root
0ba5a05219
Add a unified historical endpoint that fans out to every exchange supporting the requested (asset_class, symbol) pair, then merges the results into a single consensus candle series with per-bar divergence metrics: - candles[i].close = median across sources - candles[i].sources = count of contributing exchanges - candles[i].div_pct = (max-min)/median for that bar's close Crypto routes BTC/ETH/SOL across Bybit + Hyperliquid + Deribit; equities route to Alpaca for now (IBKR omitted from MVP because its bars endpoint takes a relative period instead of start/end). Partial failures return a warning envelope (failed_sources) instead of failing the whole request; all sources failing → HTTP 502. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
38 lines
1.4 KiB
Python
38 lines
1.4 KiB
Python
"""Pure consensus aggregation: merge per-source OHLCV candles by timestamp.
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The output is a single time-series with the median OHLC across sources,
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mean volume, the contributing source count, and a divergence % computed
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on the close range. div_pct gives a quick quality signal: 0 means full
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agreement, > X% means at least one source is suspect.
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"""
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from __future__ import annotations
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from collections import defaultdict
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from statistics import median
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from typing import Any
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def merge_candles(by_source: dict[str, list[dict[str, Any]]]) -> list[dict[str, Any]]:
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grouped: dict[int, list[dict[str, Any]]] = defaultdict(list)
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for candles in by_source.values():
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for c in candles:
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grouped[int(c["timestamp"])].append(c)
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out: list[dict[str, Any]] = []
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for ts in sorted(grouped):
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rows = grouped[ts]
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closes = [float(r["close"]) for r in rows]
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med_close = float(median(closes))
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div_pct = (max(closes) - min(closes)) / med_close if med_close else 0.0
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out.append({
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"timestamp": ts,
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"open": float(median(float(r["open"]) for r in rows)),
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"high": float(median(float(r["high"]) for r in rows)),
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"low": float(median(float(r["low"]) for r in rows)),
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"close": med_close,
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"volume": sum(float(r["volume"]) for r in rows) / len(rows),
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"sources": len(rows),
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"div_pct": div_pct,
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})
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return out
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