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6c6fcd312c
* add gst letters only * highlight events on qa/line * update hugo * add number to markers
863 lines
28 KiB
Python
863 lines
28 KiB
Python
"""Quantitative Analysis Controller Module"""
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__docformat__ = "numpy"
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import argparse
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import logging
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from datetime import datetime
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from typing import List
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import numpy as np
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import pandas as pd
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from prompt_toolkit.completion import NestedCompleter
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from gamestonk_terminal import feature_flags as gtff
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from gamestonk_terminal.common.quantitative_analysis import qa_view, rolling_view
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from gamestonk_terminal.decorators import log_start_end
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from gamestonk_terminal.helper_funcs import (
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EXPORT_ONLY_FIGURES_ALLOWED,
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EXPORT_ONLY_RAW_DATA_ALLOWED,
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check_positive,
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check_proportion_range,
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parse_known_args_and_warn,
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check_list_dates,
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)
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from gamestonk_terminal.menu import session
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from gamestonk_terminal.parent_classes import StockBaseController
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from gamestonk_terminal.rich_config import console
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from gamestonk_terminal.stocks.quantitative_analysis.factors_view import capm_view
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logger = logging.getLogger(__name__)
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class QaController(StockBaseController):
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"""Quantitative Analysis Controller class"""
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CHOICES_COMMANDS = [
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"load",
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"pick",
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"raw",
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"summary",
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"line",
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"hist",
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"cdf",
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"bw",
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"rolling",
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"decompose",
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"cusum",
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"acf",
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"spread",
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"quantile",
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"skew",
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"kurtosis",
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"normality",
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"qqplot",
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"unitroot",
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"goodness",
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"unitroot",
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"capm",
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"var",
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"es",
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]
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stock_interval = [1, 5, 15, 30, 60]
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stock_sources = ["yf", "av", "iex"]
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distributions = ["laplace", "student_t", "logistic", "normal"]
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PATH = "/stocks/qa/"
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def __init__(
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self,
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ticker: str,
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start: datetime,
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interval: str,
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stock: pd.DataFrame,
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queue: List[str] = None,
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):
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"""Constructor"""
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super().__init__(queue)
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# TODO: Move these calculations to a model
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stock["Returns"] = stock["Adj Close"].pct_change()
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stock["LogRet"] = np.log(stock["Adj Close"]) - np.log(
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stock["Adj Close"].shift(1)
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)
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stock["LogPrice"] = np.log(stock["Adj Close"])
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stock = stock.rename(columns={"Adj Close": "AdjClose"})
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stock = stock.dropna()
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stock.columns = [x.lower() for x in stock.columns]
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self.stock = stock
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self.ticker = ticker
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self.start = start
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self.interval = interval
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self.target = "returns"
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if session and gtff.USE_PROMPT_TOOLKIT:
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choices: dict = {c: {} for c in self.controller_choices}
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choices["pick"] = {c: None for c in list(stock.columns)}
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choices["load"]["-i"] = {c: None for c in self.stock_interval}
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choices["load"]["--interval"] = {c: None for c in self.stock_interval}
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choices["load"]["--source"] = {c: None for c in self.stock_sources}
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self.completer = NestedCompleter.from_nested_dict(choices)
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def print_help(self):
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"""Print help"""
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s_intraday = (f"Intraday {self.interval}", "Daily")[self.interval == "1440min"]
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if self.start:
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stock_str = (
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f"{s_intraday} {self.ticker} (from {self.start.strftime('%Y-%m-%d')})"
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)
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else:
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stock_str = f"{s_intraday} {self.ticker}"
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help_text = f"""[cmds]
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load load new ticker
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pick pick target column for analysis[/cmds]
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[param]Stock: [/param]{stock_str}
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[param]Target Column: [/param]{self.target}
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[cmds]
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[info]Statistics:[/info]
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summary brief summary statistics of loaded stock.
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normality normality statistics and tests
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unitroot unit root test for stationarity (ADF, KPSS)
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[info]Plots:[/info]
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line line plot of selected target
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hist histogram with density plot
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cdf cumulative distribution function
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bw box and whisker plot
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acf (partial) auto-correlation function differentials of prices
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qqplot residuals against standard normal curve
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[info]Rolling Metrics:[/info]
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rolling rolling mean and std deviation of prices
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spread rolling variance and std deviation of prices
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quantile rolling median and quantile of prices
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skew rolling skewness of distribution of prices
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kurtosis rolling kurtosis of distribution of prices
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[info]Risk:[/info]
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var display value at risk
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es display expected shortfall
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[info]Other:[/info]
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raw print raw data
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decompose decomposition in cyclic-trend, season, and residuals of prices
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cusum detects abrupt changes using cumulative sum algorithm of prices
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capm capital asset pricing model[/cmds]
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"""
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console.print(text=help_text, menu="Stocks - Quantitative Analysis")
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def custom_reset(self):
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"""Class specific component of reset command"""
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if self.ticker:
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if self.target:
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return ["stocks", f"load {self.ticker}", "qa", f"pick {self.target}"]
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return ["stocks", f"load {self.ticker}", "qa"]
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return []
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@log_start_end(log=logger)
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def call_pick(self, other_args: List[str]):
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"""Process pick command"""
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parser = argparse.ArgumentParser(
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formatter_class=argparse.ArgumentDefaultsHelpFormatter,
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add_help=False,
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prog="pick",
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description="""
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Change target variable
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""",
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)
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parser.add_argument(
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"-t",
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"--target",
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dest="target",
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type=lambda x: x.lower(),
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choices=list(self.stock.columns),
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help="Select variable to analyze",
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)
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if other_args and "-t" not in other_args and "-h" not in other_args:
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other_args.insert(0, "-t")
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ns_parser = parse_known_args_and_warn(parser, other_args)
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if ns_parser:
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self.target = ns_parser.target
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console.print("")
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@log_start_end(log=logger)
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def call_raw(self, other_args: List[str]):
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parser = argparse.ArgumentParser(
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formatter_class=argparse.ArgumentDefaultsHelpFormatter,
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add_help=False,
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prog="raw",
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description="""
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Print raw data to console
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""",
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)
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parser.add_argument(
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"-l",
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"--limit",
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help="Number to show",
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type=check_positive,
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default=20,
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dest="limit",
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)
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parser.add_argument(
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"-d",
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"--descend",
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action="store_true",
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default=False,
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dest="descend",
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help="Sort in descending order",
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)
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ns_parser = parse_known_args_and_warn(
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parser, other_args, export_allowed=EXPORT_ONLY_RAW_DATA_ALLOWED
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)
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if ns_parser:
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qa_view.display_raw(
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self.stock[self.target],
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num=ns_parser.limit,
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sort="",
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des=ns_parser.descend,
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export=ns_parser.export,
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)
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@log_start_end(log=logger)
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def call_summary(self, other_args: List[str]):
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"""Process summary command"""
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parser = argparse.ArgumentParser(
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formatter_class=argparse.ArgumentDefaultsHelpFormatter,
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add_help=False,
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prog="summary",
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description="""
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Summary statistics
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""",
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)
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ns_parser = parse_known_args_and_warn(
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parser, other_args, export_allowed=EXPORT_ONLY_RAW_DATA_ALLOWED
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)
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if ns_parser:
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qa_view.display_summary(df=self.stock, export=ns_parser.export)
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@log_start_end(log=logger)
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def call_line(self, other_args: List[str]):
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"""Process line command"""
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parser = argparse.ArgumentParser(
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formatter_class=argparse.ArgumentDefaultsHelpFormatter,
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add_help=False,
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prog="line",
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description="Show line plot of selected data and allow to draw lines or highlight specific datetimes.",
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)
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parser.add_argument(
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"--log",
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help="Plot with y on log scale",
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dest="log",
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action="store_true",
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default=False,
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)
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parser.add_argument(
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"-d",
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"--draw",
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help="Draw lines and annotate on the plot",
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dest="draw",
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action="store_true",
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default=False,
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)
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parser.add_argument(
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"--ml",
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help="Draw vertical line markers to highlight certain events",
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dest="ml",
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type=check_list_dates,
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default="",
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)
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parser.add_argument(
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"--ms",
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help="Draw scatter markers to highlight certain events",
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dest="ms",
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type=check_list_dates,
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default="",
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)
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ns_parser = parse_known_args_and_warn(
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parser, other_args, export_allowed=EXPORT_ONLY_FIGURES_ALLOWED
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)
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if ns_parser:
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qa_view.display_line(
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self.stock[self.target],
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title=f"{self.ticker} {self.target}",
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log_y=ns_parser.log,
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draw=ns_parser.draw,
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markers_lines=ns_parser.ml,
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markers_scatter=ns_parser.ms,
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)
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@log_start_end(log=logger)
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def call_hist(self, other_args: List[str]):
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"""Process hist command"""
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parser = argparse.ArgumentParser(
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formatter_class=argparse.ArgumentDefaultsHelpFormatter,
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add_help=False,
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prog="hist",
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description="""
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Histogram with density and rug
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""",
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)
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parser.add_argument(
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"-b", "--bins", type=check_positive, default=15, dest="n_bins"
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)
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ns_parser = parse_known_args_and_warn(parser, other_args)
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if ns_parser:
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qa_view.display_hist(
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name=self.ticker,
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df=self.stock,
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target=self.target,
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bins=ns_parser.n_bins,
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)
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@log_start_end(log=logger)
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def call_cdf(self, other_args: List[str]):
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"""Process cdf command"""
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parser = argparse.ArgumentParser(
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formatter_class=argparse.ArgumentDefaultsHelpFormatter,
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add_help=False,
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prog="cdf",
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description="""
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Cumulative distribution function
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""",
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)
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ns_parser = parse_known_args_and_warn(
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parser, other_args, export_allowed=EXPORT_ONLY_RAW_DATA_ALLOWED
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)
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if ns_parser:
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qa_view.display_cdf(
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name=self.ticker,
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df=self.stock,
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target=self.target,
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export=ns_parser.export,
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)
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@log_start_end(log=logger)
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def call_bw(self, other_args: List[str]):
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"""Process bwy command"""
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parser = argparse.ArgumentParser(
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formatter_class=argparse.ArgumentDefaultsHelpFormatter,
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add_help=False,
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prog="bw",
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description="""
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Box and Whisker plot
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""",
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)
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parser.add_argument(
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"-y",
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"--yearly",
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action="store_true",
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default=False,
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dest="year",
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help="Flag to show yearly bw plot",
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)
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ns_parser = parse_known_args_and_warn(parser, other_args)
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if ns_parser:
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qa_view.display_bw(
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name=self.ticker,
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df=self.stock,
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target=self.target,
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yearly=ns_parser.year,
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)
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@log_start_end(log=logger)
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def call_decompose(self, other_args: List[str]):
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"""Process decompose command"""
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parser = argparse.ArgumentParser(
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add_help=False,
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formatter_class=argparse.ArgumentDefaultsHelpFormatter,
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prog="decompose",
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description="""
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Decompose time series as:
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- Additive Time Series = Level + CyclicTrend + Residual + Seasonality
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- Multiplicative Time Series = Level * CyclicTrend * Residual * Seasonality
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""",
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)
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parser.add_argument(
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"-m",
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"--multiplicative",
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action="store_true",
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default=False,
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dest="multiplicative",
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help="decompose using multiplicative model instead of additive",
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)
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ns_parser = parse_known_args_and_warn(
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parser, other_args, export_allowed=EXPORT_ONLY_RAW_DATA_ALLOWED
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)
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if ns_parser:
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qa_view.display_seasonal(
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name=self.ticker,
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df=self.stock,
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target=self.target,
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multiplicative=ns_parser.multiplicative,
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export=ns_parser.export,
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)
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@log_start_end(log=logger)
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def call_cusum(self, other_args: List[str]):
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"""Process cusum command"""
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parser = argparse.ArgumentParser(
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add_help=False,
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formatter_class=argparse.ArgumentDefaultsHelpFormatter,
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prog="cusum",
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description="""
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Cumulative sum algorithm (CUSUM) to detect abrupt changes in data
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""",
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)
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parser.add_argument(
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"-t",
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"--threshold",
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dest="threshold",
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type=float,
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default=(
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max(self.stock[self.target].values)
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- min(self.stock[self.target].values)
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)
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/ 40,
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help="threshold",
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)
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parser.add_argument(
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"-d",
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"--drift",
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dest="drift",
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type=float,
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default=(
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max(self.stock[self.target].values)
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- min(self.stock[self.target].values)
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)
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/ 80,
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help="drift",
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)
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ns_parser = parse_known_args_and_warn(parser, other_args)
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if ns_parser:
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qa_view.display_cusum(
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df=self.stock,
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target=self.target,
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threshold=ns_parser.threshold,
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drift=ns_parser.drift,
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)
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@log_start_end(log=logger)
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def call_acf(self, other_args: List[str]):
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"""Process acf command"""
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parser = argparse.ArgumentParser(
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add_help=False,
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formatter_class=argparse.ArgumentDefaultsHelpFormatter,
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prog="acf",
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description="""
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Auto-Correlation and Partial Auto-Correlation Functions for diff and diff diff stock data
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""",
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)
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parser.add_argument(
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"-l",
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"--lags",
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dest="lags",
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type=check_positive,
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default=15,
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help="maximum lags to display in plots",
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)
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ns_parser = parse_known_args_and_warn(parser, other_args)
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if ns_parser:
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if self.target != "AdjClose":
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console.print(
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"Target not AdjClose. For best results, use `pick AdjClose` first."
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)
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qa_view.display_acf(
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name=self.ticker,
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df=self.stock,
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target=self.target,
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lags=ns_parser.lags,
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)
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@log_start_end(log=logger)
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def call_rolling(self, other_args: List[str]):
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"""Process rolling command"""
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parser = argparse.ArgumentParser(
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add_help=False,
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formatter_class=argparse.ArgumentDefaultsHelpFormatter,
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prog="rolling",
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description="""
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Rolling mean and std deviation
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""",
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)
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parser.add_argument(
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"-w",
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"--window",
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action="store",
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dest="n_window",
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type=check_positive,
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default=14,
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help="Window length",
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)
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ns_parser = parse_known_args_and_warn(
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parser, other_args, export_allowed=EXPORT_ONLY_RAW_DATA_ALLOWED
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)
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if ns_parser:
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rolling_view.display_mean_std(
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name=self.ticker,
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df=self.stock,
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target=self.target,
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window=ns_parser.n_window,
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export=ns_parser.export,
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)
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@log_start_end(log=logger)
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def call_spread(self, other_args: List[str]):
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"""Process spread command"""
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parser = argparse.ArgumentParser(
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add_help=False,
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formatter_class=argparse.ArgumentDefaultsHelpFormatter,
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prog="spread",
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description="""Shows rolling spread measurement
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""",
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)
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parser.add_argument(
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"-w",
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"--window",
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action="store",
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dest="n_window",
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type=check_positive,
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default=14,
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help="Window length",
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)
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ns_parser = parse_known_args_and_warn(
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parser, other_args, export_allowed=EXPORT_ONLY_RAW_DATA_ALLOWED
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)
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if ns_parser:
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rolling_view.display_spread(
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name=self.ticker,
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df=self.stock,
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target=self.target,
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window=ns_parser.n_window,
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export=ns_parser.export,
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)
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@log_start_end(log=logger)
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def call_quantile(self, other_args: List[str]):
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"""Process quantile command"""
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parser = argparse.ArgumentParser(
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add_help=False,
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formatter_class=argparse.ArgumentDefaultsHelpFormatter,
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prog="quantile",
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description="""
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The quantiles are values which divide the distribution such that
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there is a given proportion of observations below the quantile.
|
||
For example, the median is a quantile. The median is the central
|
||
value of the distribution, such that half the points are less than
|
||
or equal to it and half are greater than or equal to it.
|
||
|
||
By default, q is set at 0.5, which effectively is median. Change q to
|
||
get the desired quantile (0<q<1).
|
||
""",
|
||
)
|
||
parser.add_argument(
|
||
"-w",
|
||
"--window",
|
||
action="store",
|
||
dest="n_window",
|
||
type=check_positive,
|
||
default=14,
|
||
help="window length",
|
||
)
|
||
parser.add_argument(
|
||
"-q",
|
||
"--quantile",
|
||
action="store",
|
||
dest="f_quantile",
|
||
type=check_proportion_range,
|
||
default=0.5,
|
||
help="quantile",
|
||
)
|
||
ns_parser = parse_known_args_and_warn(
|
||
parser, other_args, export_allowed=EXPORT_ONLY_RAW_DATA_ALLOWED
|
||
)
|
||
if ns_parser:
|
||
rolling_view.display_quantile(
|
||
name=self.ticker,
|
||
df=self.stock,
|
||
target=self.target,
|
||
window=ns_parser.n_window,
|
||
quantile=ns_parser.f_quantile,
|
||
export=ns_parser.export,
|
||
)
|
||
|
||
@log_start_end(log=logger)
|
||
def call_skew(self, other_args: List[str]):
|
||
"""Process skew command"""
|
||
parser = argparse.ArgumentParser(
|
||
add_help=False,
|
||
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
|
||
prog="skew",
|
||
description="""
|
||
Skewness is a measure of asymmetry or distortion of symmetric
|
||
distribution. It measures the deviation of the given distribution
|
||
of a random variable from a symmetric distribution, such as normal
|
||
distribution. A normal distribution is without any skewness, as it is
|
||
symmetrical on both sides. Hence, a curve is regarded as skewed if
|
||
it is shifted towards the right or the left.
|
||
""",
|
||
)
|
||
parser.add_argument(
|
||
"-w",
|
||
"--window",
|
||
action="store",
|
||
dest="n_window",
|
||
type=check_positive,
|
||
default=14,
|
||
help="window length",
|
||
)
|
||
ns_parser = parse_known_args_and_warn(
|
||
parser, other_args, export_allowed=EXPORT_ONLY_RAW_DATA_ALLOWED
|
||
)
|
||
if ns_parser:
|
||
rolling_view.display_skew(
|
||
name=self.ticker,
|
||
df=self.stock,
|
||
target=self.target,
|
||
window=ns_parser.n_window,
|
||
export=ns_parser.export,
|
||
)
|
||
|
||
@log_start_end(log=logger)
|
||
def call_kurtosis(self, other_args: List[str]):
|
||
"""Process kurtosis command"""
|
||
parser = argparse.ArgumentParser(
|
||
add_help=False,
|
||
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
|
||
prog="kurtosis",
|
||
description="""
|
||
Kurtosis is a measure of the "tailedness" of the probability distribution
|
||
of a real-valued random variable. Like skewness, kurtosis describes the shape
|
||
of a probability distribution and there are different ways of quantifying it
|
||
for a theoretical distribution and corresponding ways of estimating it from
|
||
a sample from a population. Different measures of kurtosis may have different
|
||
interpretations.
|
||
""",
|
||
)
|
||
parser.add_argument(
|
||
"-w",
|
||
"--window",
|
||
action="store",
|
||
dest="n_window",
|
||
type=check_positive,
|
||
default=14,
|
||
help="window length",
|
||
)
|
||
ns_parser = parse_known_args_and_warn(
|
||
parser, other_args, export_allowed=EXPORT_ONLY_RAW_DATA_ALLOWED
|
||
)
|
||
if ns_parser:
|
||
rolling_view.display_kurtosis(
|
||
name=self.ticker,
|
||
df=self.stock,
|
||
target=self.target,
|
||
window=ns_parser.n_window,
|
||
export=ns_parser.export,
|
||
)
|
||
|
||
@log_start_end(log=logger)
|
||
def call_normality(self, other_args: List[str]):
|
||
"""Process normality command"""
|
||
parser = argparse.ArgumentParser(
|
||
add_help=False,
|
||
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
|
||
prog="normality",
|
||
description="""
|
||
Normality tests
|
||
""",
|
||
)
|
||
ns_parser = parse_known_args_and_warn(
|
||
parser, other_args, export_allowed=EXPORT_ONLY_RAW_DATA_ALLOWED
|
||
)
|
||
if ns_parser:
|
||
qa_view.display_normality(
|
||
df=self.stock, target=self.target, export=ns_parser.export
|
||
)
|
||
|
||
@log_start_end(log=logger)
|
||
def call_qqplot(self, other_args: List[str]):
|
||
"""Process qqplot command"""
|
||
parser = argparse.ArgumentParser(
|
||
add_help=False,
|
||
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
|
||
prog="qqplot",
|
||
description="""
|
||
Display QQ plot vs normal quantiles
|
||
""",
|
||
)
|
||
ns_parser = parse_known_args_and_warn(parser, other_args)
|
||
if ns_parser:
|
||
qa_view.display_qqplot(name=self.ticker, df=self.stock, target=self.target)
|
||
|
||
@log_start_end(log=logger)
|
||
def call_unitroot(self, other_args: List[str]):
|
||
"""Process unitroot command"""
|
||
parser = argparse.ArgumentParser(
|
||
add_help=False,
|
||
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
|
||
prog="unitroot",
|
||
description="""
|
||
Unit root test / stationarity (ADF, KPSS)
|
||
""",
|
||
)
|
||
parser.add_argument(
|
||
"-r",
|
||
"--fuller_reg",
|
||
help="Type of regression. Can be ‘c’,’ct’,’ctt’,’nc’ 'c' - Constant and t - trend order",
|
||
choices=["c", "ct", "ctt", "nc"],
|
||
default="c",
|
||
type=str,
|
||
dest="fuller_reg",
|
||
)
|
||
parser.add_argument(
|
||
"-k",
|
||
"--kps_reg",
|
||
help="Type of regression. Can be ‘c’,’ct'",
|
||
choices=["c", "ct"],
|
||
type=str,
|
||
dest="kpss_reg",
|
||
default="c",
|
||
)
|
||
ns_parser = parse_known_args_and_warn(
|
||
parser, other_args, export_allowed=EXPORT_ONLY_RAW_DATA_ALLOWED
|
||
)
|
||
if ns_parser:
|
||
qa_view.display_unitroot(
|
||
df=self.stock,
|
||
target=self.target,
|
||
fuller_reg=ns_parser.fuller_reg,
|
||
kpss_reg=ns_parser.kpss_reg,
|
||
export=ns_parser.export,
|
||
)
|
||
|
||
@log_start_end(log=logger)
|
||
def call_capm(self, other_args: List[str]):
|
||
"""Process capm command"""
|
||
parser = argparse.ArgumentParser(
|
||
add_help=False,
|
||
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
|
||
prog="capm",
|
||
description="""
|
||
Provides detailed information about a stock's risk compared to the market risk.
|
||
""",
|
||
)
|
||
ns_parser = parse_known_args_and_warn(parser, other_args)
|
||
if ns_parser:
|
||
capm_view(self.ticker)
|
||
|
||
@log_start_end(log=logger)
|
||
def call_var(self, other_args: List[str]):
|
||
"""Process var command"""
|
||
parser = argparse.ArgumentParser(
|
||
add_help=False,
|
||
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
|
||
prog="var",
|
||
description="""
|
||
Provides value at risk (short: VaR) of the selected stock.
|
||
""",
|
||
)
|
||
parser.add_argument(
|
||
"-m",
|
||
"--mean",
|
||
action="store_true",
|
||
default=False,
|
||
dest="use_mean",
|
||
help="If one should use the mean of the stocks return",
|
||
)
|
||
parser.add_argument(
|
||
"-a",
|
||
"--adjusted",
|
||
action="store_true",
|
||
default=False,
|
||
dest="adjusted",
|
||
help="""
|
||
If the VaR should be adjusted for skew and kurtosis (Cornish-Fisher-Expansion)
|
||
""",
|
||
)
|
||
parser.add_argument(
|
||
"-s",
|
||
"--student",
|
||
action="store_true",
|
||
default=False,
|
||
dest="student_t",
|
||
help="""
|
||
If one should use the student-t distribution
|
||
""",
|
||
)
|
||
parser.add_argument(
|
||
"-p",
|
||
"--percentile",
|
||
action="store",
|
||
dest="percentile",
|
||
type=float,
|
||
default=99.9,
|
||
help="""
|
||
Percentile used for VaR calculations, for example input 99.9 equals a 99.9 Percent VaR
|
||
""",
|
||
)
|
||
ns_parser = parse_known_args_and_warn(parser, other_args)
|
||
if ns_parser:
|
||
if ns_parser.adjusted and ns_parser.student_t:
|
||
console.print("Select the adjusted or the student_t parameter.\n")
|
||
else:
|
||
qa_view.display_var(
|
||
self.stock,
|
||
self.ticker,
|
||
ns_parser.use_mean,
|
||
ns_parser.adjusted,
|
||
ns_parser.student_t,
|
||
ns_parser.percentile / 100,
|
||
False,
|
||
)
|
||
|
||
def call_es(self, other_args: List[str]):
|
||
"""Process es command"""
|
||
parser = argparse.ArgumentParser(
|
||
add_help=False,
|
||
formatter_class=argparse.ArgumentDefaultsHelpFormatter,
|
||
prog="es",
|
||
description="""
|
||
Provides Expected Shortfall (short: ES) of the selected stock.
|
||
""",
|
||
)
|
||
parser.add_argument(
|
||
"-m",
|
||
"--mean",
|
||
action="store_true",
|
||
default=False,
|
||
dest="use_mean",
|
||
help="If one should use the mean of the stocks return",
|
||
)
|
||
parser.add_argument(
|
||
"-d",
|
||
"--dist",
|
||
"--distributions",
|
||
dest="distributions",
|
||
type=str,
|
||
choices=self.distributions,
|
||
default="normal",
|
||
help="Distribution used for the calculations",
|
||
)
|
||
parser.add_argument(
|
||
"-p",
|
||
"--percentile",
|
||
action="store",
|
||
dest="percentile",
|
||
type=float,
|
||
default=99.9,
|
||
help="""
|
||
Percentile used for ES calculations, for example input 99.9 equals a 99.9 Percent Expected Shortfall
|
||
""",
|
||
)
|
||
|
||
ns_parser = parse_known_args_and_warn(parser, other_args)
|
||
if ns_parser:
|
||
qa_view.display_es(
|
||
self.stock,
|
||
self.ticker,
|
||
ns_parser.use_mean,
|
||
ns_parser.distributions,
|
||
ns_parser.percentile / 100,
|
||
False,
|
||
)
|