"""Pure consensus aggregation: merge per-source OHLCV candles by timestamp.

The output is a single time-series with the median OHLC across sources,
mean volume, the contributing source count, and a divergence % computed
on the close range. div_pct gives a quick quality signal: 0 means full
agreement, > X% means at least one source is suspect.
"""
from __future__ import annotations

from collections import defaultdict
from statistics import median
from typing import Any


def merge_candles(by_source: dict[str, list[dict[str, Any]]]) -> list[dict[str, Any]]:
    grouped: dict[int, list[dict[str, Any]]] = defaultdict(list)
    for candles in by_source.values():
        for c in candles:
            grouped[int(c["timestamp"])].append(c)

    out: list[dict[str, Any]] = []
    for ts in sorted(grouped):
        rows = grouped[ts]
        closes = [float(r["close"]) for r in rows]
        med_close = float(median(closes))
        div_pct = (max(closes) - min(closes)) / med_close if med_close else 0.0
        out.append({
            "timestamp": ts,
            "open": float(median(float(r["open"]) for r in rows)),
            "high": float(median(float(r["high"]) for r in rows)),
            "low": float(median(float(r["low"]) for r in rows)),
            "close": med_close,
            "volume": sum(float(r["volume"]) for r in rows) / len(rows),
            "sources": len(rows),
            "div_pct": div_pct,
        })
    return out