def obv(s_interval: str, df_stock: pd.DataFrame) -> pd.DataFrame:
"""On Balance Volume
Parameters
----------
s_interval: str
Stock data interval
df_stock: pd.DataFrame
Dataframe of stock prices
Returns
-------
pd.DataFrame
Dataframe with technical indicator
"""
# Daily
if s_interval == "1440min":
df_ta = ta.obv(close=df_stock["Adj Close"],
volume=df_stock["Volume"]).dropna()
# Intraday
else:
df_ta = ta.obv(close=df_stock["Close"],
volume=df_stock["Volume"]).dropna()
return df_ta
def _compute_obs(self, index: int) -> list:
sub_df = self._dataset.iloc[self._clock.current_index -
self._clock.window_look_back:self._clock.
current_index]
# print('XX')
# print(self._dataset)
# print(sub_df)
fwma = ta.fwma(sub_df['Close'],
length=(self._clock.window_look_back - 1)).mean()
obv = ta.obv(sub_df['Close'], sub_df['Close']).mean()
###
obs = [fwma, obv]
return obs
def obv(df_stock: pd.DataFrame) -> pd.DataFrame:
"""On Balance Volume
Parameters
----------
df_stock: pd.DataFrame
Dataframe of stock prices
Returns
-------
pd.DataFrame
Dataframe with technical indicator
"""
return pd.DataFrame(
ta.obv(close=df_stock["Adj Close"],
volume=df_stock["Volume"]).dropna())
# Plot
st.header(f"Moving Average Convergence Divergence\n {company_name}")
st.line_chart(data[['macd', 'macdsignal']])
## CCI (Commodity Channel Index)
# CCI
cci = ta.trend.cci(data['High'],
data['Low'],
data['Close'],
window=31,
constant=0.015)
# Plot
st.header(f"Commodity Channel Index\n {company_name}")
st.line_chart(cci)
# ## RSI (Relative Strength Index)
# RSI
data['RSI'] = ta1.rsi(data['Adj Close'], timeperiod=14)
# Plot
st.header(f"Relative Strength Index\n {company_name}")
st.line_chart(data['RSI'])
# ## OBV (On Balance Volume)
# OBV
data['OBV'] = ta1.obv(data['Adj Close'], data['Volume']) / 10**6
# Plot
st.header(f"On Balance Volume\n {company_name}")
st.line_chart(data['OBV'])
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 obv(l_args, s_ticker, s_interval, df_stock):
parser = argparse.ArgumentParser(
prog='obv',
description=
""" The On Balance Volume (OBV) is a cumulative total of the up and
down volume. When the close is higher than the previous close, the volume is added
to the running total, and when the close is lower than the previous close, the volume
is subtracted from the running total. \n \n To interpret the OBV, look for the OBV
to move with the price or precede price moves. If the price moves before the OBV,
then it is a non-confirmed move. A series of rising peaks, or falling troughs, in the
OBV indicates a strong trend. If the OBV is flat, then the market is not trending. """
)
parser.add_argument('-o',
"--offset",
action="store",
dest="n_offset",
type=check_positive,
default=0,
help='offset')
(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":
df_ta = ta.obv(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"On-Balance Volume (OBV) on {s_ticker}")
plt.xlim(df_stock.index[0], df_stock.index[-1])
plt.ylabel(f'Share Price ($)')
plt.grid(b=True, which='major', color='#666666', linestyle='-')
plt.minorticks_on()
plt.grid(b=True,
which='minor',
color='#999999',
linestyle='-',
alpha=0.2)
axVolume = axPrice.twinx()
plt.bar(df_stock.index,
df_stock['6. volume'].values,
color='k',
alpha=0.8,
width=.3)
plt.subplot(212)
plt.plot(df_ta.index, df_ta.values, 'b', lw=1)
plt.xlim(df_stock.index[0], df_stock.index[-1])
plt.legend([f'OBV'])
plt.xlabel('Time')
plt.grid(b=True, which='major', color='#666666', linestyle='-')
plt.minorticks_on()
plt.grid(b=True,
which='minor',
color='#999999',
linestyle='-',
alpha=0.2)
plt.show()
# Intraday
else:
df_ta = ta.obv(close=df_stock['4. close'],
volume=df_stock['5. volume'],
offset=ns_parser.n_offset).dropna()
axPrice = plt.subplot(211)
plt.plot(df_stock.index,
df_stock['5. adjusted close'].values,
'k',
lw=2)
plt.title(f"On-Balance Volume (OBV) on {s_ticker}")
plt.xlim(df_stock.index[0], df_stock.index[-1])
plt.ylabel(f'Share Price ($)')
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.legend([f'OBV'])
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 obv(l_args, s_ticker, s_interval, df_stock):
parser = argparse.ArgumentParser(
prog="obv",
description="""
The On Balance Volume (OBV) is a cumulative total of the up and
down volume. When the close is higher than the previous close, the volume is added
to the running total, and when the close is lower than the previous close, the volume
is subtracted from the running total. \n \n To interpret the OBV, look for the OBV
to move with the price or precede price moves. If the price moves before the OBV,
then it is a non-confirmed move. A series of rising peaks, or falling troughs, in the
OBV indicates a strong trend. If the OBV is flat, then the market is not trending.
""",
)
parser.add_argument(
"-o",
"--offset",
action="store",
dest="n_offset",
type=check_positive,
default=0,
help="offset",
)
try:
ns_parser = parse_known_args_and_warn(parser, l_args)
# Daily
if s_interval == "1440min":
df_ta = ta.obv(
close=df_stock["5. adjusted close"],
volume=df_stock["6. volume"],
offset=ns_parser.n_offset,
).dropna()
# Intraday
else:
df_ta = ta.obv(
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"On-Balance Volume (OBV) 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.legend(["OBV"])
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("")
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