def main():
# Read Data
dates = pd.date_range('2009-01-01', '2012-12-31')
symbols = ['SPY']
df = get_data(symbols, dates)
plot_data(df)
# Compute Daily returns
daily_returns = compute_daily_returns(df)
plot_data(daily_returns, title="Daily Returns", y_label="Daily returns", )
# Plot histogram
daily_returns.hist(bins=20)
mean = daily_returns['SPY'].mean()
# print "mean: ", mean
std = daily_returns['SPY'].std()
# print "std: ", std
# plot the mean on the histogram
plt.axvline(mean, color="w", linestyle='dashed', linewidth=2)
plt.axvline(std, color="r", linestyle='dashed', linewidth=2)
plt.axvline(-std, color="r", linestyle='dashed', linewidth=2)
plt.show()
# Compute kurtosis
print daily_returns.kurtosis()
def main():
# Read SPY 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 using a 20 day window
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 SPY data, retain matplotlib axis object
ax = df['SPY'].plot(title="SPY Bollinger Bands", label="SPY")
# Add rolling mean to same plot
rm_SPY.plot(label="Rolling mean", ax=ax)
# Add the upper and lower bands to the plot
upper_band.plot(label="upper band", ax=ax)
lower_band.plot(label="lower band", ax=ax)
# Add legend
ax.legend(loc='upper left')
# Show the plot
plt.show()
def main():
# Read data
dates = pd.date_range("2009-01-01", "2012-12-31")
symbols = ['SPY', 'IBM', 'GLD']
df = get_data(symbols, dates)
# Compute daily returns
daily_returns = compute_daily_returns(df)
# Scatterplot SPY vs IBM
daily_returns.plot(kind="scatter", x="SPY", y="IBM")
beta_IBM, alpha_IBM = np.polyfit(daily_returns['SPY'], daily_returns['IBM'], 1)
print "beta_IBM: ", beta_IBM
print "alpha_IBM: ", alpha_IBM
# for every point calculate the line: ma + b
plt.plot(daily_returns['SPY'], beta_IBM *daily_returns['SPY'] + alpha_IBM, "-", color="r")
plt.show()
# Scatterplot SPY vs GLD
daily_returns.plot(kind="scatter", x="SPY", y="GLD")
beta_GLD, alpha_GLD = np.polyfit(daily_returns['SPY'], daily_returns['GLD'], 1)
plt.plot(daily_returns['SPY'], beta_GLD *daily_returns['SPY'] + alpha_GLD, "-", color="r")
plt.show()
# Calculate correlation coefficient
print daily_returns.corr(method="pearson")
def main():
# Read a month of data
dates = pd.date_range('2012-07-01', '2012-07-31')
symbols = ['SPY', 'GLD']
df = get_data(symbols, dates)
plot_data(df)
# Compute daily returns
daily_returns = compute_daily_returns(df)
plot_data(daily_returns, title="Daily returns", y_label="Daily returns")
def main():
# Read Data
dates = pd.date_range('2009-01-01', '2012-12-31')
symbols = ['SPY', 'IBM']
df = get_data(symbols, dates)
# plot_data(df)
# Compute Daily returns
daily_returns = compute_daily_returns(df)
plot_data(daily_returns, title="Daily Returns", y_label="Daily returns")
daily_returns['SPY'].hist(bins=20, label="SPY")
daily_returns['IBM'].hist(bins=20, label="IBM")
plt.legend(loc="upper right")
plt.show()