dekun
265 lines
8.9 KiB
Python
265 lines
8.9 KiB
Python
# Copyright (c) 2025-2026 马建军. All rights reserved.
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# 专有软件 — 未经授权禁止复制、传播、转售。
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# 严禁用于：带单/代客理财、向他人推荐期货品种或买卖建议、融资配资等业务。
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# 详见 LICENSE.zh-CN.txt 与 docs/软件购买与使用协议.md
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"""可开仓品种：计算、按资金过滤、SQLite 缓存。"""
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from __future__ import annotations
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import json
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import logging
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import math
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from datetime import datetime
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from typing import Callable, Optional
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from fee_specs import ensure_fee_rates_schema
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from product_recommend import _attach_turnover, list_product_recommendations
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from recommend_trend import sort_recommend_by_trend
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from symbols import product_category
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logger = logging.getLogger(__name__)
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RECOMMEND_CACHE_SQL = """
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CREATE TABLE IF NOT EXISTS product_recommend_cache (
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id INTEGER PRIMARY KEY CHECK (id = 1),
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capital REAL NOT NULL DEFAULT 0,
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rows_json TEXT NOT NULL DEFAULT '[]',
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updated_at TEXT
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)
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"""
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def ensure_recommend_tables(conn) -> None:
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conn.execute(RECOMMEND_CACHE_SQL)
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def filter_affordable_recommendations(rows: list[dict]) -> list[dict]:
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"""仅保留当前资金可开 1 手的品种（不含资金不足、无行情）。"""
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return [r for r in rows if r.get("status") in ("ok", "margin_ok")]
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def rows_missing_max_lots(rows: list[dict]) -> bool:
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"""缓存是否为旧版（缺少最大手数字段）。"""
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if not rows:
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return False
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return any("max_lots" not in r for r in rows)
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def rows_missing_trend(rows: list[dict]) -> bool:
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"""缓存是否为旧版（缺少走势字段）。"""
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if not rows:
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return False
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return any("trend" not in r for r in rows)
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def rows_missing_daily_stats(rows: list[dict]) -> bool:
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"""缓存是否为旧版（缺少跳空/量价字段）。"""
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if not rows:
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return False
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return any("gap" not in r for r in rows)
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def rows_missing_category(rows: list[dict]) -> bool:
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if not rows:
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return False
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return any("category" not in r for r in rows)
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def rows_missing_turnover(rows: list[dict]) -> bool:
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if not rows:
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return False
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return any("turnover" not in r for r in rows)
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def recommend_cache_needs_refresh(
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cached: dict,
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*,
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capital: float = 0.0,
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) -> bool:
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"""是否需要重新拉行情计算可开仓列表。"""
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if recommend_cache_stale(cached.get("updated_at")):
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return True
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rows = cached.get("rows") or []
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if rows_missing_max_lots(rows):
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return True
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if rows_missing_trend(rows):
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return True
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if rows_missing_daily_stats(rows):
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return True
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if rows_missing_category(rows):
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return True
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if rows_missing_turnover(rows):
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return True
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if float(capital or 0) > 0 and not rows:
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return True
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return False
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def enrich_recommend_rows(
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rows: list[dict],
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capital: float,
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*,
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max_margin_pct: float = 30.0,
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trading_mode: str = "simulation",
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) -> list[dict]:
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"""用当前权益与保证金比例补算最大可开手数（兼容旧缓存）。"""
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cap = float(capital or 0)
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pct = max(1.0, min(100.0, float(max_margin_pct or 30.0)))
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budget = cap * pct / 100.0 if cap > 0 else 0.0
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ctp_connected = False
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try:
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from vnpy_bridge import ctp_estimate_margin_one_lot, ctp_status
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ctp_connected = bool(ctp_status(trading_mode).get("connected"))
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except Exception:
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pass
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enriched: list[dict] = []
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for raw in rows:
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row = dict(raw)
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margin_one = 0.0
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try:
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margin_one = float(row.get("margin_one_lot") or 0)
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except (TypeError, ValueError):
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margin_one = 0.0
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price = float(row.get("price") or 0)
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main_code = (row.get("main_code") or "").strip()
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if ctp_connected and main_code and price > 0:
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ctp_margin = ctp_estimate_margin_one_lot(trading_mode, main_code, price)
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if ctp_margin and ctp_margin > 0:
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margin_one = ctp_margin
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row["margin_one_lot"] = ctp_margin
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row["margin_source"] = "ctp"
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if margin_one > 0 and budget > 0:
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lots = int(math.floor(budget / margin_one))
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else:
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try:
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lots = int(row.get("max_lots") or row.get("recommended_lots") or 0)
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except (TypeError, ValueError):
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lots = 0
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row["max_lots"] = lots
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row.pop("recommended_lots", None)
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row["margin_budget"] = round(budget, 2)
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row["max_margin_pct"] = pct
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status = row.get("status") or ""
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if lots >= 1 and status in ("ok", "margin_ok"):
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src = "柜台" if row.get("margin_source") == "ctp" else "估算"
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row["status_label"] = (
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f"最大 {lots} 手" if status == "ok" else f"最大 {lots} 手·止损偏宽"
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)
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if row.get("margin_source") == "ctp":
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row["status_label"] += f"（{src}保证金）"
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elif lots < 1 and status in ("ok", "margin_ok"):
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row["status"] = "blocked"
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row["status_label"] = "资金不足"
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if not row.get("category"):
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row["category"] = product_category(row.get("ths") or "")
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_attach_turnover(row)
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enriched.append(row)
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return enriched
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def filter_recommend_by_sizing(
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rows: list[dict],
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*,
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sizing_mode: str,
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fixed_lots: int = 1,
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) -> list[dict]:
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"""固定手数模式下：最大手数低于设定值的品种不展示。"""
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if (sizing_mode or "").strip().lower() != "fixed":
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return rows
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fl = max(1, int(fixed_lots or 1))
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return [r for r in rows if int(r.get("max_lots") or 0) >= fl]
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def refresh_recommend_cache(
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conn,
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capital: float,
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quote_fn: Callable[[str], Optional[dict]],
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*,
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trading_mode: str = "simulation",
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max_margin_pct: float = 30.0,
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) -> list[dict]:
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"""后台拉行情、筛选并写入数据库。"""
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ensure_recommend_tables(conn)
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ensure_fee_rates_schema(conn)
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all_rows = list_product_recommendations(
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capital, quote_fn, max_margin_pct=max_margin_pct, trading_mode=trading_mode,
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)
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rows = filter_affordable_recommendations(all_rows)
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if not rows and float(capital or 0) > 0:
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logger.warning(
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"recommend refresh: 0 affordable rows capital=%.2f total=%d no_price=%d blocked=%d",
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float(capital or 0),
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len(all_rows),
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sum(1 for r in all_rows if r.get("status") == "no_price"),
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sum(1 for r in all_rows if r.get("status") == "blocked"),
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)
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now = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
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conn.execute(
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"""INSERT INTO product_recommend_cache (id, capital, rows_json, updated_at)
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VALUES (1, ?, ?, ?)
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ON CONFLICT(id) DO UPDATE SET
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capital=excluded.capital,
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rows_json=excluded.rows_json,
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updated_at=excluded.updated_at""",
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(float(capital or 0), json.dumps(rows, ensure_ascii=False), now),
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)
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conn.commit()
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return rows
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def recommend_cache_stale(updated_at: Optional[str], *, now: Optional[datetime] = None) -> bool:
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"""缓存是否不是今日更新（需重新拉行情计算）。"""
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if not updated_at:
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return True
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try:
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cached_day = datetime.strptime(str(updated_at)[:10], "%Y-%m-%d").date()
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except ValueError:
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return True
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today = (now or datetime.now()).date()
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return cached_day != today
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def load_recommend_cache(conn) -> dict:
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"""优先从数据库读取可开仓品种列表。"""
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ensure_recommend_tables(conn)
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row = conn.execute("SELECT capital, rows_json, updated_at FROM product_recommend_cache WHERE id=1").fetchone()
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if not row:
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return {"capital": 0.0, "rows": [], "updated_at": None, "stale": True}
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try:
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rows = json.loads(row["rows_json"] or "[]")
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except (TypeError, ValueError, json.JSONDecodeError):
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rows = []
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updated_at = row["updated_at"]
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return {
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"capital": float(row["capital"] or 0),
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"rows": rows if isinstance(rows, list) else [],
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"updated_at": updated_at,
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"stale": recommend_cache_stale(updated_at),
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}
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def recommend_payload(
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conn,
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*,
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live_capital: float,
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max_margin_pct: float = 30.0,
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trading_mode: str = "simulation",
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sizing_mode: str = "fixed",
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fixed_lots: int = 1,
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) -> dict:
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"""读取缓存并附带当前权益（展示用，可能与缓存计算时不同）。"""
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payload = load_recommend_cache(conn)
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cap = float(live_capital or 0)
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pct = max(1.0, min(100.0, float(max_margin_pct or 30.0)))
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payload["capital"] = cap
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payload["max_margin_pct"] = pct
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rows = payload.get("rows") or []
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rows = enrich_recommend_rows(
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rows, cap, max_margin_pct=pct, trading_mode=trading_mode,
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)
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rows = filter_recommend_by_sizing(rows, sizing_mode=sizing_mode, fixed_lots=fixed_lots)
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rows = sort_recommend_by_trend(rows)
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payload["rows"] = rows
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payload["needs_refresh"] = recommend_cache_needs_refresh(payload, capital=cap)
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return payload
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