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"License :: OSI Approved :: MIT License",
"Operating System :: OS Independent",
"Programming Language :: Python :: 3",
"Programming Language :: Python :: 3.3",
"Programming Language :: Python :: 3.5",
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"description": "[](https://github.com/dc-aichara/PriceIndices)\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\", \"2020-03-16\", \"2021-03-15\") \n>>> df_history.head()\n open high low close volume market_cap date\n0 59267.429049 60540.992712 55393.165363 55907.200226 6.641937e+10 1.042946e+12 2021-03-15\n1 61221.134297 61597.918396 59302.316977 59302.316977 4.390123e+10 1.106226e+12 2021-03-14\n2 57343.370247 61683.864014 56217.972382 61243.084766 6.066983e+10 1.142369e+12 2021-03-13\n3 57821.218747 57996.619490 55376.650088 57332.088964 5.568994e+10 1.069366e+12 2021-03-12\n4 55963.180089 58091.062703 54484.593089 57805.123019 5.677234e+10 1.078136e+12 2021-03-11\n\n# Get closing price\n\n>>> price_data = history.get_price(\"bitcoin\", \"2020-03-16\", \"2021-03-15\") \n\n>>> price_data.head()\n date price\n0 2021-03-15 55907.200226\n1 2021-03-14 59302.316977\n2 2021-03-13 61243.084766\n3 2021-03-12 57332.088964\n4 2021-03-11 57805.123019\n\n```\n\n- #### Calculate Volatility Index\n\n```python\nindices = Indices(df=price_data)\n>>> df_bvol = indices.get_vola_index() \n>>> df_bvol.head()\n date price BVOL_Index\n0 2019-10-29 9427.69 0.711107\n1 2019-10-28 9256.15 0.707269\n2 2019-10-27 9551.71 0.709765\n3 2019-10-26 9244.97 0.698544\n4 2019-10-25 8660.70 0.692656\n\n```\n\n- #### Plot Volatility Index\n\n```python\n>>> indices.get_vola_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
\n\n- #### Calculate Relative Strength Index (RSI)\n\n```python\n\n>>> df_rsi = indices.get_rsi() \n\n>>> print(df_rsi.head())\n date price RSI_1 RS_Smooth RSI_2\n0 2019-10-30 9205.73 64.641855 1.624958 61.904151\n1 2019-10-29 9427.69 65.707097 1.709072 63.086984\n2 2019-10-28 9256.15 61.333433 1.597755 61.505224\n3 2019-10-27 9551.71 66.873327 2.012345 66.803267\n4 2019-10-26 9244.97 63.535368 1.791208 64.173219\n\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
\n\n- #### Get Bollinger Bands and its plot\n\n```python\n>>> df_bb = indices.get_bollinger_bands(days=20, plot=True) \n>>> df_bb.head()\n date price BB_upper BB_lower\n0 2019-10-30 9205.73 9635.043581 -8428.5855\n1 2019-10-29 9427.69 9550.707153 -8397.6225\n2 2019-10-28 9256.15 9408.263164 -8356.0250\n3 2019-10-27 9551.71 9268.466516 -8304.6565\n4 2019-10-26 9244.97 9003.752779 -8239.3520\n\n\n\"\"\"\nThis will also save Bollingers bands plot in your working directory as 'bollinger_bands.png'\n\"\"\"\n\n```\n\n
\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(plot=True)\n\"\"\"\nThis will return a pandas DataFrame and save EMA plot as 'macd.png' in working directory. \n\"\"\"\"\n>>> df_macd.head()\n date price MACD\n0 2019-10-30 9205.73 0.000000\n1 2019-10-29 9427.69 17.706211\n2 2019-10-28 9256.15 17.692715\n3 2019-10-27 9551.71 41.057952\n4 2019-10-26 9244.97 34.426864\n\n\n```\n\n
\n\n- #### Get Simple Moving Average (SMA) and its plot\n\n```python\n>>> df_sma = indices.get_simple_moving_average(days=20, plot=True) \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\n0 2019-10-30 9205.73 8467.488000\n1 2019-10-29 9427.69 8400.797333\n2 2019-10-28 9256.15 8330.597333\n3 2019-10-27 9551.71 8268.254667\n4 2019-10-26 9244.97 8187.244667\n\n\n```\n\n
\n\n- ### Get Exponential Moving Average (EMA) and its plot\n\n```python\n>>> df_ema = indices.get_exponential_moving_average(periods=[20,70], plot=True)\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 2019-10-30 9205.73 9205.730000 9205.730000\n1 2019-10-29 9427.69 9226.869048 9211.982394\n2 2019-10-28 9256.15 9229.657710 9213.226552\n3 2019-10-27 9551.71 9260.329356 9222.761297\n4 2019-10-26 9244.97 9258.866561 9223.386895\n>>> \n\n\n```\n\n
\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://dc-aichara.github.io/PriceIndices/) of PriceIndices package. \n\n### I have created a [cryptocurrency technical indicators dashboard](https://crypto-indicators-dashboard.herokuapp.com/) which uses this library. \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```",
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