Global Statistics

Let's see what is available in numpy library in order to do statistics on an n-dimensional array. First get CSV files for AAPL, GOOG, IBM and GLD, from this page: https://finance.yahoo.com/quote/AAPL/history?p=AAPL.

Plot and calculate mean, std etc...

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

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

def get_data(symbols, dates):
    df = pd.DataFrame(index=dates)
    if 'SPY' not in symbols:
        symbols.insert(0, 'SPY')

    for symbol in symbols:
        path = symbol_to_path(symbol)
        newDf = pd.read_csv(path,
                        index_col='Date',
                        parse_dates=True,
                        usecols=['Date', 'Adj Close'],
                        na_values=['nan'])
        newDf = newDf.rename(columns={'Adj Close': symbol})
        df = df.join(newDf)
        df = df.dropna()

    return df

def plot_data(df):
    df.plot()
    plt.show()

def test_run():
    # Define a date range
    dates = pd.date_range('2010-01-22', '2012-01-26')

    # Choose stock symbols to read
    symbols = ['GOOG', 'IBM', 'GLD', 'AAPL']

    # Get stock data
    df = get_data(symbols, dates)

    print "Mean:"
    print df.mean()

    print "Median:"
    print df.median()

    print "Standard deviation:"
    print df.std()

    print "Sum:"
    print df.sum()

    plot_data(df)


if __name__ == "__main__":
    test_run()

Here is the output. First lets have a look at the image, so we have better understanding of what the data look like.

Now we can check calculated numbers. Check standard deviation, the highest number is for Google, because their stocks are going up and down by a lot. Standard deviation is measure of deviation from the center, mean, of the data.

Mean:
SPY     106.943554
GOOG    276.460087
IBM     132.548075
GLD     138.086496
AAPL     41.414315
dtype: float64

Median:
SPY     107.265254
GOOG    274.101372
IBM     134.560650
GLD     135.410004
AAPL     43.072091
dtype: float64

Standard deviation:
SPY      8.521511
GOOG    25.374959
IBM     20.361991
GLD     20.455301
AAPL     8.075417
dtype: float64

Sum:
SPY      54327.325496
GOOG    140441.724183
IBM      67334.422350
GLD      70147.940030
AAPL     21038.471976
dtype: float64