graeme.py

# Graeme's Ideas

# Like the assignment says, first look at the total growth percentages and price differences
# comparing the start and end dates to figure out what the best stocks are.
# We can look at trends to see what could be important to look for/compare

# create a sliding window to find the rolling averages of the close number
# or do a linear regression to find the trendline

# same idea but with P/E


import pandas as pd
import DataPreProcessor as dp
import matplotlib.pyplot as plt
from operator import itemgetter
import matplotlib.dates as mdates


pd.set_option('display.max_columns', None)
pd.set_option('display.expand_frame_repr', False)

# df = pd.read_csv('stocks/Nasdaq-Friday-April-20-2018.csv')
# print(df)

def graemeStuff(stockName):
    nasdaq_df, nyse_df, scap_df, all_stock_df = dp.initializeDf()
    # comp_stock = dp.getStockDfByName(stockName, all_stock_df)
    # print(apple_stock.head())
    # rollingAverage(comp_stock, stockName)
    rawBest(all_stock_df)
    # print(all_stock_df)

# find the rolling average of a single stock df
def rollingAverage(stock_df, stockName):
    averageList = []
    low = stock_df['High']
    high = stock_df['Low']
    for x in range(len(stock_df)):
        avg = (float(high.iloc[x]) + float(low.iloc[x])) / 2
        averageList.append(avg)
    Date = stock_df['Date']
    dict = {'Average': averageList, 'Date': Date}
    average_df = pd.DataFrame(dict)

    xpoints = stock_df['Date'].to_numpy()
    ypoints = averageList
    rollingAvg5 = average_df.rolling(5).mean()
    rollingAvg10 = average_df.rolling(10).mean()
    rollingAvg50 = average_df.rolling(50).mean()

    plt.plot(xpoints, ypoints, label="Average")
    plt.plot(xpoints, rollingAvg5, label="Roll avg 5")
    plt.plot(xpoints, rollingAvg10, label="Roll avg 10")
    plt.plot(xpoints, rollingAvg50, label="Roll avg 50")

    # plt.plot(xpoints, high, label="High")
    # plt.plot(xpoints, low, label="Low")
    plt.title("Stock Price Over Time: " + stockName)
    plt.xlabel("Date")
    plt.ylabel("Price")
    myFmt = mdates.DateFormatter('%b')
    plt.gca().xaxis.set_major_formatter(myFmt)
    plt.legend()
    plt.show()
    # print(averageList)
    # print(len(averageList))
    # print(len(stock_df))

def rawBest(big_df):
    # make a set of the names of the companies
    # field all the names into the get stock by name
    # take the first open and the last close date to see raw total difference
    # make a dict of the company and its overall change
    # sort this dict

    # all the company names
    names = big_df['Name']
    # print(len(names))
    unique_names = names.unique()
    # print(len(unique_names))
    # print(unique_names)

    # dict of the company and its overall stock diff
    compDiff = {}

    # list to hold the dicts so that it can be sorted by stock diff
    # count = 0
    compDiffList = []
    for name in unique_names:
        stock = big_df.loc[(big_df['Name'] == name)]
        year_change = stock['YTD % Chg'].iloc[-1]
        if year_change == '...':
            year_change = 0
        year_change = float(year_change)
        if year_change > 0:
            open = stock['Open'].iloc[0]
            # if open.find(",") != -1:
            #     while open.find(",") != -1:
            #         open = open.replace(",", "")

            if open == '...':
                open = -1
            open = float(open)
            shares = int(10000/open)

            close = stock['Close'].iloc[-1]

            if close == '...':
                close = -1
            # if close.find(",") != -1:
            #     while close.find(",") != -1:
            #         close = close.replace(",", "")
            close = float(close)
            close_money = shares * close
            # print(close)
            # print(type(close))
            diff = (close_money - (open* shares))
            # print(diff)
            if diff > 1000:
                compDiff = {"Name": name, "Diff": diff}
                print(compDiff)
                compDiffList.append(compDiff)
            # count = count + 1
            # print(count)

    print(compDiffList)
    print(len(compDiffList))
    sorted_list = sorted(compDiffList, key=itemgetter('Diff'),reverse=True)
    print("Top 3 companies based off of change in Profit")
    top_three = []
    for x in range(3):
        print(sorted_list[x])
        top_three.append(sorted_list[x])

    # for item in sorted_list:
    #     for key, value in item:
    #         percent = (value/10000) * 100
    #         print(key," ",percent, "% increase in price")
    # print(compDiffList.)

    # print(dp.getStockDfByName("Eyenovia", big_df))
    # for stock in big_df:
    #     print(stock)



graemeStuff('Apple')