量化交易的单因子分析——聚宽平台
'''-------api调用聚宽-----'''
# 导入函数库
from jqdata import *
import datetime
import numpy as np
import pandas as pd
#模型选择
from sklearn.model_selection import train_test_split,cross_val_score
#数据处理
from sklearn.preprocessing import StandardScaler
# from import KBinsDiscretizer
#填充缺失值
# from import SimpleImputer
#特征选择
from sklearn.feature_selection import SelectKBest,chi2,f_regression,SelectPercentile,RFE,SelectFromModel
from scipy.stats import pearsonr
# 模型
from sklearn.svm import SVR
from sklearn.linear_model import LinearRegression,Ridge,Lasso,RandomizedLasso,LogisticRegression
from sklearn.cluster import KMeans
from sklearn.ensemble import RandomForestRegressor,RandomForestClassifier
from sklearn.tree import DecisionTreeClassifier
#调参
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import learning_curve
#模型评估
from sklearn.metrics import silhouette_score,silhouette_samples,r2_score,roc_auc_score
# 初始化函数，设定基准等等
def initialize(context):
# 设定沪深300作为基准
g.security = list(get_all_securities(types=["stock"]).index)
set_benchmark('')
# 开启动态复权模式(真实价格)
set_option('use_real_price', True)
# 输出内容到日志 ()
# 过滤掉order系列API产生的比error级别低的log
# log.set_level('order', 'error')
### 股票相关设定 ###
# 股票类每笔交易时的手续费是：买入时佣金万分之三，卖出时佣金万分之三加千分之一印花税, 每笔交易佣金最低扣5块钱
set_order_cost(OrderCost(close_tax=0.001, open_commission=0.0003, close_commission=0.0003, min_commission=5), type='stock')
#设置滑点
set_slippage(PriceRelatedSlippage(0.002))
# 开盘前运行
run_daily(before_market_open, time='before_open', reference_security='')
# 开盘时运行
run_daily(market_open, time='open', reference_security='')
# 收盘后运行
run_daily(after_market_close, time='after_close', reference_security='')
#检测因子
g.factor=valuation.market_cap
#分类数
g.class_num=10
g.day=0
#时间窗口
g.rate=22
g.stock_class_list={}
g.stock_class_close={}
g.stock_class_return={}
#收益率
for i in range(g.class_num):
g.stock_class_return["class%d"%(i)]=1
#超额收益率
g.stock_class_excess_return={}
for i in range(g.class_num):
g.stock_class_excess_return["class%d"%(i)]=1
#窗口胜率
g.stock_class_win_return={}
for i in range(g.class_num):
g.stock_class_win_return["class%d"%(i)]=0
g.benchmark_start=None
g.benchmark_open=None
## 开盘前运行函数
def before_market_open(context):
# 输出运行时间
# ('函数运行时间(before_market_open)：'+str(context.current_dt.time()))
log.info('##############################################################')
## 开盘时运行函数
def market_open(context):
if not g.benchmark_start:
g.benchmark_start=get_price('', start_date=context.current_dt, end_date=context.current_dt, frequency='daily', fields="close", skip_paused=False, fq='pre').ix[0,"close"]
if g.day%g.rate==0:
#step1:计算不同种类的当前时间窗口下的平均收益率
if g.stock_class_list:
benchmark_new=get_price('', start_date=context.current_dt, end_date=context.current_dt, frequency='daily', fields="close", skip_paused=False, fq='pre').ix[0,"close"]
for i in range(g.class_num):
new_stock_class_close=get_price(g.stock_class_list["class%d"%(i)], start_date=context.current_dt, end_date=context.current_dt, frequency='daily', fields="close", skip_paused=False, fq='pre')["close"].T.ix[:,0].values
df=pd.DataFrame({"L":g.stock_class_close["class%d"%(i)],"N":new_stock_class_close})
df["R"]=(df["N"]-df["L"])/df["L"]
g.stock_class_return["class%d"%(i)]=g.stock_class_return["class%d"%(i)]*(1+df["R"].sum()/len(df["R"]))
g.stock_class_excess_return["class%d"%(i)]=g.stock_class_return["class%d"%(i)]-((benchmark_new-g.benchmark_start)/g.benchmark_start)
g.stock_class_win_return["class%d"%(i)]=df["R"].sum()/len(df["R"])-((benchmark_new-g.benchmark_open)/g.benchmark_open)
#step2:画出每个组别的收益率曲线
record(class0=g.stock_class_win_return["class0"])
# record(class1=g.stock_class_win_return["class1"])
# record(class2=g.stock_class_win_return["class2"])
# record(class3=g.stock_class_win_return["class3"])
# record(class4=g.stock_class_win_return["class4"])
# record(class5=g.stock_class_win_return["class5"])
# record(class6=g.stock_class_win_return["class6"])
# record(class7=g.stock_class_win_return["class7"])
# record(class8=g.stock_class_win_return["class8"])
record(class9=g.stock_class_win_return["class9"])
#step3:将股票分类并且保存每个类别下个时间窗口起始收盘价数据
setclose(context,g.factor,g.class_num,sort="desc",moveNeg=True,moveNan=True)
# Momentum_factor(context,g.class_num,countNum=66,ascending_bol=False)
g.benchmark_open=get_price('', start_date=context.current_dt, end_date=context.current_dt, frequency='daily', fields="close", skip_paused=False, fq='pre').ix[0,"close"]
g.day+=1
## 收盘后运行函数
def after_market_close(context):
# (str('函数运行时间(after_market_close):'+str(context.current_dt.time())))
# #得到当天所有成交记录
# trades = get_trades()
# for _trade in ():
# ('成交记录：'+str(_trade))
# ('一天结束')
log.info('##############################################################')
#通过加减获取交易日
def getsecurities(datatime,num):
# 交易日列表
trade_days=map(lambda x:x.strftime("%Y-%m-%d"),get_all_trade_days())
index=trade_days.index(datatime)
return trade_days[index+num]
#特征排序
def feat_sort(l1,l2,reverse_bol=True):
coef_zip=zip(l1,l2)
return sorted(coef_zip, key=lambda x:abs(x[1]), reverse=reverse_bol)
def setclose(context,factor,classNum,sort="desc",moveNeg=True,moveNan=True):
q=0
if sort=="desc":
q = query(
valuation.code
,factor
).filter(
valuation.code.in_(g.security)
).order_by(
# 按市值降序排列
factor.desc()
)
else:
q = query(
valuation.code
,factor
).filter(
valuation.code.in_(g.security)
).order_by(
# 按市值升序排列
factor
)
df = get_fundamentals(q)
if moveNeg:
df=df[df.iloc[:,1]>0]
if moveNan:
df=df[~np.isnan(df.iloc[:,1])]
list_index=map(lambda x: int(x),np.linspace(0,df.shape[0],classNum+1))
for i in range(classNum):
class_list=df.iloc[list_index[i]:list_index[i+1],:].code.values.tolist()
g.stock_class_list["class%d"%(i)]=class_list
g.stock_class_close["class%d"%(i)]=get_price(class_list, start_date=context.current_dt, end_date=context.current_dt, frequency='daily', fields="close", skip_paused=False, fq='pre')["close"].T.ix[:,0].values
#动量
def Momentum_factor(context,classNum,countNum=22,ascending_bol=True):
df=get_price(g.security, count=countNum, end_date=context.current_dt, frequency='daily', fields="close", skip_paused=False, fq='pre')["close"].T
df["return"]=(df.iloc[:,-1]-df.iloc[:,0])/df.iloc[:,0]
df=df.sort("return",ascending=ascending_bol)
df=df[~np.isnan(df["return"])]
list_index=map(lambda x: int(x),np.linspace(0,df.shape[0],classNum+1))
for i in range(classNum):
class_list=df.iloc[list_index[i]:list_index[i+1],:].index.values.tolist()
g.stock_class_list["class%d"%(i)]=class_list
g.stock_class_close["class%d"%(i)]=df.ix[class_list,-2].values