{"description":"实验创建于2017/8/26","graph":{"edges":[{"to_node_id":"-57:features","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"to_node_id":"-267:features","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"to_node_id":"-102:instruments","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-62:data"},{"to_node_id":"-267:instruments","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-62:data"},{"to_node_id":"-689:input_data","from_node_id":"-57:data"},{"to_node_id":"-102:options_data","from_node_id":"-689:data"},{"to_node_id":"-57:input_data","from_node_id":"-267:data"}],"nodes":[{"node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24","module_id":"BigQuantSpace.input_features.input_features-v1","parameters":[{"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,5)>mean(close,30),1,0)\nsell_condition=where(mean(close,5)<mean(close,30),1,0)","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"features_ds","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24"}],"output_ports":[{"name":"data","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24"}],"cacheable":false,"seq_num":1,"comment":"","comment_collapsed":true},{"node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-62","module_id":"BigQuantSpace.instruments.instruments-v2","parameters":[{"name":"start_date","value":"2019-03-01","type":"Literal","bound_global_parameter":"交易日期"},{"name":"end_date","value":"2021-06-01","type":"Literal","bound_global_parameter":"交易日期"},{"name":"market","value":"CN_FUND","type":"Literal","bound_global_parameter":null},{"name":"instrument_list","value":"159941.ZOF\n515030.HOF","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-bdf8-104b137c8601-62"}],"output_ports":[{"name":"data","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-62"}],"cacheable":true,"seq_num":2,"comment":"预测数据，用于回测和模拟","comment_collapsed":false},{"node_id":"-57","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":"-57"},{"name":"features","node_id":"-57"}],"output_ports":[{"name":"data","node_id":"-57"}],"cacheable":true,"seq_num":8,"comment":"","comment_collapsed":true},{"node_id":"-102","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 # 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","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 # 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 # 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# 本代码由可视化策略环境自动生成 2021年11月19日 19:10
# 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):

    # 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))

# 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)

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,5)>mean(close,30),1,0)
sell_condition=where(mean(close,5)<mean(close,30),1,0)""",
    m_cached=False
)

m2 = M.instruments.v2(
    start_date=T.live_run_param('trading_date', '2019-03-01'),
    end_date=T.live_run_param('trading_date', '2021-06-01'),
    market='CN_FUND',
    instrument_list="""159941.ZOF
515030.HOF""",
    max_count=0
)

m6 = M.use_datasource.v1(
    instruments=m2.data,
    features=m1.data,
    datasource_id='bar1d_CN_FUND',
    start_date='',
    end_date=''
)

m8 = M.derived_feature_extractor.v3(
    input_data=m6.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,
    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=''
)

[2021-10-30 21:02:32.443952] INFO: moduleinvoker: input_features.v1 开始运行..

[2021-10-30 21:02:33.008448] INFO: moduleinvoker: input_features.v1 运行完成[0.568323s].

[2021-10-30 21:02:33.028779] INFO: moduleinvoker: instruments.v2 开始运行..

[2021-10-30 21:02:33.079397] INFO: moduleinvoker: instruments.v2 运行完成[0.050622s].

[2021-10-30 21:02:33.090171] INFO: moduleinvoker: use_datasource.v1 开始运行..

[2021-10-30 21:02:34.923903] INFO: moduleinvoker: use_datasource.v1 运行完成[1.833721s].

[2021-10-30 21:02:34.940371] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2021-10-30 21:02:35.243697] INFO: derived_feature_extractor: 提取完成 buy_condition=where(mean(close,5)>mean(close,30),1,0), 0.137s

[2021-10-30 21:02:35.253859] INFO: derived_feature_extractor: 提取完成 sell_condition=where(mean(close,5)
[2021-10-30 21:02:35.302881] INFO: derived_feature_extractor: /data, 851
[2021-10-30 21:02:35.480640] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.540261s].
[2021-10-30 21:02:35.503610] INFO: moduleinvoker: dropnan.v2 开始运行..
[2021-10-30 21:02:35.621034] INFO: dropnan: /data, 851/851
[2021-10-30 21:02:35.642417] INFO: dropnan: 行数: 851/851
[2021-10-30 21:02:35.646538] INFO: moduleinvoker: dropnan.v2 运行完成[0.142956s].
[2021-10-30 21:02:35.785755] INFO: moduleinvoker: backtest.v8 开始运行..
[2021-10-30 21:02:35.798255] INFO: backtest: biglearning backtest:V8.5.0
[2021-10-30 21:02:36.752140] INFO: backtest: product_type:stock by specified
[2021-10-30 21:02:37.042836] INFO: moduleinvoker: cached.v2 开始运行..
[2021-10-30 21:02:37.337377] INFO: backtest: 读取基金行情完成:1094
[2021-10-30 21:02:37.433242] INFO: moduleinvoker: cached.v2 运行完成[0.390409s].
[2021-10-30 21:02:37.488394] INFO: algo: TradingAlgorithm V1.8.5
[2021-10-30 21:02:46.804241] INFO: algo: trading transform...
[2021-10-30 21:02:49.320098] INFO: Performance: Simulated 548 trading days out of 548.
[2021-10-30 21:02:49.321566] INFO: Performance: first open: 2019-03-01 09:30:00+00:00
[2021-10-30 21:02:49.322640] INFO: Performance: last close: 2021-06-01 15:00:00+00:00
[2021-10-30 21:02:50.957220] INFO: moduleinvoker: backtest.v8 运行完成[15.171471s].
[2021-10-30 21:02:50.958762] INFO: moduleinvoker: trade.v4 运行完成[15.303223s].

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