# -*- coding: utf-8 -*-
"""
File Name： factor_factory_py
Author : k0180110
Modify Date: 2019-11-13
"""
import pandas as pd
import numpy as np
from sklearn.linear_model import LinearRegression
class Func:
def dtm(self, open, high):
condition1 = open[open.diff(1) < 0]
condition2 = open[open.diff(1) > 0]
open = self.max_df((high * condition2 - open * condition2), (open) * condition2.diff(1))
return open
def dbm(self, open, low):
condition2 = open[open.diff(1) > 0]
open = self.max_df((open * condition2 - low * condition2), (open) * condition2.diff(1))
return open
def max_df(self, df1, df2):
df3 = pd.concat([df1, df2], axis=0)
result = df3.max()
return result
def tr(self, high, low, close):
part1 = high - low
part2 = self.abs((high - self.delay(close)))
part3 = self.max_df(part1, part2)
part4 = low - self.delay(close)
result = self.max_df(part3, part4)
return result
def hd(self, high):
result = high - self.delay(high)
return result
def ld(self, low):
result = self.delay(low) - low
return result
def count(self, df, window=10, condition=True):
"""对df前n项条件求数量，df所有数据置为1，其中condition表示选择条件"""
df.iloc[:, :] = 1
return self.ts_sum(df * condition, window)
def FILTER(self, df, condition=True):
return df * condition
# return df[condition]
def fama(self):
pass
def sumac_df(self, df):
"""序列df过去n天累乘"""
return df.cumsum()
def rolling_decay(self, df):
"""用于wma函数"""
weight = range(1, len(df) + 1)[::-1] / np.array(range(1, len(df) + 1)).sum()
weight = np.array(weight).reshape(1, -1)
return np.dot(weight, np.asarray(df))
def decayliner(self, df, window):
df.fillna(method='ffill', inplace=True)
df.fillna(method='bfill', inplace=True)
df.fillna(value=0, inplace=True)
return df.rolling(window).apply(self.rolling_decay, raw=True)
def highday(self, df):
df.index = range(len(df))[::-1]
df = df[df == df.max()]
temp_s = pd.Series(index=df.columns)
for col in df.columns:
temp_s[col] = df[col].dropna().index.to_list()[0]
return temp_s
def lowday(self, df):
df.index = range(len(df))[::-1]
df = df[df == df.min()]
temp_s = pd.Series(index=df.columns)
for col in df.columns:
temp_s[col] = df[col].dropna().index.to_list()[0]
return temp_s
def rolling_wma(self, df):
"""用于wma函数"""
# a = [0.9 ** i for i in range(1, n + 1)] # 以前的
# return sum([a[i] * list1[-i - 1] for i in range(0, len(a))]) / sum(a)
# 网金的
# weight = (self.sequence(len(df)) - 1)[::-1] * 0.9 * 2 / (len(df) * (len(df) + 1)) # 这个为什么 时间距离越久，权重提高越高
# 自己的 网金这个为什么 时间距离越久，权重提高越高,惩罚近期数据的影响？
weight = (self.sequence(len(df)) - 1)[::-1] * 0.9 * 2 / (len(df) * (len(df) + 1))
weight = np.array(weight).reshape(1, -1)
return np.dot(weight, np.asarray(df))
def wma_df(self, df, window):
df.fillna(method='ffill', inplace=True)
df.fillna(method='bfill', inplace=True)
df.fillna(value=0, inplace=True)
result = df.rolling(window).apply(self.rolling_wma, raw=True)
return result
""" 废弃
def wma(self, df, period=10):
# 算df前period期样本加权平均值权重为0.9i，(i表示样本距离当前时点的间隔)
return df.rolling(period).apply(self.rolling_wma, raw=True)
def WMA(list1, n):
# need：(list,number) return：number 计算A前n期样本加权平均值
a = [0.9 ** i for i in range(1, n + 1)]
return sum([a[i] * list1[-i - 1] for i in range(0, len(a))]) / sum(a)
"""
def rank_df(self, df):
return df.rank(pct=True)
def ts_rank(self, df, window=10):
"""序列df的末位值在过去n天的顺序排位"""
return df.iloc[-1 * window:].rank(pct=True)
return df.rolling(window).apply(self.rank_df)
def ts_min(self, df, window=10):
"""序列df过去n天的最小值"""
return df.rolling(window).min()
def ts_max(self, df, window=10):
"""序列df过去n天的最大值"""
return df.rolling(window).max()
def delay(self, df, period=1):
"""df延迟period长度的值"""
return df.shift(period)
def ts_sum(self, df, window=10):
"""序列df过去n天求和"""
return df.rolling(window).sum()
def sumif(self, df, window=10, condition=True):
"""对df前n项条件求和，其中condition表示选择条件"""
return self.ts_sum(df * condition, window)
def sign(self, df):
"""对df取符号函数"""
return np.sign(df)
def sequence(self, n):
"""生成 1~n 的等差序列"""
return np.asarray(range(1, n + 1))
def rank(self, df):
"""向量df升序排序"""
return df.rank(axis=0, pct=True)
def rolling_prod(self, na):
"""prod 的辅助函数"""
return np.prod(na)
def prod(self, df, window=10):
"""序列df过去n天累乘"""
return df.rolling(window).apply(self.rolling_prod, raw=True)
def mean(self, df, window=10):
"""序列df过去n天均值"""
return df.rolling(window).mean()
# ------------------已用
def stddev(self, df, window=10):
"""序列df过去n天标准差"""
return df.rolling(window).std()
def correlation(self, df1, df2, windows=10):
result = df1.rolling(windows).corr(df2)
return result
def coviance(self, df1, df2, windows=10):
result = df1.rolling(windows).cov(df2)
return result
def abs(self, df):
return df.abs()
"""
def sma(self, df, n, m):
# Yi+1 =(dfi*m+Yi*(n-m))/n，其中Y表示最终结果
# Clean data
if pd.Series(df).isnull().any():
df.fillna(method='ffill', inplace=True)
df.fillna(method='bfill', inplace=True)
df.fillna(value=0, inplace=True)
y = [list(df)[0]]
for x in range(0, len(list(df)) - 1):
y.append((list(df)[x] * m + y[-1] * (n - m)) / n)
return y
for col in close.columns:
close[col] = self.sma(close[col], 3, 1)
"""
def sma_df(self, df, n, m):
"""Yi+1 =(dfi*m+Yi*(n-m))/n，其中Y表示最终结果"""
df.fillna(method='ffill', inplace=True)
df.fillna(method='bfill', inplace=True)
df.fillna(value=0, inplace=True)
y = df.iloc[0:1]
for x in range(0, len(df) - 1):
y = y.append((df.iloc[x] * m / n) + (y.iloc[-1] * (n - m) / n), ignore_index=True)
y.index = df.index
return y
# 调用方式
# qqq = self.sma_df(close, 3, 1)
# 是否用ols，是否加常数项
def reg_beta(self, x_df, y_df):
# 传进来的是df，循环每一列和df_b做回归
x_train = np.array(x_df).reshape(-1, 1)
y_train = np.array(y_df).reshape(-1, 1)
linreg = LinearRegression()
linreg.fit(x_train, y_train)
result = float(linreg.coef_)
return result
# return float(linreg.intercept_)
def reg_sigma(self, x_df, y_df):
# 传进来的是各自为一列的df
x_train = np.array(x_df).reshape(-1, 1)
y_train = np.array(y_df).reshape(-1, 1)
linreg = LinearRegression()
linreg.fit(x_train, y_train)
y_ = linreg.predict(x_train)
result = (y_train - y_).std()
return result
def ts_argmax(self, df):
"""用于highday函数"""
# 这个为什么再-1
return len(df) - np.argmax(df) - 1
def highday(self, df, window=10):
return df.rolling(window).apply(self.ts_argmax, raw=True)
def ts_argmin(self, df):
"""用于highday函数"""
# 这个为什么再-1
return len(df) - np.argmax(df) - 1
def lowday(self, df, window=10):
return df.rolling(window).apply(self.ts_argmin, raw=True)
def fama(self, close, mv, pb, ):
ret = close.pct_change(periods=1).fillna(0.0)
mkt_ret = (ret * mv).sum(axis=1) / mv.sum(axis=1)
me30 = (mv.T <= mv.quantile(0.3, axis=1)).T
me70 = (mv.T >= mv.quantile(0.7, axis=1)).T
pb30 = (pb.T <= pb.quantile(0.3, axis=1)).T
pb70 = (pb.T >= pb.quantile(0.7, axis=1)).T
smb_ret = ret[me30].mean(axis=1, skipna=True) - ret[me70].mean(axis=1, skipna=True)
hml_ret = ret[pb70].mean(axis=1, skipna=True) - ret[pb30].mean(axis=1, skipna=True)
result = pd.concat([mkt_ret, smb_ret, hml_ret], axis=1)
return result
rank
我的理解是3000只股票的结果值进行排序
ts_rank
我的理解是每只股票按天按天进行排序
wma
question1：文档应该就是时间距离现在越久，权重越高，是否是这样理解？
question2：是直接取最后一个加权得出来的值an，还是取所有值的平均值(a1+a2+a3+...+an)/n
reg_beta
question1：是否用ols，是否加常数项？
fama
question1：这个函数能帮忙发我下吗？有点复杂，我想对照代码理解下，