题：小市值策略优化
# 作者：tquan
#导入函数库
from jqdata import *
from jqfactor import get_factor_values
from jqlib.technical_analysis import *
import numpy as np
import pandas as pd
import statsmodels.api as sm
import datetime as dt
#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='fund')
# 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.stock_num = 10
g.limit_up_list = [] #记录持仓中涨停的股票
g.hold_list = [] #当前持仓的全部股票
g.history_hold_list = [] #过去一段时间内持仓过的股票
g.not_buy_again_list = [] #最近买过且涨停过的股票一段时间内不再买入
g.limit_days = 20 #不再买入的时间段天数
g.target_list = [] #开盘前预操作股票池
# 设置交易运行时间
run_daily(prepare_stock_list, time='9:05', reference_security='000300.XSHG')
run_weekly(weekly_adjustment, weekday=1, time='9:30', reference_security='000300.XSHG')
run_daily(check_limit_up, time='14:00', reference_security='000300.XSHG') #检查持仓中的涨停股是否需要卖出
run_daily(print_position_info, time='15:10', reference_security='000300.XSHG')
#1-1 选股模块
def get_factor_filter_list(context,stock_list,jqfactor,sort,p1,p2):
yesterday = context.previous_date
score_list = get_factor_values(stock_list, jqfactor, end_date=yesterday, count=1)[jqfactor].iloc[0].tolist()
df = pd.DataFrame(columns=['code','score'])
df['code'] = stock_list
df['score'] = score_list
df = df.dropna()
df.sort_values(by='score', ascending=sort, inplace=True)
filter_list = list(df.code)[int(p1*len(stock_list)):int(p2*len(stock_list))]
return filter_list
#1-2 选股模块
def get_stock_list(context):
yesterday = str(context.previous_date)
initial_list = get_all_securities().index.tolist()
initial_list = filter_new_stock(context,initial_list)
initial_list = filter_kcb_stock(context, initial_list)
initial_list = filter_st_stock(initial_list)
#SG 5年营业收入增长率
sg_list = get_factor_filter_list(context, initial_list, 'sales_growth', False, 0, 0.1)
q = query(valuation.code,valuation.circulating_market_cap,indicator.eps).filter(valuation.code.in_(sg_list)).order_by(valuation.circulating_market_cap.asc())
df = get_fundamentals(q, date=yesterday)
df = df[df['eps']>0]
sg_list = list(df.code)
#MS
factor_values = get_factor_values(initial_list, [
'operating_revenue_growth_rate', #营业收入增长率
'total_profit_growth_rate', #利润总额增长率
'net_profit_growth_rate', #净利润增长率
'earnings_growth', #5年盈利增长率
], end_date=yesterday, count=1)
df = pd.DataFrame(index=initial_list, columns=factor_values.keys())
df['operating_revenue_growth_rate'] = list(factor_values['operating_revenue_growth_rate'].T.iloc[:,0])
df['total_profit_growth_rate'] = list(factor_values['total_profit_growth_rate'].T.iloc[:,0])
df['net_profit_growth_rate'] = list(factor_values['net_profit_growth_rate'].T.iloc[:,0])
df['earnings_growth'] = list(factor_values['earnings_growth'].T.iloc[:,0])
df['total_score'] = 0.1*df['operating_revenue_growth_rate'] + 0.35*df['total_profit_growth_rate'] + 0.15*df['net_profit_growth_rate'] + 0.4*df['earnings_growth']
df = df.sort_values(by=['total_score'], ascending=False)
complex_growth_list = list(df.index)[:int(0.1*len(list(df.index)))]
q = query(valuation.code,valuation.circulating_market_cap,indicator.eps).filter(valuation.code.in_(complex_growth_list)).order_by(valuation.circulating_market_cap.asc())
df = get_fundamentals(q)
df = df[df['eps']>0]
ms_list = list(df.code)
#PEG
peg_list = get_factor_filter_list(context, initial_list, 'PEG', True, 0, 0.2)
turnover_list = get_factor_filter_list(context, peg_list, 'turnover_volatility', True, 0, 0.5)
q = query(valuation.code,valuation.circulating_market_cap,indicator.eps).filter(valuation.code.in_(turnover_list)).order_by(valuation.circulating_market_cap.asc())
df = get_fundamentals(q, date=yesterday)
peg_list = list(df.code)
final_list = [sg_list, ms_list, peg_list]
return final_list
#1-3 准备股票池
def prepare_stock_list(context):
# 1...2
#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)
#获取最近一段时间持有过的股票列表
g.history_hold_list.append(g.hold_list)
if len(g.history_hold_list) >= g.limit_days:
g.history_hold_list = g.history_hold_list[-g.limit_days:]
temp_set = set()
for hold_list in g.history_hold_list:
for stock in hold_list:
temp_set.add(stock)
g.not_buy_again_list = list(temp_set)
#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.high_limit_list = list(df.code)
else:
g.high_limit_list = []
#1-5 整体调整持仓
def weekly_adjustment(context):
#1 #获取应买入列表
all_list = get_stock_list(context)
sg_list = all_list[0][:5]
ms_list = all_list[1][:5]
peg_list = all_list[2][:5]
union_list = list(set(sg_list).union(set(ms_list)).union(set(peg_list)))
q = query(valuation.code,valuation.circulating_market_cap).filter(valuation.code.in_(union_list)).order_by(valuation.circulating_market_cap.asc())
df = get_fundamentals(q)
g.target_list = list(df.code) #...2
g.target_list = filter_paused_stock(g.target_list)
g.target_list = filter_limitup_stock(context, g.target_list)
g.target_list = filter_limitdown_stock(context, g.target_list)
#过滤最近买过且涨停过的股票
recent_limit_up_list = get_recent_limit_up_stock(context, g.target_list, g.limit_days)
black_list = list(set(g.not_buy_again_list).intersection(set(recent_limit_up_list)))
g.target_list = [stock for stock in g.target_list if stock not in black_list]
#截取不超过最大持仓数的股票量
g.target_list = g.target_list[:min(g.stock_num, len(g.target_list))]
#调仓卖出
for stock in g.hold_list:
if (stock not in g.target_list) and (stock not in g.high_limit_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(g.target_list)
if target_num > position_count:
value = context.portfolio.cash / (target_num - position_count)
for stock in g.target_list:
if context.portfolio.positions[stock].total_amount == 0:
if open_position(stock, value):
if len(context.portfolio.positions) == target_num:
break
#1-6 调整昨日涨停股票
def check_limit_up(context):
now_time = context.current_dt
if g.high_limit_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.high_limit_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 获取最近N个交易日内有涨停的股票
def get_recent_limit_up_stock(context, stock_list, recent_days):
stat_date = context.previous_date
new_list = []
for stock in stock_list:
df = get_price(stock, end_date=stat_date, frequency='daily', fields=['close','high_limit'], count=recent_days, panel=False, fill_paused=False)
df = df[df['close'] == df['high_limit']]
if len(df) > 0:
new_list.append(stock)
return new_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 过滤科创板
def filter_kcb_stock(context, stock_list):
return [stock for stock in stock_list if stock[0:3] != '688']
#2-7 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
#3-4 交易模块-调仓
def adjust_position(context, buy_stocks, stock_num):
for stock in context.portfolio.positions:
if stock not in buy_stocks:
log.info("[%s]不在应买入列表中" % (stock))
position = context.portfolio.positions[stock]
close_position(position)
else:
log.info("[%s]已经持有无需重复买入" % (stock))
position_count = len(context.portfolio.positions)
if stock_num > position_count:
value = context.portfolio.cash / (stock_num - position_count)
for stock in buy_stocks:
if context.portfolio.positions[stock].total_amount == 0:
if open_position(stock, value):
if len(context.portfolio.positions) == g.stock_num:
break
#4-1 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('———————————————————————————————————')
2023-02-21
雪球
