每日交易20%
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":"-107:instruments","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-8:data"},{"DestinationInputPortId":"-779:data1","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-15:data"},{"DestinationInputPortId":"-107:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-161:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-819:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-837:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-5546:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-819:input_data","SourceOutputPortId":"-107:data"},{"DestinationInputPortId":"-5546:training_ds","SourceOutputPortId":"-648:data"},{"DestinationInputPortId":"-161:instruments","SourceOutputPortId":"-152:data"},{"DestinationInputPortId":"-213:instruments","SourceOutputPortId":"-152:data"},{"DestinationInputPortId":"-142:instruments","SourceOutputPortId":"-152:data"},{"DestinationInputPortId":"-837:input_data","SourceOutputPortId":"-161:data"},{"DestinationInputPortId":"-5546:predict_ds","SourceOutputPortId":"-187:data"},{"DestinationInputPortId":"-2908:input_1","SourceOutputPortId":"-779:data"},{"DestinationInputPortId":"-779:data2","SourceOutputPortId":"-819:data"},{"DestinationInputPortId":"-2911:input_1","SourceOutputPortId":"-837:data"},{"DestinationInputPortId":"-224:input_data","SourceOutputPortId":"-207:data"},{"DestinationInputPortId":"-207:data1","SourceOutputPortId":"-213:data"},{"DestinationInputPortId":"-5546:test_ds","SourceOutputPortId":"-224:data"},{"DestinationInputPortId":"-648:input_data","SourceOutputPortId":"-2908:data_1"},{"DestinationInputPortId":"-187:input_data","SourceOutputPortId":"-2911:data_1"},{"DestinationInputPortId":"-207:data2","SourceOutputPortId":"-2911:data_1"},{"DestinationInputPortId":"-142:options_data","SourceOutputPortId":"-5546:predictions"}],"ModuleNodes":[{"Id":"287d2cb0-f53c-4101-bdf8-104b137c8601-8","ModuleId":"BigQuantSpace.instruments.instruments-v2","ModuleParameters":[{"Name":"start_date","Value":"2016-01-01","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"end_date","Value":"2019-01-01","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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stock_hold_now:\n # context.order_target(symbol(instrument), 0)\n # print(today,'大盘风控止损触发,全仓卖出')\n # return\n\n \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 positions = {e.symbol: p.amount * p.last_sale_price\n for e, p in context.portfolio.positions.items()}\n \n #----------------------------START:持有固定交易日天数卖出---------------------------\n today = data.current_dt.strftime('%Y-%m-%d')\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 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 dt = pd.to_datetime(D.trading_days(end_date = today).iloc[-context.options['hold_days']].values[0])\n if pd.to_datetime(equities[instrument].last_sale_date.strftime('%Y-%m-%d')) <= dt and data.can_trade(context.symbol(instrument)):\n context.order_target_percent(sid, 0)\n cash_for_buy += positions[instrument]\n #--------------------------------END:持有固定天数卖出--------------------------- \n \n\n # 3. 生成买入订单:按StockRanker预测的排序,买入前面的stock_count只股票\n buy_cash_weights = context.stock_weights\n counto = 0\n str_list = [ ]\n \n for i in range(len(buy_cash_weights)):\n while (' '.join(ranker_prediction.instrument[counto:counto+1])) in positions.keys():\n counto += 1\n str_list.append(list(ranker_prediction.instrument[counto:counto+1]))\n \n counto += 1\n\n buy_instruments = [ ]\n buy_instruments.extend([x[0] for x in str_list])\n \n 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In [1]:
# 本代码由可视化策略环境自动生成 2020年9月21日 17:24
# 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 = 3
# 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 = [0.5, 0.4, 0.1]
# 设置每只股票占用的最大资金比例
context.max_cash_per_instrument = 0.1
context.options['hold_days'] = 5
# 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):
today = data.current_dt.strftime('%Y-%m-%d')
stock_hold_now = [equity.symbol for equity in context.portfolio.positions ]
##大盘风控模块,读取风控数据
#benckmark_risk=context.benckmark_risk[today]
#context.symbol
##当risk为1时,市场有风险,全部平仓,不再执行其它操作
#if benckmark_risk > 0:
# for instrument in stock_hold_now:
# context.order_target(symbol(instrument), 0)
# print(today,'大盘风控止损触发,全仓卖出')
# return
# 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)
positions = {e.symbol: p.amount * p.last_sale_price
for e, p in context.portfolio.positions.items()}
#----------------------------START:持有固定交易日天数卖出---------------------------
today = data.current_dt.strftime('%Y-%m-%d')
# 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:
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
dt = pd.to_datetime(D.trading_days(end_date = today).iloc[-context.options['hold_days']].values[0])
if pd.to_datetime(equities[instrument].last_sale_date.strftime('%Y-%m-%d')) <= dt and data.can_trade(context.symbol(instrument)):
context.order_target_percent(sid, 0)
cash_for_buy += positions[instrument]
#--------------------------------END:持有固定天数卖出---------------------------
# 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
counto = 0
str_list = [ ]
for i in range(len(buy_cash_weights)):
while (' '.join(ranker_prediction.instrument[counto:counto+1])) in positions.keys():
counto += 1
str_list.append(list(ranker_prediction.instrument[counto:counto+1]))
counto += 1
buy_instruments = [ ]
buy_instruments.extend([x[0] for x in str_list])
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)
#print(context.symbol(instrument), cash)
def m19_prepare_bigquant_run(context):
#在数据准备函数中一次性计算每日的大盘风控条件相比于在handle中每日计算风控条件可以提高回测速度
# 多取50天的数据便于计算均值(保证回测的第一天均值不为Nan值),其中context.start_date和context.end_date是回测指定的起始时间和终止时间
start_date= (pd.to_datetime(context.start_date) - datetime.timedelta(days=50)).strftime('%Y-%m-%d')
df=DataSource('bar1d_index_CN_STOCK_A').read(start_date=start_date,end_date=context.end_date,fields=['close'])
benckmark_data=df[df.instrument=='000001.HIX']
#计算上证指数5日涨幅
benckmark_data['ret5']=benckmark_data['close']/benckmark_data['close'].shift(1)-1
#计算大盘风控条件,如果5日涨幅小于-4%则设置风险状态risk为1,否则为0
benckmark_data['risk'] = np.where(benckmark_data['ret5']<-0.04,1,0)
#修改日期格式为字符串(便于在handle中使用字符串日期索引来查看每日的风险状态)
benckmark_data['date']=benckmark_data['date'].apply(lambda x:x.strftime('%Y-%m-%d'))
#设置日期为索引
benckmark_data.set_index('date',inplace=True)
#把风控序列输出给全局变量context.benckmark_risk
context.benckmark_risk=benckmark_data['risk']
m1 = M.instruments.v2(
start_date='2016-01-01',
end_date='2019-01-01',
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, -6)/shift(close, -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="""# #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
rank_swing_volatility_60_0
swing_volatility_30_0
"""
)
m15 = M.general_feature_extractor.v7(
instruments=m1.data,
features=m3.data,
start_date='',
end_date='',
before_start_days=300
)
m24 = M.derived_feature_extractor.v3(
input_data=m15.data,
features=m3.data,
date_col='date',
instrument_col='instrument',
drop_na=False,
remove_extra_columns=False,
user_functions={}
)
m10 = M.join.v3(
data1=m2.data,
data2=m24.data,
on='date,instrument',
how='inner',
sort=False
)
m6 = M.filtet_st_stock.v7(
input_1=m10.data
)
m5 = M.dropnan.v2(
input_data=m6.data_1
)
m16 = M.instruments.v2(
start_date='2019-01-01',
end_date='2020-09-18',
market='CN_STOCK_A',
instrument_list='',
max_count=0
)
m18 = M.general_feature_extractor.v7(
instruments=m16.data,
features=m3.data,
start_date='',
end_date='',
before_start_days=300
)
m26 = M.derived_feature_extractor.v3(
input_data=m18.data,
features=m3.data,
date_col='date',
instrument_col='instrument',
drop_na=False,
remove_extra_columns=False,
user_functions={}
)
m7 = M.filtet_st_stock.v7(
input_1=m26.data
)
m22 = M.dropnan.v2(
input_data=m7.data_1
)
m9 = M.advanced_auto_labeler.v2(
instruments=m16.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, -6)/shift(close, -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
)
m8 = M.join.v3(
data1=m9.data,
data2=m7.data_1,
on='date,instrument',
how='inner',
sort=False
)
m11 = M.dropnan.v2(
input_data=m8.data
)
m4 = M.stock_ranker.v2(
training_ds=m5.data,
features=m3.data,
test_ds=m11.data,
predict_ds=m22.data,
learning_algorithm='排序',
number_of_leaves=5,
minimum_docs_per_leaf=1000,
number_of_trees=10,
learning_rate=0.1,
max_bins=1023,
feature_fraction=1,
data_row_fraction=1,
ndcg_discount_base=1,
slim_data=True
)
m19 = M.trade.v4(
instruments=m16.data,
options_data=m4.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,
order_price_field_buy='open',
order_price_field_sell='close',
capital_base=500000,
auto_cancel_non_tradable_orders=False,
data_frequency='daily',
price_type='真实价格',
product_type='股票',
plot_charts=True,
backtest_only=False,
benchmark=''
)
日志 183 条,错误日志
0 条
[2020-09-21 17:16:22.611887] INFO: moduleinvoker: instruments.v2 开始运行..
[2020-09-21 17:16:22.623594] INFO: moduleinvoker: 命中缓存
[2020-09-21 17:16:22.624867] INFO: moduleinvoker: instruments.v2 运行完成[0.012982s].
[2020-09-21 17:16:22.627784] INFO: moduleinvoker: advanced_auto_labeler.v2 开始运行..
[2020-09-21 17:16:22.632790] INFO: moduleinvoker: 命中缓存
[2020-09-21 17:16:22.634567] INFO: moduleinvoker: advanced_auto_labeler.v2 运行完成[0.006771s].
[2020-09-21 17:16:22.637133] INFO: moduleinvoker: input_features.v1 开始运行..
[2020-09-21 17:16:22.640931] INFO: moduleinvoker: 命中缓存
[2020-09-21 17:16:22.641727] INFO: moduleinvoker: input_features.v1 运行完成[0.004596s].
[2020-09-21 17:16:22.651160] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..
[2020-09-21 17:16:22.655202] INFO: moduleinvoker: 命中缓存
[2020-09-21 17:16:22.656233] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.005072s].
[2020-09-21 17:16:22.658602] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..
[2020-09-21 17:16:22.662221] INFO: moduleinvoker: 命中缓存
[2020-09-21 17:16:22.662977] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.004371s].
[2020-09-21 17:16:22.665123] INFO: moduleinvoker: join.v3 开始运行..
[2020-09-21 17:16:22.669104] INFO: moduleinvoker: 命中缓存
[2020-09-21 17:16:22.669888] INFO: moduleinvoker: join.v3 运行完成[0.004763s].
[2020-09-21 17:16:22.674524] INFO: moduleinvoker: filtet_st_stock.v7 开始运行..
[2020-09-21 17:16:22.678446] INFO: moduleinvoker: 命中缓存
[2020-09-21 17:16:22.679235] INFO: moduleinvoker: filtet_st_stock.v7 运行完成[0.004711s].
[2020-09-21 17:16:22.681453] INFO: moduleinvoker: dropnan.v2 开始运行..
[2020-09-21 17:16:22.685470] INFO: moduleinvoker: 命中缓存
[2020-09-21 17:16:22.686318] INFO: moduleinvoker: dropnan.v2 运行完成[0.00486s].
[2020-09-21 17:16:22.687597] INFO: moduleinvoker: instruments.v2 开始运行..
[2020-09-21 17:16:22.691178] INFO: moduleinvoker: 命中缓存
[2020-09-21 17:16:22.691960] INFO: moduleinvoker: instruments.v2 运行完成[0.004357s].
[2020-09-21 17:16:22.696508] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..
[2020-09-21 17:16:22.700976] INFO: moduleinvoker: 命中缓存
[2020-09-21 17:16:22.704028] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.007516s].
[2020-09-21 17:16:22.705943] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..
[2020-09-21 17:16:22.709948] INFO: moduleinvoker: 命中缓存
[2020-09-21 17:16:22.710749] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.004805s].
[2020-09-21 17:16:22.712449] INFO: moduleinvoker: filtet_st_stock.v7 开始运行..
[2020-09-21 17:16:22.716502] INFO: moduleinvoker: 命中缓存
[2020-09-21 17:16:22.717323] INFO: moduleinvoker: filtet_st_stock.v7 运行完成[0.004871s].
[2020-09-21 17:16:22.718824] INFO: moduleinvoker: dropnan.v2 开始运行..
[2020-09-21 17:16:22.722799] INFO: moduleinvoker: 命中缓存
[2020-09-21 17:16:22.723689] INFO: moduleinvoker: dropnan.v2 运行完成[0.00486s].
[2020-09-21 17:16:22.725188] INFO: moduleinvoker: advanced_auto_labeler.v2 开始运行..
[2020-09-21 17:16:22.728928] INFO: moduleinvoker: 命中缓存
[2020-09-21 17:16:22.729765] INFO: moduleinvoker: advanced_auto_labeler.v2 运行完成[0.004572s].
[2020-09-21 17:16:22.731232] INFO: moduleinvoker: join.v3 开始运行..
[2020-09-21 17:16:22.735188] INFO: moduleinvoker: 命中缓存
[2020-09-21 17:16:22.736050] INFO: moduleinvoker: join.v3 运行完成[0.004814s].
[2020-09-21 17:16:22.737621] INFO: moduleinvoker: dropnan.v2 开始运行..
[2020-09-21 17:16:22.741841] INFO: moduleinvoker: 命中缓存
[2020-09-21 17:16:22.742647] INFO: moduleinvoker: dropnan.v2 运行完成[0.005022s].
[2020-09-21 17:16:22.745372] INFO: moduleinvoker: stock_ranker.v2 开始运行..
[2020-09-21 17:16:22.754664] INFO: moduleinvoker: stock_ranker_train.v6 开始运行..
[2020-09-21 17:16:22.770742] INFO: StockRanker训练: 1e027352 准备训练: 2090488 行数, test: 1447984 rows
[2020-09-21 17:16:22.771814] INFO: StockRanker训练: AI模型训练,将在2090488*2=418.10万数据上对模型训练进行10轮迭代训练。预计将需要2~3分钟。请耐心等待。
[2020-09-21 17:16:22.785372] INFO: StockRanker训练: 正在训练 ..
[2020-09-21 17:16:22.831388] INFO: StockRanker训练: 任务状态: Pending
[2020-09-21 17:16:32.870118] INFO: StockRanker训练: 任务状态: Running
[2020-09-21 17:16:42.921671] INFO: StockRanker训练: 00:00:15.6293711, finished iteration 1
[2020-09-21 17:17:02.987455] INFO: StockRanker训练: 00:00:27.7100882, finished iteration 2
[2020-09-21 17:17:13.024151] INFO: StockRanker训练: 00:00:40.1405717, finished iteration 3
[2020-09-21 17:17:23.063779] INFO: StockRanker训练: 00:00:50.5388848, finished iteration 4
[2020-09-21 17:17:33.104879] INFO: StockRanker训练: 00:01:05.2412606, finished iteration 5
[2020-09-21 17:17:53.203193] INFO: StockRanker训练: 00:01:21.1589649, finished iteration 6
[2020-09-21 17:18:13.276742] INFO: StockRanker训练: 00:01:39.5154828, finished iteration 7
[2020-09-21 17:18:33.353569] INFO: StockRanker训练: 00:01:57.0897663, finished iteration 8
[2020-09-21 17:18:43.418887] INFO: StockRanker训练: 00:02:14.3268792, finished iteration 9
[2020-09-21 17:19:03.497765] INFO: StockRanker训练: 00:02:31.3553836, finished iteration 10
[2020-09-21 17:19:03.498889] INFO: StockRanker训练: 任务状态: Succeeded
[2020-09-21 17:19:05.714950] INFO: moduleinvoker: stock_ranker_train.v6 运行完成[162.960252s].
[2020-09-21 17:19:05.718932] INFO: moduleinvoker: stock_ranker_predict.v5 开始运行..
[2020-09-21 17:19:07.272813] INFO: StockRanker预测: /data ..
[2020-09-21 17:19:17.408424] INFO: moduleinvoker: stock_ranker_predict.v5 运行完成[11.689467s].
[2020-09-21 17:19:17.523736] INFO: moduleinvoker: stock_ranker.v2 运行完成[174.778337s].
[2020-09-21 17:19:19.381058] INFO: moduleinvoker: backtest.v8 开始运行..
[2020-09-21 17:19:19.385926] INFO: backtest: biglearning backtest:V8.4.2
[2020-09-21 17:19:19.898078] INFO: backtest: product_type:stock by specified
[2020-09-21 17:19:19.996417] INFO: moduleinvoker: cached.v2 开始运行..
[2020-09-21 17:19:20.009514] INFO: moduleinvoker: 命中缓存
[2020-09-21 17:19:20.010893] INFO: moduleinvoker: cached.v2 运行完成[0.014474s].
[2020-09-21 17:19:21.612068] INFO: algo: TradingAlgorithm V1.6.9
[2020-09-21 17:19:23.297634] INFO: algo: trading transform...
[2020-09-21 17:19:46.215864] INFO: algo: handle_splits get splits [dt:2019-05-10 00:00:00+00:00] [asset:Equity(895 [000062.SZA]), ratio:0.680497944355011]
[2020-09-21 17:19:46.217077] INFO: Position: position stock handle split[sid:895, orig_amount:1600, new_amount:2351.0, orig_cost:19.829999923706055, new_cost:13.4943, ratio:0.680497944355011, last_sale_price:13.119999885559082]
[2020-09-21 17:19:46.217947] INFO: Position: after split: PositionStock(asset:Equity(895 [000062.SZA]), amount:2351.0, cost_basis:13.4943, last_sale_price:19.279998779296875)
[2020-09-21 17:19:46.218698] INFO: Position: returning cash: 2.8791
[2020-09-21 17:19:47.303615] INFO: algo: handle_splits get splits [dt:2019-05-16 00:00:00+00:00] [asset:Equity(2964 [000031.SZA]), ratio:0.9844632744789124]
[2020-09-21 17:19:49.337929] INFO: algo: handle_splits get splits [dt:2019-05-27 00:00:00+00:00] [asset:Equity(3210 [000411.SZA]), ratio:0.9896824955940247]
[2020-09-21 17:19:49.619170] INFO: algo: handle_splits get splits [dt:2019-05-28 00:00:00+00:00] [asset:Equity(4329 [000528.SZA]), ratio:0.9779411554336548]
[2020-09-21 17:19:49.620300] INFO: algo: handle_splits get splits [dt:2019-05-28 00:00:00+00:00] [asset:Equity(3069 [000401.SZA]), ratio:0.9758598804473877]
[2020-09-21 17:19:49.621201] INFO: Position: position stock handle split[sid:3069, orig_amount:1800, new_amount:1844.0, orig_cost:16.40999984741211, new_cost:16.0139, ratio:0.9758598804473877, last_sale_price:16.169998168945312]
[2020-09-21 17:19:49.622011] INFO: Position: after split: PositionStock(asset:Equity(3069 [000401.SZA]), amount:1844.0, cost_basis:16.0139, last_sale_price:16.56999969482422)
[2020-09-21 17:19:49.622796] INFO: Position: returning cash: 8.5233
[2020-09-21 17:19:51.344095] INFO: algo: handle_splits get splits [dt:2019-06-05 00:00:00+00:00] [asset:Equity(153 [000600.SZA]), ratio:0.9850075244903564]
[2020-09-21 17:19:51.345304] INFO: Position: position stock handle split[sid:153, orig_amount:4600, new_amount:4670.0, orig_cost:6.529999732971191, new_cost:6.4321, ratio:0.9850075244903564, last_sale_price:6.570000171661377]
[2020-09-21 17:19:51.346304] INFO: Position: after split: PositionStock(asset:Equity(153 [000600.SZA]), amount:4670.0, cost_basis:6.4321, last_sale_price:6.670000076293945)
[2020-09-21 17:19:51.347143] INFO: Position: returning cash: 0.0991
[2020-09-21 17:19:52.452163] INFO: algo: handle_splits get splits [dt:2019-06-12 00:00:00+00:00] [asset:Equity(930 [000552.SZA]), ratio:0.9646643400192261]
[2020-09-21 17:19:52.453346] INFO: Position: position stock handle split[sid:930, orig_amount:11600, new_amount:12024.0, orig_cost:2.7699999809265137, new_cost:2.6721, ratio:0.9646643400192261, last_sale_price:2.7300000190734863]
[2020-09-21 17:19:52.454358] INFO: Position: after split: PositionStock(asset:Equity(930 [000552.SZA]), amount:12024.0, cost_basis:2.6721, last_sale_price:2.8299999237060547)
[2020-09-21 17:19:52.455174] INFO: Position: returning cash: 2.479
[2020-09-21 17:19:53.501393] INFO: algo: handle_splits get splits [dt:2019-06-18 00:00:00+00:00] [asset:Equity(1858 [000061.SZA]), ratio:0.9981651902198792]
[2020-09-21 17:19:53.502530] INFO: algo: handle_splits get splits [dt:2019-06-18 00:00:00+00:00] [asset:Equity(414 [000591.SZA]), ratio:0.9701492786407471]
[2020-09-21 17:19:53.503422] INFO: Position: position stock handle split[sid:1858, orig_amount:10300, new_amount:10318.0, orig_cost:5.309999942779541, new_cost:5.3003, ratio:0.9981651902198792, last_sale_price:5.440000057220459]
[2020-09-21 17:19:53.504218] INFO: Position: after split: PositionStock(asset:Equity(1858 [000061.SZA]), amount:10318.0, cost_basis:5.3003, last_sale_price:5.449999809265137)
[2020-09-21 17:19:53.505043] INFO: Position: returning cash: 5.077
[2020-09-21 17:19:53.505812] INFO: Position: position stock handle split[sid:414, orig_amount:8400, new_amount:8658.0, orig_cost:3.2200000286102295, new_cost:3.1239, ratio:0.9701492786407471, last_sale_price:3.25]
[2020-09-21 17:19:53.506590] INFO: Position: after split: PositionStock(asset:Equity(414 [000591.SZA]), amount:8658.0, cost_basis:3.1239, last_sale_price:3.3499999046325684)
[2020-09-21 17:19:53.507329] INFO: Position: returning cash: 1.4993
[2020-09-21 17:19:53.776296] INFO: algo: handle_splits get splits [dt:2019-06-19 00:00:00+00:00] [asset:Equity(3784 [000705.SZA]), ratio:0.995555579662323]
[2020-09-21 17:19:53.777508] INFO: Position: position stock handle split[sid:3784, orig_amount:4900, new_amount:4921.0, orig_cost:6.559999465942383, new_cost:6.5308, ratio:0.995555579662323, last_sale_price:6.720000267028809]
[2020-09-21 17:19:53.778448] INFO: Position: after split: PositionStock(asset:Equity(3784 [000705.SZA]), amount:4921.0, cost_basis:6.5308, last_sale_price:6.75)
[2020-09-21 17:19:53.779246] INFO: Position: returning cash: 5.8792
[2020-09-21 17:19:54.319103] INFO: algo: handle_splits get splits [dt:2019-06-21 00:00:00+00:00] [asset:Equity(793 [000963.SZA]), ratio:0.824142575263977]
[2020-09-21 17:19:54.320289] INFO: Position: position stock handle split[sid:793, orig_amount:2000, new_amount:2426.0, orig_cost:29.23000144958496, new_cost:24.0897, ratio:0.824142575263977, last_sale_price:24.51000213623047]
[2020-09-21 17:19:54.321235] INFO: Position: after split: PositionStock(asset:Equity(793 [000963.SZA]), amount:2426.0, cost_basis:24.0897, last_sale_price:29.740001678466797)
[2020-09-21 17:19:54.322061] INFO: Position: returning cash: 18.7395
[2020-09-21 17:19:57.311999] INFO: algo: handle_splits get splits [dt:2019-07-05 00:00:00+00:00] [asset:Equity(908 [002475.SZA]), ratio:0.7676056027412415]
[2020-09-21 17:19:57.313132] INFO: algo: handle_splits get splits [dt:2019-07-05 00:00:00+00:00] [asset:Equity(2318 [000046.SZA]), ratio:0.9742709994316101]
[2020-09-21 17:19:57.314029] INFO: Position: position stock handle split[sid:908, orig_amount:1500, new_amount:1954.0, orig_cost:26.500001907348633, new_cost:20.3415, ratio:0.7676056027412415, last_sale_price:19.6200008392334]
[2020-09-21 17:19:57.314827] INFO: Position: after split: PositionStock(asset:Equity(908 [002475.SZA]), amount:1954.0, cost_basis:20.3415, last_sale_price:25.560001373291016)
[2020-09-21 17:19:57.315574] INFO: Position: returning cash: 2.5216
[2020-09-21 17:19:57.316376] INFO: Position: position stock handle split[sid:2318, orig_amount:10700, new_amount:10982.0, orig_cost:5.700000286102295, new_cost:5.5533, ratio:0.9742709994316101, last_sale_price:5.680000305175781]
[2020-09-21 17:19:57.317163] INFO: Position: after split: PositionStock(asset:Equity(2318 [000046.SZA]), amount:10982.0, cost_basis:5.5533, last_sale_price:5.830000400543213)
[2020-09-21 17:19:57.317901] INFO: Position: returning cash: 3.2408
[2020-09-21 17:19:57.577915] INFO: algo: handle_splits get splits [dt:2019-07-08 00:00:00+00:00] [asset:Equity(3327 [002497.SZA]), ratio:0.9971386790275574]
[2020-09-21 17:19:57.579067] INFO: Position: position stock handle split[sid:3327, orig_amount:4500, new_amount:4512.0, orig_cost:7.140000343322754, new_cost:7.1196, ratio:0.9971386790275574, last_sale_price:6.970000267028809]
[2020-09-21 17:19:57.579911] INFO: Position: after split: PositionStock(asset:Equity(3327 [002497.SZA]), amount:4512.0, cost_basis:7.1196, last_sale_price:6.9900007247924805)
[2020-09-21 17:19:57.580627] INFO: Position: returning cash: 6.3629
[2020-09-21 17:19:57.855220] INFO: algo: handle_splits get splits [dt:2019-07-09 00:00:00+00:00] [asset:Equity(926 [002832.SZA]), ratio:0.5818777084350586]
[2020-09-21 17:19:57.856371] INFO: Position: position stock handle split[sid:926, orig_amount:600, new_amount:1031.0, orig_cost:45.51000213623047, new_cost:26.4813, ratio:0.5818777084350586, last_sale_price:26.64999771118164]
[2020-09-21 17:19:57.857360] INFO: Position: after split: PositionStock(asset:Equity(926 [002832.SZA]), amount:1031.0, cost_basis:26.4813, last_sale_price:45.79999923706055)
[2020-09-21 17:19:57.858479] INFO: Position: returning cash: 3.851
[2020-09-21 17:19:58.736980] INFO: algo: handle_splits get splits [dt:2019-07-12 00:00:00+00:00] [asset:Equity(330 [002375.SZA]), ratio:0.9928570985794067]
[2020-09-21 17:19:58.738131] INFO: Position: position stock handle split[sid:330, orig_amount:6800, new_amount:6848.0, orig_cost:5.62000036239624, new_cost:5.5799, ratio:0.9928570985794067, last_sale_price:5.559999465942383]
[2020-09-21 17:19:58.739000] INFO: Position: after split: PositionStock(asset:Equity(330 [002375.SZA]), amount:6848.0, cost_basis:5.5799, last_sale_price:5.599999904632568)
[2020-09-21 17:19:58.739754] INFO: Position: returning cash: 5.1217
[2020-09-21 17:19:59.355208] INFO: algo: handle_splits get splits [dt:2019-07-16 00:00:00+00:00] [asset:Equity(4287 [002781.SZA]), ratio:0.9960470795631409]
[2020-09-21 17:19:59.356504] INFO: Position: position stock handle split[sid:4287, orig_amount:2600, new_amount:2610.0, orig_cost:17.780000686645508, new_cost:17.7097, ratio:0.9960470795631409, last_sale_price:17.63999366760254]
[2020-09-21 17:19:59.357716] INFO: Position: after split: PositionStock(asset:Equity(4287 [002781.SZA]), amount:2610.0, cost_basis:17.7097, last_sale_price:17.709999084472656)
[2020-09-21 17:19:59.358602] INFO: Position: returning cash: 5.6162
[2020-09-21 17:20:55.958094] INFO: algo: handle_splits get splits [dt:2020-05-18 00:00:00+00:00] [asset:Equity(2870 [000011.SZA]), ratio:0.9606558084487915]
[2020-09-21 17:20:55.959213] INFO: Position: position stock handle split[sid:2870, orig_amount:6000, new_amount:6245.0, orig_cost:9.239999771118164, new_cost:8.8765, ratio:0.9606558084487915, last_sale_price:8.789999961853027]
[2020-09-21 17:20:55.960161] INFO: Position: after split: PositionStock(asset:Equity(2870 [000011.SZA]), amount:6245.0, cost_basis:8.8765, last_sale_price:9.149999618530273)
[2020-09-21 17:20:55.960970] INFO: Position: returning cash: 6.446
[2020-09-21 17:20:56.222588] INFO: algo: handle_splits get splits [dt:2020-05-19 00:00:00+00:00] [asset:Equity(712 [000739.SZA]), ratio:0.9910764694213867]
[2020-09-21 17:21:00.020853] INFO: algo: handle_splits get splits [dt:2020-06-05 00:00:00+00:00] [asset:Equity(2305 [000877.SZA]), ratio:0.9619686603546143]
[2020-09-21 17:21:00.022504] INFO: algo: handle_splits get splits [dt:2020-06-05 00:00:00+00:00] [asset:Equity(1081 [000099.SZA]), ratio:0.9927953481674194]
[2020-09-21 17:21:00.023909] INFO: Position: position stock handle split[sid:1081, orig_amount:8000, new_amount:8058.0, orig_cost:6.889999866485596, new_cost:6.8404, ratio:0.9927953481674194, last_sale_price:6.889999866485596]
[2020-09-21 17:21:00.025160] INFO: Position: after split: PositionStock(asset:Equity(1081 [000099.SZA]), amount:8058.0, cost_basis:6.8404, last_sale_price:6.940000057220459)
[2020-09-21 17:21:00.026406] INFO: Position: returning cash: 0.3823
[2020-09-21 17:21:02.668481] INFO: algo: handle_splits get splits [dt:2020-06-19 00:00:00+00:00] [asset:Equity(956 [000565.SZA]), ratio:0.9978947043418884]
[2020-09-21 17:21:02.669631] INFO: Position: position stock handle split[sid:956, orig_amount:12500, new_amount:12526.0, orig_cost:4.789999961853027, new_cost:4.7799, ratio:0.9978947043418884, last_sale_price:4.739999294281006]
[2020-09-21 17:21:02.670449] INFO: Position: after split: PositionStock(asset:Equity(956 [000565.SZA]), amount:12526.0, cost_basis:4.7799, last_sale_price:4.749999523162842)
[2020-09-21 17:21:02.671178] INFO: Position: returning cash: 1.7619
[2020-09-21 17:21:03.443136] INFO: algo: handle_splits get splits [dt:2020-06-24 00:00:00+00:00] [asset:Equity(1637 [600997.SHA]), ratio:0.9551934003829956]
[2020-09-21 17:21:03.444250] INFO: algo: handle_splits get splits [dt:2020-06-24 00:00:00+00:00] [asset:Equity(71 [600820.SHA]), ratio:0.9647058248519897]
[2020-09-21 17:21:03.445094] INFO: algo: handle_splits get splits [dt:2020-06-24 00:00:00+00:00] [asset:Equity(4297 [000581.SZA]), ratio:0.9500232934951782]
[2020-09-21 17:21:03.445892] INFO: Position: position stock handle split[sid:1637, orig_amount:5800, new_amount:6072.0, orig_cost:4.949999809265137, new_cost:4.7282, ratio:0.9551934003829956, last_sale_price:4.690000057220459]
[2020-09-21 17:21:03.446669] INFO: Position: after split: PositionStock(asset:Equity(1637 [600997.SHA]), amount:6072.0, cost_basis:4.7282, last_sale_price:4.910000324249268)
[2020-09-21 17:21:03.447374] INFO: Position: returning cash: 0.3225
[2020-09-21 17:21:03.448142] INFO: Position: position stock handle split[sid:71, orig_amount:4400, new_amount:4560.0, orig_cost:5.909999847412109, new_cost:5.7014, ratio:0.9647058248519897, last_sale_price:5.739999294281006]
[2020-09-21 17:21:03.448894] INFO: Position: after split: PositionStock(asset:Equity(71 [600820.SHA]), amount:4560.0, cost_basis:5.7014, last_sale_price:5.949999809265137)
[2020-09-21 17:21:03.449600] INFO: Position: returning cash: 5.6016
[2020-09-21 17:21:03.450332] INFO: Position: position stock handle split[sid:4297, orig_amount:1900, new_amount:1999.0, orig_cost:21.78999900817871, new_cost:20.701, ratio:0.9500232934951782, last_sale_price:20.53000259399414]
[2020-09-21 17:21:03.451079] INFO: Position: after split: PositionStock(asset:Equity(4297 [000581.SZA]), amount:1999.0, cost_basis:20.701, last_sale_price:21.60999870300293)
[2020-09-21 17:21:03.452055] INFO: Position: returning cash: 19.5233
[2020-09-21 17:21:04.855118] INFO: algo: handle_splits get splits [dt:2020-07-03 00:00:00+00:00] [asset:Equity(4274 [000656.SZA]), ratio:0.9544533491134644]
[2020-09-21 17:21:06.163431] INFO: algo: handle_splits get splits [dt:2020-07-10 00:00:00+00:00] [asset:Equity(2007 [000402.SZA]), ratio:0.9617346525192261]
[2020-09-21 17:21:06.164621] INFO: Position: position stock handle split[sid:2007, orig_amount:6000, new_amount:6238.0, orig_cost:7.760000228881836, new_cost:7.4631, ratio:0.9617346525192261, last_sale_price:7.539999961853027]
[2020-09-21 17:21:06.165452] INFO: Position: after split: PositionStock(asset:Equity(2007 [000402.SZA]), amount:6238.0, cost_basis:7.4631, last_sale_price:7.840000152587891)
[2020-09-21 17:21:06.166188] INFO: Position: returning cash: 5.482
[2020-09-21 17:21:07.590790] INFO: algo: handle_splits get splits [dt:2020-07-17 00:00:00+00:00] [asset:Equity(2964 [000031.SZA]), ratio:0.9708404541015625]
[2020-09-21 17:21:07.591936] INFO: Position: position stock handle split[sid:2964, orig_amount:5200, new_amount:5356.0, orig_cost:6.079999923706055, new_cost:5.9027, ratio:0.9708404541015625, last_sale_price:5.659999847412109]
[2020-09-21 17:21:07.592778] INFO: Position: after split: PositionStock(asset:Equity(2964 [000031.SZA]), amount:5356.0, cost_basis:5.9027, last_sale_price:5.829999923706055)
[2020-09-21 17:21:07.593492] INFO: Position: returning cash: 1.0408
[2020-09-21 17:21:13.670821] INFO: algo: handle_splits get splits [dt:2020-08-19 00:00:00+00:00] [asset:Equity(3522 [000514.SZA]), ratio:0.9931033849716187]
[2020-09-21 17:21:13.672011] INFO: algo: handle_splits get splits [dt:2020-08-19 00:00:00+00:00] [asset:Equity(848 [000681.SZA]), ratio:0.9982668161392212]
[2020-09-21 17:21:13.672923] INFO: Position: position stock handle split[sid:3522, orig_amount:17400, new_amount:17520.0, orig_cost:4.2900004386901855, new_cost:4.2604, ratio:0.9931033849716187, last_sale_price:4.319999694824219]
[2020-09-21 17:21:13.673732] INFO: Position: after split: PositionStock(asset:Equity(3522 [000514.SZA]), amount:17520.0, cost_basis:4.2604, last_sale_price:4.349999904632568)
[2020-09-21 17:21:13.674452] INFO: Position: returning cash: 3.6048
[2020-09-21 17:21:19.594814] INFO: Performance: Simulated 419 trading days out of 419.
[2020-09-21 17:21:19.597064] INFO: Performance: first open: 2019-01-02 09:30:00+00:00
[2020-09-21 17:21:19.597943] INFO: Performance: last close: 2020-09-18 15:00:00+00:00
[2020-09-21 17:21:26.715938] INFO: moduleinvoker: backtest.v8 运行完成[127.334884s].
[2020-09-21 17:21:26.717193] INFO: moduleinvoker: trade.v4 运行完成[129.188876s].
In [2]:
#m13.result.best_params_
In [4]:
m4.predictions.read_df().to_csv('1.csv')
#dt.to_csv('3.csv')