{"Description":"实验创建于2017/8/26","Summary":"","Graph":{"EdgesInternal":[{"DestinationInputPortId":"-50:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-57:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-50:instruments","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-62:data"},{"DestinationInputPortId":"-102:instruments","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-62:data"},{"DestinationInputPortId":"-57:input_data","SourceOutputPortId":"-50:data"},{"DestinationInputPortId":"-192:input_data","SourceOutputPortId":"-57:data"},{"DestinationInputPortId":"-102:options_data","SourceOutputPortId":"-192:data"}],"ModuleNodes":[{"Id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24","ModuleId":"BigQuantSpace.input_features.input_features-v1","ModuleParameters":[{"Name":"features","Value":"# #号开始的表示注释\n# 多个特征，每行一个，可以包含基础特征和衍生特征\nbuy_condition=where(mean(close_0,5)>mean(close_0,10),1,0)\nsell_condition=where(mean(close_0,5)<mean(close_0,10),1,0)","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features_ds","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24"}],"OutputPortsInternal":[{"Name":"data","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24","OutputType":null}],"UsePreviousResults":false,"moduleIdForCode":1,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"287d2cb0-f53c-4101-bdf8-104b137c8601-62","ModuleId":"BigQuantSpace.instruments.instruments-v2","ModuleParameters":[{"Name":"start_date","Value":"2018-12-01","ValueType":"Literal","LinkedGlobalParameter":"交易日期"},{"Name":"end_date","Value":"2019-01-01","ValueType":"Literal","LinkedGlobalParameter":"交易日期"},{"Name":"market","Value":"CN_STOCK_A","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"instrument_list","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"max_count","Value":"0","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"rolling_conf","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-62"}],"OutputPortsInternal":[{"Name":"data","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-62","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":2,"IsPartOfPartialRun":null,"Comment":"预测数据，用于回测和模拟","CommentCollapsed":false},{"Id":"-50","ModuleId":"BigQuantSpace.general_feature_extractor.general_feature_extractor-v7","ModuleParameters":[{"Name":"start_date","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"end_date","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"before_start_days","Value":"10","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"instruments","NodeId":"-50"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-50"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-50","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":7,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-57","ModuleId":"BigQuantSpace.derived_feature_extractor.derived_feature_extractor-v3","ModuleParameters":[{"Name":"date_col","Value":"date","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"instrument_col","Value":"instrument","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"drop_na","Value":"False","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"remove_extra_columns","Value":"False","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"user_functions","Value":"","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"input_data","NodeId":"-57"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-57"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-57","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":8,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-102","ModuleId":"BigQuantSpace.trade.trade-v4","ModuleParameters":[{"Name":"start_date","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"end_date","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"initialize","Value":"# 回测引擎：初始化函数，只执行一次\ndef bigquant_run(context):\n from zipline.finance.slippage import SlippageModel\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 \n class FixedPriceSlippage(SlippageModel):\n def process_order(self, data, order, bar_volume=0, trigger_check_price=0):\n if order.amount > 0:\n open_price = data.current(order.asset, self._price_field_buy)\n price = open_price * 0.98\n else:\n price = data.current(order.asset, self._price_field_sell)\n return (price, order.amount)\n context.fix_slippage = FixedPriceSlippage(price_field_buy=\"open\", price_field_sell=\"close\")\n context.set_slippage(us_equities=context.fix_slippage)\n","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"handle_data","Value":"# 回测引擎：每日数据处理函数，每天执行一次\ndef bigquant_run(context, data):\n # 获取今日的日期\n today = data.current_dt.strftime('%Y-%m-%d') \n # 通过positions对象，使用列表生成式的方法获取目前持仓的股票列表\n stock_hold_now = {e.symbol: p.amount * p.last_sale_price\n for e, p in context.perf_tracker.position_tracker.positions.items()}\n\n # 记录用于买入股票的可用现金,因为是早盘卖股票，需要记录卖出的股票市值并在买入下单前更新可用现金；\n # 如果是早盘买尾盘卖，则卖出时不需更新可用现金，因为尾盘卖出股票所得现金无法使用\n cash_for_buy = context.portfolio.cash \n \n try:\n buy_stock = context.daily_stock_buy[today] # 当日符合买入条件的股票\n except:\n buy_stock=[] # 如果没有符合条件的股票，就设置为空\n \n try:\n sell_stock = context.daily_stock_sell[today] # 当日符合卖出条件的股票\n except:\n sell_stock=[] # 如果没有符合条件的股票，就设置为空\n \n # 需要卖出的股票:已有持仓中符合卖出条件的股票\n stock_to_sell = [ i for i in stock_hold_now if i in sell_stock ]\n # 需要买入的股票:没有持仓且符合买入条件的股票\n stock_to_buy = [ i for i in buy_stock if i not in stock_hold_now ] \n # 需要调仓的股票：已有持仓且不符合卖出条件的股票\n stock_to_adjust=[ i for i in stock_hold_now if i not in sell_stock ]\n \n # 如果有卖出信号\n if len(stock_to_sell)>0:\n for instrument in stock_to_sell:\n sid = context.symbol(instrument) # 将标的转化为equity格式\n cur_position = context.portfolio.positions[sid].amount # 持仓\n if cur_position > 0 and data.can_trade(sid):\n context.order_target_percent(sid, 0) # 全部卖出 \n # 因为设置的是早盘卖出早盘买入，需要根据卖出的股票更新可用现金；如果设置尾盘卖出早盘买入，则不需更新可用现金(可以删除下面的语句)\n cash_for_buy += stock_hold_now[instrument]\n \n # 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# 本代码由可视化策略环境自动生成 2021年5月12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):
    from zipline.finance.slippage import SlippageModel
    
    # 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))
    
    class FixedPriceSlippage(SlippageModel):
        def process_order(self, data, order, bar_volume=0, trigger_check_price=0):
            if order.amount > 0:
                open_price = data.current(order.asset, self._price_field_buy)
                price = open_price * 0.98
            else:
                price = data.current(order.asset, self._price_field_sell)
            return (price, order.amount)
    context.fix_slippage = FixedPriceSlippage(price_field_buy="open", price_field_sell="close")
    context.set_slippage(us_equities=context.fix_slippage)

# 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):
    # 获取今日的日期
    today = data.current_dt.strftime('%Y-%m-%d')  
    # 通过positions对象，使用列表生成式的方法获取目前持仓的股票列表
    stock_hold_now = {e.symbol: p.amount * p.last_sale_price
                 for e, p in context.perf_tracker.position_tracker.positions.items()}

    # 记录用于买入股票的可用现金,因为是早盘卖股票，需要记录卖出的股票市值并在买入下单前更新可用现金；
    # 如果是早盘买尾盘卖，则卖出时不需更新可用现金，因为尾盘卖出股票所得现金无法使用
    cash_for_buy = context.portfolio.cash    
    
    try:
        buy_stock = context.daily_stock_buy[today]  # 当日符合买入条件的股票
    except:
        buy_stock=[]  # 如果没有符合条件的股票，就设置为空
    
    try:
        sell_stock = context.daily_stock_sell[today]  # 当日符合卖出条件的股票
    except:
        sell_stock=[] # 如果没有符合条件的股票，就设置为空
    
    # 需要卖出的股票:已有持仓中符合卖出条件的股票
    stock_to_sell = [ i for i in stock_hold_now if i in sell_stock ]
    # 需要买入的股票:没有持仓且符合买入条件的股票
    stock_to_buy = [ i for i in buy_stock if i not in stock_hold_now ]  
    # 需要调仓的股票：已有持仓且不符合卖出条件的股票
    stock_to_adjust=[ i for i in stock_hold_now if i not in sell_stock ]
    
    # 如果有卖出信号
    if len(stock_to_sell)>0:
        for instrument in stock_to_sell:
            sid = context.symbol(instrument) # 将标的转化为equity格式
            cur_position = context.portfolio.positions[sid].amount # 持仓
            if cur_position > 0 and data.can_trade(sid):
                context.order_target_percent(sid, 0) # 全部卖出 
                # 因为设置的是早盘卖出早盘买入，需要根据卖出的股票更新可用现金；如果设置尾盘卖出早盘买入，则不需更新可用现金(可以删除下面的语句)
                cash_for_buy += stock_hold_now[instrument]
    
    # 如果有买入信号/有持仓
    if len(stock_to_buy)+len(stock_to_adjust)>0:
        weight = 1/(len(stock_to_buy)+len(stock_to_adjust)) # 每只股票的比重为等资金比例持有
        for instrument in stock_to_buy+stock_to_adjust:
            sid = context.symbol(instrument) # 将标的转化为equity格式
            if  data.can_trade(sid):
                context.order_target_value(sid, weight*cash_for_buy) # 买入
# 回测引擎：准备数据，只执行一次
def m3_prepare_bigquant_run(context):
    # 加载预测数据
    df = context.options['data'].read_df()

    # 函数：求满足开仓条件的股票列表
    def open_pos_con(df):
        return list(df[df['buy_condition']>0].instrument)

    # 函数：求满足平仓条件的股票列表
    def close_pos_con(df):
        return list(df[df['sell_condition']>0].instrument)

    # 每日买入股票的数据框
    context.daily_stock_buy= df.groupby('date').apply(open_pos_con)
    # 每日卖出股票的数据框
    context.daily_stock_sell= df.groupby('date').apply(close_pos_con)
# 判断订单股票的最低价是否低于开盘价的98%
def m3_before_trading_start_bigquant_run(context, data):
    
    for orders in get_open_orders().values():
        for _order in orders:
            ins = _order.sid
            try:
                # 如果当日最低价高于开盘价的98%，则取消订单
                low_ = data.current(ins, "low")
                open_ = data.current(ins, "open")
                if open_ * 0.98 < low_:
                    context.cancel_order(order.id)
                    print(f"{data.current_dt}由于开盘价的98%低于最低价，取消订单{ins}")
            except:
                continue

m1 = M.input_features.v1(
    features="""# #号开始的表示注释
# 多个特征，每行一个，可以包含基础特征和衍生特征
buy_condition=where(mean(close_0,5)>mean(close_0,10),1,0)
sell_condition=where(mean(close_0,5)<mean(close_0,10),1,0)""",
    m_cached=False
)

m2 = M.instruments.v2(
    start_date=T.live_run_param('trading_date', '2018-12-01'),
    end_date=T.live_run_param('trading_date', '2019-01-01'),
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m7 = M.general_feature_extractor.v7(
    instruments=m2.data,
    features=m1.data,
    start_date='',
    end_date='',
    before_start_days=10
)

m8 = M.derived_feature_extractor.v3(
    input_data=m7.data,
    features=m1.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False
)

m4 = M.dropnan.v2(
    input_data=m8.data
)

m3 = M.trade.v4(
    instruments=m2.data,
    options_data=m4.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='open',
    capital_base=1000000,
    auto_cancel_non_tradable_orders=True,
    data_frequency='daily',
    price_type='后复权',
    product_type='股票',
    plot_charts=True,
    backtest_only=False,
    benchmark=''
)

[2021-05-12 21:44:23.756455] INFO: moduleinvoker: input_features.v1 开始运行..

[2021-05-12 21:44:23.804559] INFO: moduleinvoker: input_features.v1 运行完成[0.048104s].

[2021-05-12 21:44:23.808264] INFO: moduleinvoker: instruments.v2 开始运行..

[2021-05-12 21:44:23.814550] INFO: moduleinvoker: 命中缓存

[2021-05-12 21:44:23.816624] INFO: moduleinvoker: instruments.v2 运行完成[0.008354s].

[2021-05-12 21:44:23.826544] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2021-05-12 21:44:23.834056] INFO: moduleinvoker: 命中缓存

[2021-05-12 21:44:23.836891] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.010355s].

[2021-05-12 21:44:23.842467] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2021-05-12 21:44:24.907311] INFO: derived_feature_extractor: 提取完成 buy_condition=where(mean(close_0,5)>mean(close_0,10),1,0), 0.650s

[2021-05-12 21:44:25.585013] INFO: derived_feature_extractor: 提取完成 sell_condition=where(mean(close_0,5)
[2021-05-12 21:44:25.996153] INFO: derived_feature_extractor: /y_2018, 99095
[2021-05-12 21:44:26.096544] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[2.254082s].
[2021-05-12 21:44:26.101098] INFO: moduleinvoker: dropnan.v2 开始运行..
[2021-05-12 21:44:26.288835] INFO: dropnan: /y_2018, 99095/99095
[2021-05-12 21:44:26.365520] INFO: dropnan: 行数: 99095/99095
[2021-05-12 21:44:26.377207] INFO: moduleinvoker: dropnan.v2 运行完成[0.276093s].
[2021-05-12 21:44:26.407593] INFO: moduleinvoker: backtest.v8 开始运行..
[2021-05-12 21:44:26.420716] INFO: backtest: biglearning backtest:V8.5.0
[2021-05-12 21:44:26.673898] INFO: backtest: product_type:stock by specified
[2021-05-12 21:44:27.137569] INFO: moduleinvoker: cached.v2 开始运行..
[2021-05-12 21:44:27.145612] INFO: moduleinvoker: 命中缓存
[2021-05-12 21:44:27.147544] INFO: moduleinvoker: cached.v2 运行完成[0.010006s].
[2021-05-12 21:44:28.865461] INFO: algo: TradingAlgorithm V1.8.2
[2021-05-12 21:44:29.147950] INFO: algo: trading transform...
[2021-05-12 21:44:54.364126] INFO: Performance: Simulated 20 trading days out of 20.
[2021-05-12 21:44:54.366046] INFO: Performance: first open: 2018-12-03 09:30:00+00:00
[2021-05-12 21:44:54.368052] INFO: Performance: last close: 2018-12-28 15:00:00+00:00
[2021-05-12 21:44:59.811740] INFO: moduleinvoker: backtest.v8 运行完成[33.404153s].
[2021-05-12 21:44:59.813175] INFO: moduleinvoker: trade.v4 运行完成[33.431494s].

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