def startsimulate(_continueday, _end, _benchmark, _universe, _initialize,
_handle_data, _refresh_rate, _freq):
bt, perf, bt_by_account = quartz.backtest(start=_continueday,
end=_end,
benchmark=_benchmark,
universe=_universe,
initialize=_initialize,
handle_data=_handle_data,
refresh_rate=_refresh_rate,
freq=_freq,
accounts=accounts)
indexs = copy.copy(g_head_indexs)
i = 1
while i <= g_imaxback:
#make the table title
added = [x % (i) for x in g_security_return_value]
indexs = indexs + added
i = i + 1
data = pd.DataFrame.from_dict(data=g_security_history, orient='index')
if g_EMA:
data.to_excel('龙回头模拟交易Q3%s-%s-EMA-%d.xlsx' %
(start, _end, g_targetprice),
header=indexs)
else:
data.to_excel('龙回头模拟交易Q3%s-%s-%d.xlsx' % (start, _end, g_targetprice),
header=indexs)
cansfiltered = {}
for k, v in g_candidates.iteritems(
): #filter the candidates already bought before
if canbuy(k, 99999999., _end, g_imaxback + 1):
cansfiltered[k] = v
g_security_history.clear()
return cansfiltered
pass
def startsimulate(_continueday, _end, _benchmark, _universe, _capital_base,
_initialize, _handle_data, _refresh_rate, _freq):
bt, perf = quartz.backtest(start=_continueday,
end=_end,
benchmark=_benchmark,
universe=_universe,
capital_base=_capital_base,
initialize=_initialize,
handle_data=_handle_data,
refresh_rate=_refresh_rate,
freq=_freq)
indexs = copy.copy(g_head_indexs)
i = 1
while i <= 1:
#make the table title
added = [x % (i) for x in g_security_return_value]
indexs = indexs + added
i = i + 1
indexs = indexs + ['soldcondition']
newcol = [
sum(v['soldpoint']) / len(v['soldpoint']) / v['cost'] - 1
for k, v in g_vardic.iteritems()
]
newcol = [sum(newcol)] + newcol
global excel
excel['soldcondition'] = newcol
excel = excel[indexs]
excel.to_excel('sold.xlsx')
pass
def initialize(context): # 初始化策略运行环境
pass
def handle_data(context): # 核心策略逻辑
account = context.get_account('fantasy_account')
bt, perf, stock = qz.backtest(start=start,
end=end,
benchmark=benchmark,
universe=universe,
capital_base=100000.0,
initialize=initialize,
handle_data=handle_data,
refresh_rate=1,
freq='d',
security_base={},
security_cost={},
max_history_window=(30, 241),
accounts=accounts)
benchmark_return_hs300 = bt[['tradeDate', 'benchmark_return']]
# benchmark数据预加载
save_dir = "alpha_ml"
tmp = pd.read_csv('%s//factors_sxgb_HS300_5.csv' % save_dir)
benchmark_return_zz500 = tmp['benchmark_return']
benchmark_return_hs300 = benchmark_return_hs300['benchmark_return']
# 行业中性组合的指标对比
def startsimulate(_continueday,_end,_benchmark,_universe,_capital_base,_initialize,_handle_data,_refresh_rate,_freq):
bt, perf,bt_by_account = quartz.backtest(start = _continueday,end = _end,benchmark = _benchmark,universe = _universe,capital_base = _capital_base,initialize = _initialize,handle_data = _handle_data,refresh_rate = _refresh_rate,freq = _freq)
pass
rebalance_month=[4,8,12]
if i==6:
universe = ['510300.XSHG','510500.XSHG','513100.XSHG','163210.XSHE'] ## 波动最小，比较保守
weight = [1,1,1,2]
rebalance_month=[4,8,12]
# 生成month
# rebalance_month=[]
# step=12//(i+1)
# month_tmp=0
# for j in range(i+1):
# month_tmp+=step
# rebalance_month.append(month_tmp)
bt, acct = quartz.backtest(start = start,end = end,benchmark = benchmark, universe = universe, capital_base = capital_base, initialize = initialize, handle_data = handle_data, refresh_rate = refresh_rate)
newdf = recalculate(bt)
((newdf['position_value']-newdf['cost'])/newdf['cost']).plot(figsize=(20,6))
# print newdf
# newdf['cost'].plot()
# newdf['position_value'].plot()
# newdf['dingtou_cash'].plot()
# perf['benchmark_cumulative_returns'].plot()
pylab.legend(['A', 'usa','bond','Monetary fund','jijian','1:1:1:1','1:1:1:2'])
# In[ ]:
# 生成策略对象
#strategy = btr.TradingStrategy(initialize, handle_data)
#bt, perf = btr.backtest(sim_params, strategy, data=data)
# 开始回测
data_dict = sim_params.to_dict()
bt, perf, account = btr.backtest(start=data_dict['start'],
end=data_dict['end'],
benchmark=data_dict['major_benchmark'],
universe=data_dict['universe'],
capital_base=data_dict['capital_base'],
refresh_rate=data_dict['refresh_rate'],
freq=data_dict['freq'],
accounts=data_dict['accounts'],
initialize=initialize,
handle_data=handle_data,
preload_data=data)
print('\n策略年化收益率：', format(perf['annualized_return'], '.2%'))
print('基准年化收益率：', format(perf['benchmark_annualized_return'], '.2%'))
print('策略累计收益率：', format(perf['cumulative_returns'].values[-1], '.2%'))
print('基准累计收益率：', format(perf['benchmark_cumulative_returns'].values[-1],
'.2%'))
#
#target_stocks = target_stocks[['date', 'industry_1', 'industry_2', 'stock_code', 'stock_name',
# 'indicator', 'indicator_percent', 'pred_indicator_percent', 'cagr']]
#target_stocks.to_excel('target_stocks_5.xlsx')
