Stock Trading IPython Notebook

from datetime import datetime
from __future__ import division
import pandas as pd    
from pandas import Series,DataFrame
from pandas.io.data import DataReader
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style('whitegrid')
%matplotlib inline

end = datetime.now()
start = datetime(end.year - 1,end.month,end.day)
s = DataReader("BABA",'yahoo',start,end)
#s.ix['2015-12-24']
s.head()

s['Adj Close'].plot(legend=True,figsize=(10,4))

s['Volume'].plot(legend=True,figsize=(10,4))

ma_day = [10,20,40]
for ma in ma_day:
    column = "MA for %s days" %(str(ma))
    s[column] = pd.rolling_mean(s['Adj Close'],ma)
s[['Adj Close','MA for 10 days','MA for 20 days','MA for 40 days']].plot(subplots=False,figsize=(10,4))

s['Daily Return']=s['Adj Close'].pct_change()
# print s['Daily Return']
s['Daily Return'].dropna().plot(figsize=(10,4),legend=True,linestyle='--',marker='s')
print s['Daily Return'].mean()
print s['Daily Return'].std()

sns.plt.xlim(-0.1,0.1)
sns.distplot(s['Daily Return'].dropna(),color='0.2',bins=40,
             kde_kws={"color": sns.xkcd_rgb["pale red"], "lw": 3, 
             "bw": 0.2,"alpha": .8})
# sns.kdeplot(s['Daily Return'].dropna())

close_df = DataReader(['BABA','EBAY','AMZN','PYPL'],'yahoo',start,end)['Adj Close']
close_df['AMZN'].plot()

pct_df = close_df.pct_change()
sns.jointplot('EBAY','PYPL',pct_df,kind='scatter',joint_kws={'alpha':0.6})

sns.pairplot(pct_df.dropna())

close_df1 = DataReader(['BABA','EBAY','AMZN','YHOO','WMT'],'yahoo',start,end)['Adj Close']
pct_df1 = close_df1.pct_change()
with sns.axes_style('darkgrid'):
    fig, ax = plt.subplots(figsize=(6, 6))
    sns.corrplot(pct_df1.dropna(),annot=True,
                 cmap=sns.diverging_palette(230, 20,sep=50, 
                 n=7,as_cmap=True))   

risk_df = pct_df1

plt.ylim([0.01,0.028])
plt.xlim([-0.005,0.01])
plt.xlabel('Expected returns')
plt.ylabel('Risk')

plt.scatter(risk_df.mean(),risk_df.std(),alpha=0.5,
            s=close_df1.dropna().mean(),label='Average Adj Close')
plt.legend(loc=1)

for label, x, y in zip(risk_df.columns, risk_df.mean(), risk_df.std()):
    plt.annotate(
        label, 
        xy = (x, y), xytext = (30, 10),
        textcoords = 'offset points', ha = 'left', va = 'bottom',
        bbox = dict(boxstyle = 'round,pad=0.3', fc = 'yellow', alpha = .5),
        arrowprops = dict(arrowstyle = '-', connectionstyle = 'arc3,
        rad=-0.1'))

risk_df['BABA'].quantile(0.05)

days = 365
dt = 1/days
mu = risk_df.mean()['BABA'] # drift of expected return
sigma = risk_df.std()['BABA'] # volatility of stock

def stock_monte_carlo(start_price,days,mu,sigma):
    price = np.zeros(days)
    price[0] = start_price
    shock = np.zeros(days)
    drift = np.zeros(days)
    
    for x in xrange(1,days):
        shock[x] = np.random.normal(loc=mu * dt, scale=sigma * np.sqrt(dt))
        drift[x] = mu * dt
        price[x] = price[x-1] + (price[x-1] * (drift[x] + shock[x]))
    return price

start_price = 103.94

for run in xrange(100):
    plt.plot(stock_monte_carlo(start_price,days,mu,sigma))
    
# plt.plot(s['Adj Close'])
plt.xlabel("Days")
plt.ylabel("Price")
plt.title('Alibaba based on Monte Carlo Simulation')

# Set a large numebr of runs
runs = 10000

# Create an empty matrix to hold the end price data
simulations = np.zeros(runs)

# Set the print options of numpy to only display 0-5 points from an array to suppress output
np.set_printoptions(threshold=5)

for run in xrange(runs):    
    # Set the simulation data point as the last stock price for that run
    simulations[run] = stock_monte_carlo(start_price,days,mu,sigma)[days-1];

# Now we'lll define q as the 1% empirical qunatile, this basically means that 99% of the values should fall between here
q = np.percentile(simulations, 1)
    
# Now let's plot the distribution of the end prices
plt.hist(simulations,bins=200)

# Using plt.figtext to fill in some additional information onto the plot

# Starting Price
plt.figtext(0.65, 0.8, "Start price: $%.2f" %start_price)
# Mean ending price
plt.figtext(0.65, 0.7, "Mean final: $%.2f" % simulations.mean())

# Variance of the price (within 99% confidence interval)
plt.figtext(0.65, 0.6, "VaR(0.99): $%.2f" % (start_price - q,), color='.4')

# Display 1% quantile
plt.figtext(0.65, 0.5, "q(0.99): $%.2f" % q, color=sns.xkcd_rgb["pale red"])

# Plot a line at the 1% quantile result
plt.axvline(x=q, linewidth=3,color=sns.xkcd_rgb["pale red"],alpha=.8)
plt.axvline(x=start_price, linewidth=3,color='.6',alpha=.6)

# Title
plt.title(u"Final price distribution for Alibaba Stock after %s days" % days, weight='bold');