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"""
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交易反思 AI 分析服务 - 基于反思内容重新分析交易并提炼经验
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"""
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import json
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import logging
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import re
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from typing import Dict, List, Optional
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from sqlalchemy.orm import Session
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from app.analysis_models import TradePair, TradeReflection, TradeAIAnalysis, TradeExperience
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from app.services.ai_analysis import AIFuturesAnalyzer
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logger = logging.getLogger(__name__)
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REFLECTION_ANALYSIS_PROMPT = """你是一位资深的交易心理与策略复盘教练。请基于以下交易数据和交易者的反思内容，进行深度分析。
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=== 交易基础信息 ===
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品种：{variety}
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方向：{direction_text}
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开仓日期时间：{open_date} {open_time}
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平仓日期时间：{close_date} {close_time}
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开仓均价：{open_price}
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平仓均价：{close_price}
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交易手数：{volume}
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平仓盈亏：{close_pnl}
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手续费：{commission}
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净盈亏：{net_pnl}
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=== 交易者反思内容 ===
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入场理由：{entry_reason}
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入场时机评价：{entry_timing}
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仓位管理反思：{position_management}
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出场理由：{exit_reason}
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出场时机评价：{exit_timing}
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纪律评分（1-5）：{discipline_score}
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自由反思：{free_reflection}
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=== 当日整体反思 ===
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情绪状态：{emotion_state}
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市场判断：{market_judgment}
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当日总结：{daily_summary}
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改进方向：{daily_improvements}
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=== 任务要求 ===
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请严格按以下 JSON 格式输出分析结果（不要输出任何其他内容）：
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{{
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"overall_evaluation": "综合评价：这笔交易的执行质量、决策逻辑、与市场的匹配度等",
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"strengths": ["优点1", "优点2", "优点3"],
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"weaknesses": ["不足1", "不足2", "不足3"],
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"experience_suggestion": "提炼出的核心经验教训或改进建议，语言简洁 actionable",
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"suggestion_type": "lesson",
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"actionable_plan": ["下次可以做的具体改进1", "下次可以做的具体改进2"]
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}}
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注意：
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1. suggestion_type 只能是 lesson（经验）、tip（技巧）、warning（警告）三者之一
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2. 必须结合反思内容进行分析，不要脱离交易者自己的总结
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3. 如果交易亏损，重点分析亏损原因和避免方案
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4. 如果交易盈利，重点分析盈利是否来自计划还是运气
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"""
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def _timing_text(timing: Optional[str]) -> str:
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"""时机评价转中文"""
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mapping = {"good": "良好", "fair": "一般", "poor": "较差"}
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return mapping.get(timing, timing or "未评价")
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def _direction_text(direction: Optional[str]) -> str:
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"""方向转中文"""
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mapping = {"long": "多头", "short": "空头"}
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return mapping.get(direction, direction or "未知")
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def _get_records_text(db: Session, pair: TradePair) -> str:
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"""获取配对关联的原始记录信息"""
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from app.analysis_models import TradeRecord
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record_ids = (pair.open_record_ids or []) + (pair.close_record_ids or [])
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if not record_ids:
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return ""
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records = db.query(TradeRecord).filter(TradeRecord.id.in_(record_ids)).all()
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lines = []
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for r in sorted(records, key=lambda x: f"{x.trade_date} {x.trade_time or ''}"):
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lines.append(f"{r.trade_date} {r.trade_time or ''} {r.symbol} {r.direction}{r.offset} {r.price}×{r.volume}")
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return "\n".join(lines)
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def build_reflection_prompt(db: Session, pair: TradePair, reflection: TradeReflection,
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daily_reflection: Optional[Dict] = None) -> str:
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"""构建反思增强分析提示词"""
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daily = daily_reflection or {}
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# 获取第一条开仓和第一条平仓记录的时间
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from app.analysis_models import TradeRecord
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open_time = ""
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close_time = ""
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open_record_ids = pair.open_record_ids or []
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close_record_ids = pair.close_record_ids or []
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if open_record_ids:
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open_rec = db.query(TradeRecord).filter(TradeRecord.id == open_record_ids[0]).first()
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if open_rec:
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open_time = open_rec.trade_time or ""
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if close_record_ids:
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close_rec = db.query(TradeRecord).filter(TradeRecord.id == close_record_ids[0]).first()
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if close_rec:
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close_time = close_rec.trade_time or ""
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return REFLECTION_ANALYSIS_PROMPT.format(
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variety=pair.variety,
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direction_text=_direction_text(pair.direction),
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open_date=pair.open_date or "",
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open_time=open_time,
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close_date=pair.close_date or "",
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close_time=close_time,
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open_price=pair.open_price,
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close_price=pair.close_price,
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volume=pair.total_volume,
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close_pnl=pair.close_pnl,
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commission=pair.total_commission,
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net_pnl=pair.net_pnl,
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entry_reason=reflection.entry_reason or "未填写",
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entry_timing=_timing_text(reflection.entry_timing),
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position_management=reflection.position_management or "未填写",
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exit_reason=reflection.exit_reason or "未填写",
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exit_timing=_timing_text(reflection.exit_timing),
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discipline_score=reflection.discipline_score or "未评分",
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free_reflection=reflection.free_reflection or "未填写",
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emotion_state=daily.get("emotion_state", "未填写"),
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market_judgment=daily.get("market_judgment", "未填写"),
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daily_summary=daily.get("summary", "未填写"),
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daily_improvements=daily.get("improvements", "未填写"),
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)
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def _parse_ai_response(response: str) -> Dict:
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"""解析 AI 返回的 JSON"""
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try:
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# 尝试直接解析
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data = json.loads(response)
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return data
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except json.JSONDecodeError:
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pass
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# 尝试从 Markdown 代码块中提取
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code_block_pattern = re.compile(r'```(?:json)?\s*([\s\S]*?)```', re.MULTILINE)
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matches = code_block_pattern.findall(response)
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for match in matches:
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try:
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return json.loads(match.strip())
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except json.JSONDecodeError:
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continue
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# 尝试从文本中提取第一个 JSON 对象
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json_pattern = re.compile(r'\{[\s\S]*\}')
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matches = json_pattern.findall(response)
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for match in matches:
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try:
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return json.loads(match)
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except json.JSONDecodeError:
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continue
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# 解析失败，返回原始内容作为 overall_evaluation
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return {
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"overall_evaluation": response,
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"strengths": [],
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"weaknesses": [],
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"experience_suggestion": "",
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"suggestion_type": "lesson",
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"actionable_plan": [],
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}
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def _get_next_version(db: Session, trade_pair_id: int) -> int:
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"""获取下一个版本号"""
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latest = db.query(TradeAIAnalysis).filter(
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TradeAIAnalysis.trade_pair_id == trade_pair_id
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).order_by(TradeAIAnalysis.version.desc()).first()
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return (latest.version + 1) if latest else 1
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def analyze_with_reflection(db: Session, trade_pair_id: int) -> Dict:
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"""
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基于反思内容重新分析交易
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返回：{success, data/version, message}
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"""
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pair = db.query(TradePair).filter(TradePair.id == trade_pair_id).first()
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if not pair:
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return {"success": False, "message": "交易配对不存在"}
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reflection = db.query(TradeReflection).filter(
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TradeReflection.trade_pair_id == trade_pair_id
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).first()
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if not reflection:
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return {"success": False, "message": "该交易尚未填写反思，无法进行分析"}
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# 获取当日反思
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daily_reflection = None
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from app.analysis_models import DailyReflection
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dr = db.query(DailyReflection).filter(
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DailyReflection.reflection_date == pair.close_date
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).first()
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if dr:
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daily_reflection = {
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"emotion_state": dr.emotion_state,
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"market_judgment": dr.market_judgment,
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"summary": dr.summary,
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"improvements": dr.improvements,
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}
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# 构建提示词
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prompt = build_reflection_prompt(db, pair, reflection, daily_reflection)
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# 调用 AI
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analyzer = AIFuturesAnalyzer(db)
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model = analyzer.get_active_model()
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if not model:
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return {"success": False, "message": "未配置AI模型或模型未激活"}
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response = analyzer.call_ai_model(prompt, model)
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if not response:
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return {"success": False, "message": "AI模型返回空响应，请稍后重试"}
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# 解析结果
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parsed = _parse_ai_response(response)
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# 计算版本号
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version = _get_next_version(db, trade_pair_id)
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# 保存分析结果
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analysis = TradeAIAnalysis(
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trade_pair_id=trade_pair_id,
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version=version,
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overall_evaluation=parsed.get("overall_evaluation", ""),
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strengths=parsed.get("strengths", []),
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weaknesses=parsed.get("weaknesses", []),
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experience_suggestion=parsed.get("experience_suggestion", ""),
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suggestion_type=parsed.get("suggestion_type", "lesson"),
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raw_response=response,
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prompt_snapshot=prompt,
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)
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db.add(analysis)
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# 更新反思的 AI 分析状态
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reflection.ai_analyzed = True
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reflection.ai_version = version
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db.commit()
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return {
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"success": True,
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"data": {
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"version": version,
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"overall_evaluation": analysis.overall_evaluation,
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"strengths": analysis.strengths,
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"weaknesses": analysis.weaknesses,
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"experience_suggestion": analysis.experience_suggestion,
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"suggestion_type": analysis.suggestion_type,
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"actionable_plan": parsed.get("actionable_plan", []),
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},
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}
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def get_analysis_history(db: Session, trade_pair_id: int) -> List[Dict]:
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|
"""获取交易的所有 AI 分析版本"""
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analyses = db.query(TradeAIAnalysis).filter(
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TradeAIAnalysis.trade_pair_id == trade_pair_id
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).order_by(TradeAIAnalysis.version.desc()).all()
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return [{
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"id": a.id,
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"version": a.version,
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"overall_evaluation": a.overall_evaluation,
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"strengths": a.strengths,
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"weaknesses": a.weaknesses,
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"experience_suggestion": a.experience_suggestion,
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"suggestion_type": a.suggestion_type,
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"saved_to_experience": a.saved_to_experience,
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"experience_id": a.experience_id,
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"created_at": a.created_at.strftime('%Y-%m-%d %H:%M:%S') if a.created_at else None,
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} for a in analyses]
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def get_latest_analysis(db: Session, trade_pair_id: int) -> Optional[Dict]:
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"""获取最新 AI 分析结果"""
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analysis = db.query(TradeAIAnalysis).filter(
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TradeAIAnalysis.trade_pair_id == trade_pair_id
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).order_by(TradeAIAnalysis.version.desc()).first()
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if not analysis:
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return None
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return {
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"id": analysis.id,
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"version": analysis.version,
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"overall_evaluation": analysis.overall_evaluation,
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"strengths": analysis.strengths,
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"weaknesses": analysis.weaknesses,
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"experience_suggestion": analysis.experience_suggestion,
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"suggestion_type": analysis.suggestion_type,
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"saved_to_experience": analysis.saved_to_experience,
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"experience_id": analysis.experience_id,
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"created_at": analysis.created_at.strftime('%Y-%m-%d %H:%M:%S') if analysis.created_at else None,
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}
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def save_suggestion_as_experience(db: Session, analysis_id: int, title: Optional[str] = None,
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content: Optional[str] = None, tags: Optional[List[str]] = None) -> Dict:
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"""将 AI 分析建议保存为经验"""
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analysis = db.query(TradeAIAnalysis).filter(TradeAIAnalysis.id == analysis_id).first()
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if not analysis:
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return {"success": False, "message": "分析记录不存在"}
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pair = db.query(TradePair).filter(TradePair.id == analysis.trade_pair_id).first()
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exp_title = title or analysis.experience_suggestion[:50] if analysis.experience_suggestion else "AI提炼经验"
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|
exp_content = content or analysis.experience_suggestion or ""
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exp = TradeExperience(
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title=exp_title,
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content=exp_content,
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exp_type=analysis.suggestion_type or "lesson",
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source_pair_id=analysis.trade_pair_id,
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source_date=pair.close_date if pair else None,
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tags=tags or [],
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)
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db.add(exp)
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db.flush()
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analysis.saved_to_experience = True
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analysis.experience_id = exp.id
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db.commit()
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return {"success": True, "data": {"experience_id": exp.id}}
|
