{"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":"-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":"2020-12-31","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":"pb_lf_0\npe_ttm_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":"pe_ttm_0,pb_lf_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 # 加载股票指标数据,数据继承自m6模块\n context.indicator_data = context.options['data'].read_df()\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=0.0001, sell_cost=0.001, min_cost=5))\n # 设置股票数量\n context.stock_num = 30\n context.month_data = context.indicator_data.reset_index(drop=True)\n \n #每月调仓，改用每月调用一次主处理函数\n schedule_function(func=month_handle_data,\n date_rule=date_rules.month_start(), # 月初执行\n time_rule=time_rules.market_open()) # 开盘执行\n \n \n","type":"Literal","bound_global_parameter":null},{"name":"handle_data","value":"from datetime import timedelta\n#每月初调用一次主处理函数，在本函数进行目标股票获取并进行买卖\ndef month_handle_data(context, data):\n # 当前的日期\n date = data.current_dt.strftime('%Y-%m-%d')\n \n #根据当前时间获取上一个月的时间。当前月初往前偏移28天一定是上个月\n last_month = str(pd.to_datetime(date) + timedelta(days=-28))[0:7]\n #获取上个月末的股票池\n df = context.month_data\n tempdf = df[df['date'].astype(str).apply(lambda x:x[0:7])==last_month]\n #对股票池根据因子值列进行排序，并取前30只股票\n stocks = tempdf.sort_values('pe_ttm_0').iloc[:context.stock_num,:]\n #把股票id转换成需要购买的股票列表\n stock_to_buy = stocks['instrument'].values.tolist()\n print(\"选股日期：\",date,',选股数量：',len(stock_to_buy))\n \n \n positions = {e.symbol: p.cost_basis for e, p in context.portfolio.positions.items()}\n # 新建当日止损股票列表是为了handle_data 策略逻辑部分不再对该股票进行判断\n current_stoploss_stock = [] \n if len(positions) > 0:\n for i in positions.keys():\n stock_cost = positions[i] \n stock_market_price = data.current(context.symbol(i), 'price') \n # 亏10%就止损\n if (stock_market_price - stock_cost) / stock_cost <= -0.10: \n context.order_target_percent(context.symbol(i),0) \n current_stoploss_stock.append(i)\n# print('日期：',date,'股票：',i,'出现止损状况')\n \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 = [equity.symbol for equity in context.portfolio.positions]\n # 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\n no_need_to_sell = [i for i in stock_hold_now if i in stock_to_buy]\n # 需要卖出的股票\n stock_to_sell = [i for i in stock_hold_now if i not in no_need_to_sell]\n \n # 卖出\n for stock 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(stock)):\n context.order_target_percent(context.symbol(stock), 0)\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 # 等权重买入 \n weight = 1 / len(stock_to_buy)\n \n # 买入\n for stock in stock_to_buy:\n if data.can_trade(context.symbol(stock)):\n # 下单使得某只股票的持仓权重达到weight，因为\n # weight大于0,因此是等权重买入\n context.order_target_percent(context.symbol(stock), weight)\n\n \n \ndef bigquant_run(context, data):\n pass","type":"Literal","bound_global_parameter":null},{"name":"prepare","value":"\n\n# 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":"open","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":"后复权","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":"-32"},{"name":"options_data","node_id":"-32"},{"name":"history_ds","node_id":"-32"},{"name":"benchmark_ds","node_id":"-32"},{"name":"trading_calendar","node_id":"-32"}],"output_ports":[{"name":"raw_perf","node_id":"-32"}],"cacheable":false,"seq_num":5,"comment":"回测","comment_collapsed":true},{"node_id":"-53","module_id":"BigQuantSpace.filter.filter-v3","parameters":[{"name":"expr","value":"pb_lf_0 < 1.5 & pe_ttm_0 < 15 & amount_0 > 0 & pb_lf_0 > 0 & pe_ttm_0 > 0","type":"Literal","bound_global_parameter":null},{"name":"output_left_data","value":"False","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_data","node_id":"-53"}],"output_ports":[{"name":"data","node_id":"-53"},{"name":"left_data","node_id":"-53"}],"cacheable":true,"seq_num":6,"comment":"过滤掉不需要的股票数据","comment_collapsed":true}],"node_layout":"<node_postions><node_position Node='-290' Position='-104,20,200,200'/><node_position Node='-286' Position='315,17,200,200'/><node_position Node='-299' Position='225,137,200,200'/><node_position Node='-606' Position='229,225,200,200'/><node_position Node='-32' Position='70,430,200,200'/><node_position Node='-53' Position='215,304,200,200'/></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4月21日 15:29
# 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=0.0001, sell_cost=0.001, min_cost=5))
    # 设置股票数量
    context.stock_num = 30
    context.month_data =  context.indicator_data.reset_index(drop=True)
 
    #每月调仓，改用每月调用一次主处理函数
    schedule_function(func=month_handle_data,
        date_rule=date_rules.month_start(),   # 月初执行
        time_rule=time_rules.market_open())   # 开盘执行
    
    

from datetime import timedelta
#每月初调用一次主处理函数，在本函数进行目标股票获取并进行买卖
def month_handle_data(context, data):
    # 当前的日期
    date = data.current_dt.strftime('%Y-%m-%d')
    
    #根据当前时间获取上一个月的时间。当前月初往前偏移28天一定是上个月
    last_month = str(pd.to_datetime(date) + timedelta(days=-28))[0:7]
    #获取上个月末的股票池
    df = context.month_data
    tempdf = df[df['date'].astype(str).apply(lambda x:x[0:7])==last_month]
    #对股票池根据因子值列进行排序，并取前30只股票
    stocks = tempdf.sort_values('pe_ttm_0').iloc[:context.stock_num,:]
    #把股票id转换成需要购买的股票列表
    stock_to_buy = stocks['instrument'].values.tolist()
    print("选股日期：",date,',选股数量：',len(stock_to_buy))
    
    
    positions = {e.symbol: p.cost_basis  for e, p in context.portfolio.positions.items()}
    # 新建当日止损股票列表是为了handle_data 策略逻辑部分不再对该股票进行判断
    current_stoploss_stock = [] 
    if len(positions) > 0:
        for i in positions.keys():
            stock_cost = positions[i] 
            stock_market_price = data.current(context.symbol(i), 'price') 
            # 亏10%就止损
            if (stock_market_price - stock_cost) / stock_cost <= -0.10:   
                context.order_target_percent(context.symbol(i),0)     
                current_stoploss_stock.append(i)
#                 print('日期：',date,'股票：',i,'出现止损状况')
    
    # 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)):
            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)

            
            
def m5_handle_data_bigquant_run(context, data):
    pass


# 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='2020-12-31',
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m2 = M.input_features.v1(
    features="""pb_lf_0
pe_ttm_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='pe_ttm_0,pb_lf_0',
    group_by='instrument',
    keep_columns='--',
    ascending=True
)

m6 = M.filter.v3(
    input_data=m4.sorted_data,
    expr='pb_lf_0 < 1.5 & pe_ttm_0 < 15 & amount_0 > 0 & pb_lf_0 > 0 & pe_ttm_0 > 0',
    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=''
)

[2022-04-21 15:19:45.095370] INFO: moduleinvoker: instruments.v2 开始运行..

[2022-04-21 15:19:45.107752] INFO: moduleinvoker: 命中缓存

[2022-04-21 15:19:45.109441] INFO: moduleinvoker: instruments.v2 运行完成[0.014084s].

[2022-04-21 15:19:45.115479] INFO: moduleinvoker: input_features.v1 开始运行..

[2022-04-21 15:19:45.123434] INFO: moduleinvoker: 命中缓存

[2022-04-21 15:19:45.125016] INFO: moduleinvoker: input_features.v1 运行完成[0.009545s].

[2022-04-21 15:19:46.702134] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2022-04-21 15:19:48.854040] INFO: 基础特征抽取: 年份 2017, 特征行数=135965

[2022-04-21 15:19:51.118175] INFO: 基础特征抽取: 年份 2018, 特征行数=816987

[2022-04-21 15:19:54.651473] INFO: 基础特征抽取: 年份 2019, 特征行数=884867

[2022-04-21 15:19:57.420803] INFO: 基础特征抽取: 年份 2020, 特征行数=945961

[2022-04-21 15:19:57.530475] INFO: 基础特征抽取: 总行数: 2783780

[2022-04-21 15:19:57.542956] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[10.840836s].

[2022-04-21 15:19:57.551172] INFO: moduleinvoker: sort.v4 开始运行..

[2022-04-21 15:20:03.879891] INFO: moduleinvoker: sort.v4 运行完成[6.328709s].

[2022-04-21 15:20:03.889928] INFO: moduleinvoker: filter.v3 开始运行..

[2022-04-21 15:20:03.910960] INFO: filter: 使用表达式 pb_lf_0 < 1.5 & pe_ttm_0 < 15 & amount_0 > 0 & pb_lf_0 > 0 & pe_ttm_0 > 0 过滤

[2022-04-21 15:20:04.390301] INFO: filter: 过滤 /data, 205717/0/2783780

[2022-04-21 15:20:04.443760] INFO: moduleinvoker: filter.v3 运行完成[0.553822s].

[2022-04-21 15:20:04.484671] INFO: moduleinvoker: backtest.v8 开始运行..

[2022-04-21 15:20:04.491253] INFO: backtest: biglearning backtest:V8.6.2

[2022-04-21 15:20:04.492723] INFO: backtest: product_type:stock by specified

[2022-04-21 15:20:04.643048] INFO: moduleinvoker: cached.v2 开始运行..

[2022-04-21 15:20:13.503782] INFO: backtest: 读取股票行情完成:3794347

[2022-04-21 15:20:16.368317] INFO: moduleinvoker: cached.v2 运行完成[11.725251s].

[2022-04-21 15:20:19.838563] INFO: algo: TradingAlgorithm V1.8.7

[2022-04-21 15:20:21.378441] INFO: algo: trading transform...

[2022-04-21 15:21:17.103270] INFO: Performance: Simulated 730 trading days out of 730.

[2022-04-21 15:21:17.105661] INFO: Performance: first open: 2018-01-02 09:30:00+00:00

[2022-04-21 15:21:17.107236] INFO: Performance: last close: 2020-12-31 15:00:00+00:00

[2022-04-21 15:21:22.406349] INFO: moduleinvoker: backtest.v8 运行完成[77.921509s].

[2022-04-21 15:21:22.409936] INFO: moduleinvoker: trade.v4 运行完成[77.958931s].

选股日期： 2018-01-02 ,选股数量： 30
选股日期： 2018-02-01 ,选股数量： 30
选股日期： 2018-03-01 ,选股数量： 30
选股日期： 2018-04-02 ,选股数量： 30
选股日期： 2018-05-02 ,选股数量： 30
选股日期： 2018-06-01 ,选股数量： 30
选股日期： 2018-07-02 ,选股数量： 30
选股日期： 2018-08-01 ,选股数量： 30
选股日期： 2018-09-03 ,选股数量： 30
选股日期： 2018-10-08 ,选股数量： 30
选股日期： 2018-11-01 ,选股数量： 30
选股日期： 2018-12-03 ,选股数量： 30
选股日期： 2019-01-02 ,选股数量： 30
选股日期： 2019-02-01 ,选股数量： 30
选股日期： 2019-03-01 ,选股数量： 30
选股日期： 2019-04-01 ,选股数量： 30
选股日期： 2019-05-06 ,选股数量： 30
选股日期： 2019-06-03 ,选股数量： 30
选股日期： 2019-07-01 ,选股数量： 30
选股日期： 2019-08-01 ,选股数量： 30
选股日期： 2019-09-02 ,选股数量： 30
选股日期： 2019-10-08 ,选股数量： 30
选股日期： 2019-11-01 ,选股数量： 30
选股日期： 2019-12-02 ,选股数量： 30
选股日期： 2020-01-02 ,选股数量： 30
选股日期： 2020-02-03 ,选股数量： 30
选股日期： 2020-03-02 ,选股数量： 30
选股日期： 2020-04-01 ,选股数量： 30
选股日期： 2020-05-06 ,选股数量： 30
选股日期： 2020-06-01 ,选股数量： 30
选股日期： 2020-07-01 ,选股数量： 30
选股日期： 2020-08-03 ,选股数量： 30
选股日期： 2020-09-01 ,选股数量： 30
选股日期： 2020-10-09 ,选股数量： 30
选股日期： 2020-11-02 ,选股数量： 30
选股日期： 2020-12-01 ,选股数量： 30

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