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302 lines
14 KiB
Python
302 lines
14 KiB
Python
""" Example or something
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Is this not informative enough?
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Load a specific stock
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Then apply a specific TA on it
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Do this because AlphaVantage only allows 5 API calls per minutes.
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This way we only need 1 call, and can apply TA to the result without
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using an API request.
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Finviz for data
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"""
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from alpha_vantage.timeseries import TimeSeries
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import argparse
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import config_bot as cfg
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import pandas as pd
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import pandas_ta as ta
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from datetime import datetime
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import matplotlib
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import matplotlib.pyplot as plt
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from pandas.plotting import register_matplotlib_converters
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register_matplotlib_converters()
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# -----------------------------------------------------------------------------------------------------------------------
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def print_help():
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""" Print help
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"""
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print("What do you want to do?")
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print("view view a specific stock tick")
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print("\n--- Screeners:")
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print("gainers show latest top gainers")
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print("\n--- Technical Analysis:")
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print("sma simple moving average")
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print("ema exponential moving average")
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print("\n--- Others:")
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print("help help to see this menu again")
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print("quit to abandon the program")
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# -----------------------------------------------------------------------------------------------------------------------
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def check_positive(value):
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ivalue = int(value)
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if ivalue <= 0:
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raise argparse.ArgumentTypeError(f"{value} is an invalid positive int value")
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return ivalue
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# -----------------------------------------------------------------------------------------------------------------------
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def valid_date(s):
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try:
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return datetime.strptime(s, "%Y-%m-%d")
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except ValueError:
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raise argparse.ArgumentTypeError("Not a valid date: {s}")
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# -----------------------------------------------------------------------------------------------------------------------
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def plot_view_stock(df, symbol):
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pfig, axVolume = plt.subplots()
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plt.bar(df.index, df['5. volume'].values, color='k', alpha=0.8)
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plt.ylabel('Volume')
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axPrice = axVolume.twinx()
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plt.plot(df.index, df.iloc[:, :-1])
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plt.title(symbol + ' (Time Series)')
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plt.xlim(df.index[0], df.index[-1])
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plt.xlabel('Time')
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plt.ylabel('Share Price ($)')
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plt.legend(df.columns)
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plt.grid(b=True, which='major', color='#666666', linestyle='-')
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plt.minorticks_on()
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plt.grid(b=True, which='minor', color='#999999', linestyle='-', alpha=0.2)
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plt.show()
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# -----------------------------------------------------------------------------------------------------------------------
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def plot_stock_ta(df_stock, s_stock, df_ta, s_ta):
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plt.plot(df_stock.index, df_stock['4. close'].values)
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plt.plot(df_ta.index, df_ta.values)
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plt.title(f"{s_ta} on {s_stock}")
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plt.xlim(df_stock.index[0], df_stock.index[-1])
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plt.xlabel('Time')
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plt.ylabel('Share Price ($)')
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plt.legend([s_stock, s_ta])
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plt.grid(b=True, which='major', color='#666666', linestyle='-')
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plt.minorticks_on()
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plt.grid(b=True, which='minor', color='#999999', linestyle='-', alpha=0.2)
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plt.show()
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# -----------------------------------------------------------------------------------------------------------------------
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def main():
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""" Main function of the program
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"""
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# temporary
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#parser.add_argument('-a', action="store_true", default=False, help='example of flag')
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#parser.add_argument('-b', action="store", dest="b", help='example of string')
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#parser.add_argument('-c', action="store", dest="c", type=int, help='example of int')
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main_parser = argparse.ArgumentParser(prog='stock_market_bot', add_help=False)
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# Add list of arguments that the main parser accepts
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main_parser.add_argument('cmd', choices=['quit', 'help', 'gainers' ,'view', 'sma', 'ema'])
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# Print first welcome message and help
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print("\nWelcome to Didier's Stock Market Bot\n")
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print_help()
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print("\n")
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# Loop forever and ever
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while True:
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# Get input command from user
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as_input = input('> ')
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# Parse main command of the list of possible commands
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try:
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(ns_known_args, l_args) = main_parser.parse_known_args(as_input.split())
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except SystemExit:
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print("The command selected doesn't exist\n")
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continue
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# ---------------------------------------------------- GAINERS ----------------------------------------------------
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if ns_known_args.cmd == 'gainers':
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parser = argparse.ArgumentParser(prog='gainers',
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description='Show top ticker gainers from Yahoo Finance')
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parser.add_argument('-n', action="store", dest="n_gainers", type=int, default=5, choices=range(1, 25),
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help='Number of the top gainers stocks to retrieve (default 5)')
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try:
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(ns_parser, l_unknown_args) = parser.parse_known_args(l_args)
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except SystemExit:
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print("")
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continue
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if l_unknown_args:
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print(f"The following args couldn't be interpreted: {l_unknown_args}")
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df_gainers = pd.read_html('https://finance.yahoo.com/screener/predefined/day_gainers')[0]
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print(df_gainers.head(ns_parser.n_gainers).to_string(index=False))
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# ---------------------------------------------------- VIEW ----------------------------------------------------
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elif ns_known_args.cmd == 'view':
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parser = argparse.ArgumentParser(prog='view',
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description='Given a stock market ticker, allows to see historical data of \
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the corresponding stock. Note that the alpha_vantage key is necessary \
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for this to work!')
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parser.add_argument('-t', action="store", dest="s_ticker", required=True, help='Stock ticker')
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parser.add_argument('--type', action="store", dest="n_type", type=check_positive, default=4,
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help='1234 corresponds to types: 1. open; 2. high; 3.low; 4. close \
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while 14 corresponds to types: 1.open; 4. close')
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parser.add_argument("-s", "--start", type=valid_date, dest="s_start_date",
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help="The starting date (format YYYY-MM-DD) of the stock")
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try:
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(ns_parser, l_unknown_args) = parser.parse_known_args(l_args)
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except SystemExit:
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print("")
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continue
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if l_unknown_args:
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print(f"The following args couldn't be interpreted: {l_unknown_args}")
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# Currently only supports daily stocks
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# TODO: Intra-daily perhaps
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ts = TimeSeries(key=cfg.API_KEY, output_format='pandas')
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try:
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df_stock, d_stock_metadata = ts.get_daily(symbol=ns_parser.s_ticker, outputsize='full')
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except:
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print("Either the ticker or the API_KEY are invalids. Try again!")
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continue
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# Add function to plot data
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ln_col_idx = [int(x)-1 for x in list(str(ns_parser.n_type))]
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# Check that the types given are not bigger than 3, as there are only 4 types (0-3)
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if len([i for i in ln_col_idx if i > 3]) > 0:
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print("An index bigger than 3 was given, which is wrong. Try again")
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continue
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# Slice dataframe from the starting date YYYY-MM-DD selected
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if ns_parser.s_start_date:
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df_stock = df_stock[ns_parser.s_start_date:]
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ln_col_idx.append(4) # Append last column of df to be filtered which corresponds to: 5. Volume
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plot_view_stock(df_stock.iloc[:, ln_col_idx], ns_parser.s_ticker)
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# --------------------------------------------------------------------------------------------------------------
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# -------------------------------------------- TECHNICAL ANALYSIS ----------------------------------------------
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# --------------------------------------------------------------------------------------------------------------
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# ---------------------------------------------------- SMA ----------------------------------------------------
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elif ns_known_args.cmd == 'sma':
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parser = argparse.ArgumentParser(prog='sma',
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description=""" Moving Averages are used to smooth the data in an array to
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help eliminate noise and identify trends. The Simple Moving Average is literally
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the simplest form of a moving average. Each output value is the average of the
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previous n values. In a Simple Moving Average, each value in the time period carries
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equal weight, and values outside of the time period are not included in the average.
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This makes it less responsive to recent changes in the data, which can be useful for
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filtering out those changes. """)
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parser.add_argument('-t', action="store", dest="s_ticker", required=True, help='Stock ticker')
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parser.add_argument('--period', action="store", dest="n_time_period", type=check_positive, default=20,
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help='Number of data points used to calculate each moving average value.')
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parser.add_argument("-s", "--start", type=valid_date, dest="s_start_date",
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help="The starting date (format YYYY-MM-DD) of the stock")
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try:
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(ns_parser, l_unknown_args) = parser.parse_known_args(l_args)
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except SystemExit:
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print("")
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continue
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if l_unknown_args:
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print(f"The following args couldn't be interpreted: {l_unknown_args}")
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try: # Get stock data
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ts = TimeSeries(key=cfg.API_KEY, output_format='pandas')
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df_stock, d_stock_metadata = ts.get_daily(symbol=ns_parser.s_ticker, outputsize='full')
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except:
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print("ERROR! Try again!")
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print("Check that: 1. The ticker symbol exists and is valid")
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print(" 2. Less than 5 AlphaVantage requests have been made in 1 minute")
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print(" 3. The API_KEY is (still) valid")
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continue
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# Slice dataframe from the starting date YYYY-MM-DD selected
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if ns_parser.s_start_date:
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df_stock = df_stock[ns_parser.s_start_date:]
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df_ta = ta.sma(df_stock['4. close'], timeperiod=ns_parser.n_time_period).dropna()
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plot_stock_ta(df_stock, d_stock_metadata['2. Symbol'], df_ta, f"{ns_parser.n_time_period} SMA")
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continue
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# ---------------------------------------------------- EMA ----------------------------------------------------
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elif ns_known_args.cmd == 'ema':
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parser = argparse.ArgumentParser(prog='ema',
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description=""" The Exponential Moving Average is a staple of technical
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analysis and is used in countless technical indicators. In a Simple Moving
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Average, each value in the time period carries equal weight, and values outside
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of the time period are not included in the average. However, the Exponential
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Moving Average is a cumulative calculation, including all data. Past values have
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a diminishing contribution to the average, while more recent values have a greater
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contribution. This method allows the moving average to be more responsive to changes
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in the data. """)
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parser.add_argument('-t', action="store", dest="s_ticker", required=True, help='Stock ticker')
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parser.add_argument('--period', action="store", dest="n_time_period", type=check_positive, default=20,
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help='Number of data points used to calculate each moving average value.')
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parser.add_argument("-s", "--start", type=valid_date, dest="s_start_date",
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help="The starting date (format YYYY-MM-DD) of the stock")
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try:
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(ns_parser, l_unknown_args) = parser.parse_known_args(l_args)
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except SystemExit:
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print("")
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continue
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if l_unknown_args:
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print(f"The following args couldn't be interpreted: {l_unknown_args}")
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try: # Get stock data
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ts = TimeSeries(key=cfg.API_KEY, output_format='pandas')
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df_stock, d_stock_metadata = ts.get_daily(symbol=ns_parser.s_ticker, outputsize='full')
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except:
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print("ERROR! Try again!")
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print("Check that: 1. The ticker symbol exists and is valid")
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print(" 2. Less than 5 AlphaVantage requests have been made in 1 minute")
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print(" 3. The API_KEY is (still) valid")
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continue
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# Slice dataframe from the starting date YYYY-MM-DD selected
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if ns_parser.s_start_date:
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df_stock = df_stock[ns_parser.s_start_date:]
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df_ta = ta.ema(df_stock['4. close'], timeperiod=ns_parser.n_time_period).dropna()
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plot_stock_ta(df_stock, d_stock_metadata['2. Symbol'], df_ta, f"{ns_parser.n_time_period} EMA")
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continue
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# ---------------------------------------------------- HELP ----------------------------------------------------
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elif ns_known_args.cmd == 'help':
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print_help()
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# ---------------------------------------------------- QUIT ----------------------------------------------------
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elif ns_known_args.cmd == 'quit':
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print("Hope you made money today. Good bye my lover, good bye my friend.\n")
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break
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else:
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print('Shouldnt see this command!')
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print("\n")
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if __name__ == "__main__":
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main()
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