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Python 代码处理数据\ndef bigquant_run(data):\n \n df_tmp = data\n data=data[data['上市时间'] >365]\n data=data[data['换手rank_std20'] < 0.8]\n data=data[data['换手rank_std5'] <0.8]\n \n data=pd.DataFrame(data)\n date=data.date.unique()\n df_list=[]\n\n for i in date:\n \n cut=data.loc[data['date']==i]\n \n cut=cut[cut['市净率'] < 1.5]\n \n pb_mean = cut['市盈率'].mean()\n cut=cut[cut['市盈率'] < pb_mean]\n \n cut=cut[cut['借款比例'] < 0.33]\n\n \n \n \n df_list.append(cut)\n\n df_=pd.concat(df_list)\n \n \n \n \n \n return df_","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_data","node_id":"-137"}],"output_ports":[{"name":"data","node_id":"-137"}],"cacheable":false,"seq_num":8,"comment":"","comment_collapsed":true},{"node_id":"-190","module_id":"BigQuantSpace.hftrade.hftrade-v2","parameters":[{"name":"start_date","value":"","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"","type":"Literal","bound_global_parameter":null},{"name":"initialize","value":"# 交易引擎：初始化函数，只执行一次\ndef bigquant_run(context):\n #=========================== 加载预测数据 ===================================\n \n context.ranker_prediction = context.options['data'].read_df()\n \n #=========================== 设置交易参数 ====================================\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=0.00015, sell_cost=0.0013, min_cost=5))\n #=========================== 设置持仓参数 ====================================\n \n #最大持仓量\n context.stock_count = 5\n # 设置每只股票占用的最大资金比例\n context.max_cash_per_instrument = 0.5\n \n #持仓天数\n context.hold_days = 5\n \n #股票止损列表\n context.stop_list = {}\n \n #股票止损卖出后最小买入间隔\n context.min_stop_days = 20\n \n #=======================================功能开关\n #是否止损\n context.stop_win = True\n context.set_stock_t1(1)","type":"Literal","bound_global_parameter":null},{"name":"before_trading_start","value":"# 交易引擎：每个单位时间开盘前调用一次。\ndef bigquant_run(context, data):\n # 盘前处理，订阅行情等\n pass\n","type":"Literal","bound_global_parameter":null},{"name":"handle_tick","value":"# 交易引擎：tick数据处理函数，每个tick执行一次\ndef bigquant_run(context, tick):\n pass\n","type":"Literal","bound_global_parameter":null},{"name":"handle_data","value":"# 交易引擎：bar数据处理函数，每个时间单位执行一次\ndef bigquant_run(context, data):\n \n import datetime\n \n #初始化()\n buy_list = [] #买入列表\n sell_list = [] #卖出列表\n target_list = [] #目标列表\n #==================== 数据准备\n today = data.current_dt.strftime('%Y-%m-%d')\n time = data.current_dt\n \n today_data = context.ranker_prediction[context.ranker_prediction['date'] == today]\n #print(today_data)\n \n if len(today_data)>0:\n #获得当日预备持仓\n today_data = today_data.sort_values(by='市值', ascending = True)\n target_list = today_data.instrument.tolist()\n \n #获得当前持仓列表\n holding_list = list(context.get_account_positions().keys())\n holding_num = len(holding_list)\n long_num = 0 #初始化多头数\n \n if len(target_list)>0:\n pct = 1/len(target_list)\n else:\n pct=len(holding_list)\n \n\n #生成卖单\n for ins in holding_list:\n if ins not in target_list:\n context.order_target_percent(ins,0)\n sell_list.append(ins)\n \n #现有持仓的持仓量调整\n for ins in holding_list:\n if ins not in sell_list:\n context.order_target_percent(ins,pct)\n \n #生成买单\n for ins in target_list:\n if ins not in holding_list: \n context.order_target_percent(ins,pct)\n\n\n","type":"Literal","bound_global_parameter":null},{"name":"handle_trade","value":"# 交易引擎：成交回报处理函数，每个成交发生时执行一次\ndef bigquant_run(context, trade):\n pass\n","type":"Literal","bound_global_parameter":null},{"name":"handle_order","value":"# 交易引擎：委托回报处理函数，每个委托变化时执行一次\ndef bigquant_run(context, order):\n pass\n","type":"Literal","bound_global_parameter":null},{"name":"after_trading","value":"# 交易引擎：盘后处理函数，每日盘后执行一次\ndef bigquant_run(context, data):\n pass\n","type":"Literal","bound_global_parameter":null},{"name":"capital_base","value":"100000","type":"Literal","bound_global_parameter":null},{"name":"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":"before_start_days","value":"0","type":"Literal","bound_global_parameter":null},{"name":"volume_limit","value":1,"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":"benchmark","value":"000300.HIX","type":"Literal","bound_global_parameter":null},{"name":"plot_charts","value":"True","type":"Literal","bound_global_parameter":null},{"name":"disable_cache","value":"True","type":"Literal","bound_global_parameter":null},{"name":"replay_bdb","value":"False","type":"Literal","bound_global_parameter":null},{"name":"show_debug_info","value":"False","type":"Literal","bound_global_parameter":null},{"name":"backtest_only","value":"False","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"instruments","node_id":"-190"},{"name":"options_data","node_id":"-190"},{"name":"history_ds","node_id":"-190"},{"name":"benchmark_ds","node_id":"-190"}],"output_ports":[{"name":"raw_perf","node_id":"-190"}],"cacheable":false,"seq_num":2,"comment":"","comment_collapsed":true}],"node_layout":"<node_postions><node_position Node='287d2cb0-f53c-4101-bdf8-104b137c8601-24' Position='52,-1099,200,200'/><node_position Node='287d2cb0-f53c-4101-bdf8-104b137c8601-8' Position='-362,-1100,200,200'/><node_position Node='-1225' Position='-166,-954,200,200'/><node_position Node='-1014' Position='-137,-634,200,200'/><node_position Node='-1234' Position='-157,-843,200,200'/><node_position Node='-292' Position='-144,-752,200,200'/><node_position Node='-1260' Position='212,-955,200,200'/><node_position Node='-137' Position='-121,-539,200,200'/><node_position Node='-190' Position='-176,-408,200,200'/></node_postions>"},"nodes_readonly":false,"studio_version":"v2"}

# 本代码由可视化策略环境自动生成 2023年4月12日 20:49
# 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。


def talib_SLOPE(df, close, timeperiod=21):
    return talib.LINEARREG_SLOPE(close, timeperiod)

m12_user_functions_bigquant_run = {
'ta_slope':talib_SLOPE
}
# Python 代码处理数据
def m8_handler_bigquant_run(data):
    
    df_tmp = data
    data=data[data['上市时间'] >365]
    data=data[data['换手rank_std20'] < 0.8]
    data=data[data['换手rank_std5'] <0.8]
    
    data=pd.DataFrame(data)
    date=data.date.unique()
    df_list=[]

    for i in date:
        
        cut=data.loc[data['date']==i]
        
        cut=cut[cut['市净率'] < 1.5]
        
        pb_mean = cut['市盈率'].mean()
        cut=cut[cut['市盈率'] < pb_mean]
        
        cut=cut[cut['借款比例'] < 0.33]

        
        
        
        df_list.append(cut)

    df_=pd.concat(df_list)
    
    
    
    
    
    return df_
# 交易引擎：初始化函数，只执行一次
def m2_initialize_bigquant_run(context):
    #=========================== 加载预测数据  ===================================
    
    context.ranker_prediction = context.options['data'].read_df()
    
    #===========================  设置交易参数  ====================================
    # 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=0.00015, sell_cost=0.0013, min_cost=5))
    #===========================  设置持仓参数  ====================================
    
    #最大持仓量
    context.stock_count = 5
    # 设置每只股票占用的最大资金比例
    context.max_cash_per_instrument = 0.5
    
    #持仓天数
    context.hold_days = 5
    
    #股票止损列表
    context.stop_list = {}
    
    #股票止损卖出后最小买入间隔
    context.min_stop_days = 20
    
    #=======================================功能开关
    #是否止损
    context.stop_win = True
    context.set_stock_t1(1)
# 交易引擎：每个单位时间开盘前调用一次。
def m2_before_trading_start_bigquant_run(context, data):
    # 盘前处理，订阅行情等
    pass

# 交易引擎：tick数据处理函数，每个tick执行一次
def m2_handle_tick_bigquant_run(context, tick):
    pass

# 交易引擎：bar数据处理函数，每个时间单位执行一次
def m2_handle_data_bigquant_run(context, data):
    
    import datetime
    
    #初始化()
    buy_list = []  #买入列表
    sell_list = [] #卖出列表
    target_list = []  #目标列表
    #==================== 数据准备
    today = data.current_dt.strftime('%Y-%m-%d')
    time = data.current_dt
    
    today_data  = context.ranker_prediction[context.ranker_prediction['date'] == today]
    #print(today_data)
    
    if len(today_data)>0:
        #获得当日预备持仓
        today_data = today_data.sort_values(by='市值', ascending = True)
        target_list = today_data.instrument.tolist()
    
    #获得当前持仓列表
    holding_list = list(context.get_account_positions().keys())
    holding_num = len(holding_list)
    long_num = 0 #初始化多头数
    
    if len(target_list)>0:
        pct = 1/len(target_list)
    else:
        pct=len(holding_list)
    

    #生成卖单
    for ins in holding_list:
        if ins not in target_list:
            context.order_target_percent(ins,0)
            sell_list.append(ins)
            
    #现有持仓的持仓量调整
    for ins in holding_list:
        if ins not in sell_list:
            context.order_target_percent(ins,pct)
    
    #生成买单
    for ins in target_list:
        if ins not in holding_list:  
            context.order_target_percent(ins,pct)



# 交易引擎：成交回报处理函数，每个成交发生时执行一次
def m2_handle_trade_bigquant_run(context, trade):
    pass

# 交易引擎：委托回报处理函数，每个委托变化时执行一次
def m2_handle_order_bigquant_run(context, order):
    pass

# 交易引擎：盘后处理函数，每日盘后执行一次
def m2_after_trading_bigquant_run(context, data):
    pass


m1 = M.input_features.v1(
    features="""e=0.0000000001

上市时间=list_days_0

收益=return_10


#特征
市值=market_cap_0

#过滤特征
换手rank_std20=rank(std(turn_0,20))
换手rank_std5=rank(std(turn_0,5))


#罗伯瑞克选股指标
市净率=pb_lf_0
市盈率=pe_ttm_0
借款比例=fs_total_liability_0/(fs_current_assets_0+fs_fixed_assets_0)
现金流比率=close_0/fs_free_cash_flow_0











""",
    m_cached=False
)

m17 = M.features_short.v1(
    input_1=m1.data,
    m_cached=False
)

m3 = M.instruments.v2(
    start_date=T.live_run_param('trading_date', '2013-01-01'),
    end_date=T.live_run_param('trading_date', '2017-01-01'),
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0,
    m_cached=False
)

m4 = M.general_feature_extractor.v7(
    instruments=m3.data,
    features=m1.data,
    start_date='',
    end_date='',
    before_start_days=50,
    m_cached=False
)

m12 = M.derived_feature_extractor.v3(
    input_data=m4.data,
    features=m1.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=True,
    user_functions=m12_user_functions_bigquant_run,
    m_cached=False
)

m16 = M.fillnan.v1(
    input_data=m12.data,
    features=m17.data_1,
    fill_value='0.0',
    m_cached=False
)

m15 = M.chinaa_stock_filter.v1(
    input_data=m16.data,
    index_constituent_cond=['全部'],
    board_cond=['上证主板', '深证主板', '创业板'],
    industry_cond=['交通运输', '休闲服务', '传媒/信息服务', '公用事业', '农林牧渔', '化工', '医药生物', '商业贸易', '国防军工', '家用电器', '建筑材料/建筑建材', '建筑装饰', '房地产', '有色金属', '机械设备', '汽车/交运设备', '电子', '电气设备', '纺织服装', '综合', '计算机', '轻工制造', '通信', '采掘', '钢铁', '食品饮料'],
    st_cond=['正常'],
    delist_cond=['非退市'],
    output_left_data=False,
    m_cached=False
)

m8 = M.datahub_handler_column.v1(
    input_data=m15.data,
    handler=m8_handler_bigquant_run
)

m2 = M.hftrade.v2(
    instruments=m3.data,
    options_data=m8.data,
    start_date='',
    end_date='',
    initialize=m2_initialize_bigquant_run,
    before_trading_start=m2_before_trading_start_bigquant_run,
    handle_tick=m2_handle_tick_bigquant_run,
    handle_data=m2_handle_data_bigquant_run,
    handle_trade=m2_handle_trade_bigquant_run,
    handle_order=m2_handle_order_bigquant_run,
    after_trading=m2_after_trading_bigquant_run,
    capital_base=100000,
    frequency='daily',
    price_type='真实价格',
    product_type='股票',
    before_start_days='0',
    volume_limit=1,
    order_price_field_buy='open',
    order_price_field_sell='close',
    benchmark='000300.HIX',
    plot_charts=True,
    disable_cache=True,
    replay_bdb=False,
    show_debug_info=False,
    backtest_only=False
)

[2023-04-12 20:47:26.922467] INFO: moduleinvoker: input_features.v1 开始运行..

[2023-04-12 20:47:26.948622] INFO: moduleinvoker: input_features.v1 运行完成[0.026167s].

[2023-04-12 20:47:29.450748] INFO: moduleinvoker: features_short.v1 开始运行..

[2023-04-12 20:47:29.505180] INFO: moduleinvoker: features_short.v1 运行完成[0.054445s].

[2023-04-12 20:47:29.515500] INFO: moduleinvoker: instruments.v2 开始运行..

[2023-04-12 20:47:29.579353] INFO: moduleinvoker: instruments.v2 运行完成[0.063857s].

[2023-04-12 20:47:29.598359] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2023-04-12 20:47:30.112529] INFO: 基础特征抽取: 年份 2012, 特征行数=86573

[2023-04-12 20:47:32.263768] INFO: 基础特征抽取: 年份 2013, 特征行数=564168

[2023-04-12 20:47:34.198127] INFO: 基础特征抽取: 年份 2014, 特征行数=569948

[2023-04-12 20:47:35.849774] INFO: 基础特征抽取: 年份 2015, 特征行数=569698

[2023-04-12 20:47:37.819153] INFO: 基础特征抽取: 年份 2016, 特征行数=641546

[2023-04-12 20:47:38.157516] INFO: 基础特征抽取: 年份 2017, 特征行数=0

[2023-04-12 20:47:38.324856] INFO: 基础特征抽取: 总行数: 2431933

[2023-04-12 20:47:38.326704] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[8.728363s].

[2023-04-12 20:47:38.341782] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2023-04-12 20:47:44.101717] INFO: derived_feature_extractor: 提取完成 e=0.0000000001, 0.003s

[2023-04-12 20:47:44.105944] INFO: derived_feature_extractor: 提取完成 上市时间=list_days_0, 0.003s

[2023-04-12 20:47:44.110409] INFO: derived_feature_extractor: 提取完成 收益=return_10, 0.003s

[2023-04-12 20:47:44.117160] INFO: derived_feature_extractor: 提取完成 市值=market_cap_0, 0.005s

[2023-04-12 20:47:47.015223] INFO: derived_feature_extractor: 提取完成 换手rank_std20=rank(std(turn_0,20)), 2.897s

[2023-04-12 20:47:50.004695] INFO: derived_feature_extractor: 提取完成 换手rank_std5=rank(std(turn_0,5)), 2.987s

[2023-04-12 20:47:50.009439] INFO: derived_feature_extractor: 提取完成 市净率=pb_lf_0, 0.003s

[2023-04-12 20:47:50.014241] INFO: derived_feature_extractor: 提取完成 市盈率=pe_ttm_0, 0.003s

[2023-04-12 20:47:50.024818] INFO: derived_feature_extractor: 提取完成 借款比例=fs_total_liability_0/(fs_current_assets_0+fs_fixed_assets_0), 0.009s

[2023-04-12 20:47:50.033420] INFO: derived_feature_extractor: 提取完成 现金流比率=close_0/fs_free_cash_flow_0, 0.007s

[2023-04-12 20:47:51.635784] INFO: derived_feature_extractor: /y_2012, 86573

[2023-04-12 20:47:52.658240] INFO: derived_feature_extractor: /y_2013, 564168

[2023-04-12 20:47:53.827436] INFO: derived_feature_extractor: /y_2014, 569948

[2023-04-12 20:47:55.366294] INFO: derived_feature_extractor: /y_2015, 569698

[2023-04-12 20:47:57.026055] INFO: derived_feature_extractor: /y_2016, 641546

[2023-04-12 20:47:58.429906] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[20.088097s].

[2023-04-12 20:47:58.452043] INFO: moduleinvoker: fillnan.v1 开始运行..

[2023-04-12 20:48:04.661409] INFO: moduleinvoker: fillnan.v1 运行完成[6.209354s].

[2023-04-12 20:48:04.677714] INFO: moduleinvoker: chinaa_stock_filter.v1 开始运行..

[2023-04-12 20:48:05.824494] INFO: A股股票过滤: 过滤 /y_2012, 82102/0/86573

[2023-04-12 20:48:09.027579] INFO: A股股票过滤: 过滤 /y_2013, 534316/0/564168

[2023-04-12 20:48:12.227886] INFO: A股股票过滤: 过滤 /y_2014, 541406/0/569948

[2023-04-12 20:48:15.535630] INFO: A股股票过滤: 过滤 /y_2015, 543562/0/569698

[2023-04-12 20:48:19.329036] INFO: A股股票过滤: 过滤 /y_2016, 615743/0/641546

[2023-04-12 20:48:19.344643] INFO: A股股票过滤: 过滤完成, 2317129 + 0

[2023-04-12 20:48:19.369157] INFO: moduleinvoker: chinaa_stock_filter.v1 运行完成[14.691439s].

[2023-04-12 20:48:28.637667] INFO: 数据处理(自定义): 数据处理(自定义) 完成

[2023-04-12 20:48:28.721501] INFO: 数据处理(自定义): ds: DataSource(6b406af6eff04203939344ecbd6d1907T)

[2023-04-12 20:48:28.723014] INFO: moduleinvoker: datahub_handler_column.v1 运行完成[9.341669s].

[2023-04-12 20:48:28.779470] INFO: moduleinvoker: hfbacktest.v1 开始运行..

[2023-04-12 20:48:29.172683] INFO: hfbacktest: biglearning V1.4.21

[2023-04-12 20:48:29.174153] INFO: hfbacktest: bigtrader v1.10.0 2023-04-07