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98 lines
5.2 KiB
Python
98 lines
5.2 KiB
Python
import argparse
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import pandas_ta as ta
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from stock_market_helper_funcs import *
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from pandas.plotting import register_matplotlib_converters
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register_matplotlib_converters()
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# ----------------------------------------------------- EMA -----------------------------------------------------
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def ema(l_args, s_ticker, s_interval, df_stock):
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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('-l', "--length", action="store", dest="n_length", type=check_positive, default=10, help='length')
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parser.add_argument('-o', "--offset", action="store", dest="n_offset", type=check_positive, default=0, help='offset')
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try:
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(ns_parser, l_unknown_args) = parser.parse_known_args(l_args)
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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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# Daily
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if s_interval == "1440min":
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df_ta = ta.ema(df_stock['5. adjusted close'], length=ns_parser.n_length, offset=ns_parser.n_offset).dropna()
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plot_stock_ta(df_stock['5. adjusted close'], s_ticker, df_ta, "EMA")
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# Intraday
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else:
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df_ta = ta.ema(df_stock['4. close'], length=ns_parser.n_length, offset=ns_parser.n_offset).dropna()
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plot_stock_ta(df_stock['4. close'], s_ticker, df_ta, "EMA")
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except:
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print("ERROR!\n")
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return
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# ----------------------------------------------------- SMA -----------------------------------------------------
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def sma(l_args, s_ticker, s_interval, df_stock):
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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('-l', "--length", action="store", dest="n_length", type=check_positive, default=10, help='length')
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parser.add_argument('-o', "--offset", action="store", dest="n_offset", type=check_positive, default=0, help='offset')
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try:
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(ns_parser, l_unknown_args) = parser.parse_known_args(l_args)
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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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# Daily
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if s_interval == "1440min":
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df_ta = ta.sma(df_stock['5. adjusted close'], length=ns_parser.n_length, offset=ns_parser.n_offset).dropna()
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plot_stock_ta(df_stock['5. adjusted close'], s_ticker, df_ta, "SMA")
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# Intraday
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else:
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df_ta = ta.sma(df_stock['4. close'], length=ns_parser.n_length, offset=ns_parser.n_offset).dropna()
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plot_stock_ta(df_stock['4. close'], s_ticker, df_ta, "SMA")
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except:
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print("ERROR!\n")
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return
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# ----------------------------------------------------- VWAP -----------------------------------------------------
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def vwap(l_args, s_ticker, s_interval, df_stock):
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parser = argparse.ArgumentParser(prog='vwap',
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description=""" The Volume Weighted Average Price that measures the average typical price
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by volume. It is typically used with intraday charts to identify general direction. """)
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parser.add_argument('-o', "--offset", action="store", dest="n_offset", type=check_positive, default=0, help='offset')
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try:
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(ns_parser, l_unknown_args) = parser.parse_known_args(l_args)
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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_ta = ta.vwap(high=df_stock['2. high'], low=df_stock['3. low'], close=df_stock['4. close'],
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volume=df_stock['5. volume'], offset=ns_parser.n_offset)
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plot_stock_ta(df_stock['4. close'], s_ticker, df_ta, "VWAP")
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except:
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print("ERROR!\n")
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return
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