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01-alpha101因子信号
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## 导入函数库
from jqdata import *
from scipy.stats import rankdata
from dateutil import parser
import numpy as np
import numpy.linalg as la
import pandas as pd
from datetime import datetime
import scipy.stats as stats
# 初始化函数，设定基准等等
def initialize(context):
set_benchmark('000300.XSHG')
set_option('use_real_price', True)
set_order_cost(OrderCost(close_tax=0.001, open_commission=0.0003, close_commission=0.0003, min_commission=5), type='stock')
log.set_level('system','error')
g.stocks = get_index_stocks('000300.XSHG')
g.codes = ['601212.XSHG','600111.XSHG','002252.XSHE','000408.XSHE','601066.XSHG']
g.days = 0 # 设置建仓日
g.times = 0 # 设置每日高频范围
g.t = 0
g.buy = 0 # 买入次数
g.sell = 0 # 卖出次数
g.buy_code,g.sell_code = [],[]
# run_daily(trade, time='every_bar', reference_security='000300.XSHG')
run_daily(sell, time='14:59', reference_security='000300.XSHG')
## alpha函数
def rank(df):
return df.rank(pct=True,axis=1)
def alpha42(vwap,close):
alpha = rank((vwap - close)) / rank((vwap + close))
return alpha
## 获取买卖股票列表
# 因子前N法
def buy_sell_list1(alpha):
alpha0 = alpha.iloc[-1]
alpha_df = pd.DataFrame(alpha0,index = g.stocks)
alpha_df.columns = ['alpha']
alpha_df = alpha_df.sort_values(['alpha'],ascending=False)
buy_code = list(set(alpha_df[:10].index) & set(g.codes))
sell_code = list(set(alpha_df[-10:].index) & set(g.codes))
return buy_code,sell_code
# 因子值连续增加法
def buy_sell_list2(alpha):
alpha0 = alpha.iloc[-4:].T
alpha0.columns=['col1','col2','col3','col4']
alpha1 = alpha0.copy()
alpha1 = alpha1[alpha1.col4>alpha1.col3]
alpha1 = alpha1[alpha1.col3>alpha1.col2]
alpha1 = alpha1[alpha1.col2>alpha1.col1]
buy_code = list(set(g.codes) & set(alpha1.index))
alpha2 = alpha0.copy()
alpha2 = alpha2[alpha2.col4<alpha2.col3]
alpha2 = alpha2[alpha2.col3<alpha2.col2]
alpha2 = alpha2[alpha2.col2<alpha2.col1]
sell_code = list(set(g.codes) & set(alpha2.index))
return buy_code,sell_code
# N分钟均线阈值法
def buy_sell_list3(alpha):
a0 = alpha.iloc[-1]
a1 = alpha.iloc[-2]
b1 = alpha.rolling(5).max()
b2 = b1.iloc[-1]
c1 = alpha.rolling(5).min()
c2 = c1.iloc[-1]
dic = {'col1':list(a0),'col2':list(a1),'col3':list(b2),'col4':list(c2)}
alpha_df = pd.DataFrame(dic,index = g.stocks)
alpha_buy = alpha_df[alpha_df.col1 > alpha_df.col3]
# alpha_buy = alpha_df[alpha_df.col2 < alpha_df.col3]
alpha_sell = alpha_df[alpha_df.col1 < alpha_df.col4]
# alpha_sell = alpha_df[alpha_df.col2 > alpha_df.col3]
buy_code = list(set(g.codes) & set(alpha_buy.index))
sell_code = list(set(g.codes) & set(alpha_sell.index))
return buy_code,sell_code
## 盘中高频交易
def trade(context):
# g.t+=1
if g.days > 0 and g.times == 0:#and g.t%5 == 0, and g.times < 30
current_d = context.current_dt.strftime('%Y-%m-%d %H:%M:%S')
panel = get_price(g.stocks,count=7,end_date=current_d,frequency='1m',fields=['close','open','high','low','volume','avg'])
close = panel['close'].reset_index().drop(columns = ['index'])
vwap = panel['avg'].reset_index().drop(columns = ['index'])
alpha = alpha42(vwap,close)
g.buy_code,g.sell_code = buy_sell_list(alpha)
for code in g.buy_code:
order(code, 100)
g.buy += 1
# for code in g.sell_code:
# order(code,-100)
# g.sell+=1
# g.times += 1
if g.days > 0 and g.times == 1:
for code in g.buy_code:
order(code, -100)
# for code in g.sell_code:
# order(code, 100)
# g.times = 0
## 建仓，每日平仓
def sell(context):
print(context.portfolio.positions_value)
cash = context.portfolio.total_value*0.8
for code in g.codes:
order_target_value(code, cash/5)
g.days+=1
# g.times = 0
print(g.buy,g.sell)
g.buy = 0
g.t = 0
# current_d = context.current_dt.strftime('%Y-%m-%d %H:%M:%S')
# panel = get_price(g.stocks,count=230,end_date=current_d,frequency='1m', fields=['close','open','high','low','volume'])
# high = panel['high'].reset_index().drop(columns = ['index'])
# volume = panel['volume'].reset_index().drop(columns = ['index'])
# alpha = alpha40(high,volume)
# max_list,min_list,g.yuzhi = [],[],[]
# for i in range(12,len(alpha)):
# max_list.append(alpha.iloc[i].max())
# min_list.append(alpha.iloc[i].min())
# g.yuzhi.append(pd.Series(max_list).min())
# g.yuzhi.append(pd.Series(min_list).max())
# print(g.yuzhi)