stock_analysis (2).py

import pandas as pd
import quandl
import datetime
import pandas_datareader.data as web
from matplotlib.dates import DateFormatter, WeekdayLocator,\
    DayLocator, MONDAY
from mpl_finance import candlestick_ohlc
import numpy as np 
import matplotlib.pyplot as plt
import matplotlib.dates 
from matplotlib.dates import date2num

start_date = datetime.datetime(2019,1,1)
end_date = datetime.date.today()


s = "AAPL"
a = "MSFT"
b = "GOOG"

apple = web.DataReader(s, 'yahoo', start_date, end_date)
 
type(apple)
pd.core.frame.DataFrame
#print (apple.head())
 
#apple["Adj Close"].plot(grid = True)
#plt.show()

'''
def pandas_candlestick_ohlc(dat, stick = "day", adj = False, otherseries = None):
    
    mondays = WeekdayLocator(MONDAY)        # major ticks on the mondays
    alldays = DayLocator()              # minor ticks on the days
    dayFormatter = DateFormatter('%d')      # e.g., 12
 
    # Create a new DataFrame which includes OHLC data for each period specified by stick input
    fields = ["Open", "High", "Low", "Close"]
    if adj:
        fields = ["Adj " + s for s in fields]
    transdat = dat.loc[:,fields]
    transdat.columns = pd.Index(["Open", "High", "Low", "Close"])
    if (type(stick) == str):
        if stick == "day":
            plotdat = transdat
            stick = 1 # Used for plotting
        elif stick in ["week", "month", "year"]:
            if stick == "week":
                transdat["week"] = pd.to_datetime(transdat.index).map(lambda x: x.isocalendar()[1]) # Identify weeks
            elif stick == "month":
                transdat["month"] = pd.to_datetime(transdat.index).map(lambda x: x.month) # Identify months
            transdat["year"] = pd.to_datetime(transdat.index).map(lambda x: x.isocalendar()[0]) # Identify years
            grouped = transdat.groupby(list(set(["year",stick]))) # Group by year and other appropriate variable
            plotdat = pd.DataFrame({"Open": [], "High": [], "Low": [], "Close": []}) # Create empty data frame containing what will be plotted
            for name, group in grouped:
                plotdat = plotdat.append(pd.DataFrame({"Open": group.iloc[0,0],
                                            "High": max(group.High),
                                            "Low": min(group.Low),
                                            "Close": group.iloc[-1,3]},
                                           index = [group.index[0]]))
            if stick == "week": stick = 5
            elif stick == "month": stick = 30
            elif stick == "year": stick = 365
 
    elif (type(stick) == int and stick >= 1):
        transdat["stick"] = [np.floor(i / stick) for i in range(len(transdat.index))]
        grouped = transdat.groupby("stick")
        plotdat = pd.DataFrame({"Open": [], "High": [], "Low": [], "Close": []}) # Create empty data frame containing what will be plotted
        for name, group in grouped:
            plotdat = plotdat.append(pd.DataFrame({"Open": group.iloc[0,0],
                                        "High": max(group.High),
                                        "Low": min(group.Low),
                                        "Close": group.iloc[-1,3]},
                                       index = [group.index[0]]))
 
    else:
        raise ValueError('Valid inputs to argument "stick" include the strings "day", "week", "month", "year", or a positive integer')
 
 
    # Set plot parameters, including the axis object ax used for plotting
    fig, ax = plt.subplots()
    fig.subplots_adjust(bottom=0.2)
    if plotdat.index[-1] - plotdat.index[0] < pd.Timedelta('730 days'):
        weekFormatter = DateFormatter('Dec 13')  # e.g., Jan 12
        ax.xaxis.set_major_locator(mondays)
        ax.xaxis.set_minor_locator(alldays)
    else:
        weekFormatter = DateFormatter('Dec 13, 2019')
    ax.xaxis.set_major_formatter(weekFormatter)
 
    ax.grid(True)
 
    # Create the candelstick chart
    candlestick_ohlc(ax, list(zip(list(date2num(plotdat.index.tolist())), plotdat["Open"].tolist(), plotdat["High"].tolist(),
                      plotdat["Low"].tolist(), plotdat["Close"].tolist())),
                      colorup = "black", colordown = "red", width = stick * .4)
 
    # Plot other series (such as moving averages) as lines
    if otherseries != None:
        if type(otherseries) != list:
            otherseries = [otherseries]
        dat.loc[:,otherseries].plot(ax = ax, lw = 1.3, grid = True)
 
    ax.xaxis_date()
    ax.autoscale_view()
    plt.setp(plt.gca().get_xticklabels(), rotation=45, horizontalalignment='right')
 
    plt.show()
 
pandas_candlestick_ohlc(apple, adj=True, stick="month")
'''
microsoft, google = (web.DataReader(s, 'yahoo', start_date, end_date) for s in [a, b])
 
stocks = pd.DataFrame({s: apple["Adj Close"],
                      a: microsoft["Adj Close"],
                      b: google["Adj Close"]})

#stocks.plot(secondary_y = ["AAPL", "MSFT"], grid = True)

stock_return = stocks.apply(lambda x: x / x[0])
returns = stock_return.head() - 1

#stock_return.plot(grid = True).axhline(y = 1, color = "black", lw = 2)


spyder = web.DataReader('SPY', 'yahoo', start_date, end_date)
stocks = stocks.join(spyder.loc[:, "Adj Close"]).rename(columns={"Adj Close": "SPY"})
print (stocks.head())

stock_return = stocks.apply(lambda x: x / x[0])
stock_return.plot(grid = True).axhline(y = 1, color = "black", lw = 2)

stock_change = stocks.apply(lambda x: np.log(x) - np.log(x.shift(1)))
stock_change.plot(grid=True).axhline(y = 0, color = "black", lw = 2)


# metrics
stock_change_apr = stock_change * 252 * 100    # There are 252 trading days in a year; the 100 converts to percentages
print (stock_change_apr.tail())

tbill = web.DataReader("FRED/TB3MS", 'yahoo', start_date, end_date)
print(tbill.tail())