红利低波50策略回测

admin · 发表于 2024-12-8 04:12:11

1 获取市场中,近三年股息率最高的10%股票
2 选取PEG在0.1~2之间的股票
3 营收同比要大于1%, 净利润同比大于1%,ROE 大于1.5%
4 在满足上面的条件中,按照股息率进行排序,选出排名最高的50只股票,组成股票池
5 每月1日交易,每月进行轮动,卖出股息率不在排名前50的股票,买入新的股票
6 每天检查昨日涨停,今日未涨停的股票,卖出

策略以上证50作为基准,, 有明显的超过收益,年化收益26%, 股息率确实对选股有效。

# 标题：红利50策略
# 导入函数库
from jqdata import *
from jqfactor import *
import numpy as np
import pandas as pd
import pickle
import talib
import warnings
import pandas as pd
from jqdata import *
from jqlib.technical_analysis import *
# 初始化函数，设定基准等等
def initialize(context):
# 设定上证50作为基准
set_benchmark('000016.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(0.02))
# 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')
# 过滤order中低于error级别的日志
log.set_level('order', 'error')
# 初始化全局变量
g.no_trading_today_signal = False
g.stock_num = 50
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
# 设置交易运行时间
run_daily(prepare_stock_list, '9:05')
run_monthly(monthly_adjustment, 1, '9:30')
run_daily(check_limit_up, '14:00') # 检查持仓中的涨停股是否需要卖出
# 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 = []
def get_dividend_ratio_filter_list(context, stock_list, sort, p1, p2):
time1 = context.previous_date
time0 = time1 - datetime.timedelta(days=365*3)#最近3年分红
#获取分红数据，由于finance.run_query最多返回4000行，以防未来数据超限，最好把stock_list拆分后查询再组合
interval = 1000 #某只股票可能一年内多次分红，导致其所占行数大于1，所以interval不要取满4000
list_len = len(stock_list)
#截取不超过interval的列表并查询
q = query(finance.STK_XR_XD.code, finance.STK_XR_XD.a_registration_date, finance.STK_XR_XD.bonus_amount_rmb
).filter(
finance.STK_XR_XD.a_registration_date >= time0,
finance.STK_XR_XD.a_registration_date <= time1,
finance.STK_XR_XD.code.in_(stock_list[:min(list_len, interval)]))
df = finance.run_query(q)
#对interval的部分分别查询并拼接
if list_len > interval:
df_num = list_len // interval
for i in range(df_num):
q = query(finance.STK_XR_XD.code, finance.STK_XR_XD.a_registration_date, finance.STK_XR_XD.bonus_amount_rmb
).filter(
finance.STK_XR_XD.a_registration_date >= time0,
finance.STK_XR_XD.a_registration_date <= time1,
finance.STK_XR_XD.code.in_(stock_list[interval*(i+1):min(list_len,interval*(i+2))]))
temp_df = finance.run_query(q)
df = df.append(temp_df)
dividend = df.fillna(0)#df.fillna() 是一个 Pandas 数据处理库中的函数，它可以用来填充数据框中的空值
dividend = dividend.groupby('code').sum()
temp_list = list(dividend.index) #query查询不到无分红信息的股票，所以temp_list长度会小于stock_list
# #获取市值相关数据
q = query(valuation.code,valuation.market_cap).filter(valuation.code.in_(temp_list))
cap = get_fundamentals(q, date=time1)
cap = cap.set_index('code')
# #计算股息率
cap['dividend_ratio']=(dividend['bonus_amount_rmb']/10000)/cap['market_cap']
# #排序并筛选
cap = cap.sort_values(by=['dividend_ratio'], ascending=sort)
final_list = list(cap.index)[int(p1*len(cap)):int(p2*len(cap))]
print("近3年累计分红率排名前{0:.2%}的股有{1}只".format(p2,len(final_list)))
return final_list
def choice_hl(context, stocks):
yesterday = context.previous_date
#高股息(全市场最大10%)
stocks = get_dividend_ratio_filter_list(context, stocks, False, 0, 0.1)
# 1 选取 PEG 在 0.1 到 2之间
stocks = peg_stock(context,stocks,0.1,2)
#2 业绩筛选营收增速同比大于1%，净利润增速同比大于1%，季度ROE大于1.5%
q = query(valuation.code).filter(
valuation.code.in_(stocks),
indicator.inc_total_revenue_year_on_year>1,
indicator.inc_net_profit_year_on_year>1,
indicator.inc_return >1.5)
df=get_fundamentals(q)
stocks = list(df['code'])
stock_list = stocks[:g.stock_num] #选前前N只票
return stock_list
def peg_stock(context,stock_list,pegmin,pegmax):
q = query(valuation.code).filter(
valuation.code.in_(stock_list),
valuation.pe_ratio / indicator.inc_net_profit_year_on_year>pegmin,
valuation.pe_ratio / indicator.inc_net_profit_year_on_year<pegmax)
df=get_fundamentals(q)
stock_list = list(df['code'])
return stock_list
# 获取因子值
def get_jq_factors(stocks, end_date, factor_name):
factor_values = get_factor_values(stocks, factors=[factor_name], end_date=end_date, count=1)[factor_name].iloc[0].tolist()
df_factor = pd.DataFrame(columns=['code', factor_name])
df_factor['code'] = stocks
df_factor[factor_name] = factor_values
df_factor = df_factor.dropna()
return df_factor
# 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
initial_list = get_all_securities('stock', today).index.tolist()
# initial_list = get_stock(yesterday)
stocks = filter_kcbj_stock(initial_list)
choice = filter_st_stock(stocks)
choice = filter_paused_stock(choice)
choice = filter_new_stock(context, choice)
choice = filter_limitup_stock(context,choice)
initial_list = filter_limitdown_stock(context,choice)
stocks = choice_hl(context,initial_list)
return stocks
# 1-3 整体调整持仓
def monthly_adjustment(context):
target_list = get_stock_list(context)[0 : g.stock_num]
# 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:
print(context.portfolio.cash)
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))
# 2-1 过滤停牌股票
def filter_paused_stock(stock_list):
current_data = get_current_data()
return [stock for stock in stock_list if not current_data[stock].paused]
# 2-2 过滤ST及其他具有退市标签的股票
def filter_st_stock(stock_list):
current_data = get_current_data()
return [stock for stock in stock_list
if not current_data[stock].is_st
and 'ST' not in current_data[stock].name
and '*' not in current_data[stock].name
and '退' not in current_data[stock].name]
# 2-3 过滤科创北交股票
def filter_kcbj_stock(stock_list):
for stock in stock_list[:]:
if stock[0] == '4' or stock[0] == '8' or stock[:2] == '68' :
stock_list.remove(stock)
return stock_list
# 2-4 过滤涨停的股票
def filter_limitup_stock(context, stock_list):
last_prices = history(1, unit='1m', field='close', security_list=stock_list)
current_data = get_current_data()
return [stock for stock in stock_list if stock in context.portfolio.positions.keys()
or last_prices[stock][-1] < current_data[stock].high_limit]
# 2-5 过滤跌停的股票
def filter_limitdown_stock(context, stock_list):
last_prices = history(1, unit='1m', field='close', security_list=stock_list)
current_data = get_current_data()
return [stock for stock in stock_list if stock in context.portfolio.positions.keys()
or last_prices[stock][-1] > current_data[stock].low_limit]
# 2-6 过滤次新股
def filter_new_stock(context, stock_list):
yesterday = context.previous_date
return [stock for stock in stock_list if
not yesterday - get_security_info(stock).start_date < datetime.timedelta(days=375)]
# 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

复制代码

		自动登录	找回密码
密码			立即注册