有一个想法，把k线放荡不羁大神的策略价值选股与RSRS择时策略中的选股因子变动一下，采用另一位大神拉达姆投资的穿越牛熊基业长青的价值精选策略中的那六个选股因子，这两个策略结合起来看看效果如何，那位大神可以帮忙看看这个策略怎么写？

下面是RSRS策略源码，价值精选策略源码在回测那里见

import statsmodels.api as sm
from pandas.stats.api import ols
def initialize(context):
set_option('use_real_price', True)
set_parameter(context)
set_order_cost(OrderCost(close_tax=0.001, open_commission=0.0003, close_commission=0.0003, min_commission=5), type='stock')
run_daily(before_market_open, time='before_open', reference_security='000300.XSHG')
run_daily(market_open, time='open', reference_security='000300.XSHG')
def set_parameter(context):
g.N = 18
g.M = 1100
g.init = True
g.stock_num = 5
g.security = '000300.XSHG'
set_benchmark(g.security)
g.days = 0
g.buy = 0.7
g.sell = -0.7
g.ans = []
g.ans_rightdev= []
prices = get_price(g.security, '2005-01-05', context.previous_date, '1d', ['high', 'low'])
highs = prices.high
lows = prices.low
g.ans = []
for i in range(len(highs))[g.N:]:
data_high = highs.iloc[i-g.N 1:i 1]
data_low = lows.iloc[i-g.N 1:i 1]
X = sm.add_constant(data_low)
model = sm.OLS(data_high,X)
results = model.fit()
g.ans.append(results.params[1])
g.ans_rightdev.append(results.rsquared)
def before_market_open(context):
g.days = 1
send_message('策略正常，运行第%s天~'%g.days)
def market_open(context):
security = g.security
beta=0
r2=0
if g.init:
g.init = False
else:
prices = attribute_history(security, g.N, '1d', ['high', 'low'])
highs = prices.high
lows = prices.low
X = sm.add_constant(lows)
model = sm.OLS(highs, X)
beta = model.fit().params[1]
g.ans.append(beta)
r2=model.fit().rsquared
g.ans_rightdev.append(r2)
section = g.ans[-g.M:]
mu = np.mean(section)
sigma = np.std(section)
zscore = (section[-1]-mu)/sigma
zscore_rightdev= zscorebetar2
if zscore_rightdev > g.buy:
log.info("市场风险在合理范围")
trade_func(context)
elif (zscore_rightdev < g.sell) and (len(context.portfolio.positions.keys()) > 0):
log.info("市场风险过大，保持空仓状态")
for s in context.portfolio.positions.keys():
order_target(s, 0)
def trade_func(context):
df = get_fundamentals(query(valuation.code,valuation.pb_ratio,indicator.roe))
df = df[(df['roe']>0) & (df['pb_ratio']>0)].sort('pb_ratio')
df.index = df['code'].values
df['1/roe'] = 1/df['roe']
df['point'] = df[['pb_ratio','1/roe']].rank().T.apply(f_sum)
df = df.sort('point')[:g.stock_num]
pool = df.index
log.info('总共选出%s只股票'%len(pool))
cash = context.portfolio.total_value/len(pool)
hold_stock = context.portfolio.positions.keys()
for s in hold_stock:
if s not in pool:
order_target(s,0)
for s in pool:
order_target_value(s,cash)
def f_sum(x):
return sum(x)
def after_market_close(context):
trades = get_trades()
for _trade in trades.values():
log.info('成交记录：' str(_trade))
log.info('今日账户总资产：%s'%round(context.portfolio.total_value,2))