OpenBB/openbb_terminal/stocks/options/yfinance_view.py

"""Yfinance options view"""
__docformat__ = "numpy"

import logging
import os
import re
from bisect import bisect_left
from datetime import date, datetime, timedelta
from typing import Any, Dict, List, Optional

import matplotlib.dates as mdates
import matplotlib.pyplot as plt
import matplotlib.ticker as mtick
import numpy as np
import pandas as pd
import seaborn as sns
import yfinance as yf
from openpyxl import Workbook
from scipy.stats import binom

import openbb_terminal.config_plot as cfp
from openbb_terminal.config_plot import PLOT_DPI
from openbb_terminal.config_terminal import theme
from openbb_terminal.core.config.paths import MISCELLANEOUS_DIRECTORY
from openbb_terminal.decorators import log_start_end
from openbb_terminal.helper_funcs import (
    excel_columns,
    export_data,
    get_rf,
    is_valid_axes_count,
    plot_autoscale,
    print_rich_table,
)
from openbb_terminal.rich_config import console
from openbb_terminal.stocks.options import op_helpers, yfinance_model
from openbb_terminal.stocks.options.yfinance_model import (
    generate_data,
    get_option_chain,
    get_price,
)

# pylint: disable=C0302


logger = logging.getLogger(__name__)


def header_fmt(header: str) -> str:
    """
    Formats strings to appear as titles

    Parameters
    ----------
    header: str
        The string to be formatted

    Returns
    -------
    new_header: str
        The clean string to use as a header
    """

    words = re.findall("[A-Z][^A-Z]*", header)
    if words == []:
        words = [header]
    new_header = " ".join(words)
    new_header = new_header.replace("_", " ")
    return new_header.title()


@log_start_end(log=logger)
def display_chains(
    symbol: str,
    expiry: str,
    min_sp: float = -1,
    max_sp: float = -1,
    calls_only: bool = False,
    puts_only: bool = False,
    export: str = "",
):
    """Display option chains for given ticker and expiration

    Parameters
    ----------
    symbol: str
        Stock ticker symbol
    expiry: str
        Expiration for option chain
    min_sp: float
        Min strike
    max_sp: float
        Max strike
    calls_only: bool
        Flag to get calls only
    puts_only: bool
        Flag to get puts only
    export: str
        Format to export data

    """
    # Logic for converting calls/puts into "get calls/puts"
    call_bool = True
    put_bool = True
    if calls_only:
        call_bool = True
        put_bool = False
    if puts_only:
        call_bool = False
        put_bool = True

    option_chains = yfinance_model.get_option_chain_expiry(
        symbol=symbol,
        expiry=expiry,
        calls=call_bool,
        puts=put_bool,
        min_sp=min_sp,
        max_sp=max_sp,
    ).fillna("-")
    if option_chains.empty:
        console.print("[red]Option chains not found.[/red]")
        return

    # There are 3 possibilities.  Calls only, puts only or both.
    # If calls only or puts only, we are actually set
    # because the columns are nicely named
    if calls_only or puts_only:
        title = "Call " if calls_only else "Put "
        print_rich_table(
            option_chains,
            title=title + "Option Chain (15 min delayed) (Greeks calculated by OpenBB)",
            floatfmt=[
                ".2f",
                ".2f",
                ".2f",
                ".2f",
                ".0f",
                ".0f",
                ".3f",
                ".3f",
                ".3f",
                ".3f",
            ],
            headers=[
                "Strike",
                "Last Price",
                "Bid",
                "Ask",
                "Volume",
                "Open Interest",
                "IV",
                "Delta",
                "Gamma",
                "Theta",
            ],
        )

    # Put the columns into the order for showing them
    if calls_only and puts_only:
        option_chains = option_chains[
            [
                "impliedVolatility_call",
                "Theta_call",
                "Gamma_call",
                "Delta_call",
                "volume_call",
                "openInterest_call",
                "bid_call",
                "ask_call",
                "lastPrice_call",
                "strike",
                "lastPrice_put",
                "ask_put",
                "bid_put",
                "openInterest_put",
                "volume_put",
                "Delta_put",
                "Gamma_put",
                "Theta_put",
                "impliedVolatility_put",
            ]
        ]

        # In order to add color to call/put, the numbers will have to be strings.
        # So floatfmt will not work in print_rich_table, so lets format them now.

        float_fmt = [
            ".3f",
            ".3f",
            ".3f",
            ".3f",
            ".0f",
            ".0f",
            ".2f",
            ".2f",
            ".2f",
            ".2f",
            ".2f",
            ".2f",
            ".2f",
            ".0f",
            ".0f",
            ".3f",
            ".3f",
            ".3f",
            ".3f",
        ]
        # pylint: disable=W0640

        for idx, fmt in enumerate(float_fmt):
            option_chains.iloc[:, idx] = option_chains.iloc[:, idx].apply(
                lambda x: str("{:" + fmt + "}").format(float(x)) if x != "-" else x
            )
        # pylint: enable=W0640

    # Make anything _call green and anything _put red
    for col in option_chains.columns:
        if col.endswith("_call"):
            option_chains[col] = option_chains[col].apply(
                lambda x: f"[green]{x}[/green]"
            )
        if col.endswith("_put"):
            option_chains[col] = option_chains[col].apply(lambda x: f"[red]{x}[/red]")

    print_rich_table(
        option_chains,
        title=f"Yahoo Option Chain (15 min delayed) for {expiry} (Greeks calculated by OpenBB)",
        headers=[header_fmt(x) for x in option_chains.columns],
    )
    export_data(
        export,
        os.path.dirname(os.path.abspath(__file__)),
        "chains_yf",
        option_chains,
    )


@log_start_end(log=logger)
def plot_oi(
    symbol: str,
    expiry: str,
    min_sp: float = -1,
    max_sp: float = -1,
    calls_only: bool = False,
    puts_only: bool = False,
    export: str = "",
    external_axes: Optional[List[plt.Axes]] = None,
):
    """Plot open interest

    Parameters
    ----------
    symbol: str
        Ticker symbol
    expiry: str
        expiration date for options
    min_sp: float
        Min strike to consider
    max_sp: float
        Max strike to consider
    calls_only: bool
        Show calls only
    puts_only: bool
        Show puts only
    export: str
        Format to export file
    external_axes : Optional[List[plt.Axes]], optional
        External axes (1 axis is expected in the list), by default None
    """
    options = yfinance_model.get_option_chain(symbol, expiry)
    op_helpers.export_yf_options(export, options, "oi_yf")
    calls = options.calls
    puts = options.puts
    current_price = float(yf.Ticker(symbol).info["regularMarketPrice"])

    if min_sp == -1:
        min_strike = 0.75 * current_price
    else:
        min_strike = min_sp

    if max_sp == -1:
        max_strike = 1.25 * current_price
    else:
        max_strike = max_sp

    if calls_only and puts_only:
        console.print("Both flags selected, please select one", "\n")
        return

    call_oi = calls.set_index("strike")["openInterest"] / 1000
    put_oi = puts.set_index("strike")["openInterest"] / 1000

    df_opt = pd.merge(call_oi, put_oi, left_index=True, right_index=True)
    df_opt = df_opt.rename(
        columns={"openInterest_x": "OI_call", "openInterest_y": "OI_put"}
    )

    max_pain = op_helpers.calculate_max_pain(df_opt)
    if external_axes is None:
        _, ax = plt.subplots(figsize=plot_autoscale(), dpi=cfp.PLOT_DPI)
    elif is_valid_axes_count(external_axes, 1):
        (ax,) = external_axes
    else:
        return

    if not calls_only:
        put_oi.plot(
            x="strike",
            y="openInterest",
            label="Puts",
            ax=ax,
            marker="o",
            ls="-",
        )
    if not puts_only:
        call_oi.plot(
            x="strike",
            y="openInterest",
            label="Calls",
            ax=ax,
            marker="o",
            ls="-",
        )
    ax.axvline(current_price, lw=2, ls="--", label="Current Price", alpha=0.7)
    ax.axvline(max_pain, lw=3, label=f"Max Pain: {max_pain}", alpha=0.7)
    ax.set_xlabel("Strike Price")
    ax.set_ylabel("Open Interest [1k] ")
    ax.set_xlim(min_strike, max_strike)
    ax.legend(fontsize="x-small")
    ax.set_title(f"Open Interest for {symbol.upper()} expiring {expiry}")

    theme.style_primary_axis(ax)

    if external_axes is None:
        theme.visualize_output()


@log_start_end(log=logger)
def plot_vol(
    symbol: str,
    expiry: str,
    min_sp: float = -1,
    max_sp: float = -1,
    calls_only: bool = False,
    puts_only: bool = False,
    export: str = "",
    external_axes: Optional[List[plt.Axes]] = None,
):
    """Plot volume

    Parameters
    ----------
    symbol: str
        Ticker symbol
    expiry: str
        expiration date for options
    min_sp: float
        Min strike to consider
    max_sp: float
        Max strike to consider
    calls_only: bool
        Show calls only
    puts_only: bool
        Show puts only
    export: str
        Format to export file
    external_axes : Optional[List[plt.Axes]], optional
        External axes (1 axis is expected in the list), by default None
    """
    options = yfinance_model.get_vol(symbol, expiry)
    calls = options.calls
    puts = options.puts
    current_price = float(yf.Ticker(symbol).info["regularMarketPrice"])

    if min_sp == -1:
        min_strike = 0.75 * current_price
    else:
        min_strike = min_sp

    if max_sp == -1:
        max_strike = 1.25 * current_price
    else:
        max_strike = max_sp

    if calls_only and puts_only:
        console.print("Both flags selected, please select one", "\n")
        return

    call_v = calls.set_index("strike")["volume"] / 1000
    put_v = puts.set_index("strike")["volume"] / 1000
    if external_axes is None:
        _, ax = plt.subplots(figsize=plot_autoscale(), dpi=cfp.PLOT_DPI)
    elif is_valid_axes_count(external_axes, 1):
        (ax,) = external_axes
    else:
        return

    if not calls_only:
        put_v.plot(
            x="strike",
            y="volume",
            label="Puts",
            ax=ax,
            marker="o",
            ls="-",
        )
    if not puts_only:
        call_v.plot(
            x="strike",
            y="volume",
            label="Calls",
            ax=ax,
            marker="o",
            ls="-",
        )
    ax.axvline(current_price, lw=2, ls="--", label="Current Price", alpha=0.7)
    ax.set_xlabel("Strike Price")
    ax.set_ylabel("Volume [1k] ")
    ax.set_xlim(min_strike, max_strike)
    ax.legend(fontsize="x-small")
    ax.set_title(f"Volume for {symbol.upper()} expiring {expiry}")
    theme.style_primary_axis(ax)
    if external_axes is None:
        theme.visualize_output()

    op_helpers.export_yf_options(export, options, "vol_yf")


@log_start_end(log=logger)
def plot_volume_open_interest(
    symbol: str,
    expiry: str,
    min_sp: float = -1,
    max_sp: float = -1,
    min_vol: float = -1,
    export: str = "",
    external_axes: Optional[List[plt.Axes]] = None,
):
    """Plot volume and open interest

    Parameters
    ----------
    symbol: str
        Stock ticker symbol
    expiry: str
        Option expiration
    min_sp: float
        Min strike price
    max_sp: float
        Max strike price
    min_vol: float
        Min volume to consider
    export: str
        Format for exporting data
    external_axes : Optional[List[plt.Axes]], optional
        External axes (1 axis is expected in the list), by default None
    """
    options = yfinance_model.get_volume_open_interest(symbol, expiry)
    calls = options.calls
    puts = options.puts
    current_price = float(yf.Ticker(symbol).info["regularMarketPrice"])

    # Process Calls Data
    df_calls = 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(current_price, 2)

    df_puts = 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(current_price, 2)

    call_oi = calls.set_index("strike")["openInterest"] / 1000
    put_oi = puts.set_index("strike")["openInterest"] / 1000

    df_opt = pd.merge(call_oi, put_oi, left_index=True, right_index=True)
    df_opt = df_opt.rename(
        columns={"openInterest_x": "OI_call", "openInterest_y": "OI_put"}
    )

    max_pain = op_helpers.calculate_max_pain(df_opt)

    if min_vol == -1 and min_sp == -1 and max_sp == -1:
        # If no argument provided, we use the percentile 50 to get 50% of upper volume data
        volume_percentile_threshold = 50
        min_vol_calls = np.percentile(df_calls["oi+v"], volume_percentile_threshold)
        min_vol_puts = np.percentile(df_puts["oi+v"], volume_percentile_threshold)

        df_calls = df_calls.loc[df_calls.index.intersection(df_puts.index)]
        df_calls = (
            df_calls[df_calls["oi+v"] > min_vol_calls]
            .drop(["strike"], axis=1)
            .reset_index()
            .merge(
                df_calls[df_puts["oi+v"] < min_vol_puts][
                    ["openInterest", "volume", "type", "oi+v", "spot"]
                ].reset_index()
            )
            .set_index("strike")
        )
        df_calls["strike"] = df_calls.index

        df_puts = df_puts.loc[df_puts.index.intersection(df_calls.index)]

        df_calls = df_calls[df_calls["strike"] > 0.75 * current_price]
        df_calls = df_calls[df_calls["strike"] < 1.25 * current_price]
        df_puts = df_puts[df_puts["strike"] > 0.75 * current_price]
        df_puts = df_puts[df_puts["strike"] < 1.25 * current_price]

    else:
        df_calls = df_calls.loc[df_calls.index.intersection(df_puts.index)]
        if min_vol > -1:
            df_calls = (
                df_calls[df_calls["oi+v"] > min_vol]
                .drop(["strike"], axis=1)
                .reset_index()
                .merge(
                    df_calls[df_puts["oi+v"] < min_vol][
                        ["openInterest", "volume", "type", "oi+v", "spot"]
                    ].reset_index()
                )
                .set_index("strike")
            )
            df_calls["strike"] = df_calls.index
            df_puts = df_puts.loc[df_puts.index.intersection(df_calls.index)]

    if min_sp > -1:
        df_calls = df_calls[df_calls["strike"] > min_sp]
        df_puts = df_puts[df_puts["strike"] > min_sp]
    else:
        df_calls = df_calls[df_calls["strike"] > 0.75 * current_price]
        df_puts = df_puts[df_puts["strike"] > 0.75 * current_price]

    if max_sp > -1:
        df_calls = df_calls[df_calls["strike"] < max_sp]
        df_puts = df_puts[df_puts["strike"] < max_sp]
    else:
        df_calls = df_calls[df_calls["strike"] < 1.25 * current_price]
        df_puts = df_puts[df_puts["strike"] < 1.25 * current_price]

    if df_calls.empty and df_puts.empty:
        console.print(
            "The filtering applied is too strong, there is no data available for such conditions.\n"
        )
        return

    # Initialize the matplotlib figure
    if external_axes is None:
        _, ax = plt.subplots(figsize=plot_autoscale(), dpi=cfp.PLOT_DPI)
    elif is_valid_axes_count(external_axes, 1):
        (ax,) = external_axes
    else:
        return

    # make x axis symmetric
    axis_origin = max(abs(max(df_puts["oi+v"])), abs(max(df_calls["oi+v"])))
    ax.set_xlim(-axis_origin, +axis_origin)

    g = sns.barplot(
        x="oi+v",
        y="strike",
        data=df_calls,
        label="Calls: Open Interest",
        color="lightgreen",
        orient="h",
    )

    g = sns.barplot(
        x="volume",
        y="strike",
        data=df_calls,
        label="Calls: Volume",
        color="green",
        orient="h",
    )

    g = sns.barplot(
        x="oi+v",
        y="strike",
        data=df_puts,
        label="Puts: Open Interest",
        color="pink",
        orient="h",
    )

    g = sns.barplot(
        x="volume",
        y="strike",
        data=df_puts,
        label="Puts: Volume",
        color="red",
        orient="h",
    )

    # draw spot line
    s = [float(strike.get_text()) for strike in ax.get_yticklabels()]
    spot_index = bisect_left(s, current_price)  # find where the spot is on the graph
    spot_line = ax.axhline(spot_index, ls="--", alpha=0.3)

    # draw max pain line
    max_pain_index = bisect_left(s, max_pain)
    max_pain_line = ax.axhline(max_pain_index, ls="-", alpha=0.3, color="red")
    max_pain_line.set_linewidth(5)

    # format ticklabels without - for puts
    g.set_xticks(g.get_xticks())
    xlabels = [f"{x:,.0f}".replace("-", "") for x in g.get_xticks()]
    g.set_xticklabels(xlabels)

    ax.set_title(
        f"{symbol} volumes for {expiry} \n(open interest displayed only during market hours)",
    )
    ax.invert_yaxis()

    handles, _ = ax.get_legend_handles_labels()
    handles.append(spot_line)
    handles.append(max_pain_line)

    # 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}",
    ]

    ax.legend(fontsize="xx-small", handles=handles[:], labels=labels, loc="lower left")
    sns.despine(left=True, bottom=True)
    theme.style_primary_axis(ax)

    if external_axes is None:
        theme.visualize_output()

    op_helpers.export_yf_options(export, options, "voi_yf")


@log_start_end(log=logger)
def plot_plot(
    symbol: str,
    expiry: str,
    put: bool = False,
    x: str = "s",
    y: str = "iv",
    custom: str = "",
    export: str = "",
    external_axes: Optional[List[plt.Axes]] = None,
) -> None:
    """Generate a graph custom graph based on user input

    Parameters
    ----------
    symbol: str
        Stock ticker symbol
    expiry: str
        Option expiration
    x: str
        variable to display in x axis, choose from:
        ltd, s, lp, b, a, c, pc, v, oi, iv
    y: str
        variable to display in y axis, choose from:
        ltd, s, lp, b, a, c, pc, v, oi, iv
    custom: str
        type of plot
    put: bool
        put option instead of call
    export: str
        type of data to export
    external_axes : Optional[List[plt.Axes]], optional
        External axes (1 axis is expected in the list), by default None
    """
    convert = {
        "ltd": "lastTradeDate",
        "s": "strike",
        "lp": "lastPrice",
        "b": "bid",
        "a": "ask",
        "c": "change",
        "pc": "percentChange",
        "v": "volume",
        "oi": "openInterest",
        "iv": "impliedVolatility",
    }
    if custom == "smile":
        x = "strike"
        y = "impliedVolatility"
    else:
        if x is None:
            console.print("[red]Invalid option sent for x-axis[/red]\n")
            return
        if y is None:
            console.print("[red]Invalid option sent for y-axis[/red]\n")
            return
        if x in convert:
            x = convert[x]
        else:
            x = "strike"
            console.print(
                f"[red]'{x}' is not a valid option. Defaulting to `strike`.[/red]\n"
            )
        if y in convert:
            y = convert[y]
        else:
            y = "impliedVolatility"
            console.print(
                f"[red]'{y}' is not a valid option. Defaulting to `impliedVolatility`.[/red]\n"
            )

    varis = op_helpers.opt_chain_cols
    chain = yfinance_model.get_option_chain(symbol, expiry)
    values = chain.puts if put else chain.calls

    if external_axes is None:
        _, ax = plt.subplots(figsize=plot_autoscale(), dpi=cfp.PLOT_DPI)
    elif is_valid_axes_count(external_axes, 1):
        (ax,) = external_axes
    else:
        return

    x_data = values[x]
    y_data = values[y]
    ax.plot(x_data, y_data, "--bo")
    option = "puts" if put else "calls"
    ax.set_title(
        f"{varis[y]['label']} vs. {varis[x]['label']} for {symbol} {option} on {expiry}"
    )
    ax.set_ylabel(varis[y]["label"])
    ax.set_xlabel(varis[x]["label"])
    if varis[x]["format"] == "date":
        ax.get_xaxis().set_major_formatter(mdates.DateFormatter("%Y/%m/%d"))
        ax.get_xaxis().set_major_locator(mdates.DayLocator(interval=1))
    elif varis[x]["format"]:
        ax.get_xaxis().set_major_formatter(varis[x]["format"])
    if varis[y]["format"] == "date":
        ax.get_yaxis().set_major_formatter(mdates.DateFormatter("%Y/%m/%d"))
        ax.get_yaxis().set_major_locator(mdates.DayLocator(interval=1))
    elif varis[y]["format"]:
        ax.get_yaxis().set_major_formatter(varis[y]["format"])
    theme.style_primary_axis(ax)

    if external_axes is None:
        theme.visualize_output()
    export_data(export, os.path.dirname(os.path.abspath(__file__)), "plot")


@log_start_end(log=logger)
def plot_payoff(
    current_price: float,
    options: List[Dict[Any, Any]],
    underlying: float,
    symbol: str,
    expiry: str,
    external_axes: Optional[List[plt.Axes]] = None,
) -> None:
    """Generate a graph showing the option payoff diagram"""
    x, yb, ya = generate_data(current_price, options, underlying)

    if external_axes is None:
        _, ax = plt.subplots(figsize=plot_autoscale(), dpi=cfp.PLOT_DPI)
    elif is_valid_axes_count(external_axes, 1):
        (ax,) = external_axes
    else:
        return

    if ya:
        ax.plot(x, yb, label="Payoff Before Premium")
        ax.plot(x, ya, label="Payoff After Premium")
    else:
        ax.plot(x, yb, label="Payoff")
    ax.set_title(f"Option Payoff Diagram for {symbol} on {expiry}")
    ax.set_ylabel("Profit")
    ax.set_xlabel("Underlying Asset Price at Expiration")
    ax.legend()
    ax.xaxis.set_major_formatter("${x:.2f}")
    ax.yaxis.set_major_formatter("${x:.2f}")
    theme.style_primary_axis(ax)

    if external_axes is None:
        theme.visualize_output()


@log_start_end(log=logger)
def show_parity(
    symbol: str,
    expiry: str,
    put: bool = False,
    ask: bool = False,
    mini: float = None,
    maxi: float = None,
    export: str = "",
) -> None:
    """Prints options and whether they are under or over priced [Source: Yahoo Finance]

    Parameters
    ----------
    symbol : str
        Ticker symbol to get expirations for
    expiration : str
        Expiration to use for options
    put : bool
        Whether to use puts or calls
    ask : bool
        Whether to use ask or lastPrice
    mini : float
        Minimum strike price to show
    maxi : float
        Maximum strike price to show
    export : str
        Export data
    """
    r_date = datetime.strptime(expiry, "%Y-%m-%d").date()
    delta = (r_date - date.today()).days
    rate = ((1 + get_rf()) ** (delta / 365)) - 1
    stock = get_price(symbol)

    div_info = yfinance_model.get_dividend(symbol)
    div_dts = div_info.index.values.tolist()

    if div_dts:
        last_div = pd.to_datetime(div_dts[-1])

        if len(div_dts) > 3:
            avg_div = np.mean(div_info.to_list()[-4:])
        else:
            avg_div = np.mean(div_info.to_list())

        next_div = last_div + timedelta(days=91)
        dividends = []
        while next_div < datetime.strptime(expiry, "%Y-%m-%d"):
            day_dif = (next_div - datetime.now()).days
            dividends.append((avg_div, day_dif))
            next_div += timedelta(days=91)
        div_pvs = [x[0] / ((1 + get_rf()) ** (x[1] / 365)) for x in dividends]
        pv_dividend = sum(div_pvs)
    else:
        pv_dividend = 0

    chain = get_option_chain(symbol, expiry)
    name = "ask" if ask else "lastPrice"
    o_type = "put" if put else "call"

    calls = chain.calls[["strike", name]].copy()
    calls = calls.rename(columns={name: "callPrice"})
    puts = chain.puts[["strike", name]].copy()
    puts = puts.rename(columns={name: "putPrice"})

    opts = pd.merge(calls, puts, on="strike")
    opts = opts.dropna()
    opts = opts.loc[opts["callPrice"] * opts["putPrice"] != 0]

    opts["callParity"] = (
        opts["putPrice"] + stock - (opts["strike"] / (1 + rate)) - pv_dividend
    )
    opts["putParity"] = (
        (opts["strike"] / (1 + rate)) + opts["callPrice"] - stock + pv_dividend
    )

    diff = o_type + " Difference"
    opts[diff] = opts[o_type + "Price"] - opts[o_type + "Parity"]
    opts["distance"] = abs(stock - opts["strike"])
    filtered = opts.copy()

    if mini is None:
        mini = filtered.strike.quantile(0.25)
    if maxi is None:
        maxi = filtered.strike.quantile(0.75)

    filtered = filtered.loc[filtered["strike"] >= mini]
    filtered = filtered.loc[filtered["strike"] <= maxi]

    show = filtered[["strike", diff]].copy()

    if ask:
        console.print("Warning: Options with no current ask price not shown.\n")

    print_rich_table(
        show,
        headers=[x.title() for x in show.columns],
        show_index=False,
        title="Warning: Low volume options may be difficult to trade.",
    )

    export_data(
        export,
        os.path.dirname(os.path.abspath(__file__)),
        "parity",
        show,
    )


@log_start_end(log=logger)
def risk_neutral_vals(
    symbol: str,
    expiry: str,
    data: pd.DataFrame,
    put: bool = False,
    mini: float = None,
    maxi: float = None,
    risk: float = None,
) -> None:
    """Prints current options prices and risk neutral values [Source: Yahoo Finance]

    Parameters
    ----------
    symbol: str
        Ticker symbol to get expirations for
    expiry: str
        Expiration to use for options
    put: bool
        Whether to use puts or calls
    data: pd.DataFrame
        Estimates for stocks prices and probabilities
    mini: float
        Minimum strike price to show
    maxi: float
        Maximum strike price to show
    risk: float
        The risk-free rate for the asset
    """
    if put:
        chain = get_option_chain(symbol, expiry).puts
    else:
        chain = get_option_chain(symbol, expiry).calls

    r_date = datetime.strptime(expiry, "%Y-%m-%d").date()
    delta = (r_date - date.today()).days
    vals = []
    if risk is None:
        risk = get_rf()
    for _, row in chain.iterrows():
        vals.append(
            [
                row["strike"],
                row["lastPrice"],
                op_helpers.rn_payoff(row["strike"], data, put, delta, risk),
            ]
        )
    new_df = pd.DataFrame(vals, columns=["Strike", "Last Price", "Value"], dtype=float)
    new_df["Difference"] = new_df["Last Price"] - new_df["Value"]

    if mini is None:
        mini = new_df.Strike.quantile(0.25)
    if maxi is None:
        maxi = new_df.Strike.quantile(0.75)

    new_df = new_df[new_df["Strike"] >= mini]
    new_df = new_df[new_df["Strike"] <= maxi]

    print_rich_table(
        new_df,
        headers=[x.title() for x in new_df.columns],
        show_index=False,
        title="Risk Neutral Values",
    )


@log_start_end(log=logger)
def plot_expected_prices(
    und_vals: List[List[float]],
    p: float,
    symbol: str,
    expiry: str,
    external_axes: Optional[List[plt.Axes]] = None,
) -> None:
    """Plot expected prices of the underlying asset at expiration

    Parameters
    ----------
    und_vals : List[List[float]]
        The expected underlying values at the expiration date
    p : float
        The probability of the stock price moving upward each round
    symbol : str
        The ticker symbol of the option's underlying asset
    expiry : str
        The expiration for the option
    external_axes : Optional[List[plt.Axes]], optional
        External axes (1 axis is expected in the list), by default None
    """
    up_moves = list(range(len(und_vals[-1])))
    up_moves.reverse()
    probs = [100 * binom.pmf(r, len(up_moves), p) for r in up_moves]
    if external_axes is None:
        _, ax = plt.subplots(figsize=plot_autoscale(), dpi=cfp.PLOT_DPI)
    elif is_valid_axes_count(external_axes, 1):
        (ax,) = external_axes
    else:
        return

    ax.set_title(f"Probabilities for ending prices of {symbol} on {expiry}")
    ax.xaxis.set_major_formatter("${x:1.2f}")
    ax.yaxis.set_major_formatter(mtick.PercentFormatter())
    ax.plot(und_vals[-1], probs)
    theme.style_primary_axis(ax)

    if external_axes is None:
        theme.visualize_output()


@log_start_end(log=logger)
def export_binomial_calcs(
    up: float,
    prob_up: float,
    discount: float,
    und_vals: List[List[float]],
    opt_vals: List[List[float]],
    days: int,
    symbol: str,
) -> None:
    """Create an excel spreadsheet with binomial tables for underlying asset value and option value

    Parameters
    ----------
    up : float
        The stock's increase on an upward move
    prob_up : float
        The probability of an upward move
    discount : float
        The daily discount rate
    und_vals : List[List[float]]
        The underlying asset values at each step
    opt_vals : List[List[float]]
        The values for the option at each step
    days : int
        The number of days until the option expires
    symbol : str
        The ticker symbol for the company
    """

    opts = excel_columns()
    wb = Workbook()
    ws = wb.active

    ws["A1"] = "Up Move"
    ws["B1"] = up
    ws["A2"] = "Down Move"
    ws["B2"] = 1 / up
    ws["D1"] = "Prob Up"
    ws["E1"] = prob_up
    ws["D2"] = "Prob Down"
    ws["E2"] = 1 - prob_up
    ws["D3"] = "Discount"
    ws["E3"] = discount
    ws["A4"] = "Binomial Tree for Underlying Values"
    for i, _ in enumerate(und_vals):
        for j, _ in enumerate(und_vals[i]):
            ws[f"{opts[i]}{j+5}"] = und_vals[i][j]

    ws[f"A{days+7}"] = "Binomial Tree for Option Values"
    for i, _ in enumerate(opt_vals):
        for j, _ in enumerate(opt_vals[i]):
            ws[f"{opts[i]}{j+8+days}"] = opt_vals[i][j]

    trypath = str(
        MISCELLANEOUS_DIRECTORY
        / "exports"
        / "stocks"
        / "options"
        / f"{symbol} {datetime.now()}.xlsx"
    )
    wb.save(trypath)
    console.print(
        f"Analysis ran for {symbol}\nPlease look in {trypath} for the file.\n"
    )


@log_start_end(log=logger)
def show_binom(
    symbol: str,
    expiry: str,
    strike: float = 0,
    put: bool = False,
    europe: bool = False,
    export: str = "",
    plot: bool = False,
    vol: float = None,
) -> None:
    """Get binomial pricing for option

    Parameters
    ----------
    symbol : str
        The ticker symbol of the option's underlying asset
    expiry : str
        The expiration for the option
    strike : float
        The strike price for the option
    put : bool
        Value a put instead of a call
    europe : bool
        Value a European option instead of an American option
    export : str
        Export the options data to an excel spreadsheet
    plot : bool
        Show a graph of expected ending prices
    vol : float
        The annualized volatility for the underlying asset
    """
    up, prob_up, discount, und_vals, opt_vals, days = yfinance_model.get_binom(
        symbol, expiry, strike, put, europe, vol
    )

    if export:
        export_binomial_calcs(up, prob_up, discount, und_vals, opt_vals, days, symbol)

    if plot:
        plot_expected_prices(und_vals, prob_up, symbol, expiry)

    option = "put" if put else "call"
    console.print(
        f"{symbol} {option} at ${strike:.2f} expiring on {expiry} is worth ${opt_vals[0][0]:.2f}\n"
    )


@log_start_end(log=logger)
def display_vol_surface(
    symbol: str,
    export: str = "",
    z: str = "IV",
    external_axes: Optional[List[plt.Axes]] = None,
):
    """Display vol surface

    Parameters
    ----------
    symbol : str
        Ticker symbol to get surface for
    export : str
        Format to export data
    z : str
        The variable for the Z axis
    external_axes: Optional[List[plt.Axes]]
        External axes (1 axis is expected in the list), by default None
    """
    data = yfinance_model.get_iv_surface(symbol)
    if data.empty:
        console.print(f"No options data found for {symbol}.\n")
        return
    X = data.dte
    Y = data.strike
    if z == "IV":
        Z = data.impliedVolatility
        label = "Volatility"
    elif z == "OI":
        Z = data.openInterest
        label = "Open Interest"
    elif z == "LP":
        Z = data.lastPrice
        label = "Last Price"
    if external_axes is None:
        fig = plt.figure(figsize=plot_autoscale(), dpi=PLOT_DPI)
        ax = plt.axes(projection="3d")
    else:
        ax = external_axes[0]
    ax.plot_trisurf(X, Y, Z, cmap="jet", linewidth=0.2)
    ax.set_xlabel("DTE")
    ax.set_ylabel("Strike")
    ax.set_zlabel(z)

    if external_axes is None:
        fig.suptitle(f"{label} Surface for {symbol.upper()}")
        theme.visualize_output(force_tight_layout=False)

    export_data(
        export,
        os.path.dirname(os.path.abspath(__file__)),
        "vsurf",
        data,
    )


@log_start_end(log=logger)
def show_greeks(
    symbol: str,
    expiry: str,
    div_cont: float = 0,
    rf: float = None,
    opt_type: int = 1,
    mini: float = None,
    maxi: float = None,
    show_all: bool = False,
) -> None:
    """
    Shows the greeks for a given option

    Parameters
    ----------
    symbol: str
        The ticker symbol value of the option
    div_cont: float
        The dividend continuous rate
    expiry: str
        The date of expiration, format "YYYY-MM-DD", i.e. 2010-12-31.
    rf: float
        The risk-free rate
    opt_type: Union[1, -1]
        The option type 1 is for call and -1 is for put
    mini: float
        The minimum strike price to include in the table
    maxi: float
        The maximum strike price to include in the table
    show_all: bool
        Whether to show all greeks
    """

    df = yfinance_model.get_greeks(
        symbol, expiry, div_cont, rf, opt_type, mini, maxi, show_all
    )

    column_formatting = [
        ".1f",
        ".4f",
        ".6f",
        ".6f",
        ".6f",
        ".6f",
    ]

    if show_all:
        additional_columns = ["Rho", "Phi", "Charm", "Vanna", "Vomma"]
        column_formatting += [".6f"] * len(additional_columns)

    print_rich_table(
        df,
        headers=list(df.columns),
        show_index=False,
        title="Greeks",
        floatfmt=column_formatting,
    )

    return None