mirror of
https://github.com/OpenBB-finance/OpenBB.git
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add rolling metrics to eda (#677)
* add rolling metrics to eda * . * try fix incompatible type error * overlay median & quantile plotting
This commit is contained in:
devwolff
365
gamestonk_terminal/common/exploratory_data_analysis/rolling.py
Normal file
365
gamestonk_terminal/common/exploratory_data_analysis/rolling.py
Normal file
@@ -0,0 +1,365 @@
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"""Rolling Statistics"""
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__docformat__ = "numpy"
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import argparse
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from typing import List
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import matplotlib.pyplot as plt
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import pandas_ta as ta
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import pandas as pd
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from pandas.plotting import register_matplotlib_converters
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from gamestonk_terminal.helper_funcs import (
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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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plot_autoscale,
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)
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from gamestonk_terminal.config_plot import PLOT_DPI
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from gamestonk_terminal import feature_flags as gtff
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register_matplotlib_converters()
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def spread(
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other_args: List[str], s_ticker: str, s_interval: str, df_stock: pd.DataFrame
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):
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"""Standard Deviation & Variance
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Parameters
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----------
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other_args:List[str]
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Argparse arguments
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s_ticker: str
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Ticker
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s_interval: str
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Stock time interval
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df_stock: pd.DataFrame
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Dataframe of prices
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"""
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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="""
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""",
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)
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parser.add_argument(
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"-l",
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"--length",
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action="store",
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dest="n_length",
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type=check_positive,
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default=14,
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help="length",
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)
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parser.add_argument(
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"-o",
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"--offset",
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action="store",
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dest="n_offset",
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type=check_positive,
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default=0,
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help="offset",
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)
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try:
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ns_parser = parse_known_args_and_warn(parser, other_args)
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if not ns_parser:
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return
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# Daily
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if s_interval == "1440min":
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df_ta = ta.stdev(
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close=df_stock["Adj Close"],
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length=ns_parser.n_length,
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offset=ns_parser.n_offset,
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).dropna()
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df_ta_ = ta.variance(
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close=df_stock["Adj Close"],
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length=ns_parser.n_length,
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offset=ns_parser.n_offset,
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).dropna()
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# Intraday
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else:
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df_ta = ta.stdev(
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close=df_stock["Close"],
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length=ns_parser.n_length,
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offset=ns_parser.n_offset,
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).dropna()
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df_ta_ = ta.variance(
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close=df_stock["Adj Close"],
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length=ns_parser.n_length,
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offset=ns_parser.n_offset,
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).dropna()
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fig, axes = plt.subplots(3, 1, figsize=plot_autoscale(), dpi=PLOT_DPI)
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ax = axes[0]
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ax.set_title(f"{s_ticker} Spread")
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if s_interval == "1440min":
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ax.plot(df_stock.index, df_stock["Adj Close"].values, "fuchsia", lw=1)
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else:
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ax.plot(df_stock.index, df_stock["Close"].values, "fuchsia", lw=1)
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ax.set_xlim(df_stock.index[0], df_stock.index[-1])
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ax.set_ylabel("Share Price ($)")
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ax.yaxis.set_label_position("right")
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ax.grid(b=True, which="major", color="#666666", linestyle="-")
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ax.minorticks_on()
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ax.grid(b=True, which="minor", color="#999999", linestyle="-", alpha=0.2)
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ax1 = axes[1]
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ax1.plot(df_ta.index, df_ta.values, "b", lw=1, label="stdev")
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ax1.set_xlim(df_stock.index[0], df_stock.index[-1])
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ax1.set_ylabel("Stdev")
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ax1.yaxis.set_label_position("right")
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ax1.grid(b=True, which="major", color="#666666", linestyle="-")
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ax1.minorticks_on()
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ax1.grid(b=True, which="minor", color="#999999", linestyle="-", alpha=0.2)
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ax2 = axes[2]
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ax2.plot(df_ta_.index, df_ta_.values, "g", lw=1, label="variance")
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ax2.set_xlim(df_stock.index[0], df_stock.index[-1])
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ax2.set_ylabel("Variance")
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ax2.yaxis.set_label_position("right")
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ax2.grid(b=True, which="major", color="#666666", linestyle="-")
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ax2.minorticks_on()
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ax2.grid(b=True, which="minor", color="#999999", linestyle="-", alpha=0.2)
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if gtff.USE_ION:
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plt.ion()
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plt.gcf().autofmt_xdate()
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fig.tight_layout(pad=1)
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plt.show()
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print("")
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except Exception as e:
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print(e, "\n")
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def quantile(
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other_args: List[str], s_ticker: str, s_interval: str, df_stock: pd.DataFrame
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):
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"""Overlay Median & Quantile
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Parameters
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----------
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other_args: List[str]
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Argparse arguments
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s_ticker: str
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Ticker
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s_interval: str
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Data interval
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df_stock: pd.DataFrame
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Dataframe of dates and prices
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"""
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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.
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For example, the median is a quantile. The median is the central
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value of the distribution, such that half the points are less than
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or equal to it and half are greater than or equal to it.
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By default, q is set at 0.5, which effectively is median. Change q to
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get the desired quantile (0<q<1).
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""",
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)
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parser.add_argument(
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"-l",
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"--length",
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action="store",
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dest="n_length",
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type=check_positive,
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default=14,
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help="length",
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)
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parser.add_argument(
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"-o",
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"--offset",
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action="store",
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dest="n_offset",
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type=check_positive,
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default=0,
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help="offset",
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)
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parser.add_argument(
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"-q",
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"--quantile",
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action="store",
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dest="f_quantile",
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type=check_proportion_range,
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default=0.5,
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help="quantile",
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)
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try:
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ns_parser = parse_known_args_and_warn(parser, other_args)
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if not ns_parser:
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return
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fig, ax = plt.subplots(figsize=plot_autoscale(), dpi=PLOT_DPI)
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if s_interval == "1440min":
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plt.plot(df_stock.index, df_stock["Adj Close"].values, color="fuchsia")
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else:
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plt.plot(df_stock.index, df_stock["Close"].values, color="fuchsia")
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ax.set_title(f"{s_ticker} Median & Quantile")
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if s_interval == "1440min":
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df_ta_ = ta.median(
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close=df_stock["Adj Close"],
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length=ns_parser.n_length,
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offset=ns_parser.n_offset,
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).dropna()
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df_ta = ta.quantile(
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df_stock["Adj Close"],
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length=ns_parser.n_length,
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offset=ns_parser.n_offset,
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q=ns_parser.f_quantile,
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).dropna()
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else:
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df_ta_ = ta.median(
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close=df_stock["Adj Close"],
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length=ns_parser.n_length,
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offset=ns_parser.n_offset,
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).dropna()
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df_ta = ta.quantile(
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df_stock["Close"],
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ns_parser.n_length,
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offset=ns_parser.n_offset,
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q=ns_parser.f_quantile,
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).dropna()
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plt.plot(df_ta_.index, df_ta_.values, "g", lw=1, label="median")
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plt.plot(df_ta.index, df_ta.values, "b", lw=1, label="quantile")
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plt.title(f"Median & Quantile on {s_ticker}")
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plt.xlim(df_stock.index[0], df_stock.index[-1])
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plt.xlabel("Time")
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plt.ylabel(f"{s_ticker} Price ($)")
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plt.grid(b=True, which="major", color="#666666", linestyle="-")
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plt.minorticks_on()
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plt.grid(b=True, which="minor", color="#999999", linestyle="-", alpha=0.2)
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if gtff.USE_ION:
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plt.ion()
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plt.gcf().autofmt_xdate()
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fig.tight_layout(pad=1)
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plt.legend()
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plt.show()
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print("")
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except Exception as e:
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print(e, "\n")
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def skew(other_args: List[str], s_ticker: str, s_interval: str, df_stock: pd.DataFrame):
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"""Skewness Indicator
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Parameters
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----------
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other_args:List[str]
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Argparse arguments
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s_ticker: str
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Ticker
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s_interval: str
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Stock time interval
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df_stock: pd.DataFrame
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Dataframe of prices
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"""
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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="skew",
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description="""
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Skewness is a measure of asymmetry or distortion of symmetric
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distribution. It measures the deviation of the given distribution
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of a random variable from a symmetric distribution, such as normal
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distribution. A normal distribution is without any skewness, as it is
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symmetrical on both sides. Hence, a curve is regarded as skewed if
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it is shifted towards the right or the left.
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""",
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)
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parser.add_argument(
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"-l",
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"--length",
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action="store",
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dest="n_length",
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type=check_positive,
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default=14,
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help="length",
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)
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parser.add_argument(
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"-o",
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"--offset",
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action="store",
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dest="n_offset",
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type=check_positive,
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default=0,
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help="offset",
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)
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try:
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ns_parser = parse_known_args_and_warn(parser, other_args)
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if not ns_parser:
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return
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# Daily
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if s_interval == "1440min":
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df_ta = ta.skew(
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close=df_stock["Adj Close"],
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length=ns_parser.n_length,
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offset=ns_parser.n_offset,
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).dropna()
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# Intraday
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else:
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df_ta = ta.skew(
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close=df_stock["Close"],
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length=ns_parser.n_length,
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offset=ns_parser.n_offset,
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).dropna()
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fig, axes = plt.subplots(2, 1, figsize=plot_autoscale(), dpi=PLOT_DPI)
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ax = axes[0]
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ax.set_title(f"{s_ticker} Skewness Indicator")
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if s_interval == "1440min":
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ax.plot(df_stock.index, df_stock["Adj Close"].values, "fuchsia", lw=1)
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else:
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ax.plot(df_stock.index, df_stock["Close"].values, "fuchsia", lw=1)
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ax.set_xlim(df_stock.index[0], df_stock.index[-1])
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ax.set_ylabel("Share Price ($)")
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ax.grid(b=True, which="major", color="#666666", linestyle="-")
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ax.minorticks_on()
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ax.grid(b=True, which="minor", color="#999999", linestyle="-", alpha=0.2)
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ax2 = axes[1]
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ax2.plot(df_ta.index, df_ta.values, "b", lw=2, label="skew")
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ax2.set_xlim(df_stock.index[0], df_stock.index[-1])
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ax2.grid(b=True, which="major", color="#666666", linestyle="-")
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ax2.minorticks_on()
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ax2.grid(b=True, which="minor", color="#999999", linestyle="-", alpha=0.2)
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if gtff.USE_ION:
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plt.ion()
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plt.gcf().autofmt_xdate()
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fig.tight_layout(pad=1)
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plt.legend()
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plt.show()
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print("")
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except Exception as e:
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print(e, "\n")
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@@ -1,30 +1,26 @@
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"""Helper functions"""
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__docformat__ = "numpy"
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import argparse
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from typing import List
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from datetime import datetime, timedelta, time as Time
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import os
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import random
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import re
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import sys
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from datetime import datetime
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from datetime import time as Time
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from datetime import timedelta
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from typing import List
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import pandas as pd
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from pytz import timezone
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from prettytable import PrettyTable
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import iso8601
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import matplotlib
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import matplotlib.pyplot as plt
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import pandas as pd
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import pandas.io.formats.format
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from colorama import Fore, Style
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from holidays import US as holidaysUS
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from colorama import Fore, Style
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from pandas._config.config import get_option
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from pandas.plotting import register_matplotlib_converters
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from prettytable import PrettyTable
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from pytz import timezone
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import pandas.io.formats.format
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from screeninfo import get_monitors
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from gamestonk_terminal import config_plot as cfgPlot
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from gamestonk_terminal import feature_flags as gtff
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from gamestonk_terminal import config_plot as cfgPlot
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register_matplotlib_converters()
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if cfgPlot.BACKEND is not None:
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@@ -120,6 +116,30 @@ def check_positive(value) -> int:
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return ivalue
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def check_proportion_range(num) -> float:
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"""Checks if float is between 0 and 1. If so, return it.
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Parameters
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----------
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num: float
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Input float
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Returns
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-------
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num: float
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Input number if conditions are met
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Raises
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-------
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argparse.ArgumentTypeError
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Input number not between min and max values
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"""
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num = float(num)
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maxi = 1.0
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mini = 0.0
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if num < mini or num > maxi:
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raise argparse.ArgumentTypeError("Value must be between 0 and 1")
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return num
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def valid_date(s: str) -> datetime:
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"""Argparse type to check date is in valid format"""
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try:
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@@ -8,7 +8,7 @@ from datetime import datetime
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import pandas as pd
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from matplotlib import pyplot as plt
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from prompt_toolkit.completion import NestedCompleter
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from gamestonk_terminal.common.exploratory_data_analysis import eda_api
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from gamestonk_terminal.common.exploratory_data_analysis import eda_api, rolling
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from gamestonk_terminal import feature_flags as gtff
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from gamestonk_terminal.helper_funcs import get_flair
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from gamestonk_terminal.menu import session
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@@ -33,6 +33,9 @@ class EdaController:
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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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]
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def __init__(
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@@ -79,7 +82,13 @@ class EdaController:
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print(" cdf cumulative distribution function")
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print(" bwy box and whisker yearly plot")
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print(" bwm box and whisker monthly plot")
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print("")
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print("Rolling Metrics")
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print(" rolling rolling mean and std deviation")
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print(" spread variance and std deviation")
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print(" quantile median and quantile")
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print(" skew skewness of distribution")
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print("")
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print(" decompose decomposition in cyclic-trend, season, and residuals")
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print(" cusum detects abrupt changes using cumulative sum algorithm")
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print(" acf (partial) auto-correlation function differentials")
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@@ -168,6 +177,18 @@ class EdaController:
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"""Process acf command"""
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eda_api.acf(other_args, self.ticker, self.stock, self.start)
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def call_spread(self, other_args: List[str]):
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"""Process spread command"""
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rolling.spread(other_args, self.ticker, self.interval, self.stock)
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def call_quantile(self, other_args: List[str]):
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"""Process quantile command"""
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rolling.quantile(other_args, self.ticker, self.interval, self.stock)
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def call_skew(self, other_args: List[str]):
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"""Process skew command"""
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rolling.skew(other_args, self.ticker, self.interval, self.stock)
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def menu(ticker: str, start: datetime, interval: str, stock: pd.DataFrame):
|
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"""Statistics Menu"""
|
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|
||||
Reference in New Issue
Block a user