def var(p,c,ticker):
    from flask import Flask, request, render_template, jsonify
    import pandas.io.sql as sql
    import sqlite3
    import platform
    import datetime
    import numpy as np
    import pandas as pd
    import json
    import pygal
    import matplotlib.pyplot as plt
    from scipy.stats import norm
    from bokeh.charts import Histogram
    import plotly
    
    #from pandas.io.data import DataReader
    from pandas_datareader import wb, DataReader
    from sklearn.linear_model import LogisticRegression
    from sklearn.lda import LDA
    from sklearn.qda import QDA
    
    from bokeh.layouts import gridplot
    from bokeh.plotting import figure, show, output_file
    symbol = ticker
    def var_cov_var(P, c, mu, sigma):
        """
        Variance-Covariance calculation of daily Value-at-Risk
        using confidence level c, with mean of returns mu
        and standard deviation of returns sigma, on a portfolio
        of value P.
        """
        alpha = norm.ppf(1-c, mu, sigma)
        return P - P*(alpha + 1)

    #if __name__ == "__main__":
    historical_period_in_days = 365*7
    start = datetime.datetime.today() - datetime.timedelta(days=historical_period_in_days)
    end = datetime.datetime.today()

    citi = DataReader(symbol, "google", start, end)
    citi["rets"] = citi["Close"].pct_change()

    P = float(p)   # 1,000,000 USD
    c = float(c)  # 99% confidence interval
    mu = np.mean(citi["rets"])
    sigma = np.std(citi["rets"])

    varianza = var_cov_var(P, c, mu, sigma)
    #print "Value-at-Risk: $%0.2f" % varianza
    var1= {}
    var1['Value-at-Risk'] = "$%0.2f" % varianza
    return json.dumps(var1)
