克隆策略
{"Description":"实验创建于2017/8/26","Summary":"","Graph":{"EdgesInternal":[{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-15:instruments","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-8:data"},{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-29:instruments","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-8:data"},{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-53:data1","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-15:data"},{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-29:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-35:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-76:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-43:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-70:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-35:input_data","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-29:data"},{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-53:data2","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-35:data"},{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-60:model","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-43:model"},{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-84:input_data","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-53:data"},{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-81:options_data","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-60:predictions"},{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-70:instruments","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-62:data"},{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-81:instruments","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-62:data"},{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-76:input_data","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-70:data"},{"DestinationInputPortId":"-86:input_data","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-76:data"},{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-43:training_ds","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-84:data"},{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-60:data","SourceOutputPortId":"-86:data"}],"ModuleNodes":[{"Id":"287d2cb0-f53c-4101-bdf8-104b137c8601-8","ModuleId":"BigQuantSpace.instruments.instruments-v2","ModuleParameters":[{"Name":"start_date","Value":"2010-01-01","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"end_date","Value":"2015-01-01","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"market","Value":"CN_STOCK_A","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"instrument_list","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"max_count","Value":"0","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"rolling_conf","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-8"}],"OutputPortsInternal":[{"Name":"data","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-8","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":1,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"287d2cb0-f53c-4101-bdf8-104b137c8601-15","ModuleId":"BigQuantSpace.advanced_auto_labeler.advanced_auto_labeler-v2","ModuleParameters":[{"Name":"label_expr","Value":"# 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\nta_atr(high_0,low_0,close_0,5)\n((-1*((low_0-close_0)*(open_0**5)))/((low_0-high_0)*(close_0**5)))\nreturn_6\n-1*delta(((close_0-low_0)-(high_0-close_0))/(high_0-low_0),1)\nreturn_3\nstd(volume_0,10)\nta_ema(((high_0+low_0-0)/2-(delay(high_0,1)+delay(low_0,1))/2)*(high_0-low_0)/volume_0,15)\n(close_0-mean(close_0,12))/mean(close_0,12)*100\n(close_0-delay(close_0,6))/delay(close_0,6)*volume_0\n(volume_0-delay(volume_0,5))/delay(volume_0,5)*100\nsum(((close_0-low_0)-(high_0-close_0))/(high_0-low_0)*volume_0,20)\n(close_0-mean(close_0,24))/mean(close_0,24)*100\n((sum(close_0,7)/7)-close_0)+correlation(amount_0/volume_0*adjust_factor_0,delay(close_0,5),230)\nreturn_15\nrank((-1*((1-(open_0/close_0))**1))) \nmean(close_0,12)/close_0\nta_ema((close_0-ts_min(low_0,9))/(ts_max(high_0,9)-ts_min(low_0,9))*100,3)\nreturn_20\n(close_0-delay(close_0,20))/delay(close_0,20)*100\nclose_0-delay(close_0,5)\nta_ema(volume_0, 21)\nclose_0/delay(close_0,5)\nstd(amount_0,20)\nsum(((close_0-low_0)-(high_0-close_0))/(high_0-low_0)*volume_0,6)\n((high_0+low_0+close_0)/3-mean((high_0+low_0+close_0)/3,12))/(0.015*mean(abs(close_0-mean((high_0+low_0+close_0)/3,12)),12))\nstd(amount_0,6)\nta_ema(((ts_max(high_0,6)-close_0)/(ts_max(high_0,6)-ts_min(low_0,6))*100),20)\nta_ema(ta_ema((close_0-ts_min(low_0,9))/(ts_max(high_0,9)-ts_min(low_0,9))*100,3),3)\n(close_0-delay(close_0,6))/delay(close_0,6)*100\n(((high_0*low_0)**0.5)-amount_0/volume_0*adjust_factor_0)\n(mean(close_0,3)+mean(close_0,6)+mean(close_0,12)+mean(close_0,24))/(4*close_0)\nta_ema(close_0-delay(close_0,5),5)\nta_ema(high_0-low_0,10)/ta_ema(ta_ema(high_0-low_0,10),10)\n((high_0-ta_ema(close_0,15))-(low_0-ta_ema(close_0,15)))/close_0\n(close_0+high_0+low_0)/3\nstd(volume_0,20)\nopen_0/shift(close_0,1)-1 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In [2]:
# 本代码由可视化策略环境自动生成 2017年11月7日 23:34
# 本代码单元只能在可视化模式下编辑。您也可以拷贝代码，粘贴到新建的代码单元或者策略，然后修改。
m1 = M.instruments.v2(
start_date='2010-01-01',
end_date='2015-01-01',
market='CN_STOCK_A',
instrument_list='',
max_count=0
)
m2 = M.advanced_auto_labeler.v2(
instruments=m1.data,
label_expr="""# #号开始的表示注释
# 0. 每行一个，顺序执行，从第二个开始，可以使用label字段
# 1. 可用数据字段见 https://bigquant.com/docs/data_history_data.html
# 添加benchmark_前缀，可使用对应的benchmark数据
# 2. 可用操作符和函数见 `表达式引擎 <https://bigquant.com/docs/big_expr.html>`_
# 计算收益：5日收盘价(作为卖出价格)除以明日开盘价(作为买入价格)
shift(close, -5) / shift(open, -1)
# 极值处理：用1%和99%分位的值做clip
clip(label, all_quantile(label, 0.01), all_quantile(label, 0.99))
# 将分数映射到分类，这里使用20个分类
all_wbins(label, 20)
# 过滤掉一字涨停的情况 (设置label为NaN，在后续处理和训练中会忽略NaN的label)
where(shift(high, -1) == shift(low, -1), NaN, label)
""",
start_date='',
end_date='',
benchmark='000300.SHA',
drop_na_label=True,
cast_label_int=True
)
m3 = M.input_features.v1(
features="""return_6
fs_roe_0
fs_eps_0
fs_bps_0
fs_roa_0
return_20
rank_turn_0
rank_turn_9
#'ta_rsi(close_0,28)
rank_pb_lf_0
fs_roa_ttm_0
fs_roe_ttm_0
high_0/low_0
fs_eps_yoy_0
sqrt(high_0*low_0)-amount_0/volume_0*adjust_factor_0
sum(max(0,high_0-delay(close_0,1)),20)/sum(max(0,delay(close_0, 1)-low_0),20)*100
((close_0-open_0)/((high_0-low_0)+.001))
return_9
ta_ema(((high_0+low_0)/2-(delay(high_0,1)+delay(low_0,1))/2)*(high_0-low_0)/volume_0,7)
return_1
fs_operating_revenue_yoy_0
fs_operating_revenue_qoq_0
fs_net_profit_margin_ttm_0
fs_gross_profit_margin_ttm_0
rank_pe_lyr_0
rank_pe_ttm_0
rank_ps_ttm_0
rank_return_9
rank_fs_bps_0
rank_return_6
rank_return_15
close_1/open_0
open_0/close_0
high_0/close_1
close_0/open_0
rank_return_30
rank_return_20
rank_avg_turn_1
close_9/close_0
rank_avg_turn_6
fs_cash_ratio_0
close_4/close_0
close_6/close_0
close_2/close_0
close_3/close_0
close_5/close_0
close_1/close_0
rank_avg_turn_0
volume_0/mean(volume_0, 3)*100
rank_avg_turn_3
rank_avg_turn_9
close_20/close_0
rank_avg_turn_15
close_15/close_0
rank_avg_turn_20
rank_market_cap_0
amount_2/amount_0
rank_fs_eps_yoy_0
return_5/return_0
amount_4/amount_0
rank_fs_roe_ttm_0
return_9/return_0
amount_3/amount_0
amount_5/amount_0
(-1*correlation(open_0,volume_0,10))
(-1*delta((((close_0-low_0)-(high_0-close_0))/(close_0-low_0)),9))
ta_atr(high_0,low_0,close_0,5)
((-1*((low_0-close_0)*(open_0**5)))/((low_0-high_0)*(close_0**5)))
return_6
-1*delta(((close_0-low_0)-(high_0-close_0))/(high_0-low_0),1)
return_3
std(volume_0,10)
ta_ema(((high_0+low_0-0)/2-(delay(high_0,1)+delay(low_0,1))/2)*(high_0-low_0)/volume_0,15)
(close_0-mean(close_0,12))/mean(close_0,12)*100
(close_0-delay(close_0,6))/delay(close_0,6)*volume_0
(volume_0-delay(volume_0,5))/delay(volume_0,5)*100
sum(((close_0-low_0)-(high_0-close_0))/(high_0-low_0)*volume_0,20)
(close_0-mean(close_0,24))/mean(close_0,24)*100
((sum(close_0,7)/7)-close_0)+correlation(amount_0/volume_0*adjust_factor_0,delay(close_0,5),230)
return_15
rank((-1*((1-(open_0/close_0))**1)))
mean(close_0,12)/close_0
ta_ema((close_0-ts_min(low_0,9))/(ts_max(high_0,9)-ts_min(low_0,9))*100,3)
return_20
(close_0-delay(close_0,20))/delay(close_0,20)*100
close_0-delay(close_0,5)
ta_ema(volume_0, 21)
close_0/delay(close_0,5)
std(amount_0,20)
sum(((close_0-low_0)-(high_0-close_0))/(high_0-low_0)*volume_0,6)
((high_0+low_0+close_0)/3-mean((high_0+low_0+close_0)/3,12))/(0.015*mean(abs(close_0-mean((high_0+low_0+close_0)/3,12)),12))
std(amount_0,6)
ta_ema(((ts_max(high_0,6)-close_0)/(ts_max(high_0,6)-ts_min(low_0,6))*100),20)
ta_ema(ta_ema((close_0-ts_min(low_0,9))/(ts_max(high_0,9)-ts_min(low_0,9))*100,3),3)
(close_0-delay(close_0,6))/delay(close_0,6)*100
(((high_0*low_0)**0.5)-amount_0/volume_0*adjust_factor_0)
(mean(close_0,3)+mean(close_0,6)+mean(close_0,12)+mean(close_0,24))/(4*close_0)
ta_ema(close_0-delay(close_0,5),5)
ta_ema(high_0-low_0,10)/ta_ema(ta_ema(high_0-low_0,10),10)
((high_0-ta_ema(close_0,15))-(low_0-ta_ema(close_0,15)))/close_0
(close_0+high_0+low_0)/3
std(volume_0,20)
open_0/shift(close_0,1)-1
return_9
(mean(close_0,3)+mean(close_0,6)+mean(close_0,12)+mean(close_0,24))/4
rank(delta(((((high_0+low_0)/2)*0.2)+(amount_0/volume_0*adjust_factor_0*0.8)),4)*-1)
(rank(sign(delta((((open_0*0.85)+(high_0 *0.15))),4)))*-1)
(-1*correlation(close_0,volume_0, 10))
close_0-delay(close_0,20)
(close_0-delay(close_0,1))/delay(close_0,1)*volume_0
(close_0-delay(close_0,12))/delay(close_0,12)*volume_0
return_3
return_0
(high_0-low_0-ta_ema(high_0-low_0, 11))/ta_ema(high_0-low_0, 11)*100
return_1
mean(abs(close_0-mean(close_0,6)),6)
-1*((low_0-close_0*(open_0**5)))/((close_0-high_0)*(close_0**5))
mean(amount_0,20)
return_30
return_15
(rank((amount_0/volume_0*adjust_factor_0-close_0))/rank((amount_0/volume_0*adjust_factor_0 + close_0)))
((rank(max((amount_0/volume_0*adjust_factor_0-close_0),3))+rank(min((amount_0/volume_0*adjust_factor_0-close_0), 3)))*rank(delta(volume_0, 3)))
ta_beta(high_0,low_0,12)
correlation(amount_0/volume_0*adjust_factor_0,volume_0,5)
ta_adx(high_0,low_0,close_0,14)
rank_turn_3
rank_turn_1
correlation(high_0/low_0,volume_0,4)
rank_turn_6
#'ta_rsi(close_0,14)
rank_turn_15
rank_turn_20
rank_fs_roa_0
rank_fs_roe_0
rank_fs_eps_0
rank_return_3
rank_return_1
rank_return_0
low_0/close_1
return_4/return_0
rank_fs_roa_ttm_0
amount_1/amount_0
ta_wma(close_0,5)/close_0
mean(close_0,5)/close_0
ta_ema(close_0,5)/close_0
ta_atr(high_0,low_0,close_0,14)/close_0
avg_turn_9/turn_0
avg_turn_1/turn_0
ta_wma(close_0,30)/close_0
return_9/return_5
avg_turn_6/turn_0
return_3/return_0
ta_atr(high_0,low_0,close_0,28)/close_0
close_0/mean(close_0,10)
return_1/return_5
return_0/return_3
mean(close_0,30)/close_0
return_1/return_0
return_9/return_3
ta_ema(close_0,30)/close_0
avg_turn_3/turn_0
return_1/return_3
close_0/mean(close_0,30)
return_6/return_5
return_6/return_0
close_0/mean(close_0,20)
return_0/return_5
return_6/return_3
fs_net_profit_yoy_0
fs_net_profit_qoq_0
return_90/return_5
return_15/return_0
avg_turn_15/turn_0
return_20/return_5
return_50/return_5
rank_sh_holder_num_0
return_30/return_5
avg_turn_20/turn_0
return_30/return_0
return_30/return_3
return_20/return_0
return_20/return_3
return_15/return_5
rank_fs_cash_ratio_0
return_70/return_5
return_60/return_5
return_80/return_5
return_15/return_3
return_30/return_10
return_70/return_10
amount_0/avg_amount_5
return_80/return_10
return_50/return_10
return_20/return_10
return_90/return_10
amount_0/avg_amount_3
return_120/return_5
return_60/return_10
fs_net_profit_margin_0
(high_0-low_0)/close_0
return_120/return_10
mean(close_0,20)/mean(close_0,30)
mean(close_0,30)/mean(close_0,60)
mean(close_0,10)/mean(close_0,60)
(low_1-close_0)/close_0
rank_market_cap_float_0
mean(close_0,10)/mean(close_0,20)
(low_1-close_1)/close_0
(close_1-low_0)/close_0
(low_0-close_1)/close_0
mean(close_0,10)/mean(close_0,30)
rank_fs_net_profit_qoq_0
rank_sh_holder_avg_pct_0
fs_gross_profit_margin_0
(high_0-close_1)/close_0
(high_1-close_0)/close_0
rank_fs_net_profit_yoy_0
(open_0-close_0)/close_0
(close_1-high_0)/close_0
(high_1-close_1)/close_0
(high_0-low_0)/(close_0-open_0)
rank_fs_operating_revenue_yoy_0
rank_fs_operating_revenue_qoq_0
(open_0-close_0)/(high_0-low_0)
rank_sh_holder_avg_pct_6m_chng_0
rank_sh_holder_avg_pct_3m_chng_0
mean(close_0,3)/close_0
mean(amount_0,3)/amount_0
mean(volume_0,3)/volume_0
avg_mf_net_amount_6/mf_net_amount_0
avg_mf_net_amount_9/mf_net_amount_0
avg_mf_net_amount_3/mf_net_amount_0
avg_mf_net_amount_20/mf_net_amount_0
avg_mf_net_amount_15/mf_net_amount_0
avg_mf_net_amount_12/mf_net_amount_0
avg_mf_net_amount_9/avg_mf_net_amount_3
avg_mf_net_amount_6/avg_mf_net_amount_3
close_0/mean(close_0,3)
avg_mf_net_amount_20/avg_mf_net_amount_3
avg_mf_net_amount_12/avg_mf_net_amount_3
avg_mf_net_amount_15/avg_mf_net_amount_3
amount_0/mean(amount_0,3)
((close_0-low_0)-(high_0-close_0))/(high_0-close_0)
(high_0-low_0+high_1-low_1+high_2-low_2)/close_0
mean(close_0,6)/close_0
mean(amount_0,6)/amount_0
mean(volume_0,6)/volume_0
3/1*(high_0-low_0)/(high_0-low_0+high_1-low_1+high_2-low_2)
mean(close_0,6)/mean(close_0,3)
mean(close_0,9)/close_0
mean(amount_0,6)/mean(amount_0,3)
mean(amount_0,9)/amount_0
mean(volume_0,9)/volume_0
(mean(high_0,6)-mean(low_0,6))/close_0
mean(close_0,9)/mean(close_0,3)
mean(amount_0,9)/mean(amount_0,3)
mean(close_0,15)/close_0
(mean(high_0,9)-mean(low_0,9))/close_0
mean(amount_0,15)/amount_0
mean(volume_0,15)/volume_0
(mean(high_0,6)-mean(low_0,6))/(mean(high_0,3)-mean(low_0,3))
mean(close_0,15)/mean(close_0,3)
mean(amount_0,15)/mean(amount_0,3)
mean(close_0,20)/close_0
mean(amount_0,20)/amount_0
mean(volume_0,20)/volume_0
mean(close_0,20)/mean(close_0,3)
(mean(high_0,9)-mean(low_0,9))/(mean(high_0,3)-mean(low_0,3))
mean(amount_0,20)/mean(amount_0,3)
(sum(high_0,15)-sum(low_0,15))/close_0
(mean(high_0,15)-mean(low_0,15))/(mean(high_0,3)-mean(low_0,3))
(sum(high_0,20)-sum(low_0,20))/close_0
(mean(high_0,20)-mean(low_0,20))/(mean(high_0,3)-mean(low_0,3))"""
)
m4 = M.general_feature_extractor.v6(
instruments=m1.data,
features=m3.data,
start_date='',
end_date=''
)
m5 = M.derived_feature_extractor.v2(
input_data=m4.data,
features=m3.data,
date_col='date',
instrument_col='instrument'
)
m7 = M.join.v3(
data1=m2.data,
data2=m5.data,
on='date,instrument',
how='inner',
sort=False
)
m13 = M.dropnan.v1(
input_data=m7.data
)
m6 = M.stock_ranker_train.v5(
training_ds=m13.data,
features=m3.data,
learning_algorithm='排序',
number_of_leaves=30,
minimum_docs_per_leaf=1000,
number_of_trees=20,
learning_rate=0.1,
max_bins=1023,
feature_fraction=1,
m_lazy_run=False
)
m9 = M.instruments.v2(
start_date=T.live_run_param('trading_date', '2015-01-01'),
end_date=T.live_run_param('trading_date', '2017-01-01'),
market='CN_STOCK_A',
instrument_list='',
max_count=0
)
m10 = M.general_feature_extractor.v6(
instruments=m9.data,
features=m3.data,
start_date='',
end_date=''
)
m11 = M.derived_feature_extractor.v2(
input_data=m10.data,
features=m3.data,
date_col='date',
instrument_col='instrument'
)
m14 = M.dropnan.v1(
input_data=m11.data
)
m8 = M.stock_ranker_predict.v5(
model=m6.model,
data=m14.data,
m_lazy_run=False
)
# 回测引擎：每日数据处理函数，每天执行一次
def m12_handle_data_bigquant_run(context, data):
# 按日期过滤得到今日的预测数据
ranker_prediction = context.ranker_prediction[
context.ranker_prediction.date == data.current_dt.strftime('%Y-%m-%d')]
# 1. 资金分配
# 平均持仓时间是hold_days，每日都将买入股票，每日预期使用 1/hold_days 的资金
# 实际操作中，会存在一定的买入误差，所以在前hold_days天，等量使用资金；之后，尽量使用剩余资金（这里设置最多用等量的1.5倍）
is_staging = context.trading_day_index < context.options['hold_days'] # 是否在建仓期间（前 hold_days 天）
cash_avg = context.portfolio.portfolio_value / context.options['hold_days']
cash_for_buy = min(context.portfolio.cash, (1 if is_staging else 1.5) * cash_avg)
cash_for_sell = cash_avg - (context.portfolio.cash - cash_for_buy)
positions = {e.symbol: p.amount * p.last_sale_price
for e, p in context.perf_tracker.position_tracker.positions.items()}
# 2. 生成卖出订单：hold_days天之后才开始卖出；对持仓的股票，按StockRanker预测的排序末位淘汰
if not is_staging and cash_for_sell > 0:
equities = {e.symbol: e for e, p in context.perf_tracker.position_tracker.positions.items()}
instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(
lambda x: x in equities and not context.has_unfinished_sell_order(equities[x]))])))
# print('rank order for sell %s' % instruments)
for instrument in instruments:
context.order_target(context.symbol(instrument), 0)
cash_for_sell -= positions[instrument]
if cash_for_sell <= 0:
break
# 3. 生成买入订单：按StockRanker预测的排序，买入前面的stock_count只股票
buy_cash_weights = context.stock_weights
buy_instruments = list(ranker_prediction.instrument[:len(buy_cash_weights)])
max_cash_per_instrument = context.portfolio.portfolio_value * context.max_cash_per_instrument
for i, instrument in enumerate(buy_instruments):
cash = cash_for_buy * buy_cash_weights[i]
if cash > max_cash_per_instrument - positions.get(instrument, 0):
# 确保股票持仓量不会超过每次股票最大的占用资金量
cash = max_cash_per_instrument - positions.get(instrument, 0)
if cash > 0:
context.order_value(context.symbol(instrument), cash)
# 回测引擎：准备数据，只执行一次
def m12_prepare_bigquant_run(context):
pass
# 回测引擎：初始化函数，只执行一次
def m12_initialize_bigquant_run(context):
# 加载预测数据
context.ranker_prediction = context.options['data'].read_df()
# 系统已经设置了默认的交易手续费和滑点，要修改手续费可使用如下函数
context.set_commission(PerOrder(buy_cost=0.0003, sell_cost=0.0013, min_cost=5))
# 预测数据，通过options传入进来，使用 read_df 函数，加载到内存 (DataFrame)
# 设置买入的股票数量，这里买入预测股票列表排名靠前的5只
stock_count = 5
# 每只的股票的权重，如下的权重分配会使得靠前的股票分配多一点的资金，[0.339160, 0.213986, 0.169580, ..]
context.stock_weights = T.norm([1 / math.log(i + 2) for i in range(0, stock_count)])
# 设置每只股票占用的最大资金比例
context.max_cash_per_instrument = 0.2
context.options['hold_days'] = 5
m12 = M.trade.v3(
instruments=m9.data,
options_data=m8.predictions,
start_date='',
end_date='',
handle_data=m12_handle_data_bigquant_run,
prepare=m12_prepare_bigquant_run,
initialize=m12_initialize_bigquant_run,
volume_limit=0.025,
order_price_field_buy='open',
order_price_field_sell='close',
capital_base=1000000,
benchmark='000300.SHA',
auto_cancel_non_tradable_orders=True,
data_frequency='daily',
plot_charts=True,
backtest_only=False
)
In [4]:
m6.feature_gains.read_df()
Out[4]:
