worldquant-alpha-system/backend/app/alphas.py

"""Normalize upstream business data, without making platform or research decisions."""

import math
import re
from datetime import datetime

from sqlalchemy import or_, select, update

from .models import Alpha, Research, ResearchTag, SelfCorrelation, now
from .platform_checks import check_result, split_checks, submission_limits
from .research.provenance import source_alpha_ids

METRIC_FIELDS = (
    "sharpe", "fitness", "returns", "turnover", "margin", "drawdown",
    "sub_universe_sharpe", "robust_universe_sharpe", "two_year_sharpe", "prod_correlation", "pnl",
)


def failed_checks(checks):
    """Return failed Alpha check names, excluding submission limits and local correlation."""
    return [
        check.get("name") if isinstance(check.get("name"), str) else "未命名检查"
        for check in split_checks(checks)[0] if isinstance(check, dict) and check_result(check) == "FAIL"
    ] if isinstance(checks, list) else []


def snapshot_columns(settings, metrics, checks, *, checked=False):
    """Derive list fields from a platform snapshot, preserving missing metrics as null.

    Submission limits are excluded. Only explicit Alpha FAIL results count.
    Sync snapshots with no failures are PRE_CHECK; a completed explicit /check
    with no failures is PASS. WARNING/PENDING do not count as failures, matching
    the legacy workflow. Empty, malformed or unknown results remain PENDING.
    No submission eligibility or activity eligibility is inferred here.
    """
    settings = settings if isinstance(settings, dict) else {}
    metrics = metrics if isinstance(metrics, dict) else {}
    checks, _ = split_checks(checks)
    valid = [check for check in checks if isinstance(check, dict)]
    failures = len(failed_checks(checks))
    by_name = {check["name"]: check for check in valid if isinstance(check.get("name"), str)}
    if failures:
        check_type = "FAIL_1" if failures == 1 else "FAIL_2"
    elif not checks or len(valid) != len(checks) or any(check_result(check) not in ("PASS", "WARNING", "PENDING") for check in valid):
        check_type = "PENDING"
    else:
        check_type = "PASS" if checked else "PRE_CHECK"
    # /check values are freshest; submitted snapshots also expose a scalar in IS.
    prod_correlation = number(by_name.get("PROD_CORRELATION", {}).get("value"))
    if prod_correlation is None:
        prod_correlation = number(metrics.get("prodCorrelation"))
    neutralization = settings.get("neutralization")
    return {
        "check_type": check_type,
        "neutralization": neutralization if isinstance(neutralization, str) else None,
        "pnl": number(metrics.get("pnl")),
        "prod_correlation": prod_correlation,
        **{
            field: number(by_name.get(name, {}).get("value"))
            for field, name in (
                ("sub_universe_sharpe", "LOW_SUB_UNIVERSE_SHARPE"),
                ("robust_universe_sharpe", "LOW_ROBUST_UNIVERSE_SHARPE"),
                ("two_year_sharpe", "LOW_2Y_SHARPE"),

            )
        },
    }


def check_summary(checks, *, check_type):
    """Separate cached Alpha findings from submission limits; infer no live eligibility."""
    return {
        "check_type": check_type,
        "failed_checks": failed_checks(checks),
        "submission_limits": submission_limits(checks),
        "meaning": "PRE_CHECK 为同步无失败项；PASS 为主动检查完成且无失败项。PENDING/WARNING 不算失败，不代表全部检查项 PASS 或当前可提交",
    }


def submission_condition(submission):
    """Match the platform list contract; a missing status is never assumed submitted."""
    return Alpha.status == "UNSUBMITTED" if submission == "UNSUBMITTED" else Alpha.status != "UNSUBMITTED"


async def invalidate_correlations(db, alpha_id, regions=()):
    """A changed baseline or PnL invalidates local conclusions without touching platform checks."""
    await db.execute(
        update(SelfCorrelation)
        .where(or_(SelfCorrelation.alpha_id == alpha_id, SelfCorrelation.region.in_(regions)))
        .values(stale=True)
    )


SENSITIVE_KEYS = {
    "password",
    "token",
    "cookies",
    "cookie",
    "authorization",
    "credentials",
    "secret",
    "accesstoken",
    "refreshtoken",
    "sessiontoken",
    "clientsecret",
    "authorizationheader",
    "setcookie",
    "apikey",
    "csrftoken",
    "xsrftoken",
    "authentication",
}


def sanitize(value):
    if isinstance(value, dict):
        return {
            k: sanitize(v) for k, v in value.items() if re.sub(r"[^a-z]", "", k.lower()) not in SENSITIVE_KEYS
        }
    if isinstance(value, list):
        return [sanitize(v) for v in value]
    if isinstance(value, float) and not math.isfinite(value):
        return None
    return value


def number(value):
    if value is None or isinstance(value, bool):
        return None
    try:
        result = float(value)
        return result if math.isfinite(result) else None
    except (ValueError, TypeError):
        return None


def date(value):
    try:
        return datetime.fromisoformat(value.replace("Z", "+00:00")) if value else None
    except (ValueError, TypeError, AttributeError):
        return None


def code(value):
    return value.get("code") if isinstance(value, dict) else value if isinstance(value, str) else None


async def upsert_alpha(db, raw: dict):
    alpha_id = raw.get("id")
    if not isinstance(alpha_id, str) or not alpha_id:
        raise ValueError("Alpha 数据缺少 ID")
    item = await db.get(Alpha, alpha_id)
    previous_region = item.region if item else None
    previous_status = item.status if item else None
    if item is None:
        item = Alpha(id=alpha_id)
        db.add(item)
    settings = raw.get("settings") or {}
    metrics = raw.get("is") if isinstance(raw.get("is"), dict) else {}
    item.name = raw.get("name")
    item.expression = code(raw.get("regular"))
    item.selection, item.combo = code(raw.get("selection")), code(raw.get("combo"))
    item.alpha_type, item.language = raw.get("type"), settings.get("language")
    item.stage, item.status, item.hidden = raw.get("stage"), raw.get("status"), raw.get("hidden") is True
    item.region, item.universe = settings.get("region"), settings.get("universe")
    if previous_region != item.region or previous_status != item.status:
        regions = {
            region
            for region, status in ((previous_region, previous_status), (item.region, item.status))
            if region and status and status != "UNSUBMITTED"
        }
        await invalidate_correlations(db, alpha_id, regions)
    item.settings, item.is_metrics = sanitize(settings), sanitize(metrics)
    item.os_metrics = sanitize(raw.get("os")) if isinstance(raw.get("os"), dict) else {}
    item.checks = sanitize(metrics.get("checks") or raw.get("checks") or [])
    for key, value in snapshot_columns(item.settings, item.is_metrics, item.checks).items():
        setattr(item, key, value)
    for key in ("sharpe", "fitness", "returns", "turnover", "margin", "drawdown"):
        setattr(item, key, number(metrics.get(key)))
    item.date_created, item.date_submitted = date(raw.get("dateCreated")), date(raw.get("dateSubmitted"))
    item.synced_at, item.raw = now(), sanitize(raw)
    await db.flush()
    if await db.get(Research, alpha_id) is None:
        db.add(Research(alpha_id=alpha_id))
    return item


def list_statement(filters):
    query = select(Alpha, Research).join(Research, Research.alpha_id == Alpha.id)
    if filters.submission:
        query = query.where(submission_condition(filters.submission))
    source_filters = {k: getattr(filters, k) for k in ("source", "source_reference", "research_id", "backtest_run_id")}
    if any(source_filters.values()):
        query = query.where(Alpha.id.in_(source_alpha_ids(**source_filters)))
    q = filters.q
    if q:
        pattern = "%" + q.replace("\\", "\\\\").replace("%", "\\%").replace("_", "\\_") + "%"
        query = query.where(
            or_(
                *(
                    getattr(Alpha, f).ilike(pattern, escape="\\")
                    for f in ("id", "name", "expression", "selection", "combo")
                )
            )
        )
    for name in ("region", "universe", "alpha_type", "language", "status", "stage", "hidden", "check_type", "neutralization"):
        value = getattr(filters, name)
        if value is not None:
            query = query.where(getattr(Alpha, name) == value)
    if filters.research_state:
        query = query.where(Research.state == filters.research_state)
    if filters.favorite is not None:
        query = query.where(Research.favorite == filters.favorite)
    if filters.tag:
        query = query.where(Alpha.id.in_(select(ResearchTag.alpha_id).where(ResearchTag.tag == filters.tag)))
    if filters.created_from:
        query = query.where(Alpha.date_created >= filters.created_from)
    if filters.created_to:
        query = query.where(Alpha.date_created <= filters.created_to)
    for name in METRIC_FIELDS:
        for suffix, compare in (("min", "ge"), ("max", "le")):
            value = getattr(filters, f"{name}_{suffix}")
            if value is not None:
                column = getattr(Alpha, name)
                query = query.where(column >= value if compare == "ge" else column <= value)
    return query


def sorted_statement(query, sort, direction):
    column = getattr(Alpha, sort)
    order = column.desc() if direction == "desc" else column.asc()
    return query.order_by(order.nullslast(), Alpha.id.asc())


def summary(item: Alpha, research: Research):
    keys = (
        "id",
        "name",
        "alpha_type",
        "language",
        "stage",
        "status",
        "hidden",
        "region",
        "universe",
        "sharpe",
        "fitness",
        "returns",
        "turnover",
        "margin",
        "drawdown",
        "date_created",
        "date_submitted",
        "synced_at",
        "check_type",
        "neutralization",
        "sub_universe_sharpe",
        "robust_universe_sharpe",
        "two_year_sharpe",
        "prod_correlation",
        "pnl",
    )
    result = {k: getattr(item, k) for k in keys}
    result["failed_checks"] = failed_checks(item.checks)
    result["expression_preview"] = (item.expression or item.selection or "")[:240]
    result["research"] = {
        k: getattr(research, k) for k in ("note", "tags", "favorite", "state", "updated_at", "version")
    }
    return result


def pnl_points(raw, column=None):
    """Use schema column names, preserving missing values rather than creating zero PnL."""
    records = raw.get("records")
    schema = raw.get("schema") or {}
    properties = schema.get("properties", []) if isinstance(schema, dict) else schema
    if isinstance(properties, dict):
        names = list(properties)
    else:
        names = [p.get("name", "") if isinstance(p, dict) else str(p) for p in properties]
    normalized = [name.lower() for name in names]
    value_names = (column,) if column else ("pnl", "value")
    if not isinstance(records, list):
        raise ValueError("PnL 缺少 records")
    points = []
    for row in records:
        if isinstance(row, dict):
            row = {str(k).lower(): v for k, v in row.items()}
            timestamp = next((row[k] for k in ("date", "datetime", "timestamp") if k in row), None)
            value = next((row[k] for k in value_names if k in row), None)
        else:
            date_i = next(
                (i for i, n in enumerate(normalized) if n in ("date", "datetime", "timestamp")), None
            )
            pnl_i = next((i for i, n in enumerate(normalized) if n in value_names), None)
            if date_i is None or pnl_i is None or not isinstance(row, list) or len(row) <= date_i:
                raise ValueError("PnL schema 无法识别日期或数值列")
            if len(row) <= pnl_i and column is None:
                raise ValueError("PnL schema 无法识别日期或数值列")
            timestamp, value = row[date_i], row[pnl_i] if len(row) > pnl_i else None
        if timestamp is not None:
            if isinstance(timestamp, (int, float)):
                from datetime import timezone

                timestamp = datetime.fromtimestamp(
                    timestamp / 1000 if timestamp > 1e11 else timestamp, tz=timezone.utc
                ).isoformat()
            points.append({"date": str(timestamp), "value": number(value)})
    return sorted(points, key=lambda p: p["date"])


def glb_pnl_series(raw, points):
    """Read GLB display series from cached raw data; keep the correlation baseline intact.

    Missing columns are omitted, while missing values remain gaps. Legacy caches
    containing only normalized points still return their overall PnL.
    """
    series = [{"id": "pnl", "label": "总体 PnL", "points": points}]
    schema = raw.get("schema") or {}
    properties = schema.get("properties", []) if isinstance(schema, dict) else schema
    names = properties if isinstance(properties, dict) else [
        p.get("name", "") if isinstance(p, dict) else str(p) for p in properties
    ]
    available = {name.lower() for name in names}
    for row in raw.get("records", []):
        if isinstance(row, dict):
            available.update(str(key).lower() for key in row)
    for column, label in (
        ("investability-constrained-pnl", "可投资性约束 PnL"),
        ("amer-pnl", "AMER PnL"),
        ("apac-pnl", "APAC PnL"),
        ("emea-pnl", "EMEA PnL"),
    ):
        if column in available:
            series.append({"id": column, "label": label, "points": pnl_points(raw, column)})
    return series