@wywy1995 # 克隆自聚宽文章：https://www.joinquant.com/post/39960
# 标题：“7年40倍”策略扩容到50只
# 作者：wywy1995
from jqdata import *
import math
import pandas as pd
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)
# 设置滑点为理想情况，不同滑点影响可以在归因分析中查看
set_slippage(PriceRelatedSlippage(0.000))
# 设置交易成本
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')
# 除非需要精简信息，否则不要过滤日志，方便debug
#log.set_level('system', 'error')
#初始化全局变量
g.stock_num = 50
g.limit_up_list = []
g.hold_list = []
g.weights = [1.0, 1.0, 1.6, 0.8, 2.0]
# 设置交易时间，每天运行
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')
run_daily(print_position_info, time='15:10', reference_security='000300.XSHG')
#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 != []:
g.high_limit_list = []
for stock in g.hold_list:
df = attribute_history(stock, 1, unit='1d', fields=['close','high_limit'])
df = df.dropna()
if df.iloc[0,0] == df.iloc[0,1]:
g.high_limit_list.append(stock)
else:
g.high_limit_list = []
#1-2 选股模块
def get_stock_list(context):
# 获取前N个单位时间当时的收盘价
def get_close(stock, n, unit):
return attribute_history(stock, n, unit, 'close')['close'][0]
# 获取现价相对N个单位前价格的涨幅
def get_return(stock, n, unit):
price_before = attribute_history(stock, n, unit, 'close')['close'][0]
price_now = get_close(stock, 1, '1m')
if not isnan(price_now) and not isnan(price_before) and price_before != 0:
return price_now / price_before
else:
return 100
# 获得初始列表
yesterday = context.previous_date
initial_list = get_all_securities('stock', yesterday).index.tolist()
initial_list = filter_kcbj_stock(initial_list)
initial_list = filter_new_stock(context, initial_list, 375)
initial_list = filter_st_stock(initial_list)
q = query(
valuation.code, valuation.market_cap, valuation.circulating_market_cap
).filter(
valuation.code.in_(initial_list),
valuation.pb_ratio > 0,
indicator.inc_return > 0,
indicator.inc_total_revenue_year_on_year > 0,
indicator.inc_net_profit_year_on_year > 0
).order_by(
valuation.market_cap.asc()).limit(100)
df = get_fundamentals(q, date=yesterday)
df.index = df.code
initial_list = list(df.index)
#获取原始值
MC, CMC, PN, TV, RE = [], [], [], [], []
for stock in initial_list:
#总市值
mc = df.loc[stock]['market_cap']
MC.append(mc)
#流通市值
cmc = df.loc[stock]['circulating_market_cap']
CMC.append(cmc)
#当前价格
pricenow = get_close(stock, 1, '1m')
PN.append(pricenow)
#5日累计成交量
total_volume_n = attribute_history(stock, 1200, '1m', 'volume')['volume'].sum()
TV.append(total_volume_n)
#60日涨幅
m_days_return = get_return(stock, 60, '1d')
RE.append(m_days_return)
#合并数据
df = pd.DataFrame(index=initial_list,
columns=['market_cap','circulating_market_cap','price_now','total_volume_n','m_days_return'])
df['market_cap'] = MC
df['circulating_market_cap'] = CMC
df['price_now'] = PN
df['total_volume_n'] = TV
df['m_days_return'] = RE
df = df.dropna()
min0, min1, min2, min3, min4 = min(MC), min(CMC), min(PN), min(TV), min(RE)
#计算合成因子
temp_list = []
for i in range(len(list(df.index))):
score = g.weights[0] * math.log(min0 / df.iloc[i,0]) + g.weights[1] * math.log(min1 / df.iloc[i,1]) + g.weights[2] * math.log(min2 / df.iloc[i,2]) + g.weights[3] * math.log(min3 / df.iloc[i,3]) + g.weights[4] * math.log(min4 / df.iloc[i,4])
temp_list.append(score)
df['score'] = temp_list
#排序并返回最终选股列表
df = df.sort_values(by='score', ascending=False)
final_list = list(df.index)
return final_list
#1-4 整体调整持仓
def weekly_adjustment(context):
#获取应买入列表
target_list = get_stock_list(context)
target_list = filter_paused_stock(target_list)
target_list = filter_limitup_stock(context, target_list)
target_list = filter_limitdown_stock(context, target_list)
#截取不超过最大持仓数的股票量
target_list = target_list[:min(g.stock_num, len(target_list))]
#调仓卖出
for stock in g.hold_list:
if (stock not in 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(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
#1-5 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
#2-7 过滤次新股
def filter_new_stock(context, stock_list, d):
yesterday = context.previous_date
return [stock for stock in stock_list if not yesterday - get_security_info(stock).start_date < datetime.timedelta(days=d)]
#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) == 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('———————————————————————————————————')
print('———————————————————————————————————————分割线————————————————————————————————————————')
2023-02-11
雪球
