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relative_rotation.py
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"""Relative Rotation Studies"""
from datetime import (
date as dateType,
datetime,
timedelta,
)
from typing import List, Literal, Optional, Tuple, Union
from warnings import warn
import numpy as np
from openbb_charting.core.openbb_figure import OpenBBFigure
from openbb_core.app.command_runner import CommandRunner
from openbb_core.app.model.charts.chart import Chart
from openbb_core.app.model.obbject import OBBject
from openbb_core.app.utils import basemodel_to_df, df_to_basemodel
from openbb_core.provider.abstract.data import Data
from pandas import DataFrame, Series, to_datetime
from plotly import graph_objects as go
color_sequence = [
"burlywood",
"orange",
"grey",
"magenta",
"cyan",
"yellowgreen",
"#1f77b4",
"#aec7e8",
"#ff7f0e",
"#ffbb78",
"#d62728",
"#ff9896",
"#9467bd",
"#c5b0d5",
"#8c564b",
"#c49c94",
"#e377c2",
"#f7b6d2",
"#7f7f7f",
"#c7c7c7",
"#bcbd22",
"#dbdb8d",
"#17becf",
"#9edae5",
"#7e7e7e",
"#1b9e77",
"#d95f02",
"#7570b3",
"#e7298a",
"#66a61e",
"#e6ab02",
"#a6761d",
"#666666",
"#f0027f",
"#bf5b17",
"#d9f202",
"#8dd3c7",
"#ffffb3",
"#bebada",
"#fb8072",
"#80b1d3",
"#fdb462",
"#b3de69",
"#fccde5",
"#d9d9d9",
"#bc80bd",
"#ccebc5",
"#ffed6f",
"#6a3d9a",
"#b15928",
"#b2df8a",
"#33a02c",
"#fb9a99",
"#e31a1c",
"#fdbf6f",
"#ff7f00",
"#cab2d6",
"#6a3d9a",
"#ffff99",
"#b15928",
]
async def get_data(
symbols: List[str],
benchmark: str,
study: Literal["price", "volume", "volatility"] = "price",
date: Optional[dateType] = None,
tail_periods: Optional[int] = 16,
tail_interval: Literal["week", "month"] = "week",
provider: str = "yfinance",
) -> Tuple[DataFrame, DataFrame]:
"""
Fetch data with the OpenBB Platform Command Runner.
Parameters
----------
symbols: List[str]
List of symbols to fetch data for.
benchmark: str
Benchmark ticker symbol to compare against.
study: Literal["price", "volume", "volatility"]
The type of data to study. Default is "price".
date: Optional[dateType]
The date representing the end date of the study. Default is today.
provider: str
The OpenBB Platform data provider to use. Default is yfinance.
"""
backfill = 156
if tail_interval == "week" and tail_periods > backfill:
backfill = tail_periods
if tail_interval == "month" and tail_periods * 4 > backfill:
backfill = tail_periods * 4
if date is not None:
date = to_datetime(date)
start_date = (date - timedelta(weeks=backfill)).date()
end_date = date
if date is None:
start_date = (datetime.now() - timedelta(weeks=backfill)).date()
end_date = datetime.now().date()
target_column = "volume" if study == "volume" else "close"
tickers = symbols+ [benchmark]
runner = CommandRunner()
try:
_data = await runner.run(
"/equity/price/historical",
provider_choices={
"provider": provider,
},
standard_params={
"symbol" : ",".join(tickers),
"start_date": start_date,
"end_date": end_date,
"interval": "1d",
},
extra_params={"use_cache": False}
) # type: ignore
data = _data.to_df().pivot(columns="symbol", values=target_column)
except Exception as e:
raise RuntimeError(f"There was an error loading data for {symbols}: {e}")
try:
benchmark_data = data.pop(benchmark).to_frame()
except Exception as e:
raise RuntimeError(f"There was an error loading data for {benchmark}: {e}")
prices_data = data
return prices_data, benchmark_data
def absolute_maximum_scale(data: Series) -> Series:
"""Absolute Maximum Scale Normaliztion Method"""
return data / data.abs().max()
def min_max_scaling(data: Series) -> Series:
""""Min/Max ScalingNormalization Method"""
return (data - data.min()) / (data.max() - data.min())
def z_score_standardization(data: Series) -> Series:
"""Z-Score Standardization Method."""
return (data - data.mean()) / data.std()
def normalize(data: DataFrame, method: Literal["z", "m", "a"] = "z") -> DataFrame:
"""
Normalize a Pandas DataFrame based on method.
Parameters
-----------
data: DataFrame
Pandas DataFrame with any number of columns to be normalized.
method: Literal["z", "m", "a"]
Normalization method.
z: Z-Score Standardization
m: Min/Max Scaling
a: Absolute Maximum Scale
Returns
--------
DataFrame
Normalized DataFrame.
"""
methods = {"z": z_score_standardization, "m": min_max_scaling, "a": absolute_maximum_scale}
df = data.copy()
for col in df.columns:
df.loc[:, col] = methods[f"{method}"](df.loc[:, col])
return df
def standard_deviation(
data: DataFrame,
window: int = 21,
trading_periods: Optional[int] = None,
is_crypto: bool = False,
clean: bool = True,
) -> DataFrame:
"""
Standard deviation.
Measures how widely returns are dispersed from the average return.
It is the most common (and biased) estimator of volatility.
Parameters
----------
data : pd.DataFrame
Dataframe of OHLC prices.
window : int [default: 21]
Length of window to calculate over.
trading_periods : Optional[int] [default: 252]
Number of trading periods in a year.
is_crypto : bool [default: False]
If true, trading_periods is defined as 365.
clean : bool [default: True]
Whether to clean the data or not by dropping NaN values.
Returns
-------
pd.DataFrame : results
Dataframe with results.
"""
data = data.copy()
results = DataFrame()
if window < 2:
warn("Error: Window must be at least 2, defaulting to 21.")
window = 21
if trading_periods and is_crypto:
warn("is_crypto is overridden by trading_periods.")
if not trading_periods:
trading_periods = 365 if is_crypto else 252
for col in data.columns.tolist():
log_return = (data[col] / data[col].shift(1)).apply(np.log)
result = log_return.rolling(window=window, center=False).std() * np.sqrt(
trading_periods
)
results[col] = result
if clean:
return results.dropna()
return results
def calculate_momentum(data: Series, long_period: int = 252, short_period: int = 21) -> Series:
"""
Momentum is calculated as the log trailing 12-month return minus trailing one-month return.
Higher values indicate larger, positive momentum exposure.
Momentum = ln(1 + r12) - ln(1 + r1)
Parameters
----------
data: Series
Time series data to calculate the momentum for.
long_period: Optional[int]
Long period to base the calculation on. Default is one standard trading year.
short_period: Optional[int]
Short period to subtract from the long period. Default is one trading month.
Returns
-------
Series
Pandas Series with the calculated momentum.
"""
df = data.copy()
epsilon = 1e-10
momentum_long = np.log(1 + df.pct_change(long_period) + epsilon)
momentum_short = np.log(1 + df.pct_change(short_period) + epsilon)
data = momentum_long - momentum_short # type: ignore
return data
def get_momentum(data: DataFrame, long_period: int = 252, short_period: int = 21) -> DataFrame:
"""
Calculate the Relative-Strength Momentum Indicator. Takes the Relative Strength Ratio as the input.
Parameters
----------
data: DataFrame
Indexed time series data formatted with each column representing a ticker.
long_period: Optional[int]
Long period to base the calculation on. Default is one standard trading year.
short_period: Optional[int]
Short period to subtract from the long period. Default is one trading month.
Returns
-------
DataFrame
Pandas DataFrame with the calculated historical momentum factor exposure score.
"""
df = data.copy()
rs_momentum = DataFrame()
for ticker in df.columns.to_list():
rs_momentum.loc[:, ticker] = calculate_momentum(df.loc[:, ticker], long_period, short_period) # type: ignore
return rs_momentum
def calculate_relative_strength_ratio(
symbols_data: DataFrame,
benchmark_data: DataFrame,
) -> DataFrame:
"""
Calculate the Relative Strength Ratio for each ticker (column) in a DataFrame against the benchmark.
symbols data and benchmark data should have the same index, and each column should represent a ticker.
Parameters
-----------
symbols_data: DataFrame
Pandas DataFrame with the symbols data to compare against the benchmark.
benchmark_data: DataFrame
Pandas DataFrame with the benchmark data.
Returns
--------
DataFrame
Pandas DataFrame with the calculated relative strength ratio for each ticker joined with the benchmark values.
"""
return (
symbols_data.div(benchmark_data.iloc[:, 0], axis=0)
.multiply(100)
.join(benchmark_data.iloc[:, 0])
.dropna()
)
def process_data(
symbols_data: DataFrame,
benchmark_data: DataFrame,
long_period: int = 252,
short_period: int = 21,
normalize_method: Literal["z", "m", "a"] = "z"
) -> Tuple[DataFrame, DataFrame]:
"""
Process the raw data into normalized indicator values.
Parameters
----------
symbols_data: DataFrame
Indexed time series data formatted with each column representing a ticker.
benchmark_data: DataFrame
Indexed time series data of the benchmark symbol.
long_period: Optional[int]
Long period to base the calculation on. Default is one standard trading year.
short_period: Optional[int]
Short period to subtract from the long period. Default is one trading month.
normalize_method: Literal["z", "m", "a"]
Returns
-------
Tuple[DataFrame, DataFrame]
Tuple of Pandas DataFrames with the normalized ratio and momentum indicator values.
"""
ratio_data = calculate_relative_strength_ratio(symbols_data, benchmark_data)
momentum_data = get_momentum(ratio_data, long_period, short_period)
normalized_ratio = normalize(ratio_data, normalize_method)
normalized_momentum = normalize(momentum_data, normalize_method)
return normalized_ratio, normalized_momentum
def _create_figure_with_tails(
ratios_data: DataFrame,
momentum_data: DataFrame,
study: str,
benchmark_symbol: str,
tail_periods: int,
tail_interval: Literal["week", "month"]
):
"""Create the Plotly Figure Object with Tails."""
symbols = ratios_data.columns.to_list()
tail_dict = {"week": "W", "month": "M"}
ratios_data.index = to_datetime(ratios_data.index)
ratios_data =ratios_data.resample(tail_dict[tail_interval]).last()
momentum_data.index = to_datetime(momentum_data.index)
momentum_data = momentum_data.resample(tail_dict[tail_interval]).last()
ratios_data = ratios_data.iloc[-tail_periods:]
momentum_data = momentum_data.iloc[-tail_periods:]
_tail_periods = len(ratios_data)
tail_title = (
f"The {_tail_periods} {tail_interval.capitalize()}s "
f"Ending {ratios_data.index[-1].strftime('%Y-%m-%d')}"
)
x_min = ratios_data.min().min()
x_max = ratios_data.max().max()
y_min = momentum_data.min().min()
y_max = momentum_data.max().max()
frames = []
x_data = ratios_data
y_data = momentum_data
for i, date in enumerate(ratios_data.index):
frame_data = []
for j, symbol in enumerate(symbols):
x_frame_data = x_data[symbol].iloc[:i + 1]
y_frame_data = y_data[symbol].iloc[:i + 1]
name = symbol.upper().replace("^", "").replace(":US", "")
special_name = "-" in name or len(name) > 7
marker_size = 34 if special_name else 30
line_frame_trace = go.Scatter(
x=x_frame_data,
y=y_frame_data,
mode="markers+lines",
line=dict(color=color_sequence[j], width=2, dash="dash"),
marker=dict(size=5, color=color_sequence[j], line=dict(color="black", width=1)),
showlegend=False,
opacity=0.3,
name=name,
text=name,
hovertemplate=
"<b>%{fullData.name}</b>: " +
"RS-Ratio: %{x:.4f}, " +
"RS-Momentum: %{y:.4f}" +
"<extra></extra>",
hoverlabel=dict(font_size=10),
)
marker_frame_trace = go.Scatter(
x=[x_frame_data.iloc[-1]],
y=[y_frame_data.iloc[-1]],
mode="markers+text",
name=name,
text=name,
textposition="middle center",
textfont=dict(size=10, color="black") if len(symbol) < 4 else dict(size=7, color="black"),
line=dict(color=color_sequence[j], width=2, dash="dash"),
marker=dict(size=marker_size, color=color_sequence[j], line=dict(color="black", width=1)),
opacity=0.9,
showlegend=False,
hovertemplate="<b>%{fullData.name}</b>: RS-Ratio: %{x:.4f}, RS-Momentum: %{y:.4f}<extra></extra>",
)
frame_data.extend([line_frame_trace, marker_frame_trace])
frames.append(go.Frame(data=frame_data, name=f'Frame {i}'))
# Define the initial trace for the figure
initial_trace = frames[0]['data']
padding = 0.1
y_range = [y_min - padding * abs(y_min) - 0.3, y_max + padding * abs(y_max) + 0.3]
x_range = [x_min - padding * abs(x_min) - 0.3, x_max + padding * abs(x_max) + 0.3]
# Create the layout for the figure
layout = go.Layout(
title={
"text": (
f"Relative Rotation Against {benchmark_symbol.replace('^', '')} {study.capitalize()} For {tail_title}"
),
"x": 0.5,
"xanchor": "center",
"font": dict(size=18)
},
xaxis=dict(
title="RS-Ratio",
showgrid=True,
zeroline=True,
showline=True,
mirror=True,
ticklen=0,
zerolinecolor="black",
range=x_range,
gridcolor="lightgrey",
titlefont=dict(size=16),
showspikes=False,
),
yaxis=dict(
title="<br>RS-Momentum",
showgrid=True,
zeroline=True,
showline=True,
mirror=True,
ticklen=0,
zerolinecolor="black",
range=y_range,
gridcolor="lightgrey",
titlefont=dict(size=16),
side="left",
title_standoff=5,
),
plot_bgcolor="rgba(255,255,255,1)",
shapes=[
go.layout.Shape(
type="rect",
xref="x",
yref="y",
x0=0,
y0=0,
x1=x_range[1],
y1=y_range[1],
fillcolor="lightgreen",
opacity=0.3,
layer="below",
line_width=0,
),
go.layout.Shape(
type="rect",
xref="x",
yref="y",
x0=x_range[0],
y0=0,
x1=0,
y1=y_range[1],
fillcolor="lightblue",
opacity=0.3,
layer="below",
line_width=0,
),
go.layout.Shape(
type="rect",
xref="x",
yref="y",
x0=x_range[0],
y0=y_range[0],
x1=0,
y1=0,
fillcolor="lightpink",
opacity=0.3,
layer="below",
line_width=0,
),
go.layout.Shape(
type="rect",
xref="x",
yref="y",
x0=0,
y0=y_range[0],
x1=x_range[1],
y1=0,
fillcolor="lightyellow",
opacity=0.3,
layer="below",
line_width=0,
),
go.layout.Shape(
type="rect",
xref="x",
yref="y",
x0=x_range[0],
y0=y_range[0],
x1=x_range[1],
y1=y_range[1],
line=dict(
color="Black",
width=1,
),
fillcolor="rgba(0,0,0,0)",
layer="above",
),
],
annotations=[
go.layout.Annotation(
x=1,
xref="paper",
y=1,
yref="paper",
text="Leading",
showarrow=False,
font=dict(
size=18,
color="darkgreen",
),
),
go.layout.Annotation(
x=1,
xref="paper",
y=0,
yref="paper",
text="Weakening",
showarrow=False,
font=dict(
size=18,
color="goldenrod",
),
),
go.layout.Annotation(
x=0,
xref="paper",
y=0,
yref="paper",
text="Lagging",
showarrow=False,
font=dict(
size=18,
color="red",
),
),
go.layout.Annotation(
x=0,
xref="paper",
yref="paper",
y=1,
text="Improving",
showarrow=False,
font=dict(
size=18,
color="blue",
),
),
],
autosize=True,
margin=dict(
l=30,
r=50,
b=50,
t=50,
pad=0,
),
dragmode="pan",
hovermode="closest",
updatemenus = [{
"buttons": [{
"args": [None, {"frame": {"duration": 500, "redraw": False}, "fromcurrent": True, "transition": {"duration": 500, "easing": "linear"}}],
"label": "Play",
"method": "animate"
}],
"direction": "left",
"pad": {"r": 0, "t": 75},
"showactive": False,
"type": "buttons",
"x": -0.025,
"xanchor": "left",
"y": 0,
"yanchor": "top",
"bgcolor": "#444444", # Light gray background
"bordercolor": "#CCCCCC", # Black border
"borderwidth": 2, # Border width in pixels
"font": {"color": "#FFFFFF"}, # White text
}],
sliders=[{
"active": 0,
"yanchor": "top",
"xanchor": "center",
"currentvalue": {
"font": {"size": 16},
"prefix": "Date: ",
"visible": True,
"xanchor": "right",
},
"transition": {"duration": 300, "easing": "cubic-in-out"},
"pad": {"b": 10, "t": 50},
"len": 0.9,
"x": 0.5,
"y": 0,
"steps": [{
"label": f"{x_data.index[i].strftime('%Y-%m-%d')}",
"method": "animate",
"args": [[f'Frame {i}'], {
"mode": "immediate",
"transition": {"duration": 300},
"frame": {"duration": 300, "redraw": False},
}],
} for i in range(len(x_data.index))]
}],
)
# Create the figure and add the initial trace
fig = go.Figure(data=initial_trace, layout=layout, frames=frames)
return fig
def _create_figure(
ratios_data: DataFrame,
momentum_data: DataFrame,
benchmark_symbol: str,
date: Optional[dateType] = None,
study: Literal["price", "volume", "volatility"] = "price",
):
"""Create the Plotly Figure Object without Tails."""
if date is not None:
date = date.strftime("%Y-%m-%d") if isinstance(date, dateType) else date # type: ignore
if date not in ratios_data.index:
warn(f"Date {date} not found in data, using the last available date.")
date = ratios_data.index[-1].strftime("%Y-%m-%d") if isinstance(date, dateType) else ratios_data.index[-1]
if date is None:
date = ratios_data.index[-1].strftime("%Y-%m-%d") if isinstance(date, dateType) else ratios_data.index[-1]
# Select a single row from each dataframe
row_x = ratios_data.loc[date]
row_y = momentum_data.loc[date]
x_max = row_x.max() + 0.5
x_min = row_x.min() - 0.5
y_max = row_y.max() + 0.5
y_min = row_y.min() - 0.5
# Create an empty list to store the scatter traces
traces = []
# Loop through each column in the row_x dataframe
for i, (column_name, value_x) in enumerate(row_x.items()):
# Retrieve the corresponding value from the row_y dataframe
value_y = row_y[column_name]
marker_name = column_name.upper().replace("^", "").replace(":US", "")
special_name = "-" in marker_name or len(marker_name) > 7
marker_size = 34 if special_name else 30
# Create a scatter trace for each column
trace = go.Scatter(
x=[value_x],
y=[value_y],
mode="markers+text",
text=[marker_name],
textposition="middle center",
textfont=dict(size=10 if len(marker_name) < 4 else 8, color="black"),
marker=dict(size=marker_size, color=color_sequence[i % len(color_sequence)], line=dict(color="black", width=1)),
name=column_name,
showlegend=False,
hovertemplate=
"<b>%{text}</b>: " +
"RS-Ratio: %{x:.4f}, " +
"RS-Momentum: %{y:.4f}" +
"<extra></extra>",
)
# Add the trace to the list
traces.append(trace)
padding = 0.1
y_range = [y_min - padding * abs(y_min) - 0.3, y_max + padding * abs(y_max)]
x_range = [x_min - padding * abs(x_min), x_max + padding * abs(x_max)]
layout = go.Layout(
title={
"text": (
f"RS-Ratio vs RS-Momentum of {study.capitalize()} "
f"Against {benchmark_symbol.replace('^', '')} - {to_datetime(row_x.name).strftime('%Y-%m-%d')}"
),
"x": 0.5,
"xanchor": "center",
"font": dict(size=20)
},
xaxis=dict(
title="RS-Ratio",
showgrid=True,
zeroline=True,
showline=True,
mirror=True,
ticklen=0,
zerolinecolor="black",
range=x_range,
gridcolor="lightgrey",
titlefont=dict(size=16),
showspikes=False,
),
yaxis=dict(
title="<br>RS-Momentum",
showgrid=True,
zeroline=True,
showline=True,
mirror=True,
ticklen=0,
zerolinecolor="black",
range=y_range,
gridcolor="lightgrey",
titlefont=dict(size=16),
side="left",
title_standoff=5,
),
plot_bgcolor="rgba(255,255,255,1)",
shapes=[
go.layout.Shape(
type="rect",
xref="x",
yref="y",
x0=0,
y0=0,
x1=x_range[1],
y1=y_range[1],
fillcolor="lightgreen",
opacity=0.3,
layer="below",
line_width=0,
),
go.layout.Shape(
type="rect",
xref="x",
yref="y",
x0=x_range[0],
y0=0,
x1=0,
y1=y_range[1],
fillcolor="lightblue",
opacity=0.3,
layer="below",
line_width=0,
),
go.layout.Shape(
type="rect",
xref="x",
yref="y",
x0=x_range[0],
y0=y_range[0],
x1=0,
y1=0,
fillcolor="lightpink",
opacity=0.3,
layer="below",
line_width=0,
),
go.layout.Shape(
type="rect",
xref="x",
yref="y",
x0=0,
y0=y_range[0],
x1=x_range[1],
y1=0,
fillcolor="lightyellow",
opacity=0.3,
layer="below",
line_width=0,
),
go.layout.Shape(
type="rect",
xref="x",
yref="y",
x0=x_range[0],
y0=y_range[0],
x1=x_range[1],
y1=y_range[1],
line=dict(
color="Black",
width=1,
),
fillcolor="rgba(0,0,0,0)",
layer="above",
),
],
annotations=[
go.layout.Annotation(
x=1,
xref="paper",
y=1,
yref="paper",
text="Leading",
showarrow=False,
font=dict(
size=18,
color="darkgreen",
),
),
go.layout.Annotation(
x=1,
xref="paper",
y=0,
yref="paper",
text="Weakening",
showarrow=False,
font=dict(
size=18,
color="goldenrod",
),
),
go.layout.Annotation(
x=0,
xref="paper",
y=0,
yref="paper",
text="Lagging",
showarrow=False,
font=dict(
size=18,
color="red",
),
),
go.layout.Annotation(
x=0,
xref="paper",
yref="paper",
y=1,
text="Improving",
showarrow=False,
font=dict(
size=18,
color="blue",
),
),
],
autosize=True,
margin=dict(
l=30,
r=50,
b=50,
t=50,
pad=0,
),
dragmode="pan",
hovermode="x unified",
)
fig = go.Figure(data=traces, layout=layout)
return fig
class RelativeRotationData(Data):
"""Relative Rotation Data Model."""
symbols: List[str]
benchmark: str
study: Literal["price", "volume", "volatility"] = "price"
date: Optional[dateType] = None
long_period: Optional[int] = 252
short_period: Optional[int] = 21
window: Optional[int] = 21
trading_periods: Optional[int] = 252
normalize_method: Optional[Literal["z", "m", "a"]] = "z"
tail_periods: Optional[int] = 30
tail_interval: Literal["week", "month"] = "week"
provider: Optional[str] = None
start_date: Optional[str] = None
end_date: Optional[str] = None
symbols_data: Optional[List[Data]] = None
benchmark_data: Optional[List[Data]] = None
rs_ratios: Optional[List[Data]] = None
rs_momentum: Optional[List[Data]] = None
def show(
self,
date: Optional[dateType] = None,
show_tails: bool = True,
tail_periods: Optional[int] = 12,
tail_interval: Literal["week", "month", None] = "week",
external: bool = False,
**kwargs,
) -> Union[OpenBBFigure, None]:
"""Plot the data, optionally for a specific date in the collected data."""
ratios_data = basemodel_to_df(self.rs_ratios).set_index("date") # type: ignore
ratios_data.index = to_datetime(ratios_data.index)
momentum_data = basemodel_to_df(self.rs_momentum).set_index("date") # type: ignore
momentum_data.index = to_datetime(momentum_data.index)
if date is not None:
show_tails = False
if show_tails is False:
fig = _create_figure(
ratios_data = ratios_data,
momentum_data = momentum_data,
benchmark_symbol =self.benchmark,
date=date if date is not None else self.date,
study=self.study,
)
if show_tails is True:
fig = _create_figure_with_tails(
ratios_data = ratios_data,
momentum_data = momentum_data,
benchmark_symbol =self.benchmark,
study=self.study,
tail_periods= self.tail_periods if tail_periods is None else tail_periods, # type: ignore
tail_interval= self.tail_interval if tail_interval is None else tail_interval,
)
return (
OpenBBFigure(fig, create_backend=True).show() if external is False # type: ignore
else OpenBBFigure(fig, create_backend=True) # type: ignore
)
async def create(
symbols: Union[List[str], DataFrame, Data],
benchmark: Union[str, DataFrame, Data],
study: Optional[Literal["price", "volume", "volatility"]] = "price",
date: Optional[dateType] = None,
long_period: Optional[int] = 252,
short_period: Optional[int] = 21,
window: Optional[int] = 21,
trading_periods: Optional[int] = 252,
normalize_method: Optional[Literal["z", "m", "a"]] = "z",
tail_periods: Optional[int] = 30,
tail_interval: Literal["week", "month"] = "week",
provider:Optional[str] = None,
):
"""
Create an instance of RelativeRotationData with provided data or a list of symbols and the benchmark.
"""
try:
symbols = basemodel_to_df(symbols).set_index("date") # type: ignore
benchmark = basemodel_to_df(benchmark).set_index("date") # type: ignore
except AttributeError:
pass
if (
isinstance(symbols, list)