It is going to focus on the global statistic calculations on the DataFrame
global statistics
- pd.mean()
- pd.std()
- pd.median()
- pd.sum()
%matplotlib inline
import os
import pandas as pd
import numpy as np
def symbol_to_path(symbol, base_dir="stock/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:
# TODO: Read and join data for each symbol
df1 = pd.read_csv(symbol_to_path(symbol),index_col='Date', usecols=['Date','Adj Close'],parse_dates=True,na_values=['nan'])
df1=df1.rename(columns={'Adj Close':symbol})
df = df.join(df1)
if symbol=='SPY':
df=df.dropna()
return df
def test_run1():
# Define a date range
dates = pd.date_range('2010-01-01', '2010-01-10')
# Choose stock symbols to read
symbols = ['GOOG', 'IBM', 'AAPL']
# Get stock data
df = get_data(symbols, dates)
print df
print "the mean value is:\n%s" % df.mean()
print "the medim value is:\n %s" % df.median()
print "the deviation value is:\n %s \n and sum is:\n %s" % (df.std(), df.sum())
return
test_run()
then, we come to the output:
SPY GOOG IBM AAPL
2010-01-04 98.793254 313.062468 112.285875 27.847252
2010-01-05 99.054769 311.683844 110.929466 27.895396
2010-01-06 99.124509 303.826685 110.208865 27.451683
2010-01-07 99.542943 296.753749 109.827375 27.400936
2010-01-08 99.874198 300.709808 110.929466 27.583106
the mean value is:
SPY 99.277935
GOOG 305.207311
IBM 110.836209
AAPL 27.635675
dtype: float64
the medim value is:
SPY 99.124509
GOOG 303.826685
IBM 110.929466
AAPL 27.583106
dtype: float64
the deviation value is:
SPY 0.428374
GOOG 7.022203
IBM 0.939446
AAPL 0.225798
dtype: float64
and sum is:
SPY 496.389673
GOOG 1526.036554
IBM 554.181047
AAPL 138.178373
dtype: float64
rolling statistics
choose a time sequence like 20 days, then we calculate its mean and deviation; Next, we step one day forward and calcuate the mean and deviation of the new 20 days again.
-
Series.rolling(window=20).mean()
Get the mean value of the past 20 days of the price.
-
Series.rolling(window=20).std()
Get the standard deviation of the past 20 days of the price.
"""Bollinger Bands."""
import matplotlib.pyplot as plt
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).std()
def get_bollinger_bands(rm, rstd):
"""Return upper and lower Bollinger Bands."""
# TODO: Compute upper_band and lower_band
upper_band = rm + 2 * rstd
lower_band = rm - 2 * rstd
return upper_band, lower_band
def test_run2():
# 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()
test_run2()
