[Python] Statistical analysis of time series

import os
import pandas as pd
import matplotlib.pyplot as plt

def test_run():
    start_date=‘2017-01-01‘
    end_data=‘2017-12-15‘
    dates=pd.date_range(start_date, end_data)

    # Create an empty data frame
    df=pd.DataFrame(index=dates)

    symbols=[‘SPY‘, ‘AAPL‘, ‘IBM‘, ‘GOOG‘, ‘GLD‘]
    for symbol in symbols:
        temp=getAdjCloseForSymbol(symbol)
        df=df.join(temp, how=‘inner‘)

    return df  

if __name__ == ‘__main__‘:
    df=test_run()
    # data=data.ix[‘2017-12-01‘:‘2017-12-15‘, [‘IBM‘, ‘GOOG‘]]    
    # df=normalize_data(df)
    ax = df[‘SPY‘].plot(title="SPY rolling mean", label=‘SPY‘)
    rm = df[‘SPY‘].rolling(20).mean()
    rm.plot(label=‘Rolling mean‘, ax=ax) 
    ax.set_xlabel(‘Date‘)
    ax.set_ylabel(‘Price‘)
    ax.legend(loc="upper left")
    plt.show()

"""Bollinger Bands."""

import os
import pandas as pd
import matplotlib.pyplot as plt

def symbol_to_path(symbol, base_dir="data"):
    """Return CSV file path given ticker symbol."""
    return os.path.join(base_dir, "{}.csv".format(str(symbol)))


def get_data(symbols, dates):
    """Read stock data (adjusted close) for given symbols from CSV files."""
    df = pd.DataFrame(index=dates)
    if ‘SPY‘ not in symbols:  # add SPY for reference, if absent
        symbols.insert(0, ‘SPY‘)

    for symbol in symbols:
        df_temp = pd.read_csv(symbol_to_path(symbol), index_col=‘Date‘,
                parse_dates=True, usecols=[‘Date‘, ‘Adj Close‘], na_values=[‘nan‘])
        df_temp = df_temp.rename(columns={‘Adj Close‘: symbol})
        df = df.join(df_temp)
        if symbol == ‘SPY‘:  # drop dates SPY did not trade
            df = df.dropna(subset=["SPY"])

    return df


def plot_data(df, title="Stock prices"):
    """Plot stock prices with a custom title and meaningful axis labels."""
    ax = df.plot(title=title, fontsize=12)
    ax.set_xlabel("Date")
    ax.set_ylabel("Price")
    plt.show()


def get_rolling_mean(values, window):
    """Return rolling mean of given values, using specified window size."""
    return values.rolling(window=window).mean()


def get_rolling_std(values, window):
    """Return rolling standard deviation of given values, using specified window size."""
    # TODO: Compute and return rolling standard deviation
    return values.rolling(window=window).std()


def get_bollinger_bands(rm, rstd):
    """Return upper and lower Bollinger Bands."""
    # TODO: Compute upper_band and lower_band
    upper_band = rstd * 2 + rm
    lower_band =  rm - rstd * 2
    return upper_band, lower_band


def test_run():
    # Read data
    dates = pd.date_range(‘2012-01-01‘, ‘2012-12-31‘)
    symbols = [‘SPY‘]
    df = get_data(symbols, dates)

    # Compute Bollinger Bands
    # 1. Compute rolling mean
    rm_SPY = get_rolling_mean(df[‘SPY‘], window=20)

    # 2. Compute rolling standard deviation
    rstd_SPY = get_rolling_std(df[‘SPY‘], window=20)

    # 3. Compute upper and lower bands
    upper_band, lower_band = get_bollinger_bands(rm_SPY, rstd_SPY)
    
    # Plot raw SPY values, rolling mean and Bollinger Bands
    ax = df[‘SPY‘].plot(title="Bollinger Bands", label=‘SPY‘)
    rm_SPY.plot(label=‘Rolling mean‘, ax=ax)
    upper_band.plot(label=‘upper band‘, ax=ax)
    lower_band.plot(label=‘lower band‘, ax=ax)

    # Add axis labels and legend
    ax.set_xlabel("Date")
    ax.set_ylabel("Price")
    ax.legend(loc=‘upper left‘)
    plt.show()


if __name__ == "__main__":
    test_run()