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#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
数据库探索和自定义因子生成工具
基于现有的股票数据库表结构，提供数据查看和因子计算功能
"""
import pymysql
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
import warnings
warnings.filterwarnings('ignore')
class DatabaseExplorer:
"""数据库探索器"""
def __init__(self, host='localhost', user='root', password='root',
database='stock_cursor', charset='utf8mb4'):
"""初始化数据库连接"""
self.host = host
self.user = user
self.password = password
self.database = database
self.charset = charset
self.connection = None
def connect(self):
"""建立数据库连接"""
try:
self.connection = pymysql.connect(
host=self.host,
user=self.user,
password=self.password,
database=self.database,
charset=self.charset,
cursorclass=pymysql.cursors.DictCursor
)
print(f"✅ 成功连接到数据库: {self.database}")
return True
except Exception as e:
print(f"❌ 数据库连接失败: {e}")
return False
def close(self):
"""关闭数据库连接"""
if self.connection:
self.connection.close()
print("🔒 数据库连接已关闭")
def show_tables(self):
"""显示所有表"""
if not self.connection:
if not self.connect():
return None
try:
with self.connection.cursor() as cursor:
cursor.execute("SHOW TABLES")
tables = cursor.fetchall()
print("\n📊 数据库表列表:")
print("=" * 50)
for i, table in enumerate(tables, 1):
table_name = list(table.values())[0]
print(f"{i:2d}. {table_name}")
return [list(table.values())[0] for table in tables]
except Exception as e:
print(f"❌ 获取表列表失败: {e}")
return None
def describe_table(self, table_name):
"""查看表结构"""
if not self.connection:
if not self.connect():
return None
try:
with self.connection.cursor() as cursor:
cursor.execute(f"DESCRIBE {table_name}")
columns = cursor.fetchall()
# 获取表注释
cursor.execute(f"""
SELECT TABLE_COMMENT
FROM information_schema.TABLES
WHERE TABLE_SCHEMA = '{self.database}'
AND TABLE_NAME = '{table_name}'
""")
table_comment = cursor.fetchone()
print(f"\n📋 表结构: {table_name}")
if table_comment and table_comment['TABLE_COMMENT']:
print(f"📝 表说明: {table_comment['TABLE_COMMENT']}")
print("=" * 80)
df = pd.DataFrame(columns)
print(df.to_string(index=False))
return df
except Exception as e:
print(f"❌ 查看表结构失败: {e}")
return None
def get_table_sample(self, table_name, limit=5):
"""获取表的样本数据"""
if not self.connection:
if not self.connect():
return None
try:
query = f"SELECT * FROM {table_name} LIMIT {limit}"
df = pd.read_sql(query, self.connection)
print(f"\n📊 表 {table_name} 样本数据 (前{limit}行):")
print("=" * 100)
print(df.to_string(index=False))
return df
except Exception as e:
print(f"❌ 获取样本数据失败: {e}")
return None
def get_table_stats(self, table_name):
"""获取表的统计信息"""
if not self.connection:
if not self.connect():
return None
try:
with self.connection.cursor() as cursor:
# 获取行数
cursor.execute(f"SELECT COUNT(*) as row_count FROM {table_name}")
row_count = cursor.fetchone()['row_count']
# 获取表大小
cursor.execute(f"""
SELECT
ROUND(((data_length + index_length) / 1024 / 1024), 2) AS size_mb
FROM information_schema.tables
WHERE table_schema = '{self.database}'
AND table_name = '{table_name}'
""")
size_info = cursor.fetchone()
print(f"\n📈 表 {table_name} 统计信息:")
print("=" * 40)
print(f"📊 总行数: {row_count:,}")
print(f"💾 表大小: {size_info['size_mb']} MB")
return {
'row_count': row_count,
'size_mb': size_info['size_mb']
}
except Exception as e:
print(f"❌ 获取统计信息失败: {e}")
return None
class CustomFactorGenerator:
"""自定义因子生成器"""
def __init__(self, db_explorer):
"""初始化因子生成器"""
self.db = db_explorer
def calculate_price_momentum_factors(self, ts_code=None, start_date=None, end_date=None):
"""计算价格动量因子"""
print("\n🚀 计算价格动量因子...")
# 构建查询条件
where_conditions = []
if ts_code:
where_conditions.append(f"ts_code = '{ts_code}'")
if start_date:
where_conditions.append(f"trade_date >= '{start_date}'")
if end_date:
where_conditions.append(f"trade_date <= '{end_date}'")
where_clause = " AND ".join(where_conditions) if where_conditions else "1=1"
query = f"""
SELECT
ts_code,
trade_date,
close,
pre_close,
pct_chg,
vol,
amount,
-- 动量因子
LAG(close, 5) OVER (PARTITION BY ts_code ORDER BY trade_date) as close_5d_ago,
LAG(close, 10) OVER (PARTITION BY ts_code ORDER BY trade_date) as close_10d_ago,
LAG(close, 20) OVER (PARTITION BY ts_code ORDER BY trade_date) as close_20d_ago,
LAG(close, 60) OVER (PARTITION BY ts_code ORDER BY trade_date) as close_60d_ago,
-- 成交量动量
AVG(vol) OVER (PARTITION BY ts_code ORDER BY trade_date ROWS BETWEEN 4 PRECEDING AND CURRENT ROW) as vol_ma5,
AVG(vol) OVER (PARTITION BY ts_code ORDER BY trade_date ROWS BETWEEN 19 PRECEDING AND CURRENT ROW) as vol_ma20,
-- 价格波动率
STDDEV(pct_chg) OVER (PARTITION BY ts_code ORDER BY trade_date ROWS BETWEEN 19 PRECEDING AND CURRENT ROW) as volatility_20d
FROM stock_daily_history
WHERE {where_clause}
ORDER BY ts_code, trade_date
"""
try:
df = pd.read_sql(query, self.db.connection)
# 计算动量因子
df['momentum_5d'] = (df['close'] / df['close_5d_ago'] - 1) * 100
df['momentum_10d'] = (df['close'] / df['close_10d_ago'] - 1) * 100
df['momentum_20d'] = (df['close'] / df['close_20d_ago'] - 1) * 100
df['momentum_60d'] = (df['close'] / df['close_60d_ago'] - 1) * 100
# 成交量比率
df['volume_ratio'] = df['vol'] / df['vol_ma20']
# 相对强弱指标
df['rsi_momentum'] = df['momentum_20d'] / df['volatility_20d']
print(f"✅ 成功计算 {len(df)} 条动量因子数据")
return df
except Exception as e:
print(f"❌ 计算动量因子失败: {e}")
return None
def calculate_fundamental_factors(self, ts_code=None, end_date=None):
"""计算基本面因子"""
print("\n📊 计算基本面因子...")
where_conditions = []
if ts_code:
where_conditions.append(f"i.ts_code = '{ts_code}'")
if end_date:
where_conditions.append(f"i.end_date <= '{end_date}'")
where_clause = " AND ".join(where_conditions) if where_conditions else "1=1"
query = f"""
SELECT
i.ts_code,
i.end_date,
i.total_revenue,
i.revenue,
i.n_income,
i.n_income_attr_p,
i.basic_eps,
i.operate_profit,
i.total_profit,
b.total_assets,
b.total_liab,
b.total_hldr_eqy_inc_min_int as total_equity,
b.money_cap,
b.accounts_receiv,
b.inventories,
b.fix_assets,
c.n_cashflow_act as operating_cashflow,
c.n_cashflow_inv_act as investing_cashflow,
c.n_cash_flows_fnc_act as financing_cashflow,
c.free_cashflow,
-- 计算财务比率
CASE WHEN b.total_assets > 0 THEN i.n_income_attr_p / b.total_assets * 100 ELSE NULL END as roa,
CASE WHEN b.total_hldr_eqy_inc_min_int > 0 THEN i.n_income_attr_p / b.total_hldr_eqy_inc_min_int * 100 ELSE NULL END as roe,
CASE WHEN b.total_assets > 0 THEN b.total_liab / b.total_assets * 100 ELSE NULL END as debt_ratio,
CASE WHEN i.revenue > 0 THEN i.n_income_attr_p / i.revenue * 100 ELSE NULL END as net_margin
FROM stock_income_statement i
LEFT JOIN stock_balance_sheet b ON i.ts_code = b.ts_code AND i.end_date = b.end_date
LEFT JOIN stock_cash_flow c ON i.ts_code = c.ts_code AND i.end_date = c.end_date
WHERE {where_clause}
ORDER BY i.ts_code, i.end_date
"""
try:
df = pd.read_sql(query, self.db.connection)
# 计算增长率因子
df = df.sort_values(['ts_code', 'end_date'])
df['revenue_growth'] = df.groupby('ts_code')['revenue'].pct_change(4) * 100 # 同比增长
df['profit_growth'] = df.groupby('ts_code')['n_income_attr_p'].pct_change(4) * 100
df['asset_growth'] = df.groupby('ts_code')['total_assets'].pct_change(4) * 100
# 计算资产周转率
df['asset_turnover'] = df['revenue'] / df['total_assets']
# 计算现金流质量
df['cashflow_quality'] = df['operating_cashflow'] / df['n_income_attr_p']
print(f"✅ 成功计算 {len(df)} 条基本面因子数据")
return df
except Exception as e:
print(f"❌ 计算基本面因子失败: {e}")
return None
def calculate_technical_factors(self, ts_code=None, start_date=None, end_date=None):
"""计算技术面因子"""
print("\n📈 计算技术面因子...")
where_conditions = []
if ts_code:
where_conditions.append(f"ts_code = '{ts_code}'")
if start_date:
where_conditions.append(f"trade_date >= '{start_date}'")
if end_date:
where_conditions.append(f"trade_date <= '{end_date}'")
where_clause = " AND ".join(where_conditions) if where_conditions else "1=1"
query = f"""
SELECT
ts_code,
trade_date,
close,
high,
low,
vol,
macd_dif,
macd_dea,
macd,
kdj_k,
kdj_d,
kdj_j,
rsi_6,
rsi_12,
rsi_24,
boll_upper,
boll_mid,
boll_lower,
cci
FROM stock_factor
WHERE {where_clause}
ORDER BY ts_code, trade_date
"""
try:
df = pd.read_sql(query, self.db.connection)
# 计算布林带位置
df['boll_position'] = (df['close'] - df['boll_lower']) / (df['boll_upper'] - df['boll_lower'])
# 计算MACD信号强度
df['macd_signal_strength'] = abs(df['macd_dif'] - df['macd_dea'])
# 计算KDJ超买超卖信号
df['kdj_overbought'] = (df['kdj_k'] > 80) & (df['kdj_d'] > 80)
df['kdj_oversold'] = (df['kdj_k'] < 20) & (df['kdj_d'] < 20)
# 计算RSI综合信号
df['rsi_divergence'] = df['rsi_6'] - df['rsi_24']
# 计算技术指标一致性
conditions = [
df['macd'] > 0,
df['kdj_k'] > 50,
df['rsi_12'] > 50,
df['boll_position'] > 0.5
]
df['technical_consensus'] = sum(conditions)
print(f"✅ 成功计算 {len(df)} 条技术面因子数据")
return df
except Exception as e:
print(f"❌ 计算技术面因子失败: {e}")
return None
def calculate_market_microstructure_factors(self, ts_code=None, start_date=None, end_date=None):
"""计算市场微观结构因子"""
print("\n🔬 计算市场微观结构因子...")
where_conditions = []
if ts_code:
where_conditions.append(f"m.ts_code = '{ts_code}'")
if start_date:
where_conditions.append(f"m.trade_date >= '{start_date}'")
if end_date:
where_conditions.append(f"m.trade_date <= '{end_date}'")
where_clause = " AND ".join(where_conditions) if where_conditions else "1=1"
query = f"""
SELECT
m.ts_code,
m.trade_date,
m.buy_sm_amount,
m.sell_sm_amount,
m.buy_md_amount,
m.sell_md_amount,
m.buy_lg_amount,
m.sell_lg_amount,
m.buy_elg_amount,
m.sell_elg_amount,
m.net_mf_amount,
d.turnover_rate,
d.volume_ratio,
d.pe,
d.pb,
d.total_mv,
d.circ_mv
FROM stock_moneyflow m
LEFT JOIN stock_daily_basic d ON m.ts_code = d.ts_code AND m.trade_date = d.trade_date
WHERE {where_clause}
ORDER BY m.ts_code, m.trade_date
"""
try:
df = pd.read_sql(query, self.db.connection)
# 计算资金流向因子
df['total_buy'] = df['buy_sm_amount'] + df['buy_md_amount'] + df['buy_lg_amount'] + df['buy_elg_amount']
df['total_sell'] = df['sell_sm_amount'] + df['sell_md_amount'] + df['sell_lg_amount'] + df['sell_elg_amount']
# 大单净流入比例
df['large_order_ratio'] = (df['buy_lg_amount'] + df['buy_elg_amount'] - df['sell_lg_amount'] - df['sell_elg_amount']) / df['total_mv']
# 主力资金净流入强度
df['main_force_intensity'] = df['net_mf_amount'] / df['circ_mv']
# 散户资金比例
df['retail_ratio'] = (df['buy_sm_amount'] - df['sell_sm_amount']) / df['total_buy']
# 资金流向一致性
df['money_flow_consistency'] = (
(df['buy_lg_amount'] > df['sell_lg_amount']).astype(int) +
(df['buy_md_amount'] > df['sell_md_amount']).astype(int) +
(df['net_mf_amount'] > 0).astype(int)
)
print(f"✅ 成功计算 {len(df)} 条市场微观结构因子数据")
return df
except Exception as e:
print(f"❌ 计算市场微观结构因子失败: {e}")
return None
def save_custom_factors(self, factor_data, factor_type, factor_name):
"""保存自定义因子到数据库"""
print(f"\n💾 保存自定义因子: {factor_name}")
try:
# 首先在factor_definition表中定义因子
factor_id = f"{factor_type}_{factor_name}_{datetime.now().strftime('%Y%m%d')}"
with self.db.connection.cursor() as cursor:
# 插入因子定义
insert_definition = """
INSERT INTO factor_definition
(factor_id, factor_name, factor_formula, factor_type, description, is_active)
VALUES (%s, %s, %s, %s, %s, %s)
ON DUPLICATE KEY UPDATE
factor_name = VALUES(factor_name),
factor_formula = VALUES(factor_formula),
description = VALUES(description),
updated_at = CURRENT_TIMESTAMP
"""
cursor.execute(insert_definition, (
factor_id,
factor_name,
f"Custom {factor_type} factor calculation",
factor_type,
f"自定义{factor_type}因子: {factor_name}",
1
))
# 保存因子值（这里需要根据具体的factor_data结构来调整）
# 假设factor_data包含ts_code, trade_date和因子值列
if 'ts_code' in factor_data.columns and 'trade_date' in factor_data.columns:
for _, row in factor_data.iterrows():
# 这里可以选择保存哪些计算出的因子值
# 示例：保存第一个数值型列作为因子值
numeric_cols = factor_data.select_dtypes(include=[np.number]).columns
if len(numeric_cols) > 0:
factor_value = row[numeric_cols[0]]
insert_value = """
INSERT INTO factor_values
(ts_code, trade_date, factor_id, factor_value)
VALUES (%s, %s, %s, %s)
ON DUPLICATE KEY UPDATE
factor_value = VALUES(factor_value)
"""
cursor.execute(insert_value, (
row['ts_code'],
row['trade_date'],
factor_id,
float(factor_value) if pd.notna(factor_value) else None
))
self.db.connection.commit()
print(f"✅ 成功保存因子: {factor_id}")
except Exception as e:
print(f"❌ 保存因子失败: {e}")
self.db.connection.rollback()
def main():
"""主函数 - 演示数据库探索和因子计算功能"""
print("🚀 股票数据库探索和自定义因子生成工具")
print("=" * 60)
# 初始化数据库探索器
db_explorer = DatabaseExplorer()
if not db_explorer.connect():
return
try:
# 1. 显示所有表
tables = db_explorer.show_tables()
# 2. 查看几个重要表的结构
important_tables = ['stock_basic', 'stock_daily_history', 'stock_factor', 'stock_moneyflow']
for table in important_tables:
if table in tables:
print(f"\n{'='*20} {table} {'='*20}")
db_explorer.describe_table(table)
db_explorer.get_table_stats(table)
db_explorer.get_table_sample(table, 3)
# 3. 初始化因子生成器
factor_generator = CustomFactorGenerator(db_explorer)
# 4. 计算各类自定义因子
print("\n" + "="*60)
print("🧮 开始计算自定义因子")
print("="*60)
# 选择一个股票进行演示
sample_stock = "000001.SZ" # 平安银行
end_date = "2024-01-31"
start_date = "2023-01-01"
# 计算动量因子
momentum_factors = factor_generator.calculate_price_momentum_factors(
ts_code=sample_stock,
start_date=start_date,
end_date=end_date
)
if momentum_factors is not None and not momentum_factors.empty:
print("\n📊 动量因子样本:")
print(momentum_factors[['ts_code', 'trade_date', 'momentum_5d', 'momentum_20d', 'volume_ratio']].tail())
# 计算基本面因子
fundamental_factors = factor_generator.calculate_fundamental_factors(
ts_code=sample_stock,
end_date=end_date
)
if fundamental_factors is not None and not fundamental_factors.empty:
print("\n📊 基本面因子样本:")
print(fundamental_factors[['ts_code', 'end_date', 'roa', 'roe', 'revenue_growth', 'profit_growth']].tail())
# 计算技术面因子
technical_factors = factor_generator.calculate_technical_factors(
ts_code=sample_stock,
start_date=start_date,
end_date=end_date
)
if technical_factors is not None and not technical_factors.empty:
print("\n📊 技术面因子样本:")
print(technical_factors[['ts_code', 'trade_date', 'boll_position', 'macd_signal_strength', 'technical_consensus']].tail())
# 计算市场微观结构因子
microstructure_factors = factor_generator.calculate_market_microstructure_factors(
ts_code=sample_stock,
start_date=start_date,
end_date=end_date
)
if microstructure_factors is not None and not microstructure_factors.empty:
print("\n📊 市场微观结构因子样本:")
print(microstructure_factors[['ts_code', 'trade_date', 'large_order_ratio', 'main_force_intensity', 'money_flow_consistency']].tail())
print("\n✅ 数据库探索和因子计算完成!")
print("\n💡 提示:")
print("1. 可以修改 sample_stock 变量来分析不同股票")
print("2. 可以调整日期范围来获取不同时期的数据")
print("3. 可以在各个因子计算函数中添加更多自定义因子")
print("4. 使用 save_custom_factors() 方法可以将计算结果保存到数据库")
except Exception as e:
print(f"❌ 程序执行出错: {e}")
finally:
db_explorer.close()
if __name__ == "__main__":
main()
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