550000000000068 211 | 2017-08-08,9.550000000000011 212 | 2018-12-10,9.549999999999955 213 | 2018-07-05,9.549999999999955 214 | 2017-07-10,9.5 215 | 2017-11-30,9.5 216 | 2017-08-18,9.5 217 | 2019-04-05,9.450000000000045 218 | 2018-04-09,9.450000000000045 219 | 2019-02-06,9.399999999999977 220 | 2017-12-01,9.300000000000068 221 | 2017-08-09,9.299999999999955 222 | 2018-05-11,9.25 223 | 2018-07-27,9.200000000000045 224 | 2017-09-20,9.149999999999977 225 | 2018-11-12,9.100000000000023 226 | 2017-12-18,9.100000000000023 227 | 2018-12-14,9.100000000000023 228 | 2018-01-24,9.099999999999909 229 | 2018-06-05,9.0 230 | 2017-11-20,8.950000000000045 231 | 2017-11-08,8.800000000000011 232 | 2017-05-24,8.800000000000011 233 | 2018-10-26,8.75 234 | 2017-05-26,8.699999999999932 235 | 2019-02-27,8.549999999999955 236 | 2017-06-14,8.5 237 | 2018-10-30,8.449999999999932 238 | 2018-11-26,8.25 239 | 2018-12-04,8.100000000000023 240 | 2019-04-10,8.049999999999955 241 | 2017-06-13,8.0 242 | 2018-09-17,7.9500000000000455 243 | 2018-12-24,7.899999999999977 244 | 2017-09-18,7.899999999999977 245 | 2018-12-27,7.800000000000068 246 | 2019-02-08,7.75 247 | 2018-01-16,7.75 248 | 2019-04-22,7.7000000000000455 249 | 2017-08-17,7.699999999999989 250 | 2018-06-19,7.649999999999977 251 | 2018-08-20,7.599999999999909 252 | 2017-08-22,7.449999999999989 253 | 2017-08-03,7.400000000000034 254 | 2017-07-05,7.399999999999977 255 | 2019-03-20,7.350000000000023 256 | 2019-03-27,7.350000000000023 257 | 2017-08-24,7.300000000000011 258 | 2019-04-16,7.199999999999932 259 | 2017-11-03,7.099999999999966 260 | 2018-09-06,7.099999999999909 261 | 2018-05-15,7.0499999999999545 262 | 2018-05-29,7.0 263 | 2017-08-23,6.949999999999989 264 | 2018-06-04,6.850000000000023 265 | 2017-05-30,6.850000000000023 266 | 2018-10-17,6.849999999999909 267 | 2018-10-23,6.7999999999999545 268 | 2018-10-29,6.75 269 | 2018-01-09,6.600000000000023 270 | 2017-06-08,6.600000000000023 271 | 2017-05-31,6.550000000000068 272 | 2017-06-01,6.550000000000068 273 | 2019-03-13,6.5 274 | 2018-04-12,6.449999999999932 275 | 2018-03-20,6.449999999999932 276 | 2019-04-11,6.399999999999977 277 | 2018-08-10,6.399999999999977 278 | 2018-12-07,6.350000000000023 279 | 2018-11-15,6.350000000000023 280 | 2018-08-03,6.350000000000023 281 | 2018-06-21,6.350000000000023 282 | 2018-08-07,6.300000000000068 283 | 2018-01-15,6.25 284 | 2018-02-20,6.25 285 | 2017-12-20,6.150000000000091 286 | 2017-11-23,6.150000000000091 287 | 2018-11-21,6.100000000000023 288 | 2018-06-27,6.100000000000023 289 | 2017-09-26,6.050000000000011 290 | 2018-06-22,6.0 291 | 2017-09-04,5.9500000000000455 292 | 2017-08-29,5.899999999999977 293 | 2017-11-06,5.850000000000023 294 | 2017-08-11,5.849999999999966 295 | 2018-01-03,5.75 296 | 2018-07-20,5.7000000000000455 297 | 2017-11-17,5.650000000000034 298 | 2017-07-07,5.649999999999977 299 | 2019-02-01,5.600000000000023 300 | 2017-07-18,5.600000000000023 301 | 2017-10-23,5.599999999999966 302 | 2018-01-31,5.5499999999999545 303 | 2018-04-10,5.5 304 | 2018-01-01,5.5 305 | 2017-06-15,5.449999999999932 306 | 2019-04-30,5.399999999999977 307 | 2017-07-04,5.350000000000023 308 | 2017-09-07,5.350000000000023 309 | 2017-06-27,5.350000000000023 310 | 2019-03-11,5.2999999999999545 311 | 2019-01-21,5.25 312 | 2018-07-06,5.25 313 | 2017-11-15,5.25 314 | 2018-03-07,5.199999999999932 315 | 2018-02-12,5.199999999999932 316 | 2018-01-23,5.199999999999932 317 | 2019-02-21,5.150000000000091 318 | 2017-08-04,5.150000000000034 319 | 2018-01-17,5.100000000000023 320 | 2019-05-10,5.100000000000023 321 | 2019-02-25,5.0 322 | 2017-05-17,4.949999999999989 323 | 2017-07-11,4.949999999999932 324 | 2019-04-09,4.900000000000091 325 | 2017-11-01,4.899999999999977 326 | 2017-05-22,4.850000000000023 327 | 2017-08-01,4.849999999999966 328 | 2018-09-18,4.849999999999909 329 | 2017-08-21,4.800000000000011 330 | 2017-06-09,4.75 331 | 2017-10-19,4.75 332 | 2018-05-07,4.7000000000000455 333 | 2017-05-29,4.650000000000091 334 | 2018-10-10,4.650000000000091 335 | 2018-05-18,4.649999999999977 336 | 2018-06-11,4.600000000000136 337 | 2017-05-19,4.600000000000023 338 | 2018-08-06,4.5499999999999545 339 | 2017-09-06,4.5 340 | 2017-06-28,4.4500000000000455 341 | 2018-12-21,4.449999999999932 342 | 2018-05-24,4.449999999999818 343 | 2017-12-19,4.350000000000023 344 | 2017-10-25,4.349999999999966 345 | 2019-04-08,4.349999999999909 346 | 2018-07-02,4.25 347 | 2017-11-28,4.199999999999932 348 | 2018-05-16,4.100000000000023 349 | 2017-09-12,4.100000000000023 350 | 2018-10-16,4.099999999999909 351 | 2017-10-11,4.0 352 | 2019-04-24,4.0 353 | 2019-04-15,4.0 354 | 2018-09-07,3.9500000000000455 355 | 2019-04-18,3.9500000000000455 356 | 2019-02-22,3.9500000000000455 357 | 2018-01-25,3.900000000000091 358 | 2018-10-24,3.8999999999999773 359 | 2017-06-20,3.8500000000000227 360 | 2019-01-24,3.8500000000000227 361 | 2019-02-13,3.7999999999999545 362 | 2017-09-11,3.75 363 | 2019-05-13,3.75 364 | 2018-07-04,3.7000000000000455 365 | 2018-12-11,3.6499999999999773 366 | 2019-01-31,3.599999999999909 367 | 2018-05-17,3.5499999999999545 368 | 2018-08-29,3.5 369 | 2018-12-19,3.5 370 | 2018-03-09,3.3500000000000227 371 | 2018-02-07,3.25 372 | 2019-03-29,3.25 373 | 2017-10-30,3.1999999999999886 374 | 2019-03-07,3.1000000000000227 375 | 2017-09-28,3.099999999999966 376 | 2018-11-16,3.0 377 | 2018-11-14,3.0 378 | 2017-07-06,3.0 379 | 2017-09-29,2.9500000000000455 380 | 2017-10-12,2.9499999999999886 381 | 2017-06-16,2.8999999999999773 382 | 2019-01-02,2.8999999999999773 383 | 2017-11-29,2.8500000000000227 384 | 2019-04-03,2.8500000000000227 385 | 2019-03-15,2.7000000000000455 386 | 2018-01-11,2.6499999999999773 387 | 2017-09-15,2.5 388 | 2017-06-02,2.5 389 | 2018-01-30,2.5 390 | 2017-09-08,2.3999999999999773 391 | 2018-01-05,2.3999999999999773 392 | 2017-06-29,2.3999999999999773 393 | 2018-08-02,2.3500000000000227 394 | 2017-07-28,2.3000000000000114 395 | 2019-01-09,2.25 396 | 2017-11-07,2.25 397 | 2018-12-28,2.25 398 | 2017-10-18,2.25 399 | 2018-04-11,2.2000000000000455 400 | 2018-01-10,2.199999999999932 401 | 2018-12-17,2.1499999999999773 402 | 2017-07-31,2.0500000000000114 403 | 2018-11-07,2.0 404 | 2019-02-07,2.0 405 | 2018-09-14,1.900000000000091 406 | 2019-03-28,1.8999999999999773 407 | 2017-12-28,1.8999999999999773 408 | 2019-02-05,1.8999999999999773 409 | 2017-05-16,1.8500000000000227 410 | 2018-01-02,1.8500000000000227 411 | 2017-10-05,1.849999999999966 412 | 2017-09-21,1.8000000000000114 413 | 2019-05-07,1.7999999999999545 414 | 2017-09-13,1.75 415 | 2017-07-19,1.75 416 | 2019-03-25,1.75 417 | 2017-09-22,1.6999999999999886 418 | 2017-07-25,1.6999999999999886 419 | 2017-10-06,1.6999999999999886 420 | 2019-04-02,1.6999999999999318 421 | 2017-07-26,1.650000000000034 422 | 2017-07-27,1.6000000000000227 423 | 2018-11-22,1.5499999999999545 424 | 2018-10-03,1.5499999999999545 425 | 2019-03-08,1.5 426 | 2019-02-15,1.5 427 | 2019-03-06,1.4500000000000455 428 | 2017-12-14,1.3999999999999773 429 | 2017-06-22,1.3999999999999773 430 | 2019-01-15,1.3500000000000227 431 | 2018-03-14,1.3000000000000682 432 | 2018-08-21,1.2999999999999545 433 | 2018-03-12,1.2999999999999545 434 | 2018-05-10,1.25 435 | 2018-06-01,1.25 436 | 2017-12-26,1.25 437 | 2017-05-23,1.2000000000000455 438 | 2018-02-14,1.2000000000000455 439 | 2017-07-14,1.2000000000000455 440 | 2017-10-13,1.1999999999999886 441 | 2018-08-23,1.150000000000091 442 | 2019-01-01,1.1000000000000227 443 | 2017-08-31,1.1000000000000227 444 | 2018-03-01,1.0500000000000682 445 | 2017-06-12,1.0499999999999545 446 | 2017-10-04,1.0 447 | 2019-04-12,1.0 448 | 2017-07-17,0.9500000000000455 449 | 2017-06-19,0.9499999999999318 450 | 2017-12-15,0.9499999999999318 451 | 2017-11-02,0.8999999999999773 452 | 2018-03-27,0.8999999999999773 453 | 2018-02-27,0.8500000000000227 454 | 2017-09-25,0.8500000000000227 455 | 2017-11-24,0.849999999999909 456 | 2018-03-28,0.849999999999909 457 | 2017-12-21,0.8000000000000682 458 | 2019-04-23,0.8000000000000682 459 | 2017-08-16,0.75 460 | 2017-07-24,0.6999999999999886 461 | 2017-08-02,0.6999999999999886 462 | 2018-04-24,0.6499999999999773 463 | 2017-12-29,0.6000000000000227 464 | 2018-01-04,0.6000000000000227 465 | 2018-05-30,0.599999999999909 466 | 2019-05-08,0.5499999999999545 467 | 2017-07-13,0.5499999999999545 468 | 2017-09-19,0.5 469 | 2017-11-10,0.5 470 | 2019-02-14,0.5 471 | 2019-02-26,0.5 472 | 2019-04-01,0.4500000000000455 473 | 2017-10-09,0.4000000000000341 474 | 2017-11-09,0.39999999999997726 475 | 2017-11-21,0.39999999999997726 476 | 2018-12-31,0.39999999999997726 477 | 2018-06-07,0.39999999999997726 478 | 2018-07-17,0.34999999999990905 479 | 2019-04-04,0.2999999999999545 480 | 2017-06-30,0.2999999999999545 481 | 2017-12-11,0.25 482 | 2017-12-13,0.25 483 | 2017-09-05,0.20000000000004547 484 | 2017-06-21,0.14999999999997726 485 | 2017-10-24,0.14999999999997726 486 | 2019-05-02,0.14999999999997726 487 | 2017-09-01,0.14999999999997726 488 | 2017-12-06,0.10000000000002274 489 | 2017-10-31,0.0999999999999659 490 | 2018-12-20,0.05000000000006821 491 | 2017-07-21,0.05000000000001137 492 | 2019-05-09,0.049999999999954525 493 | 2017-07-03,0.049999999999954525 494 | 2017-05-15, 495 | -------------------------------------------------------------------------------- /NIFTY50_Data.csv: -------------------------------------------------------------------------------- 1 | "Date","Open","High","Low","Close" 2 | "05 Jul 2019","11964.75","11981.75","11797.90","11811.15" 3 | "04 Jul 2019","11928.80","11969.25","11923.65","11946.75" 4 | "03 Jul 2019","11932.15","11945.20","11887.05","11916.75" 5 | "02 Jul 2019","11890.30","11917.45","11814.70","11910.30" 6 | "01 Jul 2019","11839.90","11884.65","11830.80","11865.60" 7 | "28 Jun 2019","11861.15","11871.70","11775.50","11788.85" 8 | "27 Jun 2019","11860.85","11911.15","11821.05","11841.55" 9 | "26 Jun 2019","11768.15","11871.85","11757.55","11847.55" 10 | "25 Jun 2019","11681.00","11814.40","11651.00","11796.45" 11 | "24 Jun 2019","11725.80","11754.00","11670.20","11699.65" 12 | "21 Jun 2019","11827.60","11827.95","11705.10","11724.10" 13 | "20 Jun 2019","11653.65","11843.50","11635.05","11831.75" 14 | "19 Jun 2019","11744.45","11802.50","11625.10","11691.45" 15 | "18 Jun 2019","11677.05","11727.20","11641.15","11691.50" 16 | "17 Jun 2019","11844.00","11844.05","11657.75","11672.15" 17 | "14 Jun 2019","11910.10","11911.85","11797.70","11823.30" 18 | "13 Jun 2019","11873.90","11931.35","11817.05","11914.05" 19 | "12 Jun 2019","11962.45","11962.45","11866.35","11906.20" 20 | "11 Jun 2019","11959.85","12000.35","11904.35","11965.60" 21 | "10 Jun 2019","11934.90","11975.05","11871.75","11922.70" 22 | "07 Jun 2019","11865.20","11897.50","11769.50","11870.65" 23 | "06 Jun 2019","12039.80","12039.80","11830.25","11843.75" 24 | "04 Jun 2019","12052.65","12095.20","12005.85","12021.65" 25 | "03 Jun 2019","11953.75","12103.05","11920.10","12088.55" 26 | "31 May 2019","11999.80","12039.25","11829.45","11922.80" 27 | "30 May 2019","11865.30","11968.55","11859.40","11945.90" 28 | "29 May 2019","11905.80","11931.90","11836.80","11861.10" 29 | "28 May 2019","11958.35","11958.55","11864.90","11928.75" 30 | "27 May 2019","11855.50","11957.15","11812.40","11924.75" 31 | "24 May 2019","11748.00","11859.00","11658.10","11844.10" 32 | "23 May 2019","11901.30","12041.15","11614.50","11657.05" 33 | "22 May 2019","11727.95","11784.80","11682.40","11737.90" 34 | "21 May 2019","11863.65","11883.55","11682.80","11709.10" 35 | "20 May 2019","11651.90","11845.20","11591.70","11828.25" 36 | "17 May 2019","11261.90","11426.15","11259.85","11407.15" 37 | "16 May 2019","11180.35","11281.55","11143.35","11257.10" 38 | "15 May 2019","11271.70","11286.80","11136.95","11157.00" 39 | "14 May 2019","11151.65","11294.75","11108.30","11222.05" 40 | "13 May 2019","11258.70","11300.20","11125.60","11148.20" 41 | "10 May 2019","11314.15","11345.80","11251.05","11278.90" 42 | "09 May 2019","11322.40","11357.60","11255.05","11301.80" 43 | "08 May 2019","11478.70","11479.10","11346.95","11359.45" 44 | "07 May 2019","11651.50","11657.05","11484.45","11497.90" 45 | "06 May 2019","11605.80","11632.55","11571.35","11598.25" 46 | "03 May 2019","11722.60","11770.90","11699.35","11712.25" 47 | "02 May 2019","11725.55","11789.30","11699.55","11724.75" 48 | "30 Apr 2019","11748.75","11756.25","11655.90","11748.15" 49 | "26 Apr 2019","11683.75","11762.90","11661.75","11754.65" 50 | "25 Apr 2019","11735.70","11796.75","11624.30","11641.80" 51 | "24 Apr 2019","11601.50","11740.85","11578.85","11726.15" 52 | "23 Apr 2019","11612.95","11645.95","11564.80","11575.95" 53 | "22 Apr 2019","11727.05","11727.05","11583.95","11594.45" 54 | "18 Apr 2019","11856.15","11856.15","11738.50","11752.80" 55 | "16 Apr 2019","11736.20","11810.95","11731.55","11787.15" 56 | "15 Apr 2019","11667.00","11704.60","11648.25","11690.35" 57 | "12 Apr 2019","11612.85","11657.35","11578.80","11643.45" 58 | "11 Apr 2019","11592.55","11606.70","11550.55","11596.70" 59 | "10 Apr 2019","11646.85","11680.05","11571.75","11584.30" 60 | "09 Apr 2019","11612.05","11683.90","11569.70","11671.95" 61 | "08 Apr 2019","11704.35","11710.30","11549.10","11604.50" 62 | "05 Apr 2019","11638.40","11689.65","11609.50","11665.95" 63 | "04 Apr 2019","11660.20","11662.55","11559.20","11598.00" 64 | "03 Apr 2019","11735.30","11761.00","11629.15","11643.95" 65 | "02 Apr 2019","11711.55","11729.35","11655.85","11713.20" 66 | "01 Apr 2019","11665.20","11738.10","11644.75","11669.15" 67 | "29 Mar 2019","11625.45","11630.35","11570.15","11623.90" 68 | "28 Mar 2019","11463.65","11588.50","11452.45","11570.00" 69 | "27 Mar 2019","11531.45","11546.20","11413.00","11445.05" 70 | "26 Mar 2019","11375.20","11496.75","11352.45","11483.25" 71 | "25 Mar 2019","11395.65","11395.65","11311.60","11354.25" 72 | "22 Mar 2019","11549.20","11572.80","11434.55","11456.90" 73 | "20 Mar 2019","11553.35","11556.10","11503.10","11521.05" 74 | "19 Mar 2019","11500.30","11543.85","11451.25","11532.40" 75 | "18 Mar 2019","11473.85","11530.15","11412.50","11462.20" 76 | "15 Mar 2019","11376.85","11487.00","11370.80","11426.85" 77 | "14 Mar 2019","11382.50","11383.45","11313.75","11343.25" 78 | "13 Mar 2019","11326.20","11352.30","11276.60","11341.70" 79 | "12 Mar 2019","11231.35","11320.40","11227.00","11301.20" 80 | "11 Mar 2019","11068.75","11180.90","11059.85","11168.05" 81 | "08 Mar 2019","11038.85","11049.00","11008.95","11035.40" 82 | "07 Mar 2019","11077.95","11089.05","11027.10","11058.20" 83 | "06 Mar 2019","11024.85","11062.30","10998.85","11053.00" 84 | "05 Mar 2019","10864.85","10994.90","10817.00","10987.45" 85 | "01 Mar 2019","10842.65","10877.90","10823.10","10863.50" 86 | "28 Feb 2019","10865.70","10865.70","10784.85","10792.50" 87 | "27 Feb 2019","10881.20","10939.70","10751.20","10806.65" 88 | "26 Feb 2019","10775.30","10888.75","10729.30","10835.30" 89 | "25 Feb 2019","10813.25","10887.10","10788.05","10880.10" 90 | "22 Feb 2019","10782.70","10801.55","10758.40","10791.65" 91 | "21 Feb 2019","10744.10","10808.85","10721.50","10789.85" 92 | "20 Feb 2019","10655.45","10752.70","10646.40","10735.45" 93 | "19 Feb 2019","10636.70","10722.85","10585.65","10604.35" 94 | "18 Feb 2019","10738.65","10759.90","10628.40","10640.95" 95 | "15 Feb 2019","10780.25","10785.75","10620.40","10724.40" 96 | "14 Feb 2019","10786.10","10792.70","10718.75","10746.05" 97 | "13 Feb 2019","10870.55","10891.65","10772.10","10793.65" 98 | "12 Feb 2019","10879.70","10910.90","10823.80","10831.40" 99 | "11 Feb 2019","10930.90","10930.90","10857.10","10888.80" 100 | "08 Feb 2019","11023.50","11041.20","10925.45","10943.60" 101 | "07 Feb 2019","11070.45","11118.10","11043.60","11069.40" 102 | "06 Feb 2019","10965.10","11072.60","10962.70","11062.45" 103 | "05 Feb 2019","10908.65","10956.70","10886.70","10934.35" 104 | "04 Feb 2019","10876.75","10927.90","10814.15","10912.25" 105 | "01 Feb 2019","10851.35","10983.45","10813.45","10893.65" 106 | "31 Jan 2019","10690.55","10838.05","10678.55","10830.95" 107 | "30 Jan 2019","10702.25","10710.20","10612.85","10651.80" 108 | "29 Jan 2019","10653.70","10690.35","10583.65","10652.20" 109 | "28 Jan 2019","10792.45","10804.45","10630.95","10661.55" 110 | "25 Jan 2019","10859.75","10931.70","10756.45","10780.55" 111 | "24 Jan 2019","10844.05","10866.60","10798.65","10849.80" 112 | "23 Jan 2019","10931.05","10944.80","10811.95","10831.50" 113 | "22 Jan 2019","10949.80","10949.80","10864.15","10922.75" 114 | "21 Jan 2019","10919.35","10987.45","10885.75","10961.85" 115 | "18 Jan 2019","10914.85","10928.20","10852.20","10906.95" 116 | "17 Jan 2019","10920.85","10930.65","10844.65","10905.20" 117 | "16 Jan 2019","10899.65","10928.15","10876.90","10890.30" 118 | "15 Jan 2019","10777.55","10896.95","10777.55","10886.80" 119 | "14 Jan 2019","10807.00","10808.00","10692.35","10737.60" 120 | "11 Jan 2019","10834.75","10850.15","10739.40","10794.95" 121 | "10 Jan 2019","10859.35","10859.35","10801.80","10821.60" 122 | "09 Jan 2019","10862.40","10870.40","10749.40","10855.15" 123 | "08 Jan 2019","10786.25","10818.45","10733.25","10802.15" 124 | "07 Jan 2019","10804.85","10835.95","10750.15","10771.80" 125 | "04 Jan 2019","10699.70","10741.05","10628.65","10727.35" 126 | "03 Jan 2019","10796.80","10814.05","10661.25","10672.25" 127 | "02 Jan 2019","10868.85","10895.35","10735.05","10792.50" 128 | "01 Jan 2019","10881.70","10923.60","10807.10","10910.10" 129 | "31 Dec 2018","10913.20","10923.55","10853.20","10862.55" 130 | "28 Dec 2018","10820.95","10893.60","10817.15","10859.90" 131 | "27 Dec 2018","10817.90","10834.20","10764.45","10779.80" 132 | "26 Dec 2018","10635.45","10747.50","10534.55","10729.85" 133 | "24 Dec 2018","10780.90","10782.30","10649.25","10663.50" 134 | "21 Dec 2018","10944.25","10963.65","10738.65","10754.00" 135 | "20 Dec 2018","10885.20","10962.55","10880.05","10951.70" 136 | "19 Dec 2018","10930.55","10985.15","10928.00","10967.30" 137 | "18 Dec 2018","10850.90","10915.40","10819.10","10908.70" 138 | "17 Dec 2018","10853.20","10900.35","10844.85","10888.35" 139 | "14 Dec 2018","10784.50","10815.75","10752.10","10805.45" 140 | "13 Dec 2018","10810.75","10838.60","10749.50","10791.55" 141 | "12 Dec 2018","10591.00","10752.20","10560.80","10737.60" 142 | "11 Dec 2018","10350.05","10567.15","10333.85","10549.15" 143 | "10 Dec 2018","10508.70","10558.85","10474.95","10488.45" 144 | "07 Dec 2018","10644.80","10704.55","10599.35","10693.70" 145 | "06 Dec 2018","10718.15","10722.65","10588.25","10601.15" 146 | "05 Dec 2018","10820.45","10821.05","10747.95","10782.90" 147 | "04 Dec 2018","10877.10","10890.95","10833.35","10869.50" 148 | "03 Dec 2018","10930.70","10941.20","10845.35","10883.75" 149 | "30 Nov 2018","10892.10","10922.45","10835.10","10876.75" 150 | "29 Nov 2018","10808.70","10883.05","10782.35","10858.70" 151 | "28 Nov 2018","10708.75","10757.80","10699.85","10728.85" 152 | "27 Nov 2018","10621.45","10695.15","10596.35","10685.60" 153 | "26 Nov 2018","10568.30","10637.80","10489.75","10628.60" 154 | "22 Nov 2018","10612.65","10646.25","10512.00","10526.75" 155 | "21 Nov 2018","10670.95","10671.30","10562.35","10600.05" 156 | "20 Nov 2018","10740.10","10740.85","10640.85","10656.20" 157 | "19 Nov 2018","10731.25","10774.70","10688.80","10763.40" 158 | "16 Nov 2018","10644.00","10695.15","10631.15","10682.20" 159 | "15 Nov 2018","10580.60","10646.50","10557.50","10616.70" 160 | "14 Nov 2018","10634.90","10651.60","10532.70","10576.30" 161 | "13 Nov 2018","10451.90","10596.25","10440.55","10582.50" 162 | "12 Nov 2018","10607.80","10645.50","10464.05","10482.20" 163 | "09 Nov 2018","10614.70","10619.55","10544.85","10585.20" 164 | "07 Nov 2018","10614.45","10616.45","10582.30","10598.40" 165 | "06 Nov 2018","10552.00","10600.25","10491.45","10530.00" 166 | "05 Nov 2018","10558.75","10558.80","10477.00","10524.00" 167 | "02 Nov 2018","10462.30","10606.95","10457.70","10553.00" 168 | "01 Nov 2018","10441.70","10441.90","10341.90","10380.45" 169 | "31 Oct 2018","10209.55","10396.00","10105.10","10386.60" 170 | "30 Oct 2018","10239.40","10285.10","10175.35","10198.40" 171 | "29 Oct 2018","10078.10","10275.30","10020.35","10250.85" 172 | "26 Oct 2018","10122.35","10128.85","10004.55","10030.00" 173 | "25 Oct 2018","10135.05","10166.60","10079.30","10124.90" 174 | "24 Oct 2018","10278.15","10290.65","10126.70","10224.75" 175 | "23 Oct 2018","10152.60","10222.10","10102.35","10146.80" 176 | "22 Oct 2018","10405.85","10408.55","10224.00","10245.25" 177 | "19 Oct 2018","10339.70","10380.10","10249.60","10303.55" 178 | "17 Oct 2018","10688.70","10710.15","10436.45","10453.05" 179 | "16 Oct 2018","10550.15","10604.90","10525.30","10584.75" 180 | "15 Oct 2018","10524.20","10526.30","10410.15","10512.50" 181 | "12 Oct 2018","10331.55","10492.45","10322.15","10472.50" 182 | "11 Oct 2018","10169.80","10335.95","10138.60","10234.65" 183 | "10 Oct 2018","10331.85","10482.35","10318.25","10460.10" 184 | "09 Oct 2018","10390.30","10397.60","10279.35","10301.05" 185 | "08 Oct 2018","10310.15","10398.35","10198.40","10348.05" 186 | "05 Oct 2018","10514.10","10540.65","10261.90","10316.45" 187 | "04 Oct 2018","10754.70","10754.70","10547.25","10599.25" 188 | "03 Oct 2018","10982.70","10989.05","10843.75","10858.25" 189 | "01 Oct 2018","10930.90","11035.65","10821.55","11008.30" 190 | "28 Sep 2018","11008.10","11034.10","10850.30","10930.45" 191 | "27 Sep 2018","11079.80","11089.45","10953.35","10977.55" 192 | "26 Sep 2018","11145.55","11145.55","10993.05","11053.80" 193 | "25 Sep 2018","10969.95","11080.60","10882.85","11067.45" 194 | "24 Sep 2018","11164.40","11170.15","10943.60","10967.40" 195 | "21 Sep 2018","11271.30","11346.80","10866.45","11143.10" 196 | "19 Sep 2018","11326.65","11332.05","11210.90","11234.35" 197 | "18 Sep 2018","11381.55","11411.45","11268.95","11278.90" 198 | "17 Sep 2018","11464.95","11464.95","11366.90","11377.75" 199 | "14 Sep 2018","11443.50","11523.25","11430.55","11515.20" 200 | "12 Sep 2018","11340.10","11380.75","11250.20","11369.90" 201 | "11 Sep 2018","11476.85","11479.40","11274.00","11287.50" 202 | "10 Sep 2018","11570.25","11573.00","11427.30","11438.10" 203 | "07 Sep 2018","11558.25","11603.00","11484.40","11589.10" 204 | "06 Sep 2018","11514.15","11562.25","11436.05","11536.90" 205 | "05 Sep 2018","11514.85","11542.65","11393.85","11476.95" 206 | "04 Sep 2018","11598.75","11602.55","11496.85","11520.30" 207 | "03 Sep 2018","11751.80","11751.80","11567.40","11582.35" 208 | "31 Aug 2018","11675.85","11727.65","11640.10","11680.50" 209 | "30 Aug 2018","11694.75","11698.80","11639.70","11676.80" 210 | "29 Aug 2018","11744.95","11753.20","11678.85","11691.90" 211 | "28 Aug 2018","11731.95","11760.20","11710.50","11738.50" 212 | "27 Aug 2018","11605.85","11700.95","11595.60","11691.95" 213 | "24 Aug 2018","11566.60","11604.60","11532.00","11557.10" 214 | "23 Aug 2018","11620.70","11620.70","11546.70","11582.75" 215 | "21 Aug 2018","11576.20","11581.75","11539.60","11570.90" 216 | "20 Aug 2018","11502.10","11565.30","11499.65","11551.75" 217 | "17 Aug 2018","11437.15","11486.45","11431.80","11470.75" 218 | "16 Aug 2018","11397.15","11449.85","11366.25","11385.05" 219 | "14 Aug 2018","11381.70","11452.45","11370.80","11435.10" 220 | "13 Aug 2018","11369.60","11406.30","11340.30","11355.75" 221 | "10 Aug 2018","11474.95","11478.75","11419.65","11429.50" 222 | "09 Aug 2018","11493.25","11495.20","11454.10","11470.70" 223 | "08 Aug 2018","11412.50","11459.95","11379.30","11450.00" 224 | "07 Aug 2018","11423.15","11428.95","11359.70","11389.45" 225 | "06 Aug 2018","11401.50","11427.65","11370.60","11387.10" 226 | "03 Aug 2018","11297.80","11368.00","11294.55","11360.80" 227 | "02 Aug 2018","11328.90","11328.90","11234.95","11244.70" 228 | "01 Aug 2018","11359.80","11390.55","11313.55","11346.20" 229 | "31 Jul 2018","11311.05","11366.00","11267.75","11356.50" 230 | "30 Jul 2018","11296.65","11328.10","11261.45","11319.55" 231 | "27 Jul 2018","11232.75","11283.40","11210.25","11278.35" 232 | "26 Jul 2018","11132.95","11185.85","11125.70","11167.30" 233 | "25 Jul 2018","11148.40","11157.15","11113.25","11132.00" 234 | "24 Jul 2018","11109.00","11143.40","11092.50","11134.30" 235 | "23 Jul 2018","11019.85","11093.40","11010.95","11084.75" 236 | "20 Jul 2018","10963.50","11030.25","10946.20","11010.20" 237 | "19 Jul 2018","10999.50","11006.50","10935.45","10957.10" 238 | "18 Jul 2018","11060.20","11076.20","10956.30","10980.45" 239 | "17 Jul 2018","10939.65","11018.50","10925.60","11008.05" 240 | "16 Jul 2018","11018.95","11019.50","10926.25","10936.85" 241 | "13 Jul 2018","11056.90","11071.35","10999.75","11018.90" 242 | "12 Jul 2018","11006.95","11078.30","10999.65","11023.20" 243 | "11 Jul 2018","10956.40","10976.65","10923.00","10948.30" 244 | "10 Jul 2018","10902.75","10956.90","10876.65","10947.25" 245 | "09 Jul 2018","10838.30","10860.35","10807.15","10852.90" 246 | "06 Jul 2018","10744.15","10816.35","10735.05","10772.65" 247 | "05 Jul 2018","10786.05","10786.05","10726.25","10749.75" 248 | "04 Jul 2018","10715.00","10777.15","10677.75","10769.90" 249 | "03 Jul 2018","10668.60","10713.30","10630.25","10699.90" 250 | "02 Jul 2018","10732.35","10736.15","10604.65","10657.30" 251 | "29 Jun 2018","10612.85","10723.05","10612.35","10714.30" 252 | "28 Jun 2018","10660.80","10674.20","10557.70","10589.10" 253 | "27 Jun 2018","10785.50","10785.50","10652.40","10671.40" 254 | "26 Jun 2018","10742.70","10805.25","10732.55","10769.15" 255 | "25 Jun 2018","10822.90","10831.05","10753.05","10762.45" 256 | "22 Jun 2018","10742.70","10837.00","10710.45","10821.85" 257 | "21 Jun 2018","10808.45","10809.60","10725.90","10741.10" 258 | "20 Jun 2018","10734.65","10781.80","10724.05","10772.05" 259 | "19 Jun 2018","10789.45","10789.45","10701.20","10710.45" 260 | "18 Jun 2018","10830.20","10830.20","10787.35","10799.85" 261 | "15 Jun 2018","10808.65","10834.00","10755.40","10817.70" 262 | "14 Jun 2018","10832.90","10833.70","10773.55","10808.05" 263 | "13 Jun 2018","10887.50","10893.25","10842.65","10856.70" 264 | "12 Jun 2018","10816.15","10856.55","10789.40","10842.85" 265 | "11 Jun 2018","10781.85","10850.55","10777.05","10786.95" 266 | "08 Jun 2018","10736.40","10779.45","10709.05","10767.65" 267 | "07 Jun 2018","10722.60","10818.00","10722.60","10768.35" 268 | "06 Jun 2018","10603.45","10698.35","10587.50","10684.65" 269 | "05 Jun 2018","10630.70","10633.15","10550.90","10593.15" 270 | "04 Jun 2018","10765.95","10770.3","10618.35","10628.5" 271 | "01 Jun 2018","10738.45","10764.75","10681.5","10696.2" 272 | "31 May 2018","10670.1","10763.8","10620.4","10736.15" 273 | "30 May 2018","10579","10648.7","10558.45","10614.35" 274 | "29 May 2018","10689.4","10717.25","10616.1","10633.3" 275 | "28 May 2018","10648.35","10709.8","10640.55","10688.65" 276 | "25 May 2018","10533.05","10628.05","10524","10605.15" 277 | "24 May 2018","10464.85","10535.15","10419.8","10513.85" 278 | "23 May 2018","10521.1","10533.55","10417.8","10430.35" 279 | "22 May 2018","10518.45","10558.6","10490.55","10536.7" 280 | "21 May 2018","10616.7","10621.7","10505.8","10516.7" 281 | "18 May 2018","10671.85","10674.95","10589.1","10596.4" 282 | "17 May 2018","10775.6","10777.25","10664.5","10682.7" 283 | "16 May 2018","10751.95","10790.45","10699.7","10741.1" 284 | "15 May 2018","10812.6","10929.2","10781.4","10801.85" 285 | "14 May 2018","10815.15","10834.85","10774.75","10806.6" 286 | "11 May 2018","10741.95","10812.05","10724.45","10806.5" 287 | "10 May 2018","10779.65","10785.55","10705","10716.55" 288 | "09 May 2018","10693.35","10766.25","10689.85","10741.7" 289 | "08 May 2018","10757.9","10758.55","10689.4","10717.8" 290 | "07 May 2018","10653.15","10725.65","10635.65","10715.5" 291 | "04 May 2018","10700.45","10700.45","10601.6","10618.25" 292 | "03 May 2018","10720.15","10720.6","10647.45","10679.65" 293 | "02 May 2018","10783.85","10784.65","10689.8","10718.05" 294 | "30 Apr 2018","10705.75","10759","10704.6","10739.35" 295 | "27 Apr 2018","10651.65","10719.8","10647.55","10692.3" 296 | "26 Apr 2018","10586.5","10628.4","10559.65","10617.8" 297 | "25 Apr 2018","10612.4","10612.6","10536.45","10570.55" 298 | "24 Apr 2018","10578.1","10636.8","10569","10614.35" 299 | "23 Apr 2018","10592.8","10638.35","10514.95","10584.7" 300 | "20 Apr 2018","10560.35","10582.35","10527.45","10564.05" 301 | "19 Apr 2018","10563.65","10572.2","10546.2","10565.3" 302 | "18 Apr 2018","10578.9","10594.2","10509.7","10526.2" 303 | "17 Apr 2018","10557.3","10560.45","10495.65","10548.7" 304 | "16 Apr 2018","10398.3","10540.15","10396.35","10528.35" 305 | "13 Apr 2018","10495.3","10519.9","10451.45","10480.6" 306 | "12 Apr 2018","10410.65","10469.9","10395.25","10458.65" 307 | "11 Apr 2018","10428.15","10428.15","10355.6","10417.15" 308 | "10 Apr 2018","10412.9","10424.85","10381.5","10402.25" 309 | "09 Apr 2018","10333.7","10397.7","10328.5","10379.35" 310 | "06 Apr 2018","10322.75","10350.45","10290.85","10331.6" 311 | "05 Apr 2018","10228.45","10331.8","10227.45","10325.15" 312 | "04 Apr 2018","10274.6","10279.85","10111.3","10128.4" 313 | "03 Apr 2018","10186.85","10255.35","10171.05","10245" 314 | "02 Apr 2018","10151.65","10220.1","10127.75","10211.8" 315 | "28 Mar 2018","10143.6","10158.35","10096.9","10113.7" 316 | "27 Mar 2018","10188","10207.9","10139.65","10184.15" 317 | "26 Mar 2018","9989.15","10143.5","9958.55","10130.65" 318 | "23 Mar 2018","9968.8","10027.7","9951.9","9998.05" 319 | "22 Mar 2018","10167.5","10207.85","10105.4","10114.75" 320 | "21 Mar 2018","10181.95","10227.3","10132.95","10155.25" 321 | "20 Mar 2018","10051.55","10155.65","10049.1","10124.35" 322 | "19 Mar 2018","10215.35","10224.55","10075.3","10094.25" 323 | "16 Mar 2018","10345.15","10346.3","10180.25","10195.15" 324 | "15 Mar 2018","10405.45","10420","10346.2","10360.15" 325 | "14 Mar 2018","10393.05","10420.35","10336.3","10410.9" 326 | "13 Mar 2018","10389.5","10478.6","10377.85","10426.85" 327 | "12 Mar 2018","10301.6","10433.65","10295.45","10421.4" 328 | "09 Mar 2018","10271.3","10296.7","10211.9","10226.85" 329 | "08 Mar 2018","10216.25","10270.35","10146.4","10242.65" 330 | "07 Mar 2018","10232.95","10243.35","10141.55","10154.2" 331 | "06 Mar 2018","10420.5","10441.35","10215.9","10249.25" 332 | "05 Mar 2018","10428.3","10428.7","10323.9","10358.85" 333 | "01 Mar 2018","10479.95","10525.5","10447.15","10458.35" 334 | "28 Feb 2018","10488.95","10535.5","10461.55","10492.85" 335 | "27 Feb 2018","10615.2","10631.65","10537.25","10554.3" 336 | "26 Feb 2018","10526.55","10592.95","10520.2","10582.6" 337 | "23 Feb 2018","10408.1","10499.1","10396.65","10491.05" 338 | "22 Feb 2018","10354.35","10397.55","10340.65","10382.7" 339 | "21 Feb 2018","10426","10426.1","10349.6","10397.45" 340 | "20 Feb 2018","10391","10429.35","10347.65","10360.4" 341 | "19 Feb 2018","10488.9","10489.35","10302.75","10378.4" 342 | "16 Feb 2018","10596.2","10612.9","10434.05","10452.3" 343 | "15 Feb 2018","10537.9","10618.1","10511.05","10545.5" 344 | "14 Feb 2018","10585.75","10590.55","10456.65","10500.9" 345 | "12 Feb 2018","10518.2","10555.5","10485.4","10539.75" 346 | "09 Feb 2018","10416.5","10480.2","10398.2","10454.95" 347 | "08 Feb 2018","10518.5","10637.8","10479.55","10576.85" 348 | "07 Feb 2018","10607.2","10614","10446.4","10476.7" 349 | "06 Feb 2018","10295.15","10594.15","10276.3","10498.25" 350 | "05 Feb 2018","10604.3","10702.75","10586.8","10666.55" 351 | "02 Feb 2018","10938.2","10954.95","10736.1","10760.6" 352 | "01 Feb 2018","11044.55","11117.35","10878.8","11016.9" 353 | "31 Jan 2018","11018.8","11058.5","10979.3","11027.7" 354 | "30 Jan 2018","11120.85","11121.1","11033.9","11049.65" 355 | "29 Jan 2018","11079.35","11171.55","11075.95","11130.4" 356 | "25 Jan 2018","11095.6","11095.6","11009.2","11069.65" 357 | "24 Jan 2018","11069.35","11110.1","11046.15","11086" 358 | "23 Jan 2018","10997.4","11092.9","10994.55","11083.7" 359 | "22 Jan 2018","10883.2","10975.1","10881.4","10966.2" 360 | "19 Jan 2018","10829.2","10906.85","10793.9","10894.7" 361 | "18 Jan 2018","10873.4","10887.5","10782.4","10817" 362 | "17 Jan 2018","10702.45","10803","10666.75","10788.55" 363 | "16 Jan 2018","10761.5","10762.35","10687.85","10700.45" 364 | "15 Jan 2018","10718.5","10782.65","10713.8","10741.55" 365 | "12 Jan 2018","10682.55","10690.4","10597.1","10681.25" 366 | "11 Jan 2018","10637.05","10664.6","10612.35","10651.2" 367 | "10 Jan 2018","10652.05","10655.5","10592.7","10632.2" 368 | "09 Jan 2018","10645.1","10659.15","10603.6","10637" 369 | "08 Jan 2018","10591.7","10631.2","10588.55","10623.6" 370 | "05 Jan 2018","10534.25","10566.1","10520.1","10558.85" 371 | "04 Jan 2018","10469.4","10513","10441.45","10504.8" 372 | "03 Jan 2018","10482.65","10503.6","10429.55","10443.2" 373 | "02 Jan 2018","10477.55","10495.2","10404.65","10442.2" 374 | "01 Jan 2018","10531.7","10537.85","10423.1","10435.55" 375 | "29 Dec 2017","10492.35","10538.7","10488.65","10530.7" 376 | "28 Dec 2017","10498.2","10534.55","10460.45","10477.9" 377 | "27 Dec 2017","10531.05","10552.4","10469.25","10490.75" 378 | "26 Dec 2017","10512.3","10545.45","10477.95","10531.5" 379 | "22 Dec 2017","10457.3","10501.1","10448.25","10493" 380 | "21 Dec 2017","10473.95","10473.95","10426.9","10440.3" 381 | "20 Dec 2017","10494.4","10494.45","10437.15","10444.2" 382 | "19 Dec 2017","10414.8","10472.2","10406","10463.2" 383 | "18 Dec 2017","10263.1","10443.55","10074.8","10388.75" 384 | "15 Dec 2017","10345.65","10373.1","10319.65","10333.25" 385 | "14 Dec 2017","10229.3","10276.1","10141.55","10252.1" 386 | "13 Dec 2017","10236.6","10296.55","10169.85","10192.95" 387 | "12 Dec 2017","10324.9","10326.1","10230.2","10240.15" 388 | "11 Dec 2017","10310.5","10329.2","10282.05","10322.25" 389 | "08 Dec 2017","10198.45","10270.85","10195.25","10265.65" 390 | "07 Dec 2017","10063.45","10182.65","10061.9","10166.7" 391 | "06 Dec 2017","10088.8","10104.2","10033.35","10044.1" 392 | "05 Dec 2017","10118.25","10147.95","10069.1","10118.25" 393 | "04 Dec 2017","10175.05","10179.2","10095.7","10127.75" 394 | "01 Dec 2017","10263.7","10272.7","10108.55","10121.8" 395 | "30 Nov 2017","10332.7","10332.7","10211.25","10226.55" 396 | "29 Nov 2017","10376.65","10392.95","10345.9","10361.3" 397 | "28 Nov 2017","10387.9","10409.55","10355.2","10370.25" 398 | "27 Nov 2017","10361.05","10407.15","10340.2","10399.55" 399 | "24 Nov 2017","10366.8","10404.5","10362.25","10389.7" 400 | "23 Nov 2017","10358.45","10374.3","10307.3","10348.75" 401 | "22 Nov 2017","10350.8","10368.7","10309.55","10342.3" 402 | "21 Nov 2017","10329.25","10358.7","10315.05","10326.9" 403 | "20 Nov 2017","10287.2","10309.85","10261.5","10298.75" 404 | "17 Nov 2017","10324.55","10343.6","10268.05","10283.6" 405 | "16 Nov 2017","10152.9","10232.25","10139.2","10214.75" 406 | "15 Nov 2017","10171.95","10175.45","10094","10118.05" 407 | "14 Nov 2017","10223.4","10248","10175.55","10186.6" 408 | "13 Nov 2017","10322","10334.15","10216.25","10224.95" 409 | "10 Nov 2017","10304.35","10344.95","10254.1","10321.75" 410 | "09 Nov 2017","10358.65","10368.45","10266.95","10308.95" 411 | "08 Nov 2017","10361.95","10384.25","10285.5","10303.15" 412 | "07 Nov 2017","10477.15","10485.75","10340.8","10350.15" 413 | "06 Nov 2017","10431.75","10490.45","10413.75","10451.8" 414 | "03 Nov 2017","10461.55","10461.7","10403.6","10452.5" 415 | "02 Nov 2017","10440.5","10453","10412.55","10423.8" 416 | "01 Nov 2017","10390.35","10451.65","10383.05","10440.5" 417 | "31 Oct 2017","10364.9","10367.7","10323.95","10335.3" 418 | "30 Oct 2017","10353.85","10384.5","10344.3","10363.65" 419 | "27 Oct 2017","10362.3","10366.15","10311.3","10323.05" 420 | "26 Oct 2017","10291.8","10355.65","10271.85","10343.8" 421 | "25 Oct 2017","10321.15","10340.55","10240.9","10295.35" 422 | "24 Oct 2017","10218.55","10237.75","10182.4","10207.7" 423 | "23 Oct 2017","10176.65","10224.15","10124.5","10184.85" 424 | "19 Oct 2017","10210.35","10211.95","10123.35","10146.55" 425 | "18 Oct 2017","10209.4","10236.45","10175.75","10210.85" 426 | "17 Oct 2017","10227.65","10251.85","10212.6","10234.45" 427 | "16 Oct 2017","10207.4","10242.95","10175.1","10230.85" 428 | "13 Oct 2017","10123.7","10191.9","10120.1","10167.45" 429 | "12 Oct 2017","10011.2","10104.45","9977.1","10096.4" 430 | "11 Oct 2017","10042.6","10067.25","9955.8","9984.8" 431 | "10 Oct 2017","10013.7","10034","10002.3","10016.95" 432 | "09 Oct 2017","9988.2","10015.75","9959.45","9988.75" 433 | "06 Oct 2017","9908.15","9989.35","9906.6","9979.7" 434 | "05 Oct 2017","9927","9945.95","9881.85","9888.7" 435 | "04 Oct 2017","9884.35","9938.3","9850.65","9914.9" 436 | "03 Oct 2017","9893.3","9895.4","9831.05","9859.5" 437 | "29 Sep 2017","9814.3","9854","9775.35","9788.6" 438 | "28 Sep 2017","9736.4","9789.2","9687.55","9768.95" 439 | "27 Sep 2017","9920.6","9921.05","9714.4","9735.75" 440 | "26 Sep 2017","9875.25","9891.35","9813","9871.5" 441 | "25 Sep 2017","9960.1","9960.5","9816.05","9872.6" 442 | "22 Sep 2017","10094.35","10095.05","9952.8","9964.4" 443 | "21 Sep 2017","10139.6","10158.9","10058.6","10121.9" 444 | "20 Sep 2017","10160.95","10171.05","10134.2","10141.15" 445 | "19 Sep 2017","10175.6","10178.95","10129.95","10147.55" 446 | "18 Sep 2017","10133.1","10171.7","10131.3","10153.1" 447 | "15 Sep 2017","10062.35","10115.15","10043.65","10085.4" 448 | "14 Sep 2017","10107.4","10126.5","10070.35","10086.6" 449 | "13 Sep 2017","10099.25","10131.95","10063.15","10079.3" 450 | "12 Sep 2017","10056.85","10097.55","10028.05","10093.05" 451 | "11 Sep 2017","9971.75","10028.65","9968.8","10006.05" 452 | "08 Sep 2017","9958.65","9963.6","9913.3","9934.8" 453 | "07 Sep 2017","9945.85","9964.85","9917.2","9929.9" 454 | "06 Sep 2017","9899.25","9931.55","9882.55","9916.2" 455 | "05 Sep 2017","9933.25","9963.1","9901.05","9952.2" 456 | "04 Sep 2017","9984.15","9988.4","9861","9912.85" 457 | "01 Sep 2017","9937.65","9983.45","9909.85","9974.4" 458 | "31 Aug 2017","9905.7","9925.1","9856.95","9917.9" 459 | "30 Aug 2017","9859.5","9909.45","9850.8","9884.4" 460 | "29 Aug 2017","9886.4","9887.35","9783.75","9796.05" 461 | "28 Aug 2017","9907.15","9925.75","9882","9912.8" 462 | "24 Aug 2017","9881.2","9881.5","9848.85","9857.05" 463 | "23 Aug 2017","9803.05","9857.9","9786.75","9852.5" 464 | "22 Aug 2017","9815.75","9828.45","9752.6","9765.55" 465 | "21 Aug 2017","9864.25","9884.35","9740.1","9754.35" 466 | "18 Aug 2017","9865.95","9865.95","9783.65","9837.4" 467 | "17 Aug 2017","9945.55","9947.8","9883.75","9904.15" 468 | "16 Aug 2017","9825.85","9903.95","9773.85","9897.3" 469 | "14 Aug 2017","9755.75","9818.3","9752.1","9794.15" 470 | "11 Aug 2017","9712.15","9771.65","9685.55","9710.8" 471 | "10 Aug 2017","9872.85","9892.65","9776.2","9820.25" 472 | "09 Aug 2017","9961.15","9969.8","9893.05","9908.05" 473 | "08 Aug 2017","10068.35","10083.8","9947","9978.55" 474 | "07 Aug 2017","10074.8","10088.1","10046.35","10057.4" 475 | "04 Aug 2017","10008.6","10075.25","9988.35","10066.4" 476 | "03 Aug 2017","10081.15","10081.15","9998.25","10013.65" 477 | "02 Aug 2017","10136.3","10137.85","10054.2","10081.5" 478 | "01 Aug 2017","10101.05","10128.6","10065.75","10114.65" 479 | "31 Jul 2017","10034.7","10085.9","10016.95","10077.1" 480 | "28 Jul 2017","9996.55","10026.05","9944.5","10014.5" 481 | "27 Jul 2017","10063.25","10114.85","10005.5","10020.55" 482 | "26 Jul 2017","9983.65","10025.95","9965.95","10020.65" 483 | "25 Jul 2017","10010.55","10011.3","9949.1","9964.55" 484 | "24 Jul 2017","9936.8","9982.05","9919.6","9966.4" 485 | "21 Jul 2017","9899.6","9924.7","9838","9915.25" 486 | "20 Jul 2017","9920.2","9922.55","9863.45","9873.3" 487 | "19 Jul 2017","9855.95","9905.05","9851.65","9899.6" 488 | "18 Jul 2017","9832.7","9885.35","9792.05","9827.15" 489 | "17 Jul 2017","9908.15","9928.2","9894.7","9915.95" 490 | "14 Jul 2017","9913.3","9913.3","9845.45","9886.35" 491 | "13 Jul 2017","9855.8","9897.25","9853.45","9891.7" 492 | "12 Jul 2017","9807.3","9824.95","9787.7","9816.1" 493 | "11 Jul 2017","9797.45","9830.05","9778.85","9786.05" 494 | "10 Jul 2017","9719.3","9782.15","9646.45","9771.05" 495 | "07 Jul 2017","9670.35","9684.25","9642.65","9665.8" 496 | "06 Jul 2017","9653.6","9700.7","9639.95","9674.55" -------------------------------------------------------------------------------- /GOLD.csv: -------------------------------------------------------------------------------- 1 | Date,Price,Open,High,Low,Vol.,Change %,Pred,new 2 | "May 04, 2017",28060,28400,28482,28025,0.08K,-1.79%,738.0,117.57074041034866 3 | "May 05, 2017",28184,28136,28382,28135,0.06K,0.44%,-146.0,295.430175937443 4 | "May 08, 2017",28119,28145,28255,28097,7.85K,-0.23%,30.0,132.12371427554538 5 | "May 09, 2017",27981,28125,28192,27947,10.10K,-0.49%,357.0,101.29806419293527 6 | "May 10, 2017",28007,28060,28146,27981,9.28K,0.09%,124.0,112.15331832314767 7 | "May 11, 2017",28022,27995,28100,27945,9.72K,0.05%,149.0,182.42708892467274 8 | "May 12, 2017",28019,28088,28195,27985,9.48K,-0.01%,167.0,141.25513668036362 9 | "May 15, 2017",28008,28049,28157,27996,8.76K,-0.04%,22.0,120.06900985855101 10 | "May 16, 2017",28109,28025,28159,28025,7.73K,0.36%,34.0,218.40164139161428 11 | "May 17, 2017",28614,28170,28638,28170,15.92K,1.80%,420.0,919.3763578274776 12 | "May 18, 2017",28710,28666,28980,28551,23.80K,0.34%,234.0,475.38909320164015 13 | "May 19, 2017",28634,28660,28779,28531,13.16K,-0.26%,345.0,222.89530685920545 14 | "May 22, 2017",28783,28590,28799,28568,9.31K,0.52%,265.0,425.73848361803533 15 | "May 23, 2017",28808,28804,28930,28740,12.50K,0.09%,138.0,194.44954766875526 16 | "May 24, 2017",28719,28770,28770,28650,8.69K,-0.31%,378.0,69.28900523560151 17 | "May 25, 2017",28638,28769,28770,28600,9.93K,-0.28%,413.0,39.225874125873816 18 | "May 26, 2017",28847,28694,28866,28674,9.52K,0.73%,214.0,346.1584013391912 19 | "May 29, 2017",28874,28843,28928,28807,4.56K,0.09%,121.0,152.28142465373094 20 | "May 30, 2017",28704,28921,28960,28680,9.88K,-0.59%,491.0,63.23430962343264 21 | "May 31, 2017",28905,28683,28950,28628,7.83K,0.70%,397.0,547.1156210702793 22 | "Jun 01, 2017",28749,28877,28877,28712,1.77K,-0.54%,404.0,37.212628865978324 23 | "Jun 02, 2017",28949,28685,29011,28680,0.31K,0.70%,222.0,598.1045676429568 24 | "Jun 05, 2017",29111,29045,29180,29045,0.71K,0.56%,-3.0,201.30676536409007 25 | "Jun 06, 2017",29481,29220,29499,29216,11.14K,1.27%,259.0,546.5669153888266 26 | "Jun 07, 2017",29376,29439,29485,29310,9.82K,-0.36%,344.0,112.39406345957106 27 | "Jun 08, 2017",29049,29319,29345,28951,12.95K,-1.11%,906.0,125.33370177196049 28 | "Jun 09, 2017",28938,29001,29009,28836,8.98K,-0.38%,526.0,110.61194340407745 29 | "Jun 12, 2017",28917,28905,28971,28851,7.77K,-0.07%,174.0,132.2745138816681 30 | "Jun 13, 2017",28862,28907,28916,28711,7.71K,-0.19%,685.0,161.0781581972078 31 | "Jun 14, 2017",28946,28890,29025,28802,9.37K,0.29%,329.0,280.1149225748195 32 | "Jun 15, 2017",28686,28890,28890,28640,11.43K,-0.90%,592.0,46.40153631284921 33 | "Jun 16, 2017",28616,28697,28718,28600,6.78K,-0.24%,205.0,37.06601398601561 34 | "Jun 19, 2017",28473,28593,28593,28460,7.55K,-0.50%,292.0,13.060751932538553 35 | "Jun 20, 2017",28475,28470,28530,28420,7.69K,0.01%,150.0,115.21287825475157 36 | "Jun 21, 2017",28495,28539,28575,28403,9.08K,0.07%,420.0,128.55712424743982 37 | "Jun 22, 2017",28557,28530,28670,28519,7.48K,0.22%,-42.0,178.20119920053185 38 | "Jun 23, 2017",28648,28596,28782,28596,7.10K,0.32%,-82.0,238.33822912295363 39 | "Jun 26, 2017",28426,28302,28440,28264,4.61K,-0.77%,262.0,301.0087744126795 40 | "Jun 27, 2017",28483,28411,28572,28401,7.63K,0.20%,23.0,243.4937150100341 41 | "Jun 28, 2017",28485,28569,28660,28461,7.73K,0.01%,173.0,115.16780858016136 42 | "Jun 29, 2017",28527,28522,28550,28380,7.94K,0.15%,550.0,175.88054968287545 43 | "Jun 30, 2017",28295,28505,28505,28168,8.40K,-0.81%,928.0,128.51941919909223 44 | "Jul 03, 2017",27952,28300,28300,27925,3.24K,-1.21%,804.0,27.36257833482523 45 | "Jul 04, 2017",28043,27956,28169,27956,1.26K,0.33%,-39.0,300.66286307053815 46 | "Jul 05, 2017",28051,28120,28155,27845,0.90K,0.03%,927.0,243.29340994792443 47 | "Jul 06, 2017",28143,28200,28237,28111,10.57K,0.33%,205.0,69.14343139696211 48 | "Jul 07, 2017",27812,28101,28140,27754,16.37K,-1.18%,771.0,97.80665849967407 49 | "Jul 10, 2017",27840,27835,27887,27620,13.13K,0.10%,818.0,274.12671976828284 50 | "Jul 11, 2017",27871,27805,27929,27702,11.02K,0.11%,420.0,294.3848458595058 51 | "Jul 12, 2017",27889,27919,28033,27840,10.47K,0.06%,142.0,163.3396910919546 52 | "Jul 13, 2017",27868,27945,27968,27851,7.67K,-0.08%,199.0,40.07141574808703 53 | "Jul 14, 2017",28022,27850,28150,27753,14.38K,0.55%,432.0,572.8479803985138 54 | "Jul 17, 2017",28147,28050,28173,28050,7.52K,0.45%,71.0,220.42534759358387 55 | "Jul 18, 2017",28280,28160,28299,28160,9.35K,0.47%,101.0,259.59232954545587 56 | "Jul 19, 2017",28270,28270,28285,28150,8.47K,-0.04%,465.0,135.57548845470592 57 | "Jul 20, 2017",28345,28220,28380,28155,11.27K,0.27%,350.0,351.5183803942455 58 | "Jul 21, 2017",28541,28333,28559,28300,9.54K,0.69%,322.0,469.20561837455904 59 | "Jul 24, 2017",28530,28531,28612,28480,7.97K,-0.04%,121.0,131.2317415730322 60 | "Jul 25, 2017",28490,28535,28584,28410,11.13K,-0.14%,361.0,129.4899683210133 61 | "Jul 26, 2017",28396,28462,28462,28320,8.09K,-0.33%,436.0,76.3810734463259 62 | "Jul 27, 2017",28477,28455,28613,28376,10.46K,0.29%,202.0,259.84356498449415 63 | "Jul 28, 2017",28596,28533,28620,28405,8.24K,0.42%,551.0,279.4456961802498 64 | "Jul 31, 2017",28588,28572,28628,28510,6.20K,-0.03%,224.0,134.32283409330194 65 | "Aug 01, 2017",28514,28623,28623,28447,2.23K,-0.26%,486.0,67.41452525749628 66 | "Aug 02, 2017",28486,28451,28586,28373,0.58K,-0.10%,247.0,248.84830648856223 67 | "Aug 03, 2017",28447,28400,28500,28299,0.48K,-0.14%,398.0,249.05120322272703 68 | "Aug 04, 2017",28373,28525,28585,28355,0.07K,-0.26%,316.0,78.1460059954152 69 | "Aug 07, 2017",28419,28332,28437,28320,3.95K,0.16%,117.0,204.40900423728817 70 | "Aug 08, 2017",28357,28416,28497,28283,7.40K,-0.22%,333.0,155.55991231481673 71 | "Aug 09, 2017",28800,28415,28821,28415,11.58K,1.56%,364.0,796.5009677986965 72 | "Aug 10, 2017",29130,28845,29173,28845,12.40K,1.15%,242.0,616.2407696307855 73 | "Aug 11, 2017",29158,29147,29300,29032,11.32K,0.10%,329.0,280.16313033893675 74 | "Aug 14, 2017",29042,29100,29116,28982,7.74K,-0.40%,340.0,76.27741356704064 75 | "Aug 16, 2017",28878,28978,28978,28727,10.49K,-0.56%,804.0,152.31935113307918 76 | "Aug 17, 2017",29093,28900,29174,28800,9.73K,0.74%,512.0,570.8049305555543 77 | "Aug 18, 2017",29109,29070,29380,29013,12.43K,0.05%,-4.0,407.2143521869512 78 | "Aug 21, 2017",29224,29050,29248,29030,7.42K,0.40%,230.0,393.45683775404905 79 | "Aug 22, 2017",29061,29200,29200,28980,8.75K,-0.56%,602.0,81.61490683229931 80 | "Aug 23, 2017",29090,29063,29148,29023,7.27K,0.10%,129.0,152.28856424215337 81 | "Aug 24, 2017",29021,29051,29107,28980,7.17K,-0.24%,168.0,97.1796756383701 82 | "Aug 25, 2017",29086,29001,29134,28716,7.04K,0.22%,1177.0,508.3858476110872 83 | "Aug 28, 2017",29425,29101,29440,29095,9.56K,1.17%,333.0,672.9130434782601 84 | "Aug 29, 2017",29588,29469,29810,29425,11.07K,0.55%,73.0,506.13271028037343 85 | "Aug 30, 2017",29540,29640,29640,29405,8.46K,-0.16%,740.0,136.07889814657392 86 | "Aug 31, 2017",29756,29430,29832,29421,6.94K,0.73%,286.0,741.6798205363519 87 | "Sep 01, 2017",29788,29687,29941,29671,1.15K,0.11%,12.0,372.0646759462106 88 | "Sep 04, 2017",30174,30187,30250,29800,0.27K,1.30%,1459.0,442.6476510067113 89 | "Sep 05, 2017",30226,30389,30421,30010,0.19K,0.17%,1158.0,250.9582139286904 90 | "Sep 06, 2017",30088,30200,30298,30055,12.40K,-0.46%,258.0,131.26681084678057 91 | "Sep 07, 2017",30287,30050,30348,30010,14.46K,0.66%,336.0,578.1198267244254 92 | "Sep 08, 2017",30271,30350,30470,30221,13.38K,-0.05%,238.0,170.41196518976722 93 | "Sep 11, 2017",29942,30187,30187,29888,12.58K,-1.09%,706.0,54.540216809422425 94 | "Sep 12, 2017",29953,29910,29977,29830,9.39K,0.04%,339.0,190.60613476366046 95 | "Sep 13, 2017",29896,30011,30077,29841,10.61K,-0.19%,384.0,121.43497201836362 96 | "Sep 14, 2017",30020,29831,30059,29775,12.39K,0.41%,374.0,475.3368597816952 97 | "Sep 15, 2017",29869,30063,30096,29840,11.58K,-0.50%,471.0,62.24879356568272 98 | "Sep 18, 2017",29561,29845,29845,29550,10.85K,-1.03%,612.0,11.109813874787505 99 | "Sep 19, 2017",29628,29521,29690,29521,8.11K,0.23%,45.0,276.6125470004408 100 | "Sep 20, 2017",29780,29660,29817,29660,8.42K,0.51%,83.0,277.6351989211071 101 | "Sep 21, 2017",29584,29620,29680,29450,13.18K,-0.66%,548.0,195.0465195246179 102 | "Sep 22, 2017",29608,29660,29767,29586,8.54K,0.08%,85.0,129.13459068478187 103 | "Sep 25, 2017",30065,29580,30150,29525,13.45K,1.54%,620.0,1121.430990685858 104 | "Sep 26, 2017",29885,30129,30195,29802,11.90K,-0.60%,754.0,150.09452385746044 105 | "Sep 27, 2017",29673,29861,29944,29644,10.37K,-0.71%,409.0,112.29348266090892 106 | "Sep 28, 2017",29624,29650,29706,29530,7.99K,-0.17%,372.0,150.5602438198439 107 | "Sep 29, 2017",29549,29615,29740,29430,7.49K,-0.25%,483.0,245.25348284063872 108 | "Oct 03, 2017",29400,29420,29456,29352,1.53K,-0.50%,196.0,84.17007358953559 109 | "Oct 04, 2017",29331,29479,29513,29258,0.23K,-0.23%,554.0,107.63623624307728 110 | "Oct 05, 2017",29412,29469,29469,29361,0.06K,0.28%,318.0,51.18759579033576 111 | "Oct 06, 2017",29510,29325,29540,29274,11.30K,0.33%,359.0,453.14442850311025 112 | "Oct 09, 2017",29670,29540,29718,29540,6.17K,0.54%,82.0,308.7833446174664 113 | "Oct 10, 2017",29768,29700,29798,29700,6.98K,0.33%,38.0,166.22437710437592 114 | "Oct 11, 2017",29659,29717,29770,29638,5.43K,-0.37%,147.0,74.09352857817794 115 | "Oct 12, 2017",29756,29725,29840,29692,6.88K,0.33%,79.0,179.31900848713485 116 | "Oct 13, 2017",29800,29750,29817,29660,7.31K,0.15%,393.0,207.74106540795762 117 | "Oct 16, 2017",29808,29809,29896,29790,5.77K,0.03%,-13.0,105.0640483383686 118 | "Oct 17, 2017",29565,29751,29751,29550,8.27K,-0.82%,432.0,15.102030456851935 119 | "Oct 18, 2017",29536,29640,29643,29504,5.08K,-0.10%,333.0,35.150759219090105 120 | "Oct 19, 2017",29654,29600,29669,29593,1.70K,0.40%,67.0,130.15665866927884 121 | "Oct 20, 2017",29541,29600,29622,29524,3.22K,-0.38%,164.0,39.05642866820199 122 | "Oct 23, 2017",29499,29502,29550,29364,7.21K,-0.14%,498.0,183.85512872905747 123 | "Oct 24, 2017",29498,29515,29537,29444,5.50K,-0.00%,228.0,76.17056106507151 124 | "Oct 25, 2017",29362,29475,29475,29275,8.11K,-0.46%,574.0,87.59436379163162 125 | "Oct 26, 2017",29236,29375,29443,29211,8.37K,-0.43%,310.0,93.19855533874217 126 | "Oct 27, 2017",29272,29223,29299,29169,6.53K,0.12%,238.0,179.4590489903676 127 | "Oct 30, 2017",29349,29263,29369,29204,4.82K,0.26%,302.0,251.8192370908109 128 | "Oct 31, 2017",29040,29300,29337,28958,5.80K,-1.05%,811.0,120.07320947579136 129 | "Nov 01, 2017",29120,29048,29260,28991,2.16K,0.28%,160.0,342.19695767651865 130 | "Nov 02, 2017",29242,29301,29318,29168,0.34K,0.42%,397.0,91.38055403181716 131 | "Nov 03, 2017",29191,29230,29456,29002,0.05K,-0.17%,608.0,417.9586235432034 132 | "Nov 06, 2017",29363,29130,29388,29130,7.66K,0.59%,208.0,493.0636457260552 133 | "Nov 07, 2017",29457,29349,29490,29333,8.21K,0.32%,139.0,265.6636893601062 134 | "Nov 08, 2017",29562,29451,29600,29438,7.90K,0.36%,125.0,273.68238331408435 135 | "Nov 09, 2017",29670,29530,29689,29466,9.36K,0.37%,377.0,364.54388108328203 136 | "Nov 10, 2017",29508,29640,29700,29468,9.68K,-0.55%,364.0,100.31491787701816 137 | "Nov 13, 2017",29619,29504,29636,29473,6.19K,0.38%,222.0,278.8074508872524 138 | "Nov 14, 2017",29620,29550,29660,29411,10.22K,0.00%,586.0,320.7694400054388 139 | "Nov 15, 2017",29528,29600,29740,29512,9.84K,-0.31%,68.0,156.12361073461582 140 | "Nov 16, 2017",29491,29544,29555,29452,7.79K,-0.13%,251.0,50.1363914165413 141 | "Nov 17, 2017",29658,29462,29689,29361,9.90K,0.57%,569.0,527.3178706447325 142 | "Nov 20, 2017",29339,29700,29729,29268,10.65K,-1.08%,977.0,101.11832034986946 143 | "Nov 21, 2017",29334,29410,29425,29277,7.66K,-0.02%,365.0,72.28814427707765 144 | "Nov 22, 2017",29504,29341,29525,29280,7.80K,0.58%,386.0,409.8743169398913 145 | "Nov 23, 2017",29420,29475,29510,29393,5.78K,-0.28%,183.0,62.10747456877653 146 | "Nov 24, 2017",29375,29475,29500,29357,5.80K,-0.15%,247.0,43.08767925878057 147 | "Nov 27, 2017",29460,29450,29510,29402,6.55K,0.29%,152.0,118.21304673151462 148 | "Nov 28, 2017",29381,29450,29456,29359,5.80K,-0.27%,220.0,28.0726863994023 149 | "Nov 29, 2017",29215,29430,29441,29188,5.89K,-0.56%,527.0,38.2340345347402 150 | "Nov 30, 2017",28992,29287,29287,28927,6.54K,-0.76%,850.0,65.8089328309179 151 | "Dec 01, 2017",29217,29045,29344,29038,2.16K,0.78%,73.0,479.8862869343611 152 | "Dec 04, 2017",29059,29021,29200,28970,0.09K,-0.54%,101.0,268.70659302726926 153 | "Dec 05, 2017",28893,29095,29170,28865,0.05K,-0.57%,441.0,103.29586003810982 154 | "Dec 06, 2017",28972,28950,29039,28922,6.38K,0.27%,67.0,139.20226816955983 155 | "Dec 07, 2017",28670,28932,28932,28653,7.38K,-1.04%,592.0,17.165532404982518 156 | "Dec 08, 2017",28588,28650,28650,28500,8.42K,-0.29%,476.0,88.46315789473738 157 | "Dec 11, 2017",28429,28550,28573,28412,5.92K,-0.56%,287.0,40.09633253554785 158 | "Dec 12, 2017",28318,28432,28475,28271,7.58K,-0.39%,373.0,90.33914612146692 159 | "Dec 13, 2017",28377,28350,28445,28320,4.66K,0.21%,79.0,152.25158898305017 160 | "Dec 14, 2017",28500,28434,28589,28421,6.63K,0.43%,29.0,234.46697864255475 161 | "Dec 15, 2017",28441,28474,28559,28395,5.54K,-0.21%,165.0,131.26568057756594 162 | "Dec 18, 2017",28663,28492,28683,28470,6.17K,0.78%,239.0,385.4439409905172 163 | "Dec 19, 2017",28555,28656,28680,28535,5.95K,-0.38%,258.0,44.10162957771171 164 | "Dec 20, 2017",28617,28618,28658,28557,6.07K,0.22%,202.0,100.21220716461904 165 | "Dec 21, 2017",28596,28645,28665,28542,5.63K,-0.07%,294.0,74.23270969098303 166 | "Dec 22, 2017",28757,28560,28790,28560,6.08K,0.56%,164.0,428.58648459383767 167 | "Dec 26, 2017",28924,28796,28933,28778,4.33K,0.58%,191.0,283.7863645840589 168 | "Dec 27, 2017",29092,28890,29110,28888,5.16K,0.58%,192.0,425.5677097756852 169 | "Dec 28, 2017",29171,29150,29220,29099,5.06K,0.27%,176.0,142.2993917316744 170 | "Dec 29, 2017",29372,29202,29400,29134,6.93K,0.69%,414.0,438.1729937530035 171 | "Jan 01, 2018",29324,29332,29385,29275,1.58K,-0.16%,159.0,102.18411614005163 172 | "Jan 02, 2018",29392,29332,29463,29332,1.70K,0.23%,-11.0,191.26796672576165 173 | "Jan 03, 2018",29481,29385,29492,29385,0.19K,0.30%,85.0,203.34956610515653 174 | "Jan 04, 2018",29472,29365,29475,29276,0.20K,-0.03%,460.0,307.3322858313986 175 | "Jan 05, 2018",29465,29500,29550,29440,0.19K,-0.02%,120.0,75.0934103260879 176 | "Jan 08, 2018",29270,29235,29290,29176,5.90K,-0.66%,251.0,149.36728818206757 177 | "Jan 09, 2018",29173,29265,29278,29153,5.42K,-0.33%,251.0,33.08575446780742 178 | "Jan 10, 2018",29343,29155,29444,29123,10.83K,0.58%,215.0,511.4248875459271 179 | "Jan 11, 2018",29404,29338,29420,29315,5.81K,0.21%,142.0,171.31877878219166 180 | "Jan 12, 2018",29553,29425,29590,29425,9.23K,0.51%,91.0,293.7177570093445 181 | "Jan 15, 2018",29763,29590,29785,29590,6.00K,0.71%,151.0,369.14008110848226 182 | "Jan 16, 2018",29830,29780,29860,29754,8.69K,0.23%,124.0,156.27075351213352 183 | "Jan 17, 2018",29761,29876,29884,29705,6.83K,-0.23%,446.0,64.33745160747276 184 | "Jan 18, 2018",29644,29702,29702,29501,9.53K,-0.39%,688.0,143.9743059557295 185 | "Jan 19, 2018",29743,29615,29775,29600,5.95K,0.33%,156.0,303.84543918919013 186 | "Jan 22, 2018",29806,29701,29825,29701,5.52K,0.21%,86.0,229.43836907848163 187 | "Jan 23, 2018",29876,29836,29924,29752,7.02K,0.23%,328.0,212.71685937079747 188 | "Jan 24, 2018",30244,29907,30290,29879,11.08K,1.23%,403.0,753.0207503597849 189 | "Jan 25, 2018",30367,30310,30476,30185,9.88K,0.41%,448.0,349.7545800894477 190 | "Jan 29, 2018",30074,30285,30285,30004,8.63K,-0.96%,702.0,70.65557925609754 191 | "Jan 30, 2018",30052,30000,30197,29960,6.81K,-0.07%,67.0,289.7277703604814 192 | "Jan 31, 2018",30064,30085,30172,29976,5.48K,0.04%,307.0,175.57539364825058 193 | "Feb 01, 2018",30512,30125,30550,30050,1.81K,1.49%,649.0,894.6871880199651 194 | "Feb 02, 2018",30399,30780,30817,30200,0.66K,-0.37%,1521.0,240.06566225165443 195 | "Feb 05, 2018",30286,30385,30498,30008,0.19K,-0.37%,1197.0,395.5394561450266 196 | "Feb 06, 2018",30182,30320,30650,30160,12.07K,-0.34%,34.0,352.35742705570374 197 | "Feb 07, 2018",29976,30219,30259,29950,8.73K,-0.68%,550.0,66.2682470784639 198 | "Feb 08, 2018",30103,29905,30147,29783,11.10K,0.42%,642.0,565.910955914449 199 | "Feb 09, 2018",29999,30060,30110,29889,9.20K,-0.35%,512.0,160.8133427013272 200 | "Feb 12, 2018",30133,30102,30175,30017,6.40K,0.45%,329.0,189.6105873338456 201 | "Feb 13, 2018",30195,30170,30221,30091,3.53K,0.21%,315.0,155.44930377853962 202 | "Feb 14, 2018",30605,30220,30693,30066,12.87K,1.36%,913.0,1023.2403711833977 203 | "Feb 15, 2018",30549,30612,30644,30501,7.63K,-0.18%,286.0,80.22504180190663 204 | "Feb 16, 2018",30785,30600,30811,30600,8.67K,0.77%,159.0,397.2756535947701 205 | "Feb 19, 2018",30715,30717,30777,30678,4.03K,-0.23%,92.0,97.11940152552415 206 | "Feb 20, 2018",30524,30668,30737,30505,9.25K,-0.62%,295.0,88.1445009014933 207 | "Feb 21, 2018",30459,30500,30516,30371,7.08K,-0.21%,418.0,104.42013763129215 208 | "Feb 22, 2018",30564,30450,30580,30380,6.53K,0.34%,378.0,315.21132323897467 209 | "Feb 23, 2018",30492,30497,30508,30426,5.14K,-0.24%,263.0,77.17787418654916 210 | "Feb 26, 2018",30518,30520,30620,30492,5.84K,0.09%,8.0,126.10914338186922 211 | "Feb 27, 2018",30306,30518,30597,30270,7.86K,-0.69%,489.0,115.38889990089228 212 | "Feb 28, 2018",30423,30319,30475,30277,5.12K,0.39%,220.0,302.95478415959224 213 | "Mar 01, 2018",30181,30307,30372,30111,1.47K,-0.80%,467.0,135.60675500647628 214 | "Mar 02, 2018",30473,30445,30575,30400,0.15K,0.97%,106.0,203.42023026315655 215 | "Mar 05, 2018",30429,30400,30535,30310,0.18K,-0.14%,283.0,254.88337182447867 216 | "Mar 06, 2018",30568,30400,30669,30365,10.81K,0.46%,207.0,474.03233986497435 217 | "Mar 07, 2018",30461,30620,30639,30404,10.10K,-0.35%,527.0,76.44056703065506 218 | "Mar 08, 2018",30436,30491,30533,30390,7.75K,-0.08%,252.0,88.21645278052166 219 | "Mar 09, 2018",30408,30413,30474,30290,10.96K,-0.09%,421.0,179.7168042258163 220 | "Mar 12, 2018",30340,30380,30432,30284,7.37K,-0.22%,252.0,108.27367586844412 221 | "Mar 13, 2018",30387,30350,30409,30222,10.98K,0.15%,527.0,225.02094500694875 222 | "Mar 14, 2018",30413,30399,30545,30364,9.56K,0.09%,22.0,195.29208931629543 223 | "Mar 15, 2018",30314,30456,30474,30288,8.33K,-0.33%,370.0,44.15966719493008 224 | "Mar 16, 2018",30251,30275,30364,30184,10.02K,-0.21%,227.0,156.39954943016346 225 | "Mar 19, 2018",30432,30222,30453,30129,11.62K,0.60%,561.0,537.2583889276102 226 | "Mar 20, 2018",30284,30393,30393,30220,9.13K,-0.49%,474.0,64.36637988087386 227 | "Mar 21, 2018",30464,30310,30539,30284,11.32K,0.59%,183.0,410.51565182934786 228 | "Mar 22, 2018",30506,30522,30615,30472,11.33K,0.14%,75.0,127.15955631399264 229 | "Mar 23, 2018",30901,30550,30947,30550,13.64K,1.29%,305.0,752.561276595745 230 | "Mar 26, 2018",30889,30840,30915,30771,7.33K,-0.04%,299.0,193.55220824802743 231 | "Mar 27, 2018",30717,30871,30888,30685,9.46K,-0.56%,419.0,49.211699527455494 232 | "Mar 28, 2018",30518,30732,30750,30490,8.56K,-0.65%,522.0,46.23876680879039 233 | "Mar 29, 2018",30426,30482,30578,30403,3.20K,-0.30%,108.0,119.1323882511606 234 | "Apr 02, 2018",30961,30580,31080,30510,1.38K,1.76%,542.0,959.4257620452299 235 | "Apr 03, 2018",30625,30893,30893,30552,0.33K,-1.09%,828.0,73.81477481015827 236 | "Apr 04, 2018",30886,30660,30972,30600,0.07K,0.85%,380.0,601.4768627450976 237 | "Apr 05, 2018",30505,31500,31500,30450,0.06K,-1.23%,2210.0,56.89655172413768 238 | "Apr 06, 2018",30605,30571,30650,30413,13.42K,0.33%,621.0,272.49620228192003 239 | "Apr 09, 2018",30675,30600,30693,30556,7.60K,0.23%,233.0,212.53354496661998 240 | "Apr 10, 2018",30798,30613,30819,30585,8.76K,0.40%,276.0,420.6296223639037 241 | "Apr 11, 2018",31244,30820,31422,30820,18.12K,1.45%,246.0,1034.2818948734605 242 | "Apr 12, 2018",30897,31176,31217,30857,13.21K,-1.11%,677.0,81.46666882717182 243 | "Apr 13, 2018",31005,30922,31075,30770,9.89K,0.35%,621.0,390.3293792655168 244 | "Apr 16, 2018",31167,31055,31227,31050,8.09K,0.52%,72.0,289.66695652173803 245 | "Apr 17, 2018",31257,31139,31280,31089,9.19K,0.29%,295.0,310.03213355206026 246 | "Apr 18, 2018",31410,31243,31483,31177,9.90K,0.49%,358.0,475.2868781473517 247 | "Apr 19, 2018",31479,31449,31517,31305,8.78K,0.22%,568.0,243.17834211787343 248 | "Apr 20, 2018",31314,31405,31440,31285,6.02K,-0.52%,263.0,64.14367907943233 249 | "Apr 23, 2018",31164,31300,31300,31110,6.67K,-0.48%,488.0,54.32979749276637 250 | "Apr 24, 2018",31262,31160,31280,31068,6.35K,0.31%,452.0,315.32380584524435 251 | "Apr 25, 2018",31301,31298,31324,31215,6.17K,0.12%,312.0,112.30030434086076 252 | "Apr 26, 2018",31158,31251,31276,31112,6.27K,-0.46%,345.0,71.24247878632012 253 | "Apr 27, 2018",31129,31101,31189,31047,6.10K,-0.09%,184.0,170.37504428769168 254 | "Apr 30, 2018",30947,31090,31090,30892,6.38K,-0.58%,506.0,55.352518451378266 255 | "May 01, 2018",30874,30855,30917,30735,0.86K,-0.24%,456.0,201.82310069952652 256 | "May 02, 2018",30926,31020,31350,30860,0.50K,0.17%,122.0,397.0479585223584 257 | "May 03, 2018",31003,30925,31146,30861,0.21K,0.25%,191.0,364.31136385729405 258 | "May 04, 2018",31068,30907,31154,30907,0.13K,0.21%,75.0,409.2866664509674 259 | "May 07, 2018",31247,31125,31300,31125,6.16K,0.58%,69.0,297.68594377510084 260 | "May 08, 2018",31200,31220,31238,31075,8.69K,-0.15%,522.0,143.655671761866 261 | "May 09, 2018",31303,31219,31364,31156,9.93K,0.33%,275.0,292.98138400308017 262 | "May 10, 2018",31352,31338,31470,31231,10.73K,0.16%,324.0,253.92597099036357 263 | "May 11, 2018",31500,31340,31545,31340,8.84K,0.47%,115.0,366.0465858328025 264 | "May 14, 2018",31496,31450,31544,31380,7.90K,-0.01%,278.0,210.6062460165704 265 | "May 15, 2018",31186,31425,31499,31172,14.51K,-0.98%,460.0,88.14686256897039 266 | "May 16, 2018",31007,31171,31171,30941,10.73K,-0.57%,592.0,66.49061116318262 267 | "May 17, 2018",30986,31001,31001,30852,8.54K,-0.07%,566.0,134.6471541553219 268 | "May 18, 2018",31093,30971,31115,30950,7.38K,0.35%,184.0,287.7623586429727 269 | "May 21, 2018",31114,31035,31139,30925,8.03K,0.07%,494.0,294.30787388843964 270 | "May 22, 2018",31111,31025,31173,31005,7.33K,-0.01%,104.0,254.57435897435792 271 | "May 23, 2018",31186,31121,31369,31103,11.98K,0.24%,-46.0,331.7098350641427 272 | "May 24, 2018",31461,31241,31486,31235,11.06K,0.88%,219.0,472.8161037297905 273 | "May 25, 2018",31194,31370,31427,31172,10.05K,-0.85%,383.0,79.17996920313091 274 | "May 28, 2018",30971,31150,31150,30885,6.81K,-0.71%,702.0,86.73789865630533 275 | "May 29, 2018",31184,31020,31320,31020,12.38K,0.69%,28.0,465.5860735009665 276 | "May 30, 2018",31030,31120,31123,30937,9.14K,-0.49%,549.0,96.55913630927536 277 | "May 31, 2018",30882,31050,31120,30837,6.07K,-0.48%,446.0,115.41297791614124 278 | "Jun 01, 2018",30568,30796,30839,30458,2.07K,-1.02%,853.0,154.37599317092463 279 | "Jun 04, 2018",30708,30650,30812,30580,0.34K,0.46%,234.0,290.9710922171362 280 | "Jun 05, 2018",30699,30673,30935,30656,0.08K,-0.03%,-142.0,305.3913426409199 281 | "Jun 06, 2018",30755,30876,30913,30720,6.33K,0.18%,345.0,72.21988932291787 282 | "Jun 07, 2018",31055,30820,31075,30789,8.21K,0.98%,339.0,523.4708824580193 283 | "Jun 08, 2018",31076,31011,31196,30950,8.18K,0.07%,189.0,312.00148626817463 284 | "Jun 11, 2018",31035,31070,31083,30920,7.52K,-0.13%,517.0,128.6062419146183 285 | "Jun 12, 2018",30952,31001,31001,30873,6.95K,-0.27%,414.0,79.32753538690667 286 | "Jun 13, 2018",30952,30936,30994,30920,3.91K,0.00%,38.0,90.07658473480114 287 | "Jun 14, 2018",31145,30973,31178,30973,8.59K,0.62%,139.0,378.13841087398765 288 | "Jun 15, 2018",30804,31180,31195,30755,11.17K,-1.09%,933.0,64.70102422370474 289 | "Jun 18, 2018",30773,30799,30836,30721,4.81K,-0.10%,223.0,89.19465512190435 290 | "Jun 19, 2018",30702,30848,30899,30673,7.27K,-0.23%,357.0,80.21367326312975 291 | "Jun 20, 2018",30605,30680,30702,30580,4.93K,-0.32%,228.0,47.099738391105355 292 | "Jun 21, 2018",30451,30575,30575,30367,6.94K,-0.50%,584.0,84.57536141205857 293 | "Jun 22, 2018",30498,30410,30554,30387,6.81K,0.15%,124.0,255.61003060519212 294 | "Jun 25, 2018",30540,30500,30648,30500,4.98K,0.14%,-68.0,188.1940983606546 295 | "Jun 26, 2018",30480,30512,30574,30335,6.41K,-0.20%,582.0,208.14240975770736 296 | "Jun 27, 2018",30606,30466,30631,30435,6.00K,0.41%,239.0,337.10123213405683 297 | "Jun 28, 2018",30431,30588,30604,30408,4.32K,-0.57%,390.0,39.148250460406416 298 | "Jun 29, 2018",30363,30404,30449,30288,5.61K,-0.22%,337.0,120.39867274168137 299 | "Jul 02, 2018",30153,30380,30412,30000,2.26K,-0.69%,1034.0,187.10120000000094 300 | "Jul 03, 2018",30469,30185,30600,30100,0.19K,1.05%,493.0,790.1295681063131 301 | "Jul 04, 2018",30510,30463,30550,30463,0.04K,0.13%,7.0,134.13422840823114 302 | "Jul 05, 2018",30541,30600,30640,30495,0.02K,0.10%,262.0,86.21872438104765 303 | "Jul 06, 2018",30582,30650,30740,30570,4.84K,0.13%,94.0,102.06673209028305 304 | "Jul 09, 2018",30638,30613,30788,30613,7.26K,0.18%,-125.0,200.142913141477 305 | "Jul 10, 2018",30551,30663,30679,30436,7.52K,-0.28%,668.0,131.91815613089784 306 | "Jul 11, 2018",30344,30502,30545,30325,6.02K,-0.68%,349.0,62.13784006595233 307 | "Jul 12, 2018",30175,30315,30315,30161,7.75K,-0.56%,336.0,14.07148304101429 308 | "Jul 13, 2018",30131,30169,30210,30035,7.17K,-0.15%,419.0,137.55934742799946 309 | "Jul 16, 2018",30103,30151,30248,30074,5.56K,-0.09%,115.0,126.16778612755294 310 | "Jul 17, 2018",29757,30102,30120,29740,9.28K,-1.15%,740.0,35.21721587088177 311 | "Jul 18, 2018",29799,29999,29999,29671,7.92K,0.14%,912.0,129.41498432813023 312 | "Jul 19, 2018",29913,29671,29968,29624,8.40K,0.38%,375.0,589.3559276262495 313 | "Jul 20, 2018",29957,29788,29977,29767,7.07K,0.15%,233.0,380.3404105217196 314 | "Jul 23, 2018",29914,29922,30043,29862,7.10K,-0.14%,103.0,173.3151831759424 315 | "Jul 24, 2018",29912,29860,29978,29780,7.69K,-0.01%,306.0,250.87763599731258 316 | "Jul 25, 2018",29959,29867,30035,29835,7.58K,0.16%,144.0,292.8312384782985 317 | "Jul 26, 2018",29836,29912,29952,29816,5.77K,-0.41%,192.0,60.09122618728361 318 | "Jul 27, 2018",29805,29960,29960,29682,7.53K,-0.10%,802.0,124.15201131999129 319 | "Jul 30, 2018",29755,29726,29782,29700,4.90K,-0.17%,106.0,111.15185185185328 320 | "Jul 31, 2018",29638,29730,29740,29561,6.63K,-0.39%,482.0,87.46625621596105 321 | "Aug 01, 2018",29462,29591,29670,29441,2.14K,-0.59%,263.0,100.16334363642454 322 | "Aug 02, 2018",29520,29532,29638,29477,0.92K,0.20%,90.0,149.23486107813002 323 | "Aug 03, 2018",29800,29520,29900,29425,0.11K,0.95%,560.0,761.0535259133394 324 | "Aug 06, 2018",29563,29656,29656,29538,4.02K,-0.80%,286.0,25.099871352154878 325 | "Aug 07, 2018",29534,29604,29668,29520,5.98K,-0.10%,132.0,78.07018970189529 326 | "Aug 08, 2018",29544,29560,29630,29500,6.45K,0.03%,138.0,114.19389830508591 327 | "Aug 09, 2018",29605,29565,29665,29529,5.58K,0.21%,124.0,176.3500287852621 328 | "Aug 10, 2018",29706,29625,29744,29540,7.31K,0.34%,383.0,286.146377792822 329 | "Aug 13, 2018",29665,29750,29836,29644,7.72K,-0.14%,168.0,107.13601403319261 330 | "Aug 14, 2018",29664,29665,29740,29575,4.99K,-0.00%,283.0,164.49653423499694 331 | "Aug 16, 2018",29275,29615,29615,29222,10.27K,-1.31%,892.0,53.712784888099115 332 | "Aug 17, 2018",29275,29291,29375,29184,6.00K,0.00%,312.0,175.59556606359547 333 | "Aug 20, 2018",29435,29388,29459,29350,5.66K,0.55%,175.0,156.31567291311873 334 | "Aug 21, 2018",29542,29460,29565,29436,6.27K,0.36%,155.0,211.46453322462185 335 | "Aug 22, 2018",29627,29450,29733,29450,4.61K,0.29%,71.0,461.7008828522921 336 | "Aug 23, 2018",29576,29666,29666,29490,5.98K,-0.17%,524.0,86.51325873177484 337 | "Aug 24, 2018",29882,29550,29900,29550,9.03K,1.03%,314.0,685.9323181049076 338 | "Aug 27, 2018",29973,29930,30045,29863,6.67K,0.30%,239.0,225.67039480293167 339 | "Aug 28, 2018",29981,30015,30170,29951,6.78K,0.03%,33.0,185.21935828519784 340 | "Aug 29, 2018",30144,29977,30168,29977,6.08K,0.54%,143.0,359.0640491043123 341 | "Aug 30, 2018",30131,30110,30269,30082,6.31K,-0.04%,-5.0,208.30460075792868 342 | "Aug 31, 2018",30116,30224,30298,30055,5.78K,-0.05%,386.0,135.49319580768497 343 | "Sep 03, 2018",30327,30075,30448,30062,1.44K,0.70%,183.0,641.402634555252 344 | "Sep 04, 2018",30221,30238,30300,30154,0.17K,-0.35%,240.0,129.32440140611652 345 | "Sep 05, 2018",30310,30257,30494,30250,0.05K,0.29%,-103.0,297.483966942149 346 | "Sep 06, 2018",30565,30366,30693,30366,14.80K,0.84%,71.0,528.142955937561 347 | "Sep 07, 2018",30509,30643,30643,30405,9.44K,-0.18%,684.0,104.81407663213396 348 | "Sep 10, 2018",30725,30549,30748,30526,11.42K,0.71%,245.0,399.4472253161257 349 | "Sep 11, 2018",30732,30660,30744,30559,11.40K,0.02%,464.0,258.0473183022986 350 | "Sep 12, 2018",30664,30682,30779,30443,15.88K,-0.22%,823.0,320.4391814210176 351 | "Sep 13, 2018",30470,30576,30690,30450,6.63K,-0.63%,178.0,134.15763546798192 352 | "Sep 14, 2018",30453,30501,30648,30401,11.82K,-0.06%,157.0,199.4224861024304 353 | "Sep 17, 2018",30778,30509,30800,30509,13.77K,1.07%,247.0,562.5657674784488 354 | "Sep 18, 2018",30811,30710,30925,30652,11.96K,0.11%,219.0,375.41612292835634 355 | "Sep 19, 2018",30649,30821,30936,30634,13.10K,-0.53%,289.0,130.14787491023162 356 | "Sep 20, 2018",30623,30700,30700,30585,3.97K,-0.08%,306.0,38.14288049697643 357 | "Sep 21, 2018",30609,30731,30910,30455,15.51K,-0.05%,681.0,335.3007716302745 358 | "Sep 24, 2018",30862,30620,30899,30620,10.49K,0.83%,205.0,523.2050293925531 359 | "Sep 25, 2018",30838,30821,30864,30755,7.69K,-0.08%,255.0,126.29416355064312 360 | "Sep 26, 2018",30608,30824,30830,30587,9.12K,-0.75%,510.0,27.166835583746433 361 | "Sep 27, 2018",30356,30738,30738,30321,10.28K,-0.82%,904.0,35.481349559711816 362 | "Sep 28, 2018",30479,30398,30520,30264,8.85K,0.41%,576.0,338.81866243721737 363 | "Oct 01, 2018",30614,30399,30660,30376,2.22K,0.44%,261.0,501.22517777192485 364 | "Oct 03, 2018",31025,30890,31168,30889,0.19K,1.34%,-4.0,415.2283984589994 365 | "Oct 04, 2018",31136,31072,31164,31015,0.04K,0.36%,264.0,213.5812993712716 366 | "Oct 05, 2018",31233,31101,31288,30985,0.07K,0.31%,541.0,437.42517347103416 367 | "Oct 08, 2018",31096,31369,31389,31083,9.96K,-0.44%,578.0,33.12797992471678 368 | "Oct 09, 2018",31161,31200,31275,31115,7.31K,0.21%,187.0,121.23654186083876 369 | "Oct 10, 2018",31247,31235,31265,31151,6.58K,0.28%,330.0,126.35132098487884 370 | "Oct 11, 2018",31793,31300,31815,31250,17.38K,1.75%,671.0,1067.817439999999 371 | "Oct 12, 2018",31670,31699,31720,31574,10.61K,-0.39%,421.0,117.44390954582924 372 | "Oct 15, 2018",31877,31723,32075,31723,14.31K,0.65%,-44.0,507.7087917284006 373 | "Oct 16, 2018",31701,31877,31957,31680,10.71K,-0.55%,356.0,101.18361742424169 374 | "Oct 17, 2018",31702,31670,31839,31635,8.63K,0.00%,35.0,236.4320531057383 375 | "Oct 18, 2018",31885,31670,31909,31638,5.54K,0.58%,319.0,488.1157152790948 376 | "Oct 19, 2018",31737,31805,31867,31703,7.59K,-0.46%,210.0,96.17588240860641 377 | "Oct 22, 2018",31729,31738,31749,31641,6.73K,-0.03%,359.0,99.30036977339478 378 | "Oct 23, 2018",31871,31721,32053,31721,13.47K,0.45%,-32.0,483.5699378960308 379 | "Oct 24, 2018",31723,31820,31844,31585,9.82K,-0.46%,722.0,163.1316131074891 380 | "Oct 25, 2018",31683,31800,31912,31663,9.54K,-0.13%,202.0,132.15728136942198 381 | "Oct 26, 2018",31764,31779,31993,31725,13.31K,0.26%,-28.0,253.3294562647752 382 | "Oct 29, 2018",31730,31847,31847,31692,6.67K,-0.11%,386.0,38.18585131894361 383 | "Oct 30, 2018",31725,31755,31789,31651,5.96K,-0.02%,322.0,108.32264383431902 384 | "Oct 31, 2018",31631,31650,31780,31590,5.67K,-0.30%,72.0,171.24659702437566 385 | "Nov 01, 2018",31783,31671,31818,31619,1.58K,0.48%,285.0,312.0321642050658 386 | "Nov 02, 2018",31737,31737,31999,31530,0.54K,-0.14%,566.0,472.0790675547105 387 | "Nov 05, 2018",31783,31785,31898,31630,0.18K,0.14%,503.0,267.2963642111936 388 | "Nov 06, 2018",31657,31718,31869,31641,9.16K,-0.40%,35.0,167.11529344837254 389 | "Nov 07, 2018",31602,31660,31678,31530,1.80K,-0.17%,386.0,90.33796384395828 390 | "Nov 08, 2018",31408,31551,31551,31234,6.11K,-0.61%,982.0,175.7659601716077 391 | "Nov 09, 2018",31023,31302,31383,30965,10.71K,-1.23%,709.0,139.78294849023223 392 | "Nov 12, 2018",31014,31106,31178,30975,7.77K,-0.03%,268.0,111.2555932203395 393 | "Nov 13, 2018",30778,31110,31110,30756,8.28K,-0.76%,752.0,22.253218884121452 394 | "Nov 14, 2018",30838,30700,30867,30620,7.55K,0.19%,429.0,386.7585238406282 395 | "Nov 15, 2018",30831,30830,30939,30753,8.71K,-0.02%,201.0,187.47175885279648 396 | "Nov 16, 2018",31017,30856,31158,30830,10.10K,0.60%,124.0,490.9894907557573 397 | "Nov 19, 2018",30888,31068,31068,30785,8.15K,-0.42%,772.0,103.94685723566728 398 | "Nov 20, 2018",30830,30811,30994,30800,8.35K,-0.19%,-101.0,213.1889610389626 399 | "Nov 21, 2018",30839,30800,30910,30750,7.74K,0.03%,168.0,199.4630894308939 400 | "Nov 22, 2018",30613,30852,30899,30555,9.81K,-0.73%,663.0,105.6529864179356 401 | "Nov 23, 2018",30513,30551,30566,30427,4.66K,-0.33%,405.0,101.3928747493992 402 | "Nov 26, 2018",30574,30472,30672,30470,7.68K,0.20%,12.0,304.6894650475879 403 | "Nov 27, 2018",30423,30585,30648,30389,7.86K,-0.49%,397.0,97.28977590575596 404 | "Nov 28, 2018",30457,30400,30593,30270,8.40K,0.11%,441.0,381.9954079947129 405 | "Nov 29, 2018",30253,30399,30399,30210,6.69K,-0.67%,464.0,43.269016881826246 406 | "Nov 30, 2018",30186,30233,30320,30155,7.63K,-0.22%,131.0,118.16962361134209 407 | "Dec 03, 2018",30787,30322,30850,30257,1.60K,1.99%,662.0,1068.3873483821917 408 | "Dec 04, 2018",30964,30874,31085,30874,0.08K,0.57%,-31.0,301.6150806503865 409 | "Dec 05, 2018",30926,30830,30970,30825,0.04K,-0.12%,72.0,241.47510137875 410 | "Dec 06, 2018",31005,31039,31196,30974,10.55K,0.26%,35.0,188.22218634984162 411 | "Dec 07, 2018",31440,31011,31480,30941,11.24K,1.40%,669.0,976.6927054717053 412 | "Dec 10, 2018",31895,31459,31966,31420,14.09K,1.45%,521.0,990.2542966263536 413 | "Dec 11, 2018",31812,31850,31850,31618,10.53K,-0.26%,,195.4234929470549 414 | "Dec 12, 2018",31626,31749,31749,31582,7.57K,-0.58%,,44.232664175797254 415 | "Dec 13, 2018",31414,31550,31600,31337,8.43K,-0.67%,,127.64623288764051 416 | "Dec 14, 2018",31437,31440,31514,31384,6.75K,0.07%,,127.21953861840302 417 | "Dec 17, 2018",31501,31369,31530,31291,5.97K,0.20%,,372.60397558403196 418 | "Dec 18, 2018",31075,31589,31650,31041,10.18K,-1.35%,,95.66705325214936 419 | "Dec 19, 2018",31139,31167,31500,30967,8.76K,0.21%,,507.96044176058416 420 | "Dec 20, 2018",31219,31027,31250,30914,12.97K,0.26%,,531.3150029113021 421 | "Dec 21, 2018",31126,31115,31292,31075,9.91K,-0.30%,,228.35613837490018 422 | "Dec 24, 2018",31375,31300,31395,31177,8.57K,0.80%,,294.3844821503044 423 | "Dec 26, 2018",31463,31430,31643,31400,10.03K,0.28%,,276.4875477706992 424 | "Dec 27, 2018",31611,31499,31647,31426,8.01K,0.47%,,334.3009928085012 425 | "Dec 28, 2018",31506,31576,31629,31462,6.64K,-0.33%,,97.23355158603954 426 | "Dec 31, 2018",31489,31452,31583,31284,6.60K,-0.05%,,337.95930827259866 427 | "Jan 01, 2019",31560,31550,31651,31410,0.68K,0.23%,,252.1509073543457 428 | "Jan 02, 2019",31934,31530,32500,31510,0.40K,1.19%,,1407.321485242777 429 | "Jan 03, 2019",31939,32000,32045,31836,0.17K,0.02%,,148.67618419399514 430 | "Jan 04, 2019",31679,32686,32686,31460,0.06K,-0.81%,,227.53445645263855 431 | "Jan 07, 2019",31652,31536,31748,31456,12.80K,-0.09%,,409.8194303153614 432 | "Jan 08, 2019",31736,31616,31758,31572,10.84K,0.27%,,306.9661725579645 433 | "Jan 09, 2019",31993,31685,32016,31639,14.63K,0.81%,,689.2181484876272 434 | "Jan 10, 2019",31878,32090,32119,31855,13.33K,-0.36%,,52.19061371841235 435 | "Jan 11, 2019",31924,31907,32065,31888,10.56K,0.14%,,194.19982438534862 436 | "Jan 14, 2019",32130,32050,32194,31963,13.65K,0.65%,,312.2069267590668 437 | "Jan 15, 2019",32169,32066,32273,32064,12.59K,0.12%,,312.68441242515115 438 | "Jan 16, 2019",32272,32148,32299,32103,11.36K,0.32%,,321.0318038812584 439 | "Jan 17, 2019",32253,32300,32363,32190,12.92K,-0.06%,,126.3385834109977 440 | "Jan 18, 2019",32089,32295,32305,32052,12.63K,-0.51%,,47.29205665793053 441 | "Jan 21, 2019",32039,32052,32184,31985,8.90K,-0.16%,,186.335969985932 442 | "Jan 22, 2019",32154,32030,32189,31949,12.04K,0.36%,,365.53995430217043 443 | "Jan 23, 2019",32135,32105,32199,32060,10.91K,-0.06%,,169.32517155333701 444 | "Jan 24, 2019",32016,32100,32147,31986,9.55K,-0.37%,,77.15100356406037 445 | "Jan 25, 2019",32339,32057,32367,32019,13.03K,1.01%,,633.4779349761084 446 | "Jan 28, 2019",32536,32383,32570,32383,10.90K,0.61%,,340.8835191304097 447 | "Jan 29, 2019",32891,32570,32967,32530,13.05K,1.09%,,762.8495849984612 448 | "Jan 30, 2019",32903,32900,32975,32807,9.82K,0.04%,,171.49160240192575 449 | "Jan 31, 2019",33100,32937,33245,32903,10.39K,0.60%,,507.04765522900567 450 | "Feb 01, 2019",33405,33119,33500,32997,3.12K,0.92%,,795.2194744976805 451 | "Feb 04, 2019",33455,34247,34400,33285,0.42K,0.15%,,328.6947573982252 452 | "Feb 05, 2019",33381,33549,33549,33250,0.13K,-0.22%,,132.17801503759256 453 | "Feb 06, 2019",33222,33199,33378,33175,7.27K,-0.48%,,226.28759608138355 454 | "Feb 07, 2019",33056,33169,33200,32951,10.60K,-0.50%,,136.79345088161062 455 | "Feb 08, 2019",33125,33000,33142,32910,9.43K,0.21%,,358.51564873898315 456 | "Feb 11, 2019",32931,33060,33082,32811,11.15K,-0.59%,,142.99113102313274 457 | "Feb 12, 2019",32747,32915,32945,32721,9.36K,-0.56%,,56.17798967024282 458 | "Feb 13, 2019",32909,32727,33028,32680,10.85K,0.49%,,532.4385556915513 459 | "Feb 14, 2019",32970,32862,33000,32765,11.45K,0.19%,,344.47031893789244 460 | "Feb 15, 2019",33263,32992,33280,32992,11.79K,0.89%,,561.3656644034927 461 | "Feb 18, 2019",33463,33333,33484,33333,8.88K,0.60%,,281.58890588906064 462 | "Feb 19, 2019",33753,33499,33797,33434,11.91K,0.87%,,620.4634503798516 463 | "Feb 20, 2019",33719,33800,33877,33680,9.09K,-0.10%,,116.22811757720045 464 | "Feb 21, 2019",33302,33630,33658,33265,14.50K,-1.24%,,65.43712610852526 465 | "Feb 22, 2019",33369,33302,33413,33190,9.55K,0.20%,,291.20268153057987 466 | "Feb 25, 2019",33159,33385,33398,33130,8.44K,-0.63%,,42.23459100512992 467 | "Feb 26, 2019",33228,33225,33450,33205,9.01K,0.21%,,248.16970335792573 468 | "Feb 27, 2019",33171,33300,33466,33138,9.40K,-0.17%,,199.3266340756818 469 | "Feb 28, 2019",32902,33189,33260,32877,8.13K,-0.81%,,96.29123703500956 470 | "Mar 01, 2019",32533,32857,32953,31915,1.56K,-1.12%,,734.0997650007847 471 | "Mar 04, 2019",32505,32394,32601,32315,0.15K,-0.09%,,398.68157202537265 472 | "Mar 05, 2019",32015,33435,33440,32000,0.24K,-1.51%,,20.67500000000291 473 | "Mar 06, 2019",31954,32136,32300,31885,16.71K,-0.19%,,233.89807119335086 474 | "Mar 07, 2019",31946,31959,31980,31800,14.75K,-0.03%,,167.8264150943396 475 | "Mar 08, 2019",32174,31980,32235,31955,16.62K,0.71%,,475.9189485213573 476 | "Mar 11, 2019",31915,32214,32214,31897,12.41K,-0.80%,,18.17888829670483 477 | "Mar 12, 2019",32015,31906,32056,31885,12.06K,0.31%,,280.6971930374784 478 | "Mar 13, 2019",32225,32088,32330,32088,13.82K,0.66%,,380.0332211418609 479 | "Mar 14, 2019",31832,32274,32274,31813,17.21K,-1.22%,,19.275327696224853 480 | "Mar 15, 2019",31853,31895,31960,31768,13.20K,0.07%,,150.51372450264537 481 | "Mar 18, 2019",31685,31800,31815,31590,12.27K,-0.53%,,110.67663817663923 482 | "Mar 19, 2019",31985,31750,32017,31691,14.67K,0.95%,,564.0243286737567 483 | "Mar 20, 2019",31740,31961,31961,31704,14.38K,-0.77%,,36.29182437547206 484 | "Mar 21, 2019",31842,31899,32064,31806,9.41K,0.32%,,201.2920203735157 485 | "Mar 22, 2019",32155,31869,32184,31748,19.21K,0.98%,,727.5893914577283 486 | "Mar 25, 2019",32223,32199,32245,32083,14.02K,0.21%,,186.70691643549438 487 | "Mar 26, 2019",32113,32200,32200,32031,13.04K,-0.34%,,82.43264337672736 488 | "Mar 27, 2019",32019,32101,32182,32000,17.72K,-0.29%,,100.1080624999995 489 | "Mar 28, 2019",31624,31993,32030,31591,15.06K,-1.23%,,70.45857997530766 490 | "Mar 29, 2019",31703,31600,31788,31510,11.36K,0.25%,,382.7027610282457 491 | "Apr 01, 2019",31570,31588,31730,31542,4.52K,-0.42%,,170.16688859298662 492 | "Apr 02, 2019",31535,31550,31590,31359,0.39K,-0.11%,,217.2964699129443 493 | "Apr 03, 2019",31450,31499,31607,31318,0.17K,-0.27%,,241.2180854460676 494 | "Apr 04, 2019",31503,31499,31549,31350,0.07K,0.17%,,203.9711961722496 495 | "Apr 05, 2019",31566,31535,31620,31400,0.06K,0.20%,,252.1630573248404 496 | "Apr 08, 2019",32076,31795,32155,31795,12.90K,1.62%,,644.1816323321291 497 | "Apr 09, 2019",32098,32131,32171,32020,11.01K,0.07%,,118.36783260462109 498 | "Apr 10, 2019",32174,32070,32197,31968,11.74K,0.24%,,334.47566316316437 499 | "Apr 11, 2019",31663,32148,32148,31638,17.11K,-1.59%,,25.40299639673685 500 | "Apr 12, 2019",31760,31721,31871,31705,11.01K,0.31%,,205.2879671976043 501 | "Apr 15, 2019",31760,31710,31791,31570,8.54K,0.00%,,272.33006018371816 502 | "Apr 16, 2019",31474,31700,31768,31453,14.48K,-0.90%,,89.21031380154456 503 | "Apr 17, 2019",31410,31500,31525,31393,5.65K,-0.20%,,42.07148090338524 504 | "Apr 18, 2019",31403,31474,31544,31355,9.87K,-0.02%,,118.28933184500055 505 | "Apr 22, 2019",31531,31497,31697,31497,9.89K,0.41%,,234.21589357716584 506 | "Apr 23, 2019",31525,31490,31566,31401,9.92K,-0.02%,,200.65157160599847 507 | "Apr 24, 2019",31759,31480,31799,31450,12.64K,0.74%,,631.4289666136721 508 | "Apr 25, 2019",31843,31750,31926,31641,11.64K,0.26%,,379.81947473215035 509 | "Apr 26, 2019",31868,31851,31934,31705,9.67K,0.08%,,247.1773221889307 510 | "Apr 30, 2019",31625,31800,31824,31597,6.44K,-0.76%,,52.201158337815286 511 | "May 01, 2019",31563,31604,31657,31503,1.55K,-0.20%,,113.29330539948569 512 | "May 02, 2019",31203,31420,31425,31160,0.48K,-1.14%,,48.36569319640694 513 | "May 03, 2019",31341,31250,31500,31163,0.08K,0.44%,,429.9249109520897 514 | -------------------------------------------------------------------------------- /30 stocks/largecaps/dlf_stock_data.csv: -------------------------------------------------------------------------------- 1 | "Symbol","Series","Date","Prev Close","Open Price","High Price","Low Price","Last Price","Close Price","Average Price","Total Traded Quantity","Turnover","No. of Trades","Deliverable Qty","% Dly Qt to Traded Qty" 2 | "DLF","EQ","16-Jul-2018"," 187.70"," 188.00"," 188.60"," 177.00"," 178.35"," 178.05"," 181.36"," 4266013"," 773701682.25"," 28055"," 601748"," 14.11" 3 | "DLF","EQ","17-Jul-2018"," 178.05"," 177.05"," 182.00"," 176.75"," 180.00"," 179.95"," 180.04"," 5742276"," 1033833369.30"," 38110"," 1964833"," 34.22" 4 | "DLF","EQ","18-Jul-2018"," 179.95"," 181.70"," 181.70"," 170.00"," 171.10"," 170.70"," 173.82"," 5559072"," 966271043.55"," 38514"," 1127994"," 20.29" 5 | "DLF","EQ","19-Jul-2018"," 170.70"," 171.90"," 175.50"," 168.00"," 169.50"," 169.85"," 171.73"," 7272656"," 1248923077.45"," 47821"," 1117544"," 15.37" 6 | "DLF","EQ","20-Jul-2018"," 169.85"," 170.80"," 172.35"," 168.00"," 169.65"," 170.40"," 170.24"," 3249890"," 553248310.90"," 23225"," 278633"," 8.57" 7 | "DLF","EQ","23-Jul-2018"," 170.40"," 170.50"," 179.00"," 170.50"," 177.25"," 177.65"," 174.80"," 9216233"," 1611029266.70"," 73743"," 3523566"," 38.23" 8 | "DLF","EQ","24-Jul-2018"," 177.65"," 179.45"," 190.95"," 177.65"," 188.40"," 189.10"," 185.52"," 9610781"," 1783001585.40"," 60269"," 1521046"," 15.83" 9 | "DLF","EQ","25-Jul-2018"," 189.10"," 189.75"," 189.75"," 182.25"," 182.85"," 183.50"," 185.41"," 4839118"," 897238928.20"," 30246"," 437873"," 9.05" 10 | "DLF","EQ","26-Jul-2018"," 183.50"," 183.85"," 192.90"," 181.60"," 191.95"," 190.20"," 187.03"," 6391773"," 1195448870.75"," 42256"," 1108839"," 17.35" 11 | "DLF","EQ","27-Jul-2018"," 190.20"," 191.05"," 194.50"," 188.25"," 190.35"," 190.50"," 191.24"," 4558851"," 871850451.95"," 32630"," 303518"," 6.66" 12 | "DLF","EQ","30-Jul-2018"," 190.50"," 190.10"," 191.20"," 186.05"," 188.45"," 188.90"," 188.82"," 3271250"," 617672791.70"," 28332"," 235100"," 7.19" 13 | "DLF","EQ","31-Jul-2018"," 188.90"," 189.50"," 198.85"," 189.10"," 195.75"," 196.30"," 196.32"," 10925285"," 2144867473.85"," 78042"," 2175130"," 19.91" 14 | "DLF","EQ","01-Aug-2018"," 196.30"," 195.90"," 197.25"," 189.35"," 192.80"," 193.35"," 192.88"," 5890923"," 1136222521.20"," 48748"," 1159693"," 19.69" 15 | "DLF","EQ","02-Aug-2018"," 193.35"," 192.00"," 192.00"," 186.40"," 187.55"," 187.75"," 188.53"," 4912319"," 926113991.70"," 39304"," 716928"," 14.59" 16 | "DLF","EQ","03-Aug-2018"," 187.75"," 189.60"," 193.50"," 188.20"," 188.35"," 188.90"," 190.32"," 5986116"," 1139265146.50"," 40523"," 826183"," 13.80" 17 | "DLF","EQ","06-Aug-2018"," 188.90"," 190.00"," 191.95"," 187.55"," 189.10"," 189.35"," 189.98"," 3743820"," 711234256.15"," 24108"," 441114"," 11.78" 18 | "DLF","EQ","07-Aug-2018"," 189.35"," 189.60"," 190.90"," 184.05"," 186.85"," 186.80"," 186.79"," 4195521"," 783675501.85"," 29448"," 342637"," 8.17" 19 | "DLF","EQ","08-Aug-2018"," 186.80"," 186.80"," 191.55"," 185.80"," 189.25"," 188.95"," 188.81"," 5579043"," 1053397564.50"," 35757"," 1181777"," 21.18" 20 | "DLF","EQ","09-Aug-2018"," 188.95"," 189.60"," 203.50"," 189.05"," 200.50"," 200.60"," 197.77"," 12195178"," 2411889314.60"," 73803"," 1569254"," 12.87" 21 | "DLF","EQ","10-Aug-2018"," 200.60"," 202.40"," 202.80"," 193.60"," 196.40"," 196.55"," 198.06"," 8356670"," 1655091564.80"," 56166"," 702813"," 8.41" 22 | "DLF","EQ","13-Aug-2018"," 196.55"," 195.00"," 197.50"," 191.25"," 194.60"," 194.15"," 194.18"," 4981483"," 967291262.40"," 37283"," 274770"," 5.52" 23 | "DLF","EQ","14-Aug-2018"," 194.15"," 194.25"," 204.70"," 194.25"," 202.50"," 202.80"," 202.01"," 11487982"," 2320736521.85"," 71815"," 1623336"," 14.13" 24 | "DLF","EQ","16-Aug-2018"," 202.80"," 201.35"," 208.85"," 198.55"," 207.30"," 206.70"," 204.06"," 9989854"," 2038521972.15"," 66562"," 1065466"," 10.67" 25 | "DLF","EQ","17-Aug-2018"," 206.70"," 208.95"," 211.85"," 208.15"," 210.45"," 210.15"," 210.01"," 7728061"," 1622954909.85"," 51519"," 852464"," 11.03" 26 | "DLF","EQ","20-Aug-2018"," 210.15"," 212.80"," 216.45"," 212.30"," 215.00"," 214.85"," 214.79"," 8027156"," 1724155216.15"," 57124"," 882643"," 11.00" 27 | "DLF","EQ","21-Aug-2018"," 214.85"," 214.85"," 215.40"," 207.25"," 208.35"," 208.20"," 209.35"," 6488641"," 1358414344.70"," 45193"," 812714"," 12.53" 28 | "DLF","EQ","23-Aug-2018"," 208.20"," 209.00"," 214.35"," 205.95"," 212.35"," 211.90"," 211.09"," 7578138"," 1599670258.85"," 49828"," 454601"," 6.00" 29 | "DLF","EQ","24-Aug-2018"," 211.90"," 211.00"," 215.85"," 210.50"," 212.00"," 212.95"," 213.29"," 6562786"," 1399763475.10"," 43697"," 444935"," 6.78" 30 | "DLF","EQ","27-Aug-2018"," 212.95"," 213.95"," 218.30"," 211.90"," 213.45"," 213.65"," 215.33"," 7327329"," 1577796860.80"," 47204"," 724808"," 9.89" 31 | "DLF","EQ","28-Aug-2018"," 213.65"," 214.50"," 216.55"," 210.55"," 213.60"," 213.35"," 213.18"," 5328214"," 1135873099.60"," 33570"," 449828"," 8.44" 32 | "DLF","EQ","29-Aug-2018"," 213.35"," 213.65"," 221.50"," 212.25"," 218.00"," 217.50"," 217.79"," 8597454"," 1872425857.00"," 58524"," 1368148"," 15.91" 33 | "DLF","BL","30-Aug-2018"," 215.25"," 217.00"," 217.00"," 217.00"," 217.00"," 217.00"," 217.00"," 16200000"," 3515400000.00"," 2"," 16200000"," 100.00" 34 | "DLF","EQ","30-Aug-2018"," 217.50"," 217.85"," 223.50"," 214.80"," 221.65"," 221.10"," 219.51"," 11203811"," 2459388079.70"," 66526"," 2164189"," 19.32" 35 | "DLF","EQ","31-Aug-2018"," 221.10"," 221.10"," 222.30"," 217.20"," 220.75"," 220.80"," 219.99"," 6129569"," 1348439045.85"," 40028"," 564593"," 9.21" 36 | "DLF","EQ","03-Sep-2018"," 220.80"," 213.10"," 216.55"," 210.30"," 210.50"," 212.45"," 214.01"," 9075098"," 1942129959.20"," 57717"," 1021158"," 11.25" 37 | "DLF","EQ","04-Sep-2018"," 212.45"," 210.50"," 212.35"," 198.40"," 201.65"," 201.85"," 205.30"," 9686643"," 1988685174.30"," 56989"," 1547847"," 15.98" 38 | "DLF","EQ","05-Sep-2018"," 201.85"," 202.00"," 203.90"," 196.35"," 203.25"," 202.35"," 199.88"," 7278094"," 1454779631.80"," 54826"," 1302942"," 17.90" 39 | "DLF","EQ","06-Sep-2018"," 202.35"," 204.50"," 207.00"," 201.25"," 205.00"," 204.50"," 204.19"," 4795832"," 979276617.20"," 33220"," 322588"," 6.73" 40 | "DLF","EQ","07-Sep-2018"," 204.50"," 205.60"," 209.30"," 201.70"," 208.60"," 208.35"," 206.32"," 4237143"," 874192776.00"," 32278"," 552503"," 13.04" 41 | "DLF","EQ","10-Sep-2018"," 208.35"," 207.45"," 209.45"," 203.00"," 205.00"," 204.60"," 205.67"," 3767850"," 774917799.55"," 33449"," 273948"," 7.27" 42 | "DLF","EQ","11-Sep-2018"," 204.60"," 205.40"," 206.95"," 197.60"," 198.20"," 198.90"," 202.37"," 3593534"," 727227879.45"," 31599"," 280031"," 7.79" 43 | "DLF","EQ","12-Sep-2018"," 198.90"," 198.95"," 203.60"," 193.35"," 202.90"," 202.45"," 198.76"," 5717235"," 1136356001.95"," 39348"," 282462"," 4.94" 44 | "DLF","EQ","14-Sep-2018"," 202.45"," 204.50"," 210.50"," 203.40"," 209.25"," 209.25"," 207.60"," 5547982"," 1151759563.35"," 40126"," 1118064"," 20.15" 45 | "DLF","EQ","17-Sep-2018"," 209.25"," 207.00"," 214.25"," 206.15"," 212.30"," 213.20"," 211.42"," 7225702"," 1527676822.85"," 50293"," 1243607"," 17.21" 46 | "DLF","EQ","18-Sep-2018"," 213.20"," 213.00"," 214.00"," 201.60"," 202.95"," 202.80"," 207.35"," 5891263"," 1221580594.05"," 47203"," 592985"," 10.07" 47 | "DLF","EQ","19-Sep-2018"," 202.80"," 201.70"," 205.80"," 198.55"," 201.00"," 201.25"," 202.13"," 5211308"," 1053337247.20"," 53187"," 427353"," 8.20" 48 | "DLF","EQ","21-Sep-2018"," 201.25"," 202.10"," 205.35"," 184.50"," 198.60"," 199.55"," 197.47"," 12303546"," 2429596858.05"," 108572"," 3111496"," 25.29" 49 | "DLF","EQ","24-Sep-2018"," 199.55"," 199.85"," 199.85"," 182.05"," 184.65"," 185.10"," 187.41"," 10192254"," 1910140657.75"," 99558"," 1542791"," 15.14" 50 | "DLF","EQ","25-Sep-2018"," 185.10"," 184.10"," 187.35"," 166.05"," 178.20"," 177.60"," 176.60"," 16192061"," 2859502839.05"," 130255"," 2581252"," 15.94" 51 | "DLF","EQ","26-Sep-2018"," 177.60"," 179.70"," 184.90"," 176.35"," 182.40"," 182.60"," 181.17"," 15397405"," 2789591572.55"," 109098"," 4765773"," 30.95" 52 | "DLF","EQ","27-Sep-2018"," 182.60"," 181.90"," 181.90"," 170.40"," 172.30"," 171.90"," 174.39"," 9691018"," 1690058916.85"," 71736"," 1867944"," 19.28" 53 | "DLF","EQ","28-Sep-2018"," 171.90"," 173.50"," 174.70"," 159.60"," 161.60"," 162.55"," 165.02"," 13888498"," 2291829820.45"," 111302"," 3456842"," 24.89" 54 | "DLF","EQ","01-Oct-2018"," 162.55"," 159.00"," 162.60"," 147.65"," 160.00"," 158.45"," 155.10"," 21910391"," 3398293083.30"," 166070"," 5642860"," 25.75" 55 | "DLF","EQ","03-Oct-2018"," 158.45"," 157.50"," 161.75"," 151.70"," 151.95"," 154.80"," 158.69"," 19784307"," 3139502183.35"," 102382"," 2364455"," 11.95" 56 | "DLF","EQ","04-Oct-2018"," 154.80"," 150.00"," 160.85"," 147.55"," 157.30"," 158.40"," 154.63"," 13713891"," 2120548177.45"," 100467"," 2008097"," 14.64" 57 | "DLF","EQ","05-Oct-2018"," 158.40"," 157.05"," 163.85"," 145.30"," 147.45"," 149.25"," 155.97"," 11093634"," 1730262068.85"," 98470"," 1555644"," 14.02" 58 | "DLF","EQ","08-Oct-2018"," 149.25"," 149.25"," 156.75"," 146.15"," 154.00"," 151.85"," 151.91"," 15115736"," 2296156539.70"," 130197"," 3172366"," 20.99" 59 | "DLF","EQ","09-Oct-2018"," 151.85"," 152.95"," 155.80"," 152.40"," 153.10"," 153.50"," 153.83"," 9331488"," 1435447621.00"," 70398"," 1920366"," 20.58" 60 | "DLF","EQ","10-Oct-2018"," 153.50"," 155.00"," 165.40"," 153.55"," 164.00"," 163.70"," 159.19"," 13311026"," 2118987828.40"," 87896"," 3279160"," 24.63" 61 | "DLF","EQ","11-Oct-2018"," 163.70"," 155.90"," 160.70"," 152.45"," 153.55"," 153.45"," 155.54"," 8930073"," 1389023552.30"," 82536"," 1152137"," 12.90" 62 | "DLF","EQ","12-Oct-2018"," 153.45"," 155.70"," 161.05"," 155.70"," 157.00"," 157.65"," 158.11"," 7037376"," 1112702461.60"," 61633"," 1137021"," 16.16" 63 | "DLF","EQ","15-Oct-2018"," 157.65"," 157.15"," 159.20"," 154.20"," 157.60"," 156.80"," 156.94"," 4457305"," 699507583.30"," 38195"," 311171"," 6.98" 64 | "DLF","EQ","16-Oct-2018"," 156.80"," 158.05"," 161.35"," 157.05"," 158.10"," 158.15"," 158.91"," 5218314"," 829252461.80"," 39241"," 496594"," 9.52" 65 | "DLF","EQ","17-Oct-2018"," 158.15"," 161.00"," 162.40"," 143.00"," 144.90"," 144.35"," 151.14"," 11909786"," 1800025362.70"," 79326"," 2089842"," 17.55" 66 | "DLF","EQ","19-Oct-2018"," 144.35"," 142.90"," 154.50"," 142.00"," 152.95"," 153.15"," 150.57"," 19254568"," 2899242887.40"," 142568"," 4001438"," 20.78" 67 | "DLF","EQ","22-Oct-2018"," 153.15"," 154.00"," 155.80"," 149.50"," 153.45"," 152.80"," 152.12"," 8007346"," 1218081314.80"," 65602"," 567062"," 7.08" 68 | "DLF","EQ","23-Oct-2018"," 152.80"," 150.30"," 155.00"," 149.75"," 154.90"," 153.45"," 151.87"," 5919153"," 898946978.60"," 47194"," 533187"," 9.01" 69 | "DLF","EQ","24-Oct-2018"," 153.45"," 156.00"," 158.75"," 152.65"," 157.25"," 156.65"," 155.99"," 5771458"," 900268783.25"," 53143"," 371755"," 6.44" 70 | "DLF","EQ","25-Oct-2018"," 156.65"," 153.50"," 157.30"," 150.85"," 155.70"," 154.95"," 153.80"," 4732368"," 727819891.05"," 43757"," 250280"," 5.29" 71 | "DLF","EQ","26-Oct-2018"," 154.95"," 155.00"," 156.00"," 150.35"," 154.00"," 153.85"," 153.84"," 5328152"," 819705091.90"," 48297"," 176985"," 3.32" 72 | "DLF","EQ","29-Oct-2018"," 153.85"," 155.00"," 166.40"," 152.60"," 164.15"," 164.45"," 160.17"," 6936987"," 1111068016.65"," 52108"," 654717"," 9.44" 73 | "DLF","EQ","30-Oct-2018"," 164.45"," 163.95"," 166.50"," 159.75"," 161.65"," 161.85"," 163.00"," 5747572"," 936867551.80"," 51980"," 368562"," 6.41" 74 | "DLF","EQ","31-Oct-2018"," 161.85"," 163.80"," 165.80"," 157.80"," 164.00"," 164.75"," 162.06"," 6249997"," 1012877713.65"," 50309"," 484759"," 7.76" 75 | "DLF","EQ","01-Nov-2018"," 164.75"," 165.45"," 173.45"," 164.00"," 171.40"," 171.85"," 169.88"," 8059831"," 1369197675.40"," 80443"," 667610"," 8.28" 76 | "DLF","EQ","02-Nov-2018"," 171.85"," 178.90"," 178.90"," 168.10"," 170.40"," 169.95"," 173.78"," 12042541"," 2092699669.95"," 103473"," 1907771"," 15.84" 77 | "DLF","EQ","05-Nov-2018"," 169.95"," 170.00"," 175.65"," 168.45"," 173.00"," 173.20"," 172.28"," 5399483"," 930216029.05"," 52705"," 362003"," 6.70" 78 | "DLF","EQ","06-Nov-2018"," 173.20"," 174.05"," 174.50"," 171.10"," 172.00"," 172.50"," 172.78"," 3241788"," 560128454.90"," 33613"," 164582"," 5.08" 79 | "DLF","EQ","07-Nov-2018"," 172.50"," 174.00"," 174.10"," 171.60"," 172.15"," 172.95"," 172.88"," 483591"," 83604210.45"," 4478"," 85116"," 17.60" 80 | "DLF","EQ","09-Nov-2018"," 172.95"," 172.90"," 175.00"," 171.25"," 174.50"," 174.35"," 173.10"," 2796209"," 484018432.05"," 26418"," 419525"," 15.00" 81 | "DLF","EQ","12-Nov-2018"," 174.35"," 175.50"," 176.15"," 170.80"," 171.00"," 171.40"," 172.77"," 3201720"," 553145584.45"," 29170"," 366179"," 11.44" 82 | "DLF","EQ","13-Nov-2018"," 171.40"," 170.40"," 172.90"," 167.75"," 172.75"," 171.40"," 170.07"," 4712188"," 801421874.30"," 37115"," 1235791"," 26.23" 83 | "DLF","EQ","14-Nov-2018"," 171.40"," 173.20"," 173.95"," 161.75"," 163.25"," 162.65"," 166.61"," 6001775"," 999954087.30"," 46112"," 1564191"," 26.06" 84 | "DLF","EQ","15-Nov-2018"," 162.65"," 163.80"," 171.30"," 162.40"," 168.90"," 169.85"," 166.84"," 5375427"," 896858285.10"," 44682"," 1224080"," 22.77" 85 | "DLF","EQ","16-Nov-2018"," 169.85"," 170.15"," 171.60"," 164.80"," 169.30"," 169.40"," 167.97"," 3566071"," 598984700.05"," 32810"," 317537"," 8.90" 86 | "DLF","EQ","19-Nov-2018"," 169.40"," 170.25"," 176.25"," 168.10"," 173.15"," 172.75"," 173.28"," 4385661"," 759938953.35"," 37843"," 429286"," 9.79" 87 | "DLF","EQ","20-Nov-2018"," 172.75"," 169.90"," 175.65"," 169.90"," 174.20"," 174.90"," 173.33"," 6984983"," 1210722195.65"," 47819"," 1562487"," 22.37" 88 | "DLF","EQ","21-Nov-2018"," 174.90"," 174.00"," 183.50"," 173.55"," 182.30"," 182.80"," 179.84"," 7925047"," 1425221384.35"," 64281"," 909330"," 11.47" 89 | "DLF","EQ","22-Nov-2018"," 182.80"," 182.50"," 182.50"," 176.45"," 177.00"," 177.10"," 178.95"," 4309429"," 771181788.30"," 38128"," 706864"," 16.40" 90 | "DLF","EQ","26-Nov-2018"," 177.10"," 178.00"," 179.00"," 171.60"," 178.10"," 177.50"," 175.23"," 4103380"," 719037911.30"," 35447"," 251677"," 6.13" 91 | "DLF","EQ","27-Nov-2018"," 177.50"," 178.50"," 183.35"," 176.00"," 178.60"," 178.15"," 180.60"," 6761380"," 1221094424.90"," 55709"," 649994"," 9.61" 92 | "DLF","EQ","28-Nov-2018"," 178.15"," 178.70"," 179.25"," 172.50"," 176.30"," 175.95"," 175.38"," 5394839"," 946141115.00"," 44427"," 272656"," 5.05" 93 | "DLF","EQ","29-Nov-2018"," 175.95"," 177.80"," 181.00"," 174.15"," 179.65"," 179.45"," 177.73"," 5321521"," 945775500.20"," 42872"," 612923"," 11.52" 94 | "DLF","EQ","30-Nov-2018"," 179.45"," 180.00"," 180.00"," 175.95"," 178.55"," 178.10"," 177.89"," 3513726"," 625042408.25"," 31392"," 451271"," 12.84" 95 | "DLF","EQ","03-Dec-2018"," 178.10"," 179.00"," 181.80"," 176.20"," 180.55"," 180.45"," 179.10"," 3700585"," 662786278.20"," 30357"," 189892"," 5.13" 96 | "DLF","EQ","04-Dec-2018"," 180.45"," 179.10"," 180.45"," 172.10"," 173.80"," 173.80"," 176.36"," 4669519"," 823518705.15"," 36191"," 519857"," 11.13" 97 | "DLF","EQ","05-Dec-2018"," 173.80"," 172.95"," 178.00"," 171.65"," 177.40"," 177.00"," 175.35"," 6536199"," 1146141664.15"," 49271"," 578758"," 8.85" 98 | "DLF","EQ","06-Dec-2018"," 177.00"," 174.70"," 174.70"," 165.65"," 166.80"," 167.40"," 168.49"," 7596610"," 1279989557.95"," 59200"," 518856"," 6.83" 99 | "DLF","EQ","07-Dec-2018"," 167.40"," 168.40"," 173.00"," 165.10"," 172.05"," 172.55"," 169.65"," 6402016"," 1086077627.55"," 44736"," 417015"," 6.51" 100 | "DLF","EQ","10-Dec-2018"," 172.55"," 169.65"," 169.65"," 164.50"," 166.80"," 166.65"," 166.79"," 4046041"," 674833644.45"," 33798"," 222649"," 5.50" 101 | "DLF","EQ","11-Dec-2018"," 166.65"," 164.00"," 172.90"," 163.00"," 172.45"," 171.45"," 169.09"," 4612431"," 779901666.90"," 35598"," 209107"," 4.53" 102 | "DLF","EQ","12-Dec-2018"," 171.45"," 172.80"," 181.30"," 171.10"," 178.00"," 179.25"," 177.70"," 7437148"," 1321561384.25"," 49390"," 516582"," 6.95" 103 | "DLF","EQ","13-Dec-2018"," 179.25"," 176.95"," 182.85"," 174.70"," 178.20"," 178.85"," 178.80"," 8244217"," 1474055992.35"," 57381"," 339172"," 4.11" 104 | "DLF","EQ","14-Dec-2018"," 178.85"," 178.10"," 180.55"," 176.25"," 177.95"," 178.75"," 178.60"," 4029066"," 719577564.85"," 32577"," 202887"," 5.04" 105 | "DLF","EQ","17-Dec-2018"," 178.75"," 179.75"," 180.70"," 174.90"," 177.95"," 178.30"," 177.57"," 3643033"," 646883350.00"," 23959"," 281292"," 7.72" 106 | "DLF","EQ","18-Dec-2018"," 178.30"," 177.00"," 180.95"," 176.25"," 178.20"," 178.05"," 177.86"," 3786975"," 673555276.40"," 25088"," 275976"," 7.29" 107 | "DLF","EQ","19-Dec-2018"," 178.05"," 179.35"," 193.30"," 178.40"," 192.80"," 191.50"," 188.49"," 16441945"," 3099064400.90"," 84435"," 2021090"," 12.29" 108 | "DLF","EQ","20-Dec-2018"," 191.50"," 192.80"," 193.70"," 186.70"," 190.00"," 189.85"," 190.32"," 8962611"," 1705726042.00"," 61538"," 652294"," 7.28" 109 | "DLF","EQ","21-Dec-2018"," 189.85"," 190.00"," 191.80"," 185.15"," 187.10"," 187.10"," 188.60"," 8223560"," 1550998611.60"," 60764"," 1281669"," 15.59" 110 | "DLF","EQ","24-Dec-2018"," 187.10"," 186.00"," 186.30"," 177.10"," 179.00"," 178.10"," 180.30"," 8945739"," 1612876836.00"," 66389"," 1148212"," 12.84" 111 | "DLF","EQ","26-Dec-2018"," 178.10"," 179.00"," 179.75"," 171.80"," 177.30"," 177.60"," 175.42"," 7574429"," 1328736649.10"," 54950"," 333278"," 4.40" 112 | "DLF","EQ","27-Dec-2018"," 177.60"," 179.80"," 181.40"," 174.90"," 175.25"," 175.65"," 177.86"," 6183377"," 1099799352.45"," 50741"," 561195"," 9.08" 113 | "DLF","EQ","28-Dec-2018"," 175.65"," 177.90"," 180.70"," 176.70"," 177.70"," 177.95"," 178.69"," 4300844"," 768521951.80"," 28103"," 303119"," 7.05" 114 | "DLF","EQ","31-Dec-2018"," 177.95"," 178.70"," 179.95"," 176.35"," 177.15"," 177.55"," 177.80"," 4653719"," 827435241.00"," 30134"," 324171"," 6.97" 115 | "DLF","EQ","01-Jan-2019"," 177.55"," 177.10"," 182.15"," 177.00"," 180.20"," 180.30"," 180.28"," 6246700"," 1126135080.50"," 41137"," 417340"," 6.68" 116 | "DLF","EQ","02-Jan-2019"," 180.30"," 179.90"," 179.90"," 172.25"," 173.10"," 173.10"," 175.40"," 6604177"," 1158364183.75"," 41658"," 1177344"," 17.83" 117 | "DLF","EQ","03-Jan-2019"," 173.10"," 173.10"," 174.95"," 171.15"," 171.95"," 172.45"," 173.17"," 5873745"," 1017148394.50"," 39144"," 278923"," 4.75" 118 | "DLF","EQ","04-Jan-2019"," 172.45"," 173.05"," 175.80"," 170.65"," 175.50"," 175.15"," 173.76"," 5556175"," 965446594.20"," 72667"," 429369"," 7.73" 119 | "DLF","EQ","07-Jan-2019"," 175.15"," 178.00"," 184.00"," 177.10"," 179.05"," 179.80"," 181.08"," 10127291"," 1833842397.60"," 68451"," 1036404"," 10.23" 120 | "DLF","EQ","08-Jan-2019"," 179.80"," 179.80"," 182.80"," 178.55"," 181.35"," 181.50"," 180.98"," 4704040"," 851359269.60"," 34802"," 435579"," 9.26" 121 | "DLF","EQ","09-Jan-2019"," 181.50"," 182.25"," 184.70"," 178.10"," 182.30"," 182.10"," 182.11"," 5570072"," 1014372110.50"," 40886"," 385900"," 6.93" 122 | "DLF","EQ","10-Jan-2019"," 182.10"," 183.10"," 187.15"," 181.35"," 183.50"," 183.55"," 184.37"," 9198151"," 1695819677.70"," 64564"," 888351"," 9.66" 123 | "DLF","EQ","11-Jan-2019"," 183.55"," 182.50"," 183.60"," 179.00"," 182.30"," 182.15"," 181.18"," 4565679"," 827213428.35"," 35670"," 224623"," 4.92" 124 | "DLF","EQ","14-Jan-2019"," 182.15"," 182.30"," 182.30"," 178.20"," 181.70"," 181.15"," 180.44"," 3741265"," 675081441.10"," 27860"," 183340"," 4.90" 125 | "DLF","EQ","15-Jan-2019"," 181.15"," 182.50"," 187.20"," 182.00"," 185.85"," 185.95"," 184.03"," 4060162"," 747184664.05"," 27574"," 351244"," 8.65" 126 | "DLF","EQ","16-Jan-2019"," 185.95"," 185.60"," 187.75"," 182.50"," 183.30"," 182.95"," 184.63"," 4131732"," 762835193.20"," 27697"," 510413"," 12.35" 127 | "DLF","EQ","17-Jan-2019"," 182.95"," 183.70"," 185.80"," 181.55"," 184.20"," 184.20"," 183.88"," 4558933"," 838318470.15"," 30926"," 386512"," 8.48" 128 | "DLF","EQ","18-Jan-2019"," 184.20"," 184.50"," 184.60"," 179.30"," 180.55"," 180.35"," 181.15"," 4243642"," 768739285.00"," 27552"," 444992"," 10.49" 129 | "DLF","EQ","21-Jan-2019"," 180.35"," 180.55"," 182.10"," 176.20"," 176.40"," 177.25"," 178.93"," 3361204"," 601432617.80"," 22984"," 322109"," 9.58" 130 | "DLF","EQ","22-Jan-2019"," 177.25"," 177.00"," 180.60"," 174.75"," 179.80"," 180.05"," 177.46"," 4744583"," 841990824.35"," 33185"," 572458"," 12.07" 131 | "DLF","EQ","23-Jan-2019"," 180.05"," 180.40"," 180.85"," 176.20"," 177.10"," 177.25"," 177.84"," 4567262"," 812228401.20"," 28252"," 236255"," 5.17" 132 | "DLF","EQ","24-Jan-2019"," 177.25"," 177.90"," 183.70"," 176.65"," 177.40"," 177.75"," 179.71"," 5827123"," 1047167739.25"," 42045"," 652731"," 11.20" 133 | "DLF","EQ","25-Jan-2019"," 177.75"," 178.85"," 180.10"," 143.10"," 159.50"," 158.00"," 164.10"," 20781142"," 3410226323.20"," 123258"," 2667683"," 12.84" 134 | "DLF","EQ","28-Jan-2019"," 158.00"," 160.00"," 167.60"," 158.50"," 163.90"," 164.05"," 162.35"," 11812368"," 1917700814.35"," 91440"," 1342758"," 11.37" 135 | "DLF","EQ","29-Jan-2019"," 164.05"," 163.00"," 168.20"," 162.05"," 165.50"," 164.90"," 164.77"," 4196603"," 691454462.10"," 46064"," 315124"," 7.51" 136 | "DLF","EQ","30-Jan-2019"," 164.90"," 166.00"," 166.40"," 160.05"," 163.95"," 164.20"," 162.78"," 4379914"," 712970997.15"," 43479"," 219652"," 5.01" 137 | "DLF","EQ","31-Jan-2019"," 164.20"," 165.00"," 168.00"," 158.50"," 164.40"," 164.85"," 163.04"," 5587357"," 910960702.80"," 47955"," 736764"," 13.19" 138 | "DLF","EQ","01-Feb-2019"," 164.85"," 164.95"," 181.05"," 162.65"," 166.45"," 165.70"," 170.36"," 13045972"," 2222518937.20"," 95810"," 1013361"," 7.77" 139 | "DLF","EQ","04-Feb-2019"," 165.70"," 165.90"," 165.95"," 158.20"," 163.60"," 163.60"," 161.64"," 5652935"," 913750250.50"," 54896"," 480531"," 8.50" 140 | "DLF","EQ","05-Feb-2019"," 163.60"," 163.05"," 165.35"," 154.55"," 160.00"," 159.75"," 159.89"," 7095047"," 1134445142.00"," 62699"," 598410"," 8.43" 141 | "DLF","EQ","06-Feb-2019"," 159.75"," 158.00"," 162.50"," 152.05"," 161.15"," 160.35"," 157.46"," 10746796"," 1692186702.85"," 91632"," 532531"," 4.96" 142 | "DLF","EQ","07-Feb-2019"," 160.35"," 161.00"," 163.65"," 157.20"," 159.90"," 160.40"," 160.25"," 7154145"," 1146425699.25"," 59544"," 472159"," 6.60" 143 | "DLF","EQ","08-Feb-2019"," 160.40"," 160.00"," 169.00"," 157.85"," 165.95"," 164.75"," 163.53"," 10455050"," 1709745404.40"," 87789"," 807229"," 7.72" 144 | "DLF","EQ","11-Feb-2019"," 164.75"," 164.80"," 165.95"," 161.10"," 164.00"," 163.70"," 163.60"," 5269340"," 862051275.90"," 45104"," 617415"," 11.72" 145 | "DLF","EQ","12-Feb-2019"," 163.70"," 164.00"," 165.65"," 157.60"," 159.80"," 159.15"," 160.71"," 5780211"," 928922907.90"," 46082"," 497185"," 8.60" 146 | "DLF","EQ","13-Feb-2019"," 159.15"," 160.40"," 161.90"," 157.00"," 157.90"," 157.95"," 158.87"," 4614030"," 733032275.60"," 36034"," 403270"," 8.74" 147 | "DLF","EQ","14-Feb-2019"," 157.95"," 157.00"," 164.25"," 156.65"," 162.05"," 162.30"," 161.11"," 4477707"," 721387015.75"," 43337"," 455258"," 10.17" 148 | "DLF","EQ","15-Feb-2019"," 162.30"," 162.00"," 162.05"," 154.40"," 158.10"," 158.10"," 158.05"," 5111337"," 807832167.40"," 40051"," 400937"," 7.84" 149 | "DLF","EQ","18-Feb-2019"," 158.10"," 159.00"," 160.20"," 155.60"," 155.90"," 156.00"," 157.44"," 3876032"," 610249673.70"," 30423"," 348537"," 8.99" 150 | "DLF","EQ","19-Feb-2019"," 156.00"," 157.90"," 166.90"," 157.60"," 162.90"," 163.40"," 163.18"," 15335372"," 2502432434.85"," 87564"," 848255"," 5.53" 151 | "DLF","EQ","20-Feb-2019"," 163.40"," 163.60"," 165.30"," 159.00"," 162.95"," 163.00"," 162.32"," 9359061"," 1519152190.65"," 63114"," 545473"," 5.83" 152 | "DLF","EQ","21-Feb-2019"," 163.00"," 163.00"," 166.90"," 162.10"," 166.25"," 165.30"," 164.90"," 5377001"," 886668782.30"," 42558"," 580242"," 10.79" 153 | "DLF","EQ","22-Feb-2019"," 165.30"," 165.50"," 170.00"," 164.70"," 169.30"," 169.25"," 168.06"," 6024582"," 1012502491.05"," 40091"," 972066"," 16.13" 154 | "DLF","EQ","25-Feb-2019"," 169.25"," 170.55"," 173.30"," 167.05"," 168.05"," 168.15"," 169.49"," 13038685"," 2209886050.30"," 77517"," 1373896"," 10.54" 155 | "DLF","EQ","26-Feb-2019"," 168.15"," 165.10"," 167.75"," 162.10"," 166.50"," 166.40"," 165.28"," 5572262"," 920984480.30"," 37819"," 795040"," 14.27" 156 | "DLF","EQ","27-Feb-2019"," 166.40"," 167.00"," 169.20"," 163.00"," 163.95"," 164.35"," 165.80"," 4545778"," 753691699.55"," 41276"," 525951"," 11.57" 157 | "DLF","EQ","28-Feb-2019"," 164.35"," 163.00"," 166.40"," 161.85"," 166.20"," 164.70"," 164.01"," 5881165"," 964546645.65"," 36316"," 1953144"," 33.21" 158 | "DLF","EQ","01-Mar-2019"," 164.70"," 166.00"," 169.20"," 165.20"," 167.45"," 167.25"," 167.06"," 3845718"," 642459294.60"," 26732"," 378735"," 9.85" 159 | "DLF","EQ","05-Mar-2019"," 167.25"," 167.50"," 177.00"," 167.50"," 174.85"," 175.60"," 172.43"," 6150185"," 1060477418.20"," 45411"," 1452143"," 23.61" 160 | "DLF","EQ","06-Mar-2019"," 175.60"," 176.00"," 180.75"," 174.35"," 176.85"," 177.35"," 177.72"," 6513459"," 1157557257.15"," 52282"," 1070829"," 16.44" 161 | "DLF","EQ","07-Mar-2019"," 177.35"," 176.95"," 179.05"," 173.60"," 175.15"," 175.95"," 176.15"," 4201421"," 740086222.10"," 35941"," 658727"," 15.68" 162 | "DLF","EQ","08-Mar-2019"," 175.95"," 175.30"," 176.75"," 173.80"," 175.60"," 175.35"," 175.08"," 3005893"," 526272222.60"," 21494"," 173474"," 5.77" 163 | "DLF","EQ","11-Mar-2019"," 175.35"," 175.35"," 178.30"," 175.15"," 177.00"," 176.85"," 176.92"," 2795266"," 494546224.05"," 19205"," 211463"," 7.57" 164 | "DLF","EQ","12-Mar-2019"," 176.85"," 179.00"," 188.70"," 177.60"," 187.10"," 187.75"," 185.76"," 13173333"," 2447095693.30"," 91663"," 1428210"," 10.84" 165 | "DLF","EQ","13-Mar-2019"," 187.75"," 190.50"," 194.95"," 190.00"," 191.70"," 192.15"," 192.40"," 16127714"," 3102929906.05"," 97808"," 1390716"," 8.62" 166 | "DLF","EQ","14-Mar-2019"," 192.15"," 192.80"," 203.15"," 190.05"," 201.75"," 201.85"," 198.09"," 18351272"," 3635251862.25"," 118247"," 1885642"," 10.28" 167 | "DLF","EQ","15-Mar-2019"," 201.85"," 202.15"," 203.75"," 196.55"," 197.40"," 197.45"," 200.19"," 9317938"," 1865380409.10"," 78700"," 2058149"," 22.09" 168 | "DLF","EQ","18-Mar-2019"," 197.45"," 198.45"," 201.20"," 195.25"," 197.85"," 197.75"," 198.08"," 6926554"," 1371981417.65"," 44939"," 1865132"," 26.93" 169 | "DLF","EQ","19-Mar-2019"," 197.75"," 198.75"," 199.25"," 192.60"," 194.00"," 194.00"," 196.11"," 7149471"," 1402081219.85"," 54161"," 1526518"," 21.35" 170 | "DLF","EQ","20-Mar-2019"," 194.00"," 196.00"," 200.75"," 195.50"," 199.75"," 199.35"," 198.69"," 9822900"," 1951685772.50"," 61756"," 1948907"," 19.84" 171 | "DLF","EQ","22-Mar-2019"," 199.35"," 200.10"," 202.70"," 195.40"," 196.25"," 196.15"," 199.19"," 9153838"," 1823344864.30"," 75505"," 1203873"," 13.15" 172 | "DLF","EQ","25-Mar-2019"," 196.15"," 194.45"," 194.75"," 188.00"," 189.20"," 189.25"," 190.81"," 5217787"," 995602436.35"," 36065"," 438609"," 8.41" 173 | "DLF","EQ","26-Mar-2019"," 189.25"," 191.40"," 205.35"," 188.50"," 196.40"," 196.55"," 200.44"," 28150981"," 5642638080.75"," 178483"," 1724897"," 6.13" 174 | "DLF","EQ","27-Mar-2019"," 196.55"," 199.10"," 199.40"," 189.60"," 190.45"," 190.40"," 193.42"," 11282628"," 2182313624.35"," 72839"," 2806464"," 24.87" 175 | "DLF","EQ","28-Mar-2019"," 190.40"," 191.90"," 197.80"," 191.60"," 194.30"," 194.05"," 195.22"," 10010007"," 1954150288.55"," 75926"," 1706909"," 17.05" 176 | "DLF","EQ","29-Mar-2019"," 194.05"," 195.95"," 206.40"," 193.80"," 201.80"," 202.45"," 201.23"," 19479636"," 3919885269.60"," 124467"," 3964617"," 20.35" 177 | "DLF","EQ","01-Apr-2019"," 202.45"," 203.00"," 204.50"," 194.95"," 195.05"," 195.55"," 198.73"," 16311067"," 3241538535.50"," 126989"," 6595460"," 40.44" 178 | "DLF","EQ","02-Apr-2019"," 195.55"," 195.60"," 200.80"," 194.20"," 199.10"," 199.50"," 197.76"," 18498248"," 3658271721.30"," 134933"," 6218003"," 33.61" 179 | "DLF","EQ","03-Apr-2019"," 199.50"," 200.70"," 209.65"," 199.60"," 202.90"," 202.10"," 205.06"," 28104238"," 5762923703.15"," 181535"," 7601208"," 27.05" 180 | "DLF","EQ","04-Apr-2019"," 202.10"," 203.35"," 204.80"," 198.10"," 199.90"," 200.85"," 202.15"," 14167501"," 2863919009.70"," 89712"," 3979126"," 28.09" 181 | "DLF","EQ","05-Apr-2019"," 200.85"," 200.90"," 204.75"," 199.40"," 201.55"," 201.60"," 202.25"," 13045209"," 2638328600.70"," 70718"," 4099604"," 31.43" 182 | "DLF","EQ","08-Apr-2019"," 201.60"," 203.20"," 203.20"," 183.55"," 184.50"," 184.60"," 190.71"," 110839025"," 21138118592.40"," 215230"," 72048104"," 65.00" 183 | "DLF","EQ","09-Apr-2019"," 184.60"," 186.00"," 189.50"," 183.40"," 187.60"," 188.15"," 186.54"," 24874709"," 4640252556.60"," 137630"," 3082158"," 12.39" 184 | "DLF","EQ","10-Apr-2019"," 188.15"," 187.75"," 191.20"," 179.05"," 180.10"," 180.10"," 185.13"," 22351331"," 4137950607.40"," 142524"," 7441711"," 33.29" 185 | "DLF","EQ","11-Apr-2019"," 180.10"," 180.50"," 182.45"," 175.80"," 176.60"," 177.70"," 178.12"," 20379941"," 3630112866.80"," 119167"," 5470334"," 26.84" 186 | "DLF","EQ","12-Apr-2019"," 177.70"," 177.70"," 183.65"," 177.30"," 182.00"," 181.85"," 181.76"," 21022203"," 3821024796.35"," 114346"," 7658533"," 36.43" 187 | "DLF","EQ","15-Apr-2019"," 181.85"," 183.00"," 185.20"," 181.45"," 184.55"," 184.75"," 183.83"," 13223734"," 2430930454.20"," 75610"," 5794124"," 43.82" 188 | "DLF","EQ","16-Apr-2019"," 184.75"," 186.70"," 187.90"," 182.10"," 182.70"," 183.35"," 185.06"," 15829170"," 2929354991.15"," 96538"," 6361173"," 40.19" 189 | "DLF","EQ","18-Apr-2019"," 183.35"," 183.85"," 185.70"," 179.25"," 181.75"," 183.10"," 182.40"," 14326897"," 2613238522.75"," 100729"," 6114410"," 42.68" 190 | "DLF","EQ","22-Apr-2019"," 183.10"," 181.00"," 181.40"," 172.30"," 172.70"," 172.95"," 175.31"," 15806621"," 2771129121.55"," 83658"," 6176259"," 39.07" 191 | "DLF","EQ","23-Apr-2019"," 172.95"," 173.00"," 176.60"," 170.60"," 171.70"," 172.00"," 174.43"," 10583075"," 1846022689.45"," 68683"," 2741088"," 25.90" 192 | "DLF","EQ","24-Apr-2019"," 172.00"," 172.45"," 177.00"," 171.15"," 176.25"," 176.05"," 174.12"," 8682385"," 1511787601.15"," 57579"," 2628521"," 30.27" 193 | "DLF","EQ","25-Apr-2019"," 176.05"," 177.00"," 178.70"," 173.00"," 175.50"," 173.60"," 174.55"," 44558136"," 7777463077.15"," 103726"," 29943663"," 67.20" 194 | "DLF","EQ","26-Apr-2019"," 173.60"," 176.50"," 177.30"," 172.80"," 174.60"," 174.30"," 174.55"," 15119128"," 2639116973.20"," 51828"," 8215756"," 54.34" 195 | "DLF","EQ","30-Apr-2019"," 174.30"," 174.00"," 174.30"," 167.05"," 171.90"," 172.75"," 171.18"," 15575038"," 2666118668.25"," 84816"," 7552746"," 48.49" 196 | "DLF","EQ","02-May-2019"," 172.75"," 171.05"," 175.60"," 169.70"," 172.10"," 171.85"," 173.36"," 9657298"," 1674195686.65"," 63803"," 3640542"," 37.70" 197 | "DLF","EQ","03-May-2019"," 171.85"," 172.95"," 179.20"," 171.95"," 177.90"," 177.95"," 176.68"," 11791575"," 2083353698.65"," 83478"," 4011040"," 34.02" 198 | "DLF","EQ","06-May-2019"," 177.95"," 175.95"," 177.35"," 173.05"," 173.45"," 173.90"," 175.22"," 5468266"," 958139484.25"," 43584"," 1503927"," 27.50" 199 | "DLF","EQ","07-May-2019"," 173.90"," 175.70"," 176.20"," 167.40"," 169.75"," 169.60"," 171.96"," 8443779"," 1452003140.35"," 47504"," 1815521"," 21.50" 200 | "DLF","EQ","08-May-2019"," 169.60"," 169.50"," 170.15"," 165.60"," 168.20"," 168.95"," 168.04"," 6741133"," 1132765204.45"," 45240"," 1279607"," 18.98" 201 | "DLF","EQ","09-May-2019"," 168.95"," 168.70"," 170.80"," 166.50"," 167.90"," 167.80"," 168.40"," 4647668"," 782672720.20"," 30817"," 525348"," 11.30" 202 | "DLF","EQ","10-May-2019"," 167.80"," 168.50"," 172.50"," 165.75"," 166.10"," 166.50"," 168.96"," 7393523"," 1249174741.25"," 44654"," 1344432"," 18.18" 203 | "DLF","EQ","13-May-2019"," 166.50"," 165.10"," 167.45"," 163.00"," 163.40"," 164.35"," 165.69"," 6157794"," 1020275860.40"," 41916"," 1037091"," 16.84" 204 | "DLF","EQ","14-May-2019"," 164.35"," 163.05"," 167.80"," 160.75"," 166.60"," 166.25"," 164.64"," 8190348"," 1348450877.75"," 49968"," 1846520"," 22.55" 205 | "DLF","EQ","15-May-2019"," 166.25"," 166.95"," 166.95"," 160.60"," 162.15"," 161.45"," 163.12"," 5421371"," 884309739.75"," 35186"," 1156013"," 21.32" 206 | "DLF","EQ","16-May-2019"," 161.45"," 162.85"," 164.50"," 158.15"," 162.65"," 162.90"," 161.54"," 9992081"," 1614142621.40"," 56214"," 3241958"," 32.45" 207 | "DLF","EQ","17-May-2019"," 162.90"," 162.65"," 167.00"," 161.85"," 164.05"," 164.45"," 164.12"," 6369300"," 1045343397.60"," 52669"," 1099114"," 17.26" 208 | "DLF","EQ","20-May-2019"," 164.45"," 169.25"," 174.70"," 168.25"," 173.60"," 173.75"," 172.02"," 7512324"," 1292251390.50"," 67200"," 1764083"," 23.48" 209 | "DLF","EQ","21-May-2019"," 173.75"," 174.50"," 176.45"," 169.50"," 171.25"," 171.50"," 172.42"," 12077133"," 2082384952.10"," 104896"," 3909705"," 32.37" 210 | "DLF","EQ","22-May-2019"," 171.50"," 175.90"," 181.20"," 172.00"," 173.95"," 173.90"," 175.95"," 24314232"," 4278205811.35"," 142568"," 4071895"," 16.75" 211 | "DLF","EQ","23-May-2019"," 173.90"," 177.00"," 184.60"," 174.00"," 181.00"," 180.70"," 179.33"," 26290847"," 4714736696.70"," 213136"," 9237540"," 35.14" 212 | "DLF","EQ","24-May-2019"," 180.70"," 181.75"," 193.20"," 181.50"," 191.15"," 191.65"," 188.64"," 22407285"," 4226980450.10"," 151437"," 6211530"," 27.72" 213 | "DLF","EQ","27-May-2019"," 191.65"," 191.40"," 200.20"," 190.10"," 196.35"," 197.00"," 195.89"," 15049131"," 2947993073.55"," 104509"," 4419720"," 29.37" 214 | "DLF","EQ","28-May-2019"," 197.00"," 197.50"," 197.60"," 191.60"," 195.15"," 195.40"," 194.78"," 8700693"," 1694692849.80"," 70085"," 2359306"," 27.12" 215 | "DLF","EQ","29-May-2019"," 195.40"," 194.90"," 197.25"," 191.80"," 193.05"," 193.70"," 194.47"," 7979267"," 1551718269.45"," 72983"," 2300190"," 28.83" 216 | "DLF","EQ","30-May-2019"," 193.70"," 194.95"," 196.50"," 190.65"," 195.40"," 194.70"," 193.70"," 10437577"," 2021719291.90"," 66392"," 3559004"," 34.10" 217 | "DLF","EQ","31-May-2019"," 194.70"," 195.50"," 196.20"," 184.75"," 190.05"," 191.20"," 190.92"," 11214402"," 2141045837.55"," 83201"," 2453095"," 21.87" 218 | "DLF","EQ","03-Jun-2019"," 191.20"," 191.00"," 196.90"," 188.75"," 195.80"," 196.15"," 193.48"," 7573474"," 1465310601.35"," 52636"," 2143072"," 28.30" 219 | "DLF","EQ","04-Jun-2019"," 196.15"," 195.00"," 202.65"," 194.30"," 195.30"," 195.45"," 198.30"," 14559889"," 2887169028.65"," 96267"," 3546357"," 24.36" 220 | "DLF","EQ","06-Jun-2019"," 195.45"," 195.90"," 197.15"," 187.15"," 188.95"," 190.40"," 191.90"," 9595362"," 1841319512.75"," 64731"," 1129471"," 11.77" 221 | "DLF","EQ","07-Jun-2019"," 190.40"," 190.00"," 194.20"," 186.60"," 190.40"," 190.05"," 190.47"," 8188075"," 1559605473.60"," 76518"," 1214721"," 14.84" 222 | "DLF","EQ","10-Jun-2019"," 190.05"," 191.40"," 193.10"," 185.60"," 188.80"," 189.25"," 189.58"," 7653848"," 1451007091.05"," 71184"," 1709590"," 22.34" 223 | "DLF","EQ","11-Jun-2019"," 189.25"," 190.00"," 192.90"," 185.70"," 191.25"," 191.55"," 189.60"," 6158116"," 1167561061.25"," 56896"," 691273"," 11.23" 224 | "DLF","EQ","12-Jun-2019"," 191.55"," 191.00"," 191.00"," 183.70"," 184.75"," 184.55"," 186.67"," 8066923"," 1505834000.00"," 60978"," 1311350"," 16.26" 225 | "DLF","EQ","13-Jun-2019"," 184.55"," 183.95"," 186.75"," 181.60"," 184.30"," 184.95"," 184.43"," 8810333"," 1624896222.50"," 77253"," 2062243"," 23.41" 226 | "DLF","EQ","14-Jun-2019"," 184.95"," 183.75"," 184.50"," 175.50"," 176.80"," 177.85"," 178.48"," 11855210"," 2115949351.00"," 101856"," 2335812"," 19.70" 227 | "DLF","EQ","17-Jun-2019"," 177.85"," 177.85"," 177.90"," 170.35"," 171.75"," 172.65"," 173.21"," 11335861"," 1963448900.45"," 75935"," 2517890"," 21.62" 228 | "DLF","EQ","18-Jun-2019"," 172.65"," 172.60"," 175.50"," 170.00"," 173.95"," 173.70"," 173.45"," 9460128"," 1640838750.55"," 62625"," 3160606"," 32.52" 229 | "DLF","EQ","19-Jun-2019"," 173.70"," 174.90"," 179.50"," 171.80"," 176.50"," 176.40"," 176.25"," 11090503"," 1954690524.50"," 91792"," 2625233"," 23.67" 230 | "DLF","EQ","20-Jun-2019"," 176.40"," 176.40"," 183.25"," 173.35"," 181.20"," 181.35"," 179.81"," 9873647"," 1775379070.75"," 78849"," 2283611"," 23.13" 231 | "DLF","EQ","21-Jun-2019"," 181.35"," 179.95"," 181.90"," 176.55"," 176.85"," 177.55"," 179.11"," 10517709"," 1883842724.85"," 76206"," 4692216"," 44.61" 232 | "DLF","EQ","24-Jun-2019"," 177.55"," 178.40"," 179.40"," 173.95"," 175.55"," 175.55"," 176.79"," 7231257"," 1278405665.40"," 63085"," 1339100"," 18.52" 233 | "DLF","EQ","25-Jun-2019"," 175.55"," 174.60"," 177.35"," 174.05"," 176.30"," 176.25"," 175.99"," 4769918"," 839479306.45"," 41545"," 570526"," 11.96" 234 | "DLF","EQ","26-Jun-2019"," 176.25"," 175.95"," 185.80"," 175.30"," 184.80"," 184.60"," 182.42"," 10243007"," 1868562927.25"," 78172"," 2197976"," 21.46" 235 | "DLF","EQ","27-Jun-2019"," 184.60"," 184.00"," 187.00"," 182.80"," 186.55"," 185.60"," 185.16"," 16068076"," 2975194436.70"," 83471"," 7896698"," 49.15" 236 | "DLF","EQ","28-Jun-2019"," 185.60"," 186.55"," 191.35"," 185.10"," 188.90"," 188.55"," 188.88"," 9413259"," 1777932698.45"," 58040"," 1779795"," 18.91" 237 | "DLF","EQ","01-Jul-2019"," 188.55"," 189.70"," 191.45"," 187.60"," 191.00"," 190.80"," 189.94"," 5467507"," 1038493407.45"," 43283"," 647820"," 11.85" 238 | "DLF","EQ","02-Jul-2019"," 190.80"," 191.85"," 193.45"," 189.40"," 193.10"," 192.50"," 191.52"," 6054505"," 1159574006.00"," 43145"," 982809"," 16.23" 239 | "DLF","EQ","03-Jul-2019"," 192.50"," 193.50"," 194.70"," 191.10"," 193.35"," 193.65"," 193.00"," 5299636"," 1022833101.50"," 32755"," 777957"," 14.68" 240 | "DLF","EQ","04-Jul-2019"," 193.65"," 193.70"," 196.90"," 192.50"," 194.10"," 194.60"," 194.75"," 9837342"," 1915793813.60"," 43873"," 2949195"," 29.98" 241 | "DLF","EQ","05-Jul-2019"," 194.60"," 196.20"," 196.60"," 185.50"," 185.75"," 186.95"," 192.57"," 15489840"," 2982853201.25"," 70438"," 4418819"," 28.53" 242 | "DLF","EQ","08-Jul-2019"," 186.95"," 185.40"," 187.20"," 177.50"," 182.55"," 182.40"," 182.11"," 7421180"," 1351435255.60"," 59350"," 1583852"," 21.34" 243 | "DLF","EQ","09-Jul-2019"," 182.40"," 182.35"," 185.30"," 180.40"," 183.50"," 183.65"," 183.34"," 5311932"," 973897710.65"," 34806"," 1149051"," 21.63" 244 | "DLF","EQ","10-Jul-2019"," 183.65"," 184.05"," 184.05"," 175.25"," 177.50"," 177.70"," 178.33"," 9142852"," 1630487471.20"," 65528"," 1939992"," 21.22" 245 | "DLF","EQ","11-Jul-2019"," 177.70"," 179.00"," 187.70"," 178.95"," 185.10"," 186.00"," 184.81"," 11089171"," 2049349224.50"," 86534"," 1985433"," 17.90" 246 | "DLF","EQ","12-Jul-2019"," 186.00"," 184.00"," 188.35"," 183.25"," 186.00"," 186.15"," 186.22"," 6872502"," 1279789033.15"," 47646"," 1482959"," 21.58" 247 | "DLF","EQ","15-Jul-2019"," 186.15"," 186.50"," 187.65"," 184.65"," 186.00"," 186.05"," 186.08"," 3848689"," 716176599.20"," 37675"," 744069"," 19.33" 248 | -------------------------------------------------------------------------------- /30 stocks/largecaps/bhel_stock_data.csv: -------------------------------------------------------------------------------- 1 | "Symbol","Series","Date","Prev Close","Open Price","High Price","Low Price","Last Price","Close Price","Average Price","Total Traded Quantity","Turnover","No. of Trades","Deliverable Qty","% Dly Qt to Traded Qty" 2 | "BHEL","EQ","16-Jul-2018"," 67.15"," 67.15"," 67.25"," 64.60"," 65.10"," 65.00"," 65.76"," 5498218"," 361562305.70"," 21922"," 1869571"," 34.00" 3 | "BHEL","EQ","17-Jul-2018"," 65.00"," 65.50"," 68.30"," 64.45"," 68.05"," 68.05"," 67.10"," 7675970"," 515088316.60"," 29716"," 1869191"," 24.35" 4 | "BHEL","EQ","18-Jul-2018"," 68.05"," 68.10"," 68.55"," 65.25"," 66.45"," 66.55"," 66.45"," 5743139"," 381649790.95"," 18600"," 1173205"," 20.43" 5 | "BHEL","EQ","19-Jul-2018"," 66.55"," 66.95"," 67.75"," 65.45"," 66.20"," 66.15"," 66.48"," 4872954"," 323947220.75"," 16885"," 1083044"," 22.23" 6 | "BHEL","EQ","20-Jul-2018"," 66.15"," 66.30"," 68.20"," 65.90"," 67.75"," 67.50"," 67.05"," 5073743"," 340185448.95"," 19540"," 995321"," 19.62" 7 | "BHEL","EQ","23-Jul-2018"," 67.50"," 67.75"," 69.90"," 67.05"," 69.50"," 69.50"," 68.67"," 5048661"," 346691781.80"," 21219"," 1109242"," 21.97" 8 | "BHEL","EQ","24-Jul-2018"," 69.50"," 69.55"," 73.90"," 69.30"," 73.70"," 73.45"," 72.41"," 14241131"," 1031136071.40"," 55279"," 4203572"," 29.52" 9 | "BHEL","EQ","25-Jul-2018"," 73.45"," 73.95"," 78.35"," 70.80"," 71.40"," 71.65"," 74.38"," 47859275"," 3559998711.50"," 143412"," 7340443"," 15.34" 10 | "BHEL","EQ","26-Jul-2018"," 71.65"," 71.95"," 72.40"," 70.30"," 70.70"," 70.70"," 71.22"," 13088082"," 932175400.10"," 36546"," 2601103"," 19.87" 11 | "BHEL","EQ","27-Jul-2018"," 70.70"," 71.00"," 72.10"," 70.50"," 71.20"," 71.15"," 71.30"," 5959511"," 424931299.80"," 20825"," 1130481"," 18.97" 12 | "BHEL","EQ","30-Jul-2018"," 71.15"," 71.40"," 73.60"," 71.25"," 73.30"," 73.30"," 72.89"," 9169279"," 668362914.65"," 30274"," 2972702"," 32.42" 13 | "BHEL","EQ","31-Jul-2018"," 73.30"," 73.50"," 74.50"," 72.55"," 73.90"," 74.05"," 73.69"," 9110753"," 671408533.15"," 36315"," 2492662"," 27.36" 14 | "BHEL","EQ","01-Aug-2018"," 74.05"," 74.30"," 74.65"," 72.80"," 73.50"," 73.55"," 73.63"," 7218091"," 531481475.95"," 24359"," 2881059"," 39.91" 15 | "BHEL","EQ","02-Aug-2018"," 73.55"," 73.45"," 74.70"," 72.75"," 73.55"," 73.70"," 73.69"," 5908968"," 435417603.50"," 20356"," 1112583"," 18.83" 16 | "BHEL","EQ","03-Aug-2018"," 73.70"," 74.00"," 74.60"," 73.65"," 74.05"," 74.00"," 74.08"," 4949579"," 366659824.90"," 15212"," 1131622"," 22.86" 17 | "BHEL","EQ","06-Aug-2018"," 74.00"," 74.30"," 75.85"," 74.25"," 75.20"," 75.05"," 75.19"," 6672395"," 501728079.20"," 24747"," 2319760"," 34.77" 18 | "BHEL","EQ","07-Aug-2018"," 75.05"," 75.45"," 75.45"," 72.70"," 73.45"," 73.45"," 73.86"," 5763228"," 425673352.95"," 19970"," 1646875"," 28.58" 19 | "BHEL","EQ","08-Aug-2018"," 73.45"," 73.45"," 74.30"," 72.80"," 73.50"," 73.55"," 73.50"," 3694645"," 271539192.90"," 13274"," 547110"," 14.81" 20 | "BHEL","EQ","09-Aug-2018"," 73.55"," 73.55"," 75.10"," 73.20"," 74.50"," 74.50"," 74.39"," 3849977"," 286398317.35"," 12657"," 896401"," 23.28" 21 | "BHEL","EQ","10-Aug-2018"," 74.50"," 74.50"," 74.90"," 72.55"," 73.05"," 73.20"," 73.50"," 3550580"," 260973653.30"," 12625"," 716643"," 20.18" 22 | "BHEL","EQ","13-Aug-2018"," 73.20"," 72.80"," 73.75"," 72.00"," 72.60"," 72.60"," 72.88"," 3555510"," 259142791.00"," 15045"," 923504"," 25.97" 23 | "BHEL","EQ","14-Aug-2018"," 72.60"," 72.90"," 73.15"," 71.95"," 72.20"," 72.25"," 72.48"," 2812094"," 203809539.35"," 11491"," 688922"," 24.50" 24 | "BHEL","EQ","16-Aug-2018"," 72.25"," 72.00"," 73.70"," 71.70"," 72.45"," 72.60"," 72.87"," 4576252"," 333478831.85"," 16301"," 1594078"," 34.83" 25 | "BHEL","EQ","17-Aug-2018"," 72.60"," 73.00"," 74.60"," 72.70"," 74.00"," 73.85"," 73.60"," 5001845"," 368137721.05"," 17199"," 1412727"," 28.24" 26 | "BHEL","EQ","20-Aug-2018"," 73.85"," 74.60"," 75.40"," 74.40"," 75.15"," 75.00"," 74.87"," 4923730"," 368652477.55"," 18369"," 1599649"," 32.49" 27 | "BHEL","EQ","21-Aug-2018"," 75.00"," 75.50"," 76.50"," 75.10"," 75.45"," 75.50"," 75.69"," 5001773"," 378600574.40"," 19649"," 1382634"," 27.64" 28 | "BHEL","EQ","23-Aug-2018"," 75.50"," 75.30"," 75.90"," 73.30"," 73.80"," 73.90"," 74.19"," 4221464"," 313193661.60"," 19356"," 1043608"," 24.72" 29 | "BHEL","EQ","24-Aug-2018"," 73.90"," 73.90"," 80.80"," 73.15"," 80.10"," 80.20"," 78.40"," 31797538"," 2493076146.25"," 96393"," 5058095"," 15.91" 30 | "BHEL","EQ","27-Aug-2018"," 80.20"," 80.95"," 81.25"," 79.05"," 80.00"," 79.95"," 80.11"," 13621490"," 1091255075.70"," 43611"," 1631314"," 11.98" 31 | "BHEL","EQ","28-Aug-2018"," 79.95"," 80.10"," 80.75"," 76.55"," 77.60"," 77.70"," 78.41"," 10860282"," 851600032.65"," 33279"," 2566503"," 23.63" 32 | "BHEL","EQ","29-Aug-2018"," 77.70"," 77.65"," 82.65"," 77.00"," 80.55"," 80.65"," 80.97"," 20108730"," 1628236756.45"," 62533"," 3167477"," 15.75" 33 | "BHEL","EQ","30-Aug-2018"," 80.65"," 80.60"," 81.90"," 80.00"," 81.20"," 80.85"," 80.94"," 9498914"," 768862889.35"," 27407"," 1637096"," 17.23" 34 | "BHEL","EQ","31-Aug-2018"," 80.85"," 81.20"," 82.20"," 79.10"," 80.70"," 80.60"," 80.53"," 8388264"," 675493178.75"," 29215"," 1378105"," 16.43" 35 | "BHEL","EQ","03-Sep-2018"," 80.60"," 81.00"," 82.90"," 80.80"," 81.90"," 81.75"," 81.87"," 9935832"," 813488708.00"," 26053"," 2277246"," 22.92" 36 | "BHEL","EQ","04-Sep-2018"," 81.75"," 81.85"," 81.90"," 77.30"," 79.25"," 78.70"," 79.27"," 8166498"," 647356872.55"," 26568"," 2306390"," 28.24" 37 | "BHEL","EQ","05-Sep-2018"," 78.70"," 78.50"," 80.30"," 75.35"," 80.30"," 78.95"," 77.79"," 13213861"," 1027971520.00"," 35174"," 2111532"," 15.98" 38 | "BHEL","EQ","06-Sep-2018"," 78.95"," 80.15"," 82.35"," 78.70"," 81.35"," 81.40"," 80.77"," 11046623"," 892267435.25"," 41462"," 1466195"," 13.27" 39 | "BHEL","EQ","07-Sep-2018"," 81.40"," 81.40"," 83.30"," 78.30"," 78.85"," 79.35"," 80.66"," 14671712"," 1183405688.40"," 38936"," 5337947"," 36.38" 40 | "BHEL","EQ","10-Sep-2018"," 79.35"," 79.00"," 79.95"," 77.30"," 77.70"," 77.75"," 78.39"," 6815427"," 534281686.05"," 30055"," 1443703"," 21.18" 41 | "BHEL","EQ","11-Sep-2018"," 77.75"," 77.35"," 78.40"," 76.05"," 76.85"," 77.10"," 77.18"," 10091442"," 778872037.85"," 42114"," 2130403"," 21.11" 42 | "BHEL","EQ","12-Sep-2018"," 77.10"," 77.10"," 77.70"," 74.80"," 75.25"," 75.10"," 76.12"," 9414455"," 716588090.60"," 30255"," 2619959"," 27.83" 43 | "BHEL","EQ","14-Sep-2018"," 75.10"," 75.55"," 78.20"," 75.55"," 76.90"," 77.05"," 77.00"," 10398967"," 800692978.65"," 32199"," 2862882"," 27.53" 44 | "BHEL","EQ","17-Sep-2018"," 77.05"," 76.80"," 76.80"," 75.60"," 76.05"," 76.15"," 76.24"," 3755063"," 286294691.45"," 16413"," 683308"," 18.20" 45 | "BHEL","EQ","18-Sep-2018"," 76.15"," 76.25"," 76.75"," 72.80"," 73.80"," 73.85"," 74.41"," 8685643"," 646299406.75"," 26346"," 2820476"," 32.47" 46 | "BHEL","EQ","19-Sep-2018"," 73.85"," 75.00"," 75.40"," 73.85"," 75.00"," 74.85"," 74.69"," 6108676"," 456282233.00"," 22855"," 1369769"," 22.42" 47 | "BHEL","EQ","21-Sep-2018"," 74.85"," 76.00"," 76.00"," 67.50"," 71.90"," 71.50"," 72.63"," 11280098"," 819277530.50"," 40673"," 3552868"," 31.50" 48 | "BHEL","EQ","24-Sep-2018"," 71.50"," 72.50"," 72.50"," 69.50"," 70.35"," 70.55"," 70.53"," 4974635"," 350841237.95"," 16834"," 942548"," 18.95" 49 | "BHEL","EQ","25-Sep-2018"," 70.55"," 70.00"," 72.70"," 69.40"," 71.70"," 71.65"," 71.03"," 7388125"," 524795557.95"," 28354"," 1677934"," 22.71" 50 | "BHEL","EQ","26-Sep-2018"," 71.65"," 72.00"," 72.85"," 71.75"," 72.15"," 72.15"," 72.22"," 3739806"," 270093868.45"," 14421"," 614653"," 16.44" 51 | "BHEL","EQ","27-Sep-2018"," 72.15"," 72.20"," 73.00"," 69.80"," 70.75"," 71.10"," 71.37"," 7884479"," 562722194.15"," 25010"," 2959416"," 37.53" 52 | "BHEL","EQ","28-Sep-2018"," 71.10"," 71.20"," 71.45"," 67.75"," 68.55"," 68.50"," 69.15"," 7062306"," 488327237.55"," 23183"," 1659787"," 23.50" 53 | "BHEL","EQ","01-Oct-2018"," 68.50"," 68.40"," 71.45"," 66.35"," 71.20"," 71.10"," 68.74"," 7191129"," 494284025.35"," 23345"," 1340566"," 18.64" 54 | "BHEL","EQ","03-Oct-2018"," 71.10"," 71.90"," 76.35"," 70.60"," 73.30"," 73.50"," 74.12"," 16456814"," 1219766839.05"," 66911"," 3719009"," 22.60" 55 | "BHEL","EQ","04-Oct-2018"," 73.50"," 72.45"," 74.90"," 72.10"," 73.00"," 73.50"," 73.57"," 10444564"," 768457349.30"," 38053"," 3191083"," 30.55" 56 | "BHEL","EQ","05-Oct-2018"," 73.50"," 72.80"," 74.00"," 69.75"," 70.35"," 70.40"," 72.28"," 9385085"," 678382689.70"," 45402"," 3282757"," 34.98" 57 | "BHEL","EQ","08-Oct-2018"," 70.40"," 69.95"," 71.00"," 67.95"," 69.55"," 69.60"," 69.44"," 9132523"," 634118625.90"," 50839"," 2258062"," 24.73" 58 | "BHEL","EQ","09-Oct-2018"," 69.60"," 69.75"," 71.50"," 68.10"," 70.10"," 70.25"," 69.93"," 7821977"," 547019836.60"," 45188"," 1323689"," 16.92" 59 | "BHEL","EQ","10-Oct-2018"," 70.25"," 70.90"," 74.65"," 70.50"," 74.15"," 74.15"," 72.99"," 7848645"," 572878250.20"," 28366"," 1358856"," 17.31" 60 | "BHEL","EQ","11-Oct-2018"," 74.15"," 71.55"," 74.90"," 71.05"," 73.10"," 73.30"," 73.20"," 6359958"," 465564291.35"," 30210"," 1350690"," 21.24" 61 | "BHEL","EQ","12-Oct-2018"," 73.30"," 74.10"," 76.45"," 74.00"," 74.80"," 75.15"," 75.43"," 7655675"," 577487552.05"," 28728"," 1481710"," 19.35" 62 | "BHEL","EQ","15-Oct-2018"," 75.15"," 75.40"," 76.70"," 73.85"," 76.10"," 75.85"," 75.39"," 5209684"," 392782490.30"," 19722"," 610692"," 11.72" 63 | "BHEL","EQ","16-Oct-2018"," 75.85"," 76.00"," 76.70"," 75.55"," 76.25"," 76.20"," 76.15"," 4799630"," 365509488.85"," 26292"," 1572867"," 32.77" 64 | "BHEL","EQ","17-Oct-2018"," 76.20"," 77.00"," 77.65"," 73.55"," 73.85"," 74.15"," 75.95"," 7900337"," 600010311.25"," 23688"," 1901117"," 24.06" 65 | "BHEL","EQ","19-Oct-2018"," 74.15"," 73.55"," 75.20"," 72.60"," 73.80"," 73.50"," 73.73"," 5638133"," 415682722.70"," 19582"," 968199"," 17.17" 66 | "BHEL","EQ","22-Oct-2018"," 73.50"," 74.05"," 75.20"," 72.65"," 73.95"," 73.95"," 73.89"," 5188175"," 383368930.30"," 15572"," 751940"," 14.49" 67 | "BHEL","EQ","23-Oct-2018"," 73.95"," 75.00"," 76.05"," 72.85"," 74.35"," 73.85"," 74.49"," 13765726"," 1025451091.85"," 38464"," 4952398"," 35.98" 68 | "BHEL","EQ","24-Oct-2018"," 73.85"," 75.25"," 76.45"," 73.90"," 76.25"," 75.95"," 75.16"," 8048611"," 604968538.65"," 30871"," 1590968"," 19.77" 69 | "BHEL","EQ","25-Oct-2018"," 75.95"," 74.90"," 76.50"," 69.25"," 69.90"," 70.25"," 71.85"," 46914007"," 3370909556.25"," 113640"," 16556336"," 35.29" 70 | "BHEL","EQ","26-Oct-2018"," 70.25"," 71.00"," 71.05"," 67.25"," 67.40"," 67.50"," 68.56"," 26351665"," 1806711273.60"," 84792"," 8757867"," 33.23" 71 | "BHEL","EQ","29-Oct-2018"," 67.50"," 68.35"," 68.45"," 66.55"," 68.05"," 67.95"," 67.66"," 16596499"," 1122838471.90"," 55895"," 6524531"," 39.31" 72 | "BHEL","EQ","30-Oct-2018"," 67.95"," 68.25"," 69.95"," 68.00"," 69.25"," 69.15"," 69.03"," 17763932"," 1226308268.65"," 64314"," 7481324"," 42.12" 73 | "BHEL","EQ","31-Oct-2018"," 69.15"," 69.25"," 69.50"," 67.60"," 68.85"," 68.75"," 68.43"," 18658155"," 1276849052.40"," 58958"," 8616268"," 46.18" 74 | "BHEL","EQ","01-Nov-2018"," 68.75"," 69.20"," 70.75"," 69.20"," 70.45"," 70.20"," 70.23"," 18514431"," 1300341001.45"," 48387"," 8477130"," 45.79" 75 | "BHEL","EQ","02-Nov-2018"," 70.20"," 70.95"," 73.95"," 70.75"," 73.35"," 73.00"," 72.73"," 25054634"," 1822270558.05"," 57409"," 10543971"," 42.08" 76 | "BHEL","EQ","05-Nov-2018"," 73.00"," 71.45"," 71.45"," 69.30"," 70.05"," 70.00"," 70.14"," 12435322"," 872211419.25"," 35528"," 4068431"," 32.72" 77 | "BHEL","EQ","06-Nov-2018"," 70.00"," 70.10"," 71.10"," 69.00"," 69.20"," 69.30"," 69.88"," 9397104"," 656701599.40"," 26279"," 3204343"," 34.10" 78 | "BHEL","EQ","07-Nov-2018"," 69.30"," 69.95"," 69.95"," 69.45"," 69.70"," 69.70"," 69.71"," 1121760"," 78192680.60"," 5658"," 580885"," 51.78" 79 | "BHEL","EQ","09-Nov-2018"," 69.70"," 69.75"," 70.40"," 69.20"," 69.95"," 70.00"," 69.85"," 5825050"," 406873077.35"," 22199"," 1751174"," 30.06" 80 | "BHEL","EQ","12-Nov-2018"," 70.00"," 70.00"," 70.55"," 68.50"," 68.70"," 68.90"," 69.56"," 8027643"," 558403204.45"," 27795"," 2853467"," 35.55" 81 | "BHEL","EQ","13-Nov-2018"," 68.90"," 68.70"," 69.45"," 67.80"," 68.80"," 68.70"," 68.68"," 5492697"," 377224657.80"," 20909"," 1047559"," 19.07" 82 | "BHEL","EQ","14-Nov-2018"," 68.70"," 68.80"," 69.20"," 65.70"," 66.20"," 66.05"," 66.70"," 17411472"," 1161297888.15"," 61084"," 7610672"," 43.71" 83 | "BHEL","EQ","15-Nov-2018"," 66.05"," 66.10"," 67.70"," 65.10"," 66.85"," 66.85"," 66.59"," 12196075"," 812085816.40"," 40929"," 3032237"," 24.86" 84 | "BHEL","EQ","16-Nov-2018"," 66.85"," 67.00"," 67.05"," 65.65"," 66.65"," 66.75"," 66.38"," 8934462"," 593104758.30"," 34857"," 3199359"," 35.81" 85 | "BHEL","EQ","19-Nov-2018"," 66.75"," 66.80"," 68.35"," 66.80"," 68.05"," 68.00"," 67.77"," 6923682"," 469211317.30"," 26321"," 2367465"," 34.19" 86 | "BHEL","EQ","20-Nov-2018"," 68.00"," 67.90"," 68.05"," 66.95"," 67.40"," 67.40"," 67.48"," 11370903"," 767363209.50"," 30356"," 7309321"," 64.28" 87 | "BHEL","EQ","21-Nov-2018"," 67.40"," 67.80"," 67.80"," 65.90"," 66.70"," 66.60"," 66.62"," 8588079"," 572140949.40"," 29144"," 3489689"," 40.63" 88 | "BHEL","EQ","22-Nov-2018"," 66.60"," 66.85"," 67.35"," 65.85"," 66.05"," 66.15"," 66.57"," 8808101"," 586381148.10"," 26053"," 3550763"," 40.31" 89 | "BHEL","EQ","26-Nov-2018"," 66.15"," 66.20"," 67.15"," 65.50"," 67.05"," 67.00"," 66.65"," 7523888"," 501503129.60"," 41489"," 2753802"," 36.60" 90 | "BHEL","EQ","27-Nov-2018"," 67.00"," 66.40"," 68.10"," 66.30"," 67.15"," 67.00"," 67.13"," 9725050"," 652832504.10"," 28805"," 4864938"," 50.02" 91 | "BHEL","EQ","28-Nov-2018"," 67.00"," 67.10"," 67.25"," 66.35"," 67.00"," 67.00"," 66.74"," 8965627"," 598355078.90"," 21944"," 4865845"," 54.27" 92 | "BHEL","EQ","29-Nov-2018"," 67.00"," 67.20"," 68.80"," 65.70"," 68.70"," 68.20"," 67.42"," 10339738"," 697107819.70"," 24362"," 4310165"," 41.69" 93 | "BHEL","EQ","30-Nov-2018"," 68.20"," 68.75"," 70.35"," 66.15"," 68.20"," 68.15"," 68.00"," 123227256"," 8378840704.60"," 123936"," 85762660"," 69.60" 94 | "BHEL","EQ","03-Dec-2018"," 68.15"," 68.80"," 71.30"," 67.40"," 70.50"," 70.85"," 69.89"," 16129731"," 1127261624.75"," 43398"," 4253410"," 26.37" 95 | "BHEL","EQ","04-Dec-2018"," 70.85"," 70.15"," 71.40"," 69.65"," 70.35"," 70.50"," 70.50"," 7323826"," 516296013.25"," 19678"," 1775259"," 24.24" 96 | "BHEL","EQ","05-Dec-2018"," 70.50"," 69.75"," 70.25"," 66.10"," 66.45"," 66.45"," 67.54"," 21703542"," 1465847514.05"," 50425"," 10199148"," 46.99" 97 | "BHEL","EQ","06-Dec-2018"," 66.45"," 66.35"," 66.60"," 65.05"," 65.70"," 65.65"," 65.73"," 13418588"," 882025891.95"," 36561"," 4843032"," 36.09" 98 | "BHEL","EQ","07-Dec-2018"," 65.65"," 65.80"," 66.75"," 64.35"," 65.85"," 65.95"," 65.71"," 10787228"," 708846199.55"," 37077"," 2327148"," 21.57" 99 | "BHEL","EQ","10-Dec-2018"," 65.95"," 65.00"," 65.40"," 62.60"," 63.10"," 62.95"," 63.75"," 13939033"," 888602954.25"," 36668"," 6932184"," 49.73" 100 | "BHEL","EQ","11-Dec-2018"," 62.95"," 62.05"," 65.50"," 61.70"," 64.95"," 64.60"," 63.80"," 21465276"," 1369572804.55"," 56304"," 6343583"," 29.55" 101 | "BHEL","EQ","12-Dec-2018"," 64.60"," 65.00"," 66.95"," 64.60"," 66.35"," 66.35"," 65.92"," 9954488"," 656245188.45"," 34831"," 3618735"," 36.35" 102 | "BHEL","EQ","13-Dec-2018"," 66.35"," 66.90"," 67.70"," 66.30"," 66.70"," 66.75"," 67.06"," 8670196"," 581438123.60"," 25244"," 2833654"," 32.68" 103 | "BHEL","EQ","14-Dec-2018"," 66.75"," 66.50"," 67.90"," 65.80"," 66.50"," 66.60"," 66.92"," 6485337"," 433986185.55"," 19225"," 1260624"," 19.44" 104 | "BHEL","EQ","17-Dec-2018"," 66.60"," 66.70"," 68.45"," 66.50"," 67.65"," 67.70"," 67.62"," 8413741"," 568924567.85"," 22839"," 2723229"," 32.37" 105 | "BHEL","EQ","18-Dec-2018"," 67.70"," 67.65"," 70.15"," 67.30"," 70.00"," 69.90"," 69.54"," 17072720"," 1187310035.30"," 50065"," 5306056"," 31.08" 106 | "BHEL","EQ","19-Dec-2018"," 69.90"," 70.20"," 71.00"," 69.40"," 70.00"," 70.10"," 70.28"," 10669275"," 749845582.75"," 36330"," 2955203"," 27.70" 107 | "BHEL","EQ","20-Dec-2018"," 70.10"," 69.80"," 71.00"," 69.25"," 70.70"," 70.55"," 70.39"," 6354028"," 447265410.40"," 21436"," 1241269"," 19.54" 108 | "BHEL","EQ","21-Dec-2018"," 70.55"," 71.80"," 71.90"," 69.40"," 69.75"," 69.85"," 70.38"," 15851379"," 1115596733.55"," 43438"," 5777884"," 36.45" 109 | "BHEL","EQ","24-Dec-2018"," 69.85"," 70.05"," 73.00"," 69.50"," 71.10"," 71.25"," 71.45"," 22943907"," 1639308628.85"," 56530"," 8345529"," 36.37" 110 | "BHEL","EQ","26-Dec-2018"," 71.25"," 70.70"," 71.85"," 69.25"," 71.50"," 71.55"," 70.80"," 13578782"," 961393951.60"," 45223"," 5229050"," 38.51" 111 | "BHEL","EQ","27-Dec-2018"," 71.55"," 72.00"," 72.40"," 69.75"," 69.85"," 70.00"," 70.47"," 22742988"," 1602786925.30"," 34233"," 14312579"," 62.93" 112 | "BHEL","EQ","28-Dec-2018"," 70.00"," 70.05"," 72.40"," 70.05"," 72.20"," 72.05"," 71.76"," 9774725"," 701460298.50"," 28070"," 2933188"," 30.01" 113 | "BHEL","EQ","31-Dec-2018"," 72.05"," 72.30"," 73.50"," 72.20"," 73.00"," 73.10"," 73.02"," 7130426"," 520659172.60"," 28029"," 1572192"," 22.05" 114 | "BHEL","EQ","01-Jan-2019"," 73.10"," 73.75"," 73.95"," 72.80"," 73.75"," 73.80"," 73.49"," 6440421"," 473286080.90"," 21312"," 972389"," 15.10" 115 | "BHEL","EQ","02-Jan-2019"," 73.80"," 73.40"," 74.80"," 72.45"," 72.75"," 73.00"," 73.63"," 8615103"," 634349434.85"," 28628"," 1288334"," 14.95" 116 | "BHEL","EQ","03-Jan-2019"," 73.00"," 73.00"," 73.00"," 70.40"," 70.70"," 70.75"," 71.65"," 8083906"," 579195933.80"," 25943"," 1645836"," 20.36" 117 | "BHEL","EQ","04-Jan-2019"," 70.75"," 70.80"," 71.85"," 70.05"," 71.80"," 71.40"," 70.87"," 20439249"," 1448547508.60"," 43945"," 9409049"," 46.03" 118 | "BHEL","EQ","07-Jan-2019"," 71.40"," 72.00"," 72.95"," 71.75"," 72.00"," 72.25"," 72.41"," 8163279"," 591120977.95"," 26975"," 2843935"," 34.84" 119 | "BHEL","EQ","08-Jan-2019"," 72.25"," 72.10"," 72.25"," 71.00"," 71.30"," 71.35"," 71.44"," 7453809"," 532463136.70"," 22696"," 3166308"," 42.48" 120 | "BHEL","EQ","09-Jan-2019"," 71.35"," 71.80"," 71.90"," 69.70"," 70.10"," 70.25"," 70.62"," 7124203"," 503135492.70"," 21065"," 1973357"," 27.70" 121 | "BHEL","EQ","10-Jan-2019"," 70.25"," 70.25"," 70.50"," 68.70"," 68.80"," 68.85"," 69.36"," 7810193"," 541724757.55"," 26260"," 2924497"," 37.44" 122 | "BHEL","EQ","11-Jan-2019"," 68.85"," 69.35"," 69.35"," 68.10"," 68.30"," 68.55"," 68.59"," 7359392"," 504770921.25"," 27138"," 2005849"," 27.26" 123 | "BHEL","EQ","14-Jan-2019"," 68.55"," 68.75"," 68.75"," 67.50"," 67.80"," 67.80"," 67.95"," 4735962"," 321808521.90"," 17489"," 826348"," 17.45" 124 | "BHEL","EQ","15-Jan-2019"," 67.80"," 68.10"," 68.80"," 67.80"," 68.45"," 68.35"," 68.27"," 5007754"," 341865110.65"," 15397"," 1319729"," 26.35" 125 | "BHEL","EQ","16-Jan-2019"," 68.35"," 68.45"," 69.60"," 68.15"," 68.20"," 68.35"," 68.73"," 5484091"," 376917811.65"," 17332"," 1316699"," 24.01" 126 | "BHEL","EQ","17-Jan-2019"," 68.35"," 68.60"," 70.40"," 68.00"," 70.05"," 70.15"," 69.26"," 9573414"," 663018131.30"," 40565"," 2419660"," 25.27" 127 | "BHEL","EQ","18-Jan-2019"," 70.15"," 70.40"," 71.15"," 69.70"," 71.00"," 70.95"," 70.79"," 17158253"," 1214612046.85"," 39858"," 9852692"," 57.42" 128 | "BHEL","EQ","21-Jan-2019"," 70.95"," 71.10"," 72.25"," 70.70"," 71.00"," 70.95"," 71.44"," 10341030"," 738792589.85"," 33781"," 3971617"," 38.41" 129 | "BHEL","EQ","22-Jan-2019"," 70.95"," 70.95"," 71.55"," 69.95"," 71.10"," 71.20"," 70.72"," 6256263"," 442426648.00"," 44561"," 2172982"," 34.73" 130 | "BHEL","EQ","23-Jan-2019"," 71.20"," 71.25"," 71.60"," 69.05"," 69.20"," 69.35"," 70.44"," 5371992"," 378394712.35"," 21809"," 1255371"," 23.37" 131 | "BHEL","EQ","24-Jan-2019"," 69.35"," 69.25"," 70.10"," 68.65"," 68.85"," 69.05"," 69.33"," 6289323"," 436016029.65"," 19721"," 2189460"," 34.81" 132 | "BHEL","EQ","25-Jan-2019"," 69.05"," 69.15"," 69.50"," 66.05"," 66.25"," 66.40"," 67.75"," 7134722"," 483393801.00"," 23615"," 2400598"," 33.65" 133 | "BHEL","EQ","28-Jan-2019"," 66.40"," 66.50"," 66.75"," 63.45"," 63.70"," 63.75"," 64.43"," 8737901"," 562954439.15"," 29171"," 3052186"," 34.93" 134 | "BHEL","EQ","29-Jan-2019"," 63.75"," 63.20"," 65.35"," 63.20"," 64.50"," 64.40"," 64.50"," 8758306"," 564904953.75"," 33714"," 3558506"," 40.63" 135 | "BHEL","EQ","30-Jan-2019"," 64.40"," 64.95"," 64.95"," 63.60"," 64.00"," 63.90"," 64.18"," 7509058"," 481910836.35"," 19323"," 3151864"," 41.97" 136 | "BHEL","EQ","31-Jan-2019"," 63.90"," 64.50"," 65.00"," 63.70"," 64.50"," 64.70"," 64.47"," 12325961"," 794681065.65"," 24228"," 6756777"," 54.82" 137 | "BHEL","EQ","01-Feb-2019"," 64.70"," 64.95"," 65.70"," 63.65"," 64.35"," 64.35"," 64.76"," 6031620"," 390584541.70"," 20007"," 1089175"," 18.06" 138 | "BHEL","EQ","04-Feb-2019"," 64.35"," 64.55"," 65.30"," 63.10"," 64.95"," 64.95"," 64.39"," 6807730"," 438320556.30"," 21415"," 1831842"," 26.91" 139 | "BHEL","EQ","05-Feb-2019"," 64.95"," 64.95"," 65.15"," 56.20"," 58.60"," 58.85"," 59.61"," 48929404"," 2916510843.65"," 133168"," 12904543"," 26.37" 140 | "BHEL","EQ","06-Feb-2019"," 58.85"," 59.35"," 61.15"," 57.60"," 60.90"," 60.80"," 59.54"," 19423985"," 1156454574.80"," 63746"," 6017542"," 30.98" 141 | "BHEL","EQ","07-Feb-2019"," 60.80"," 61.00"," 62.50"," 60.60"," 62.40"," 62.20"," 61.67"," 8069603"," 497671088.75"," 24606"," 2290341"," 28.38" 142 | "BHEL","EQ","08-Feb-2019"," 62.20"," 62.25"," 63.25"," 61.80"," 62.10"," 62.25"," 62.47"," 8295240"," 518218027.65"," 43130"," 3231802"," 38.96" 143 | "BHEL","EQ","11-Feb-2019"," 62.25"," 61.20"," 63.25"," 61.00"," 62.70"," 62.75"," 62.24"," 9511824"," 591975025.30"," 34014"," 3259572"," 34.27" 144 | "BHEL","EQ","12-Feb-2019"," 62.75"," 62.50"," 62.60"," 61.50"," 61.60"," 61.80"," 61.99"," 5528900"," 342734157.05"," 17619"," 2264425"," 40.96" 145 | "BHEL","EQ","13-Feb-2019"," 61.80"," 61.90"," 62.10"," 60.80"," 61.00"," 61.00"," 61.19"," 7374677"," 451292746.00"," 24379"," 3994862"," 54.17" 146 | "BHEL","EQ","14-Feb-2019"," 61.00"," 60.25"," 62.80"," 60.15"," 62.00"," 62.30"," 61.70"," 7971447"," 491860988.60"," 23133"," 2722389"," 34.15" 147 | "BHEL","EQ","15-Feb-2019"," 62.30"," 61.95"," 62.00"," 60.70"," 61.00"," 61.05"," 61.12"," 7013360"," 428623098.55"," 18278"," 3057188"," 43.59" 148 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6564549"," 421397674.40"," 21009"," 2153834"," 32.81" 155 | "BHEL","EQ","27-Feb-2019"," 64.85"," 65.10"," 66.00"," 64.60"," 64.70"," 64.95"," 65.07"," 6764048"," 440116743.55"," 23923"," 2579048"," 38.13" 156 | "BHEL","EQ","28-Feb-2019"," 64.95"," 65.00"," 65.15"," 64.10"," 64.55"," 64.45"," 64.55"," 14167452"," 914574342.55"," 11579"," 9532512"," 67.28" 157 | "BHEL","EQ","01-Mar-2019"," 64.45"," 64.80"," 66.60"," 64.65"," 66.10"," 66.15"," 65.96"," 9309674"," 614063258.55"," 14981"," 5269901"," 56.61" 158 | "BHEL","EQ","05-Mar-2019"," 66.15"," 67.75"," 68.00"," 66.40"," 67.20"," 67.30"," 67.34"," 9082342"," 611580240.95"," 24799"," 2879950"," 31.71" 159 | "BHEL","EQ","06-Mar-2019"," 67.30"," 67.65"," 70.15"," 67.50"," 69.05"," 69.10"," 69.18"," 13004838"," 899663515.15"," 37721"," 4064798"," 31.26" 160 | "BHEL","EQ","07-Mar-2019"," 69.10"," 69.20"," 69.25"," 67.75"," 68.00"," 68.10"," 68.33"," 4192540"," 286470958.35"," 14811"," 857546"," 20.45" 161 | "BHEL","EQ","08-Mar-2019"," 68.10"," 67.90"," 67.90"," 66.50"," 67.10"," 67.10"," 66.95"," 9335968"," 625085323.35"," 16628"," 5085608"," 54.47" 162 | "BHEL","EQ","11-Mar-2019"," 67.10"," 67.70"," 70.60"," 67.20"," 70.00"," 70.05"," 69.54"," 12701280"," 883285096.40"," 28773"," 6314886"," 49.72" 163 | "BHEL","EQ","12-Mar-2019"," 70.05"," 70.45"," 71.35"," 69.55"," 69.75"," 69.80"," 70.29"," 7526822"," 529046859.25"," 26630"," 2805005"," 37.27" 164 | "BHEL","EQ","13-Mar-2019"," 69.80"," 70.05"," 70.20"," 67.75"," 68.10"," 68.00"," 68.57"," 5268092"," 361237724.25"," 16651"," 1887379"," 35.83" 165 | "BHEL","EQ","14-Mar-2019"," 68.00"," 67.75"," 68.65"," 66.60"," 67.15"," 67.25"," 67.29"," 5767075"," 388070445.35"," 16801"," 1307575"," 22.67" 166 | "BHEL","EQ","15-Mar-2019"," 67.25"," 66.25"," 68.05"," 66.10"," 67.90"," 67.80"," 67.32"," 6878337"," 463067793.30"," 21734"," 1997145"," 29.04" 167 | "BHEL","EQ","18-Mar-2019"," 67.80"," 68.20"," 69.15"," 66.45"," 67.25"," 67.15"," 67.25"," 11419966"," 768019682.45"," 26102"," 4002295"," 35.05" 168 | "BHEL","EQ","19-Mar-2019"," 67.15"," 67.50"," 68.45"," 67.15"," 68.10"," 68.10"," 67.95"," 10369254"," 704607069.40"," 24318"," 4013246"," 38.70" 169 | "BHEL","EQ","20-Mar-2019"," 68.10"," 68.10"," 72.40"," 68.10"," 68.60"," 68.55"," 70.41"," 34728590"," 2445281596.55"," 79814"," 8641183"," 24.88" 170 | "BHEL","EQ","22-Mar-2019"," 68.55"," 68.75"," 69.35"," 66.90"," 67.40"," 67.55"," 67.98"," 12856428"," 873979181.65"," 32136"," 4829524"," 37.57" 171 | "BHEL","EQ","25-Mar-2019"," 67.55"," 67.10"," 70.10"," 66.55"," 69.20"," 69.10"," 68.84"," 26125367"," 1798472400.95"," 54813"," 12361425"," 47.32" 172 | "BHEL","EQ","26-Mar-2019"," 69.10"," 69.95"," 72.70"," 69.75"," 71.20"," 71.05"," 71.37"," 35481702"," 2532300294.35"," 83824"," 13477600"," 37.98" 173 | "BHEL","EQ","27-Mar-2019"," 71.05"," 72.00"," 72.75"," 70.65"," 71.30"," 71.30"," 71.57"," 26105730"," 1868515116.95"," 56104"," 11915107"," 45.64" 174 | "BHEL","EQ","28-Mar-2019"," 71.30"," 72.10"," 72.40"," 70.85"," 71.00"," 71.55"," 71.63"," 45326931"," 3246974683.35"," 68739"," 25121026"," 55.42" 175 | "BHEL","EQ","29-Mar-2019"," 71.55"," 71.70"," 76.35"," 71.65"," 75.00"," 74.95"," 74.48"," 35964901"," 2678532967.40"," 71269"," 11116659"," 30.91" 176 | "BHEL","EQ","01-Apr-2019"," 74.95"," 75.45"," 77.85"," 74.80"," 75.95"," 75.95"," 76.64"," 25156745"," 1927944473.50"," 45079"," 9163086"," 36.42" 177 | "BHEL","EQ","02-Apr-2019"," 75.95"," 76.25"," 77.15"," 73.70"," 75.45"," 74.70"," 75.35"," 20278983"," 1528009133.25"," 37762"," 5610219"," 27.67" 178 | "BHEL","EQ","03-Apr-2019"," 74.70"," 75.40"," 76.15"," 73.00"," 73.80"," 73.85"," 74.73"," 19628856"," 1466877532.00"," 36373"," 7020444"," 35.77" 179 | "BHEL","EQ","04-Apr-2019"," 73.85"," 74.30"," 74.95"," 72.00"," 73.50"," 73.20"," 73.57"," 13697381"," 1007691073.05"," 25014"," 4371184"," 31.91" 180 | "BHEL","EQ","05-Apr-2019"," 73.20"," 73.80"," 74.60"," 72.40"," 72.70"," 72.70"," 73.45"," 12657002"," 929710525.75"," 20924"," 4863116"," 38.42" 181 | "BHEL","EQ","08-Apr-2019"," 72.70"," 72.90"," 73.85"," 72.10"," 72.90"," 72.60"," 72.89"," 13220181"," 963584107.50"," 28663"," 4676254"," 35.37" 182 | "BHEL","EQ","09-Apr-2019"," 72.60"," 73.00"," 73.15"," 71.10"," 72.00"," 71.65"," 71.62"," 17283821"," 1237935903.65"," 33534"," 7092497"," 41.04" 183 | "BHEL","EQ","10-Apr-2019"," 71.65"," 71.75"," 73.30"," 71.70"," 72.00"," 72.20"," 72.46"," 10842680"," 785672853.15"," 20758"," 3331964"," 30.73" 184 | "BHEL","EQ","11-Apr-2019"," 72.20"," 72.10"," 74.80"," 71.30"," 74.75"," 74.35"," 73.36"," 24219236"," 1776799183.20"," 61538"," 11562001"," 47.74" 185 | "BHEL","EQ","12-Apr-2019"," 74.35"," 74.70"," 78.30"," 74.50"," 77.60"," 77.70"," 76.76"," 43815259"," 3363228660.30"," 89161"," 18791174"," 42.89" 186 | "BHEL","EQ","15-Apr-2019"," 77.70"," 78.10"," 78.25"," 76.65"," 77.40"," 77.30"," 77.26"," 11053354"," 854032100.65"," 24734"," 3191661"," 28.88" 187 | "BHEL","EQ","16-Apr-2019"," 77.30"," 77.50"," 78.85"," 76.20"," 77.05"," 76.70"," 77.68"," 11087979"," 861291422.45"," 28557"," 2163822"," 19.52" 188 | "BHEL","EQ","18-Apr-2019"," 76.70"," 78.00"," 78.00"," 75.75"," 75.90"," 76.05"," 76.56"," 7161272"," 548285380.20"," 17059"," 867766"," 12.12" 189 | "BHEL","EQ","22-Apr-2019"," 76.05"," 75.85"," 75.85"," 73.00"," 73.40"," 73.15"," 73.88"," 6499049"," 480125414.10"," 16678"," 1397918"," 21.51" 190 | "BHEL","EQ","23-Apr-2019"," 73.15"," 73.00"," 74.55"," 72.80"," 73.75"," 73.50"," 73.87"," 6018400"," 444596628.05"," 16425"," 786346"," 13.07" 191 | "BHEL","EQ","24-Apr-2019"," 73.50"," 73.40"," 74.35"," 72.90"," 73.40"," 73.45"," 73.40"," 7705263"," 565531686.55"," 13837"," 3269285"," 42.43" 192 | "BHEL","EQ","25-Apr-2019"," 73.45"," 73.85"," 75.15"," 71.70"," 72.15"," 72.25"," 73.20"," 17295969"," 1265993949.55"," 35015"," 7690524"," 44.46" 193 | "BHEL","EQ","26-Apr-2019"," 72.25"," 72.35"," 73.20"," 70.55"," 71.20"," 71.25"," 71.83"," 9643069"," 692623255.55"," 22332"," 2488117"," 25.80" 194 | "BHEL","EQ","30-Apr-2019"," 71.25"," 70.55"," 72.20"," 69.40"," 70.65"," 70.80"," 70.62"," 14315500"," 1011025761.30"," 42470"," 4549733"," 31.78" 195 | "BHEL","EQ","02-May-2019"," 70.80"," 70.95"," 71.65"," 69.35"," 69.60"," 69.60"," 70.46"," 10325875"," 727531745.35"," 27725"," 3298445"," 31.94" 196 | "BHEL","EQ","03-May-2019"," 69.60"," 69.70"," 70.60"," 68.40"," 68.50"," 68.65"," 69.43"," 8390333"," 582572051.80"," 25626"," 2514899"," 29.97" 197 | "BHEL","EQ","06-May-2019"," 68.65"," 68.35"," 68.35"," 65.65"," 65.75"," 65.90"," 66.81"," 9020087"," 602591756.50"," 26115"," 2772828"," 30.74" 198 | "BHEL","EQ","07-May-2019"," 65.90"," 66.10"," 66.70"," 63.35"," 63.80"," 63.80"," 65.13"," 8647726"," 563217905.25"," 22296"," 1888590"," 21.84" 199 | "BHEL","EQ","08-May-2019"," 63.80"," 63.90"," 63.90"," 61.55"," 61.85"," 61.75"," 62.70"," 15303026"," 959553648.95"," 37103"," 3341451"," 21.84" 200 | "BHEL","EQ","09-May-2019"," 61.75"," 61.80"," 62.85"," 61.55"," 62.70"," 62.45"," 62.31"," 13936931"," 868423796.75"," 25374"," 4887663"," 35.07" 201 | "BHEL","EQ","10-May-2019"," 62.45"," 62.70"," 63.50"," 61.85"," 62.90"," 63.15"," 62.78"," 11398050"," 715566537.50"," 19967"," 2585535"," 22.68" 202 | "BHEL","EQ","13-May-2019"," 63.15"," 62.60"," 63.00"," 59.20"," 60.10"," 59.85"," 61.18"," 10847294"," 663651682.00"," 26873"," 3041674"," 28.04" 203 | "BHEL","EQ","14-May-2019"," 59.85"," 60.25"," 64.50"," 59.80"," 64.05"," 63.95"," 62.94"," 25941657"," 1632885475.75"," 54208"," 8255335"," 31.82" 204 | "BHEL","EQ","15-May-2019"," 63.95"," 63.85"," 64.30"," 60.25"," 60.95"," 60.60"," 62.40"," 11735485"," 732290465.15"," 33877"," 3021010"," 25.74" 205 | "BHEL","EQ","16-May-2019"," 60.60"," 60.60"," 62.70"," 60.50"," 62.00"," 62.20"," 61.72"," 13662204"," 843198243.05"," 33203"," 3845124"," 28.14" 206 | "BHEL","EQ","17-May-2019"," 62.20"," 62.00"," 62.65"," 61.20"," 62.00"," 62.10"," 61.94"," 8777033"," 543636178.25"," 21984"," 2457317"," 28.00" 207 | "BHEL","EQ","20-May-2019"," 62.10"," 64.50"," 66.75"," 63.95"," 65.75"," 65.95"," 65.68"," 15063392"," 989320470.55"," 36948"," 4640922"," 30.81" 208 | "BHEL","EQ","21-May-2019"," 65.95"," 65.95"," 66.15"," 62.85"," 63.35"," 63.25"," 64.49"," 10096863"," 651159376.55"," 24270"," 2474284"," 24.51" 209 | "BHEL","EQ","22-May-2019"," 63.25"," 63.60"," 65.60"," 62.15"," 65.35"," 65.25"," 63.99"," 12714321"," 813534866.55"," 27783"," 2721707"," 21.41" 210 | "BHEL","EQ","23-May-2019"," 65.25"," 67.20"," 70.25"," 66.00"," 66.65"," 66.55"," 67.70"," 22390610"," 1515764859.55"," 49480"," 6617978"," 29.56" 211 | "BHEL","EQ","24-May-2019"," 66.55"," 66.80"," 69.45"," 66.75"," 69.10"," 69.00"," 68.50"," 10037711"," 687609668.85"," 25643"," 2099844"," 20.92" 212 | "BHEL","EQ","27-May-2019"," 69.00"," 69.10"," 73.25"," 68.65"," 72.70"," 72.70"," 71.01"," 35842146"," 2545224840.05"," 85986"," 5518156"," 15.40" 213 | "BHEL","EQ","28-May-2019"," 72.70"," 73.50"," 73.70"," 71.00"," 72.40"," 72.60"," 72.26"," 21729849"," 1570267207.65"," 53729"," 6488244"," 29.86" 214 | "BHEL","EQ","29-May-2019"," 72.60"," 72.40"," 72.40"," 70.45"," 71.00"," 70.95"," 71.30"," 8006184"," 570825866.50"," 20771"," 1461862"," 18.26" 215 | "BHEL","EQ","30-May-2019"," 70.95"," 71.20"," 72.15"," 70.60"," 71.70"," 71.65"," 71.36"," 9835370"," 701810630.75"," 24913"," 3355109"," 34.11" 216 | "BHEL","EQ","31-May-2019"," 71.65"," 71.85"," 71.90"," 68.85"," 70.10"," 70.20"," 70.16"," 11824619"," 829636143.30"," 29686"," 2134759"," 18.05" 217 | "BHEL","EQ","03-Jun-2019"," 70.20"," 69.80"," 72.25"," 69.55"," 71.70"," 71.55"," 71.20"," 11879171"," 845756014.60"," 34257"," 3195802"," 26.90" 218 | "BHEL","EQ","04-Jun-2019"," 71.55"," 71.90"," 73.00"," 70.70"," 70.90"," 70.90"," 71.92"," 15631724"," 1124287444.70"," 33264"," 3820937"," 24.44" 219 | "BHEL","EQ","06-Jun-2019"," 70.90"," 71.00"," 71.80"," 67.70"," 68.60"," 68.55"," 69.51"," 14182515"," 985876585.60"," 34586"," 3107479"," 21.91" 220 | "BHEL","EQ","07-Jun-2019"," 68.55"," 68.50"," 70.50"," 67.35"," 68.55"," 68.50"," 68.86"," 14654515"," 1009049525.85"," 33169"," 3557471"," 24.28" 221 | "BHEL","EQ","10-Jun-2019"," 68.50"," 68.75"," 69.65"," 67.85"," 68.30"," 68.20"," 68.63"," 8628229"," 592121790.10"," 23699"," 1966333"," 22.79" 222 | "BHEL","EQ","11-Jun-2019"," 68.20"," 68.30"," 70.15"," 67.75"," 69.95"," 69.70"," 69.32"," 10633415"," 737087816.55"," 29016"," 3109212"," 29.24" 223 | "BHEL","EQ","12-Jun-2019"," 69.70"," 69.95"," 70.65"," 67.95"," 68.20"," 68.20"," 69.13"," 11129594"," 769393546.25"," 31679"," 2714538"," 24.39" 224 | "BHEL","EQ","13-Jun-2019"," 68.20"," 68.20"," 69.25"," 67.80"," 68.75"," 68.70"," 68.61"," 9467662"," 649610187.25"," 29465"," 2534162"," 26.77" 225 | "BHEL","EQ","14-Jun-2019"," 68.70"," 69.95"," 71.00"," 68.30"," 68.50"," 68.65"," 69.72"," 17332932"," 1208415876.15"," 45795"," 3204910"," 18.49" 226 | "BHEL","EQ","17-Jun-2019"," 68.65"," 69.55"," 69.95"," 67.40"," 67.80"," 67.65"," 68.60"," 14424170"," 989491874.10"," 40910"," 3488800"," 22.62" 227 | "BHEL","EQ","18-Jun-2019"," 67.65"," 68.00"," 69.10"," 67.05"," 68.90"," 68.60"," 68.01"," 10325816"," 702285761.15"," 34294"," 3029613"," 28.07" 228 | "BHEL","EQ","19-Jun-2019"," 68.60"," 69.00"," 69.45"," 66.40"," 67.70"," 67.65"," 68.12"," 11533322"," 785613777.10"," 26988"," 2924194"," 25.35" 229 | "BHEL","EQ","20-Jun-2019"," 67.65"," 67.75"," 71.05"," 67.20"," 70.95"," 70.80"," 69.43"," 13970908"," 969980509.00"," 33564"," 4123643"," 29.52" 230 | "BHEL","EQ","21-Jun-2019"," 70.80"," 70.95"," 71.85"," 70.05"," 71.25"," 71.40"," 71.20"," 11601602"," 826073637.45"," 35906"," 3378633"," 29.12" 231 | "BHEL","EQ","24-Jun-2019"," 71.40"," 73.00"," 74.00"," 72.00"," 73.80"," 73.70"," 73.01"," 18372701"," 1341375351.70"," 52449"," 5401569"," 29.40" 232 | "BHEL","EQ","25-Jun-2019"," 73.70"," 73.30"," 74.25"," 72.30"," 74.00"," 73.90"," 73.21"," 8306005"," 608081357.05"," 26455"," 1639283"," 19.74" 233 | "BHEL","EQ","26-Jun-2019"," 73.90"," 73.50"," 75.50"," 73.40"," 75.20"," 75.20"," 74.72"," 16456718"," 1229587125.85"," 48957"," 4243830"," 25.79" 234 | "BHEL","EQ","27-Jun-2019"," 75.20"," 75.05"," 75.50"," 74.35"," 74.65"," 74.70"," 74.94"," 10529610"," 789137735.00"," 35042"," 3715951"," 35.29" 235 | "BHEL","EQ","28-Jun-2019"," 74.70"," 74.40"," 74.55"," 72.55"," 73.50"," 73.15"," 73.59"," 7498136"," 551791933.20"," 19297"," 1712449"," 22.84" 236 | "BHEL","EQ","01-Jul-2019"," 73.15"," 73.40"," 74.25"," 71.70"," 73.00"," 73.05"," 72.93"," 6618565"," 482667680.40"," 18577"," 908595"," 13.73" 237 | "BHEL","EQ","02-Jul-2019"," 73.05"," 73.10"," 73.30"," 71.55"," 71.90"," 72.05"," 72.10"," 5825625"," 420004833.40"," 14515"," 954662"," 16.39" 238 | "BHEL","EQ","03-Jul-2019"," 72.05"," 72.00"," 73.75"," 71.75"," 73.05"," 72.95"," 72.75"," 9271047"," 674438633.55"," 23606"," 2253343"," 24.31" 239 | "BHEL","EQ","04-Jul-2019"," 72.95"," 73.15"," 73.65"," 72.45"," 73.15"," 73.10"," 73.17"," 5004601"," 366193682.45"," 13484"," 589199"," 11.77" 240 | "BHEL","EQ","05-Jul-2019"," 73.10"," 73.45"," 73.50"," 69.75"," 70.20"," 70.15"," 71.93"," 10632584"," 764773281.70"," 22078"," 3002085"," 28.23" 241 | "BHEL","EQ","08-Jul-2019"," 70.15"," 69.50"," 70.35"," 66.60"," 67.15"," 67.30"," 68.08"," 9496461"," 646479564.50"," 35457"," 2165266"," 22.80" 242 | "BHEL","EQ","09-Jul-2019"," 67.30"," 67.15"," 67.75"," 65.85"," 67.15"," 67.20"," 66.78"," 8178805"," 546192048.20"," 25547"," 1110985"," 13.58" 243 | "BHEL","EQ","10-Jul-2019"," 67.20"," 67.95"," 68.20"," 66.20"," 66.55"," 66.75"," 66.96"," 7966262"," 533440125.00"," 22506"," 1288423"," 16.17" 244 | "BHEL","EQ","11-Jul-2019"," 66.75"," 67.00"," 67.15"," 64.85"," 64.95"," 65.05"," 65.38"," 20836630"," 1362329155.55"," 31475"," 12592940"," 60.44" 245 | "BHEL","EQ","12-Jul-2019"," 65.05"," 65.00"," 65.60"," 64.40"," 64.80"," 64.80"," 65.04"," 10910933"," 709642896.45"," 22931"," 4075662"," 37.35" 246 | "BHEL","EQ","15-Jul-2019"," 64.80"," 65.30"," 65.55"," 63.20"," 64.25"," 64.25"," 64.08"," 13043104"," 835833052.15"," 29191"," 3084521"," 23.65" 247 | --------------------------------------------------------------------------------