{"description":"实验创建于2017/8/26","graph":{"edges":[{"to_node_id":"-4862:features","from_node_id":"-4857:data"},{"to_node_id":"-6998:features","from_node_id":"-4857:data"},{"to_node_id":"-4862:instruments","from_node_id":"-4849:data"},{"to_node_id":"-281:instruments","from_node_id":"-4849:data"},{"to_node_id":"-2646:instruments","from_node_id":"-4849:data"},{"to_node_id":"-6998:input_data","from_node_id":"-4862:data"},{"to_node_id":"-288:data1","from_node_id":"-2687:data"},{"to_node_id":"-2687:input_data","from_node_id":"-6998:data"},{"to_node_id":"-2646:options_data","from_node_id":"-288:data"},{"to_node_id":"-288:data2","from_node_id":"-281:data"}],"nodes":[{"node_id":"-4857","module_id":"BigQuantSpace.input_features.input_features-v1","parameters":[{"name":"features","value":"# #号开始的表示注释，注释需单独一行\n\nbuy_condition=where(close_0>mean(close_0,10),1,0)\nsell_condition=where(close_0>mean(close_0,60),1,0)\n\n\n\n\n\n","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"features_ds","node_id":"-4857"}],"output_ports":[{"name":"data","node_id":"-4857"}],"cacheable":true,"seq_num":2,"comment":"","comment_collapsed":true},{"node_id":"-4849","module_id":"BigQuantSpace.instruments.instruments-v2","parameters":[{"name":"start_date","value":"2018-01-01","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"2022-08-14","type":"Literal","bound_global_parameter":"交易日期"},{"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":"-4849"}],"output_ports":[{"name":"data","node_id":"-4849"}],"cacheable":true,"seq_num":11,"comment":"","comment_collapsed":true},{"node_id":"-4862","module_id":"BigQuantSpace.general_feature_extractor.general_feature_extractor-v7","parameters":[{"name":"start_date","value":"","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"","type":"Literal","bound_global_parameter":null},{"name":"before_start_days","value":"120","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"instruments","node_id":"-4862"},{"name":"features","node_id":"-4862"}],"output_ports":[{"name":"data","node_id":"-4862"}],"cacheable":true,"seq_num":12,"comment":"","comment_collapsed":true},{"node_id":"-2687","module_id":"BigQuantSpace.chinaa_stock_filter.chinaa_stock_filter-v1","parameters":[{"name":"index_constituent_cond","value":"%7B%22enumItems%22%3A%5B%7B%22value%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22displayValue%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E4%B8%8A%E8%AF%8150%22%2C%22displayValue%22%3A%22%E4%B8%8A%E8%AF%8150%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E6%B2%AA%E6%B7%B1300%22%2C%22displayValue%22%3A%22%E6%B2%AA%E6%B7%B1300%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E4%B8%AD%E8%AF%81500%22%2C%22displayValue%22%3A%22%E4%B8%AD%E8%AF%81500%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E4%B8%AD%E8%AF%81800%22%2C%22displayValue%22%3A%22%E4%B8%AD%E8%AF%81800%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E4%B8%8A%E8%AF%81180%22%2C%22displayValue%22%3A%22%E4%B8%8A%E8%AF%81180%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E4%B8%AD%E8%AF%81100%22%2C%22displayValue%22%3A%22%E4%B8%AD%E8%AF%81100%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E6%B7%B1%E8%AF%81100%22%2C%22displayValue%22%3A%22%E6%B7%B1%E8%AF%81100%22%2C%22selected%22%3Afalse%7D%5D%7D","type":"Literal","bound_global_parameter":null},{"name":"board_cond","value":"%7B%22enumItems%22%3A%5B%7B%22value%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22displayValue%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E4%B8%8A%E8%AF%81%E4%B8%BB%E6%9D%BF%22%2C%22displayValue%22%3A%22%E4%B8%8A%E8%AF%81%E4%B8%BB%E6%9D%BF%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E6%B7%B1%E8%AF%81%E4%B8%BB%E6%9D%BF%22%2C%22displayValue%22%3A%22%E6%B7%B1%E8%AF%81%E4%B8%BB%E6%9D%BF%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E5%88%9B%E4%B8%9A%E6%9D%BF%22%2C%22displayValue%22%3A%22%E5%88%9B%E4%B8%9A%E6%9D%BF%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E7%A7%91%E5%88%9B%E6%9D%BF%22%2C%22displayValue%22%3A%22%E7%A7%91%E5%88%9B%E6%9D%BF%22%2C%22selected%22%3Afalse%7D%5D%7D","type":"Literal","bound_global_parameter":null},{"name":"industry_cond","value":"%7B%22enumItems%22%3A%5B%7B%22value%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22displayValue%22%3A%22%E5%85%A8%E9%83%A8%22%2C%22selected%22%3Atrue%7D%2C%7B%22value%22%3A%22%E4%BA%A4%E9%80%9A%E8%BF%90%E8%BE%93%22%2C%22displayValue%22%3A%22%E4%BA%A4%E9%80%9A%E8%BF%90%E8%BE%93%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E4%BC%91%E9%97%B2%E6%9C%8D%E5%8A%A1%22%2C%22displayValue%22%3A%22%E4%BC%91%E9%97%B2%E6%9C%8D%E5%8A%A1%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E4%BC%A0%E5%AA%92%2F%E4%BF%A1%E6%81%AF%E6%9C%8D%E5%8A%A1%22%2C%22displayValue%22%3A%22%E4%BC%A0%E5%AA%92%2F%E4%BF%A1%E6%81%AF%E6%9C%8D%E5%8A%A1%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%85%AC%E7%94%A8%E4%BA%8B%E4%B8%9A%22%2C%22displayValue%22%3A%22%E5%85%AC%E7%94%A8%E4%BA%8B%E4%B8%9A%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%86%9C%E6%9E%97%E7%89%A7%E6%B8%94%22%2C%22displayValue%22%3A%22%E5%86%9C%E6%9E%97%E7%89%A7%E6%B8%94%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%8C%96%E5%B7%A5%22%2C%22displayValue%22%3A%22%E5%8C%96%E5%B7%A5%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%8C%BB%E8%8D%AF%E7%94%9F%E7%89%A9%22%2C%22displayValue%22%3A%22%E5%8C%BB%E8%8D%AF%E7%94%9F%E7%89%A9%22%2C%22selected%22%3Afalse%7D%2C%7B%22value%22%3A%22%E5%95%86%E4%B8%9A%E8%B4%B8%E6%98%9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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 = 5\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 = 0.2\n context.hold_days = 5\n","type":"Literal","bound_global_parameter":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.hold_days # 是否在建仓期间（前 hold_days 天）\n cash_avg = context.portfolio.portfolio_value / context.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.perf_tracker.position_tracker.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StockRanker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.perf_tracker.position_tracker.positions.items()}\n instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(\n lambda x: x in equities and not context.has_unfinished_sell_order(equities[x]))])))\n # print('rank order for sell %s' % instruments)\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StockRanker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","type":"Literal","bound_global_parameter":null},{"name":"prepare","value":"# 回测引擎：准备数据，只执行一次\ndef bigquant_run(context):\n pass\n","type":"Literal","bound_global_parameter":null},{"name":"before_trading_start","value":"# 回测引擎：每个单位时间开始前调用一次，即每日开盘前调用一次。\ndef bigquant_run(context, data):\n pass\n","type":"Literal","bound_global_parameter":null},{"name":"volume_limit","value":0.025,"type":"Literal","bound_global_parameter":null},{"name":"order_price_field_buy","value":"open","type":"Literal","bound_global_parameter":null},{"name":"order_price_field_sell","value":"close","type":"Literal","bound_global_parameter":null},{"name":"capital_base","value":1000000,"type":"Literal","bound_global_parameter":null},{"name":"auto_cancel_non_tradable_orders","value":"True","type":"Literal","bound_global_parameter":null},{"name":"data_frequency","value":"daily","type":"Literal","bound_global_parameter":null},{"name":"price_type","value":"真实价格","type":"Literal","bound_global_parameter":null},{"name":"product_type","value":"股票","type":"Literal","bound_global_parameter":null},{"name":"plot_charts","value":"True","type":"Literal","bound_global_parameter":null},{"name":"backtest_only","value":"False","type":"Literal","bound_global_parameter":null},{"name":"benchmark","value":"000300.HIX","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"instruments","node_id":"-2646"},{"name":"options_data","node_id":"-2646"},{"name":"history_ds","node_id":"-2646"},{"name":"benchmark_ds","node_id":"-2646"},{"name":"trading_calendar","node_id":"-2646"}],"output_ports":[{"name":"raw_perf","node_id":"-2646"}],"cacheable":false,"seq_num":3,"comment":"","comment_collapsed":true}],"node_layout":"<node_postions><node_position Node='-4857' Position='562,-133,200,200'/><node_position Node='-4849' Position='-21,-113,200,200'/><node_position Node='-4862' Position='401,184,200,200'/><node_position Node='-2687' Position='427.86004638671875,584.34130859375,200,200'/><node_position Node='-6998' Position='454,403,200,200'/><node_position Node='-288' Position='425,996,200,200'/><node_position Node='-281' Position='-47,509,200,200'/><node_position Node='-2646' Position='364.479248046875,1299.4931030273438,200,200'/></node_postions>"},"nodes_readonly":false,"studio_version":"v2"}

# 本代码由可视化策略环境自动生成 2022年9月6日 21:46
# 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 m3_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.hold_days = 5

# 回测引擎：每日数据处理函数，每天执行一次
def m3_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.hold_days # 是否在建仓期间（前 hold_days 天）
    cash_avg = context.portfolio.portfolio_value / context.hold_days
    cash_for_buy = min(context.portfolio.cash, (1 if is_staging else 1.5) * cash_avg)
    cash_for_sell = cash_avg - (context.portfolio.cash - cash_for_buy)
    positions = {e.symbol: p.amount * p.last_sale_price
                 for e, p in context.perf_tracker.position_tracker.positions.items()}

    # 2. 生成卖出订单：hold_days天之后才开始卖出；对持仓的股票，按StockRanker预测的排序末位淘汰
    if not is_staging and cash_for_sell > 0:
        equities = {e.symbol: e for e, p in context.perf_tracker.position_tracker.positions.items()}
        instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(
                lambda x: x in equities and not context.has_unfinished_sell_order(equities[x]))])))
        # print('rank order for sell %s' % instruments)
        for instrument in instruments:
            context.order_target(context.symbol(instrument), 0)
            cash_for_sell -= positions[instrument]
            if cash_for_sell <= 0:
                break

    # 3. 生成买入订单：按StockRanker预测的排序，买入前面的stock_count只股票
    buy_cash_weights = context.stock_weights
    buy_instruments = list(ranker_prediction.instrument[:len(buy_cash_weights)])
    max_cash_per_instrument = context.portfolio.portfolio_value * context.max_cash_per_instrument
    for i, instrument in enumerate(buy_instruments):
        cash = cash_for_buy * buy_cash_weights[i]
        if cash > max_cash_per_instrument - positions.get(instrument, 0):
            # 确保股票持仓量不会超过每次股票最大的占用资金量
            cash = max_cash_per_instrument - positions.get(instrument, 0)
        if cash > 0:
            context.order_value(context.symbol(instrument), cash)

# 回测引擎：准备数据，只执行一次
def m3_prepare_bigquant_run(context):
    pass

# 回测引擎：每个单位时间开始前调用一次，即每日开盘前调用一次。
def m3_before_trading_start_bigquant_run(context, data):
    pass


m2 = M.input_features.v1(
    features="""# #号开始的表示注释，注释需单独一行

buy_condition=where(close_0>mean(close_0,10),1,0)
sell_condition=where(close_0>mean(close_0,60),1,0)





"""
)

m11 = M.instruments.v2(
    start_date='2018-01-01',
    end_date=T.live_run_param('trading_date', '2022-08-14'),
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m12 = M.general_feature_extractor.v7(
    instruments=m11.data,
    features=m2.data,
    start_date='',
    end_date='',
    before_start_days=120
)

m14 = M.derived_feature_extractor.v3(
    input_data=m12.data,
    features=m2.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False,
    user_functions={}
)

m13 = M.chinaa_stock_filter.v1(
    input_data=m14.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
)

m1 = M.use_datasource.v1(
    instruments=m11.data,
    datasource_id='instruments_CN_STOCK_A',
    start_date='',
    end_date=''
)

m18 = M.join.v3(
    data1=m13.data,
    data2=m1.data,
    on='date,instrument',
    how='left',
    sort=False
)

m3 = M.trade.v4(
    instruments=m11.data,
    options_data=m18.data,
    start_date='',
    end_date='',
    initialize=m3_initialize_bigquant_run,
    handle_data=m3_handle_data_bigquant_run,
    prepare=m3_prepare_bigquant_run,
    before_trading_start=m3_before_trading_start_bigquant_run,
    volume_limit=0.025,
    order_price_field_buy='open',
    order_price_field_sell='close',
    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.HIX'
)

[2022-09-06 21:41:51.464993] INFO: moduleinvoker: input_features.v1 开始运行..

[2022-09-06 21:41:51.488583] INFO: moduleinvoker: 命中缓存

[2022-09-06 21:41:51.491488] INFO: moduleinvoker: input_features.v1 运行完成[0.026505s].

[2022-09-06 21:41:52.187034] INFO: moduleinvoker: instruments.v2 开始运行..

[2022-09-06 21:41:52.212153] INFO: moduleinvoker: 命中缓存

[2022-09-06 21:41:52.214681] INFO: moduleinvoker: instruments.v2 运行完成[0.027646s].

[2022-09-06 21:41:52.277914] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2022-09-06 21:42:01.704594] INFO: 基础特征抽取: 年份 2017, 特征行数=256263

[2022-09-06 21:42:06.335466] INFO: 基础特征抽取: 年份 2018, 特征行数=816987

[2022-09-06 21:42:13.261309] INFO: 基础特征抽取: 年份 2019, 特征行数=884867

[2022-09-06 21:42:19.422230] INFO: 基础特征抽取: 年份 2020, 特征行数=945961

[2022-09-06 21:42:26.127445] INFO: 基础特征抽取: 年份 2021, 特征行数=1061527

[2022-09-06 21:42:30.098937] INFO: 基础特征抽取: 年份 2022, 特征行数=704964

[2022-09-06 21:42:30.352417] INFO: 基础特征抽取: 总行数: 4670569

[2022-09-06 21:42:30.376770] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[38.098865s].

[2022-09-06 21:42:30.394149] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-09-06 21:42:45.646600] INFO: derived_feature_extractor: 提取完成 buy_condition=where(close_0>mean(close_0,10),1,0), 4.813s

[2022-09-06 21:42:50.458893] INFO: derived_feature_extractor: 提取完成 sell_condition=where(close_0>mean(close_0,60),1,0), 4.810s

[2022-09-06 21:42:51.110528] INFO: derived_feature_extractor: /y_2017, 256263

[2022-09-06 21:42:52.909468] INFO: derived_feature_extractor: /y_2018, 816987

[2022-09-06 21:42:54.992651] INFO: derived_feature_extractor: /y_2019, 884867

[2022-09-06 21:42:57.163471] INFO: derived_feature_extractor: /y_2020, 945961

[2022-09-06 21:42:59.602592] INFO: derived_feature_extractor: /y_2021, 1061527

[2022-09-06 21:43:01.386018] INFO: derived_feature_extractor: /y_2022, 704964

[2022-09-06 21:43:02.284837] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[31.890685s].

[2022-09-06 21:43:02.300412] INFO: moduleinvoker: chinaa_stock_filter.v1 开始运行..

[2022-09-06 21:43:04.198688] INFO: A股股票过滤: 过滤 /y_2017, 251995/0/256263

[2022-09-06 21:43:07.458176] INFO: A股股票过滤: 过滤 /y_2018, 800233/0/816987

[2022-09-06 21:43:11.127501] INFO: A股股票过滤: 过滤 /y_2019, 852507/0/884867

[2022-09-06 21:43:14.871134] INFO: A股股票过滤: 过滤 /y_2020, 870321/0/945961

[2022-09-06 21:43:19.022170] INFO: A股股票过滤: 过滤 /y_2021, 940451/0/1061527

[2022-09-06 21:43:22.094266] INFO: A股股票过滤: 过滤 /y_2022, 606166/0/704964

[2022-09-06 21:43:22.100488] INFO: A股股票过滤: 过滤完成, 4321673 + 0

[2022-09-06 21:43:22.123546] INFO: moduleinvoker: chinaa_stock_filter.v1 运行完成[19.823122s].

[2022-09-06 21:43:22.131940] INFO: moduleinvoker: use_datasource.v1 开始运行..

[2022-09-06 21:43:22.138705] INFO: moduleinvoker: 命中缓存

[2022-09-06 21:43:22.141371] INFO: moduleinvoker: use_datasource.v1 运行完成[0.009431s].

[2022-09-06 21:43:22.158879] INFO: moduleinvoker: join.v3 开始运行..

[2022-09-06 21:43:26.575399] INFO: join: /y_2017, 行数=251995/251995, 耗时=2.560739s

[2022-09-06 21:43:30.449633] INFO: join: /y_2018, 行数=800233/800233, 耗时=3.869757s

[2022-09-06 21:43:34.255053] INFO: join: /y_2019, 行数=852507/852507, 耗时=3.796572s

[2022-09-06 21:43:37.948594] INFO: join: /y_2020, 行数=870321/870321, 耗时=3.684373s

[2022-09-06 21:43:42.099081] INFO: join: /y_2021, 行数=940451/940451, 耗时=4.139353s

[2022-09-06 21:43:45.470728] INFO: join: /y_2022, 行数=606166/606166, 耗时=3.359662s

[2022-09-06 21:43:45.570441] INFO: join: 最终行数: 4321673

[2022-09-06 21:43:45.610431] INFO: moduleinvoker: join.v3 运行完成[23.451531s].

[2022-09-06 21:43:51.084806] INFO: moduleinvoker: backtest.v8 开始运行..

[2022-09-06 21:43:51.100837] INFO: backtest: biglearning backtest:V8.6.3

[2022-09-06 21:43:51.103654] INFO: backtest: product_type:stock by specified

[2022-09-06 21:43:51.446963] INFO: moduleinvoker: cached.v2 开始运行..

[2022-09-06 21:44:11.358904] INFO: backtest: 读取股票行情完成:5937344

[2022-09-06 21:44:18.393609] INFO: moduleinvoker: cached.v2 运行完成[26.946659s].

[2022-09-06 21:44:25.981680] INFO: algo: TradingAlgorithm V1.8.8

[2022-09-06 21:44:31.170791] INFO: algo: trading transform...

[2022-09-06 21:44:37.324911] INFO: algo: handle_splits get splits [dt:2018-06-22 00:00:00+00:00] [asset:Equity(1169 [000006.SZA]), ratio:0.970588207244873]

[2022-09-06 21:44:37.326814] INFO: Position: position stock handle split[sid:1169, orig_amount:5300, new_amount:5460.0, orig_cost:6.1600004557108035, new_cost:5.9788, ratio:0.970588207244873, last_sale_price:5.939999580383301]

[2022-09-06 21:44:37.328801] INFO: Position: after split: PositionStock(asset:Equity(1169 [000006.SZA]), amount:5460.0, cost_basis:5.9788, last_sale_price:6.119999885559082)

[2022-09-06 21:44:37.344455] INFO: Position: returning cash: 3.6009

[2022-09-06 21:44:37.951579] INFO: algo: handle_splits get splits [dt:2018-07-12 00:00:00+00:00] [asset:Equity(935 [000001.SZA]), ratio:0.9840546250343323]

[2022-09-06 21:44:37.963859] INFO: Position: position stock handle split[sid:935, orig_amount:18300, new_amount:18596.0, orig_cost:12.984863469878192, new_cost:12.7778, ratio:0.9840546250343323, last_sale_price:8.640000343322754]

[2022-09-06 21:44:37.965974] INFO: Position: after split: PositionStock(asset:Equity(935 [000001.SZA]), amount:18596.0, cost_basis:12.7778, last_sale_price:8.780000686645508)

[2022-09-06 21:44:37.967627] INFO: Position: returning cash: 4.5673

[2022-09-06 21:44:39.250119] INFO: algo: handle_splits get splits [dt:2018-08-23 00:00:00+00:00] [asset:Equity(4021 [000002.SZA]), ratio:0.9623745083808899]

[2022-09-06 21:44:39.252792] INFO: Position: position stock handle split[sid:4021, orig_amount:7500, new_amount:7793.0, orig_cost:31.817868119115914, new_cost:30.6207, ratio:0.9623745083808899, last_sale_price:23.01999855041504]

[2022-09-06 21:44:39.255456] INFO: Position: after split: PositionStock(asset:Equity(4021 [000002.SZA]), amount:7793.0, cost_basis:30.6207, last_sale_price:23.920000076293945)

[2022-09-06 21:44:39.257804] INFO: Position: returning cash: 5.1537

[2022-09-06 21:44:47.877433] INFO: algo: handle_splits get splits [dt:2019-06-19 00:00:00+00:00] [asset:Equity(1169 [000006.SZA]), ratio:0.9661319255828857]

[2022-09-06 21:44:47.880150] INFO: Position: position stock handle split[sid:1169, orig_amount:6200, new_amount:6417.0, orig_cost:5.620000617672315, new_cost:5.4297, ratio:0.9661319255828857, last_sale_price:5.419999599456787]

[2022-09-06 21:44:47.882612] INFO: Position: after split: PositionStock(asset:Equity(1169 [000006.SZA]), amount:6417.0, cost_basis:5.4297, last_sale_price:5.609999656677246)

[2022-09-06 21:44:47.884667] INFO: Position: returning cash: 1.8593

[2022-09-06 21:44:48.155067] INFO: algo: handle_splits get splits [dt:2019-06-26 00:00:00+00:00] [asset:Equity(935 [000001.SZA]), ratio:0.9895756244659424]

[2022-09-06 21:44:48.157634] INFO: Position: position stock handle split[sid:935, orig_amount:18596.0, new_amount:18791.0, orig_cost:12.7778, new_cost:12.6446, ratio:0.9895756244659424, last_sale_price:13.289999961853027]

[2022-09-06 21:44:48.159857] INFO: Position: after split: PositionStock(asset:Equity(935 [000001.SZA]), amount:18791.0, cost_basis:12.6446, last_sale_price:13.429999351501465)

[2022-09-06 21:44:48.161900] INFO: Position: returning cash: 11.878

[2022-09-06 21:44:49.706410] INFO: algo: handle_splits get splits [dt:2019-08-15 00:00:00+00:00] [asset:Equity(4021 [000002.SZA]), ratio:0.9612975120544434]

[2022-09-06 21:44:49.708741] INFO: Position: position stock handle split[sid:4021, orig_amount:6300, new_amount:6553.0, orig_cost:27.53999902036751, new_cost:26.4741, ratio:0.9612975120544434, last_sale_price:26.079999923706055]

[2022-09-06 21:44:49.710408] INFO: Position: after split: PositionStock(asset:Equity(4021 [000002.SZA]), amount:6553.0, cost_basis:26.4741, last_sale_price:27.1299991607666)

[2022-09-06 21:44:49.711842] INFO: Position: returning cash: 16.7502

[2022-09-06 21:44:58.109705] INFO: algo: handle_splits get splits [dt:2020-05-28 00:00:00+00:00] [asset:Equity(935 [000001.SZA]), ratio:0.9830769300460815]

[2022-09-06 21:44:58.112254] INFO: Position: position stock handle split[sid:935, orig_amount:18791.0, new_amount:19114.0, orig_cost:12.6446, new_cost:12.4306, ratio:0.9830769300460815, last_sale_price:12.779999732971191]

[2022-09-06 21:44:58.114382] INFO: Position: after split: PositionStock(asset:Equity(935 [000001.SZA]), amount:19114.0, cost_basis:12.4306, last_sale_price:13.0)

[2022-09-06 21:44:58.116476] INFO: Position: returning cash: 6.0783

[2022-09-06 21:44:58.821189] INFO: algo: handle_splits get splits [dt:2020-06-18 00:00:00+00:00] [asset:Equity(1169 [000006.SZA]), ratio:0.9703947305679321]

[2022-09-06 21:44:58.823885] INFO: Position: position stock handle split[sid:1169, orig_amount:5800, new_amount:5976.0, orig_cost:6.009999755321769, new_cost:5.8321, ratio:0.9703947305679321, last_sale_price:5.900000095367432]

[2022-09-06 21:44:58.826252] INFO: Position: after split: PositionStock(asset:Equity(1169 [000006.SZA]), amount:5976.0, cost_basis:5.8321, last_sale_price:6.079999923706055)

[2022-09-06 21:44:58.829138] INFO: Position: returning cash: 5.6002

[2022-09-06 21:45:00.606640] INFO: algo: handle_splits get splits [dt:2020-08-14 00:00:00+00:00] [asset:Equity(4021 [000002.SZA]), ratio:0.9643356204032898]

[2022-09-06 21:45:00.610918] INFO: Position: position stock handle split[sid:4021, orig_amount:7200, new_amount:7466.0, orig_cost:30.06847315410749, new_cost:28.9961, ratio:0.9643356204032898, last_sale_price:27.580001831054688]

[2022-09-06 21:45:00.612974] INFO: Position: after split: PositionStock(asset:Equity(4021 [000002.SZA]), amount:7466.0, cost_basis:28.9961, last_sale_price:28.60000228881836)

[2022-09-06 21:45:00.614792] INFO: Position: returning cash: 7.7294

[2022-09-06 21:45:09.579128] INFO: algo: handle_splits get splits [dt:2021-05-14 00:00:00+00:00] [asset:Equity(935 [000001.SZA]), ratio:0.9921976923942566]

[2022-09-06 21:45:09.582036] INFO: Position: position stock handle split[sid:935, orig_amount:19114.0, new_amount:19264.0, orig_cost:12.4306, new_cost:12.3336, ratio:0.9921976923942566, last_sale_price:22.89000129699707]

[2022-09-06 21:45:09.585206] INFO: Position: after split: PositionStock(asset:Equity(935 [000001.SZA]), amount:19264.0, cost_basis:12.3336, last_sale_price:23.06999969482422)

[2022-09-06 21:45:09.587150] INFO: Position: returning cash: 7.0053

[2022-09-06 21:45:10.954005] INFO: algo: handle_splits get splits [dt:2021-06-23 00:00:00+00:00] [asset:Equity(1169 [000006.SZA]), ratio:0.9638094902038574]

[2022-09-06 21:45:10.956270] INFO: Position: position stock handle split[sid:1169, orig_amount:8200, new_amount:8507.0, orig_cost:5.240000345192473, new_cost:5.0504, ratio:0.9638094902038574, last_sale_price:5.059999942779541]

[2022-09-06 21:45:10.958215] INFO: Position: after split: PositionStock(asset:Equity(1169 [000006.SZA]), amount:8507.0, cost_basis:5.0504, last_sale_price:5.25)

[2022-09-06 21:45:10.959910] INFO: Position: returning cash: 4.5815

[2022-09-06 21:45:13.375226] INFO: algo: handle_splits get splits [dt:2021-08-25 00:00:00+00:00] [asset:Equity(4021 [000002.SZA]), ratio:0.9456993341445923]

[2022-09-06 21:45:13.377694] INFO: Position: position stock handle split[sid:4021, orig_amount:2600, new_amount:2749.0, orig_cost:22.770000460528, new_cost:21.5336, ratio:0.9456993341445923, last_sale_price:21.76999855041504]

[2022-09-06 21:45:13.380602] INFO: Position: after split: PositionStock(asset:Equity(4021 [000002.SZA]), amount:2749.0, cost_basis:21.5336, last_sale_price:23.020000457763672)

[2022-09-06 21:45:13.382627] INFO: Position: returning cash: 6.2737

[2022-09-06 21:45:22.701784] INFO: algo: handle_splits get splits [dt:2022-06-21 00:00:00+00:00] [asset:Equity(1169 [000006.SZA]), ratio:0.9727272391319275]

[2022-09-06 21:45:22.704396] INFO: Position: position stock handle split[sid:1169, orig_amount:9800, new_amount:10074.0, orig_cost:4.290000051883398, new_cost:4.173, ratio:0.9727272391319275, last_sale_price:4.279999256134033]

[2022-09-06 21:45:22.707981] INFO: Position: after split: PositionStock(asset:Equity(1169 [000006.SZA]), amount:10074.0, cost_basis:4.173, last_sale_price:4.399999618530273)

[2022-09-06 21:45:22.714303] INFO: Position: returning cash: 3.2815

[2022-09-06 21:45:23.908562] INFO: algo: handle_splits get splits [dt:2022-07-22 00:00:00+00:00] [asset:Equity(935 [000001.SZA]), ratio:0.9823213219642639]

[2022-09-06 21:45:23.920523] INFO: Position: position stock handle split[sid:935, orig_amount:19264.0, new_amount:19610.0, orig_cost:12.3336, new_cost:12.1156, ratio:0.9823213219642639, last_sale_price:12.779999732971191]

[2022-09-06 21:45:23.923044] INFO: Position: after split: PositionStock(asset:Equity(935 [000001.SZA]), amount:19610.0, cost_basis:12.1156, last_sale_price:13.00999927520752)

[2022-09-06 21:45:23.925495] INFO: Position: returning cash: 8.8322

[2022-09-06 21:45:24.861114] INFO: Performance: Simulated 1121 trading days out of 1121.

[2022-09-06 21:45:24.869481] INFO: Performance: first open: 2018-01-02 09:30:00+00:00

[2022-09-06 21:45:24.881532] INFO: Performance: last close: 2022-08-12 15:00:00+00:00

[2022-09-06 21:45:37.791173] INFO: moduleinvoker: backtest.v8 运行完成[106.706347s].

[2022-09-06 21:45:37.799341] INFO: moduleinvoker: trade.v4 运行完成[112.169165s].

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