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"""
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V2 交易计划生成器 - 5维度综合评分 + 多周期共振分析
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"""
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import json
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import logging
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from pathlib import Path
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from typing import Dict, List, Optional, Tuple
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from datetime import datetime
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from sqlalchemy.orm import Session
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from app.services.cache import get_cached_data
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from app.analysis_models import (
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ReviewPlanV2, SymbolScoreV2, TradingPlanV2, SectorHeat,
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)
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logger = logging.getLogger(__name__)
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# 品种配置加载
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CONFIG_DIR = Path(__file__).resolve().parent.parent.parent / "config"
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# 板块分类
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SECTOR_MAP = {
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"贵金属": ["沪银", "沪金"],
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"有色金属": ["沪铜", "沪锌", "沪锡", "沪铅", "沪铝", "铝合金", "沪镍"],
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"黑色系": ["焦炭", "焦煤", "螺纹钢", "热卷", "铁矿石"],
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"能源": ["原油", "低硫燃油", "燃油"],
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"化工": ["PTA", "甲醇", "尿素", "PVC", "乙二醇", "合成橡胶", "烧碱", "纯碱", "玻璃", "橡胶"],
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"农产品": ["白糖", "棉花", "棕榈油", "豆粕"],
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"新能源": ["氧化铝", "多晶硅", "碳酸锂", "工业硅"],
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"股指": ["中证1000"],
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"航运": ["集运欧线"],
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"橡胶": ["20号胶"],
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}
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# 权重
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WEIGHTS = {
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"amplitude": 0.20,
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"volume": 0.15,
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"change": 0.15,
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"trend": 0.35,
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"activity": 0.15,
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}
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def load_symbols_config() -> Dict[str, str]:
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"""加载品种配置 {中文名: 合约代码}"""
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config_path = CONFIG_DIR / "symbols_config.json"
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if not config_path.exists():
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logger.warning(f"品种配置文件不存在: {config_path}")
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return {}
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with open(config_path, "r", encoding="utf-8") as f:
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data = json.load(f)
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return data.get("futures", {})
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def _get_candle_values(candles: List[dict]) -> Tuple[list, list, list, list, list]:
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"""从K线列表提取 OHLCV"""
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opens, highs, lows, closes, volumes = [], [], [], [], []
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for c in candles:
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opens.append(float(c.get("open", 0)))
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highs.append(float(c.get("high", 0)))
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lows.append(float(c.get("low", 0)))
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closes.append(float(c.get("close", 0)))
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volumes.append(float(c.get("volume", 0)))
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return opens, highs, lows, closes, volumes
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def _calc_single_period_trend(candles: List[dict]) -> float:
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"""
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计算单周期趋势分 (-50 ~ +50)
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MA排列: 多头+50 / 空头-50
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MACD: DIF>DEA +30 / DIF<DEA -30
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综合: -50 ~ +50 (归一化后映射)
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"""
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if len(candles) < 5:
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return 0.0
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last = candles[-1]
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ma10 = last.get("ma10")
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ma20 = last.get("ma20")
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macd_dif = last.get("macd_dif", 0) or 0
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macd_dea = last.get("macd_dea", 0) or 0
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score = 0.0
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# MA排列
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if ma10 and ma20:
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if ma10 > ma20:
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score += 50
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else:
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score -= 50
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# MACD
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if macd_dif > macd_dea:
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score += 30
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else:
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score -= 30
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# 归一化到 -50 ~ +50
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return max(-50, min(50, score * 50 / 80))
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def _calc_trend_score(t60: float, t15: float, t5: float) -> Tuple[float, str, str]:
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"""
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三周期共振判定
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Returns: (趋势总分 0-100, 方向标签, 方向简写)
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"""
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# 共振判定
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if t60 > 20 and t15 > 20 and t5 > 20:
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direction = "多头共振"
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tag = "强多"
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raw = 80 + min(20, (t60 + t15 + t5) / 3 * 20 / 50)
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elif t60 < -20 and t15 < -20 and t5 < -20:
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direction = "空头共振"
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tag = "强空"
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raw = 80 + min(20, abs(t60 + t15 + t5) / 3 * 20 / 50)
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elif t60 > 0 and t15 > 0:
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direction = "偏多震荡"
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tag = "偏多"
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raw = 50 + min(20, (t60 + t15) / 2 * 20 / 50)
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elif t60 < 0 and t15 < 0:
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direction = "偏空震荡"
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tag = "偏空"
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raw = 50 + min(20, abs(t60 + t15) / 2 * 20 / 50)
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else:
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direction = "多空交织"
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tag = "震荡"
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raw = max(0, 40 - abs(t60 + t15 + t5) / 3)
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return round(raw, 1), direction, tag
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def _calc_pivot_points(high: float, low: float, close: float) -> dict:
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|
"""计算枢轴点和支撑/阻力位"""
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|
pivot = (high + low + close) / 3
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r1 = 2 * pivot - low
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s1 = 2 * pivot - high
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r2 = pivot + (high - low)
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s2 = pivot - (high - low)
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return {"pivot": pivot, "r1": r1, "r2": r2, "s1": s1, "s2": s2}
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def _normalize_scores(values: List[float], reverse: bool = False) -> List[float]:
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"""将一组值归一化到 0-100"""
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if not values:
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return []
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min_v = min(values)
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|
max_v = max(values)
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|
|
if max_v == min_v:
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return [50.0] * len(values)
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result = []
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for v in values:
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|
if reverse:
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|
score = (max_v - v) / (max_v - min_v) * 100
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else:
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|
score = (v - min_v) / (max_v - min_v) * 100
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|
|
result.append(round(score, 1))
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return result
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|
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def _normalize_abs_scores(values: List[float]) -> List[float]:
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|
|
"""将绝对值归一化到 0-100（涨跌幅用绝对值排名）"""
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|
|
abs_values = [abs(v) for v in values]
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|
|
return _normalize_scores(abs_values)
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def generate_plan(db: Session, review_date_str: str, week_day: str) -> dict:
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|
|
"""
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|
|
生成V2复盘与交易计划
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|
|
|
|
|
Returns:
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|
|
{
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|
|
"review_date_id": int,
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|
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"summary": {...},
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|
|
"scores": [...],
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|
|
"plans": [...],
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|
|
"sectors": [...],
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|
|
}
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|
"""
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|
|
symbols_config = load_symbols_config()
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|
|
if not symbols_config:
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|
|
raise ValueError("品种配置文件为空")
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# 反转映射: 合约代码 -> 中文名
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|
|
code_to_name = {v: k for k, v in symbols_config.items()}
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all_symbols = list(symbols_config.values())
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logger.info(f"开始生成交易计划: {review_date_str} ({week_day}), 共 {len(all_symbols)} 个品种")
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# 将复盘日期作为数据截止时间（当天 23:59:59）
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|
|
try:
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|
|
end_time = datetime.strptime(review_date_str, "%Y-%m-%d").replace(hour=23, minute=59, second=59)
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|
|
except ValueError:
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|
|
end_time = datetime.now()
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|
|
# ==================== 第一步: 数据收集 ====================
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|
|
symbol_data = {}
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|
|
periods_needed = ["daily", "60min", "15min", "5min"]
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|
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|
|
|
for symbol in all_symbols:
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|
|
try:
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|
|
data = get_cached_data(
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|
|
db, symbol, "futures",
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|
|
periods=periods_needed,
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|
|
end_time=end_time,
|
|
|
max_candles=100,
|
|
|
)
|
|
|
if data and data.get("timeframes"):
|
|
|
symbol_data[symbol] = data
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|
|
else:
|
|
|
logger.warning(f"{symbol} 无数据")
|
|
|
except Exception as e:
|
|
|
logger.error(f"获取 {symbol} 数据失败: {e}")
|
|
|
|
|
|
if not symbol_data:
|
|
|
raise ValueError("无法从数据库获取任何品种数据，请先进行数据更新")
|
|
|
|
|
|
logger.info(f"成功获取 {len(symbol_data)}/{len(all_symbols)} 个品种数据")
|
|
|
|
|
|
# ==================== 第二步: 多维度打分 ====================
|
|
|
raw_scores = []
|
|
|
|
|
|
for symbol, data in symbol_data.items():
|
|
|
name = code_to_name.get(symbol, symbol)
|
|
|
tf = data.get("timeframes", {})
|
|
|
|
|
|
daily = tf.get("daily", [])
|
|
|
h1 = tf.get("60min", [])
|
|
|
m15 = tf.get("15min", [])
|
|
|
m5 = tf.get("5min", [])
|
|
|
|
|
|
# 日线数据
|
|
|
if not daily or len(daily) < 2:
|
|
|
continue
|
|
|
|
|
|
_, d_highs, d_lows, d_closes, d_volumes = _get_candle_values(daily)
|
|
|
today_high = d_highs[-1]
|
|
|
today_low = d_lows[-1]
|
|
|
today_close = d_closes[-1]
|
|
|
prev_close = d_closes[-2] if len(d_closes) >= 2 else today_close
|
|
|
today_volume = d_volumes[-1]
|
|
|
|
|
|
# 近5日均量
|
|
|
recent_vols = d_volumes[-6:-1] if len(d_volumes) >= 6 else d_volumes[:-1]
|
|
|
avg_vol_5 = sum(recent_vols) / len(recent_vols) if recent_vols else today_volume
|
|
|
|
|
|
# A. 振幅
|
|
|
amplitude_pct = (today_high - today_low) / today_close * 100 if today_close else 0
|
|
|
|
|
|
# B. 量比
|
|
|
volume_ratio = today_volume / avg_vol_5 if avg_vol_5 > 0 else 1.0
|
|
|
|
|
|
# C. 涨跌幅
|
|
|
change_pct = (today_close - prev_close) / prev_close * 100 if prev_close else 0
|
|
|
|
|
|
# D. 多周期趋势
|
|
|
t60 = _calc_single_period_trend(h1) if h1 else 0
|
|
|
t15 = _calc_single_period_trend(m15) if m15 else 0
|
|
|
t5 = _calc_single_period_trend(m5) if m5 else 0
|
|
|
trend_total, direction, direction_tag = _calc_trend_score(t60, t15, t5)
|
|
|
|
|
|
# E. 活跃度 (5min K线近10根)
|
|
|
if m5 and len(m5) >= 5:
|
|
|
recent_m5 = m5[-10:] if len(m5) >= 10 else m5
|
|
|
_, _, _, m5_closes, m5_vols = _get_candle_values(recent_m5)
|
|
|
# 价格变动率
|
|
|
if m5_closes and m5_closes[0] > 0:
|
|
|
price_change_rate = abs(m5_closes[-1] - m5_closes[0]) / m5_closes[0] * 100
|
|
|
else:
|
|
|
price_change_rate = 0
|
|
|
# 量能活跃度
|
|
|
avg_m5_vol = sum(m5_vols) / len(m5_vols) if m5_vols else 0
|
|
|
vol_activity = min(100, avg_m5_vol / 100) if avg_m5_vol > 0 else 0
|
|
|
price_activity = min(100, price_change_rate * 50)
|
|
|
activity_score = (vol_activity + price_activity) / 2
|
|
|
else:
|
|
|
activity_score = 0
|
|
|
|
|
|
# 关键点位
|
|
|
pivots = _calc_pivot_points(today_high, today_low, today_close)
|
|
|
|
|
|
raw_scores.append({
|
|
|
"symbol": symbol,
|
|
|
"name": name,
|
|
|
"close_price": today_close,
|
|
|
"prev_close": prev_close,
|
|
|
"high_price": today_high,
|
|
|
"low_price": today_low,
|
|
|
"volume": today_volume,
|
|
|
"avg_volume_5": avg_vol_5,
|
|
|
"amplitude_pct": round(amplitude_pct, 2),
|
|
|
"volume_ratio": round(volume_ratio, 2),
|
|
|
"change_pct": round(change_pct, 2),
|
|
|
"amplitude_raw": amplitude_pct,
|
|
|
"volume_raw": volume_ratio,
|
|
|
"change_raw": abs(change_pct),
|
|
|
"trend_score": trend_total,
|
|
|
"trend_60m": round(t60, 1),
|
|
|
"trend_15m": round(t15, 1),
|
|
|
"trend_5m": round(t5, 1),
|
|
|
"direction": direction,
|
|
|
"direction_tag": direction_tag,
|
|
|
"activity_raw": activity_score,
|
|
|
"pivots": pivots,
|
|
|
})
|
|
|
|
|
|
if not raw_scores:
|
|
|
raise ValueError("没有足够的数据进行评分计算")
|
|
|
|
|
|
# ==================== 第三步: 归一化评分 ====================
|
|
|
amp_scores = _normalize_scores([s["amplitude_raw"] for s in raw_scores])
|
|
|
vol_scores = _normalize_scores([s["volume_raw"] for s in raw_scores])
|
|
|
chg_scores = _normalize_abs_scores([s["change_raw"] for s in raw_scores])
|
|
|
act_scores = _normalize_scores([s["activity_raw"] for s in raw_scores])
|
|
|
|
|
|
for i, s in enumerate(raw_scores):
|
|
|
s["amplitude_score"] = amp_scores[i]
|
|
|
s["volume_score"] = vol_scores[i]
|
|
|
s["change_score"] = chg_scores[i]
|
|
|
s["activity_score"] = act_scores[i]
|
|
|
|
|
|
# 综合评分 = 振幅×0.20 + 量能×0.15 + 涨跌幅×0.15 + 趋势×0.35 + 活跃度×0.15
|
|
|
# 趋势分映射: trend_score 0-100 直接使用
|
|
|
s["composite_score"] = round(
|
|
|
amp_scores[i] * WEIGHTS["amplitude"]
|
|
|
+ vol_scores[i] * WEIGHTS["volume"]
|
|
|
+ chg_scores[i] * WEIGHTS["change"]
|
|
|
+ s["trend_score"] * WEIGHTS["trend"]
|
|
|
+ act_scores[i] * WEIGHTS["activity"],
|
|
|
1
|
|
|
)
|
|
|
|
|
|
# 排序
|
|
|
raw_scores.sort(key=lambda x: x["composite_score"], reverse=True)
|
|
|
|
|
|
# ==================== 第四步: 分类 ====================
|
|
|
for rank, s in enumerate(raw_scores, 1):
|
|
|
s["rank"] = rank
|
|
|
score = s["composite_score"]
|
|
|
trend = s["trend_score"]
|
|
|
|
|
|
if score >= 55 and trend >= 50:
|
|
|
s["category"] = "green"
|
|
|
elif score >= 45 or (score >= 40 and 30 <= trend < 50):
|
|
|
s["category"] = "yellow"
|
|
|
else:
|
|
|
s["category"] = "red"
|
|
|
|
|
|
# ==================== 第五步: 生成交易计划 ====================
|
|
|
plans = []
|
|
|
green_items = [s for s in raw_scores if s["category"] == "green"]
|
|
|
|
|
|
for s in green_items:
|
|
|
direction = "long" if s["trend_score"] >= 50 else "short"
|
|
|
pivots = s["pivots"]
|
|
|
|
|
|
if direction == "long":
|
|
|
entry_low = round(pivots["s1"], 2)
|
|
|
entry_high = round(pivots["pivot"], 2)
|
|
|
stop_loss = round(pivots["s2"], 2)
|
|
|
target1 = round(pivots["r1"], 2)
|
|
|
target2 = round(pivots["r2"], 2)
|
|
|
else:
|
|
|
entry_low = round(pivots["pivot"], 2)
|
|
|
entry_high = round(pivots["r1"], 2)
|
|
|
stop_loss = round(pivots["r2"], 2)
|
|
|
target1 = round(pivots["s1"], 2)
|
|
|
target2 = round(pivots["s2"], 2)
|
|
|
|
|
|
plans.append({
|
|
|
"symbol": s["symbol"],
|
|
|
"name": s["name"],
|
|
|
"direction": direction,
|
|
|
"composite_score": s["composite_score"],
|
|
|
"entry_low": entry_low,
|
|
|
"entry_high": entry_high,
|
|
|
"stop_loss": stop_loss,
|
|
|
"target1": target1,
|
|
|
"target2": target2,
|
|
|
"trigger": "回踩支撑企稳 + 5m放量突破",
|
|
|
"amplitude_score": s["amplitude_score"],
|
|
|
"volume_score": s["volume_score"],
|
|
|
"trend_score": s["trend_score"],
|
|
|
"activity_score": s["activity_score"],
|
|
|
"category": s["category"],
|
|
|
})
|
|
|
|
|
|
# ==================== 第六步: 板块热度 ====================
|
|
|
sectors = _calc_sector_heat(raw_scores, code_to_name)
|
|
|
|
|
|
# ==================== 第七步: 汇总 ====================
|
|
|
bull_count = sum(1 for s in raw_scores if s["trend_score"] >= 50)
|
|
|
bear_count = sum(1 for s in raw_scores if s["trend_score"] < 30)
|
|
|
neutral_count = len(raw_scores) - bull_count - bear_count
|
|
|
|
|
|
# 核心结论
|
|
|
top3 = raw_scores[:3]
|
|
|
top_names = [f"{s['symbol']} {s['name']}" for s in top3]
|
|
|
main_direction = "多头" if bull_count > bear_count else "空头" if bear_count > bull_count else "震荡"
|
|
|
core_conclusion = f"{main_direction}格局{'延续' if bull_count > 15 else '分化'}，{'贵金属+黑色系共振做多' if bull_count > 20 else '板块轮动明显'}"
|
|
|
|
|
|
# 风险提示
|
|
|
risk_warnings = []
|
|
|
if top3:
|
|
|
top_sym = top3[0]
|
|
|
risk_warnings.append(
|
|
|
f"极值品种严禁追{'多' if top_sym['trend_score'] >= 50 else '空'}："
|
|
|
f"{top_sym['symbol']}({top_sym['composite_score']}分) "
|
|
|
f"{'周一大概率高开' if top_sym['change_pct'] > 2 else '注意风险控制'}"
|
|
|
)
|
|
|
if bear_count > 0:
|
|
|
bear_syms = [s for s in raw_scores if s["category"] == "red" and s["trend_score"] < 30][:2]
|
|
|
if bear_syms:
|
|
|
names = "/".join(s["symbol"] for s in bear_syms)
|
|
|
risk_warnings.append(f"空头品种谨慎：{names} 趋势偏空，需严格止损")
|
|
|
risk_warnings.append("关注宏观数据和消息面变化，可能影响开盘方向")
|
|
|
if any(s["volume_ratio"] > 2.5 for s in raw_scores):
|
|
|
high_vol = [s for s in raw_scores if s["volume_ratio"] > 2.5]
|
|
|
risk_warnings.append(
|
|
|
f"量能异常品种：{', '.join(s['symbol'] for s in high_vol[:3])} "
|
|
|
f"量比超2.5倍，需确认量能可持续性"
|
|
|
)
|
|
|
|
|
|
return {
|
|
|
"review_date": review_date_str,
|
|
|
"week_day": week_day,
|
|
|
"data_basis": f"{review_date_str} 收盘 ({len(symbol_data)} 品种) | 多周期共振分析",
|
|
|
"core_conclusion": core_conclusion,
|
|
|
"bull_count": bull_count,
|
|
|
"bear_count": bear_count,
|
|
|
"neutral_count": neutral_count,
|
|
|
"opportunity_count": len(green_items),
|
|
|
"risk_warnings": risk_warnings,
|
|
|
"scores": raw_scores,
|
|
|
"plans": plans,
|
|
|
"sectors": sectors,
|
|
|
"symbol_count": len(symbol_data),
|
|
|
}
|
|
|
|
|
|
|
|
|
def _calc_sector_heat(scores: List[dict], code_to_name: dict) -> List[dict]:
|
|
|
"""计算板块热度"""
|
|
|
# 构建 symbol -> score 映射
|
|
|
name_to_score = {}
|
|
|
for s in scores:
|
|
|
name_to_score[s["name"]] = s
|
|
|
|
|
|
sectors = []
|
|
|
for sector_name, members_names in SECTOR_MAP.items():
|
|
|
members = []
|
|
|
for mn in members_names:
|
|
|
if mn in name_to_score:
|
|
|
sc = name_to_score[mn]
|
|
|
members.append({
|
|
|
"symbol": sc["symbol"],
|
|
|
"name": sc["name"],
|
|
|
"score": sc["composite_score"],
|
|
|
"trend": sc["trend_score"],
|
|
|
"change_pct": sc["change_pct"],
|
|
|
})
|
|
|
|
|
|
if not members:
|
|
|
continue
|
|
|
|
|
|
avg_score = round(sum(m["score"] for m in members) / len(members), 1)
|
|
|
avg_trend = round(sum(m["trend"] for m in members) / len(members), 0)
|
|
|
|
|
|
if avg_trend > 20:
|
|
|
direction = "多头"
|
|
|
elif avg_trend < -20:
|
|
|
direction = "空头"
|
|
|
else:
|
|
|
direction = "震荡"
|
|
|
|
|
|
# 热度等级
|
|
|
if avg_score >= 75:
|
|
|
heat = 3
|
|
|
elif avg_score >= 60:
|
|
|
heat = 2
|
|
|
elif avg_score >= 45:
|
|
|
heat = 1
|
|
|
else:
|
|
|
heat = 0
|
|
|
|
|
|
# 龙头
|
|
|
leader = max(members, key=lambda m: m["score"])
|
|
|
|
|
|
sectors.append({
|
|
|
"sector_name": sector_name,
|
|
|
"avg_score": avg_score,
|
|
|
"avg_trend": int(avg_trend),
|
|
|
"direction": direction,
|
|
|
"heat_level": heat,
|
|
|
"leader_symbol": leader["symbol"],
|
|
|
"leader_score": leader["score"],
|
|
|
"members": sorted(members, key=lambda m: m["score"], reverse=True),
|
|
|
})
|
|
|
|
|
|
sectors.sort(key=lambda s: s["avg_score"], reverse=True)
|
|
|
return sectors
|
|
|
|
|
|
|
|
|
def save_plan_to_db(db: Session, plan_data: dict) -> int:
|
|
|
"""将计划数据保存到数据库"""
|
|
|
review_date_str = plan_data["review_date"]
|
|
|
week_day = plan_data["week_day"]
|
|
|
|
|
|
# 清理旧数据
|
|
|
existing = db.query(ReviewPlanV2).filter_by(review_date=review_date_str).first()
|
|
|
if existing:
|
|
|
review_plan_id = existing.id
|
|
|
# 清理关联数据
|
|
|
db.query(SymbolScoreV2).filter_by(review_date_id=review_plan_id).delete()
|
|
|
db.query(TradingPlanV2).filter_by(review_date_id=review_plan_id).delete()
|
|
|
db.query(SectorHeat).filter_by(review_date_id=review_plan_id).delete()
|
|
|
# 更新元数据
|
|
|
existing.week_day = week_day
|
|
|
existing.data_basis = plan_data.get("data_basis")
|
|
|
existing.core_conclusion = plan_data.get("core_conclusion")
|
|
|
existing.bull_count = plan_data.get("bull_count")
|
|
|
existing.bear_count = plan_data.get("bear_count")
|
|
|
existing.neutral_count = plan_data.get("neutral_count")
|
|
|
existing.opportunity_count = plan_data.get("opportunity_count")
|
|
|
existing.risk_warnings = plan_data.get("risk_warnings")
|
|
|
else:
|
|
|
review_plan = ReviewPlanV2(
|
|
|
review_date=review_date_str,
|
|
|
week_day=week_day,
|
|
|
data_basis=plan_data.get("data_basis"),
|
|
|
core_conclusion=plan_data.get("core_conclusion"),
|
|
|
bull_count=plan_data.get("bull_count"),
|
|
|
bear_count=plan_data.get("bear_count"),
|
|
|
neutral_count=plan_data.get("neutral_count"),
|
|
|
opportunity_count=plan_data.get("opportunity_count"),
|
|
|
risk_warnings=plan_data.get("risk_warnings"),
|
|
|
)
|
|
|
db.add(review_plan)
|
|
|
db.flush()
|
|
|
review_plan_id = review_plan.id
|
|
|
|
|
|
# 保存评分数据
|
|
|
for s in plan_data.get("scores", []):
|
|
|
pivots = s.get("pivots", {})
|
|
|
score_record = SymbolScoreV2(
|
|
|
review_date_id=review_plan_id,
|
|
|
symbol=s["symbol"],
|
|
|
name=s["name"],
|
|
|
close_price=s.get("close_price"),
|
|
|
prev_close=s.get("prev_close"),
|
|
|
high_price=s.get("high_price"),
|
|
|
low_price=s.get("low_price"),
|
|
|
volume=s.get("volume"),
|
|
|
avg_volume_5=s.get("avg_volume_5"),
|
|
|
amplitude_score=s.get("amplitude_score"),
|
|
|
volume_score=s.get("volume_score"),
|
|
|
change_score=s.get("change_score"),
|
|
|
trend_score=s.get("trend_score"),
|
|
|
activity_score=s.get("activity_score"),
|
|
|
composite_score=s.get("composite_score"),
|
|
|
amplitude_pct=s.get("amplitude_pct"),
|
|
|
change_pct=s.get("change_pct"),
|
|
|
volume_ratio=s.get("volume_ratio"),
|
|
|
trend_60m=s.get("trend_60m"),
|
|
|
trend_15m=s.get("trend_15m"),
|
|
|
trend_5m=s.get("trend_5m"),
|
|
|
direction=s.get("direction"),
|
|
|
direction_tag=s.get("direction_tag"),
|
|
|
category=s.get("category"),
|
|
|
pivot=pivots.get("pivot"),
|
|
|
r1=pivots.get("r1"),
|
|
|
r2=pivots.get("r2"),
|
|
|
s1=pivots.get("s1"),
|
|
|
s2=pivots.get("s2"),
|
|
|
rank=s.get("rank"),
|
|
|
)
|
|
|
db.add(score_record)
|
|
|
|
|
|
# 保存交易计划
|
|
|
for p in plan_data.get("plans", []):
|
|
|
plan_record = TradingPlanV2(
|
|
|
review_date_id=review_plan_id,
|
|
|
symbol=p["symbol"],
|
|
|
name=p["name"],
|
|
|
direction=p["direction"],
|
|
|
composite_score=p.get("composite_score"),
|
|
|
entry_low=p.get("entry_low"),
|
|
|
entry_high=p.get("entry_high"),
|
|
|
stop_loss=p.get("stop_loss"),
|
|
|
target1=p.get("target1"),
|
|
|
target2=p.get("target2"),
|
|
|
trigger=p.get("trigger"),
|
|
|
amplitude_score=p.get("amplitude_score"),
|
|
|
volume_score=p.get("volume_score"),
|
|
|
trend_score=p.get("trend_score"),
|
|
|
activity_score=p.get("activity_score"),
|
|
|
category=p.get("category"),
|
|
|
)
|
|
|
db.add(plan_record)
|
|
|
|
|
|
# 保存板块热度
|
|
|
for sec in plan_data.get("sectors", []):
|
|
|
sector_record = SectorHeat(
|
|
|
review_date_id=review_plan_id,
|
|
|
sector_name=sec["sector_name"],
|
|
|
avg_score=sec.get("avg_score"),
|
|
|
avg_trend=sec.get("avg_trend"),
|
|
|
direction=sec.get("direction"),
|
|
|
heat_level=sec.get("heat_level"),
|
|
|
leader_symbol=sec.get("leader_symbol"),
|
|
|
leader_score=sec.get("leader_score"),
|
|
|
members=sec.get("members"),
|
|
|
)
|
|
|
db.add(sector_record)
|
|
|
|
|
|
db.commit()
|
|
|
logger.info(f"交易计划已保存: {review_date_str}, ID={review_plan_id}")
|
|
|
return review_plan_id
|
|
|
|
|
|
|
|
|
def get_plan_data(db: Session, review_plan_id: int) -> Optional[dict]:
|
|
|
"""从数据库读取完整的计划数据"""
|
|
|
plan_meta = db.query(ReviewPlanV2).filter_by(id=review_plan_id).first()
|
|
|
if not plan_meta:
|
|
|
return None
|
|
|
|
|
|
scores = db.query(SymbolScoreV2).filter_by(
|
|
|
review_date_id=review_plan_id
|
|
|
).order_by(SymbolScoreV2.composite_score.desc()).all()
|
|
|
|
|
|
plans = db.query(TradingPlanV2).filter_by(
|
|
|
review_date_id=review_plan_id
|
|
|
).order_by(TradingPlanV2.composite_score.desc()).all()
|
|
|
|
|
|
sectors = db.query(SectorHeat).filter_by(
|
|
|
review_date_id=review_plan_id
|
|
|
).order_by(SectorHeat.avg_score.desc()).all()
|
|
|
|
|
|
return {
|
|
|
"meta": {
|
|
|
"id": plan_meta.id,
|
|
|
"review_date": plan_meta.review_date,
|
|
|
"week_day": plan_meta.week_day,
|
|
|
"data_basis": plan_meta.data_basis,
|
|
|
"core_conclusion": plan_meta.core_conclusion,
|
|
|
"bull_count": plan_meta.bull_count,
|
|
|
"bear_count": plan_meta.bear_count,
|
|
|
"neutral_count": plan_meta.neutral_count,
|
|
|
"opportunity_count": plan_meta.opportunity_count,
|
|
|
"risk_warnings": plan_meta.risk_warnings,
|
|
|
"created_at": plan_meta.created_at.isoformat() if plan_meta.created_at else None,
|
|
|
},
|
|
|
"scores": [_score_to_dict(s) for s in scores],
|
|
|
"plans": [_plan_to_dict(p) for p in plans],
|
|
|
"sectors": [_sector_to_dict(s) for s in sectors],
|
|
|
}
|
|
|
|
|
|
|
|
|
def _score_to_dict(s: SymbolScoreV2) -> dict:
|
|
|
return {
|
|
|
"id": s.id,
|
|
|
"symbol": s.symbol,
|
|
|
"name": s.name,
|
|
|
"close_price": s.close_price,
|
|
|
"prev_close": s.prev_close,
|
|
|
"high_price": s.high_price,
|
|
|
"low_price": s.low_price,
|
|
|
"volume": s.volume,
|
|
|
"avg_volume_5": s.avg_volume_5,
|
|
|
"amplitude_score": s.amplitude_score,
|
|
|
"volume_score": s.volume_score,
|
|
|
"change_score": s.change_score,
|
|
|
"trend_score": s.trend_score,
|
|
|
"activity_score": s.activity_score,
|
|
|
"composite_score": s.composite_score,
|
|
|
"amplitude_pct": s.amplitude_pct,
|
|
|
"change_pct": s.change_pct,
|
|
|
"volume_ratio": s.volume_ratio,
|
|
|
"trend_60m": s.trend_60m,
|
|
|
"trend_15m": s.trend_15m,
|
|
|
"trend_5m": s.trend_5m,
|
|
|
"direction": s.direction,
|
|
|
"direction_tag": s.direction_tag,
|
|
|
"category": s.category,
|
|
|
"pivot": s.pivot,
|
|
|
"r1": s.r1,
|
|
|
"r2": s.r2,
|
|
|
"s1": s.s1,
|
|
|
"s2": s.s2,
|
|
|
"rank": s.rank,
|
|
|
}
|
|
|
|
|
|
|
|
|
def _plan_to_dict(p: TradingPlanV2) -> dict:
|
|
|
return {
|
|
|
"id": p.id,
|
|
|
"symbol": p.symbol,
|
|
|
"name": p.name,
|
|
|
"direction": p.direction,
|
|
|
"composite_score": p.composite_score,
|
|
|
"entry_low": p.entry_low,
|
|
|
"entry_high": p.entry_high,
|
|
|
"stop_loss": p.stop_loss,
|
|
|
"target1": p.target1,
|
|
|
"target2": p.target2,
|
|
|
"trigger": p.trigger,
|
|
|
"amplitude_score": p.amplitude_score,
|
|
|
"volume_score": p.volume_score,
|
|
|
"trend_score": p.trend_score,
|
|
|
"activity_score": p.activity_score,
|
|
|
"category": p.category,
|
|
|
}
|
|
|
|
|
|
|
|
|
def _sector_to_dict(s: SectorHeat) -> dict:
|
|
|
return {
|
|
|
"id": s.id,
|
|
|
"sector_name": s.sector_name,
|
|
|
"avg_score": s.avg_score,
|
|
|
"avg_trend": s.avg_trend,
|
|
|
"direction": s.direction,
|
|
|
"heat_level": s.heat_level,
|
|
|
"leader_symbol": s.leader_symbol,
|
|
|
"leader_score": s.leader_score,
|
|
|
"members": s.members,
|
|
|
}
|
