def ad(df_stock: pd.DataFrame, use_open: bool) -> pd.DataFrame:
"""Calculate AD technical indicator
Parameters
----------
df_stock : pd.DataFrame
Dataframe of prices
use_open : bool
Whether to use open prices
Returns
-------
pd.DataFrame
Dataframe with technical indicator
"""
if use_open:
df_ta = ta.ad(
high=df_stock["High"],
low=df_stock["Low"],
close=df_stock["Close"],
volume=df_stock["Volume"],
open_=df_stock["Open"],
).dropna()
# Do not use open stock values
else:
df_ta = ta.ad(
high=df_stock["High"],
low=df_stock["Low"],
close=df_stock["Close"],
volume=df_stock["Volume"],
).dropna()
return pd.DataFrame(df_ta)
def add_plots(df: pd.DataFrame, additional_charts: Dict[str, bool]):
"""Add additional plots to the candle chart.
Parameters
----------
df : pd.DataFrame
The source data
additional_charts : Dict[str, bool]
A dictionary of flags to include additional charts
Returns
-------
Tuple
Tuple of lists containing the plots, legends and subplot legends
"""
panel_number = 2
plots_to_add = []
legends = []
subplot_legends = []
if additional_charts["ad"]:
ad = ta.ad(df["High"], df["Low"], df["Close"], df["Volume"])
ad_plot = mpf.make_addplot(ad, panel=panel_number)
plots_to_add.append(ad_plot)
subplot_legends.extend([panel_number * 2, ["AD"]])
panel_number += 1
if additional_charts["bbands"]:
bbands = ta.bbands(df["Close"])
bbands = bbands.drop("BBB_5_2.0", axis=1)
bbands_plot = mpf.make_addplot(bbands, panel=0)
plots_to_add.append(bbands_plot)
legends.extend(["Lower BBand", "Middle BBand", "Upper BBand"])
if additional_charts["cci"]:
cci = ta.cci(df["High"], df["Low"], df["Close"])
cci_plot = mpf.make_addplot(cci, panel=panel_number)
plots_to_add.append(cci_plot)
subplot_legends.extend([panel_number * 2, ["CCI"]])
panel_number += 1
if additional_charts["ema"]:
ema = ta.ema(df["Close"])
ema_plot = mpf.make_addplot(ema, panel=0)
plots_to_add.append(ema_plot)
legends.append("10 EMA")
if additional_charts["rsi"]:
rsi = ta.rsi(df["Close"])
rsi_plot = mpf.make_addplot(rsi, panel=panel_number)
plots_to_add.append(rsi_plot)
subplot_legends.extend([panel_number * 2, ["RSI"]])
panel_number += 1
if additional_charts["obv"]:
obv = ta.obv(df["Close"], df["Volume"])
obv_plot = mpf.make_addplot(obv, panel=panel_number)
plots_to_add.append(obv_plot)
subplot_legends.extend([panel_number * 2, ["OBV"]])
panel_number += 1
if additional_charts["sma"]:
sma_length = [20, 50]
for length in sma_length:
sma = ta.sma(df["Close"], length=length)
sma_plot = mpf.make_addplot(sma, panel=0)
plots_to_add.append(sma_plot)
legends.append(f"{length} SMA")
if additional_charts["vwap"]:
vwap = ta.vwap(df["High"], df["Low"], df["Close"], df["Volume"])
vwap_plot = mpf.make_addplot(vwap, panel=0)
plots_to_add.append(vwap_plot)
legends.append("vwap")
return plots_to_add, legends, subplot_legends
def ad(l_args, s_ticker, s_interval, df_stock):
parser = argparse.ArgumentParser(
prog='ad',
description=
""" The Accumulation/Distribution Line is similar to the On Balance
Volume (OBV), which sums the volume times +1/-1 based on whether the close is
higher than the previous close. The Accumulation/Distribution indicator, however
multiplies the volume by the close location value (CLV). The CLV is based on the
movement of the issue within a single bar and can be +1, -1 or zero. \n \n
The Accumulation/Distribution Line is interpreted by looking for a divergence in
the direction of the indicator relative to price. If the Accumulation/Distribution
Line is trending upward it indicates that the price may follow. Also, if the
Accumulation/Distribution Line becomes flat while the price is still rising (or falling)
then it signals an impending flattening of the price."""
)
parser.add_argument('-o',
"--offset",
action="store",
dest="n_offset",
type=check_positive,
default=0,
help='offset')
parser.add_argument('--open',
action="store_true",
default=False,
dest="b_use_open",
help='uses open value of stock')
(ns_parser, l_unknown_args) = parser.parse_known_args(l_args)
if l_unknown_args:
print(
f"The following args couldn't be interpreted: {l_unknown_args}\n")
return
# Daily
if s_interval == "1440min":
# Use open stock values
if ns_parser.b_use_open:
df_ta = ta.ad(high=df_stock['2. high'],
low=df_stock['3. low'],
close=df_stock['5. adjusted close'],
volume=df_stock['6. volume'],
offset=ns_parser.n_offset,
open_=df_stock['1. open']).dropna()
# Do not use open stock values
else:
df_ta = ta.ad(high=df_stock['2. high'],
low=df_stock['3. low'],
close=df_stock['5. adjusted close'],
volume=df_stock['6. volume'],
offset=ns_parser.n_offset).dropna()
axPrice = plt.subplot(211)
plt.plot(df_stock.index,
df_stock['5. adjusted close'].values,
'k',
lw=2)
plt.title(f"Accumulation/Distribution Line (AD) on {s_ticker}")
plt.xlim(df_stock.index[0], df_stock.index[-1])
plt.ylabel(f'Share Price ($)')
plt.grid(b=True, which='major', color='#666666', linestyle='-')
plt.minorticks_on()
plt.grid(b=True,
which='minor',
color='#999999',
linestyle='-',
alpha=0.2)
axVolume = axPrice.twinx()
plt.bar(df_stock.index,
df_stock['6. volume'].values,
color='k',
alpha=0.8,
width=.3)
plt.subplot(212)
plt.plot(df_ta.index, df_ta.values, 'b', lw=1)
plt.xlim(df_stock.index[0], df_stock.index[-1])
plt.axhline(0, linewidth=2, color='k', ls='--')
plt.legend([f'Chaikin Oscillator'])
plt.xlabel('Time')
plt.grid(b=True, which='major', color='#666666', linestyle='-')
plt.minorticks_on()
plt.grid(b=True,
which='minor',
color='#999999',
linestyle='-',
alpha=0.2)
plt.show()
# Intraday
else:
# Use open stock values
if ns_parser.b_use_open:
df_ta = ta.ad(high=df_stock['2. high'],
low=df_stock['3. low'],
close=df_stock['4. close'],
volume=df_stock['5. volume'],
offset=ns_parser.n_offset,
open_=df_stock['1. open']).dropna()
# Do not use open stock values
else:
df_ta = ta.ad(high=df_stock['2. high'],
low=df_stock['3. low'],
close=df_stock['4. close'],
volume=df_stock['5. volume'],
offset=ns_parser.n_offset).dropna()
axPrice = plt.subplot(211)
plt.plot(df_stock.index, df_stock['4. close'].values, 'k', lw=2)
plt.title(f"Accumulation/Distribution Line (AD) on {s_ticker}")
plt.xlim(df_stock.index[0], df_stock.index[-1])
plt.ylabel(f'Share Price ($)')
plt.grid(b=True, which='major', color='#666666', linestyle='-')
plt.minorticks_on()
plt.grid(b=True,
which='minor',
color='#999999',
linestyle='-',
alpha=0.2)
axVolume = axPrice.twinx()
plt.bar(df_stock.index,
df_stock['5. volume'].values,
color='k',
alpha=0.8,
width=.3)
plt.subplot(212)
plt.plot(df_ta.index, df_ta.values, 'b', lw=1)
plt.xlim(df_stock.index[0], df_stock.index[-1])
plt.axhline(0, linewidth=2, color='k', ls='--')
plt.legend([f'Chaikin Oscillator'])
plt.xlabel('Time')
plt.grid(b=True, which='major', color='#666666', linestyle='-')
plt.minorticks_on()
plt.grid(b=True,
which='minor',
color='#999999',
linestyle='-',
alpha=0.2)
plt.show()
print("")
def ad(l_args, s_ticker, s_interval, df_stock):
parser = argparse.ArgumentParser(
prog="ad",
description="""
The Accumulation/Distribution Line is similar to the On Balance
Volume (OBV), which sums the volume times +1/-1 based on whether the close is
higher than the previous close. The Accumulation/Distribution indicator, however
multiplies the volume by the close location value (CLV). The CLV is based on the
movement of the issue within a single bar and can be +1, -1 or zero. \n \n
The Accumulation/Distribution Line is interpreted by looking for a divergence in
the direction of the indicator relative to price. If the Accumulation/Distribution
Line is trending upward it indicates that the price may follow. Also, if the
Accumulation/Distribution Line becomes flat while the price is still rising (or falling)
then it signals an impending flattening of the price.
""",
)
parser.add_argument(
"-o",
"--offset",
action="store",
dest="n_offset",
type=check_positive,
default=0,
help="offset",
)
parser.add_argument(
"--open",
action="store_true",
default=False,
dest="b_use_open",
help="uses open value of stock",
)
try:
ns_parser = parse_known_args_and_warn(parser, l_args)
# Daily
if s_interval == "1440min":
# Use open stock values
if ns_parser.b_use_open:
df_ta = ta.ad(
high=df_stock["2. high"],
low=df_stock["3. low"],
close=df_stock["5. adjusted close"],
volume=df_stock["6. volume"],
offset=ns_parser.n_offset,
open_=df_stock["1. open"],
).dropna()
# Do not use open stock values
else:
df_ta = ta.ad(
high=df_stock["2. high"],
low=df_stock["3. low"],
close=df_stock["5. adjusted close"],
volume=df_stock["6. volume"],
offset=ns_parser.n_offset,
).dropna()
# Intraday
else:
# Use open stock values
if ns_parser.b_use_open:
df_ta = ta.ad(
high=df_stock["2. high"],
low=df_stock["3. low"],
close=df_stock["4. close"],
volume=df_stock["5. volume"],
offset=ns_parser.n_offset,
open_=df_stock["1. open"],
).dropna()
# Do not use open stock values
else:
df_ta = ta.ad(
high=df_stock["2. high"],
low=df_stock["3. low"],
close=df_stock["4. close"],
volume=df_stock["5. volume"],
offset=ns_parser.n_offset,
).dropna()
plt.figure()
axPrice = plt.subplot(211)
if s_interval == "1440min":
plt.plot(df_stock.index,
df_stock["5. adjusted close"].values,
"k",
lw=2)
else:
plt.plot(df_stock.index, df_stock["4. close"].values, "k", lw=2)
plt.title(f"Accumulation/Distribution Line (AD) on {s_ticker}")
plt.xlim(df_stock.index[0], df_stock.index[-1])
plt.ylabel("Share Price ($)")
plt.grid(b=True, which="major", color="#666666", linestyle="-")
plt.minorticks_on()
plt.grid(b=True,
which="minor",
color="#999999",
linestyle="-",
alpha=0.2)
_ = axPrice.twinx()
if s_interval == "1440min":
plt.bar(
df_stock.index,
df_stock["6. volume"].values,
color="k",
alpha=0.8,
width=0.3,
)
else:
plt.bar(
df_stock.index,
df_stock["5. volume"].values,
color="k",
alpha=0.8,
width=0.3,
)
plt.subplot(212)
plt.plot(df_ta.index, df_ta.values, "b", lw=1)
plt.xlim(df_stock.index[0], df_stock.index[-1])
plt.axhline(0, linewidth=2, color="k", ls="--")
plt.legend(["Chaikin Oscillator"])
plt.xlabel("Time")
plt.grid(b=True, which="major", color="#666666", linestyle="-")
plt.minorticks_on()
plt.grid(b=True,
which="minor",
color="#999999",
linestyle="-",
alpha=0.2)
plt.ion()
plt.show()
print("")
except Exception as e:
print(e)
print("")
return
def add_plots(df, ns_parser):
panel_number = 2
plots_to_add = []
legends = []
subplot_legends = []
if ns_parser.ad:
ad = ta.ad(df["High"], df["Low"], df["Close"], df["Volume"])
ad_plot = mpf.make_addplot(ad, panel=panel_number)
plots_to_add.append(ad_plot)
subplot_legends.extend([panel_number * 2, ["AD"]])
panel_number += 1
if ns_parser.bbands:
bbands = ta.bbands(df["Close"])
bbands = bbands.drop("BBB_5_2.0", axis=1)
bbands_plot = mpf.make_addplot(bbands, panel=0)
plots_to_add.append(bbands_plot)
legends.extend(["Lower BBand", "Middle BBand", "Upper BBand"])
if ns_parser.cci:
cci = ta.cci(df["High"], df["Low"], df["Close"])
cci_plot = mpf.make_addplot(cci, panel=panel_number)
plots_to_add.append(cci_plot)
subplot_legends.extend([panel_number * 2, ["CCI"]])
panel_number += 1
if ns_parser.ema:
ema = ta.ema(df["Close"])
ema_plot = mpf.make_addplot(ema, panel=0)
plots_to_add.append(ema_plot)
legends.append("10 EMA")
if ns_parser.rsi:
rsi = ta.rsi(df["Close"])
rsi_plot = mpf.make_addplot(rsi, panel=panel_number)
plots_to_add.append(rsi_plot)
subplot_legends.extend([panel_number * 2, ["RSI"]])
panel_number += 1
if ns_parser.obv:
obv = ta.obv(df["Close"], df["Volume"])
obv_plot = mpf.make_addplot(obv, panel=panel_number)
plots_to_add.append(obv_plot)
subplot_legends.extend([panel_number * 2, ["OBV"]])
panel_number += 1
if ns_parser.sma:
sma_length = [20, 50]
for length in sma_length:
sma = ta.sma(df["Close"], length=length)
sma_plot = mpf.make_addplot(sma, panel=0)
plots_to_add.append(sma_plot)
legends.append(f"{length} SMA")
if ns_parser.vwap:
vwap = ta.vwap(df["High"], df["Low"], df["Close"], df["Volume"])
vwap_plot = mpf.make_addplot(vwap, panel=0)
plots_to_add.append(vwap_plot)
legends.append("vwap")
return plots_to_add, legends, subplot_legends
