"""Join reported EPS surprises to adjusted-price windows and inspect the size and direction of the subsequent move.

Research example. Live observations can change.
"""

from datetime import UTC, datetime
from decimal import Decimal
from textwrap import fill

import matplotlib.pyplot as plt
import pandas as pd

AS_OF = datetime.now(UTC).date()
NAVY, BLUE, PINK = "#132244", "#8fb9ff", "#e7a2cc"


def fetch(qj):
    return {
        "vix_history": qj.fred.get_fred_data_series_by_id(search_id="VIXCLS", start="2020-01-01", end="2025-12-31"),
        "earnings": qj.fmp.get_earnings_surprises(symbol="AAPL"),
        "prices": qj.eod.get_historical_prices(
            symbols=["AAPL"], start_date="2020-01-01", end_date="2025-12-31", frequency="1d"
        ),
    }


def analyze(raw):
    events = frame(raw["earnings"])
    price = history(raw["prices"], "AAPL")["price"]
    vix = macro(raw["vix_history"], "VIXCLS")
    rows = []
    for event in events.to_dict("records"):
        if event["date"] < price.index.min() or pd.isna(event["surprisePercentage"]):
            continue
        window = price.loc[price.index > event["date"]]
        if len(window) < 6:
            continue
        prior_vix = vix.loc[vix.index < event["date"]]
        if prior_vix.empty:
            continue
        rows.append(
            {
                "Prior VIX": prior_vix.iloc[-1],
                "Event": event["date"],
                "EPS surprise %": float(event["surprisePercentage"]),
                "Next-window return %": 100 * (ratio(window.iloc[5], window.iloc[0]) - 1),
            }
        )
    result = pd.DataFrame(rows)
    if result.empty:
        raise ValueError("No earnings events within available price coverage")
    fig, (ax,) = canvas("AAPL · earnings surprise versus subsequent move", "Five-session adjusted return %")
    points = ax.scatter(
        result["EPS surprise %"], result["Next-window return %"], s=55, c=result["Prior VIX"], cmap="cool", alpha=0.9
    )
    fig.colorbar(points, ax=ax, label="VIX before the event date")
    ax.axhline(0, color="#afc1dc", lw=0.8)
    ax.axvline(0, color="#afc1dc", lw=0.8)
    ax.set_xlabel("Reported EPS surprise % · FMP")
    return result, fig


def payload(response):
    if hasattr(response, "to_dict"):
        response = response.to_dict()
    while isinstance(response, dict):
        if response.get("error") or response.get("error_code") or response.get("success") is False:
            raise ValueError("Provider request failed")
        if "data" in response:
            response = response["data"]
        elif "value" in response:
            response = response["value"]
        else:
            break
    return response


def frame(response, date="date"):
    rows = payload(response)
    if not isinstance(rows, list) or not rows:
        raise ValueError("No provider observations returned")
    result = pd.DataFrame(rows)
    if date:
        result[date] = pd.to_datetime(result[date], utc=True)
        result = result.sort_values(date)
    return result


def decimal(value):
    if pd.isna(value):
        raise ValueError("Required monetary value or quantity is missing")
    return Decimal(str(value))


def ratio(numerator, denominator):
    denominator = decimal(denominator)
    if denominator == 0:
        raise ValueError("Required denominator is zero")
    return float(decimal(numerator) / denominator)


def history(response, symbol, adjusted=True):
    values = payload(response)
    if not isinstance(values, dict) or symbol not in values:
        raise ValueError(f"No price coverage for {symbol}")
    result = frame(values[symbol]).set_index("date")
    field = "adjusted_close" if adjusted else "close"
    result["price"] = result[field].map(decimal)
    result["volume"] = result["volume"].map(decimal)
    if result.index.has_duplicates:
        raise ValueError("Duplicate trading dates")
    return result


def macro(response, column):
    values = frame(response).set_index("date")[column]
    values = pd.to_numeric(values, errors="coerce").dropna()
    if values.empty:
        raise ValueError(f"No numeric observations for {column}")
    return values


def canvas(title, ylabel, panels=1):
    plt.rcParams.update(
        {
            "figure.facecolor": NAVY,
            "axes.facecolor": NAVY,
            "savefig.facecolor": NAVY,
            "text.color": "#e9eef8",
            "axes.labelcolor": "#afc1dc",
            "xtick.color": "#afc1dc",
            "ytick.color": "#afc1dc",
            "axes.edgecolor": "#34486b",
            "grid.color": "#34486b",
            "font.size": 10,
            "axes.prop_cycle": plt.cycler(color=[BLUE, PINK, "#ba9dea"]),
        }
    )
    fig, axes = plt.subplots(panels, 1, figsize=(9, 6.1), squeeze=False, layout="constrained")
    fig.suptitle(fill(title, width=58), fontsize=17, x=0.04, ha="left", weight="medium")
    for ax in axes.flat:
        ax.grid(axis="y", alpha=0.45)
        ax.set_axisbelow(True)
        ax.spines[["top", "right"]].set_visible(False)
    axes.flat[0].set_ylabel(ylabel)
    return fig, list(axes.flat)


def run():
    from quantjourney.sdk import QuantJourneyAPI

    qj = QuantJourneyAPI.from_env()
    raw = fetch(qj)
    table, fig = analyze(raw)
    print(table.to_string(index=False))
    fig.savefig(OUTPUT, dpi=180)
    plt.show()


OUTPUT = "sdk_earnings_reaction.png"

if __name__ == "__main__":
    run()