{"description":"实验创建于2019/4/20","graph":{"edges":[{"to_node_id":"-299:instruments","from_node_id":"-290:data"},{"to_node_id":"-32:instruments","from_node_id":"-290:data"},{"to_node_id":"-299:features","from_node_id":"-286:data"},{"to_node_id":"-606:input_ds","from_node_id":"-299:data"},{"to_node_id":"-53:input_data","from_node_id":"-606:sorted_data"},{"to_node_id":"-58:input_1","from_node_id":"-32:raw_perf"},{"to_node_id":"-32:options_data","from_node_id":"-53:data"}],"nodes":[{"node_id":"-290","module_id":"BigQuantSpace.instruments.instruments-v2","parameters":[{"name":"start_date","value":"2018-01-01","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"2021-11-01","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":"-290"}],"output_ports":[{"name":"data","node_id":"-290"}],"cacheable":true,"seq_num":1,"comment":"输入证券","comment_collapsed":true},{"node_id":"-286","module_id":"BigQuantSpace.input_features.input_features-v1","parameters":[{"name":"features","value":"market_cap_float_0\namount_0","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"features_ds","node_id":"-286"}],"output_ports":[{"name":"data","node_id":"-286"}],"cacheable":true,"seq_num":2,"comment":"输入特征","comment_collapsed":true},{"node_id":"-299","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":"-299"},{"name":"features","node_id":"-299"}],"output_ports":[{"name":"data","node_id":"-299"}],"cacheable":true,"seq_num":3,"comment":"","comment_collapsed":true},{"node_id":"-606","module_id":"BigQuantSpace.sort.sort-v4","parameters":[{"name":"sort_by","value":"market_cap_float_0","type":"Literal","bound_global_parameter":null},{"name":"group_by","value":"instrument","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":"-606"},{"name":"sort_by_ds","node_id":"-606"}],"output_ports":[{"name":"sorted_data","node_id":"-606"}],"cacheable":true,"seq_num":4,"comment":"依据某个因子排序","comment_collapsed":true},{"node_id":"-32","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 # 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# 本代码由可视化策略环境自动生成 2021年12月20日 14:04
# 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 m5_initialize_bigquant_run(context):
    # 加载股票指标数据,数据继承自m6模块
    context.indicator_data = 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))
    
    # 设置股票数量
    context.stock_num = 30
    
    # 调仓天数,22个交易日大概就是一个月。可以理解为一个月换仓一次
    context.rebalance_days = 22
    
    # 如果策略运行中，需要将数据进行保存，可以借用extension这个对象，类型为dict
    # 比如当前运行的k线的索引，比如个股持仓天数、买入均价
    if 'index' not in context.extension:
        context.extension['index'] = 0
 
    

# 回测引擎：每日数据处理函数，每天执行一次
def m5_handle_data_bigquant_run(context, data):
    
    
    context.extension['index'] += 1
    # 不在换仓日就return,相当于后面的代码只会一个月运行一次，买入的股票会持有1个月
    if  context.extension['index'] % context.rebalance_days != 0:
        return 
    
    # 当前的日期
    date = data.current_dt.strftime('%Y-%m-%d')
    
    cur_data = context.indicator_data[context.indicator_data['date'] == date]
    # 根据日期获取调仓需要买入的股票的列表
    stock_to_buy = list(cur_data.instrument[:context.stock_num])
    # 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 = [equity.symbol for equity in context.portfolio.positions]
    # 继续持有的股票：调仓时，如果买入的股票已经存在于目前的持仓里，那么应继续持有
    no_need_to_sell = [i for i in stock_hold_now if i in stock_to_buy]
    # 需要卖出的股票
    stock_to_sell = [i for i in stock_hold_now if i not in no_need_to_sell]
  
    # 卖出
    for stock 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(stock)):
            # 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
            context.order_target_percent(context.symbol(stock), 0)
    
    # 如果当天没有买入的股票，就返回
    if len(stock_to_buy) == 0:
        return

    # 等权重买入 
    weight =  1 / len(stock_to_buy)
    
    # 买入
    for stock in stock_to_buy:
        if data.can_trade(context.symbol(stock)):
            # 下单使得某只股票的持仓权重达到weight，因为
            # weight大于0,因此是等权重买入
            context.order_target_percent(context.symbol(stock), weight)
 
# 回测引擎：准备数据，只执行一次
def m5_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 m5_before_trading_start_bigquant_run(context, data):
    pass


m1 = M.instruments.v2(
    start_date='2018-01-01',
    end_date='2021-11-01',
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m2 = M.input_features.v1(
    features="""market_cap_float_0
amount_0"""
)

m3 = M.general_feature_extractor.v7(
    instruments=m1.data,
    features=m2.data,
    start_date='',
    end_date='',
    before_start_days=60
)

m4 = M.sort.v4(
    input_ds=m3.data,
    sort_by='market_cap_float_0',
    group_by='instrument',
    keep_columns='--',
    ascending=True
)

m6 = M.filter.v3(
    input_data=m4.sorted_data,
    expr='amount_0 > 10000',
    output_left_data=False
)

m5 = M.trade.v4(
    instruments=m1.data,
    options_data=m6.data,
    start_date='',
    end_date='',
    initialize=m5_initialize_bigquant_run,
    handle_data=m5_handle_data_bigquant_run,
    prepare=m5_prepare_bigquant_run,
    before_trading_start=m5_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=''
)

m7 = M.strategy_ret_risk_analysis.v2(
    input_1=m5.raw_perf,
    analysis_flag='absolute',
    benchmark_index='000906.HIX',
    terms='long'
)

[2021-12-20 13:53:13.627581] INFO: moduleinvoker: instruments.v2 开始运行..

[2021-12-20 13:53:13.736378] INFO: moduleinvoker: instruments.v2 运行完成[0.108788s].

[2021-12-20 13:53:13.747442] INFO: moduleinvoker: input_features.v1 开始运行..

[2021-12-20 13:53:13.791262] INFO: moduleinvoker: 命中缓存

[2021-12-20 13:53:13.792776] INFO: moduleinvoker: input_features.v1 运行完成[0.045344s].

[2021-12-20 13:53:13.844129] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2021-12-20 13:54:19.751353] INFO: 基础特征抽取: 年份 2017, 特征行数=135965

[2021-12-20 13:54:22.996383] INFO: 基础特征抽取: 年份 2018, 特征行数=816987

[2021-12-20 13:54:26.793488] INFO: 基础特征抽取: 年份 2019, 特征行数=884867

[2021-12-20 13:54:30.756619] INFO: 基础特征抽取: 年份 2020, 特征行数=945961

[2021-12-20 13:54:34.127164] INFO: 基础特征抽取: 年份 2021, 特征行数=858486

[2021-12-20 13:54:34.248060] INFO: 基础特征抽取: 总行数: 3642266

[2021-12-20 13:54:34.256946] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[80.41283s].

[2021-12-20 13:54:34.268982] INFO: moduleinvoker: sort.v4 开始运行..

[2021-12-20 13:54:40.145552] INFO: moduleinvoker: sort.v4 运行完成[5.876558s].

[2021-12-20 13:54:40.161608] INFO: moduleinvoker: filter.v3 开始运行..

[2021-12-20 13:54:40.185988] INFO: filter: 使用表达式 amount_0 > 10000 过滤

[2021-12-20 13:54:41.640846] INFO: filter: 过滤 /data, 3642127/0/3642266

[2021-12-20 13:54:41.681738] INFO: moduleinvoker: filter.v3 运行完成[1.520124s].

[2021-12-20 13:54:43.785101] INFO: moduleinvoker: backtest.v8 开始运行..

[2021-12-20 13:54:43.791566] INFO: backtest: biglearning backtest:V8.6.0

[2021-12-20 13:54:43.793920] INFO: backtest: product_type:stock by specified

[2021-12-20 13:54:43.924838] INFO: moduleinvoker: cached.v2 开始运行..

[2021-12-20 13:54:58.147254] INFO: backtest: 读取股票行情完成:4716458

[2021-12-20 13:55:05.706872] INFO: moduleinvoker: cached.v2 运行完成[21.782071s].

[2021-12-20 13:55:10.139549] INFO: algo: TradingAlgorithm V1.8.6

[2021-12-20 13:55:12.594305] INFO: algo: trading transform...

[2021-12-20 13:55:26.883880] INFO: Performance: Simulated 929 trading days out of 929.

[2021-12-20 13:55:26.886046] INFO: Performance: first open: 2018-01-02 09:30:00+00:00

[2021-12-20 13:55:26.888076] INFO: Performance: last close: 2021-11-01 15:00:00+00:00

[2021-12-20 13:55:38.687134] INFO: moduleinvoker: backtest.v8 运行完成[54.90204s].

[2021-12-20 13:55:38.689930] INFO: moduleinvoker: trade.v4 运行完成[56.982361s].

[2021-12-20 13:55:38.708417] INFO: moduleinvoker: strategy_ret_risk_analysis.v2 开始运行..

[2021-12-20 14:01:57.470964] INFO: moduleinvoker: strategy_ret_risk_analysis.v2 运行完成[378.762551s].

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