Quickly compare a list of stocks or indices prices.

stock_compare.py

import yfinance as yf
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import argparse


def parse_args():
    parser = argparse.ArgumentParser(description='Quickly compare a list of stocks or indices prices.')
    parser.add_argument('--history', type=str, default='ytd', help='History period of the price data', choices=["1d", "5d", "1mo", "3mo", "6mo", "1y", "2y", "5y", "10y", "ytd", "max"])
    parser.add_argument('--granularity', type=str, default='1d', help='Granularity of the price data', choices=["1m", "2m", "5m", "15m", "30m", "60m", "90m", "1h", "1d", "5d", "1wk", "1mo", "3mo"])
    parser.add_argument('--stocks', nargs='+', help='List of stocks to compare')
    parser.add_argument('--indices', nargs='+', help='List of indices to compare', choices=["SP500", "NASDAQ", "DOWJONES"])
    parser.add_argument('--log', action='store_true', help='Use log scale for the y-axis')
    parser.add_argument('--timecolor', action='store_true', help='Use color to indicate time')
    parser.add_argument('--pricelines', action='store_true', help='Adds lines at regular price intervals')
    parser.add_argument('--theme', type=str, default='dark', help='Theme of the plot', choices=["dark", "light", "seaborn"])
    args = parser.parse_args()
    return args


def main(args):
    print("Hello!")
    
    if args.stocks is None and args.indices is None:
        print("You need to specify at least one stock or index to compare.")
        return
    elif args.stocks is None:
        print("You are comparing the following indices: {}".format(args.indices))
    elif args.indices is None:
        print("You are comparing the following stocks: {}".format(args.stocks))
    else:
        print("You are comparing the following stocks: {} with the following indices {}".format(args.stocks, args.indices))

    if args.indices is not None and args.stocks is not None:
        print("WARNING: You are comparing stocks and indices on the same plot. This can make reading the plot difficult. Consider using --stocks and --indices separately.")

    if args.stocks is not None and len(args.stocks) > 10:
        print("WARNING: You are comparing more than 10 stocks. This can make reading the plot difficult.")

    if args.theme is not None:
        themes_map = {
            "dark": "dark_background",
            "light": "default",
            "seaborn": "seaborn",
        }

        if args.theme == "seaborn":
            import seaborn as sns
            sns.set_theme()

            print("WARNING: When using the seaborn theme consider disabling the --timecolor and --pricelines flags.")
        else:
            plt.style.use(themes_map[args.theme])


    # Get the data for the indices.
    if args.indices:
        indices_ticker_map =  {
            "SP500": "^GSPC",
            "NASDAQ": "^IXIC",
            "DOWJONES": "^DJI"
        }

        indices_tickers = [indices_ticker_map[index] for index in args.indices]
        indices_data = yf.download(indices_tickers, period=args.history, interval=args.granularity, progress=False)
        
        if len(indices_tickers) == 1:
            indices_data.columns = pd.MultiIndex.from_product([indices_data.columns, indices_tickers])
        
        for ticker in args.indices:
            close_price = indices_data["Close", indices_ticker_map[ticker]].to_numpy()
            plt.plot(close_price, linestyle='dashed', label=ticker + " ({})".format(indices_ticker_map[ticker]))

    # Get the data for the stocks.
    data = yf.download(args.stocks, period=args.history, interval=args.granularity, progress=False)

    if len(args.stocks) == 1:
        data.columns = pd.MultiIndex.from_product([data.columns, args.stocks])

    for ticker in args.stocks:
        close_price = data["Close", ticker].to_numpy()
        high_price = data["High", ticker].to_numpy()
        low_price = data["Low", ticker].to_numpy()

        plt.plot(close_price, label=ticker)
        plt.fill_between(range(len(close_price)), high_price, low_price, alpha=0.3)

    # Try to only keep about a dozen labels.
    if len(data.index) > 12:
        xticks_indices = range(0, len(data.index), int(len(data.index) / 12))
        xticks_labels = data.index[xticks_indices]
    else:
        xticks_indices = range(len(data.index))
        xticks_labels = data.index

    # Adjust labels depending on granularity.
    if args.granularity in ["1m", "2m", "5m", "15m", "30m", "60m", "90m", "1h"]:
        xticks_labels = [date.strftime("%H:%M\n%d/%m/%Y") for date in xticks_labels]
    elif args.granularity in ["1d", "5d", "1wk"]:
        xticks_labels = [date.strftime("%d/%m/%Y") for date in xticks_labels]
    elif args.granularity in ["1mo", "3mo"]:
        xticks_labels = [date.strftime("%d/%m/%Y") for date in xticks_labels]
    else:
        xticks_labels = [date.strftime("%m/%Y") for date in xticks_labels]

    if args.log:
        plt.yscale("log")

    if args.timecolor:
        for i, xi in enumerate(xticks_indices):
            if i % 2 == 0 and i < len(xticks_indices) - 1:
                plt.axvspan(xi, xticks_indices[i + 1], facecolor='grey', alpha=0.1)

    if args.pricelines:
        plt.grid(True)

    plt.xticks(xticks_indices, xticks_labels, rotation=45)
    plt.xlabel("Date")
    plt.ylabel("Price (at close, with high/low)")
    plt.legend()
    plt.title("Comparison of close prices of stocks ({}/{})".format(args.history, args.granularity))
    plt.margins(x=0, tight=True)
    plt.show()


if __name__ == "__main__":
    args = parse_args()
    main(args)