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        "author": "Dayal Chand Aichara",
        "author_email": "dc.aichara@gmail.com",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 4 - Beta",
            "Intended Audience :: Education",
            "License :: OSI Approved :: MIT License",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.3",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6",
            "Programming Language :: Python :: 3.7",
            "Topic :: Software Development :: Build Tools"
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        "description": "\n\n\n## Installation \n\n### pip \n\n```\npip install PriceIndics\n```\n\n### From Source (Github)\n\n git clone https://github.com/dc-aichara/Price-Indices.git\n\n cd Price-Indices \n\n python3 setup.py install\n\n## Usages \n\n```python\nfrom PriceIndices import MarketHistory, Indices\n\n```\n## Examples \n\n- #### Get market history and closing price\n\n```python\n>>> history = MarketHistory()\n\n# Get Market History \n\n>>> df_history = history.get_history('bitcoin', '20130428', '20190624')  \n>>> df_history.head()\n        Date     Open*      High       Low   Close**       Volume    Market Cap\n0 2019-06-23  10696.69  11246.14  10556.10  10855.37  20998326502  192970090355\n1 2019-06-22  10175.92  11157.35  10107.04  10701.69  29995204861  190214124824\n2 2019-06-21   9525.07  10144.56   9525.07  10144.56  20624008643  180293241528\n3 2019-06-20   9273.06   9594.42   9232.48   9527.16  17846823784  169304784791\n4 2019-06-19   9078.73   9299.62   9070.40   9273.52  15546809946  164780855869\n\n# Get closing price\n\n>>> price_data  =  history.get_price('bitcoin', '20130428', '20190624')  \n\n>>> price_data .head()\n        date     price\n0 2019-06-23  10855.37\n1 2019-06-22  10701.69\n2 2019-06-21  10144.56\n3 2019-06-20   9527.16\n4 2019-06-19   9273.52\n\n```\n\n- #### Calculate Volatility Index\n\n```python\n\n\n>>> df_bvol = Indices.get_bvol_index(price_data )  \n>>> df_bvol.head()\n        date     price  BVOL_Index\n0 2019-06-22  10701.69    0.636482\n1 2019-06-21  10144.56    0.636414\n2 2019-06-20   9527.16    0.619886\n3 2019-06-19   9273.52    0.608403\n4 2019-06-18   9081.76    0.604174\n\n```\n\n- #### Plot Volatility Index\n\n```python\n>>> Indices.get_bvol_graph(df_bvol)   \n\n\"\"\"\nThis will return a plot of BVOL index against time also save volatility index plot in your working directory as 'bvol_index.png'\n\"\"\"\n```\n\n<img src= 'plots/bvol_index.png' >\n\n- #### Calculate Relative Strength Index (RSI)\n\n```python\n\n>>> df_rsi = Indices.get_rsi(price_data)   \n\n>>> print(df_rsi.tail())\n           date   price  price_change   gain   loss  gain_average  loss_average        RS      RSI_1  RS_Smooth      RSI_2\n2217 2013-05-02  105.21          7.46   7.46   0.00      1.532143      2.500000  0.612857  37.998229   0.561117  35.943306\n2218 2013-05-01  116.99         11.78  11.78   0.00      2.373571      2.175714  1.090939  52.174596   0.975319  49.375257\n2219 2013-04-30  139.00         22.01  22.01   0.00      3.945714      1.981429  1.991348  66.570258   1.869110  65.145981\n2220 2013-04-29  144.54          5.54   5.54   0.00      3.878571      1.981429  1.957462  66.187226   2.206422  68.812592\n2221 2013-04-28  134.21        -10.33   0.00  10.33      3.878571      2.506429  1.547449  60.745050   1.397158  58.283931\n\n```\n\n- #### Plot RSI\n\n```python\n>>> Indices.get_rsi_graph(df_rsi)  \n\n\"\"\"\nThis will return a plot of RSI against time and also save RSI plot in your working directory as 'rsi.png'\n\"\"\"\n```\n\n<img src='plots/rsi.png' >\n\n- #### Get Bollinger Bands and its plot\n\n```python\n>>> df_bb = Indices.get_bollinger_bands(price_data , 20) \n>>> df_bb.tail()\n           date   price       SMA         SD       pluse     minus\n2243 2013-05-02  105.21  115.2345   6.339257  127.913013 -115.2345\n2244 2013-05-01  116.99  114.9400   6.097587  127.135174 -114.9400\n2245 2013-04-30  139.00  115.7900   8.016499  131.822998 -115.7900\n2246 2013-04-29  144.54  116.9175  10.217936  137.353372 -116.9175\n2247 2013-04-28  134.21  117.4530  10.842616  139.138233 -117.4530\n\n\"\"\"\nThis will also save Bollingers bands plot in your working directory as 'bollinger_bands.png'\n\"\"\"\n\n```\n\n\n<img src='plots/bollinger_bands.png'>\n\n\n- #### Get Moving Average Convergence Divergence (MACD) and its plot\n\n```python\n\n>>> df_macd = Indices.get_moving_average_convergence_divergence(price_data)\n\"\"\"This will return a pandas DataFrame and save EMA plot as 'macd.png' in working directory. \n\"\"\"\"\n>>> df_macd.head()\n         date    price      EMA_12        EMA_26        MACD\n19 2019-06-18  9081.76    10415.979340  10886.327599 -470.348259\n20 2019-06-17  9320.35    10247.420980  10770.329259 -522.908279\n21 2019-06-16  8994.49    10054.662368  10638.785610 -584.123242\n22 2019-06-15  8838.38    9867.542004   10505.422231 -637.880228\n23 2019-06-14  8693.83    9686.970926   10371.230214 -684.259288\n\n```\n\n<img src='plots/macd.png'>\n\n- #### Get Simple Moving Average (SMA) and its plot\n\n```python\n>>> df_sma = Indices.get_simple_moving_average(price_data, 20) \n\"\"\"This will return a pandas DataFrame and save EMA plot as 'sma.png' in working directory. \n\"\"\"\"\n>>> df_sma.head()\n         date    price         SMA\n19 2019-06-18  9081.76  10998.4180\n20 2019-06-17  9320.35  10891.8930\n21 2019-06-16  8994.49  10781.1900\n22 2019-06-15  8838.38  10674.1860\n23 2019-06-14  8693.83  10548.1055\n\n```\n\n<img src='plots/sma.png'>\n\n- ### Get Exponential Moving Average (EMA) and its plot\n\n```python\n>>> df_ema = Indices.get_exponential_moving_average(price_data, [20,70])\n\"\"\"This will return a pandas DataFrame and save EMA plot as 'ema.png' in working directory. \n\"\"\"\"\n\n>>> df_ema.head()\n        date     price        EMA_20        EMA_70\n0 2019-07-07  11450.85  11450.850000  11450.850000\n1 2019-07-06  11208.55  11427.773810  11444.024648\n2 2019-07-05  10978.46  11384.982018  11430.910151\n3 2019-07-04  11215.44  11368.835159  11424.840569\n4 2019-07-03  11961.27  11425.257525  11439.951257\n\n```\n\n<img src='plots/ema.png' >\n\n### License\n\n[MIT](https://choosealicense.com/licenses/mit/) \u00a9 [Dayal Chand Aichara](https://github.com/dc-aichara)\n\n\n### Check out [webpage](https://github.com/dc-aichara/PriceIndices) of this PriceIndices package. \n\n### Disclaimer: \n\n```\nAll content provided here, is for educational purpose and your general information only, procured  from third party sources.\nI make no warranties of any kind in relation to this content, including but  not limited to accuracy\nand updatedness. No part of the content that I provide  constitutes  financial  advice, legal advice \nor any other form of advice meant for your specific reliance for any purpose. Any use or reliance on\nmy content is solely at your own risk  and  discretion. You should conduct your own research, review, \nanalyse and  verify my content  before relying  on them. Trading is a highly risky activity that can \nlead to  major  losses, please  therefore  consult your financial advisor before making any decision.\nNo content on this Site is meant to be a solicitation or offer.\n```\n\n\n",
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