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Python 代码入口函数，input_1/2/3 对应三个输入端，data_1/2/3 对应三个输出端\ndef bigquant_run(input_1, input_2, input_3):\n # 输入代码列表\n instruments = input_1.read()['instruments']\n # 输入开始日期\n start_date = input_1.read()['start_date']\n # 向前取90天\n start_date = pd.to_datetime(start_date) + datetime.timedelta(days=-90)\n start_date = start_date.strftime('%Y-%m-%d')\n # 输入结束日期\n end_date = input_1.read()['end_date']\n # 输入特征列表\n features = input_2.read()\n \n # 提取专利数据\n patents = DataSource('patent_CN_STOCK_A').read(instruments=instruments, start_date=start_date, end_date=end_date)\n patents = patents[features + ['date','instrument']]\n # 提取交易日历\n trade_days = DataSource(\"trading_days\").read(start_date=start_date, end_date=end_date)\n trade_days = trade_days[trade_days.country_code=='CN']\n \n # 将月频数据转换为日频数据\n def cpt_daily(x):\n df = pd.merge(x, trade_days, on='date', how='outer')\n df = df.sort_values('date').fillna(method='ffill')\n return df\n df = patents.groupby('instrument').apply(cpt_daily).reset_index(drop=True)\n \n data_1 = DataSource.write_df(df)\n return Outputs(data_1=data_1, data_2=None, data_3=None)\n","type":"Literal","bound_global_parameter":null},{"name":"post_run","value":"# 后处理函数，可选。输入是主函数的输出，可以在这里对数据做处理，或者返回更友好的outputs数据格式。此函数输出不会被缓存。\ndef bigquant_run(outputs):\n return outputs\n","type":"Literal","bound_global_parameter":null},{"name":"input_ports","value":"","type":"Literal","bound_global_parameter":null},{"name":"params","value":"{}","type":"Literal","bound_global_parameter":null},{"name":"output_ports","value":"","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_1","node_id":"-1409"},{"name":"input_2","node_id":"-1409"},{"name":"input_3","node_id":"-1409"}],"output_ports":[{"name":"data_1","node_id":"-1409"},{"name":"data_2","node_id":"-1409"},{"name":"data_3","node_id":"-1409"}],"cacheable":true,"seq_num":7,"comment":"1.提取专利数据\n2.转换为日频数据","comment_collapsed":false},{"node_id":"-1417","module_id":"BigQuantSpace.input_features.input_features-v1","parameters":[{"name":"features","value":"\n# #号开始的表示注释，注释需单独一行\n# 多个特征，每行一个，可以包含基础特征和衍生特征，特征须为本平台特征\nTG1_V011 ","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"features_ds","node_id":"-1417"}],"output_ports":[{"name":"data","node_id":"-1417"}],"cacheable":true,"seq_num":4,"comment":"","comment_collapsed":true},{"node_id":"-1422","module_id":"BigQuantSpace.dropnan.dropnan-v2","parameters":[],"input_ports":[{"name":"input_data","node_id":"-1422"},{"name":"features","node_id":"-1422"}],"output_ports":[{"name":"data","node_id":"-1422"}],"cacheable":true,"seq_num":5,"comment":"","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 # 最大持仓占比\n context.max_positions = 0.9\n # 设置一个 symbols 用于存放历史周期上的股票池\n context.symbols = pd.Series()\n context.extension['index'] = 0\n \n # 月末尾盘执行选股，第二天买入\n schedule_function(func=month_handle_data,\n date_rule=date_rules.month_end(),\n time_rule=time_rules.market_close())\n \n \n","type":"Literal","bound_global_parameter":null},{"name":"handle_data","value":"#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 from datetime import timedelta\n factor = m4.data.read()[0]\n \n # 获取当前日期\n date = data.current_dt.strftime(\"%Y-%m-%d\")\n # 提取当日的数据\n cur_data = context.indicator_data[context.indicator_data.date==date]\n # 股票池\n symbols =list(cur_data.instrument)[:context.stock_num]\n # 持仓股\n holdings = [equity.symbol for equity in context.portfolio.positions]\n # 待卖出的股票：当前持仓不在股票池即为卖出\n stocks_to_sell = [x for x in holdings if x not in symbols]\n \n # 计算股票池大小和选股重复率\n len_stks = len(cur_data[cur_data[factor] > 0])\n context.extension['index'] += 1\n context.symbols[str(context.extension['index'])] = symbols\n if context.extension['index'] > 1 and len(symbols) > 0: \n # 提取上一日的股票池\n last_symbols = context.symbols[str(context.extension['index']-1)]\n # 计算两期股票池的交集\n dup_len = len(list(set(last_symbols).intersection(set(symbols))))\n dup_ratio = dup_len/len(symbols)\n # print(date,\"买入股票和持仓股的重复率：\",round(dup_ratio*100,2),\"，股票池大小：\",len_stks)\n \n # 发单：卖出\n for stock in stocks_to_sell:\n code = context.symbol(stock)\n # 如果股票停牌，则无法成交\n if data.can_trade(code):\n context.order_target_percent(code, 0)\n \n # 如果当天没有买入的股票，则直接返回：\n if len(symbols) == 0:\n return\n \n # 确定权重\n weight = context.max_positions / len(symbols)\n \n # 发单：买入\n for stock in symbols:\n code = context.symbol(stock)\n if data.can_trade(code):\n context.order_target_percent(code,weight)\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":9,"comment":"回测","comment_collapsed":true},{"node_id":"-1947","module_id":"BigQuantSpace.sort.sort-v5","parameters":[{"name":"sort_by","value":"--","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":"False","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_ds","node_id":"-1947"},{"name":"sort_by_ds","node_id":"-1947"}],"output_ports":[{"name":"sorted_data","node_id":"-1947"}],"cacheable":true,"seq_num":10,"comment":"","comment_collapsed":true},{"node_id":"-2421","module_id":"BigQuantSpace.filter.filter-v3","parameters":[{"name":"expr","value":"TG1_V011 > 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":"-2421"}],"output_ports":[{"name":"data","node_id":"-2421"},{"name":"left_data","node_id":"-2421"}],"cacheable":true,"seq_num":11,"comment":"","comment_collapsed":true}],"node_layout":"<node_postions><node_position Node='-672' Position='155,23,200,200'/><node_position Node='-681' Position='360,241,200,200'/><node_position Node='-687' Position='413,105,200,200'/><node_position Node='-703' Position='138,383,200,200'/><node_position Node='-715' Position='137,480,200,200'/><node_position Node='-1409' Position='-35,216,200,200'/><node_position Node='-1417' Position='-142,90,200,200'/><node_position Node='-1422' Position='136,671,200,200'/><node_position Node='-32' Position='32,857,200,200'/><node_position Node='-1947' Position='132,759,200,200'/><node_position Node='-2421' Position='137,577,200,200'/></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6月2日 15:06
# 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。


# Python 代码入口函数，input_1/2/3 对应三个输入端，data_1/2/3 对应三个输出端
def m7_run_bigquant_run(input_1, input_2, input_3):
    # 输入代码列表
    instruments = input_1.read()['instruments']
    # 输入开始日期
    start_date = input_1.read()['start_date']
    # 向前取90天
    start_date = pd.to_datetime(start_date) + datetime.timedelta(days=-90)
    start_date = start_date.strftime('%Y-%m-%d')
    # 输入结束日期
    end_date = input_1.read()['end_date']
    # 输入特征列表
    features = input_2.read()
    
    # 提取专利数据
    patents = DataSource('patent_CN_STOCK_A').read(instruments=instruments, start_date=start_date, end_date=end_date)
    patents = patents[features + ['date','instrument']]
    # 提取交易日历
    trade_days = DataSource("trading_days").read(start_date=start_date, end_date=end_date)
    trade_days = trade_days[trade_days.country_code=='CN']
    
    # 将月频数据转换为日频数据
    def cpt_daily(x):
        df = pd.merge(x, trade_days, on='date', how='outer')
        df = df.sort_values('date').fillna(method='ffill')
        return df
    df = patents.groupby('instrument').apply(cpt_daily).reset_index(drop=True)
    
    data_1 = DataSource.write_df(df)
    return Outputs(data_1=data_1, data_2=None, data_3=None)

# 后处理函数，可选。输入是主函数的输出，可以在这里对数据做处理，或者返回更友好的outputs数据格式。此函数输出不会被缓存。
def m7_post_run_bigquant_run(outputs):
    return outputs

# 回测引擎：初始化函数，只执行一次
def m9_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
    # 最大持仓占比
    context.max_positions = 0.9
    # 设置一个 symbols 用于存放历史周期上的股票池
    context.symbols = pd.Series()
    context.extension['index'] = 0
    
    # 月末尾盘执行选股，第二天买入
    schedule_function(func=month_handle_data,
        date_rule=date_rules.month_end(),
        time_rule=time_rules.market_close())
    
    

#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):
    from datetime import timedelta
    factor = m4.data.read()[0]
    
    # 获取当前日期
    date = data.current_dt.strftime("%Y-%m-%d")
    # 提取当日的数据
    cur_data = context.indicator_data[context.indicator_data.date==date]
    # 股票池
    symbols =list(cur_data.instrument)[:context.stock_num]
    # 持仓股
    holdings = [equity.symbol for equity in context.portfolio.positions]
    # 待卖出的股票：当前持仓不在股票池即为卖出
    stocks_to_sell = [x for x in holdings if x not in symbols]
    
    # 计算股票池大小和选股重复率
    len_stks = len(cur_data[cur_data[factor] > 0])
    context.extension['index'] += 1
    context.symbols[str(context.extension['index'])] = symbols
    if context.extension['index'] > 1 and len(symbols) > 0:  
        # 提取上一日的股票池
        last_symbols = context.symbols[str(context.extension['index']-1)]
        # 计算两期股票池的交集
        dup_len = len(list(set(last_symbols).intersection(set(symbols))))
        dup_ratio = dup_len/len(symbols)
        # print(date,"买入股票和持仓股的重复率：",round(dup_ratio*100,2),"，股票池大小：",len_stks)
    
    # 发单：卖出
    for stock in stocks_to_sell:
        code = context.symbol(stock)
        # 如果股票停牌，则无法成交
        if data.can_trade(code):
            context.order_target_percent(code, 0)
    
    # 如果当天没有买入的股票，则直接返回：
    if len(symbols) == 0:
        return
    
    # 确定权重
    weight = context.max_positions / len(symbols)
    
    # 发单：买入
    for stock in symbols:
        code = context.symbol(stock)
        if data.can_trade(code):
            context.order_target_percent(code,weight)
        
        
def m9_handle_data_bigquant_run(context, data):
    pass


# 回测引擎：准备数据，只执行一次
def m9_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 m9_before_trading_start_bigquant_run(context, data):
    pass


m1 = M.instruments.v2(
    start_date='2010-01-01',
    end_date='2022-05-30',
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m3 = M.input_features.v1(
    features="""
# #号开始的表示注释，注释需单独一行
# 多个特征，每行一个，可以包含基础特征和衍生特征，特征须为本平台特征
close_0
"""
)

m2 = M.general_feature_extractor.v7(
    instruments=m1.data,
    features=m3.data,
    start_date='',
    end_date='',
    before_start_days=90
)

m4 = M.input_features.v1(
    features="""
# #号开始的表示注释，注释需单独一行
# 多个特征，每行一个，可以包含基础特征和衍生特征，特征须为本平台特征
TG1_V011 """
)

m7 = M.cached.v3(
    input_1=m1.data,
    input_2=m4.data,
    run=m7_run_bigquant_run,
    post_run=m7_post_run_bigquant_run,
    input_ports='',
    params='{}',
    output_ports=''
)

m6 = M.join.v3(
    data1=m7.data_1,
    data2=m2.data,
    on='date,instrument',
    how='right',
    sort=False
)

m8 = M.chinaa_stock_filter.v1(
    input_data=m6.data,
    index_constituent_cond=['全部'],
    board_cond=['全部'],
    industry_cond=['公用事业', '农林牧渔', '化工', '医药生物', '国防军工', '家用电器', '建筑材料/建筑建材', '建筑装饰', '有色金属', '机械设备', '汽车/交运设备', '电子', '电气设备', '纺织服装', '计算机', '轻工制造', '通信', '采掘', '钢铁', '食品饮料'],
    st_cond=['正常'],
    delist_cond=['全部'],
    output_left_data=False
)

m11 = M.filter.v3(
    input_data=m8.data,
    expr='TG1_V011  > 0',
    output_left_data=False
)

m5 = M.dropnan.v2(
    input_data=m11.data
)

m10 = M.sort.v5(
    input_ds=m5.data,
    sort_by_ds=m4.data,
    sort_by='--',
    group_by='date',
    keep_columns='--',
    ascending=False
)

m9 = M.trade.v4(
    instruments=m1.data,
    options_data=m10.sorted_data,
    start_date='',
    end_date='',
    initialize=m9_initialize_bigquant_run,
    handle_data=m9_handle_data_bigquant_run,
    prepare=m9_prepare_bigquant_run,
    before_trading_start=m9_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=''
)

[2022-06-02 15:04:39.201996] INFO: moduleinvoker: instruments.v2 开始运行..

[2022-06-02 15:04:39.373164] INFO: moduleinvoker: 命中缓存

[2022-06-02 15:04:39.375326] INFO: moduleinvoker: instruments.v2 运行完成[0.173334s].

[2022-06-02 15:04:39.380977] INFO: moduleinvoker: input_features.v1 开始运行..

[2022-06-02 15:04:39.389226] INFO: moduleinvoker: 命中缓存

[2022-06-02 15:04:39.391050] INFO: moduleinvoker: input_features.v1 运行完成[0.010078s].

[2022-06-02 15:04:39.407123] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2022-06-02 15:04:39.419145] INFO: moduleinvoker: 命中缓存

[2022-06-02 15:04:39.421242] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.014148s].

[2022-06-02 15:04:39.426264] INFO: moduleinvoker: input_features.v1 开始运行..

[2022-06-02 15:04:39.435967] INFO: moduleinvoker: 命中缓存

[2022-06-02 15:04:39.438321] INFO: moduleinvoker: input_features.v1 运行完成[0.012112s].

[2022-06-02 15:04:39.454356] INFO: moduleinvoker: cached.v3 开始运行..

[2022-06-02 15:04:39.464584] INFO: moduleinvoker: 命中缓存

[2022-06-02 15:04:39.466711] INFO: moduleinvoker: cached.v3 运行完成[0.012357s].

[2022-06-02 15:04:39.478130] INFO: moduleinvoker: join.v3 开始运行..

[2022-06-02 15:04:39.486768] INFO: moduleinvoker: 命中缓存

[2022-06-02 15:04:39.488571] INFO: moduleinvoker: join.v3 运行完成[0.010508s].

[2022-06-02 15:04:39.498565] INFO: moduleinvoker: chinaa_stock_filter.v1 开始运行..

[2022-06-02 15:04:39.519041] INFO: moduleinvoker: 命中缓存

[2022-06-02 15:04:39.525132] INFO: moduleinvoker: chinaa_stock_filter.v1 运行完成[0.026585s].

[2022-06-02 15:04:39.534861] INFO: moduleinvoker: filter.v3 开始运行..

[2022-06-02 15:04:39.555265] INFO: filter: 使用表达式 TG1_V011  > 0 过滤

[2022-06-02 15:04:39.870868] INFO: filter: 过滤 /y_2009, 0/0/66636

[2022-06-02 15:04:40.053437] INFO: filter: 过滤 /y_2010, 1730/0/311722

[2022-06-02 15:04:40.224267] INFO: filter: 过滤 /y_2011, 2111/0/385100

[2022-06-02 15:04:40.466262] INFO: filter: 过滤 /y_2012, 2288/0/432619

[2022-06-02 15:04:40.675519] INFO: filter: 过滤 /y_2013, 3337/0/433144

[2022-06-02 15:04:40.877744] INFO: filter: 过滤 /y_2014, 3967/0/440171

[2022-06-02 15:04:41.071589] INFO: filter: 过滤 /y_2015, 3982/0/447523

[2022-06-02 15:04:41.334587] INFO: filter: 过滤 /y_2016, 3132/0/508936

[2022-06-02 15:04:41.575464] INFO: filter: 过滤 /y_2017, 7222/0/595600

[2022-06-02 15:04:41.853364] INFO: filter: 过滤 /y_2018, 9455/0/654389

[2022-06-02 15:04:42.113189] INFO: filter: 过滤 /y_2019, 11459/0/698438

[2022-06-02 15:04:42.389316] INFO: filter: 过滤 /y_2020, 14981/0/744170

[2022-06-02 15:04:42.683969] INFO: filter: 过滤 /y_2021, 12172/0/849490

[2022-06-02 15:04:42.856785] INFO: filter: 过滤 /y_2022, 813/0/347566

[2022-06-02 15:04:42.899116] INFO: moduleinvoker: filter.v3 运行完成[3.364182s].

[2022-06-02 15:04:42.911075] INFO: moduleinvoker: dropnan.v2 开始运行..

[2022-06-02 15:04:43.102075] INFO: dropnan: /y_2010, 1730/1730

[2022-06-02 15:04:43.163908] INFO: dropnan: /y_2011, 2111/2111

[2022-06-02 15:04:43.213063] INFO: dropnan: /y_2012, 2288/2288

[2022-06-02 15:04:43.273840] INFO: dropnan: /y_2013, 3337/3337

[2022-06-02 15:04:43.329161] INFO: dropnan: /y_2014, 3967/3967

[2022-06-02 15:04:43.466009] INFO: dropnan: /y_2015, 3982/3982

[2022-06-02 15:04:43.517170] INFO: dropnan: /y_2016, 3132/3132

[2022-06-02 15:04:43.584307] INFO: dropnan: /y_2017, 7222/7222

[2022-06-02 15:04:43.653029] INFO: dropnan: /y_2018, 9455/9455

[2022-06-02 15:04:43.706689] INFO: dropnan: /y_2019, 11459/11459

[2022-06-02 15:04:43.784210] INFO: dropnan: /y_2020, 14981/14981

[2022-06-02 15:04:43.897626] INFO: dropnan: /y_2021, 12172/12172

[2022-06-02 15:04:43.964887] INFO: dropnan: /y_2022, 813/813

[2022-06-02 15:04:44.044456] INFO: dropnan: 行数: 76649/76649

[2022-06-02 15:04:44.050515] INFO: moduleinvoker: dropnan.v2 运行完成[1.139358s].

[2022-06-02 15:04:44.057009] INFO: moduleinvoker: sort.v5 开始运行..

[2022-06-02 15:04:48.716296] INFO: moduleinvoker: sort.v5 运行完成[4.65925s].

[2022-06-02 15:04:48.780047] INFO: moduleinvoker: backtest.v8 开始运行..

[2022-06-02 15:04:48.787028] INFO: backtest: biglearning backtest:V8.6.2

[2022-06-02 15:04:48.788413] INFO: backtest: product_type:stock by specified

[2022-06-02 15:04:49.302769] INFO: moduleinvoker: cached.v2 开始运行..

[2022-06-02 15:04:49.312013] INFO: moduleinvoker: 命中缓存

[2022-06-02 15:04:49.314015] INFO: moduleinvoker: cached.v2 运行完成[0.011265s].

[2022-06-02 15:05:01.119877] INFO: algo: TradingAlgorithm V1.8.7

[2022-06-02 15:05:05.503783] INFO: algo: trading transform...

[2022-06-02 15:05:33.527183] INFO: Performance: Simulated 3012 trading days out of 3012.

[2022-06-02 15:05:33.529240] INFO: Performance: first open: 2010-01-04 09:30:00+00:00

[2022-06-02 15:05:33.531175] INFO: Performance: last close: 2022-05-30 15:00:00+00:00

[2022-06-02 15:05:52.271789] INFO: moduleinvoker: backtest.v8 运行完成[63.491757s].

[2022-06-02 15:05:52.274135] INFO: moduleinvoker: trade.v4 运行完成[63.548303s].

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