mirror of
https://github.com/OpenBB-finance/OpenBB.git
synced 2026-10-29 05:21:48 +00:00
Refactor options menu
This commit is contained in:
didier
@@ -9,7 +9,7 @@ import yfinance as yf
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from prompt_toolkit.completion import NestedCompleter
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from gamestonk_terminal.helper_funcs import get_flair, parse_known_args_and_warn
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from gamestonk_terminal import feature_flags as gtff
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from gamestonk_terminal.options import volume
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from gamestonk_terminal.options import volume_view
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from gamestonk_terminal.menu import session
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@@ -17,16 +17,15 @@ class OptionsController:
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"""Options Controller class."""
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# Command choices
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CHOICES = ["help", "q", "quit", "exp", "volume"]
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CHOICES = ["help", "q", "quit", "exp", "voi"]
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def __init__(
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self,
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ticker: str,
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):
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def __init__(self, ticker: str, last_adj_close_price: float):
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"""Construct data."""
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self.ticker = ticker
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self.raw_data_options = yf.Ticker(self.ticker)
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self.expiry_date = self.raw_data_options.options[0]
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self.yf_ticker_data = yf.Ticker(self.ticker)
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self.expiry_date = self.yf_ticker_data.options[0]
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self.options = self.yf_ticker_data.option_chain(self.expiry_date)
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self.last_adj_close_price = last_adj_close_price
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self.op_parser = argparse.ArgumentParser(add_help=False, prog="op")
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self.op_parser.add_argument(
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"cmd",
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@@ -48,7 +47,7 @@ class OptionsController:
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action="store",
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type=int,
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default=-1,
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choices=range(len(self.raw_data_options.options)),
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choices=range(len(self.yf_ticker_data.options)),
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help=f"Expiry date index for {self.ticker}.",
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)
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@@ -64,12 +63,13 @@ class OptionsController:
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# Print possible expiry dates
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if ns_parser.n_date == -1:
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print("\nAvailable expiry dates:")
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for i, d in enumerate(self.raw_data_options.options):
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for i, d in enumerate(self.yf_ticker_data.options):
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print(f" {(2-len(str(i)))*' '}{i}. {d}")
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# It means an expiry date was correctly selected
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else:
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self.expiry_date = self.raw_data_options.options[ns_parser.n_date]
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self.expiry_date = self.yf_ticker_data.options[ns_parser.n_date]
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self.options = self.yf_ticker_data.option_chain(self.expiry_date)
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print(f"\nSelected expiry date : {self.expiry_date}")
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except Exception as e:
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@@ -89,7 +89,7 @@ class OptionsController:
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print(f"Selected expiry date: {expiry_date}")
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print("")
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print(" exp see/set expiry date")
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print(" volume plot options trading volume / open interest")
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print(" voi volume + open interest options trading plot")
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print("")
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return
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@@ -121,26 +121,27 @@ class OptionsController:
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"""Process Quit command - quit the program."""
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return True
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def call_volume(self, _):
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"""Process volume command."""
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volume.volume_graph(
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self.raw_data_options,
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def call_voi(self, other_args: List[str]):
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"""Process voi command."""
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volume_view.volume_open_interest_graph(
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other_args,
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self.ticker,
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self.expiry_date,
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volume_percentile_threshold=60,
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self.last_adj_close_price,
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self.options.calls,
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self.options.puts,
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)
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print("")
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def call_exp(self, other_args: List[str]):
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"""Process exp command."""
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self.expiry_dates(self, other_args)
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def menu(ticker: str):
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"""Options info Menu."""
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def menu(ticker: str, last_adj_close_price: float):
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""" Options Menu. """
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try:
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op_controller = OptionsController(ticker)
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op_controller = OptionsController(ticker, last_adj_close_price)
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op_controller.call_help(None)
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except IndexError:
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print("No options found for " + ticker)
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@@ -1,176 +0,0 @@
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"""options info. [Source: Yahoo Finance]."""
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from bisect import bisect_left
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import seaborn as sns
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import pandas as pd
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import matplotlib.pyplot as plt
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import numpy as np
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from gamestonk_terminal.helper_funcs import plot_autoscale
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from gamestonk_terminal import config_plot as cfgPlot
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def volume_graph(raw_data, ticker_name, exp_date, volume_percentile_threshold=50):
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"""Docstring make linter hap."""
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# SET VOLUME TO BE FILTERED, default = 50
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PERCENTILE_THRESHOLD = volume_percentile_threshold
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TICKER_NAME = ticker_name
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raw_data_options = raw_data
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EXP_DATE = exp_date
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# current stock price
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spot = __get_current_spot(raw_data_options)
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calls = __parse_opt_data(raw_data_options, EXP_DATE)
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puts = __parse_opt_data(raw_data_options, EXP_DATE, is_calls=False)
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calls = __add_max_pain_data(calls, spot)
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puts = __add_max_pain_data(puts, spot, is_calls=False)
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max_pain = __calc_max_pain(calls, puts)
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# Initialize the matplotlib figure
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_, ax = plt.subplots(figsize=plot_autoscale(), dpi=cfgPlot.PLOT_DPI)
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# _, ax = plt.subplots(figsize=(12, 10))
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# make x axis symmetric
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axis_origin = max(abs(max(puts["oi+v"])), abs(max(calls["oi+v"])))
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ax.set_xlim(-axis_origin, +axis_origin)
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VOLUME_THRESHOLD = np.percentile(calls["oi+v"], PERCENTILE_THRESHOLD)
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sns.set_style(style="darkgrid")
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g = sns.barplot(
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x="oi+v",
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y="strike",
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data=calls[calls["oi+v"] > VOLUME_THRESHOLD],
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label="Calls: Open Interest",
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color="lightgreen",
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orient="h",
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)
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g = sns.barplot(
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x="volume",
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y="strike",
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data=calls[calls["oi+v"] > VOLUME_THRESHOLD],
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label="Calls: Volume",
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color="green",
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orient="h",
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)
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g = sns.barplot(
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x="oi+v",
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y="strike",
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data=puts[puts["oi+v"] < -VOLUME_THRESHOLD],
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label="Puts: Open Interest",
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color="pink",
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orient="h",
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)
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g = sns.barplot(
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x="volume",
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y="strike",
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data=puts[puts["oi+v"] < -VOLUME_THRESHOLD],
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label="Puts: Volume",
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color="red",
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orient="h",
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)
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# draw spot line
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s = [float(strike.get_text()) for strike in ax.get_yticklabels()]
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spot_index = bisect_left(s, spot) # find where the spot is on the graph
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spot_line = ax.axhline(spot_index, ls="--", color="dodgerblue", alpha=0.3)
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# draw max pain line
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max_pain_index = bisect_left(s, max_pain)
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max_pain_line = ax.axhline(max_pain_index, ls="-", color="black", alpha=0.3)
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max_pain_line.set_linewidth(5)
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# ax.axhline(max_pain_index, ls='--')
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# format ticklabels without - for puts
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g.set_xticks(g.get_xticks())
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xlabels = [f"{x:,.0f}".replace("-", "") for x in g.get_xticks()]
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g.set_xticklabels(xlabels)
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plt.title(
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f"{TICKER_NAME.upper()} volumes for {EXP_DATE} (open interest displayed only during market hours)"
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)
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ax.invert_yaxis()
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# ax.spines['left'].set_position('center')
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_ = ax.legend()
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handles, _ = ax.get_legend_handles_labels()
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handles.append(spot_line)
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handles.append(max_pain_line)
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# create legend labels + add to graph
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labels = [
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"Calls open interest",
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"Calls volume ",
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"Puts open interest",
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"Puts volume",
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"Current stock price",
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f"Max pain = {max_pain}",
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]
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plt.legend(handles=handles[:], labels=labels)
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sns.despine(left=True, bottom=True)
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plt.show()
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def __add_max_pain_data(df, spot, is_calls=True):
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# max pain parsing + calculation
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df["spot"] = round(spot, 2)
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if is_calls:
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df["dv"] = spot - df.index
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else:
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df["dv"] = df.index - spot
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df["dv"] = df["dv"].apply(lambda x: max(0, x))
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df["dv"] = abs(df["dv"] * df["volume"])
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return df
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def __calc_max_pain(calls, puts):
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df = pd.merge(calls, puts, left_index=True, right_index=True)
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df["dv"] = round(df["dv_x"] + df["dv_y"], 2)
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max_pain = df["dv"].idxmax()
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return max_pain
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def __parse_opt_data(raw_data_options, exp_date, is_calls=True):
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# get option chain for specific expiration
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opt = raw_data_options.option_chain(exp_date)
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# PARSE DATA
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if is_calls:
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option_data = opt.calls
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flag = "calls"
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else:
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option_data = opt.puts
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flag = "puts"
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data = option_data.pivot_table(
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index="strike", values=["volume", "openInterest"], aggfunc="sum"
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).reindex()
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data["strike"] = data.index
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data["type"] = flag
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if is_calls:
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data["openInterest"] = data["openInterest"]
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data["volume"] = data["volume"]
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else:
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data["openInterest"] = -data["openInterest"]
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data["volume"] = -data["volume"]
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data["oi+v"] = data["openInterest"] + data["volume"]
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return data
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def __get_current_spot(raw_data_options):
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return raw_data_options.history().tail(1)["Close"].iloc[0]
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273
gamestonk_terminal/options/volume_view.py
Normal file
273
gamestonk_terminal/options/volume_view.py
Normal file
@@ -0,0 +1,273 @@
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""" Volume view """
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__docformat__ = "numpy"
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from bisect import bisect_left
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import seaborn as sns
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from typing import List
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import argparse
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import pandas as pd
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import matplotlib.pyplot as plt
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import numpy as np
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import yfinance
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from gamestonk_terminal.helper_funcs import (
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plot_autoscale,
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check_non_negative,
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parse_known_args_and_warn,
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)
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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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def volume_open_interest_graph(
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other_args: List[str],
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ticker: str,
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exp_date: str,
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last_adj_close_price: float,
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op_calls: pd.DataFrame,
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op_puts: pd.DataFrame,
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):
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"""Options volume and open interest
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Parameters
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----------
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other_args : List[str]
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Command line arguments to be processed with argparse
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ticker : str
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Main ticker to compare income
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exp_date : str
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Expiry date of the option
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last_adj_close_price: float
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Last adjusted closing price
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op_calls: pd.DataFrame
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Option data calls
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op_puts: pd.DataFrame
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Option data puts
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"""
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parser = argparse.ArgumentParser(
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add_help=False,
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prog="voi",
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description="""
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Plots Volume + Open Interest of calls vs puts. [Source: Yahoo Finance]
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""",
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)
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parser.add_argument(
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"-v",
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"--minv",
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dest="min_vol",
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type=check_non_negative,
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default=-1,
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help="minimum volume (considering open interest) threshold of the plot.",
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)
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parser.add_argument(
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"-m",
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"--min",
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dest="min_sp",
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type=check_non_negative,
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default=-1,
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help="minimum strike price to consider in the plot.",
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)
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parser.add_argument(
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"-M",
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"--max",
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dest="max_sp",
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type=check_non_negative,
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default=-1,
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help="maximum strike price to consider in the plot.",
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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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df_calls, df_puts, max_pain = get_calls_puts_maxpain(
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op_calls, op_puts, last_adj_close_price
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)
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if (
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ns_parser.min_vol == -1
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and ns_parser.min_sp == -1
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and ns_parser.max_sp == -1
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):
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# If no argument provided, we use the percentile 50 to get 50% of upper volume data
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volume_percentile_threshold = 50
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min_vol_calls = np.percentile(df_calls["oi+v"], volume_percentile_threshold)
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min_vol_puts = np.percentile(df_puts["oi+v"], volume_percentile_threshold)
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df_calls = df_calls[df_calls["oi+v"] > min_vol_calls]
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df_puts = df_puts[df_puts["oi+v"] < min_vol_puts]
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else:
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if ns_parser.min_vol > -1:
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df_calls = df_calls[df_calls["oi+v"] > ns_parser.min_vol]
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df_puts = df_puts[df_puts["oi+v"] < -ns_parser.min_vol]
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if ns_parser.min_sp > -1:
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df_calls = df_calls[df_calls["strike"] > ns_parser.min_sp]
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df_puts = df_puts[df_puts["strike"] > ns_parser.min_sp]
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if ns_parser.max_sp > -1:
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df_calls = df_calls[df_calls["strike"] < ns_parser.max_sp]
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df_puts = df_puts[df_puts["strike"] < ns_parser.max_sp]
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if df_calls.empty and df_puts.empty:
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print(
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"The filtering applied is too strong, there is no data available for such conditions.\n"
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)
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return
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# Initialize the matplotlib figure
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_, ax = plt.subplots(figsize=plot_autoscale(), dpi=cfgPlot.PLOT_DPI)
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# make x axis symmetric
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axis_origin = max(abs(max(df_puts["oi+v"])), abs(max(df_calls["oi+v"])))
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ax.set_xlim(-axis_origin, +axis_origin)
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sns.set_style(style="darkgrid")
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g = sns.barplot(
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x="oi+v",
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y="strike",
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data=df_calls,
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label="Calls: Open Interest",
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color="lightgreen",
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orient="h",
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)
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g = sns.barplot(
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x="volume",
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y="strike",
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data=df_calls,
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label="Calls: Volume",
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color="green",
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orient="h",
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)
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g = sns.barplot(
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x="oi+v",
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y="strike",
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data=df_puts,
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label="Puts: Open Interest",
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color="pink",
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orient="h",
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)
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g = sns.barplot(
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x="volume",
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y="strike",
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data=df_puts,
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label="Puts: Volume",
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color="red",
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orient="h",
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)
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# draw spot line
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s = [float(strike.get_text()) for strike in ax.get_yticklabels()]
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spot_index = bisect_left(
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s, last_adj_close_price
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) # find where the spot is on the graph
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spot_line = ax.axhline(spot_index, ls="--", color="dodgerblue", alpha=0.3)
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# draw max pain line
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max_pain_index = bisect_left(s, max_pain)
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max_pain_line = ax.axhline(max_pain_index, ls="-", color="black", alpha=0.3)
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max_pain_line.set_linewidth(5)
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# format ticklabels without - for puts
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g.set_xticks(g.get_xticks())
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xlabels = [f"{x:,.0f}".replace("-", "") for x in g.get_xticks()]
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g.set_xticklabels(xlabels)
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plt.title(
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f"{ticker} volumes for {exp_date} (open interest displayed only during market hours)"
|
||||
)
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ax.invert_yaxis()
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||||
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_ = ax.legend()
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||||
handles, _ = ax.get_legend_handles_labels()
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handles.append(spot_line)
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handles.append(max_pain_line)
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|
||||
# create legend labels + add to graph
|
||||
labels = [
|
||||
"Calls open interest",
|
||||
"Calls volume ",
|
||||
"Puts open interest",
|
||||
"Puts volume",
|
||||
"Current stock price",
|
||||
f"Max pain = {max_pain}",
|
||||
]
|
||||
|
||||
plt.legend(handles=handles[:], labels=labels)
|
||||
sns.despine(left=True, bottom=True)
|
||||
|
||||
if gtff.USE_ION:
|
||||
plt.ion()
|
||||
plt.show()
|
||||
|
||||
print("")
|
||||
|
||||
except Exception as e:
|
||||
print(e, "\n")
|
||||
return
|
||||
|
||||
|
||||
def get_calls_puts_maxpain(
|
||||
op_calls: pd.DataFrame, op_puts: pd.DataFrame, last_adj_close_price: float
|
||||
):
|
||||
"""Get calls and puts dataframes, and max pain
|
||||
|
||||
Parameters
|
||||
----------
|
||||
op_calls: pd.DataFrame
|
||||
Option data calls
|
||||
op_puts: pd.DataFrame
|
||||
Option data puts
|
||||
last_adj_close_price: float
|
||||
Last adjusted closing price
|
||||
|
||||
Returns
|
||||
----------
|
||||
pd.DataFrame
|
||||
Processed calls dataframe
|
||||
pd.DataFrame
|
||||
Processed puts dataframe
|
||||
float
|
||||
Max pain
|
||||
"""
|
||||
# Process Calls Data
|
||||
df_calls = op_calls.pivot_table(
|
||||
index="strike", values=["volume", "openInterest"], aggfunc="sum"
|
||||
).reindex()
|
||||
df_calls["strike"] = df_calls.index
|
||||
df_calls["type"] = "calls"
|
||||
df_calls["openInterest"] = df_calls["openInterest"]
|
||||
df_calls["volume"] = df_calls["volume"]
|
||||
df_calls["oi+v"] = df_calls["openInterest"] + df_calls["volume"]
|
||||
df_calls["spot"] = round(last_adj_close_price, 2)
|
||||
df_calls["dv"] = last_adj_close_price - df_calls.index
|
||||
df_calls["dv"] = df_calls["dv"].apply(lambda x: max(0, x))
|
||||
df_calls["dv"] = abs(df_calls["dv"] * df_calls["volume"])
|
||||
|
||||
# Process Puts Data
|
||||
df_puts = op_puts.pivot_table(
|
||||
index="strike", values=["volume", "openInterest"], aggfunc="sum"
|
||||
).reindex()
|
||||
df_puts["strike"] = df_puts.index
|
||||
df_puts["type"] = "puts"
|
||||
df_puts["openInterest"] = df_puts["openInterest"]
|
||||
df_puts["volume"] = -df_puts["volume"]
|
||||
df_puts["openInterest"] = -df_puts["openInterest"]
|
||||
df_puts["oi+v"] = df_puts["openInterest"] + df_puts["volume"]
|
||||
df_puts["spot"] = round(last_adj_close_price, 2)
|
||||
df_puts["dv"] = df_puts.index - last_adj_close_price
|
||||
df_puts["dv"] = df_puts["dv"].apply(lambda x: max(0, x))
|
||||
df_puts["dv"] = abs(df_puts["dv"] * df_puts["volume"])
|
||||
|
||||
# Get max pain
|
||||
df_opt = pd.merge(df_calls, df_puts, left_index=True, right_index=True)
|
||||
df_opt["dv"] = round(df_opt["dv_x"] + df_opt["dv_y"], 2)
|
||||
max_pain = df_opt["dv"].idxmax()
|
||||
|
||||
return df_calls, df_puts, max_pain
|
||||
@@ -231,7 +231,9 @@ def main():
|
||||
b_quit = eda_controller.menu(df_stock, s_ticker, s_start, s_interval)
|
||||
|
||||
elif ns_known_args.opt == "op":
|
||||
b_quit = op_controller.menu(s_ticker)
|
||||
b_quit = op_controller.menu(
|
||||
s_ticker, df_stock["5. adjusted close"].values[-1]
|
||||
)
|
||||
|
||||
elif ns_known_args.opt == "fred":
|
||||
b_quit = fred_controller.menu()
|
||||
|
||||
Reference in New Issue
Block a user