"""Normalize net noncommercial futures-and-options positions by open interest and compare them with the equity-market path.

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 {
        "cot": qj.cftc.get_cot_data(
            symbol="13874A", report_type="legacy", start_date="2020-01-01", end_date="2025-12-31"
        ),
        "prices": qj.eod.get_historical_prices(
            symbols=["SPY"], start_date="2020-01-01", end_date="2025-12-31", frequency="1d"
        ),
    }


def analyze(raw):
    cot = frame(raw["cot"], "report_date_as_yyyy_mm_dd")
    cot = cot.loc[cot["futonly_or_combined"] == "Combined"].copy()
    cot["Net / OI %"] = [
        100 * ratio(decimal(long) - decimal(short), interest)
        for long, short, interest in zip(
            cot["noncomm_positions_long_all"], cot["noncomm_positions_short_all"], cot["open_interest_all"], strict=True
        )
    ]
    cot = cot.set_index("report_date_as_yyyy_mm_dd")
    prices = history(raw["prices"], "SPY")["price"].rename("SPY")
    joined = pd.merge_asof(
        cot[["Net / OI %"]],
        prices.to_frame(),
        left_index=True,
        right_index=True,
        direction="backward",
        tolerance=pd.Timedelta(days=7),
    ).dropna()
    if joined.empty:
        raise ValueError("No matching report dates and market observations")
    joined["SPY index"] = joined["SPY"].map(lambda x: 100 * ratio(x, joined["SPY"].iloc[0]))
    fig, (top, bottom) = canvas("Positioning · the speculative balance", "Net noncommercial / OI %", panels=2)
    top.plot(joined.index, joined["Net / OI %"], color=BLUE)
    top.axhline(0, color="#afc1dc", lw=0.8)
    bottom.plot(joined.index, joined["SPY index"], color=PINK)
    bottom.set_ylabel("SPY adjusted index · start = 100")
    bottom.set_xlabel("CFTC report date · publication occurs later")
    return joined[["Net / OI %", "SPY index"]].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 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 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_cot.png"

if __name__ == "__main__":
    run()