Question

THIS IS A PYTHON PROGRAM

Part 1 – Stocks Research and Program Setup

Find five (5) of your favorite stocks. In your research you will find the daily closing prices of those stocks from February 11, 2019 through February 15, 2019. Write prices out in a table (5 rows by 5 columns).

Part 2 – Python Program

Begin writing a Python program that will do the following:

• create a 2D list named stocks to store the data for each of your companies.
• include a 1D list of five (5) strings (named companies) that represent the names of the companies.
• include a separate function named average that will calculate (and display to the screen) the average closing price for each stock.
• call the function to execute within the “main” method

Answer 1

import matplotlib.pyplot as plt   # Import matplotlib # This line is necessary for the plot to appear in a Jupyter notebook %matplotlib inline # Control the default size of figures in this Jupyter notebook %pylab inline pylab.rcParams['figure.figsize'] = (15, 9)   # Change the size of plots apple["Adj Close"].plot(grid = True) # Plot the adjusted closing price of AAPL

Populating the interactive namespace from numpy and matplotlib

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A linechart is fine, but there are at least four variables involved for each date (open, high, low, and close), and we would like to have some visual way to see all four variables that does not require plotting four separate lines. Financial data is often plotted with a Japanese candlestick plot, so named because it was first created by 18th century Japanese rice traders. Such a chart can be created with matplotlib, though it requires considerable effort.

I have made a function you are welcome to use to more easily create candlestick charts from pandas data frames, and use it to plot our stock data. (Code is based off this example, and you can read the documentation for the functions involved here.)

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from matplotlib.dates import DateFormatter, WeekdayLocator,\     DayLocator, MONDAY from matplotlib.finance import candlestick_ohlc def pandas_candlestick_ohlc(dat, stick = "day", otherseries = None):     """     :param dat: pandas DataFrame object with datetime64 index, and float columns "Open", "High", "Low", and "Close", likely created via DataReader from "yahoo"     :param stick: A string or number indicating the period of time covered by a single candlestick. Valid string inputs include "day", "week", "month", and "year", ("day" default), and any numeric input indicates the number of trading days included in a period     :param otherseries: An iterable that will be coerced into a list, containing the columns of dat that hold other series to be plotted as lines     This will show a Japanese candlestick plot for stock data stored in dat, also plotting other series if passed.     """     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     transdat = dat.loc[:,["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('%b %d') # e.g., Jan 12         ax.xaxis.set_major_locator(mondays)         ax.xaxis.set_minor_locator(alldays)     else:         weekFormatter = DateFormatter('%b %d, %Y')     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)