{"description":"实验创建于2017/8/26","graph":{"edges":[{"to_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-15:instruments","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-8:data"},{"to_node_id":"-215:instruments","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-8:data"},{"to_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-53:data1","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-15:data"},{"to_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-43:features","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"to_node_id":"-215:features","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"to_node_id":"-222:features","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"to_node_id":"-231:features","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"to_node_id":"-238:features","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"to_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-60:model","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-43:model"},{"to_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-84:input_data","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-53:data"},{"to_node_id":"-250:options_data","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-60:predictions"},{"to_node_id":"-231:instruments","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-62:data"},{"to_node_id":"-250:instruments","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-62:data"},{"to_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-43:training_ds","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-84:data"},{"to_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-60:data","from_node_id":"-86:data"},{"to_node_id":"-222:input_data","from_node_id":"-215:data"},{"to_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-53:data2","from_node_id":"-222:data"},{"to_node_id":"-238:input_data","from_node_id":"-231:data"},{"to_node_id":"-86:input_data","from_node_id":"-238:data"}],"nodes":[{"node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-8","module_id":"BigQuantSpace.instruments.instruments-v2","parameters":[{"name":"start_date","value":"2010-01-01","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"2015-01-01","type":"Literal","bound_global_parameter":null},{"name":"market","value":"CN_STOCK_A","type":"Literal","bound_global_parameter":null},{"name":"instrument_list","value":"","type":"Literal","bound_global_parameter":null},{"name":"max_count","value":"0","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"rolling_conf","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-8"}],"output_ports":[{"name":"data","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-8"}],"cacheable":true,"seq_num":1,"comment":"","comment_collapsed":true},{"node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-15","module_id":"BigQuantSpace.advanced_auto_labeler.advanced_auto_labeler-v2","parameters":[{"name":"label_expr","value":"# 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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. 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bigquant_run(\n bq_graph,\n inputs,\n trading_days_market='CN', # 使用那个市场的交易日历, TODO\n train_instruments_mid='m1', # 训练数据 证券代码列表 模块id\n test_instruments_mid='m9', # 测试数据 证券代码列表 模块id\n predict_mid='m8', # 预测 模块id\n trade_mid='m19', # 回测 模块id\n start_date='2014-01-01', # 数据开始日期\n end_date=T.live_run_param('trading_date', '2017-01-01'), # 数据结束日期\n train_update_days=250, # 更新周期，按交易日计算，每多少天更新一次\n train_update_days_for_live=None, #模拟实盘模式下的更新周期，按交易日计算，每多少天更新一次。如果需要在模拟实盘阶段使用不同的模型更新周期，可以设置这个参数\n train_data_min_days=250, # 最小数据天数，按交易日计算，所以第一个滚动的结束日期是 从开始日期到开始日期+最小数据天数\n train_data_max_days=250, # 最大数据天数，按交易日计算，0，表示没有限制，否则每一个滚动的开始日期=max(此滚动的结束日期-最大数据天数, 开始日期\n rolling_count_for_live=1, #实盘模式下滚动次数，模拟实盘模式下，取最后多少次滚动。一般在模拟实盘模式下，只用到最后一次滚动训练的模型，这里可以设置为1；如果你的滚动训练数据时间段很短，以至于期间可能没有训练数据，这里可以设置大一点。0表示没有限制\n):\n def merge_datasources(input_1):\n df_list = [ds[0].read_df().set_index('date').loc[ds[1]:].reset_index() for ds in input_1]\n df = pd.concat(df_list)\n instrument_data = {\n 'start_date': df['date'].min().strftime('%Y-%m-%d'),\n 'end_date': df['date'].max().strftime('%Y-%m-%d'),\n 'instruments': list(set(df['instrument'])),\n }\n return Outputs(data=DataSource.write_df(df), instrument_data=DataSource.write_pickle(instrument_data))\n\n def gen_rolling_dates(trading_days_market, start_date, end_date, train_update_days, train_update_days_for_live, train_data_min_days, train_data_max_days, rolling_count_for_live):\n # 是否实盘模式\n tdays = list(D.trading_days(market=trading_days_market, start_date=start_date, end_date=end_date)['date'])\n is_live_run = T.live_run_param('trading_date', None) is not None\n\n if is_live_run and train_update_days_for_live:\n train_update_days = train_update_days_for_live\n\n rollings = []\n train_end_date = train_data_min_days\n while train_end_date < len(tdays):\n if train_data_max_days is not None and train_data_max_days > 0:\n train_start_date = max(train_end_date - train_data_max_days, 0)\n else:\n train_start_date = 0\n rollings.append({\n 'train_start_date': tdays[train_start_date].strftime('%Y-%m-%d'),\n 'train_end_date': tdays[train_end_date - 1].strftime('%Y-%m-%d'),\n 'test_start_date': tdays[train_end_date].strftime('%Y-%m-%d'),\n 'test_end_date': tdays[min(train_end_date + train_update_days, len(tdays)) - 1].strftime('%Y-%m-%d'),\n })\n train_end_date += train_update_days\n\n if not rollings:\n raise Exception('没有滚动需要执行，请检查配置')\n\n if is_live_run and rolling_count_for_live:\n rollings = rollings[-rolling_count_for_live:]\n\n return rollings\n\n g = bq_graph\n\n rolling_dates = gen_rolling_dates(\n trading_days_market, start_date, end_date, train_update_days, train_update_days_for_live, train_data_min_days, train_data_max_days, rolling_count_for_live)\n\n # 训练和预测\n results = []\n for rolling in rolling_dates:\n parameters = {}\n # 先禁用回测\n parameters[trade_mid + '.__enabled__'] = False\n parameters[train_instruments_mid + '.start_date'] = rolling['train_start_date']\n parameters[train_instruments_mid + '.end_date'] = rolling['train_end_date']\n parameters[test_instruments_mid + '.start_date'] = rolling['test_start_date']\n parameters[test_instruments_mid + '.end_date'] = rolling['test_end_date']\n # print('------ rolling_train:', parameters)\n results.append(g.run(parameters))\n\n # 合并预测结果并回测\n mx = M.cached.v3(run=merge_datasources, input_1=[[result[predict_mid].predictions, result[test_instruments_mid].data.read_pickle()['start_date']] for result in results])\n parameters = {}\n parameters['*.__enabled__'] = False\n parameters[trade_mid + '.__enabled__'] = True\n parameters[trade_mid + '.instruments'] = mx.instrument_data\n parameters[trade_mid + '.options_data'] = mx.data\n\n trade = g.run(parameters)\n\n return {'rollings': results, 'trade': trade}\n","type":"Literal","bound_global_parameter":null},{"name":"run_now","value":"True","type":"Literal","bound_global_parameter":null},{"name":"bq_graph","value":"True","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"bq_graph_port","node_id":"-1223"},{"name":"input_1","node_id":"-1223"},{"name":"input_2","node_id":"-1223"},{"name":"input_3","node_id":"-1223"}],"output_ports":[{"name":"result","node_id":"-1223"}],"cacheable":false,"seq_num":4,"comment":"","comment_collapsed":true}],"node_layout":"<node_postions><node_position Node='287d2cb0-f53c-4101-bdf8-104b137c8601-8' Position='211,64,200,200'/><node_position Node='287d2cb0-f53c-4101-bdf8-104b137c8601-15' Position='70,183,200,200'/><node_position Node='287d2cb0-f53c-4101-bdf8-104b137c8601-24' Position='765,21,200,200'/><node_position Node='287d2cb0-f53c-4101-bdf8-104b137c8601-43' Position='638,561,200,200'/><node_position Node='287d2cb0-f53c-4101-bdf8-104b137c8601-53' Position='249,375,200,200'/><node_position Node='287d2cb0-f53c-4101-bdf8-104b137c8601-60' Position='906,647,200,200'/><node_position Node='287d2cb0-f53c-4101-bdf8-104b137c8601-62' Position='1074,127,200,200'/><node_position Node='287d2cb0-f53c-4101-bdf8-104b137c8601-84' Position='376,467,200,200'/><node_position Node='-86' Position='1078,418,200,200'/><node_position Node='-215' Position='381,188,200,200'/><node_position Node='-222' Position='385,280,200,200'/><node_position Node='-231' Position='1078,236,200,200'/><node_position Node='-238' Position='1081,327,200,200'/><node_position Node='-250' Position='1037,751,200,200'/><node_position Node='-1223' Position='162.773193359375,669.051513671875,200,200'/></node_postions>"},"nodes_readonly":false,"studio_version":"v2"}

# 本代码由可视化策略环境自动生成 2021年10月27日 14:54
# 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 = 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

# 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)

# 回测引擎：准备数据，只执行一次
def m19_prepare_bigquant_run(context):
    pass


g = T.Graph({

    'm1': 'M.instruments.v2',
    'm1.start_date': '2010-01-01',
    'm1.end_date': '2015-01-01',
    'm1.market': 'CN_STOCK_A',
    'm1.instrument_list': '',
    'm1.max_count': 0,

    'm2': 'M.advanced_auto_labeler.v2',
    'm2.instruments': T.Graph.OutputPort('m1.data'),
    'm2.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, -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)
""",
    'm2.start_date': '',
    'm2.end_date': '',
    'm2.benchmark': '000300.SHA',
    'm2.drop_na_label': True,
    'm2.cast_label_int': True,

    'm3': 'M.input_features.v1',
    'm3.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
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
""",

    'm15': 'M.general_feature_extractor.v7',
    'm15.instruments': T.Graph.OutputPort('m1.data'),
    'm15.features': T.Graph.OutputPort('m3.data'),
    'm15.start_date': '',
    'm15.end_date': '',
    'm15.before_start_days': 90,

    'm16': 'M.derived_feature_extractor.v3',
    'm16.input_data': T.Graph.OutputPort('m15.data'),
    'm16.features': T.Graph.OutputPort('m3.data'),
    'm16.date_col': 'date',
    'm16.instrument_col': 'instrument',
    'm16.drop_na': False,
    'm16.remove_extra_columns': False,

    'm7': 'M.join.v3',
    'm7.data1': T.Graph.OutputPort('m2.data'),
    'm7.data2': T.Graph.OutputPort('m16.data'),
    'm7.on': 'date,instrument',
    'm7.how': 'inner',
    'm7.sort': False,

    'm13': 'M.dropnan.v1',
    'm13.input_data': T.Graph.OutputPort('m7.data'),

    'm6': 'M.stock_ranker_train.v5',
    'm6.training_ds': T.Graph.OutputPort('m13.data'),
    'm6.features': T.Graph.OutputPort('m3.data'),
    'm6.learning_algorithm': '排序',
    'm6.number_of_leaves': 30,
    'm6.minimum_docs_per_leaf': 1000,
    'm6.number_of_trees': 20,
    'm6.learning_rate': 0.1,
    'm6.max_bins': 1023,
    'm6.feature_fraction': 1,
    'm6.m_lazy_run': False,

    'm9': 'M.instruments.v2',
    'm9.start_date': T.live_run_param('trading_date', '2015-01-01'),
    'm9.end_date': T.live_run_param('trading_date', '2017-01-01'),
    'm9.market': 'CN_STOCK_A',
    'm9.instrument_list': '',
    'm9.max_count': 0,

    'm17': 'M.general_feature_extractor.v7',
    'm17.instruments': T.Graph.OutputPort('m9.data'),
    'm17.features': T.Graph.OutputPort('m3.data'),
    'm17.start_date': '',
    'm17.end_date': '',
    'm17.before_start_days': 90,

    'm18': 'M.derived_feature_extractor.v3',
    'm18.input_data': T.Graph.OutputPort('m17.data'),
    'm18.features': T.Graph.OutputPort('m3.data'),
    'm18.date_col': 'date',
    'm18.instrument_col': 'instrument',
    'm18.drop_na': False,
    'm18.remove_extra_columns': False,

    'm14': 'M.dropnan.v1',
    'm14.input_data': T.Graph.OutputPort('m18.data'),

    'm8': 'M.stock_ranker_predict.v5',
    'm8.model': T.Graph.OutputPort('m6.model'),
    'm8.data': T.Graph.OutputPort('m14.data'),
    'm8.m_lazy_run': False,

    'm19': 'M.trade.v4',
    'm19.instruments': T.Graph.OutputPort('m9.data'),
    'm19.options_data': T.Graph.OutputPort('m8.predictions'),
    'm19.start_date': '',
    'm19.end_date': '',
    'm19.initialize': m19_initialize_bigquant_run,
    'm19.handle_data': m19_handle_data_bigquant_run,
    'm19.prepare': m19_prepare_bigquant_run,
    'm19.volume_limit': 0.025,
    'm19.order_price_field_buy': 'open',
    'm19.order_price_field_sell': 'close',
    'm19.capital_base': 1000000,
    'm19.auto_cancel_non_tradable_orders': True,
    'm19.data_frequency': 'daily',
    'm19.price_type': '真实价格',
    'm19.product_type': '股票',
    'm19.plot_charts': True,
    'm19.backtest_only': False,
    'm19.benchmark': '000300.SHA',
})

# g.run({})


def m4_run_bigquant_run(
    bq_graph,
    inputs,
    trading_days_market='CN', # 使用那个市场的交易日历, TODO
    train_instruments_mid='m1', # 训练数据 证券代码列表 模块id
    test_instruments_mid='m9', # 测试数据 证券代码列表 模块id
    predict_mid='m8', # 预测 模块id
    trade_mid='m19', # 回测 模块id
    start_date='2014-01-01', # 数据开始日期
    end_date=T.live_run_param('trading_date', '2017-01-01'), # 数据结束日期
    train_update_days=250, # 更新周期，按交易日计算，每多少天更新一次
    train_update_days_for_live=None, #模拟实盘模式下的更新周期，按交易日计算，每多少天更新一次。如果需要在模拟实盘阶段使用不同的模型更新周期，可以设置这个参数
    train_data_min_days=250, # 最小数据天数，按交易日计算，所以第一个滚动的结束日期是 从开始日期到开始日期+最小数据天数
    train_data_max_days=250, # 最大数据天数，按交易日计算，0，表示没有限制，否则每一个滚动的开始日期=max(此滚动的结束日期-最大数据天数, 开始日期
    rolling_count_for_live=1, #实盘模式下滚动次数，模拟实盘模式下，取最后多少次滚动。一般在模拟实盘模式下，只用到最后一次滚动训练的模型，这里可以设置为1；如果你的滚动训练数据时间段很短，以至于期间可能没有训练数据，这里可以设置大一点。0表示没有限制
):
    def merge_datasources(input_1):
        df_list = [ds[0].read_df().set_index('date').loc[ds[1]:].reset_index() for ds in input_1]
        df = pd.concat(df_list)
        instrument_data = {
            'start_date': df['date'].min().strftime('%Y-%m-%d'),
            'end_date': df['date'].max().strftime('%Y-%m-%d'),
            'instruments': list(set(df['instrument'])),
        }
        return Outputs(data=DataSource.write_df(df), instrument_data=DataSource.write_pickle(instrument_data))

    def gen_rolling_dates(trading_days_market, start_date, end_date, train_update_days, train_update_days_for_live, train_data_min_days, train_data_max_days, rolling_count_for_live):
        # 是否实盘模式
        tdays = list(D.trading_days(market=trading_days_market, start_date=start_date, end_date=end_date)['date'])
        is_live_run = T.live_run_param('trading_date', None) is not None

        if is_live_run and train_update_days_for_live:
            train_update_days = train_update_days_for_live

        rollings = []
        train_end_date = train_data_min_days
        while train_end_date < len(tdays):
            if train_data_max_days is not None and train_data_max_days > 0:
                train_start_date = max(train_end_date - train_data_max_days, 0)
            else:
                train_start_date = 0
            rollings.append({
                'train_start_date': tdays[train_start_date].strftime('%Y-%m-%d'),
                'train_end_date': tdays[train_end_date - 1].strftime('%Y-%m-%d'),
                'test_start_date': tdays[train_end_date].strftime('%Y-%m-%d'),
                'test_end_date': tdays[min(train_end_date + train_update_days, len(tdays)) - 1].strftime('%Y-%m-%d'),
            })
            train_end_date += train_update_days

        if not rollings:
            raise Exception('没有滚动需要执行，请检查配置')

        if is_live_run and rolling_count_for_live:
            rollings = rollings[-rolling_count_for_live:]

        return rollings

    g = bq_graph

    rolling_dates = gen_rolling_dates(
        trading_days_market, start_date, end_date, train_update_days, train_update_days_for_live, train_data_min_days, train_data_max_days, rolling_count_for_live)

    # 训练和预测
    results = []
    for rolling in rolling_dates:
        parameters = {}
        # 先禁用回测
        parameters[trade_mid + '.__enabled__'] = False
        parameters[train_instruments_mid + '.start_date'] = rolling['train_start_date']
        parameters[train_instruments_mid + '.end_date'] = rolling['train_end_date']
        parameters[test_instruments_mid + '.start_date'] = rolling['test_start_date']
        parameters[test_instruments_mid + '.end_date'] = rolling['test_end_date']
        # print('------ rolling_train:', parameters)
        results.append(g.run(parameters))

    # 合并预测结果并回测
    mx = M.cached.v3(run=merge_datasources, input_1=[[result[predict_mid].predictions, result[test_instruments_mid].data.read_pickle()['start_date']] for result in results])
    parameters = {}
    parameters['*.__enabled__'] = False
    parameters[trade_mid + '.__enabled__'] = True
    parameters[trade_mid + '.instruments'] = mx.instrument_data
    parameters[trade_mid + '.options_data'] = mx.data

    trade = g.run(parameters)

    return {'rollings': results, 'trade': trade}


m4 = M.hyper_rolling_train.v1(
    run=m4_run_bigquant_run,
    run_now=True,
    bq_graph=g
)

[2021-10-27 14:44:11.894017] INFO: moduleinvoker: instruments.v2 开始运行..

[2021-10-27 14:44:11.919347] INFO: moduleinvoker: 命中缓存

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[2021-10-27 14:44:12.024225] INFO: moduleinvoker: advanced_auto_labeler.v2 开始运行..

[2021-10-27 14:44:12.039902] INFO: moduleinvoker: 命中缓存

[2021-10-27 14:44:12.047645] INFO: moduleinvoker: advanced_auto_labeler.v2 运行完成[0.023419s].

[2021-10-27 14:44:12.335966] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2021-10-27 14:44:12.350733] INFO: moduleinvoker: 命中缓存

[2021-10-27 14:44:12.353486] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.017544s].

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[2021-10-27 14:44:12.397554] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

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[2021-10-27 14:44:12.456412] INFO: moduleinvoker: join.v3 开始运行..

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[2021-10-27 14:44:12.468607] INFO: moduleinvoker: join.v3 运行完成[0.012212s].

[2021-10-27 14:44:12.485365] INFO: moduleinvoker: dropnan.v1 开始运行..

[2021-10-27 14:44:12.500595] INFO: moduleinvoker: 命中缓存

[2021-10-27 14:44:12.503730] INFO: moduleinvoker: dropnan.v1 运行完成[0.018352s].

[2021-10-27 14:44:12.516701] INFO: moduleinvoker: dropnan.v1 开始运行..

[2021-10-27 14:44:12.533399] INFO: moduleinvoker: 命中缓存

[2021-10-27 14:44:12.535836] INFO: moduleinvoker: dropnan.v1 运行完成[0.019157s].

[2021-10-27 14:44:12.554335] INFO: moduleinvoker: stock_ranker_train.v5 开始运行..

[2021-10-27 14:44:12.575512] INFO: moduleinvoker: 命中缓存

[2021-10-27 14:44:12.834894] INFO: moduleinvoker: stock_ranker_train.v5 运行完成[0.280553s].

[2021-10-27 14:44:12.854097] INFO: moduleinvoker: stock_ranker_predict.v5 开始运行..

[2021-10-27 14:44:12.867925] INFO: moduleinvoker: 命中缓存

[2021-10-27 14:44:12.871610] INFO: moduleinvoker: stock_ranker_predict.v5 运行完成[0.017597s].

[2021-10-27 14:44:12.878194] INFO: moduleinvoker: instruments.v2 开始运行..

[2021-10-27 14:44:12.906626] INFO: moduleinvoker: 命中缓存

[2021-10-27 14:44:12.908700] INFO: moduleinvoker: instruments.v2 运行完成[0.030508s].

[2021-10-27 14:44:12.913060] INFO: moduleinvoker: input_features.v1 开始运行..

[2021-10-27 14:44:12.920352] INFO: moduleinvoker: 命中缓存

[2021-10-27 14:44:12.922081] INFO: moduleinvoker: input_features.v1 运行完成[0.009038s].

[2021-10-27 14:44:12.929372] INFO: moduleinvoker: instruments.v2 开始运行..

[2021-10-27 14:44:12.938830] INFO: moduleinvoker: 命中缓存

[2021-10-27 14:44:12.940913] INFO: moduleinvoker: instruments.v2 运行完成[0.011597s].

[2021-10-27 14:44:12.952037] INFO: moduleinvoker: advanced_auto_labeler.v2 开始运行..

[2021-10-27 14:44:12.965248] INFO: moduleinvoker: 命中缓存

[2021-10-27 14:44:12.967261] INFO: moduleinvoker: advanced_auto_labeler.v2 运行完成[0.015235s].

[2021-10-27 14:44:12.997125] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2021-10-27 14:44:13.004277] INFO: moduleinvoker: 命中缓存

[2021-10-27 14:44:13.006631] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.009533s].

[2021-10-27 14:44:13.021933] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2021-10-27 14:44:13.035154] INFO: moduleinvoker: 命中缓存

[2021-10-27 14:44:13.038014] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.016099s].

[2021-10-27 14:44:13.052150] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2021-10-27 14:44:13.059761] INFO: moduleinvoker: 命中缓存

[2021-10-27 14:44:13.062177] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.010055s].

[2021-10-27 14:44:13.069652] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2021-10-27 14:44:13.081679] INFO: moduleinvoker: 命中缓存

[2021-10-27 14:44:13.083581] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.013931s].

[2021-10-27 14:44:13.091797] INFO: moduleinvoker: join.v3 开始运行..

[2021-10-27 14:44:13.102534] INFO: moduleinvoker: 命中缓存

[2021-10-27 14:44:13.105318] INFO: moduleinvoker: join.v3 运行完成[0.013579s].

[2021-10-27 14:44:13.115539] INFO: moduleinvoker: dropnan.v1 开始运行..

[2021-10-27 14:44:13.123139] INFO: moduleinvoker: 命中缓存

[2021-10-27 14:44:13.125525] INFO: moduleinvoker: dropnan.v1 运行完成[0.009996s].

[2021-10-27 14:44:13.136656] INFO: moduleinvoker: dropnan.v1 开始运行..

[2021-10-27 14:44:13.147151] INFO: moduleinvoker: 命中缓存

[2021-10-27 14:44:13.149241] INFO: moduleinvoker: dropnan.v1 运行完成[0.012567s].

[2021-10-27 14:44:13.159898] INFO: moduleinvoker: stock_ranker_train.v5 开始运行..

[2021-10-27 14:44:13.174034] INFO: moduleinvoker: 命中缓存

[2021-10-27 14:44:13.391619] INFO: moduleinvoker: stock_ranker_train.v5 运行完成[0.231717s].

[2021-10-27 14:44:13.402318] INFO: moduleinvoker: stock_ranker_predict.v5 开始运行..

[2021-10-27 14:44:13.419546] INFO: moduleinvoker: 命中缓存

[2021-10-27 14:44:13.421354] INFO: moduleinvoker: stock_ranker_predict.v5 运行完成[0.019053s].

[2021-10-27 14:44:13.462941] INFO: moduleinvoker: cached.v3 开始运行..

[2021-10-27 14:44:13.477727] INFO: moduleinvoker: 命中缓存

[2021-10-27 14:44:13.479515] INFO: moduleinvoker: cached.v3 运行完成[0.016649s].

[2021-10-27 14:44:15.635452] INFO: moduleinvoker: backtest.v8 开始运行..

[2021-10-27 14:44:15.641679] INFO: backtest: biglearning backtest:V8.5.0

[2021-10-27 14:44:15.643466] INFO: backtest: product_type:stock by specified

[2021-10-27 14:44:15.759011] INFO: moduleinvoker: cached.v2 开始运行..

[2021-10-27 14:44:15.769768] INFO: moduleinvoker: 命中缓存

[2021-10-27 14:44:15.771424] INFO: moduleinvoker: cached.v2 运行完成[0.012479s].

[2021-10-27 14:44:18.238593] INFO: algo: TradingAlgorithm V1.8.5

[2021-10-27 14:44:19.687917] INFO: algo: trading transform...

[2021-10-27 14:44:22.262027] INFO: algo: handle_splits get splits [dt:2015-04-21 00:00:00+00:00] [asset:Equity(3091 [300195.SZA]), ratio:0.49782368540763855]

[2021-10-27 14:44:22.263927] INFO: Position: position stock handle split[sid:3091, orig_amount:1100, new_amount:2209.0, orig_cost:56.54999583537557, new_cost:28.1519, ratio:0.49782368540763855, last_sale_price:27.449996948242188]

[2021-10-27 14:44:22.265125] INFO: Position: after split: PositionStock(asset:Equity(3091 [300195.SZA]), amount:2209.0, cost_basis:28.1519, last_sale_price:55.13999938964844)

[2021-10-27 14:44:22.266457] INFO: Position: returning cash: 16.9544

[2021-10-27 14:44:23.170752] INFO: algo: handle_splits get splits [dt:2015-05-26 00:00:00+00:00] [asset:Equity(2368 [002278.SZA]), ratio:0.8650537729263306]

[2021-10-27 14:44:23.172653] INFO: Position: position stock handle split[sid:2368, orig_amount:9200, new_amount:10635.0, orig_cost:17.00000033372181, new_cost:14.7059, ratio:0.8650537729263306, last_sale_price:16.09000015258789]

[2021-10-27 14:44:23.174078] INFO: Position: after split: PositionStock(asset:Equity(2368 [002278.SZA]), amount:10635.0, cost_basis:14.7059, last_sale_price:18.600000381469727)

[2021-10-27 14:44:23.175254] INFO: Position: returning cash: 2.8481

[2021-10-27 14:44:23.254792] INFO: algo: handle_splits get splits [dt:2015-05-28 00:00:00+00:00] [asset:Equity(3283 [000935.SZA]), ratio:0.9951314330101013]

[2021-10-27 14:44:23.636884] INFO: algo: handle_splits get splits [dt:2015-06-11 00:00:00+00:00] [asset:Equity(441 [000635.SZA]), ratio:0.9989212155342102]

[2021-10-27 14:44:23.677017] INFO: algo: handle_splits get splits [dt:2015-06-12 00:00:00+00:00] [asset:Equity(1182 [300337.SZA]), ratio:0.9988328218460083]

[2021-10-27 14:44:23.762935] INFO: algo: handle_splits get splits [dt:2015-06-16 00:00:00+00:00] [asset:Equity(953 [600218.SHA]), ratio:0.995171308517456]

[2021-10-27 14:44:23.764618] INFO: Position: position stock handle split[sid:953, orig_amount:6500, new_amount:6531.0, orig_cost:20.41000042459708, new_cost:20.3114, ratio:0.995171308517456, last_sale_price:20.60999870300293]

[2021-10-27 14:44:23.766253] INFO: Position: after split: PositionStock(asset:Equity(953 [600218.SHA]), amount:6531.0, cost_basis:20.3114, last_sale_price:20.71000099182129)

[2021-10-27 14:44:23.767804] INFO: Position: returning cash: 11.1044

[2021-10-27 14:44:23.939097] INFO: algo: handle_splits get splits [dt:2015-06-23 00:00:00+00:00] [asset:Equity(394 [300077.SZA]), ratio:0.9998031258583069]

[2021-10-27 14:44:23.941490] INFO: Position: position stock handle split[sid:394, orig_amount:1600, new_amount:1600.0, orig_cost:51.989997928733004, new_cost:51.9798, ratio:0.9998031258583069, last_sale_price:50.77000045776367]

[2021-10-27 14:44:23.944103] INFO: Position: after split: PositionStock(asset:Equity(394 [300077.SZA]), amount:1600.0, cost_basis:51.9798, last_sale_price:50.779998779296875)

[2021-10-27 14:44:23.946121] INFO: Position: returning cash: 15.9956

[2021-10-27 14:44:24.069969] INFO: algo: handle_splits get splits [dt:2015-06-26 00:00:00+00:00] [asset:Equity(671 [600703.SHA]), ratio:0.9935043454170227]

[2021-10-27 14:44:24.408321] INFO: algo: handle_splits get splits [dt:2015-07-08 00:00:00+00:00] [asset:Equity(2147 [000062.SZA]), ratio:0.998516321182251]

[2021-10-27 14:44:24.410342] INFO: algo: handle_splits get splits [dt:2015-07-08 00:00:00+00:00] [asset:Equity(1789 [002552.SZA]), ratio:0.9986004829406738]

[2021-10-27 14:44:24.413190] INFO: Position: position stock handle split[sid:2147, orig_amount:1200, new_amount:1201.0, orig_cost:33.71999749571256, new_cost:33.67, ratio:0.998516321182251, last_sale_price:33.650001525878906]

[2021-10-27 14:44:24.415158] INFO: Position: after split: PositionStock(asset:Equity(2147 [000062.SZA]), amount:1201.0, cost_basis:33.67, last_sale_price:33.70000076293945)

[2021-10-27 14:44:24.416393] INFO: Position: returning cash: 26.35

[2021-10-27 14:44:24.472216] INFO: algo: handle_splits get splits [dt:2015-07-09 00:00:00+00:00] [asset:Equity(622 [000830.SZA]), ratio:0.9541283845901489]

[2021-10-27 14:44:24.474039] INFO: Position: position stock handle split[sid:622, orig_amount:6700, new_amount:7022.0, orig_cost:9.18000030947306, new_cost:8.7589, ratio:0.9541283845901489, last_sale_price:6.239999771118164]

[2021-10-27 14:44:24.475540] INFO: Position: after split: PositionStock(asset:Equity(622 [000830.SZA]), amount:7022.0, cost_basis:8.7589, last_sale_price:6.539999961853027)

[2021-10-27 14:44:24.476569] INFO: Position: returning cash: 0.7226

[2021-10-27 14:44:24.527918] INFO: algo: handle_splits get splits [dt:2015-07-10 00:00:00+00:00] [asset:Equity(2128 [600493.SHA]), ratio:0.9978116750717163]

[2021-10-27 14:44:24.688553] INFO: algo: handle_splits get splits [dt:2015-07-16 00:00:00+00:00] [asset:Equity(3203 [600783.SHA]), ratio:0.9938850402832031]

[2021-10-27 14:44:24.690096] INFO: Position: position stock handle split[sid:3203, orig_amount:4200, new_amount:4225.0, orig_cost:22.09000027418059, new_cost:21.9549, ratio:0.9938850402832031, last_sale_price:24.3799991607666]

[2021-10-27 14:44:24.691519] INFO: Position: after split: PositionStock(asset:Equity(3203 [600783.SHA]), amount:4225.0, cost_basis:21.9549, last_sale_price:24.529998779296875)

[2021-10-27 14:44:24.692683] INFO: Position: returning cash: 20.4998

[2021-10-27 14:44:24.832937] INFO: algo: handle_splits get splits [dt:2015-07-21 00:00:00+00:00] [asset:Equity(391 [002634.SZA]), ratio:0.9914237856864929]

[2021-10-27 14:44:24.834757] INFO: Position: position stock handle split[sid:391, orig_amount:10400, new_amount:10489.0, orig_cost:22.650001286474506, new_cost:22.4558, ratio:0.9914237856864929, last_sale_price:23.12000274658203]

[2021-10-27 14:44:24.837261] INFO: Position: after split: PositionStock(asset:Equity(391 [002634.SZA]), amount:10489.0, cost_basis:22.4558, last_sale_price:23.31999969482422)

[2021-10-27 14:44:24.839462] INFO: Position: returning cash: 22.2919

[2021-10-27 14:44:26.535663] INFO: algo: handle_splits get splits [dt:2015-09-22 00:00:00+00:00] [asset:Equity(1079 [603686.SHA]), ratio:0.5]

[2021-10-27 14:44:26.537888] INFO: Position: position stock handle split[sid:1079, orig_amount:700, new_amount:1400.0, orig_cost:73.30000378718402, new_cost:36.65, ratio:0.5, last_sale_price:39.589996337890625]

[2021-10-27 14:44:26.539394] INFO: Position: after split: PositionStock(asset:Equity(1079 [603686.SHA]), amount:1400.0, cost_basis:36.65, last_sale_price:79.17999267578125)

[2021-10-27 14:44:26.541361] INFO: Position: returning cash: 0.0

[2021-10-27 14:44:26.786874] INFO: algo: handle_splits get splits [dt:2015-10-08 00:00:00+00:00] [asset:Equity(1299 [002639.SZA]), ratio:0.3333333432674408]

[2021-10-27 14:44:26.789135] INFO: Position: position stock handle split[sid:1299, orig_amount:2400, new_amount:7199.0, orig_cost:26.799999459307912, new_cost:8.9333, ratio:0.3333333432674408, last_sale_price:8.640000343322754]

[2021-10-27 14:44:26.790761] INFO: Position: after split: PositionStock(asset:Equity(1299 [002639.SZA]), amount:7199.0, cost_basis:8.9333, last_sale_price:25.920000076293945)

[2021-10-27 14:44:26.792744] INFO: Position: returning cash: 8.6381

[2021-10-27 14:44:30.295810] INFO: algo: handle_splits get splits [dt:2016-04-06 00:00:00+00:00] [asset:Equity(1273 [002503.SZA]), ratio:0.7680311799049377]

[2021-10-27 14:44:30.950851] INFO: algo: handle_splits get splits [dt:2016-05-10 00:00:00+00:00] [asset:Equity(2403 [002120.SZA]), ratio:0.9971453547477722]

[2021-10-27 14:44:30.979144] INFO: algo: handle_splits get splits [dt:2016-05-11 00:00:00+00:00] [asset:Equity(1637 [002381.SZA]), ratio:0.9839613437652588]

[2021-10-27 14:44:30.980850] INFO: Position: position stock handle split[sid:1637, orig_amount:5200, new_amount:5284.0, orig_cost:12.95000235242694, new_cost:12.7423, ratio:0.9839613437652588, last_sale_price:12.269998550415039]

[2021-10-27 14:44:30.981976] INFO: Position: after split: PositionStock(asset:Equity(1637 [002381.SZA]), amount:5284.0, cost_basis:12.7423, last_sale_price:12.470000267028809)

[2021-10-27 14:44:30.983319] INFO: Position: returning cash: 9.3308

[2021-10-27 14:44:31.100519] INFO: algo: handle_splits get splits [dt:2016-05-17 00:00:00+00:00] [asset:Equity(226 [300121.SZA]), ratio:0.99640291929245]

[2021-10-27 14:44:31.102274] INFO: Position: position stock handle split[sid:226, orig_amount:22800, new_amount:22882.0, orig_cost:10.637258938956723, new_cost:10.599, ratio:0.99640291929245, last_sale_price:11.080000877380371]

[2021-10-27 14:44:31.103446] INFO: Position: after split: PositionStock(asset:Equity(226 [300121.SZA]), amount:22882.0, cost_basis:10.599, last_sale_price:11.120000839233398)

[2021-10-27 14:44:31.104442] INFO: Position: returning cash: 3.4294

[2021-10-27 14:44:31.193886] INFO: algo: handle_splits get splits [dt:2016-05-20 00:00:00+00:00] [asset:Equity(149 [300423.SZA]), ratio:0.8309080004692078]

[2021-10-27 14:44:31.195508] INFO: Position: position stock handle split[sid:149, orig_amount:8200, new_amount:9868.0, orig_cost:31.644583373403883, new_cost:26.2937, ratio:0.8309080004692078, last_sale_price:28.549999237060547]

[2021-10-27 14:44:31.197123] INFO: Position: after split: PositionStock(asset:Equity(149 [300423.SZA]), amount:9868.0, cost_basis:26.2937, last_sale_price:34.36000061035156)

[2021-10-27 14:44:31.198597] INFO: Position: returning cash: 20.6109

[2021-10-27 14:44:31.272269] INFO: algo: handle_splits get splits [dt:2016-05-24 00:00:00+00:00] [asset:Equity(468 [002690.SZA]), ratio:0.9857617616653442]

[2021-10-27 14:44:31.274115] INFO: Position: position stock handle split[sid:468, orig_amount:3800, new_amount:3854.0, orig_cost:20.460008607604404, new_cost:20.1687, ratio:0.9857617616653442, last_sale_price:20.770000457763672]

[2021-10-27 14:44:31.275314] INFO: Position: after split: PositionStock(asset:Equity(468 [002690.SZA]), amount:3854.0, cost_basis:20.1687, last_sale_price:21.06999969482422)

[2021-10-27 14:44:31.276602] INFO: Position: returning cash: 18.4188

[2021-10-27 14:44:31.335820] INFO: algo: handle_splits get splits [dt:2016-05-26 00:00:00+00:00] [asset:Equity(2387 [300108.SZA]), ratio:0.6610305905342102]

[2021-10-27 14:44:31.429015] INFO: algo: handle_splits get splits [dt:2016-05-31 00:00:00+00:00] [asset:Equity(978 [603606.SHA]), ratio:0.9983764886856079]

[2021-10-27 14:44:31.430836] INFO: Position: position stock handle split[sid:978, orig_amount:5300, new_amount:5308.0, orig_cost:12.340006477474601, new_cost:12.32, ratio:0.9983764886856079, last_sale_price:12.29999828338623]

[2021-10-27 14:44:31.432040] INFO: Position: after split: PositionStock(asset:Equity(978 [603606.SHA]), amount:5308.0, cost_basis:12.32, last_sale_price:12.319999694824219)

[2021-10-27 14:44:31.433378] INFO: Position: returning cash: 7.6088

[2021-10-27 14:44:31.550375] INFO: algo: handle_splits get splits [dt:2016-06-06 00:00:00+00:00] [asset:Equity(466 [002493.SZA]), ratio:0.9983592629432678]

[2021-10-27 14:44:31.551937] INFO: Position: position stock handle split[sid:466, orig_amount:6500, new_amount:6510.0, orig_cost:12.320001596729725, new_cost:12.2998, ratio:0.9983592629432678, last_sale_price:12.169999122619629]

[2021-10-27 14:44:31.553081] INFO: Position: after split: PositionStock(asset:Equity(466 [002493.SZA]), amount:6510.0, cost_basis:12.2998, last_sale_price:12.1899995803833)

[2021-10-27 14:44:31.554403] INFO: Position: returning cash: 8.3038

[2021-10-27 14:44:31.755072] INFO: algo: handle_splits get splits [dt:2016-06-17 00:00:00+00:00] [asset:Equity(2472 [603558.SHA]), ratio:0.9942626953125]

[2021-10-27 14:44:31.834526] INFO: algo: handle_splits get splits [dt:2016-06-22 00:00:00+00:00] [asset:Equity(3056 [600505.SHA]), ratio:0.9965397715568542]

[2021-10-27 14:44:31.836075] INFO: Position: position stock handle split[sid:3056, orig_amount:12400, new_amount:12443.0, orig_cost:8.760022234471004, new_cost:8.7297, ratio:0.9965397715568542, last_sale_price:8.640000343322754]

[2021-10-27 14:44:31.837289] INFO: Position: after split: PositionStock(asset:Equity(3056 [600505.SHA]), amount:12443.0, cost_basis:8.7297, last_sale_price:8.670000076293945)

[2021-10-27 14:44:31.838548] INFO: Position: returning cash: 0.4822

[2021-10-27 14:44:31.867077] INFO: algo: handle_splits get splits [dt:2016-06-23 00:00:00+00:00] [asset:Equity(2650 [600917.SHA]), ratio:0.9849853515625]

[2021-10-27 14:44:31.868583] INFO: Position: position stock handle split[sid:2650, orig_amount:6300, new_amount:6396.0, orig_cost:9.77000640205391, new_cost:9.6233, ratio:0.9849853515625, last_sale_price:9.84000301361084]

[2021-10-27 14:44:31.870660] INFO: Position: after split: PositionStock(asset:Equity(2650 [600917.SHA]), amount:6396.0, cost_basis:9.6233, last_sale_price:9.989999771118164)

[2021-10-27 14:44:31.872628] INFO: Position: returning cash: 0.3366

[2021-10-27 14:44:32.018836] INFO: algo: handle_splits get splits [dt:2016-06-30 00:00:00+00:00] [asset:Equity(113 [601010.SHA]), ratio:0.9924242496490479]

[2021-10-27 14:44:32.020402] INFO: Position: position stock handle split[sid:113, orig_amount:14500, new_amount:14610.0, orig_cost:5.230000842241598, new_cost:5.1904, ratio:0.9924242496490479, last_sale_price:5.240000247955322]

[2021-10-27 14:44:32.021925] INFO: Position: after split: PositionStock(asset:Equity(113 [601010.SHA]), amount:14610.0, cost_basis:5.1904, last_sale_price:5.28000020980835)

[2021-10-27 14:44:32.023490] INFO: Position: returning cash: 3.5994

[2021-10-27 14:44:32.185181] INFO: algo: handle_splits get splits [dt:2016-07-08 00:00:00+00:00] [asset:Equity(2203 [603699.SHA]), ratio:0.9860337376594543]

[2021-10-27 14:44:32.186975] INFO: Position: position stock handle split[sid:2203, orig_amount:12700, new_amount:12879.0, orig_cost:18.07757020230939, new_cost:17.8251, ratio:0.9860337376594543, last_sale_price:17.65000343322754]

[2021-10-27 14:44:32.188356] INFO: Position: after split: PositionStock(asset:Equity(2203 [603699.SHA]), amount:12879.0, cost_basis:17.8251, last_sale_price:17.899999618530273)

[2021-10-27 14:44:32.189469] INFO: Position: returning cash: 15.5997

[2021-10-27 14:44:35.844681] INFO: Performance: Simulated 483 trading days out of 483.

[2021-10-27 14:44:35.846168] INFO: Performance: first open: 2015-01-12 09:30:00+00:00

[2021-10-27 14:44:35.848914] INFO: Performance: last close: 2016-12-30 15:00:00+00:00

[2021-10-27 14:44:41.943598] INFO: moduleinvoker: backtest.v8 运行完成[26.308194s].

[2021-10-27 14:44:41.946157] INFO: moduleinvoker: trade.v4 运行完成[28.452688s].

[2021-10-27 14:44:41.949124] INFO: moduleinvoker: hyper_rolling_train.v1 运行完成[30.115662s].

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