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# 原文网址：https://www.joinquant.com/post/44699
# 标题：10年52倍，年化59%，全新因子方法超稳定
# 作者：小白F
#导入函数库
from jqdata import *
from jqfactor import *
import numpy as np
import pandas as pd
industry_code = ['HY001', 'HY002', 'HY003', 'HY004', 'HY005', 'HY006', 'HY007', 'HY008', 'HY009', 'HY010', 'HY011']
#初始化函数
def initialize(context):
# 设定基准
set_benchmark('000905.XSHG')
# 用真实价格交易
set_option('use_real_price', True)
# 打开防未来函数
set_option("avoid_future_data", True)
# 将滑点设置为0
set_slippage(FixedSlippage(0))
# 设置交易成本万分之三，不同滑点影响可在归因分析中查看
set_order_cost(OrderCost(open_tax=0, close_tax=0.001, open_commission=0.0003, close_commission=0.0003, close_today_commission=0, min_commission=5),type='stock')
# 过滤order中低于error级别的日志
log.set_level('order', 'error')
log.set_level('system', 'error')
#初始化全局变量
g.no_trading_today_signal = False
g.stock_num = 3
g.hold_list = [] #当前持仓的全部股票
g.yesterday_HL_list = [] #记录持仓中昨日涨停的股票
g.factor_list = [
{'ARBR': (-0.9996444781983547, 0.9986148448690932)}
]
g.chosen_factor = ['ARBR']
g.month_day = 1
# 设置交易运行时间
run_daily(prepare_stock_list, '9:05')
run_weekly(weekly_adjustment,g.month_day, '9:30')
run_daily(check_limit_up, '14:00') #检查持仓中的涨停股是否需要卖出
run_daily(close_account, '14:30')
# run_daily(print_position_info, '15:10')
#1-1 准备股票池
def prepare_stock_list(context):
#获取已持有列表
g.hold_list= []
for position in list(context.portfolio.positions.values()):
stock = position.security
g.hold_list.append(stock)
#获取昨日涨停列表
if g.hold_list != []:
df = get_price(g.hold_list, end_date=context.previous_date, frequency='daily', fields=['close','high_limit'], count=1, panel=False, fill_paused=False)
df = df[df['close'] == df['high_limit']]
g.yesterday_HL_list = list(df.code)
else:
g.yesterday_HL_list = []
#判断今天是否为账户资金再平衡的日期
g.no_trading_today_signal = today_is_between(context, '04-05', '04-30')
#1-2 选股模块
def get_stock_list(context):
#指定日期防止未来数据
yesterday = context.previous_date
today = context.current_dt
#获取初始列表
initial_list = get_all_securities('stock', today).index.tolist()
initial_list = filter_all_stock2(context, initial_list)
final_list = []
#MS
factor_list = list(g.factor_list[0].keys())
# print(factor_list)
factor_data = get_factor_values(initial_list,factor_list, end_date=yesterday, count=1)
df_jq_factor_value = pd.DataFrame(index=initial_list, columns=factor_list)
for factor in factor_list:
df_jq_factor_value[factor] = list(factor_data[factor].T.iloc[:,0])
#print('before:df_jq_factor_value is \n%s'% df_jq_factor_value)
df_jq_factor_value = data_preprocessing(df_jq_factor_value,initial_list,industry_code,yesterday) #标准化
#print('after:df_jq_factor_value is \n%s'% df_jq_factor_value)
#print('df_jq_factor_value.info is \n%s'% df_jq_factor_value.info)
# tar = g.model.predict_proba(df_jq_factor_value)
df = df_jq_factor_value
df = df.dropna()
for factor in g.chosen_factor :
# print(df[factor_list[i]],factor_value[i][0])
df = df[(df[factor]>=g.factor_list[0][factor][0]) & (df[factor]<=g.factor_list[0][factor][1])]
print(f'过滤完 {factor} ，剩余：{len(df)}')
# df['total_score'] = list(tar[:,1])
# df = df.sort_values(by=['total_score'], ascending=False) #分数越高即预测未来收益越高，排序默认降序
# postive_list = list(df.index)[:int(0.1*len(list(df.index)))]
postive_list = list(df.index)
log.info(f'因子筛选后的数量：{len(postive_list)}/{len(df)}')
# negative_list = list(df.index)[int(0.3*len(list(df.index))):-1]
q = query(valuation.code,valuation.circulating_market_cap,indicator.eps).filter(valuation.code.in_(postive_list)).order_by(valuation.circulating_market_cap.asc())
# q = query(valuation.code,valuation.circulating_market_cap,indicator.eps).filter(valuation.code.in_(postive_list)).order_by(indicator.eps.desc())
df2 = get_fundamentals(q)
df2 = df2[df2['eps']>0]
lst = list(df2.code)
lst = lst[:min(g.stock_num, len(lst))]
# df['chosen'] = str(lst)
# log.info(df[['total_score', 'chosen']].head(6))
for stock in lst:
if stock not in final_list:
final_list.append(stock)
return final_list
#1-3 整体调整持仓
def weekly_adjustment(context):
if g.no_trading_today_signal == False:
#获取应买入列表
target_list = get_stock_list(context)
#调仓卖出
for stock in g.hold_list:
if (stock not in target_list) and (stock not in g.yesterday_HL_list):
log.info("卖出[%s]" % (stock))
position = context.portfolio.positions[stock]
close_position(position)
else:
log.info("已持有[%s]" % (stock))
#调仓买入
position_count = len(context.portfolio.positions)
target_num = len(target_list)
if target_num > position_count:
value = context.portfolio.cash / (target_num - position_count)
for stock in target_list:
if context.portfolio.positions[stock].total_amount == 0:
if open_position(stock, value):
if len(context.portfolio.positions) == target_num:
break
#1-4 调整昨日涨停股票
def check_limit_up(context):
now_time = context.current_dt
if g.yesterday_HL_list != []:
#对昨日涨停股票观察到尾盘如不涨停则提前卖出，如果涨停即使不在应买入列表仍暂时持有
for stock in g.yesterday_HL_list:
current_data = get_price(stock, end_date=now_time, frequency='1m', fields=['close','high_limit'], skip_paused=False, fq='pre', count=1, panel=False, fill_paused=True)
if current_data.iloc[0,0] < current_data.iloc[0,1]:
log.info("[%s]涨停打开，卖出" % (stock))
position = context.portfolio.positions[stock]
close_position(position)
else:
log.info("[%s]涨停，继续持有" % (stock))
# 过滤股票，过滤停牌退市ST股票，选股时使用
def filter_all_stock2(context, stock_list):
# 过滤次新股（新股、老股的分界日期，两种指定方法）
# 新老股的分界日期, 自然日180天
# by_date = context.previous_date - datetime.timedelta(days=180)
# 新老股的分界日期，120个交易日
by_date = get_trade_days(end_date=context.previous_date, count=252)[0]
all_stocks = get_all_securities(date=by_date).index.tolist()
stock_list = list(set(stock_list).intersection(set(all_stocks)))
curr_data = get_current_data()
return [stock for stock in stock_list if not (
stock.startswith(('3', '68', '4', '8')) or # 创业，科创，北交所
curr_data[stock].paused or # 停牌
curr_data[stock].is_st or # ST
('ST' in curr_data[stock].name) or # ST
('*' in curr_data[stock].name) or # 退市
('退' in curr_data[stock].name) or # 退市
(curr_data[stock].day_open == curr_data[stock].high_limit) or # 涨停开盘, 其它时间用last_price
(curr_data[stock].day_open == curr_data[stock].low_limit) # 跌停开盘, 其它时间用last_price
)]
#3-1 交易模块-自定义下单
def order_target_value_(security, value):
if value == 0:
log.debug("Selling out %s" % (security))
else:
log.debug("Order %s to value %f" % (security, value))
return order_target_value(security, value)
#3-2 交易模块-开仓
def open_position(security, value):
order = order_target_value_(security, value)
if order != None and order.filled > 0:
return True
return False
#3-3 交易模块-平仓
def close_position(position):
security = position.security
order = order_target_value_(security, 0) # 可能会因停牌失败
if order != None:
if order.status == OrderStatus.held and order.filled == order.amount:
return True
return False
#4-1 判断今天是否为账户资金再平衡的日期
def today_is_between(context, start_date, end_date):
today = context.current_dt.strftime('%m-%d')
if (start_date <= today) and (today <= end_date):
return True
else:
return False
#4-2 清仓后次日资金可转
def close_account(context):
if g.no_trading_today_signal == True:
if len(g.hold_list) != 0:
for stock in g.hold_list:
position = context.portfolio.positions[stock]
close_position(position)
log.info("卖出[%s]" % (stock))
#4-3 打印每日持仓信息
def print_position_info(context):
#打印当天成交记录
trades = get_trades()
for _trade in trades.values():
print('成交记录：'+str(_trade))
#打印账户信息
for position in list(context.portfolio.positions.values()):
securities=position.security
cost=position.avg_cost
price=position.price
ret=100*(price/cost-1)
value=position.value
amount=position.total_amount
print('代码:{}'.format(securities))
print('成本价:{}'.format(format(cost,'.2f')))
print('现价:{}'.format(price))
print('收益率:{}%'.format(format(ret,'.2f')))
print('持仓(股):{}'.format(amount))
print('市值:{}'.format(format(value,'.2f')))
print('———————————————————————————————————')
print('———————————————————————————————————————分割线————————————————————————————————————————')
#取股票对应行业
def get_industry_name(i_Constituent_Stocks, value):
return [k for k, v in i_Constituent_Stocks.items() if value in v]
#缺失值处理
def replace_nan_indu(factor_data,stockList,industry_code,date):
#把nan用行业平均值代替，依然会有nan，此时用所有股票平均值代替
i_Constituent_Stocks={}
data_temp=pd.DataFrame(index=industry_code,columns=factor_data.columns)
for i in industry_code:
temp = get_industry_stocks(i, date)
i_Constituent_Stocks[i] = list(set(temp).intersection(set(stockList)))
data_temp.loc[i]=mean(factor_data.loc[i_Constituent_Stocks[i],:])
for factor in data_temp.columns:
#行业缺失值用所有行业平均值代替
null_industry=list(data_temp.loc[pd.isnull(data_temp[factor]),factor].keys())
for i in null_industry:
data_temp.loc[i,factor]=mean(data_temp[factor])
null_stock=list(factor_data.loc[pd.isnull(factor_data[factor]),factor].keys())
for i in null_stock:
industry=get_industry_name(i_Constituent_Stocks, i)
if industry:
factor_data.loc[i,factor]=data_temp.loc[industry[0],factor]
else:
factor_data.loc[i,factor]=mean(factor_data[factor])
return factor_data
#数据预处理
def data_preprocessing(factor_data,stockList,industry_code,date):
#去极值
factor_data = winsorize_med(factor_data, scale=5, inf2nan=False,axis=0)
#缺失值处理
factor_data = replace_nan_indu(factor_data,stockList,industry_code,date)
#标准化处理
factor_data = standardlize(factor_data,axis=0)
return factor_data
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