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"""Normalization: provider frames -> canonical OHLCV schema + content hashes."""
from __future__ import annotations
import hashlib
import json
from typing import Any
import pandas as pd
SCHEMA_VERSION = "1"
NORMALIZATION_VERSION = "1"
# Canonical instrument identity format: "<EXCHANGE>#<CODE>", e.g. SH#600000.
# Prices are never substituted across canonical symbols.
# AKShare (Chinese) -> canonical column names. Non-mapped fields are preserved
# verbatim as raw provider fields (units/precision untouched).
COLUMN_MAP = {
"日期": "date",
"开盘": "open",
"收盘": "close",
"最高": "high",
"最低": "low",
"成交量": "volume",
}
REQUIRED = ["date", "open", "high", "low", "close", "volume"]
class NormalizeError(ValueError):
def __init__(self, code: str, message: str, details: Any = None):
super().__init__(message)
self.code = code
self.details = details
def map_columns(df: pd.DataFrame) -> pd.DataFrame:
return df.rename(columns=COLUMN_MAP)
def normalize_frame(df: pd.DataFrame, symbol: str) -> pd.DataFrame:
if df is None or df.empty:
raise NormalizeError("empty_response", "provider returned no rows")
missing_req = {c for c in REQUIRED if c not in df.columns}
if missing_req:
raise NormalizeError(
"missing_columns",
f"provider frame missing standard columns: {sorted(missing_req)}",
{"columns": list(df.columns)},
)
out = df.copy()
out["date"] = pd.to_datetime(out["date"], errors="coerce")
if out["date"].isna().any():
bad = out.loc[out["date"].isna()].index.tolist()
raise NormalizeError(
"bad_date", "unparseable date values", {"rows": [int(i) for i in bad[:5]]}
)
for col in ("open", "high", "low", "close", "volume"):
out[col] = pd.to_numeric(out[col], errors="coerce")
if out[col].isna().any():
rows = out.loc[out[col].isna()].index.tolist()
first = out.loc[rows[0], "date"].date().isoformat()
raise NormalizeError(
"missing_ohlcv",
f"missing or non-numeric '{col}' on {first} — gap kept, never imputed",
{"column": col, "row_count": len(rows), "rows": [int(i) for i in rows[:5]]},
)
out["date"] = out["date"].dt.strftime("%Y-%m-%d")
out = out.sort_values("date", kind="mergesort")
out = out.drop_duplicates(subset="date", keep="last")
# canonical identity, prices never substituted across symbols
out["symbol"] = symbol
return out.reset_index(drop=True)
def frame_records(df: pd.DataFrame) -> list[dict]:
return json.loads(df.to_json(orient="records", force_ascii=False))
def compute_object_hash(df: pd.DataFrame) -> str:
"""Content hash over canonical records; independent of request/user ids."""
recs = frame_records(df)
return hashlib.sha256(
json.dumps(recs, sort_keys=True, ensure_ascii=False).encode("utf-8")
).hexdigest()
def coverage_summary(df_or_dates) -> dict:
if isinstance(df_or_dates, pd.DataFrame):
dates = pd.to_datetime(df_or_dates["date"]).tolist()
else:
dates = pd.to_datetime(list(df_or_dates)).tolist()
if not dates:
return {"actual_start": None, "actual_end": None, "segments": [], "gaps": []}
segs = [[dates[0], dates[0]]]
gaps: list[dict] = []
for prev, cur in zip(dates, dates[1:]):
delta = cur - prev
if delta.days == 1:
segs[-1][1] = cur
else:
segs.append([cur, cur])
gaps.append({"after": prev.date().isoformat(), "before": cur.date().isoformat(),
"calendar_days": delta.days - 1})
fmt = lambda d: d.date().isoformat()
return {
"actual_start": fmt(dates[0]),
"actual_end": fmt(dates[-1]),
"segments": [[fmt(a), fmt(b)] for a, b in segs],
"gaps": gaps,
}
def build_manifest_entry(
df: pd.DataFrame,
*,
instrument: dict,
provider: str,
endpoint: str,
params: dict,
adjustment: str,
requested_start: str,
requested_end: str,
raw_object_hash: str,
warnings: list,
schema_version: str = SCHEMA_VERSION,
normalization_version: str = NORMALIZATION_VERSION,
path: str,
fetched_at: str,
akshare_version: str,
) -> dict:
cov = coverage_summary(df)
return {
"instrument": {
"symbol": instrument["symbol"],
"market": instrument["market"],
"asset_type": instrument["asset_type"],
**({"name": instrument["name"]} if instrument.get("name") else {}),
},
"object_hash": compute_object_hash(df),
"path": path, # internal-only: backend rewrites before client exposure
"raw_object_hash": raw_object_hash,
"provider": provider,
"endpoint": endpoint,
"params": params,
"akshare_version": akshare_version,
"fetched_at": fetched_at,
"schema_version": schema_version,
"normalization_version": normalization_version,
"adjustment": adjustment,
"requested_start": requested_start,
"requested_end": requested_end,
"actual_start": cov["actual_start"],
"actual_end": cov["actual_end"],
"coverage": {"segments": cov["segments"], "gaps": cov["gaps"]},
"row_count": int(len(df)),
"columns": list(df.columns),
"warnings": list(warnings),
"immutable": True,
}
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