求问，最近在学习量化策略，从书上复制的聚宽策略到研究环境中，为什么没有任何交易，请帮忙看看哪里有问题，谢谢！代码如下：
import talib
import pandas as pd
import numpy as np
def initialize(context):
set_params() # 设置参数
set_backtest() # 设置回测条件
def set_params(): # 设置参数
g.stocknum = 20 # 买入的股票数量
g.period = 5 # 调仓周期
g.days = 0 # 计时器
g.lag = 20
def set_backtest(): # 设置回测条件
# 对比标的
set_benchmark('000300.XSHG')
# 当前价格的百分比设置滑点
set_slippage(PriceRelatedSlippage(0.002))
# 设置佣金
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')
# 用真实价格交易
set_option('use_real_price',True)
# 设置报错等级，过滤比error低的报错，只保留error
log.set_level('order','error')
# 设置tralling_stop_day，每天open价格执行
run_daily(tralling_stop_day, 'open')
# 定义函数 getStockPrice
# 取得股票某个区间内的所有收盘价（用于取前20日和当前 收盘价）
# 输入：stock, interval
# 输出：h['close'].values[0] , h['close'].values[-1]
def getStockPrice(stock, interval): # 输入stock证券名，interval期
h = attribute_history(stock, interval, unit='1d', fields=('close'), skip_paused=True)
return (h['close'].values[0] , h['close'].values[-1])
# 0是第一个（interval周期的值,-1是最近的一个值(昨天收盘价)）
# 追踪止损模块
def tralling_stop_day(context):
for stock in context.portfolio.positions.keys():
if tralling_stop(context, stock) == 1 and mem_300_500_stop(context) == 1:
log.info("追踪止损: selling %s %s股" % (stock, context.portfolio.positions[stock].closeable_amount))
order_target(stock, 0)
# 计算是否需要追踪止损
def tralling_stop(context, stock_code):
Data_ATR = attribute_history('000001.XSHG', 30, '1d',['close','high','low'] , df=False)
close_ATR = Data_ATR['close']
high_ATR = Data_ATR['high']
low_ATR = Data_ATR['low']
atr = talib.ATR(high_ATR, low_ATR, close_ATR)
highest20 = max(close_ATR[-20:])
if ((highest20 - close_ATR[-1]) > (3*atr[-1])):
return 1
else:
return 0
# 计算300和500指数动量是否需要追踪止损
def mem_300_500_stop(context):
# 计算300和500指数的增长率，用于快速清仓
interval300,Yesterday300 = getStockPrice('000300.XSHG', g.lag)
interval500,Yesterday500 = getStockPrice('000905.XSHG', g.lag)
hs300increase = (Yesterday300 - interval300) / interval300
zz500increase = (Yesterday500 - interval500) / interval500
if (hs300increase <= 0 and zz500increase <= 0 ):
return 1
else:
return 0
# 1、过滤停牌股票
def filter_paused_stock(stock_list):
current_data = get_current_data()
stock_list = [stock for stock in stock_list if not current_data[stock].paused]
return stock_list
# 2、过滤退市
def delisted_filter(security_list):
current_data = get_current_data()
security_list = [stock for stock in security_list if not '退' in current_data[stock].name]
return security_list
# 3、过滤St
def st_filter(security_list):
current_data = get_current_data()
security_list = [stock for stock in security_list if not current_data[stock].is_st]
return security_list
# 4、过滤次新股
# 通过context.current_dt.date实现
def remove_new_stocks(context, security_list):
for stock in security_list:
days_public = (context.current_dt.date() - get_security_info(stock).start_date).days
if days_public < 365:
security_list.remove(stock)
return security_list
# 5、过滤涨停板股
# 通过history获取1分钟数据，再通过current_data获取涨停价格
def high_limit_filter(security_list):
prices = history(1, unit='1m', field='close', security_list = security_list)
current_data = get_current_data()
security_list = [stock for stock in security_list if not prices[stock][-1] == current_data[stock].high_limit]
return security_list
def handle_data(context, data):
# 获得当前时间
hour = context.current_dt.hour
minute = context.current_dt.minute
# 每天14:53调仓
if hour == 14 and minute == 53:
# 计算300和500指数的增长率
interval300,Yesterday300 = getStockPrice('000300.XSHG', g.lag)
interval500,Yesterday500 = getStockPrice('000905.XSHG', g.lag)
hs300increase = (Yesterday300 - interval300) / interval300
zz500increase = (Yesterday500 - interval500) / interval500
h = attribute_history('000001.XSHG', 20, unit='1d', fields=('close', 'high', 'low'), skip_paused=True)
h_now = h['close'][-1]
# 阶段内大盘最高最低价
low_price = h.close.min()
# 如果非止损区间，且小盘股动量大于大盘股
if tralling_stop(context, '000001.XSHG') == 0 \
and zz500increase >= 0 and zz500increase - hs300increase >= 0.01 :
buy_market_stocks(context)
g.days += 1
# 定义函数买入股票
def buy_market_stocks(context):
# 在每个调仓周期
g.run_today = g.days % g.period == 0
if g.run_today == False:
return
# 获取当前时间
date = context.current_dt.strftime("%Y-%m-%d")
list_stock = get_index_stocks('000002.XSHG') + get_index_stocks('399107.XSHE')
# 000002.XSHG A股指数 和 399107.XSHE 深证A指
#过滤停牌、退市、st牌股、次新股、涨停板股
filter1 = filter_paused_stock(list_stock)
filter2 = delisted_filter(filter1)
filter3 = st_filter(filter2)
filter4 = remove_new_stocks(context,filter3)
filter5 = high_limit_filter(filter4)
# 选出在过滤停牌、退市和st后的股票代码，并按照当前时间市值从小到大排序
df = get_fundamentals(query(
valuation.code,
valuation.market_cap, # 总市值
valuation.pe_ratio, # PE市盈率
indicator.inc_net_profit_year_on_year, # 净利润同比增长率(%)
indicator.inc_net_profit_annual, # 净利润环比增长率(%)
balance.total_assets, # 资产总计(元)
balance.total_liability, # 负债总计(元)
).filter(
# 市盈率相对盈利增长比率
valuation.pe_ratio / indicator.inc_net_profit_year_on_year>0,
valuation.pe_ratio / indicator.inc_net_profit_year_on_year<1,
# 资产负债率
balance.total_liability / balance.total_assets<0.5,
indicator.inc_net_profit_annual > 0.10,
valuation.code.in_(filter5)
).order_by(
valuation.market_cap.asc()
).limit(
# 最多取前stocknum只
g.stocknum
), date=date)
# 取出前g.buyStockCount名的股票代码，并转成list类型
g.stocks = list(df['code'])
order_stock_sell(context,g.stocks)
order_stock_buy(context,g.stocks)
# 执行卖出
def order_stock_sell(context,order_list):
# 对于不需要持仓的股票，全仓卖出
for stock in context.portfolio.positions:
# 除去buy_list内的股票，其他都卖出
if stock not in order_list:
order_target_value(stock, 0)
# 执行买入
def order_stock_buy(context,order_list):
# 先求出可用资金，如果持仓个数小于g.stocknum
if len(context.portfolio.positions) < g.stocknum:
# 求出要买的数量num
num = g.stocknum - len(context.portfolio.positions)
# 求出每只股票要买的金额cash
g.each_stock_cash = context.portfolio.available_cash/num
else:
# 如果持仓个数满足要求，不再计算g.each_stock_cash
cash = 0
num = 0
# 执行买入
for stock in order_list:
if stock not in context.portfolio.positions:
order_target_value(stock, g.each_stock_cash)
2019-06-17
雪球
