添加数据预处理

2025-07-25 14:42:38 +08:00
commit 607f7d2806
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+import pandas as pd
+from pymongo import MongoClient
+import numpy as np
+from datetime import datetime
+from concurrent.futures import ThreadPoolExecutor
+import statsmodels.api as sm
+# 数据库连接配置
+config = {
+    'host': 'www.bldcapital.cn',
+    'port': 27217,
+    'username': 'ZhangZH',
+    'password': 'S$#r)JAHE_2C',
+    'authSource': 'Alpha101',  # 指定认证数据库
+    'authMechanism': 'SCRAM-SHA-1'  # 指定认证机制
+}
+client = MongoClient(**config)
+client.admin.command('ping')
+print("成功连接到MongoDB服务器")  
+db = client['Alpha101']
+#取中证500只
+stocklist = pd.read_csv("C:/Users/86185/Desktop/实习/500stock.csv")  
+# 提取 "Stock Code" 列，转成列表
+selected = stocklist['Stock Code'].tolist()
+
+START = datetime(2020, 1, 1)
+END   = datetime(2021, 1, 1)
+projection={
+    '_id':0,
+    'alpha004':1,
+    'time':1
+    }
+def fetch(code: str) -> pd.DataFrame:
+    cur = db[code].find({'time': {'$gte': START, '$lt': END}}, projection)
+    df = pd.DataFrame(list(cur))
+    df['Code'] = code
+    return df
+
+with ThreadPoolExecutor(max_workers=32) as pool:
+    dfs = list(pool.map(fetch, selected))
+
+# 合并
+df_all = pd.concat(dfs, ignore_index=True)
+df_all['time'] = pd.to_datetime(df_all['time'])  # 转日期类型
+df_all_ = df_all.pivot(index='Code', columns='time', values='alpha004')
+df_all_.info()
+#---------------------去极值-------------------------
+def extreme_3sigma(dt,n=3):
+
+    mean = dt.mean()           # 截面数据均值
+    std = dt.std()             # 截面数据标准差
+    dt_up = mean + n*std       # 上限
+    dt_down = mean - n*std     # 下限
+    return dt.clip(dt_down, dt_up, axis=1)   # 超出上下限的值，赋值为上下限
+
+df1=extreme_3sigma(df_all_,3)
+print('step1 finished')
+#---------------------标准化-------------------------
+def standardize_z(dt):
+    mean = dt.mean()     #  截面数据均值
+    std = dt.std()       #  截面数据标准差
+    return (dt - mean)/std     # 标准化处理
+
+df2 = standardize_z(df1)
+print('step2 finished')
+#----------------------3.行业中性化--------------------------
+#1生成行业哑变量矩阵
+def generate_industry_dummy_matrix(csv_file_path):
+    # 从 CSV 文件中读取数据
+    industry= pd.read_csv(csv_file_path)
+    # 获取所有唯一的股票代码和行业名称
+    unique_stocks = industry['code'].unique()
+    unique_industries = industry['industry_name'].unique()
+    # 创建一个全 0 的 DataFrame 作为初始的哑变量矩阵
+    dummy_matrix = pd.DataFrame(0, index=unique_stocks, columns=unique_industries)
+    # 遍历每一行数据，将对应股票在其所属行业的位置赋值为 1
+    for index, row in industry.iterrows():
+        stock = row['code']
+        industry = row['industry_name']
+        dummy_matrix.loc[stock, industry] = 1
+    return dummy_matrix
+dummy_matrix= generate_industry_dummy_matrix('C:/Users/86185/Desktop/实习/industry.csv')
+##取股票交集
+market=pd.read_csv('C:/Users/86185/Desktop/实习/market.csv')
+market.set_index(market.columns[0], inplace=True)
+common_stocks = list(
+    set(df2.index) & 
+    set(dummy_matrix.index)&
+    set(market.index)
+)
+# 筛选三个数据框，仅保留共同的股票
+df2_f= df2.loc[common_stocks].copy()
+industry_f = dummy_matrix.loc[common_stocks].copy()
+market_f = market.loc[common_stocks].copy().astype(float)
+#2实现行业中性化
+def neutralization(factor, industry_matrix):
+    Y = factor
+    df= pd.DataFrame(index=Y.index, columns=Y.columns)
+    # 按日期循环进行行业中性化
+    for i in range(Y.shape[1]):
+        # 获取当天的因子值（删除缺失值）
+        y = Y.iloc[:, i].dropna()        
+        # 获取对应的行业哑变量（并确保与 y 索引一致）
+        X = industry_matrix.loc[y.index]     
+        # 添加常数项
+        X = sm.add_constant(X)     
+        # 执行线性回归
+        try:
+            result = sm.OLS(y, X).fit()
+            # 保存残差（即行业中性化后的因子值）
+            df.loc[y.index, Y.columns[i]] = result.resid
+        except Exception as e:
+            print(f"日期 {Y.columns[i]} 中性化失败: {e}")
+            # 失败时保留原始值或填充 NaN
+            df.loc[y.index, Y.columns[i]] = y    
+    return df
+df3=neutralization(df2_f, industry_f)
+print('step3 finished')
+#----------------------4.市值中性化--------------------------
+def neutralization_size(factor, market_cap):
+    Y = factor.astype(float)
+    df= pd.DataFrame(index=Y.index, columns=Y.columns)    
+    for i in range(Y.shape[1]):
+        # 获取当天的因子值（删除缺失值）
+        y = Y.iloc[:, i].dropna()
+        # 获取当天的市值并计算对数（删除缺失值和非正数）
+        ln_mkt_cap = np.log(market_cap.iloc[:, i])
+        ln_mkt_cap = ln_mkt_cap.replace([np.inf, -np.inf], np.nan).dropna()
+        # 确保市值和因子的股票索引一致
+        common_idx = list(set(y.index).intersection(set(ln_mkt_cap.index)))
+        y = y.loc[common_idx]
+        ln_mkt_cap = ln_mkt_cap.loc[common_idx]
+        # 添加常数项
+        X = sm.add_constant(ln_mkt_cap)
+        # 执行线性回归
+        try:
+            result = sm.OLS(y, X).fit()
+            # 保存残差（即市值中性化后的因子值）
+            df.loc[common_idx, Y.columns[i]] = result.resid
+        except Exception as e:
+            print(f"日期 {Y.columns[i]} 中性化失败: {e}")
+            # 失败时保留原始值或填充 NaN
+            df.loc[common_idx, Y.columns[i]] = y
+    
+    return df
+df4=neutralization_size(df3, market_f)
+df4.to_csv('df4.csv')
+print('step4 finished')