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OpenBB/openbb_terminal/common/quantitative_analysis/qa_view.py
Pratyush Shukla 13283fbfce CI listing quick fix (#6002) 
* BIGGGG LINTING

* fixing lints

* fixing lints

* black

* very ruff

* no export

* fix hedge_view again

* lints

* platform lints

* lints

* black

* black it @hjoaquim

* fix some more linting

---------

Co-authored-by: hjoaquim <h.joaquim@campus.fct.unl.pt>
2024-01-26 17:08:42 +00:00

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"""Quantitative Analysis View"""
__docformat__ = "numpy"
# pylint: disable=C0302
import logging
import os
import warnings
from datetime import datetime
from typing import Any, Dict, List, Optional, Union
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from detecta import detect_cusum
from pandas.plotting import register_matplotlib_converters
from scipy import stats
from statsmodels.graphics.gofplots import qqplot
from openbb_terminal import OpenBBFigure, theme
from openbb_terminal.common.quantitative_analysis import qa_model
from openbb_terminal.decorators import log_start_end
from openbb_terminal.helper_funcs import export_data, print_rich_table
from openbb_terminal.rich_config import console
logger = logging.getLogger(__name__)
register_matplotlib_converters()
def lambda_color_red(val: Any) -> str:
"""Adds red to dataframe value"""
if val > 0.05:
return f"[red]{round(val,4)}[/red]"
return round(val, 4)
@log_start_end(log=logger)
def display_summary(
data: pd.DataFrame, export: str = "", sheet_name: Optional[str] = None
) -> None:
"""Prints table showing summary statistics
Parameters
----------
data : pd.DataFrame
DataFrame to get statistics of
export : str
Format to export data
"""
summary = qa_model.get_summary(data)
print_rich_table(
summary,
headers=list(summary.columns),
floatfmt=".3f",
show_index=True,
title="[bold]Summary Statistics[/bold]",
export=bool(export),
)
export_data(
export,
os.path.dirname(os.path.abspath(__file__)).replace("common", "stocks"),
"summary",
summary,
sheet_name,
)
@log_start_end(log=logger)
def display_hist(
data: pd.DataFrame,
target: str,
symbol: str = "",
bins: int = 15,
external_axes: bool = False,
) -> Union[OpenBBFigure, None]:
"""Plots histogram of data
Parameters
----------
data : pd.DataFrame
Dataframe to look at
target : str
Data column to get histogram of the dataframe
symbol : str
Name of dataset
bins : int
Number of bins in histogram
external_axes : bool, optional
Whether to return the figure object or not, by default False
Examples
--------
>>> from openbb_terminal.sdk import openbb
>>> df = openbb.stocks.load("AAPL")
>>> openbb.qa.hist(data=df, target="Adj Close")
"""
data = data[target]
fig = OpenBBFigure.create_subplots(1, 1, shared_yaxes=False)
if isinstance(data.index[0], datetime):
start = data.index[0]
fig.set_title(
f"Histogram of {symbol} {target} from {start.strftime('%Y-%m-%d')}"
)
else:
fig.set_title(f"Histogram of {symbol} {target}")
fig.add_histplot(
data,
name=["Univariate distribution", "Marginal distributions"],
colors=[theme.up_color],
bins=bins,
curve="kde",
)
fig.update_layout(
xaxis_title="Value",
margin=dict(r=40),
yaxis=dict(title="Proportion", title_standoff=40),
bargap=0.01,
bargroupgap=0,
)
fig.update_traces(
selector=dict(type="histogram"),
marker=dict(
color=theme.up_color,
line=dict(color="white", width=2.5),
),
)
return fig.show(external=external_axes)
@log_start_end(log=logger)
def display_cdf(
data: pd.DataFrame,
target: str,
symbol: str = "",
export: str = "",
sheet_name: Optional[str] = None,
external_axes: bool = False,
) -> Union[OpenBBFigure, None]:
"""Plots Cumulative Distribution Function
Parameters
----------
data : pd.DataFrame
Dataframe to look at
target : str
Data column
symbol : str
Name of dataset
export : str
Format to export data
external_axes : bool, optional
Whether to return the figure object or not, by default False
Examples
--------
>>> from openbb_terminal.sdk import openbb
>>> df = openbb.stocks.load("AAPL")
>>> openbb.qa.cdf(data=df, target="Adj Close")
"""
data = data[target]
start = data.index[0]
cdf = data.value_counts().sort_index().div(len(data)).cumsum()
minVal = data.values.min()
q25 = np.quantile(data.values, 0.25)
medianVal = np.quantile(data.values, 0.5)
q75 = np.quantile(data.values, 0.75)
labels = [
(minVal, q25),
(0.25, 0.25),
"Q1",
(q25, q25),
(0, 0.25),
"Q1",
(minVal, medianVal),
(0.5, 0.5),
"Median",
(medianVal, medianVal),
(0, 0.5),
"Median",
(minVal, q75),
(0.75, 0.75),
"Q3",
(q75, q75),
(0, 0.75),
"Q3",
]
fig = OpenBBFigure(xaxis_title=target.title(), yaxis_title="Probability")
fig.set_title(
f"Cumulative Distribution Function of {symbol} {target} from {start.strftime('%Y-%m-%d')}"
)
fig.add_scatter(
x=cdf.index,
y=cdf.values,
name="Cumulative Distribution Function",
marker_color=theme.up_color,
mode="lines",
)
for xt, yt, label in zip(labels[::3], labels[1::3], labels[2::3]):
fig.add_annotation(
x=minVal + (xt[1] - minVal) / 2,
y=yt[1] + 0.04,
text=label,
font=dict(color=theme.down_color),
)
fig.add_shape(
type="line",
x0=xt[0],
y0=yt[0],
x1=xt[1],
y1=yt[1],
line=dict(color=theme.down_color, width=1.5, dash="dash"),
)
export_data(
export,
os.path.dirname(os.path.abspath(__file__)).replace("common", "stocks"),
"cdf",
pd.DataFrame(cdf),
sheet_name,
fig,
)
return fig.show(external=external_axes)
@log_start_end(log=logger)
def display_bw(
data: pd.DataFrame,
target: str,
symbol: str = "",
yearly: bool = True,
external_axes: bool = False,
) -> Union[OpenBBFigure, None]:
"""Plots box and whisker plots
Parameters
----------
symbol : str
Name of dataset
data : pd.DataFrame
Dataframe to look at
target : str
Data column to look at
yearly : bool
Flag to indicate yearly accumulation
external_axes : bool, optional
Whether to return the figure object or not, by default False
Examples
--------
>>> from openbb_terminal.sdk import openbb
>>> df = openbb.stocks.load("AAPL")
>>> openbb.qa.bw(data=df, target="Adj Close")
"""
data = data.copy()
start = data[target].index[0]
colors = theme.get_colors()
color = colors[0]
pd.options.mode.chained_assignment = None
data["x_data"] = data[target].index.year if yearly else data[target].index.month
pd.options.mode.chained_assignment = "warn"
l_months = [
"Jan",
"Feb",
"Mar",
"Apr",
"May",
"Jun",
"Jul",
"Aug",
"Sep",
"Oct",
"Nov",
"Dec",
]
fig = OpenBBFigure(
title=(
f"{['Monthly','Yearly'][yearly]} box plot of {symbol.upper()} "
f"{target} from {start.strftime('%Y-%m-%d')}"
),
yaxis_title=target,
xaxis_title=["Monthly", "Yearly"][yearly],
)
for i, group in enumerate(data["x_data"].unique()):
x = group if yearly else l_months[group - 1]
y = data[data["x_data"] == group][target]
fig.add_box(
y=y,
x=[x] * len(y),
name=str(x),
marker=dict(
color=theme.up_color,
outliercolor=theme.up_color,
),
fillcolor=colors[i % len(colors)],
line_color=color,
boxmean=True,
whiskerwidth=1,
boxpoints="suspectedoutliers",
hoveron="points",
showlegend=False,
)
fig.add_shape(
type="line",
x0=x,
y0=y.min(),
x1=x,
y1=y.max(),
line=dict(color=color, width=1.5, dash="dash"),
)
return fig.show(external=external_axes)
@log_start_end(log=logger)
def display_acf(
data: pd.DataFrame,
target: str,
symbol: str = "",
lags: int = 15,
external_axes: bool = False,
) -> Union[OpenBBFigure, None]:
"""Plots Auto and Partial Auto Correlation of returns and change in returns
Parameters
----------
data : pd.DataFrame
Dataframe to look at
target : str
Data column to look at
symbol : str
Name of dataset
lags : int
Max number of lags to look at
external_axes : bool, optional
Whether to return the figure object or not, by default False
Examples
--------
>>> from openbb_terminal.sdk import openbb
>>> df = openbb.stocks.load("AAPL")
>>> openbb.qa.acf(data=df, target="Adj Close")
"""
data = data[target]
start = data.index[0]
fig = OpenBBFigure.create_subplots(
rows=2,
cols=2,
shared_xaxes=False,
subplot_titles=[
f"{symbol} Returns Auto-Correlation",
f"{symbol} Returns Partial Auto-Correlation",
f"Change in {symbol} Returns Auto-Correlation",
f"Change in {symbol} Returns Partial Auto-Correlation",
],
vertical_spacing=0.1,
horizontal_spacing=0.1,
x_title="Lag",
).set_title(title=f"ACF differentials starting from {start.strftime('%Y-%m-%d')}")
kwargs = dict(marker=dict(color=theme.get_colors()[0]), line=dict(color="white"))
np_diff = np.diff(data.values)
# Diff Auto - correlation function for original time series
fig.add_corr_plot(np_diff, lags, row=1, col=1, **kwargs) # type: ignore
# Diff Partial auto - correlation function for original time series
fig.add_corr_plot(np_diff, lags, row=1, col=2, pacf=True, method="ywm", **kwargs) # type: ignore
# Diff Diff Auto-correlation function for original time series
fig.add_corr_plot(np.diff(np_diff), lags, row=2, col=1, **kwargs) # type: ignore
# Diff Diff Partial auto-correlation function for original time series
fig.add_corr_plot(
np.diff(np_diff), lags, row=2, col=2, pacf=True, method="ywm", **kwargs # type: ignore
)
return fig.show(external=external_axes)
@log_start_end(log=logger)
def display_qqplot(
data: pd.DataFrame,
target: str,
symbol: str = "",
external_axes: bool = False,
) -> Union[OpenBBFigure, None]:
"""Plots QQ plot for data against normal quantiles
Parameters
----------
data : pd.DataFrame
Dataframe
target : str
Column in data to look at
symbol : str
Stock ticker
external_axes : bool, optional
Whether to return the figure object or not, by default False
Examples
--------
>>> from openbb_terminal.sdk import openbb
>>> df = openbb.stocks.load("AAPL")
>>> openbb.qa.qqplot(data=df, target="Adj Close")
"""
# Statsmodels has a UserWarning for marker kwarg-- which we don't use
warnings.filterwarnings(category=UserWarning, action="ignore")
data = data[target]
fig = OpenBBFigure(
xaxis_title="Theoretical quantiles", yaxis_title="Sample quantiles"
)
fig.set_title(title=f"Q-Q plot for {symbol} {target}")
qqplot_data = (
qqplot(
data,
stats.distributions.norm,
fit=True,
line="45",
color=theme.down_color,
)
.gca()
.lines
)
fig.add_scatter(
x=qqplot_data[0].get_xdata(),
y=qqplot_data[0].get_ydata(),
mode="markers",
showlegend=False,
)
fig.add_scatter(
x=qqplot_data[1].get_xdata(),
y=qqplot_data[1].get_ydata(),
mode="lines",
line_color=theme.up_color,
showlegend=False,
)
plt.close()
return fig.show(external=external_axes)
@log_start_end(log=logger)
def display_cusum(
data: pd.DataFrame,
target: str,
threshold: float = 5,
drift: float = 2.1,
external_axes: bool = False,
) -> Union[OpenBBFigure, None]:
"""Plots Cumulative sum algorithm (CUSUM) to detect abrupt changes in data
Parameters
----------
data : pd.DataFrame
Dataframe
target : str
Column of data to look at
threshold : float
Threshold value
drift : float
Drift parameter
external_axes : bool, optional
Whether to return the figure object or not, by default False
Examples
--------
>>> from openbb_terminal.sdk import openbb
>>> df = openbb.stocks.load("AAPL")
>>> openbb.qa.cusum(data=df, target="Adj Close")
"""
target_series = data[target].values
# The code for this plot was adapted from detecta's sources because at the
# time of writing this detect_cusum had a bug related to external axes support.
# see https: // github.com/demotu/detecta/pull/3
tap, tan = 0, 0
ta, tai, taf, _ = detect_cusum(
x=target_series,
threshold=threshold,
drift=drift,
ending=True,
show=False,
)
# Thus some variable names are left unchanged and unreadable...
gp, gn = np.zeros(target_series.size), np.zeros(target_series.size)
for i in range(1, target_series.size):
s = target_series[i] - target_series[i - 1]
gp[i] = gp[i - 1] + s - drift # cumulative sum for + change
gn[i] = gn[i - 1] - s - drift # cumulative sum for - change
if gp[i] < 0:
gp[i], tap = 0, i
if gn[i] < 0:
gn[i], tan = 0, i
if gp[i] > threshold or gn[i] > threshold: # change detected!
ta = np.append(ta, i) # alarm index
tai = np.append(tai, tap if gp[i] > threshold else tan) # start
gp[i], gn[i] = 0, 0 # reset alarm
fig = OpenBBFigure.create_subplots(
2,
1,
x_title="Data points",
subplot_titles=[
"Time series and detected changes "
+ f"(threshold= {threshold:.3g}, drift= {drift:.3g}): N changes = {len(tai)}",
"Time series of the cumulative sums of positive and negative changes",
],
vertical_spacing=0.1,
)
target_series_indexes = range(data[target].size)
fig.add_scatter(
x=[*target_series_indexes], y=target_series, showlegend=False, row=1, col=1
)
if len(ta):
fig.add_scatter(
x=tai,
y=target_series[tai],
mode="markers",
marker=dict(
color=theme.up_color,
size=8,
symbol="triangle-right",
),
name="Start",
row=1,
col=1,
)
fig.add_scatter(
x=taf,
y=target_series[taf],
mode="markers",
marker=dict(
color=theme.down_color,
size=8,
symbol="triangle-left",
),
name="Ending",
row=1,
col=1,
)
fig.add_scatter(
x=ta,
y=target_series[ta],
mode="markers",
marker=dict(
color=theme.get_colors()[-1],
size=5,
symbol="circle",
line=dict(
color=theme.get_colors()[-2],
width=1,
),
),
name="Alarm",
row=1,
col=1,
)
fig.set_yaxis_title("Amplitude", row=1, col=1)
fig.add_scatter(
x=[*target_series_indexes],
y=gp,
mode="lines",
name="+",
row=2,
col=1,
line=dict(color=theme.get_colors()[0]),
)
fig.add_scatter(
x=[*target_series_indexes],
y=gn,
mode="lines",
name="-",
row=2,
col=1,
line=dict(color=theme.get_colors()[1]),
)
fig.add_hline(threshold, row=2, col=1)
fig.update_yaxes(
position=0.0, row=2, col=1, range=[0, max(gp.max(), gn.max(), threshold) * 1.1]
)
return fig.show(external=external_axes)
@log_start_end(log=logger)
def display_seasonal(
symbol: str,
data: pd.DataFrame,
target: str,
multiplicative: bool = False,
export: str = "",
sheet_name: Optional[str] = None,
external_axes: bool = False,
) -> Union[OpenBBFigure, None]:
"""Plots seasonal decomposition data
Parameters
----------
symbol : str
Name of dataset
data : pd.DataFrame
DataFrame
target : str
Column of data to look at
multiplicative : bool
Boolean to indicate multiplication instead of addition
export : str
Format to export trend and cycle data
external_axes : bool, optional
Whether to return the figure object or not, by default False
"""
data = data[target]
result, cycle, trend = qa_model.get_seasonal_decomposition(data, multiplicative)
plot_data = data.copy()
for rmerge, suffix in zip(
[result.trend, result.seasonal, result.resid, cycle, trend],
["_result.trend", "_result.seasonal", "_result.resid", "_cycle", "_trend"],
):
plot_data = pd.merge(
plot_data,
rmerge,
how="outer",
left_index=True,
right_index=True,
suffixes=("", suffix),
)
# Multiplicative model
fig = OpenBBFigure.create_subplots(4, 1, vertical_spacing=0.06)
fig.set_title(f"{symbol} (Time-Series) {target} seasonal decomposition")
colors = iter(theme.get_colors())
fig.add_scatter(
x=plot_data.index,
y=plot_data[target],
name="Values",
line_color=next(colors),
legend="legend",
row=1,
col=1,
)
for i, (column, name) in enumerate(
zip(
[("trend", "trend_cycle"), ("trend_trend", "seasonal")],
[
("Cyclic-Trend", "Cycle component"),
("Trend component", "Seasonal effect"),
],
)
):
for j in range(2):
fig.add_scatter(
x=plot_data.index,
y=plot_data[column[j]],
name=name[j],
line_color=next(colors) if i != 0 else theme.down_color,
legend=f"legend{i + 2}",
row=i + 2,
col=1,
)
fig.add_scatter(
x=plot_data.index,
y=plot_data["resid"],
name="Residuals",
line_color=next(colors),
legend="legend4",
row=4,
col=1,
)
legend_defaults = dict(
xanchor="left",
yanchor="top",
xref="paper",
yref="paper",
font=dict(size=13),
bgcolor="rgba(0, 0, 0, 0.4)",
)
fig.update_traces(selector=dict(name="Cyclic-Trend"), line_color=theme.down_color)
fig.update_traces(
selector=dict(name="Cycle component"),
line=dict(color=theme.up_color, dash="dash"),
)
fig.update_yaxes(nticks=5)
fig.update_layout(
legend=dict(**legend_defaults),
legend2=dict(x=0.01, y=0.73, **legend_defaults),
legend3=dict(x=0.01, y=0.46, **legend_defaults),
legend4=dict(x=0.01, y=0.20, **legend_defaults),
)
# From #https: // otexts.com/fpp2/seasonal-strength.html
console.print("Time-Series Level is " + str(round(data.mean(), 2)))
Ft = max(0, 1 - np.var(result.resid)) / np.var(result.trend + result.resid)
console.print(f"Strength of Trend: {Ft:.4f}")
Fs = max(
0,
1 - np.var(result.resid) / np.var(result.seasonal + result.resid),
)
console.print(f"Strength of Seasonality: {Fs:.4f}\n")
export_data(
export,
os.path.dirname(os.path.abspath(__file__)).replace("common", "stocks"),
"summary",
cycle.join(trend),
sheet_name,
fig,
)
return fig.show(external=external_axes)
@log_start_end(log=logger)
def display_normality(
data: pd.DataFrame, target: str, export: str = "", sheet_name: Optional[str] = None
) -> None:
"""Prints table showing normality statistics
Parameters
----------
data : pd.DataFrame
DataFrame
target : str
Column in data to look at
export : str
Format to export data
"""
data = data[target]
normal = qa_model.get_normality(data)
stats1 = normal.copy().T
stats1.iloc[:, 1] = stats1.iloc[:, 1].apply(lambda x: lambda_color_red(x))
print_rich_table(
stats1,
show_index=True,
headers=["Statistic", "p-value"],
floatfmt=".4f",
title="[bold]Normality Statistics[/bold]",
export=bool(export),
)
export_data(
export,
os.path.dirname(os.path.abspath(__file__)).replace("common", "stocks"),
"normality",
normal,
sheet_name,
)
@log_start_end(log=logger)
def display_unitroot(
data: pd.DataFrame,
target: str,
fuller_reg: str = "c",
kpss_reg: str = "c",
export: str = "",
sheet_name: Optional[str] = None,
):
"""Prints table showing unit root test calculations
Parameters
----------
data : pd.DataFrame
DataFrame
target : str
Column of data to look at
fuller_reg : str
Type of regression of ADF test. Can be ‘c’,’ct’,’ctt’,’nc’ 'c' - Constant and t - trend order
kpss_reg : str
Type of regression for KPSS test. Can be ‘c’,’ct'
export : str
Format for exporting data
"""
data = data[target]
data = qa_model.get_unitroot(data, fuller_reg, kpss_reg)
print_rich_table(
data,
show_index=True,
headers=list(data.columns),
title="[bold]Unit Root Calculation[/bold]",
floatfmt=".4f",
export=bool(export),
)
export_data(
export,
os.path.dirname(os.path.abspath(__file__)).replace("common", "stocks"),
"unitroot",
data,
sheet_name,
)
@log_start_end(log=logger)
def display_raw(
data: pd.DataFrame,
sortby: str = "",
ascend: bool = False,
limit: int = 20,
export: str = "",
sheet_name: Optional[str] = None,
) -> None:
"""Prints table showing raw stock data
Parameters
----------
data : DataFrame
DataFrame with historical information
sortby : str
The column to sort by
ascend : bool
Whether to sort descending
limit : int
Number of rows to show
export : str
Export data as CSV, JSON, XLSX
"""
df1 = pd.DataFrame(data) if isinstance(data, pd.Series) else data.copy()
if sortby:
sortby = sortby.replace("_", " ")
try:
sort_col = [x.lower().replace(" ", "") for x in df1.columns].index(
sortby.lower().replace(" ", "")
)
except ValueError:
console.print("[red]The provided column is not a valid option[/red]\n")
return
df1 = df1.sort_values(by=data.columns[sort_col], ascending=ascend)
else:
df1 = df1.sort_index(ascending=ascend)
print_rich_table(
df1,
headers=[x.title() if x != "" else "Date" for x in df1.columns],
title="[bold]Raw Data[/bold]",
show_index=True,
floatfmt=".3f",
export=bool(export),
limit=limit,
)
export_data(
export,
os.path.dirname(os.path.abspath(__file__)),
"raw",
data,
sheet_name,
)
@log_start_end(log=logger)
def display_line(
data: pd.Series,
title: str = "",
log_y: bool = True,
markers_lines: Optional[List[datetime]] = None,
markers_scatter: Optional[List[datetime]] = None,
export: str = "",
sheet_name: Optional[str] = None,
external_axes: bool = False,
) -> Union[OpenBBFigure, None]:
"""Display line plot of data
Parameters
----------
data: pd.Series
Data to plot
title: str
Title for plot
log_y: bool
Flag for showing y on log scale
markers_lines: Optional[List[datetime]]
List of dates to highlight using vertical lines
markers_scatter: Optional[List[datetime]]
List of dates to highlight using scatter
sheet_name: str
Optionally specify the name of the sheet the data is exported to.
export: str
Format to export data
external_axes : bool, optional
Whether to return the figure object or not, by default False
Examples
--------
>>> from openbb_terminal.sdk import openbb
>>> df = openbb.stocks.load("AAPL")
>>> openbb.qa.line(data=df["Adj Close"])
"""
fig = OpenBBFigure(yaxis_title=data.name)
if log_y:
fig.add_scatter(
x=data.index,
y=data.values,
name="",
mode="lines",
showlegend=False,
)
fig.update_layout(yaxis_type="log")
else:
fig.add_scatter(
x=data.index,
y=data.values,
name="",
mode="lines",
showlegend=False,
)
if markers_lines:
for marker_date in markers_lines:
fig.add_vline(x=marker_date, line=dict(color=theme.up_color, width=2))
if markers_scatter:
scatter: Dict[str, list] = dict(x=[], y=[])
for n, marker_date in enumerate(markers_scatter):
price_location_idx = data.index.get_loc(marker_date, method="nearest")
# algo to improve text placement of highlight event number
if (
price_location_idx > 0
and data.iloc[price_location_idx - 1]
> data.iloc[price_location_idx]
):
text_loc = (0, -20)
else:
text_loc = (0, 10)
fig.add_annotation(
x=marker_date,
y=data.iloc[price_location_idx],
text=str(n + 1),
xshift=text_loc[0],
yshift=text_loc[1],
)
scatter["x"].append(marker_date.strftime("%Y-%m-%d"))
scatter["y"].append(data.iloc[price_location_idx])
fig.add_scatter(
x=scatter["x"],
y=scatter["y"],
mode="markers",
marker=dict(
size=12,
color=theme.up_color,
line=dict(
width=2,
color=theme.up_color,
),
),
)
if title:
fig.set_title(title)
export_data(
export,
os.path.dirname(os.path.abspath(__file__)).replace("common", "stocks"),
"line",
sheet_name=sheet_name,
figure=fig,
)
return fig.show(external=external_axes)
def display_var(
data: pd.DataFrame,
symbol: str = "",
use_mean: bool = False,
adjusted_var: bool = False,
student_t: bool = False,
percentile: float = 99.9,
data_range: int = 0,
portfolio: bool = False,
) -> None:
"""Prints table showing VaR of dataframe.
Parameters
----------
data: pd.Dataframe
Data dataframe
use_mean: bool
if one should use the data mean return
symbol: str
name of the data
adjusted_var: bool
if one should have VaR adjusted for skew and kurtosis (Cornish-Fisher-Expansion)
student_t: bool
If one should use the student-t distribution
percentile: int
var percentile
data_range: int
Number of rows you want to use VaR over
portfolio: bool
If the data is a portfolio
"""
if data_range > 0:
df = qa_model.get_var(
data[-data_range:], use_mean, adjusted_var, student_t, percentile, portfolio
)
else:
df = qa_model.get_var(
data, use_mean, adjusted_var, student_t, percentile, portfolio
)
if adjusted_var:
str_title = "Adjusted "
elif student_t:
str_title = "Student-t "
else:
str_title = ""
if symbol != "":
symbol += " "
print_rich_table(
df,
show_index=True,
headers=list(df.columns),
title=f"[bold]{symbol}{str_title}Value at Risk[/bold]",
floatfmt=".2f",
)
def display_es(
data: pd.DataFrame,
symbol: str = "",
use_mean: bool = False,
distribution: str = "normal",
percentile: float = 99.9,
portfolio: bool = False,
) -> None:
"""Prints table showing expected shortfall.
Parameters
----------
data: pd.DataFrame
Data dataframe
use_mean:
if one should use the data mean return
symbol: str
name of the data
distribution: str
choose distribution to use: logistic, laplace, normal
percentile: int
es percentile
portfolio: bool
If the data is a portfolio
"""
df = qa_model.get_es(data, use_mean, distribution, percentile, portfolio)
if distribution == "laplace":
str_title = "Laplace "
elif distribution == "student_t":
str_title = "Student-t "
elif distribution == "logistic":
str_title = "Logistic "
else:
str_title = ""
if symbol != "":
symbol += " "
print_rich_table(
df,
show_index=True,
headers=list(df.columns),
title=f"[bold]{symbol}{str_title}Expected Shortfall[/bold]",
floatfmt=".2f",
)
def display_sharpe(
data: pd.DataFrame, rfr: float = 0, window: float = 252, external_axes: bool = False
) -> Union[OpenBBFigure, None]:
"""Plots Calculated the sharpe ratio
Parameters
----------
data: pd.DataFrame
selected dataframe column
rfr: float
risk free rate
window: float
length of the rolling window
external_axes : bool, optional
Whether to return the figure object or not, by default False
"""
sharpe_ratio = qa_model.get_sharpe(data, rfr, window)
data = sharpe_ratio[int(window - 1) :]
fig = OpenBBFigure(yaxis_title="Sharpe Ratio", xaxis_title="Date")
fig.set_title(f"Sharpe Ratio - over a {window} day window")
fig.add_scatter(x=data.index, y=data)
return fig.show(external=external_axes)
def display_sortino(
data: pd.DataFrame,
target_return: float,
window: float,
adjusted: bool,
external_axes: bool = False,
) -> Union[OpenBBFigure, None]:
"""Plots the sortino ratio
Parameters
----------
data: pd.DataFrame
selected dataframe
target_return: float
target return of the asset
window: float
length of the rolling window
adjusted: bool
adjust the sortino ratio
external_axes : bool, optional
Whether to return the figure object or not, by default False
"""
sortino_ratio = qa_model.get_sortino(data, target_return, window, adjusted)
str_adjusted = "Adjusted " if adjusted else ""
data = sortino_ratio[int(window - 1) :]
fig = OpenBBFigure(yaxis_title="Sortino Ratio", xaxis_title="Date")
fig.set_title(f"{str_adjusted}Sortino Ratio - over a {window} day window")
fig.add_scatter(x=data.index, y=data)
return fig.show(external=external_axes)
def display_omega(
data: pd.DataFrame,
threshold_start: float = 0,
threshold_end: float = 1.5,
external_axes: bool = False,
) -> Union[OpenBBFigure, None]:
"""Plots the omega ratio
Parameters
----------
data: pd.DataFrame
stock dataframe
threshold_start: float
annualized target return threshold start of plotted threshold range
threshold_end: float
annualized target return threshold end of plotted threshold range
external_axes : bool, optional
Whether to return the figure object or not, by default False
"""
df = qa_model.get_omega(data, threshold_start, threshold_end)
# Plotting
fig = OpenBBFigure(yaxis_title="Omega Ratio", xaxis_title="Threshold (%)")
fig.set_title(f"Omega Curve - over last {len(data)}'s period")
fig.add_scatter(x=df["threshold"], y=df["omega"], name="Omega Curve")
return fig.show(external=external_axes)
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