克隆策略
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#号开始的表示注释\n# 多个特征,每行一个,可以包含基础特征和衍生特征\n\n\n#max(turn_0,turn_1)-max(turn_2,turn_3,turn_4,turn_5,turn_6,turn_7,turn_8,turn_9)\n#avg_turn_0\n#avg_turn_13\n#avg_turn_5\n#sum(where(return_0>1,where(turn_0>turn_1,1,-1),where(turn_0<turn_1,1,-1)),10)\ndaily_return_0\n\ndaily_return_1\ndaily_return_2\n#mto=100*shift(open_0, -1)/close_0-100\n\nreturn_5\nreturn_10\nreturn_20\navg_amount_0/avg_amount_5\navg_amount_5/avg_amount_20\nrank_avg_amount_0/rank_avg_amount_5\nrank_avg_amount_5/rank_avg_amount_10\nrank_return_0\nrank_return_5\nrank_return_10\nrank_return_0/rank_return_5\nrank_return_5/rank_return_10\npe_ttm_0\n\n(high_0-open_0/2-close_0/2)/open_0*10\n(high_1-open_1/2-close_1/2)/open_1*10\n(open_0/2+close_0/2-low_0)/open_0*10\n(open_1/2+close_1/2-low_1)/open_1*10\n(close_0-open_0)/open_0\n(close_1-open_1)/open_1\n(high_0-open_0/2-close_0/2)/(close_0-open_0)\n(high_1-open_1/2-close_1/2)/(close_1-open_1)\nrank((high_0-open_0/2-close_0/2)/(close_0-open_0))\nrank((high_1-open_1/2-close_1/2)/(close_1-open_1))\n(high_0+low_0)/close_1\n(high_1+low_1)/close_2\n((high_0+low_0)/close_1)/((high_1+low_1)/close_2)\n#((high_1+low_1)/close_2)/((high_2+low_2)/close_3)\n\nrank_avg_mf_net_amount_3\n#rank_avg_mf_net_amount_0\nrank_avg_mf_net_amount_10\nrank_avg_mf_net_amount_20","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features_ds","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24"}],"OutputPortsInternal":[{"Name":"data","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":3,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"287d2cb0-f53c-4101-bdf8-104b137c8601-53","ModuleId":"BigQuantSpace.join.join-v3","ModuleParameters":[{"Name":"on","Value":"date,instrument","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"how","Value":"inner","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"sort","Value":"False","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"data1","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-53"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"data2","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-53"}],"OutputPortsInternal":[{"Name":"data","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-53","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":7,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"287d2cb0-f53c-4101-bdf8-104b137c8601-60","ModuleId":"BigQuantSpace.stock_ranker_predict.stock_ranker_predict-v5","ModuleParameters":[{"Name":"m_lazy_run","Value":"False","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"model","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-60"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"data","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-60"}],"OutputPortsInternal":[{"Name":"predictions","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-60","OutputType":null},{"Name":"m_lazy_run","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-60","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":8,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"287d2cb0-f53c-4101-bdf8-104b137c8601-62","ModuleId":"BigQuantSpace.instruments.instruments-v2","ModuleParameters":[{"Name":"start_date","Value":"2020-06-01","ValueType":"Literal","LinkedGlobalParameter":"交易日期"},{"Name":"end_date","Value":"2020-07-31","ValueType":"Literal","LinkedGlobalParameter":"交易日期"},{"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-bdf8-104b137c8601-62"}],"OutputPortsInternal":[{"Name":"data","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-62","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":9,"IsPartOfPartialRun":null,"Comment":"预测数据,用于回测和模拟","CommentCollapsed":false},{"Id":"287d2cb0-f53c-4101-bdf8-104b137c8601-84","ModuleId":"BigQuantSpace.dropnan.dropnan-v1","ModuleParameters":[],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-84"}],"OutputPortsInternal":[{"Name":"data","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-84","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":13,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-86","ModuleId":"BigQuantSpace.dropnan.dropnan-v1","ModuleParameters":[],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"-86"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-86","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":14,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-215","ModuleId":"Bi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回测引擎:初始化函数,只执行一次\ndef bigquant_run(context):\n # 加载预测数据\n context.ranker_prediction = context.options['data'].read_df()\n\n # 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\n context.set_commission(PerOrder(buy_cost=0.0003, sell_cost=0.0013, min_cost=5))\n # 预测数据,通过options传入进来,使用 read_df 函数,加载到内存 (DataFrame)\n # 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\n stock_count = 1\n # 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, ..]\n context.stock_weights = T.norm([1 / math.log(i + 2) for i in range(0, stock_count)])\n # 设置每只股票占用的最大资金比例\n context.max_cash_per_instrument = 0.5\n context.options['hold_days'] = 0\n","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"handle_data","Value":"# 回测引擎:每日数据处理函数,每天执行一次\ndef bigquant_run(context, data):\n # 按日期过滤得到今日的预测数据\n ranker_prediction = context.ranker_prediction[\n context.ranker_prediction.date == data.current_dt.strftime('%Y-%m-%d')]\n\n # 1. 资金分配\n # 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\n # 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)\n is_staging = context.trading_day_index < context.options['hold_days'] # 是否在建仓期间(前 hold_days 天)\n cash_avg = context.portfolio.portfolio_value / context.options['hold_days']\n cash_for_buy = min(context.portfolio.cash, (1 if is_staging else 1.5) * cash_avg)\n cash_for_sell = cash_avg - (context.portfolio.cash - cash_for_buy)\n positions = {e.symbol: p.amount * p.last_sale_price\n for e, p in context.portfolio.positions.items()}\n\n # 2. 生成卖出订单:hold_days天之后才开始卖出;对持仓的股票,按机器学习算法预测的排序末位淘汰\n if not is_staging and cash_for_sell > 0:\n equities = {e.symbol: e for e, p in context.portfolio.positions.items()}\n instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(\n lambda x: x in equities)])))\n\n for instrument in instruments:\n context.order_target(context.symbol(instrument), 0)\n cash_for_sell -= positions[instrument]\n if cash_for_sell <= 0:\n break\n\n # 3. 生成买入订单:按机器学习算法预测的排序,买入前面的stock_count只股票\n buy_cash_weights = context.stock_weights\n buy_instruments = list(ranker_prediction.instrument[:len(buy_cash_weights)])\n max_cash_per_instrument = context.portfolio.portfolio_value * context.max_cash_per_instrument\n for i, instrument in enumerate(buy_instruments):\n cash = cash_for_buy * buy_cash_weights[i]\n if cash > max_cash_per_instrument - positions.get(instrument, 0):\n # 确保股票持仓量不会超过每次股票最大的占用资金量\n cash = max_cash_per_instrument - positions.get(instrument, 0)\n if cash > 0:\n context.order_value(context.symbol(instrument), cash)\n #----------------------------START:持有固定天数卖出---------------------------\n today = data.current_dt\n # 不是建仓期(在前hold_days属于建仓期)\n if not is_staging:\n equities = {e.symbol: p for e, p in context.portfolio.positions.items() if p.amount>0}\n for instrument in equities:\n# print('last_sale_date: ', equities[instrument].last_sale_date)\n sid = equities[instrument].sid # 交易标的\n # 今天和上次交易的时间相隔hold_days就全部卖出\n if 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In [88]:
# 本代码由可视化策略环境自动生成 2020年7月31日 10:35
# 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。
# 回测引擎:初始化函数,只执行一次
def m19_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 = 1
# 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 = 0.5
context.options['hold_days'] = 0
# 回测引擎:每日数据处理函数,每天执行一次
def m19_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.portfolio.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,按机器学习算法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.portfolio.positions.items()}
instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(
lambda x: x in equities)])))
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:按机器学习算法预测的排序,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)
#----------------------------START:持有固定天数卖出---------------------------
today = data.current_dt
# 不是建仓期(在前hold_days属于建仓期)
if not is_staging:
equities = {e.symbol: p for e, p in context.portfolio.positions.items() if p.amount>0}
for instrument in equities:
# print('last_sale_date: ', equities[instrument].last_sale_date)
sid = equities[instrument].sid # 交易标的
# 今天和上次交易的时间相隔hold_days就全部卖出
if today-equities[instrument].last_sale_date>=datetime.timedelta(context.options['hold_days']) and data.can_trade(context.symbol(instrument)):
context.order_target_percent(sid, 0)
#--------------------------------END:持有固定天数卖出---------------------------
# 回测引擎:准备数据,只执行一次
def m19_prepare_bigquant_run(context):
pass
m1 = M.instruments.v2(
start_date='2019-01-01',
end_date='2020-07-30',
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/develop/datasource/deprecated/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/develop/bigexpr/usage.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, -1) /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='399006.ZIX',
drop_na_label=True,
cast_label_int=True
)
m3 = M.input_features.v1(
features="""# #号开始的表示注释
# 多个特征,每行一个,可以包含基础特征和衍生特征
#max(turn_0,turn_1)-max(turn_2,turn_3,turn_4,turn_5,turn_6,turn_7,turn_8,turn_9)
#avg_turn_0
#avg_turn_13
#avg_turn_5
#sum(where(return_0>1,where(turn_0>turn_1,1,-1),where(turn_0<turn_1,1,-1)),10)
daily_return_0
daily_return_1
daily_return_2
#mto=100*shift(open_0, -1)/close_0-100
return_5
return_10
return_20
avg_amount_0/avg_amount_5
avg_amount_5/avg_amount_20
rank_avg_amount_0/rank_avg_amount_5
rank_avg_amount_5/rank_avg_amount_10
rank_return_0
rank_return_5
rank_return_10
rank_return_0/rank_return_5
rank_return_5/rank_return_10
pe_ttm_0
(high_0-open_0/2-close_0/2)/open_0*10
(high_1-open_1/2-close_1/2)/open_1*10
(open_0/2+close_0/2-low_0)/open_0*10
(open_1/2+close_1/2-low_1)/open_1*10
(close_0-open_0)/open_0
(close_1-open_1)/open_1
(high_0-open_0/2-close_0/2)/(close_0-open_0)
(high_1-open_1/2-close_1/2)/(close_1-open_1)
rank((high_0-open_0/2-close_0/2)/(close_0-open_0))
rank((high_1-open_1/2-close_1/2)/(close_1-open_1))
(high_0+low_0)/close_1
(high_1+low_1)/close_2
((high_0+low_0)/close_1)/((high_1+low_1)/close_2)
#((high_1+low_1)/close_2)/((high_2+low_2)/close_3)
rank_avg_mf_net_amount_3
#rank_avg_mf_net_amount_0
rank_avg_mf_net_amount_10
rank_avg_mf_net_amount_20"""
)
m15 = M.general_feature_extractor.v7(
instruments=m1.data,
features=m3.data,
start_date='',
end_date='',
before_start_days=90
)
m11 = M.chinaa_stock_filter.v1(
input_data=m15.data,
index_constituent_cond=['全部'],
board_cond=['上证主板', '深证主板', '创业板'],
industry_cond=['全部'],
st_cond=['正常'],
delist_cond=['非退市'],
output_left_data=False
)
m16 = M.derived_feature_extractor.v3(
input_data=m11.data,
features=m3.data,
date_col='date',
instrument_col='instrument',
drop_na=False,
remove_extra_columns=False
)
m7 = M.join.v3(
data1=m2.data,
data2=m16.data,
on='date,instrument',
how='inner',
sort=False
)
m13 = M.dropnan.v1(
input_data=m7.data
)
m10 = M.filter.v3(
input_data=m13.data,
expr='daily_return_0>1.095',
output_left_data=False
)
m4 = M.stock_ranker_train.v6(
training_ds=m10.data,
features=m3.data,
learning_algorithm='排序',
number_of_leaves=30,
minimum_docs_per_leaf=1000,
number_of_trees=450,
learning_rate=0.3,
max_bins=1023,
feature_fraction=1,
data_row_fraction=1,
ndcg_discount_base=1,
m_lazy_run=False
)
m9 = M.instruments.v2(
start_date=T.live_run_param('trading_date', '2020-06-01'),
end_date=T.live_run_param('trading_date', '2020-07-31'),
market='CN_STOCK_A',
instrument_list='',
max_count=0
)
m17 = M.general_feature_extractor.v7(
instruments=m9.data,
features=m3.data,
start_date='',
end_date='',
before_start_days=90
)
m12 = M.chinaa_stock_filter.v1(
input_data=m17.data,
index_constituent_cond=['全部'],
board_cond=['上证主板', '深证主板', '创业板'],
industry_cond=['全部'],
st_cond=['正常'],
delist_cond=['非退市'],
output_left_data=False
)
m18 = M.derived_feature_extractor.v3(
input_data=m12.data,
features=m3.data,
date_col='date',
instrument_col='instrument',
drop_na=False,
remove_extra_columns=False
)
m14 = M.dropnan.v1(
input_data=m18.data
)
m20 = M.filter.v3(
input_data=m14.data,
expr='daily_return_0>1.095',
output_left_data=False
)
m8 = M.stock_ranker_predict.v5(
model=m4.model,
data=m20.data,
m_lazy_run=False
)
m19 = M.trade.v4(
instruments=m9.data,
options_data=m8.predictions,
start_date='',
end_date='',
initialize=m19_initialize_bigquant_run,
handle_data=m19_handle_data_bigquant_run,
prepare=m19_prepare_bigquant_run,
volume_limit=0.025,
order_price_field_buy='open',
order_price_field_sell='open',
capital_base=1000000,
auto_cancel_non_tradable_orders=True,
data_frequency='daily',
price_type='真实价格',
product_type='股票',
plot_charts=True,
backtest_only=False,
benchmark='000300.SHA'
)
m6 = M.input_features.v1(
features="""# #号开始的表示注释
# 多个特征,每行一个,可以包含基础特征和衍生特征
zf=(shift(close_0, -1)/shift(open_0, -1)-1)*100
zfo=(shift(open_0, -2)/shift(open_0, -1)-1)*100
zfc=(shift(close_0, -2)/shift(open_0, -1)-1)*100
zfh=(shift(high_0, -2)/shift(open_0, -1)-1)*100
mto=100*shift(open_0, -1)/close_0-100"""
)
m21 = M.general_feature_extractor.v7(
instruments=m9.data,
features=m6.data,
start_date='',
end_date='',
before_start_days=90
)
m22 = M.derived_feature_extractor.v3(
input_data=m21.data,
features=m6.data,
date_col='date',
instrument_col='instrument',
drop_na=False,
remove_extra_columns=False
)
m5 = M.join.v3(
data1=m8.predictions,
data2=m22.data,
on='date,instrument',
how='inner',
sort=True,
m_cached=False
)
日志 105 条,错误日志
0 条
[2020-07-31 10:33:14.882321] INFO: moduleinvoker: instruments.v2 开始运行..
[2020-07-31 10:33:14.888618] INFO: moduleinvoker: 命中缓存
[2020-07-31 10:33:14.889962] INFO: moduleinvoker: instruments.v2 运行完成[0.007631s].
[2020-07-31 10:33:14.891768] INFO: moduleinvoker: advanced_auto_labeler.v2 开始运行..
[2020-07-31 10:33:14.935103] INFO: moduleinvoker: 命中缓存
[2020-07-31 10:33:14.936833] INFO: moduleinvoker: advanced_auto_labeler.v2 运行完成[0.045047s].
[2020-07-31 10:33:14.938562] INFO: moduleinvoker: input_features.v1 开始运行..
[2020-07-31 10:33:14.988241] INFO: moduleinvoker: 命中缓存
[2020-07-31 10:33:14.989860] INFO: moduleinvoker: input_features.v1 运行完成[0.051279s].
[2020-07-31 10:33:14.997671] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..
[2020-07-31 10:33:15.004436] INFO: moduleinvoker: 命中缓存
[2020-07-31 10:33:15.005730] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.008057s].
[2020-07-31 10:33:15.007626] INFO: moduleinvoker: chinaa_stock_filter.v1 开始运行..
[2020-07-31 10:33:15.012398] INFO: moduleinvoker: 命中缓存
[2020-07-31 10:33:15.013634] INFO: moduleinvoker: chinaa_stock_filter.v1 运行完成[0.005998s].
[2020-07-31 10:33:15.015279] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..
[2020-07-31 10:33:15.025215] INFO: moduleinvoker: 命中缓存
[2020-07-31 10:33:15.026617] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.011318s].
[2020-07-31 10:33:15.029272] INFO: moduleinvoker: join.v3 开始运行..
[2020-07-31 10:33:15.034913] INFO: moduleinvoker: 命中缓存
[2020-07-31 10:33:15.036074] INFO: moduleinvoker: join.v3 运行完成[0.006793s].
[2020-07-31 10:33:15.038013] INFO: moduleinvoker: dropnan.v1 开始运行..
[2020-07-31 10:33:15.042876] INFO: moduleinvoker: 命中缓存
[2020-07-31 10:33:15.044187] INFO: moduleinvoker: dropnan.v1 运行完成[0.006167s].
[2020-07-31 10:33:15.046047] INFO: moduleinvoker: filter.v3 开始运行..
[2020-07-31 10:33:15.053780] INFO: moduleinvoker: 命中缓存
[2020-07-31 10:33:15.054936] INFO: moduleinvoker: filter.v3 运行完成[0.008884s].
[2020-07-31 10:33:15.056554] INFO: moduleinvoker: stock_ranker_train.v6 开始运行..
[2020-07-31 10:33:15.376365] INFO: moduleinvoker: 命中缓存
[2020-07-31 10:33:15.540448] INFO: moduleinvoker: stock_ranker_train.v6 运行完成[0.483867s].
[2020-07-31 10:33:15.542541] INFO: moduleinvoker: instruments.v2 开始运行..
[2020-07-31 10:33:15.630973] INFO: moduleinvoker: instruments.v2 运行完成[0.08839s].
[2020-07-31 10:33:15.641451] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..
[2020-07-31 10:33:17.414645] INFO: 基础特征抽取: 年份 2020, 特征行数=389290
[2020-07-31 10:33:17.767759] INFO: 基础特征抽取: 总行数: 389290
[2020-07-31 10:33:17.777559] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[2.136117s].
[2020-07-31 10:33:17.779402] INFO: moduleinvoker: chinaa_stock_filter.v1 开始运行..
[2020-07-31 10:33:20.103904] INFO: A股股票过滤: 过滤 /y_2020, 361415/0/389290
[2020-07-31 10:33:20.120354] INFO: A股股票过滤: 过滤完成, 361415 + 0
[2020-07-31 10:33:20.478952] INFO: moduleinvoker: chinaa_stock_filter.v1 运行完成[2.699529s].
[2020-07-31 10:33:20.480840] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..
[2020-07-31 10:33:20.713265] INFO: derived_feature_extractor: 提取完成 avg_amount_0/avg_amount_5, 0.003s
[2020-07-31 10:33:20.719358] INFO: derived_feature_extractor: 提取完成 avg_amount_5/avg_amount_20, 0.004s
[2020-07-31 10:33:20.724182] INFO: derived_feature_extractor: 提取完成 rank_avg_amount_0/rank_avg_amount_5, 0.003s
[2020-07-31 10:33:20.728892] INFO: derived_feature_extractor: 提取完成 rank_avg_amount_5/rank_avg_amount_10, 0.002s
[2020-07-31 10:33:20.732452] INFO: derived_feature_extractor: 提取完成 rank_return_0/rank_return_5, 0.002s
[2020-07-31 10:33:20.735235] INFO: derived_feature_extractor: 提取完成 rank_return_5/rank_return_10, 0.001s
[2020-07-31 10:33:20.737749] INFO: derived_feature_extractor: 提取完成 (high_0-open_0/2-close_0/2)/open_0*10, 0.002s
[2020-07-31 10:33:20.740050] INFO: derived_feature_extractor: 提取完成 (high_1-open_1/2-close_1/2)/open_1*10, 0.001s
[2020-07-31 10:33:20.742483] INFO: derived_feature_extractor: 提取完成 (open_0/2+close_0/2-low_0)/open_0*10, 0.002s
[2020-07-31 10:33:20.744767] INFO: derived_feature_extractor: 提取完成 (open_1/2+close_1/2-low_1)/open_1*10, 0.001s
[2020-07-31 10:33:20.746620] INFO: derived_feature_extractor: 提取完成 (close_0-open_0)/open_0, 0.001s
[2020-07-31 10:33:20.748391] INFO: derived_feature_extractor: 提取完成 (close_1-open_1)/open_1, 0.001s
[2020-07-31 10:33:20.750540] INFO: derived_feature_extractor: 提取完成 (high_0-open_0/2-close_0/2)/(close_0-open_0), 0.001s
[2020-07-31 10:33:20.752619] INFO: derived_feature_extractor: 提取完成 (high_1-open_1/2-close_1/2)/(close_1-open_1), 0.001s
[2020-07-31 10:33:20.988746] INFO: derived_feature_extractor: 提取完成 rank((high_0-open_0/2-close_0/2)/(close_0-open_0)), 0.235s
[2020-07-31 10:33:21.085785] INFO: derived_feature_extractor: 提取完成 rank((high_1-open_1/2-close_1/2)/(close_1-open_1)), 0.095s
[2020-07-31 10:33:21.090362] INFO: derived_feature_extractor: 提取完成 (high_0+low_0)/close_1, 0.003s
[2020-07-31 10:33:21.096365] INFO: derived_feature_extractor: 提取完成 (high_1+low_1)/close_2, 0.005s
[2020-07-31 10:33:21.099853] INFO: derived_feature_extractor: 提取完成 ((high_0+low_0)/close_1)/((high_1+low_1)/close_2), 0.002s
[2020-07-31 10:33:21.343084] INFO: derived_feature_extractor: /y_2020, 361415
[2020-07-31 10:33:22.331375] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[1.850519s].
[2020-07-31 10:33:22.334910] INFO: moduleinvoker: dropnan.v1 开始运行..
[2020-07-31 10:33:23.136716] INFO: dropnan: /y_2020, 308133/361415
[2020-07-31 10:33:23.531014] INFO: dropnan: 行数: 308133/361415
[2020-07-31 10:33:23.545762] INFO: moduleinvoker: dropnan.v1 运行完成[1.210863s].
[2020-07-31 10:33:23.547552] INFO: moduleinvoker: filter.v3 开始运行..
[2020-07-31 10:33:23.557376] INFO: filter: 使用表达式 daily_return_0>1.095 过滤
[2020-07-31 10:33:23.976594] INFO: filter: 过滤 /y_2020, 5599/0/308133
[2020-07-31 10:33:24.255552] INFO: moduleinvoker: filter.v3 运行完成[0.707983s].
[2020-07-31 10:33:24.257455] INFO: moduleinvoker: stock_ranker_predict.v5 开始运行..
[2020-07-31 10:33:24.845231] INFO: StockRanker预测: /y_2020 ..
[2020-07-31 10:33:25.997111] INFO: moduleinvoker: stock_ranker_predict.v5 运行完成[1.73962s].
[2020-07-31 10:33:26.045926] INFO: moduleinvoker: backtest.v8 开始运行..
[2020-07-31 10:33:26.049698] INFO: backtest: biglearning backtest:V8.4.2
[2020-07-31 10:33:26.050594] INFO: backtest: product_type:stock by specified
[2020-07-31 10:33:26.140743] INFO: moduleinvoker: cached.v2 开始运行..
[2020-07-31 10:33:26.147621] INFO: moduleinvoker: 命中缓存
[2020-07-31 10:33:26.148697] INFO: moduleinvoker: cached.v2 运行完成[0.007967s].
[2020-07-31 10:33:26.823727] INFO: algo: TradingAlgorithm V1.6.8
[2020-07-31 10:33:27.316747] INFO: algo: trading transform...
[2020-07-31 10:33:27.763584] INFO: Performance: Simulated 43 trading days out of 43.
[2020-07-31 10:33:27.764670] INFO: Performance: first open: 2020-06-01 09:30:00+00:00
[2020-07-31 10:33:27.765540] INFO: Performance: last close: 2020-07-31 15:00:00+00:00
[2020-07-31 10:33:32.397918] INFO: moduleinvoker: backtest.v8 运行完成[6.351988s].
[2020-07-31 10:33:32.399699] INFO: moduleinvoker: trade.v4 运行完成[6.400168s].
[2020-07-31 10:33:32.402016] INFO: moduleinvoker: input_features.v1 开始运行..
[2020-07-31 10:33:32.521939] INFO: moduleinvoker: 命中缓存
[2020-07-31 10:33:32.523556] INFO: moduleinvoker: input_features.v1 运行完成[0.121541s].
[2020-07-31 10:33:32.532371] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..
[2020-07-31 10:33:33.188788] INFO: 基础特征抽取: 年份 2020, 特征行数=389290
[2020-07-31 10:33:33.353546] INFO: 基础特征抽取: 总行数: 389290
[2020-07-31 10:33:33.364739] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.832362s].
[2020-07-31 10:33:33.366399] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..
[2020-07-31 10:33:33.646915] INFO: derived_feature_extractor: 提取完成 zf=(shift(close_0, -1)/shift(open_0, -1)-1)*100, 0.128s
[2020-07-31 10:33:33.751331] INFO: derived_feature_extractor: 提取完成 zfo=(shift(open_0, -2)/shift(open_0, -1)-1)*100, 0.103s
[2020-07-31 10:33:33.879418] INFO: derived_feature_extractor: 提取完成 zfc=(shift(close_0, -2)/shift(open_0, -1)-1)*100, 0.127s
[2020-07-31 10:33:34.002114] INFO: derived_feature_extractor: 提取完成 zfh=(shift(high_0, -2)/shift(open_0, -1)-1)*100, 0.122s
[2020-07-31 10:33:34.077730] INFO: derived_feature_extractor: 提取完成 mto=100*shift(open_0, -1)/close_0-100, 0.073s
[2020-07-31 10:33:34.355817] INFO: derived_feature_extractor: /y_2020, 389290
[2020-07-31 10:33:35.839819] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[2.473396s].
[2020-07-31 10:33:35.841825] INFO: moduleinvoker: join.v3 开始运行..
[2020-07-31 10:33:36.188760] INFO: join: /y_2020, 行数=5599/389290, 耗时=0.297239s
[2020-07-31 10:33:36.670736] INFO: join: 最终行数: 5599
[2020-07-31 10:33:36.671846] INFO: moduleinvoker: join.v3 运行完成[0.830022s].
In [89]:
#### a=m8.predictions.read_all_df()
#a=a[a.position.isin([1,2,3])].tail(12)
#score =m19.read_raw_perf()
#score.returns.sum()
#b=a["instrument"].tail(10)
c=m1.data.read()
c=c['end_date']
b=m5.data.read_all_df()
b=b[b.position.isin([1,2,3])]
b=b[b.date==c]
#b.to_csv('450tree.csv',header=False,mode='a')
b
Out[89]: