mirror of
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174 lines
9.3 KiB
Python
174 lines
9.3 KiB
Python
import argparse
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import pandas_ta as ta
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from 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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# ------------------------------------------------------- AD -------------------------------------------------------
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def ad(l_args, s_ticker, s_interval, df_stock):
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parser = argparse.ArgumentParser(prog='ad',
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description=""" The Accumulation/Distribution Line is similar to the On Balance
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Volume (OBV), which sums the volume times +1/-1 based on whether the close is
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higher than the previous close. The Accumulation/Distribution indicator, however
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multiplies the volume by the close location value (CLV). The CLV is based on the
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movement of the issue within a single bar and can be +1, -1 or zero. \n \n
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The Accumulation/Distribution Line is interpreted by looking for a divergence in
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the direction of the indicator relative to price. If the Accumulation/Distribution
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Line is trending upward it indicates that the price may follow. Also, if the
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Accumulation/Distribution Line becomes flat while the price is still rising (or falling)
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then it signals an impending flattening of the price.""")
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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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parser.add_argument('--open', action="store_true", default=False, dest="b_use_open", help='uses open value of 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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if l_unknown_args:
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print(f"The following args couldn't be interpreted: {l_unknown_args}\n")
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return
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# Daily
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if s_interval == "1440min":
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# Use open stock values
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if ns_parser.b_use_open:
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df_ta = ta.ad(high=df_stock['2. high'], low=df_stock['3. low'], close=df_stock['5. adjusted close'],
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volume=df_stock['6. volume'], offset=ns_parser.n_offset, open_=df_stock['1. open']).dropna()
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# Do not use open stock values
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else:
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df_ta = ta.ad(high=df_stock['2. high'], low=df_stock['3. low'], close=df_stock['5. adjusted close'],
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volume=df_stock['6. volume'], offset=ns_parser.n_offset).dropna()
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axPrice = plt.subplot(211)
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plt.plot(df_stock.index, df_stock['5. adjusted close'].values, 'k', lw=2)
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plt.title(f"Accumulation/Distribution Line (AD) on {s_ticker}")
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plt.xlim(df_stock.index[0], df_stock.index[-1])
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plt.ylabel(f'Share Price ($)')
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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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axVolume = axPrice.twinx()
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plt.bar(df_stock.index, df_stock['6. volume'].values, color='k', alpha=0.8, width=.3)
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plt.subplot(212)
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plt.plot(df_ta.index, df_ta.values, 'b', lw=1)
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plt.xlim(df_stock.index[0], df_stock.index[-1])
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plt.axhline(0, linewidth=2, color='k', ls='--')
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plt.legend([f'Chaikin Oscillator'])
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plt.xlabel('Time')
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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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# Intraday
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else:
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# Use open stock values
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if ns_parser.b_use_open:
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df_ta = ta.ad(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, open_=df_stock['1. open']).dropna()
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# Do not use open stock values
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else:
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df_ta = ta.ad(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).dropna()
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axPrice = plt.subplot(211)
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plt.plot(df_stock.index, df_stock['4. close'].values, 'k', lw=2)
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plt.title(f"Accumulation/Distribution Line (AD) on {s_ticker}")
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plt.xlim(df_stock.index[0], df_stock.index[-1])
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plt.ylabel(f'Share Price ($)')
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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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axVolume = axPrice.twinx()
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plt.bar(df_stock.index, df_stock['5. volume'].values, color='k', alpha=0.8, width=.3)
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plt.subplot(212)
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plt.plot(df_ta.index, df_ta.values, 'b', lw=1)
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plt.xlim(df_stock.index[0], df_stock.index[-1])
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plt.axhline(0, linewidth=2, color='k', ls='--')
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plt.legend([f'Chaikin Oscillator'])
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plt.xlabel('Time')
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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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print("")
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except:
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print("")
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return
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# ------------------------------------------------------- OBV -------------------------------------------------------
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def obv(l_args, s_ticker, s_interval, df_stock):
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parser = argparse.ArgumentParser(prog='obv',
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description=""" The On Balance Volume (OBV) is a cumulative total of the up and
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down volume. When the close is higher than the previous close, the volume is added
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to the running total, and when the close is lower than the previous close, the volume
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is subtracted from the running total. \n \n To interpret the OBV, look for the OBV
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to move with the price or precede price moves. If the price moves before the OBV,
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then it is a non-confirmed move. A series of rising peaks, or falling troughs, in the
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OBV indicates a strong trend. If the OBV is flat, then the market is not trending. """)
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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}\n")
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return
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# Daily
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if s_interval == "1440min":
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df_ta = ta.obv(close=df_stock['5. adjusted close'], volume=df_stock['6. volume'], offset=ns_parser.n_offset).dropna()
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axPrice = plt.subplot(211)
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plt.plot(df_stock.index, df_stock['5. adjusted close'].values, 'k', lw=2)
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plt.title(f"On-Balance Volume (OBV) on {s_ticker}")
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plt.xlim(df_stock.index[0], df_stock.index[-1])
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plt.ylabel(f'Share Price ($)')
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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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axVolume = axPrice.twinx()
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plt.bar(df_stock.index, df_stock['6. volume'].values, color='k', alpha=0.8, width=.3)
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plt.subplot(212)
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plt.plot(df_ta.index, df_ta.values, 'b', lw=1)
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plt.xlim(df_stock.index[0], df_stock.index[-1])
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plt.legend([f'OBV'])
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plt.xlabel('Time')
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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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# Intraday
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else:
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df_ta = ta.obv(close=df_stock['4. close'], volume=df_stock['5. volume'], offset=ns_parser.n_offset).dropna()
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axPrice = plt.subplot(211)
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plt.plot(df_stock.index, df_stock['5. adjusted close'].values, 'k', lw=2)
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plt.title(f"On-Balance Volume (OBV) on {s_ticker}")
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plt.xlim(df_stock.index[0], df_stock.index[-1])
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plt.ylabel(f'Share Price ($)')
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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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axVolume = axPrice.twinx()
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plt.bar(df_stock.index, df_stock['5. volume'].values, color='k', alpha=0.8, width=.3)
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plt.subplot(212)
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plt.plot(df_ta.index, df_ta.values, 'b', lw=1)
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plt.xlim(df_stock.index[0], df_stock.index[-1])
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plt.legend([f'OBV'])
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plt.xlabel('Time')
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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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print("")
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except:
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print("")
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