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"""
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数据导入路由
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"""
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import pandas as pd
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import logging
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from fastapi import APIRouter, Depends, UploadFile, File, HTTPException
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from sqlalchemy.orm import Session
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from datetime import datetime, date
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from app.db.session import get_db
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from app.schemas.base import ResponseModel
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from app.models.stock_basic import StockBasic, IndexBasic, IndexTrade
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from app.core.security import get_current_user
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from app.models.user import User
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router = APIRouter()
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logger = logging.getLogger(__name__)
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INDEX_TRADE_COLUMN_MAP = {
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'证券代码': 'index_code',
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'证券名称': 'name',
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'成分个数 [交易日期]最新': 'component_count',
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'开盘价 [交易日期]最新': 'open',
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'收盘价 [交易日期]最新': 'close',
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'成交量 [交易日期]最新 [单位]股': 'volume',
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'成交额 [交易日期]最新 [单位]百万元': 'amount',
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'总市值 [截止日期]最新 [单位]百万元': 'total_market_value',
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'自由流通市值 [交易日期]最新 [单位]百万元': 'float_market_value',
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'涨跌幅 [交易日期]最新 [单位]%': 'change_pct',
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'最高价 [交易日期]最新': 'high',
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'最低价 [交易日期]最新': 'low',
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'上涨家数 [交易日期]最新': 'up_count',
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'下跌家数 [交易日期]最新': 'down_count',
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'平盘家数 [交易日期]最新': 'flat_count',
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'涨停家数 [交易日期]最新': 'limit_up_count',
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'跌停家数 [交易日期]最新': 'limit_down_count',
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'停牌家数 [交易日期]最新': 'suspend_count',
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'近期创历史新高 [交易日期]最新 [近N日内]300 [复权方式]不复权': 'is_new_high',
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'近期创历史新低 [交易日期]最新 [近N日内]300 [复权方式]不复权': 'is_new_low',
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'市盈率PE(TTM) [交易日期]最新 [剔除规则]不调整': 'pe_ratio',
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'市盈率PE(TTM)中位值 [交易日期]最新 [剔除规则]不调整': 'pe_median'
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}
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@router.post("/index-data", response_model=ResponseModel)
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async def import_index_data(
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file: UploadFile = File(...),
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trade_date: str = None,
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db: Session = Depends(get_db),
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current_user: User = Depends(get_current_user)
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):
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"""导入指数数据（同时更新指数基础表和指数交易表）"""
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if not file.filename.endswith(('.xls', '.xlsx')):
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raise HTTPException(status_code=400, detail="只支持xls或xlsx格式文件")
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if not trade_date:
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raise HTTPException(status_code=400, detail="请提供交易日期参数(YYYY-MM-DD格式)")
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try:
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trade_date_obj = datetime.strptime(trade_date, '%Y-%m-%d').date()
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except:
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raise HTTPException(status_code=400, detail="交易日期格式错误，请使用YYYY-MM-DD格式")
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try:
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df = pd.read_excel(file.file)
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df.columns = df.columns.str.strip()
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renamed_df = df.rename(columns=INDEX_TRADE_COLUMN_MAP)
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if 'index_code' not in renamed_df.columns:
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raise HTTPException(status_code=400, detail="缺少必要列：证券代码")
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success_count = 0
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error_count = 0
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index_basic_updated = 0
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index_basic_added = 0
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for _, row in renamed_df.iterrows():
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try:
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index_code = str(row['index_code']).strip()
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if not index_code:
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continue
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name = str(row.get('name', '')) if pd.notna(row.get('name')) else None
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component_count = int(row.get('component_count')) if pd.notna(row.get('component_count')) else None
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index_basic = db.query(IndexBasic).filter(IndexBasic.code == index_code).first()
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if index_basic:
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if component_count and index_basic.component_count != component_count:
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index_basic.component_count = component_count
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index_basic.name = name or index_basic.name
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index_basic.updated_at = datetime.utcnow()
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index_basic_updated += 1
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else:
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index_basic = IndexBasic(
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code=index_code,
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name=name,
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component_count=component_count
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)
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db.add(index_basic)
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db.flush()
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index_basic_added += 1
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existing_trade = db.query(IndexTrade).filter(
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IndexTrade.index_code == index_code,
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IndexTrade.trade_date == trade_date_obj
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).first()
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def get_float_val(col_name):
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val = row.get(col_name)
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if pd.notna(val):
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try:
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return float(val)
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except:
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return None
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return None
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def get_int_val(col_name):
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val = row.get(col_name)
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if pd.notna(val):
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try:
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return int(float(val))
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except:
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return None
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return None
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def get_bool_val(col_name):
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val = row.get(col_name)
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if pd.notna(val):
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if isinstance(val, bool):
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return val
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if isinstance(val, str):
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return val.lower() in ['true', '1', 'yes', '是']
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return bool(val)
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return False
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open_price = get_float_val('open')
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close_price = get_float_val('close')
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high_price = get_float_val('high')
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low_price = get_float_val('low')
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change_pct = get_float_val('change_pct')
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volume = get_int_val('volume')
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amount = get_float_val('amount')
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total_market_value = get_float_val('total_market_value')
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float_market_value = get_float_val('float_market_value')
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up_count = get_int_val('up_count')
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down_count = get_int_val('down_count')
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flat_count = get_int_val('flat_count')
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limit_up_count = get_int_val('limit_up_count')
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limit_down_count = get_int_val('limit_down_count')
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suspend_count = get_int_val('suspend_count')
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pe_ratio = get_float_val('pe_ratio')
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pe_median = get_float_val('pe_median')
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is_new_high = get_bool_val('is_new_high')
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is_new_low = get_bool_val('is_new_low')
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if existing_trade:
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existing_trade.open = open_price
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existing_trade.close = close_price
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existing_trade.high = high_price
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existing_trade.low = low_price
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existing_trade.change_pct = change_pct
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existing_trade.volume = volume
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existing_trade.amount = amount
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existing_trade.total_market_value = total_market_value
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existing_trade.float_market_value = float_market_value
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existing_trade.up_count = up_count
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existing_trade.down_count = down_count
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existing_trade.flat_count = flat_count
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existing_trade.limit_up_count = limit_up_count
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existing_trade.limit_down_count = limit_down_count
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existing_trade.suspend_count = suspend_count
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existing_trade.pe_ratio = pe_ratio
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existing_trade.pe_median = pe_median
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existing_trade.is_new_high = is_new_high
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existing_trade.is_new_low = is_new_low
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existing_trade.updated_at = datetime.utcnow()
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else:
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trade = IndexTrade(
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index_code=index_code,
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trade_date=trade_date_obj,
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open=open_price,
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close=close_price,
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high=high_price,
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low=low_price,
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change_pct=change_pct,
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volume=volume,
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amount=amount,
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total_market_value=total_market_value,
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float_market_value=float_market_value,
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up_count=up_count,
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down_count=down_count,
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flat_count=flat_count,
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limit_up_count=limit_up_count,
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limit_down_count=limit_down_count,
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suspend_count=suspend_count,
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pe_ratio=pe_ratio,
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pe_median=pe_median,
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is_new_high=is_new_high,
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is_new_low=is_new_low
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)
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db.add(trade)
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success_count += 1
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except Exception as e:
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logger.error(f"导入指数{row.get('index_code')}失败: {str(e)}")
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error_count += 1
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db.commit()
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return ResponseModel(data={
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"success_count": success_count,
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"error_count": error_count,
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"total_count": len(df),
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"index_basic_added": index_basic_added,
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"index_basic_updated": index_basic_updated,
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"trade_date": trade_date
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})
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except Exception as e:
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logger.error(f"导入指数数据失败: {str(e)}")
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raise HTTPException(status_code=500, detail=str(e))
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@router.post("/stock-basic", response_model=ResponseModel)
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async def import_stock_basic(
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file: UploadFile = File(...),
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db: Session = Depends(get_db),
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current_user: User = Depends(get_current_user)
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):
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"""导入股票基础数据"""
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if not file.filename.endswith(('.xls', '.xlsx')):
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raise HTTPException(status_code=400, detail="只支持xls或xlsx格式文件")
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try:
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df = pd.read_excel(file.file)
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required_columns = ['code', 'name', 'total_shares', 'float_shares',
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'industry_index_name', 'industry_index_code',
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'institution_hold_ratio', 'industry_level3', 'list_date']
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missing_columns = [col for col in required_columns if col not in df.columns]
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if missing_columns:
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raise HTTPException(status_code=400, detail=f"缺少必要列: {missing_columns}")
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success_count = 0
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error_count = 0
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for _, row in df.iterrows():
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try:
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existing = db.query(StockBasic).filter(StockBasic.code == str(row['code'])).first()
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list_date = None
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if pd.notna(row['list_date']):
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if isinstance(row['list_date'], datetime):
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list_date = row['list_date'].date()
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elif isinstance(row['list_date'], str):
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list_date = datetime.strptime(row['list_date'], '%Y-%m-%d').date()
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if existing:
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existing.name = str(row.get('name', existing.name))
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existing.total_shares = int(row.get('total_shares', existing.total_shares)) if pd.notna(row.get('total_shares')) else existing.total_shares
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existing.float_shares = int(row.get('float_shares', existing.float_shares)) if pd.notna(row.get('float_shares')) else existing.float_shares
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existing.industry_index_name = str(row.get('industry_index_name', existing.industry_index_name)) if pd.notna(row.get('industry_index_name')) else existing.industry_index_name
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existing.industry_index_code = str(row.get('industry_index_code', existing.industry_index_code)) if pd.notna(row.get('industry_index_code')) else existing.industry_index_code
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existing.institution_hold_ratio = float(row.get('institution_hold_ratio', existing.institution_hold_ratio)) if pd.notna(row.get('institution_hold_ratio')) else existing.institution_hold_ratio
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existing.industry_level3 = str(row.get('industry_level3', existing.industry_level3)) if pd.notna(row.get('industry_level3')) else existing.industry_level3
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existing.list_date = list_date
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existing.updated_at = datetime.utcnow()
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else:
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stock = StockBasic(
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code=str(row['code']),
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name=str(row.get('name', '')),
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total_shares=int(row['total_shares']) if pd.notna(row['total_shares']) else None,
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float_shares=int(row['float_shares']) if pd.notna(row['float_shares']) else None,
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industry_index_name=str(row.get('industry_index_name', '')) if pd.notna(row.get('industry_index_name')) else None,
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industry_index_code=str(row.get('industry_index_code', '')) if pd.notna(row.get('industry_index_code')) else None,
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institution_hold_ratio=float(row['institution_hold_ratio']) if pd.notna(row['institution_hold_ratio']) else None,
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|
industry_level3=str(row.get('industry_level3', '')) if pd.notna(row.get('industry_level3')) else None,
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list_date=list_date
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)
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db.add(stock)
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success_count += 1
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except Exception as e:
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logger.error(f"导入股票{row.get('code')}失败: {str(e)}")
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error_count += 1
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db.commit()
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return ResponseModel(data={
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"success_count": success_count,
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"error_count": error_count,
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|
"total_count": len(df)
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|
})
|
|
|
|
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|
except Exception as e:
|
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|
logger.error(f"导入股票基础数据失败: {str(e)}")
|
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|
raise HTTPException(status_code=500, detail=str(e))
|
|
|
|
|
|
|
|
|
@router.post("/index-basic", response_model=ResponseModel)
|
|
|
async def import_index_basic(
|
|
|
file: UploadFile = File(...),
|
|
|
db: Session = Depends(get_db),
|
|
|
current_user: User = Depends(get_current_user)
|
|
|
):
|
|
|
"""导入指数基础数据"""
|
|
|
if not file.filename.endswith(('.xls', '.xlsx')):
|
|
|
raise HTTPException(status_code=400, detail="只支持xls或xlsx格式文件")
|
|
|
|
|
|
try:
|
|
|
df = pd.read_excel(file.file)
|
|
|
|
|
|
required_columns = ['code', 'name', 'component_count']
|
|
|
missing_columns = [col for col in required_columns if col not in df.columns]
|
|
|
if missing_columns:
|
|
|
raise HTTPException(status_code=400, detail=f"缺少必要列: {missing_columns}")
|
|
|
|
|
|
success_count = 0
|
|
|
error_count = 0
|
|
|
|
|
|
for _, row in df.iterrows():
|
|
|
try:
|
|
|
existing = db.query(IndexBasic).filter(IndexBasic.code == str(row['code'])).first()
|
|
|
|
|
|
if existing:
|
|
|
existing.name = str(row.get('name', existing.name))
|
|
|
existing.component_count = int(row.get('component_count', existing.component_count)) if pd.notna(row.get('component_count')) else existing.component_count
|
|
|
existing.updated_at = datetime.utcnow()
|
|
|
else:
|
|
|
index = IndexBasic(
|
|
|
code=str(row['code']),
|
|
|
name=str(row.get('name', '')),
|
|
|
component_count=int(row['component_count']) if pd.notna(row['component_count']) else None
|
|
|
)
|
|
|
db.add(index)
|
|
|
|
|
|
success_count += 1
|
|
|
except Exception as e:
|
|
|
logger.error(f"导入指数{row.get('code')}失败: {str(e)}")
|
|
|
error_count += 1
|
|
|
|
|
|
db.commit()
|
|
|
|
|
|
return ResponseModel(data={
|
|
|
"success_count": success_count,
|
|
|
"error_count": error_count,
|
|
|
"total_count": len(df)
|
|
|
})
|
|
|
|
|
|
except Exception as e:
|
|
|
logger.error(f"导入指数基础数据失败: {str(e)}")
|
|
|
raise HTTPException(status_code=500, detail=str(e))
|
|
|
|
|
|
|
|
|
@router.post("/index-trade", response_model=ResponseModel)
|
|
|
async def import_index_trade(
|
|
|
file: UploadFile = File(...),
|
|
|
db: Session = Depends(get_db),
|
|
|
current_user: User = Depends(get_current_user)
|
|
|
):
|
|
|
"""导入指数交易数据"""
|
|
|
if not file.filename.endswith(('.xls', '.xlsx')):
|
|
|
raise HTTPException(status_code=400, detail="只支持xls或xlsx格式文件")
|
|
|
|
|
|
try:
|
|
|
df = pd.read_excel(file.file)
|
|
|
|
|
|
required_columns = ['index_code', 'trade_date', 'open', 'close', 'high', 'low']
|
|
|
missing_columns = [col for col in required_columns if col not in df.columns]
|
|
|
if missing_columns:
|
|
|
raise HTTPException(status_code=400, detail=f"缺少必要列: {missing_columns}")
|
|
|
|
|
|
success_count = 0
|
|
|
error_count = 0
|
|
|
|
|
|
for _, row in df.iterrows():
|
|
|
try:
|
|
|
trade_date = None
|
|
|
if pd.notna(row['trade_date']):
|
|
|
if isinstance(row['trade_date'], datetime):
|
|
|
trade_date = row['trade_date'].date()
|
|
|
elif isinstance(row['trade_date'], str):
|
|
|
trade_date = datetime.strptime(row['trade_date'], '%Y-%m-%d').date()
|
|
|
|
|
|
existing = db.query(IndexTrade).filter(
|
|
|
IndexTrade.index_code == str(row['index_code']),
|
|
|
IndexTrade.trade_date == trade_date
|
|
|
).first()
|
|
|
|
|
|
if existing:
|
|
|
existing.open = float(row.get('open', existing.open)) if pd.notna(row.get('open')) else existing.open
|
|
|
existing.close = float(row.get('close', existing.close)) if pd.notna(row.get('close')) else existing.close
|
|
|
existing.high = float(row.get('high', existing.high)) if pd.notna(row.get('high')) else existing.high
|
|
|
existing.low = float(row.get('low', existing.low)) if pd.notna(row.get('low')) else existing.low
|
|
|
existing.change_pct = float(row.get('change_pct', existing.change_pct)) if pd.notna(row.get('change_pct')) else existing.change_pct
|
|
|
existing.volume = int(row.get('volume', existing.volume)) if pd.notna(row.get('volume')) else existing.volume
|
|
|
existing.amount = float(row.get('amount', existing.amount)) if pd.notna(row.get('amount')) else existing.amount
|
|
|
existing.total_market_value = float(row.get('total_market_value', existing.total_market_value)) if pd.notna(row.get('total_market_value')) else existing.total_market_value
|
|
|
existing.float_market_value = float(row.get('float_market_value', existing.float_market_value)) if pd.notna(row.get('float_market_value')) else existing.float_market_value
|
|
|
existing.up_count = int(row.get('up_count', existing.up_count)) if pd.notna(row.get('up_count')) else existing.up_count
|
|
|
existing.down_count = int(row.get('down_count', existing.down_count)) if pd.notna(row.get('down_count')) else existing.down_count
|
|
|
existing.flat_count = int(row.get('flat_count', existing.flat_count)) if pd.notna(row.get('flat_count')) else existing.flat_count
|
|
|
existing.limit_up_count = int(row.get('limit_up_count', existing.limit_up_count)) if pd.notna(row.get('limit_up_count')) else existing.limit_up_count
|
|
|
existing.limit_down_count = int(row.get('limit_down_count', existing.limit_down_count)) if pd.notna(row.get('limit_down_count')) else existing.limit_down_count
|
|
|
existing.suspend_count = int(row.get('suspend_count', existing.suspend_count)) if pd.notna(row.get('suspend_count')) else existing.suspend_count
|
|
|
existing.pe_ratio = float(row.get('pe_ratio', existing.pe_ratio)) if pd.notna(row.get('pe_ratio')) else existing.pe_ratio
|
|
|
existing.pe_median = float(row.get('pe_median', existing.pe_median)) if pd.notna(row.get('pe_median')) else existing.pe_median
|
|
|
existing.is_new_high = bool(row.get('is_new_high', existing.is_new_high)) if pd.notna(row.get('is_new_high')) else existing.is_new_high
|
|
|
existing.is_new_low = bool(row.get('is_new_low', existing.is_new_low)) if pd.notna(row.get('is_new_low')) else existing.is_new_low
|
|
|
existing.updated_at = datetime.utcnow()
|
|
|
else:
|
|
|
trade = IndexTrade(
|
|
|
index_code=str(row['index_code']),
|
|
|
trade_date=trade_date,
|
|
|
open=float(row['open']) if pd.notna(row['open']) else None,
|
|
|
close=float(row['close']) if pd.notna(row['close']) else None,
|
|
|
high=float(row['high']) if pd.notna(row['high']) else None,
|
|
|
low=float(row['low']) if pd.notna(row['low']) else None,
|
|
|
change_pct=float(row.get('change_pct')) if pd.notna(row.get('change_pct')) else None,
|
|
|
volume=int(row.get('volume')) if pd.notna(row.get('volume')) else None,
|
|
|
amount=float(row.get('amount')) if pd.notna(row.get('amount')) else None,
|
|
|
total_market_value=float(row.get('total_market_value')) if pd.notna(row.get('total_market_value')) else None,
|
|
|
float_market_value=float(row.get('float_market_value')) if pd.notna(row.get('float_market_value')) else None,
|
|
|
up_count=int(row.get('up_count')) if pd.notna(row.get('up_count')) else None,
|
|
|
down_count=int(row.get('down_count')) if pd.notna(row.get('down_count')) else None,
|
|
|
flat_count=int(row.get('flat_count')) if pd.notna(row.get('flat_count')) else None,
|
|
|
limit_up_count=int(row.get('limit_up_count')) if pd.notna(row.get('limit_up_count')) else None,
|
|
|
limit_down_count=int(row.get('limit_down_count')) if pd.notna(row.get('limit_down_count')) else None,
|
|
|
suspend_count=int(row.get('suspend_count')) if pd.notna(row.get('suspend_count')) else None,
|
|
|
pe_ratio=float(row.get('pe_ratio')) if pd.notna(row.get('pe_ratio')) else None,
|
|
|
pe_median=float(row.get('pe_median')) if pd.notna(row.get('pe_median')) else None,
|
|
|
is_new_high=bool(row.get('is_new_high')) if pd.notna(row.get('is_new_high')) else False,
|
|
|
is_new_low=bool(row.get('is_new_low')) if pd.notna(row.get('is_new_low')) else False
|
|
|
)
|
|
|
db.add(trade)
|
|
|
|
|
|
success_count += 1
|
|
|
except Exception as e:
|
|
|
logger.error(f"导入指数交易{row.get('index_code')}-{row.get('trade_date')}失败: {str(e)}")
|
|
|
error_count += 1
|
|
|
|
|
|
db.commit()
|
|
|
|
|
|
return ResponseModel(data={
|
|
|
"success_count": success_count,
|
|
|
"error_count": error_count,
|
|
|
"total_count": len(df)
|
|
|
})
|
|
|
|
|
|
except Exception as e:
|
|
|
logger.error(f"导入指数交易数据失败: {str(e)}")
|
|
|
raise HTTPException(status_code=500, detail=str(e)) |
