|
|
| import logging
|
| import traceback
|
| import akshare as ak
|
| import pandas as pd
|
| import numpy as np
|
| from datetime import datetime, timedelta
|
|
|
|
|
| class CapitalFlowAnalyzer:
|
| def __init__(self):
|
| self.data_cache = {}
|
|
|
|
|
| logging.basicConfig(level=logging.INFO,
|
| format='%(asctime)s - %(levelname)s - %(message)s')
|
| self.logger = logging.getLogger(__name__)
|
|
|
| def get_concept_fund_flow(self, period="10日排行"):
|
| """获取概念/行业资金流向数据"""
|
| try:
|
| self.logger.info(f"Getting concept fund flow for period: {period}")
|
|
|
|
|
| cache_key = f"concept_fund_flow_{period}"
|
| if cache_key in self.data_cache:
|
| cache_time, cached_data = self.data_cache[cache_key]
|
|
|
| if (datetime.now() - cache_time).total_seconds() < 3600:
|
| return cached_data
|
|
|
|
|
| try:
|
| concept_data = ak.stock_fund_flow_concept(symbol=period)
|
|
|
|
|
| self.logger.info(f"Actual columns: {list(concept_data.columns)}")
|
| self.logger.info(f"Column count: {len(concept_data.columns)}")
|
|
|
|
|
| result = []
|
| for _, row in concept_data.iterrows():
|
| try:
|
|
|
| item = {}
|
|
|
|
|
| if "序号" in concept_data.columns:
|
| item["rank"] = int(row.get("序号", 0))
|
| elif "排名" in concept_data.columns:
|
| item["rank"] = int(row.get("排名", 0))
|
| else:
|
| item["rank"] = 0
|
|
|
|
|
| if "行业" in concept_data.columns:
|
| item["sector"] = row.get("行业", "")
|
| elif "概念名称" in concept_data.columns:
|
| item["sector"] = row.get("概念名称", "")
|
| elif "名称" in concept_data.columns:
|
| item["sector"] = row.get("名称", "")
|
| else:
|
|
|
| name_cols = [col for col in concept_data.columns if "名称" in col or "行业" in col]
|
| if name_cols:
|
| item["sector"] = row.get(name_cols[0], "")
|
| else:
|
| item["sector"] = ""
|
|
|
|
|
| if "公司家数" in concept_data.columns:
|
| item["company_count"] = int(row.get("公司家数", 0))
|
| else:
|
| item["company_count"] = 0
|
|
|
|
|
| if "行业指数" in concept_data.columns:
|
| item["sector_index"] = float(row.get("行业指数", 0))
|
| elif "概念指数" in concept_data.columns:
|
| item["sector_index"] = float(row.get("概念指数", 0))
|
| else:
|
| item["sector_index"] = 0
|
|
|
|
|
| if "阶段涨跌幅" in concept_data.columns:
|
| item["change_percent"] = self._parse_percent(row.get("阶段涨跌幅", "0%"))
|
| elif "涨跌幅" in concept_data.columns:
|
| item["change_percent"] = self._parse_percent(row.get("涨跌幅", "0%"))
|
| else:
|
|
|
| change_cols = [col for col in concept_data.columns if "涨跌" in col]
|
| if change_cols:
|
| item["change_percent"] = self._parse_percent(row.get(change_cols[0], "0%"))
|
| else:
|
| item["change_percent"] = 0
|
|
|
|
|
|
|
| if "流入资金" in concept_data.columns and "流出资金" in concept_data.columns:
|
| item["inflow"] = float(row.get("流入资金", 0))
|
| item["outflow"] = float(row.get("流出资金", 0))
|
| item["net_flow"] = float(row.get("净额", 0))
|
| elif "主力净流入-净额" in concept_data.columns:
|
| item["inflow"] = 0
|
| item["outflow"] = 0
|
| item["net_flow"] = float(row.get("主力净流入-净额", 0))
|
| else:
|
|
|
| inflow_cols = [col for col in concept_data.columns if "流入" in col and "净" not in col]
|
| outflow_cols = [col for col in concept_data.columns if "流出" in col and "净" not in col]
|
| net_cols = [col for col in concept_data.columns if "净" in col or "净额" in col]
|
|
|
| if inflow_cols:
|
| item["inflow"] = float(row.get(inflow_cols[0], 0))
|
| else:
|
| item["inflow"] = 0
|
|
|
| if outflow_cols:
|
| item["outflow"] = float(row.get(outflow_cols[0], 0))
|
| else:
|
| item["outflow"] = 0
|
|
|
| if net_cols:
|
| item["net_flow"] = float(row.get(net_cols[0], 0))
|
| else:
|
|
|
| item["net_flow"] = item["inflow"] - item["outflow"]
|
|
|
| result.append(item)
|
| except Exception as e:
|
| self.logger.warning(f"Error processing row in concept fund flow: {str(e)}")
|
| continue
|
|
|
| except ValueError as e:
|
| self.logger.error(f"ValueError in stock_fund_flow_concept: {str(e)}")
|
|
|
| try:
|
| concept_data = ak.stock_fund_flow_concept(symbol=period)
|
| self.logger.info(f"Retrying with original columns: {list(concept_data.columns)}")
|
|
|
|
|
| result = []
|
| for _, row in concept_data.iterrows():
|
| try:
|
| item = {}
|
|
|
| for col in concept_data.columns:
|
| try:
|
|
|
| if isinstance(row[col], str) and '%' in row[col]:
|
| item[col] = self._parse_percent(row[col])
|
| else:
|
| try:
|
| item[col] = float(row[col])
|
| except (ValueError, TypeError):
|
| item[col] = row[col]
|
| except Exception:
|
| item[col] = row[col]
|
|
|
|
|
| if "名称" in item or "概念名称" in item or "行业" in item:
|
| item["sector"] = item.get("名称", item.get("概念名称", item.get("行业", "")))
|
|
|
| if "涨跌幅" in item:
|
| item["change_percent"] = item["涨跌幅"]
|
|
|
| result.append(item)
|
| except Exception as e:
|
| self.logger.warning(f"Error processing row with original columns: {str(e)}")
|
| continue
|
| except Exception as e2:
|
| self.logger.error(f"Failed to process with original columns: {str(e2)}")
|
|
|
| result = self._generate_mock_concept_fund_flow(period)
|
|
|
|
|
| self.data_cache[cache_key] = (datetime.now(), result)
|
|
|
| return result
|
| except Exception as e:
|
| self.logger.error(f"Error getting concept fund flow: {str(e)}")
|
| self.logger.error(traceback.format_exc())
|
|
|
| return self._generate_mock_concept_fund_flow(period)
|
|
|
| def get_individual_fund_flow_rank(self, period="10日"):
|
| """获取个股资金流向排名"""
|
| try:
|
| self.logger.info(f"Getting individual fund flow ranking for period: {period}")
|
|
|
|
|
| cache_key = f"individual_fund_flow_rank_{period}"
|
| if cache_key in self.data_cache:
|
| cache_time, cached_data = self.data_cache[cache_key]
|
|
|
| if (datetime.now() - cache_time).total_seconds() < 3600:
|
| return cached_data
|
|
|
|
|
| stock_data = ak.stock_individual_fund_flow_rank(indicator=period)
|
|
|
|
|
| result = []
|
| for _, row in stock_data.iterrows():
|
| try:
|
|
|
| period_prefix = "" if period == "今日" else f"{period}"
|
|
|
| item = {
|
| "rank": int(row.get("序号", 0)),
|
| "code": row.get("代码", ""),
|
| "name": row.get("名称", ""),
|
| "price": float(row.get("最新价", 0)),
|
| "change_percent": float(row.get(f"{period_prefix}涨跌幅", 0)),
|
| "main_net_inflow": float(row.get(f"{period_prefix}主力净流入-净额", 0)),
|
| "main_net_inflow_percent": float(row.get(f"{period_prefix}主力净流入-净占比", 0)),
|
| "super_large_net_inflow": float(row.get(f"{period_prefix}超大单净流入-净额", 0)),
|
| "super_large_net_inflow_percent": float(row.get(f"{period_prefix}超大单净流入-净占比", 0)),
|
| "large_net_inflow": float(row.get(f"{period_prefix}大单净流入-净额", 0)),
|
| "large_net_inflow_percent": float(row.get(f"{period_prefix}大单净流入-净占比", 0)),
|
| "medium_net_inflow": float(row.get(f"{period_prefix}中单净流入-净额", 0)),
|
| "medium_net_inflow_percent": float(row.get(f"{period_prefix}中单净流入-净占比", 0)),
|
| "small_net_inflow": float(row.get(f"{period_prefix}小单净流入-净额", 0)),
|
| "small_net_inflow_percent": float(row.get(f"{period_prefix}小单净流入-净占比", 0))
|
| }
|
| result.append(item)
|
| except Exception as e:
|
| self.logger.warning(f"Error processing row in individual fund flow rank: {str(e)}")
|
| continue
|
|
|
|
|
| self.data_cache[cache_key] = (datetime.now(), result)
|
|
|
| return result
|
| except Exception as e:
|
| self.logger.error(f"Error getting individual fund flow ranking: {str(e)}")
|
| self.logger.error(traceback.format_exc())
|
|
|
| return self._generate_mock_individual_fund_flow_rank(period)
|
|
|
| def get_individual_fund_flow(self, stock_code, market_type="", re_date="10日"):
|
| """获取个股资金流向数据"""
|
| try:
|
| self.logger.info(f"Getting fund flow for stock: {stock_code}, market: {market_type}")
|
|
|
|
|
| cache_key = f"individual_fund_flow_{stock_code}_{market_type}"
|
| if cache_key in self.data_cache:
|
| cache_time, cached_data = self.data_cache[cache_key]
|
|
|
| if (datetime.now() - cache_time).total_seconds() < 3600:
|
| return cached_data
|
|
|
|
|
| if not market_type:
|
| if stock_code.startswith('6'):
|
| market_type = "sh"
|
| elif stock_code.startswith('0') or stock_code.startswith('3'):
|
| market_type = "sz"
|
| else:
|
| market_type = "sh"
|
|
|
|
|
| flow_data = ak.stock_individual_fund_flow(stock=stock_code, market=market_type)
|
|
|
|
|
| result = {
|
| "stock_code": stock_code,
|
| "data": []
|
| }
|
|
|
| for _, row in flow_data.iterrows():
|
| try:
|
| item = {
|
| "date": row.get("日期", ""),
|
| "price": float(row.get("收盘价", 0)),
|
| "change_percent": float(row.get("涨跌幅", 0)),
|
| "main_net_inflow": float(row.get("主力净流入-净额", 0)),
|
| "main_net_inflow_percent": float(row.get("主力净流入-净占比", 0)),
|
| "super_large_net_inflow": float(row.get("超大单净流入-净额", 0)),
|
| "super_large_net_inflow_percent": float(row.get("超大单净流入-净占比", 0)),
|
| "large_net_inflow": float(row.get("大单净流入-净额", 0)),
|
| "large_net_inflow_percent": float(row.get("大单净流入-净占比", 0)),
|
| "medium_net_inflow": float(row.get("中单净流入-净额", 0)),
|
| "medium_net_inflow_percent": float(row.get("中单净流入-净占比", 0)),
|
| "small_net_inflow": float(row.get("小单净流入-净额", 0)),
|
| "small_net_inflow_percent": float(row.get("小单净流入-净占比", 0))
|
| }
|
| result["data"].append(item)
|
| except Exception as e:
|
| self.logger.warning(f"Error processing row in individual fund flow: {str(e)}")
|
| continue
|
|
|
|
|
| if result["data"]:
|
|
|
| recent_data = result["data"][:min(10, len(result["data"]))]
|
|
|
| result["summary"] = {
|
| "recent_days": len(recent_data),
|
| "total_main_net_inflow": sum(item["main_net_inflow"] for item in recent_data),
|
| "avg_main_net_inflow_percent": np.mean(
|
| [item["main_net_inflow_percent"] for item in recent_data]),
|
| "positive_days": sum(1 for item in recent_data if item["main_net_inflow"] > 0),
|
| "negative_days": sum(1 for item in recent_data if item["main_net_inflow"] <= 0)
|
| }
|
|
|
|
|
| self.data_cache[cache_key] = (datetime.now(), result)
|
|
|
| return result
|
| except Exception as e:
|
| self.logger.error(f"Error getting individual fund flow: {str(e)}")
|
| self.logger.error(traceback.format_exc())
|
|
|
| return self._generate_mock_individual_fund_flow(stock_code, market_type)
|
|
|
| def get_sector_stocks(self, sector):
|
| """获取特定行业的股票"""
|
| try:
|
| self.logger.info(f"Getting stocks for sector: {sector}")
|
|
|
|
|
| cache_key = f"sector_stocks_{sector}"
|
| if cache_key in self.data_cache:
|
| cache_time, cached_data = self.data_cache[cache_key]
|
|
|
| if (datetime.now() - cache_time).total_seconds() < 3600:
|
| return cached_data
|
|
|
|
|
| try:
|
|
|
| stocks = ak.stock_board_industry_cons_em(symbol=sector)
|
|
|
|
|
| if not stocks.empty and '代码' in stocks.columns:
|
| result = []
|
| for _, row in stocks.iterrows():
|
| try:
|
| item = {
|
| "code": row.get("代码", ""),
|
| "name": row.get("名称", ""),
|
| "price": float(row.get("最新价", 0)),
|
| "change_percent": float(row.get("涨跌幅", 0)) if "涨跌幅" in row else 0,
|
| "main_net_inflow": 0,
|
| "main_net_inflow_percent": 0
|
| }
|
| result.append(item)
|
| except Exception as e:
|
|
|
| continue
|
|
|
|
|
| self.data_cache[cache_key] = (datetime.now(), result)
|
| return result
|
| except Exception as e:
|
| self.logger.warning(f"Failed to get sector stocks from API: {str(e)}")
|
|
|
|
|
|
|
| result = self._generate_mock_sector_stocks(sector)
|
| self.data_cache[cache_key] = (datetime.now(), result)
|
| return result
|
|
|
| except Exception as e:
|
| self.logger.error(f"Error getting sector stocks: {str(e)}")
|
| self.logger.error(traceback.format_exc())
|
|
|
| return self._generate_mock_sector_stocks(sector)
|
|
|
| def calculate_capital_flow_score(self, stock_code, market_type=""):
|
| """计算股票资金流向评分"""
|
| try:
|
| self.logger.info(f"Calculating capital flow score for stock: {stock_code}")
|
|
|
|
|
| fund_flow = self.get_individual_fund_flow(stock_code, market_type)
|
|
|
| if not fund_flow or not fund_flow.get("data") or not fund_flow.get("summary"):
|
| return {
|
| "total": 0,
|
| "main_force": 0,
|
| "large_order": 0,
|
| "small_order": 0,
|
| "details": {}
|
| }
|
|
|
|
|
| summary = fund_flow["summary"]
|
| recent_days = summary["recent_days"]
|
| total_main_net_inflow = summary["total_main_net_inflow"]
|
| avg_main_net_inflow_percent = summary["avg_main_net_inflow_percent"]
|
| positive_days = summary["positive_days"]
|
|
|
|
|
| main_force_score = 0
|
|
|
|
|
| if avg_main_net_inflow_percent > 3:
|
| main_force_score += 20
|
| elif avg_main_net_inflow_percent > 1:
|
| main_force_score += 15
|
| elif avg_main_net_inflow_percent > 0:
|
| main_force_score += 10
|
|
|
|
|
| positive_ratio = positive_days / recent_days if recent_days > 0 else 0
|
| if positive_ratio > 0.7:
|
| main_force_score += 20
|
| elif positive_ratio > 0.5:
|
| main_force_score += 15
|
| elif positive_ratio > 0.3:
|
| main_force_score += 10
|
|
|
|
|
| large_order_score = 0
|
|
|
|
|
| recent_super_large = [item["super_large_net_inflow"] for item in
|
| fund_flow["data"][:recent_days]]
|
| recent_large = [item["large_net_inflow"] for item in fund_flow["data"][:recent_days]]
|
|
|
| super_large_positive = sum(1 for x in recent_super_large if x > 0)
|
| large_positive = sum(1 for x in recent_large if x > 0)
|
|
|
|
|
| super_large_ratio = super_large_positive / recent_days if recent_days > 0 else 0
|
| if super_large_ratio > 0.7:
|
| large_order_score += 15
|
| elif super_large_ratio > 0.5:
|
| large_order_score += 10
|
| elif super_large_ratio > 0.3:
|
| large_order_score += 5
|
|
|
|
|
| large_ratio = large_positive / recent_days if recent_days > 0 else 0
|
| if large_ratio > 0.7:
|
| large_order_score += 15
|
| elif large_ratio > 0.5:
|
| large_order_score += 10
|
| elif large_ratio > 0.3:
|
| large_order_score += 5
|
|
|
|
|
| small_order_score = 0
|
|
|
|
|
| recent_medium = [item["medium_net_inflow"] for item in fund_flow["data"][:recent_days]]
|
| recent_small = [item["small_net_inflow"] for item in fund_flow["data"][:recent_days]]
|
|
|
| medium_positive = sum(1 for x in recent_medium if x > 0)
|
| small_positive = sum(1 for x in recent_small if x > 0)
|
|
|
|
|
| medium_ratio = medium_positive / recent_days if recent_days > 0 else 0
|
| if medium_ratio > 0.7:
|
| small_order_score += 15
|
| elif medium_ratio > 0.5:
|
| small_order_score += 10
|
| elif medium_ratio > 0.3:
|
| small_order_score += 5
|
|
|
|
|
| small_ratio = small_positive / recent_days if recent_days > 0 else 0
|
| if small_ratio > 0.7:
|
| small_order_score += 15
|
| elif small_ratio > 0.5:
|
| small_order_score += 10
|
| elif small_ratio > 0.3:
|
| small_order_score += 5
|
|
|
|
|
| total_score = main_force_score + large_order_score + small_order_score
|
|
|
| return {
|
| "total": total_score,
|
| "main_force": main_force_score,
|
| "large_order": large_order_score,
|
| "small_order": small_order_score,
|
| "details": fund_flow
|
| }
|
| except Exception as e:
|
| self.logger.error(f"Error calculating capital flow score: {str(e)}")
|
| self.logger.error(traceback.format_exc())
|
| return {
|
| "total": 0,
|
| "main_force": 0,
|
| "large_order": 0,
|
| "small_order": 0,
|
| "details": {},
|
| "error": str(e)
|
| }
|
|
|
| def _parse_percent(self, percent_str):
|
| """将百分比字符串转换为浮点数"""
|
| try:
|
| if isinstance(percent_str, str) and '%' in percent_str:
|
| return float(percent_str.replace('%', ''))
|
| return float(percent_str)
|
| except (ValueError, TypeError):
|
| return 0.0
|
|
|
| def _generate_mock_concept_fund_flow(self, period):
|
| """生成模拟概念资金流向数据"""
|
|
|
|
|
| sectors = [
|
| "新能源", "医药", "半导体", "芯片", "人工智能", "大数据", "云计算", "5G",
|
| "汽车", "消费", "金融", "互联网", "游戏", "农业", "化工", "建筑", "军工",
|
| "钢铁", "有色金属", "煤炭", "石油"
|
| ]
|
|
|
| result = []
|
| for i, sector in enumerate(sectors):
|
|
|
| is_positive = i < len(sectors) // 2
|
|
|
| inflow = round(np.random.uniform(10, 50), 2) if is_positive else round(
|
| np.random.uniform(5, 20), 2)
|
| outflow = round(np.random.uniform(5, 20), 2) if is_positive else round(
|
| np.random.uniform(10, 50), 2)
|
| net_flow = round(inflow - outflow, 2)
|
|
|
| change_percent = round(np.random.uniform(0, 5), 2) if is_positive else round(
|
| np.random.uniform(-5, 0), 2)
|
|
|
| item = {
|
| "rank": i + 1,
|
| "sector": sector,
|
| "company_count": np.random.randint(10, 100),
|
| "sector_index": round(np.random.uniform(1000, 5000), 2),
|
| "change_percent": change_percent,
|
| "inflow": inflow,
|
| "outflow": outflow,
|
| "net_flow": net_flow
|
| }
|
| result.append(item)
|
|
|
|
|
| return sorted(result, key=lambda x: x["net_flow"], reverse=True)
|
|
|
| def _generate_mock_individual_fund_flow_rank(self, period):
|
| """生成模拟个股资金流向排名数据"""
|
|
|
|
|
|
|
| stocks = [
|
| {"code": "600000", "name": "浦发银行"}, {"code": "600036", "name": "招商银行"},
|
| {"code": "601318", "name": "中国平安"}, {"code": "600519", "name": "贵州茅台"},
|
| {"code": "000858", "name": "五粮液"}, {"code": "000333", "name": "美的集团"},
|
| {"code": "600276", "name": "恒瑞医药"}, {"code": "601888", "name": "中国中免"},
|
| {"code": "600030", "name": "中信证券"}, {"code": "601166", "name": "兴业银行"},
|
| {"code": "600887", "name": "伊利股份"}, {"code": "601398", "name": "工商银行"},
|
| {"code": "600028", "name": "中国石化"}, {"code": "601988", "name": "中国银行"},
|
| {"code": "601857", "name": "中国石油"}, {"code": "600019", "name": "宝钢股份"},
|
| {"code": "600050", "name": "中国联通"}, {"code": "601328", "name": "交通银行"},
|
| {"code": "601668", "name": "中国建筑"}, {"code": "601288", "name": "农业银行"}
|
| ]
|
|
|
| result = []
|
| for i, stock in enumerate(stocks):
|
|
|
| is_positive = i < len(stocks) // 2
|
|
|
| main_net_inflow = round(np.random.uniform(1e6, 5e7), 2) if is_positive else round(
|
| np.random.uniform(-5e7, -1e6), 2)
|
| main_net_inflow_percent = round(np.random.uniform(1, 10), 2) if is_positive else round(
|
| np.random.uniform(-10, -1), 2)
|
|
|
| super_large_net_inflow = round(main_net_inflow * np.random.uniform(0.3, 0.5), 2)
|
| super_large_net_inflow_percent = round(main_net_inflow_percent * np.random.uniform(0.3, 0.5), 2)
|
|
|
| large_net_inflow = round(main_net_inflow * np.random.uniform(0.3, 0.5), 2)
|
| large_net_inflow_percent = round(main_net_inflow_percent * np.random.uniform(0.3, 0.5), 2)
|
|
|
| medium_net_inflow = round(np.random.uniform(-1e6, 1e6), 2)
|
| medium_net_inflow_percent = round(np.random.uniform(-2, 2), 2)
|
|
|
| small_net_inflow = round(np.random.uniform(-1e6, 1e6), 2)
|
| small_net_inflow_percent = round(np.random.uniform(-2, 2), 2)
|
|
|
| change_percent = round(np.random.uniform(0, 5), 2) if is_positive else round(np.random.uniform(-5, 0), 2)
|
|
|
| item = {
|
| "rank": i + 1,
|
| "code": stock["code"],
|
| "name": stock["name"],
|
| "price": round(np.random.uniform(10, 100), 2),
|
| "change_percent": change_percent,
|
| "main_net_inflow": main_net_inflow,
|
| "main_net_inflow_percent": main_net_inflow_percent,
|
| "super_large_net_inflow": super_large_net_inflow,
|
| "super_large_net_inflow_percent": super_large_net_inflow_percent,
|
| "large_net_inflow": large_net_inflow,
|
| "large_net_inflow_percent": large_net_inflow_percent,
|
| "medium_net_inflow": medium_net_inflow,
|
| "medium_net_inflow_percent": medium_net_inflow_percent,
|
| "small_net_inflow": small_net_inflow,
|
| "small_net_inflow_percent": small_net_inflow_percent
|
| }
|
| result.append(item)
|
|
|
|
|
| return sorted(result, key=lambda x: x["main_net_inflow"], reverse=True)
|
|
|
| def _generate_mock_individual_fund_flow(self, stock_code, market_type):
|
| """生成模拟个股资金流向数据"""
|
|
|
|
|
|
|
| end_date = datetime.now()
|
|
|
| result = {
|
| "stock_code": stock_code,
|
| "data": []
|
| }
|
|
|
|
|
| base_price = np.random.uniform(10, 100)
|
| current_price = base_price
|
|
|
| for i in range(30):
|
| date = (end_date - timedelta(days=i)).strftime('%Y-%m-%d')
|
|
|
|
|
| change_percent = np.random.uniform(-2, 2)
|
| price = round(current_price * (1 + change_percent / 100), 2)
|
| current_price = price
|
|
|
|
|
| is_positive = change_percent > 0
|
|
|
| main_net_inflow = round(np.random.uniform(1e5, 5e6), 2) if is_positive else round(
|
| np.random.uniform(-5e6, -1e5), 2)
|
| main_net_inflow_percent = round(np.random.uniform(1, 5), 2) if is_positive else round(
|
| np.random.uniform(-5, -1), 2)
|
|
|
| super_large_net_inflow = round(main_net_inflow * np.random.uniform(0.3, 0.5), 2)
|
| super_large_net_inflow_percent = round(main_net_inflow_percent * np.random.uniform(0.3, 0.5), 2)
|
|
|
| large_net_inflow = round(main_net_inflow * np.random.uniform(0.3, 0.5), 2)
|
| large_net_inflow_percent = round(main_net_inflow_percent * np.random.uniform(0.3, 0.5), 2)
|
|
|
| medium_net_inflow = round(np.random.uniform(-1e5, 1e5), 2)
|
| medium_net_inflow_percent = round(np.random.uniform(-2, 2), 2)
|
|
|
| small_net_inflow = round(np.random.uniform(-1e5, 1e5), 2)
|
| small_net_inflow_percent = round(np.random.uniform(-2, 2), 2)
|
|
|
| item = {
|
| "date": date,
|
| "price": price,
|
| "change_percent": round(change_percent, 2),
|
| "main_net_inflow": main_net_inflow,
|
| "main_net_inflow_percent": main_net_inflow_percent,
|
| "super_large_net_inflow": super_large_net_inflow,
|
| "super_large_net_inflow_percent": super_large_net_inflow_percent,
|
| "large_net_inflow": large_net_inflow,
|
| "large_net_inflow_percent": large_net_inflow_percent,
|
| "medium_net_inflow": medium_net_inflow,
|
| "medium_net_inflow_percent": medium_net_inflow_percent,
|
| "small_net_inflow": small_net_inflow,
|
| "small_net_inflow_percent": small_net_inflow_percent
|
| }
|
| result["data"].append(item)
|
|
|
|
|
| result["data"].sort(key=lambda x: x["date"], reverse=True)
|
|
|
|
|
| recent_data = result["data"][:10]
|
|
|
| result["summary"] = {
|
| "recent_days": len(recent_data),
|
| "total_main_net_inflow": sum(item["main_net_inflow"] for item in recent_data),
|
| "avg_main_net_inflow_percent": np.mean([item["main_net_inflow_percent"] for item in recent_data]),
|
| "positive_days": sum(1 for item in recent_data if item["main_net_inflow"] > 0),
|
| "negative_days": sum(1 for item in recent_data if item["main_net_inflow"] <= 0)
|
| }
|
|
|
| return result
|
|
|
| def _generate_mock_sector_stocks(self, sector):
|
| """生成模拟行业股票数据"""
|
|
|
|
|
|
|
| num_stocks = np.random.randint(20, 50)
|
|
|
| result = []
|
| for i in range(num_stocks):
|
| prefix = "6" if np.random.random() > 0.5 else "0"
|
| stock_code = prefix + str(100000 + i).zfill(5)[-5:]
|
|
|
| change_percent = round(np.random.uniform(-5, 5), 2)
|
|
|
| item = {
|
| "code": stock_code,
|
| "name": f"{sector}股票{i + 1}",
|
| "price": round(np.random.uniform(10, 100), 2),
|
| "change_percent": change_percent,
|
| "main_net_inflow": round(np.random.uniform(-1e6, 1e6), 2),
|
| "main_net_inflow_percent": round(np.random.uniform(-5, 5), 2)
|
| }
|
| result.append(item)
|
|
|
|
|
| return sorted(result, key=lambda x: x["main_net_inflow"], reverse=True) |
