{"description":"实验创建于2018/6/27","graph":{"edges":[{"to_node_id":"-353:instruments","from_node_id":"-51:data"},{"to_node_id":"-370:instruments","from_node_id":"-51:data"},{"to_node_id":"-353:features","from_node_id":"-59:data"},{"to_node_id":"-360:features","from_node_id":"-59:data"},{"to_node_id":"-360:input_data","from_node_id":"-353:data"},{"to_node_id":"-390:input_ds","from_node_id":"-360:data"},{"to_node_id":"-370:options_data","from_node_id":"-390:sorted_data"}],"nodes":[{"node_id":"-51","module_id":"BigQuantSpace.instruments.instruments-v2","parameters":[{"name":"start_date","value":"2021-06-20","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"2021-07-01","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":"-51"}],"output_ports":[{"name":"data","node_id":"-51"}],"cacheable":true,"seq_num":1,"comment":"","comment_collapsed":true},{"node_id":"-59","module_id":"BigQuantSpace.input_features.input_features-v1","parameters":[{"name":"features","value":"\n# #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((open_0>close_1)&(mean(close_0,5)>mean(close_0,10)),1,0)\nsell_condition=where(mean(close_0,5)<mean(close_0,10),1,0)\npe_ttm_0\n","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"features_ds","node_id":"-59"}],"output_ports":[{"name":"data","node_id":"-59"}],"cacheable":true,"seq_num":2,"comment":"","comment_collapsed":true},{"node_id":"-353","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":"60","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"instruments","node_id":"-353"},{"name":"features","node_id":"-353"}],"output_ports":[{"name":"data","node_id":"-353"}],"cacheable":false,"seq_num":5,"comment":"","comment_collapsed":true},{"node_id":"-360","module_id":"BigQuantSpace.derived_feature_extractor.derived_feature_extractor-v3","parameters":[{"name":"date_col","value":"date","type":"Literal","bound_global_parameter":null},{"name":"instrument_col","value":"instrument","type":"Literal","bound_global_parameter":null},{"name":"drop_na","value":"False","type":"Literal","bound_global_parameter":null},{"name":"remove_extra_columns","value":"False","type":"Literal","bound_global_parameter":null},{"name":"user_functions","value":"{}","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_data","node_id":"-360"},{"name":"features","node_id":"-360"}],"output_ports":[{"name":"data","node_id":"-360"}],"cacheable":true,"seq_num":7,"comment":"","comment_collapsed":true},{"node_id":"-370","module_id":"BigQuantSpace.trade.trade-v4","parameters":[{"name":"start_date","value":"","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"","type":"Literal","bound_global_parameter":null},{"name":"initialize","value":"# 回测引擎：初始化函数，只执行一次\ndef bigquant_run(context):\n\n # 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日期\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和对应的最新市值\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\n cash_for_buy = context.portfolio.cash\n \n # 获取当日符合买入/卖出条件的股票列表\n try:\n buy_stock = context.daily_buy_stock[today] # 当日符合买入条件的股票\n except:\n buy_stock=[]\n try:\n sell_stock = context.daily_sell_stock[today] # 当日符合卖出条件的股票\n except:\n sell_stock = []\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 # 卖出\n for instrument in stock_to_sell:\n # 如果该股票停牌，则没法成交。因此需要用can_trade方法检查下该股票的状态\n # 如果返回真值，则可以正常下单，否则会出错\n # 因为stock是字符串格式，我们用symbol方法将其转化成平台可以接受的形式：Equity格式\n if data.can_trade(context.symbol(instrument)):\n # order_target_percent是平台的一个下单接口，表明下单使得该股票的权重为0，即卖出全部股票，可参考回测文档\n if(today==\"2021-06-23\"):\n from zipline.finance.execution import LimitOrder\n price = data.current(context.symbol(instrument), \"close\")#不一定按照此价格成交，需要看初始化函数中自定义买卖价格那里如何设置的\n rv = context.order_target(context.symbol(instrument), 0, limit_price=price)\n else:\n context.order_target_percent(context.symbol(instrument), 0)\n # 开盘卖出后所得资金可用来买入股票，更新当日可用现金\n cash_for_buy += stock_hold_now[instrument]\n \n # 如果当天没有买入的股票，就返回\n if len(stock_to_buy) == 0:\n return\n \n # 买入\n for instrument in stock_to_buy:\n # 利用当日可用现金使用等资金比例下单买入\n cash = cash_for_buy / len(stock_to_buy)\n if data.can_trade(context.symbol(instrument)):\n current_price = data.current(context.symbol(instrument), 'price')\n amount = math.floor(cash / current_price / 100) * 100\n context.order(context.symbol(instrument), amount)","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 # 加载预测数据\n df = context.options['data'].read_df()\n # 函数：求满足开仓条件的股票列表\n def open_pos_con(df):\n return list(df[df['buy_condition']>0].instrument)[:10]\n\n # 函数：求满足平仓条件的股票列表\n def close_pos_con(df):\n return list(df[df['sell_condition']>0].instrument)\n \n # 每日买入股票的数据框\n context.daily_buy_stock= df.groupby('date').apply(open_pos_con)\n # 每日卖出股票的数据框\n context.daily_sell_stock= df.groupby('date').apply(close_pos_con) \n \n \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":"","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"instruments","node_id":"-370"},{"name":"options_data","node_id":"-370"},{"name":"history_ds","node_id":"-370"},{"name":"benchmark_ds","node_id":"-370"},{"name":"trading_calendar","node_id":"-370"}],"output_ports":[{"name":"raw_perf","node_id":"-370"}],"cacheable":false,"seq_num":8,"comment":"","comment_collapsed":true},{"node_id":"-390","module_id":"BigQuantSpace.sort.sort-v4","parameters":[{"name":"sort_by","value":"pe_ttm_0","type":"Literal","bound_global_parameter":null},{"name":"group_by","value":"date","type":"Literal","bound_global_parameter":null},{"name":"keep_columns","value":"--","type":"Literal","bound_global_parameter":null},{"name":"ascending","value":"True","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_ds","node_id":"-390"},{"name":"sort_by_ds","node_id":"-390"}],"output_ports":[{"name":"sorted_data","node_id":"-390"}],"cacheable":true,"seq_num":9,"comment":"","comment_collapsed":true}],"node_layout":"<node_postions><node_position Node='-51' Position='89,21,200,200'/><node_position Node='-59' Position='448,124,200,200'/><node_position Node='-353' Position='257,236,200,200'/><node_position Node='-360' Position='239,338,200,200'/><node_position Node='-370' Position='198.12310791015625,549,200,200'/><node_position Node='-390' Position='291,437,200,200'/></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7月10日17:42
# 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 m8_initialize_bigquant_run(context):

    # 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.limit is None:
                price_field = self._price_field_buy if order.amount > 0 else self._price_field_sell
                price = data.current(order.asset, price_field)
            else:
                price = data.current(order.asset, "open")
                print("==============开盘价成交 ","order.asset=",order.asset,"open price=",price)
            
            # 返回希望成交的价格和数量
            return (price, order.amount)
    # 如果未限价，设置滑点范围为最低价到最高价，即未限价时按照最低价买入、最高价卖出
    context.fix_slippage = FixedPriceSlippage(price_field_buy='open', price_field_sell='close')
    context.set_slippage(us_equities=context.fix_slippage) # us是universe的简写，如果是期货，需要传入us_future


# 回测引擎：每日数据处理函数，每天执行一次
def m8_handle_data_bigquant_run(context, data):
    # 回测引擎：每日数据处理函数，每天执行一次
    today = data.current_dt.strftime('%Y-%m-%d') # 日期
    # 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和对应的最新市值
    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
    cash_for_buy = context.portfolio.cash
    
    # 获取当日符合买入/卖出条件的股票列表
    try:
        buy_stock = context.daily_buy_stock[today]  # 当日符合买入条件的股票
    except:
        buy_stock=[]
    try:
        sell_stock = context.daily_sell_stock[today]  # 当日符合卖出条件的股票
    except:
        sell_stock = []

    # 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]
    # 卖出
    for instrument in stock_to_sell:
        # 如果该股票停牌，则没法成交。因此需要用can_trade方法检查下该股票的状态
        # 如果返回真值，则可以正常下单，否则会出错
        # 因为stock是字符串格式，我们用symbol方法将其转化成平台可以接受的形式：Equity格式
        if data.can_trade(context.symbol(instrument)):
            # order_target_percent是平台的一个下单接口，表明下单使得该股票的权重为0，即卖出全部股票，可参考回测文档
            if(today=="2021-06-23"):
                from zipline.finance.execution import LimitOrder
                price = data.current(context.symbol(instrument), "close")#不一定按照此价格成交，需要看初始化函数中自定义买卖价格那里如何设置的
                rv = context.order_target(context.symbol(instrument), 0, limit_price=price)
            else:
                context.order_target_percent(context.symbol(instrument), 0)
            # 开盘卖出后所得资金可用来买入股票，更新当日可用现金
            cash_for_buy += stock_hold_now[instrument]
            
    # 如果当天没有买入的股票，就返回
    if len(stock_to_buy) == 0:
        return
    
    # 买入
    for instrument in stock_to_buy:
        # 利用当日可用现金使用等资金比例下单买入
        cash = cash_for_buy / len(stock_to_buy)
        if data.can_trade(context.symbol(instrument)):
            current_price = data.current(context.symbol(instrument), 'price')
            amount = math.floor(cash / current_price / 100) * 100
            context.order(context.symbol(instrument), amount)
# 回测引擎：准备数据，只执行一次
def m8_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)[:10]

    # 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_buy_stock= df.groupby('date').apply(open_pos_con)
    # 每日卖出股票的数据框
    context.daily_sell_stock= df.groupby('date').apply(close_pos_con)    
    
    

# 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 m8_before_trading_start_bigquant_run(context, data):
    pass


m1 = M.instruments.v2(
    start_date='2021-06-20',
    end_date=T.live_run_param('trading_date', '2021-07-01'),
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m2 = 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((open_0>close_1)&(mean(close_0,5)>mean(close_0,10)),1,0)
sell_condition=where(mean(close_0,5)<mean(close_0,10),1,0)
pe_ttm_0
"""
)

m5 = M.general_feature_extractor.v7(
    instruments=m1.data,
    features=m2.data,
    start_date='',
    end_date='',
    before_start_days=60,
    m_cached=False
)

m7 = M.derived_feature_extractor.v3(
    input_data=m5.data,
    features=m2.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False,
    user_functions={}
)

m9 = M.sort.v4(
    input_ds=m7.data,
    sort_by='pe_ttm_0',
    group_by='date',
    keep_columns='--',
    ascending=True
)

m8 = M.trade.v4(
    instruments=m1.data,
    options_data=m9.sorted_data,
    start_date='',
    end_date='',
    initialize=m8_initialize_bigquant_run,
    handle_data=m8_handle_data_bigquant_run,
    prepare=m8_prepare_bigquant_run,
    before_trading_start=m8_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=''
)

[2021-07-10 17:42:16.567119] INFO: moduleinvoker: instruments.v2 开始运行..

[2021-07-10 17:42:16.575189] INFO: moduleinvoker: 命中缓存

[2021-07-10 17:42:16.576325] INFO: moduleinvoker: instruments.v2 运行完成[0.00921s].

[2021-07-10 17:42:16.578554] INFO: moduleinvoker: input_features.v1 开始运行..

[2021-07-10 17:42:16.584133] INFO: moduleinvoker: 命中缓存

[2021-07-10 17:42:16.585322] INFO: moduleinvoker: input_features.v1 运行完成[0.00677s].

[2021-07-10 17:42:16.593219] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2021-07-10 17:42:17.288187] INFO: 基础特征抽取: 年份 2021, 特征行数=206463

[2021-07-10 17:42:17.348193] INFO: 基础特征抽取: 总行数: 206463

[2021-07-10 17:42:17.357228] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.764009s].

[2021-07-10 17:42:17.359533] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2021-07-10 17:42:18.501593] INFO: derived_feature_extractor: 提取完成 buy_condition=where((open_0>close_1)&(mean(close_0,5)>mean(close_0,10)),1,0), 0.653s

[2021-07-10 17:42:19.136642] INFO: derived_feature_extractor: 提取完成 sell_condition=where(mean(close_0,5)
[2021-07-10 17:42:19.846634] INFO: derived_feature_extractor: /y_2021, 206463
[2021-07-10 17:42:19.994307] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[2.634746s].
[2021-07-10 17:42:19.997018] INFO: moduleinvoker: sort.v4 开始运行..
[2021-07-10 17:42:20.559753] INFO: moduleinvoker: sort.v4 运行完成[0.562737s].
[2021-07-10 17:42:20.606141] INFO: moduleinvoker: backtest.v8 开始运行..
[2021-07-10 17:42:20.610385] INFO: backtest: biglearning backtest:V8.5.0
[2021-07-10 17:42:20.827017] INFO: backtest: product_type:stock by specified
[2021-07-10 17:42:21.208350] INFO: moduleinvoker: cached.v2 开始运行..
[2021-07-10 17:42:21.215958] INFO: moduleinvoker: 命中缓存
[2021-07-10 17:42:21.217221] INFO: moduleinvoker: cached.v2 运行完成[0.00889s].
[2021-07-10 17:42:22.697154] INFO: algo: TradingAlgorithm V1.8.3
[2021-07-10 17:42:22.880750] INFO: algo: trading transform...
[2021-07-10 17:42:23.214163] INFO: Performance: Simulated 9 trading days out of 9.
[2021-07-10 17:42:23.215731] INFO: Performance: first open: 2021-06-21 09:30:00+00:00
[2021-07-10 17:42:23.216524] INFO: Performance: last close: 2021-07-01 15:00:00+00:00
[2021-07-10 17:42:27.364161] INFO: moduleinvoker: backtest.v8 运行完成[6.758009s].
[2021-07-10 17:42:27.365592] INFO: moduleinvoker: trade.v4 运行完成[6.803379s].

==============开盘价成交  order.asset= Equity(1928 [600241.SHA]) open price= 7.2899995

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