"""Combine CNN Fear & Greed observations, VIX closes and SPY returns without treating sentiment as a trade signal.

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 numpy as np
import pandas as pd

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


def fetch(qj):
    return {
        "cnn": qj.domains.call(
            "sentiment.fear_greed.get_index_range", provider="cnnf", start_date="2026-08-01", end_date="2026-10-30"
        ),
        "vix_current": qj.domains.call(
            "macro.indicators.get_fred_data_series_by_id",
            provider="fred",
            search_id="VIXCLS",
            start="2026-01-01",
            end="2026-10-30",
        ),
        "recent_prices": qj.domains.call(
            "equity.pricing.get_historical_prices",
            provider="eod",
            symbols=["SPY"],
            start_date="2026-01-01",
            end_date="2026-10-30",
            frequency="1d",
        ),
    }


def analyze(raw):
    observations = frame(raw["cnn"])
    observations["fg_value"] = pd.to_numeric(observations["fg_value"], errors="raise")
    sentiment = observations.groupby("date")["fg_value"].mean().rename("CNN daily mean")
    prices = history(raw["recent_prices"], "SPY")["price"]
    returns = (100 * change(prices)).rename("SPY daily return %")
    vix = macro(raw["vix_current"], "VIXCLS").rename("VIX close")
    data = pd.concat([sentiment, returns, vix], axis=1, join="inner").dropna()
    if len(data) < 20:
        raise ValueError("Need at least 20 common sentiment, VIX and price dates")
    fig, (top, bottom) = canvas("Sentiment · price action and market risk", "CNN Fear & Greed · daily mean", panels=2)
    top.plot(data.index, data["CNN daily mean"], color=BLUE)
    top.set_ylim(0, 100)
    points = bottom.scatter(data["VIX close"], data["SPY daily return %"], c=data["CNN daily mean"], cmap="cool", s=35)
    bottom.set_xlabel("VIX close")
    bottom.set_ylabel("SPY daily adjusted return %")
    bottom.axhline(0, color="#afc1dc", lw=0.8)
    fig.colorbar(points, ax=bottom, label="CNN daily mean")
    return data.reset_index(), 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 change(values, periods=1):
    previous = values.shift(periods)
    return pd.Series(
        [ratio(x, y) - 1 if pd.notna(x) and pd.notna(y) else np.nan for x, y in zip(values, previous, strict=True)],
        index=values.index,
    )


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_sentiment.png"

if __name__ == "__main__":
    run()