Histogram

The histogram is created from this data that you can see on this chart below.

Then we transformed data into daily returns.

Now, we can take daily returns and transform it to histogram. We also add mean and standard deviation lines.

Here is the code we used to generate the three charts above.

import pandas as pd
import matplotlib.pyplot as plt
from util import get_data, plot_data, compute_daily_returns

def test_run():
    dates = pd.date_range('2009-01-01', '2012-12-31')
    symbols = ['SPY']
    df = get_data(symbols, dates)
    plot_data(df)

    daily_returns = compute_daily_returns(df)
    plot_data(daily_returns, title="Daily returns", ylabel="Daily returns")

    daily_returns.hist(bins=20, color='c')

    mean = daily_returns['SPY'].mean()
    std = daily_returns['SPY'].std()
    kurtosis = daily_returns.kurtosis()
    print kurtosis  # if positive, we got fat tails, if negative, tails are skinny

    plt.axvline(mean, color='b', linestyle='dashed', linewidth=2)
    plt.axvline(std, color='r', linestyle='dotted', linewidth=2)
    plt.axvline(-std, color='r', linestyle='dotted', linewidth=2)
    plt.show()

if __name__ == "__main__":
    test_run()

We have moved some of the utility code into util.py.

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

def compute_daily_returns(df):
    """Compute and return the daily return values."""
    daily_returns = (df / df.shift(1)) - 1
    daily_returns.ix[0, :] = 0
    return daily_returns