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"""
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交易配对引擎 - 将开仓和平仓配对为完整交易，持久化到 TradePair 表
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"""
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import logging
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from typing import Optional
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from sqlalchemy.orm import Session
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from collections import defaultdict
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from app.analysis_models import TradeRecord, TradePair, TradeReflection, DailyReflection
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logger = logging.getLogger(__name__)
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def auto_pair_trades(db: Session, trade_date: str) -> dict:
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"""
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自动配对指定日期及之前未平仓的交易记录
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按品种+方向分组，FIFO 规则配对
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返回：{created: 新建配对数, updated: 更新配对数, unpaired: 未配对记录数}
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"""
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# 查询指定日期的记录
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current_records = db.query(TradeRecord).filter(
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TradeRecord.trade_date == trade_date
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).order_by(
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TradeRecord.variety, TradeRecord.symbol, TradeRecord.trade_date, TradeRecord.trade_time
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).all()
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if not current_records:
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return {"created": 0, "updated": 0, "unpaired": 0}
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# 查询之前未配对的记录（跨日持仓）
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# 找出所有已经配对的记录 ID
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all_pairs = db.query(TradePair).all()
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paired_ids = set()
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for p in all_pairs:
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paired_ids.update(p.open_record_ids or [])
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paired_ids.update(p.close_record_ids or [])
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# 查询 trade_date 之前的未配对记录
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previous_unpaired = db.query(TradeRecord).filter(
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TradeRecord.trade_date < trade_date,
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TradeRecord.id.notin_(paired_ids) if paired_ids else True,
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).order_by(
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TradeRecord.variety, TradeRecord.symbol, TradeRecord.trade_date, TradeRecord.trade_time
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).all()
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# 合并当前记录和之前未配对记录
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all_records = previous_unpaired + current_records
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# 按品种分组
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by_variety = defaultdict(list)
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for r in all_records:
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by_variety[r.variety].append(r)
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result = {"created": 0, "updated": 0, "unpaired": 0}
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for variety, recs in by_variety.items():
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pair_result = _pair_single_variety(db, variety, recs)
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result["created"] += pair_result["created"]
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result["updated"] += pair_result["updated"]
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result["unpaired"] += pair_result["unpaired"]
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return result
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def _pair_single_variety(db: Session, variety: str, records: list) -> dict:
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"""单个品种的配对逻辑"""
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# 分离开仓和平仓
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opens = [r for r in records if r.offset == '开']
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closes = [r for r in records if r.offset == '平']
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result = {"created": 0, "updated": 0, "unpaired": 0}
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# 按方向分组
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long_opens = [r for r in opens if r.direction == '买']
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short_opens = [r for r in opens if r.direction == '卖']
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long_closes = [r for r in closes if r.direction == '卖'] # 卖平 = 多头平仓
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short_closes = [r for r in closes if r.direction == '买'] # 买平 = 空头平仓
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# 多头配对
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result["created"] += _pair_direction(db, variety, long_opens, long_closes, "long")
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# 空头配对
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result["created"] += _pair_direction(db, variety, short_opens, short_closes, "short")
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# 统计未配对
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all_paired_open_ids = set()
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all_paired_close_ids = set()
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existing_pairs = db.query(TradePair).filter(
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TradePair.variety == variety,
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).all()
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for p in existing_pairs:
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all_paired_open_ids.update(p.open_record_ids or [])
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all_paired_close_ids.update(p.close_record_ids or [])
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unpaired_count = 0
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for r in records:
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if r.id not in all_paired_open_ids and r.id not in all_paired_close_ids:
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unpaired_count += 1
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result["unpaired"] = unpaired_count
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return result
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def _pair_direction(db: Session, variety: str, opens: list, closes: list, direction: str) -> int:
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"""单方向配对（FIFO，支持多开合并），返回新建配对数
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每个平仓记录会尽可能与前面的开仓记录配对，形成一个完整配对。
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如果多个开仓合并才能匹配一个平仓，则这些开仓记录会归入同一个配对。
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"""
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created = 0
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# 开仓队列，每个元素是 (record, remaining_volume)
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open_queue = [(r, r.volume or 0) for r in opens]
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close_queue = [(r, r.volume or 0) for r in closes]
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while open_queue and close_queue:
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close_rec, close_remaining = close_queue[0]
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# 从开仓队列中取足够手数来匹配这个平仓
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needed = close_remaining
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used_opens = [] # (record, take_volume)
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while needed > 0 and open_queue:
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open_rec, open_remaining = open_queue[0]
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take = min(open_remaining, needed)
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used_opens.append((open_rec, take))
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needed -= take
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if take >= open_remaining:
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open_queue.pop(0)
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else:
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open_queue[0] = (open_rec, open_remaining - take)
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if not used_opens:
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break
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# 实际匹配的手数
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matched_volume = close_remaining - needed
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# 更新平仓队列
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if matched_volume >= close_remaining:
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close_queue.pop(0)
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else:
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close_queue[0] = (close_rec, close_remaining - matched_volume)
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# 计算均价（按手数加权）
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open_volume_sum = sum(take for _, take in used_opens)
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open_price = sum((r.price or 0) * take for r, take in used_opens) / open_volume_sum if open_volume_sum > 0 else 0
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close_price = close_rec.price or 0
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# 计算盈亏和手续费
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# 平仓盈亏按比例分配到该平仓记录
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close_pnl = (close_rec.close_pnl or 0) * (matched_volume / close_rec.volume) if close_rec.volume else 0
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total_commission = sum((r.commission or 0) * (take / r.volume) for r, take in used_opens if r.volume) + \
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(close_rec.commission or 0) * (matched_volume / close_rec.volume) if close_rec.volume else 0
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net_pnl = close_pnl - total_commission
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# 最早开仓日期和最早平仓日期
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open_date = min(r.trade_date for r, _ in used_opens)
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close_date = close_rec.trade_date
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pair = TradePair(
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variety=variety,
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direction=direction,
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open_record_ids=[r.id for r, _ in used_opens],
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close_record_ids=[close_rec.id],
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open_price=round(open_price, 2),
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close_price=round(close_price, 2),
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total_volume=matched_volume,
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close_pnl=round(close_pnl, 2),
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total_commission=round(total_commission, 2),
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net_pnl=round(net_pnl, 2),
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open_date=open_date,
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close_date=close_date,
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)
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db.add(pair)
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created += 1
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db.flush()
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return created
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def get_daily_trades_with_pairs(db: Session, trade_date: str) -> dict:
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"""
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获取指定日期的交易数据，包含配对信息和反思状态
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返回：{date, stats, pairs, unpaired_records, daily_reflection}
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"""
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# 获取所有配对
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pairs = db.query(TradePair).filter(
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(TradePair.open_date == trade_date) | (TradePair.close_date == trade_date)
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).order_by(TradePair.open_date, TradePair.close_date).all()
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# 获取所有交易记录
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records = db.query(TradeRecord).filter(
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TradeRecord.trade_date == trade_date
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).order_by(TradeRecord.trade_date, TradeRecord.trade_time).all()
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# 计算统计
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total_pnl = sum(p.net_pnl or 0 for p in pairs)
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total_trades = len(pairs) + len(records) # 配对 + 未配对
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winning_trades = sum(1 for p in pairs if (p.net_pnl or 0) > 0)
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win_rate = round(winning_trades / total_trades * 100, 1) if total_trades > 0 else 0
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# 获取反思状态
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pair_ids = [p.id for p in pairs]
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reflections = db.query(TradeReflection).filter(
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TradeReflection.trade_pair_id.in_(pair_ids)
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).all()
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reflection_map = {r.trade_pair_id: r for r in reflections}
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# 获取当日反思
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daily_reflection = db.query(DailyReflection).filter(
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DailyReflection.reflection_date == trade_date
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).first()
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# 构建返回数据
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pairs_data = []
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for p in pairs:
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reflection = reflection_map.get(p.id)
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pairs_data.append({
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"id": p.id,
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"variety": p.variety,
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"direction": p.direction,
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"open_price": p.open_price,
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"close_price": p.close_price,
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"total_volume": p.total_volume,
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"close_pnl": p.close_pnl,
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"total_commission": p.total_commission,
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"net_pnl": p.net_pnl,
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"open_date": p.open_date,
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"close_date": p.close_date,
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"open_record_ids": p.open_record_ids,
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"close_record_ids": p.close_record_ids,
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"reflection_status": "done" if reflection else ("pending" if _has_unpaired_records(db, p) else "none"),
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"ai_analyzed": reflection.ai_analyzed if reflection else False,
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"ai_version": reflection.ai_version if reflection else None,
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})
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# 未配对记录
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paired_record_ids = set()
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for p in pairs:
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paired_record_ids.update(p.open_record_ids or [])
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paired_record_ids.update(p.close_record_ids or [])
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unpaired = [r for r in records if r.id not in paired_record_ids]
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unpaired_data = [{
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"id": r.id,
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"symbol": r.symbol,
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"variety": r.variety,
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"direction": r.direction,
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"offset": r.offset,
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"price": r.price,
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"volume": r.volume,
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"trade_date": r.trade_date,
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"trade_time": r.trade_time,
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} for r in unpaired]
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return {
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"date": trade_date,
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"stats": {
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"total_pnl": round(total_pnl, 2),
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"total_trades": total_trades,
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"winning_trades": winning_trades,
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"win_rate": win_rate,
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"paired_count": len(pairs),
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"unpaired_count": len(unpaired),
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"reflected_count": sum(1 for r in reflection_map.values()),
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},
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"pairs": pairs_data,
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"unpaired_records": unpaired_data,
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"daily_reflection": {
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"id": daily_reflection.id if daily_reflection else None,
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"emotion_state": daily_reflection.emotion_state if daily_reflection else None,
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"market_judgment": daily_reflection.market_judgment if daily_reflection else None,
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"discipline_score": daily_reflection.discipline_score if daily_reflection else None,
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"overall_rating": daily_reflection.overall_rating if daily_reflection else None,
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"summary": daily_reflection.summary if daily_reflection else None,
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"improvements": daily_reflection.improvements if daily_reflection else None,
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} if daily_reflection else None,
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}
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def _has_unpaired_records(db: Session, pair: TradePair) -> bool:
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"""检查配对是否有未配对的开仓或平仓记录"""
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# 简化逻辑：如果开仓日期和平仓日期不同，可能存在跨日未配对
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return pair.open_date != pair.close_date
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def create_manual_pair(db: Session, open_record_ids: list[int], close_record_ids: list[int]) -> TradePair:
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"""手动创建配对"""
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open_records = db.query(TradeRecord).filter(TradeRecord.id.in_(open_record_ids)).all()
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close_records = db.query(TradeRecord).filter(TradeRecord.id.in_(close_record_ids)).all()
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if not open_records or not close_records:
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raise ValueError("开仓或平仓记录不存在")
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variety = open_records[0].variety
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direction = "long" if open_records[0].direction == '买' else "short"
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# 计算统计
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total_volume = sum(r.volume or 0 for r in open_records)
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total_close_pnl = sum(r.close_pnl or 0 for r in close_records)
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total_commission = sum((r.commission or 0) for r in open_records) + sum((r.commission or 0) for r in close_records)
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net_pnl = total_close_pnl - total_commission
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avg_open_price = sum(r.price or 0 for r in open_records) / len(open_records) if open_records else 0
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avg_close_price = sum(r.price or 0 for r in close_records) / len(close_records) if close_records else 0
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pair = TradePair(
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variety=variety,
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direction=direction,
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open_record_ids=open_record_ids,
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close_record_ids=close_record_ids,
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open_price=round(avg_open_price, 2),
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close_price=round(avg_close_price, 2),
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total_volume=total_volume,
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close_pnl=total_close_pnl,
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total_commission=total_commission,
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net_pnl=round(net_pnl, 2),
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open_date=open_records[0].trade_date,
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close_date=close_records[-1].trade_date,
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)
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db.add(pair)
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db.flush()
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return pair
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def delete_pair(db: Session, pair_id: int) -> bool:
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"""删除配对（不删除原始交易记录）"""
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pair = db.query(TradePair).filter(TradePair.id == pair_id).first()
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if not pair:
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return False
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# 删除关联的反思
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db.query(TradeReflection).filter(TradeReflection.trade_pair_id == pair_id).delete()
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# 删除配对
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db.delete(pair)
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db.flush()
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return True
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