多因子相关性

2023-10-12 09:27:34

factor = “return_on_invested_capital”

----------------1. 准备因子数据----------------

1.1 获取因子数据

获取这一年的交易日期

date_data = get_trading_dates(start_date=“2020-01-01”, end_date=“2021-01-01”)

定义df数据集

all_data = pd.DataFrame()

for date in date_data:
# 获取当天因子数据
q = query(
fundamentals.financial_indicator.return_on_invested_capital
)

# 获取截面数据
fund = get_fundamentals(q, entry_date=date).iloc[:, 0, :]

# 创建日期列
fund["date"] = date

# 拼接
all_data = pd.concat([all_data, fund])

设置双重索引, 变成一个MultiIndex DataFrame

multiindex_df = all_data.set_index([“date”, all_data.index])

1.2 处理因子数据 (去极值, 标准化)

def mad(factor):
“”“3倍中位数去极值”""

# 求出因子值的中位数
median = np.median(factor)

# 求出因子值与中位数的差值, 进行绝对值
mad = np.median(abs(factor - median))

# 定义几倍的中位数上下限
high = median + (3 * 1.4826 * mad)
low = median - (3 * 1.4826 * mad)

# 替换上下限
factor = np.where(factor > high, high, factor)
factor = np.where(factor < low, low, factor)
return factor

def stand(factor):
“”“数据标准化”""
mean = factor.mean(http://www.yesedata.com/)
std = factor.std()
return (factor - mean) / std

multiindex_df[factor] = mad(multiindex_df[factor])
multiindex_df[factor] = stand(multiindex_df[factor])

----------------2. 准备收盘价数据----------------

转换为Series

singlefactor_series = multiindex_df[factor]

获取所有股票基础信息

stocks = all_instruments(“CS”)

得到合约代码

stocks_list = stocks[“order_book_id”]

获取收盘价

price = get_price(stocks_list ,start_date=“2020-01-01”, end_date=“2021-01-01”, fields=“close”)

----------------3. 生成通用Alphalens数据----------------

factor_return = utils.get_clean_factor_and_forward_returns(singlefactor_series.astype(float), price.astype(float))

----------------4. 生成通用Alphalens数据----------------

IC 值, 默认每天的IC结构

return_invested_capital_IC = performance.factor_information_coefficient(factor_return)

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