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# 本代码由可视化策略环境自动生成 2022年6月28日 17:58
# 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 m2_run_bigquant_run(input_1, input_2, input_3,start_date_input,end_date_input):
    # 示例代码如下。在这里编写您的代码
    df = DataSource("bar1d_CN_CONBOND").read(start_date=start_date_input, end_date=end_date_input)
    df1 = DataSource("market_performance_CN_CONBOND").read(start_date=start_date_input, end_date=end_date_input)
    df2 = df.drop(['close'],axis = 1)
    df3 = pd.merge(df1,df2,on=['date','instrument'],how='inner')
    data_1 = DataSource.write_df(df3)
    data_2 = DataSource.write_df(df3)
    return Outputs(data_1=data_1, data_2=data_2, 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 m2_post_run_bigquant_run(outputs):
    return outputs

# 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 m14_run_bigquant_run(input_1, input_2, input_3, stock_count, hold_days):
    # 示例代码如下。在这里编写您的代码
    param = {
        "stock_count": stock_count,
        "hold_days": hold_days
        }
    data_1 = DataSource.write_pickle(param)
    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 m14_post_run_bigquant_run(outputs):
    return outputs

# 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 m1_run_bigquant_run(input_1, input_2, input_3,start_date_input,end_date_input):
    # 示例代码如下。在这里编写您的代码
    df = DataSource("bar1d_CN_CONBOND").read(start_date=start_date_input, end_date=end_date_input)
    df1 = DataSource("market_performance_CN_CONBOND").read(start_date=start_date_input, end_date=end_date_input)
    df2 = df.drop(['close'],axis = 1)
    df3 = pd.merge(df1,df2,on=['date','instrument'],how='inner')
    data_1 = DataSource.write_df(df3)
    data_2 = DataSource.write_df(df3)
    return Outputs(data_1=data_1, data_2=data_2, 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 m1_post_run_bigquant_run(outputs):
    return outputs

def del_input(input_1):
    df = input_1.read_df()
    df = df.drop(["trigger_cond_item_desc",'revise_item_desc','trigger_item_desc'],axis=1)
    df = df.dropna(axis=0, how='all', thresh=None, subset=None, inplace=False)
    print(df.columns)
    #df.sort_values(by=['date','double_low'],axis=0,ascending=True,inplace = True)
    df = df.sort_index(axis = 1)
    df = df.reset_index(drop = True)
    df = df.groupby('date').head(10)
    #print(df[['double_low','date']])
    data_1 = DataSource.write_df(df)
    return data_1
# 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 m23_run_bigquant_run(input_1, input_2, input_3):
    data_1 = del_input(input_1)
    data_2 = del_input(input_2)
    return Outputs(data_1=data_1, data_2=data_2, 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 m23_post_run_bigquant_run(outputs):
    return outputs

# 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 m11_run_bigquant_run(input_1, input_2, input_3):
    # 示例代码如下。在这里编写您的代码
    print('m11.input1',input_1)
    df = input_1.read()
    param = input_2.read()
    
    data = {
        "param": param,
        "data": df
    }
    data_1 = DataSource.write_pickle(data)
    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 m11_post_run_bigquant_run(outputs):
    return outputs

# 回测引擎：初始化函数，只执行一次
def m21_initialize_bigquant_run(context):
    print('初始化函数开始：')
    print('context.options',context.options)
    # 加载预测数据
    context.ranker_prediction = context.options.get('data').read()['data']
    context.param = context.options['data'].read()["param"]
    # 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=0.0003, min_cost=5))
    # 预测数据，通过options传入进来，使用 read_df 函数，加载到内存 (DataFrame)
    # 设置买入的股票数量，这里买入预测股票列表排名靠前的5只
    stock_count = context.param["stock_count"]
    # 每只的股票的权重，如下的权重分配会使得靠前的股票分配多一点的资金，[0.339160, 0.213986, 0.169580, ..]
    context.stock_weights = T.norm([1 / math.log(i + 2) for i in range(0, stock_count)])
    # 设置每只股票占用的最大资金比例
    context.max_cash_per_instrument = 0.4
    context.hold_days = context.param["hold_days"]
    print('初始化函数结束')

# 交易引擎：每个单位时间开盘前调用一次。
def m21_before_trading_start_bigquant_run(context, data):
    # 盘前处理，订阅行情等
    print('订阅行情前',data)
    context.subscribe(context.instruments)
    print('订阅行情后',data)
    pass

# 交易引擎：tick数据处理函数，每个tick执行一次
def m21_handle_tick_bigquant_run(context, tick):
    pass

import math
# 回测引擎：每日数据处理函数，每天执行一次
def m21_handle_data_bigquant_run(context, data):
    print('当前日期data.current_dt',data.current_dt)
    print('data',data)
    try:
        context.ranker_prediction = context.options.get('data').read()['data']
        # 相隔几天（hold_days）进行一下换仓
        if context.trading_day_index % context.hold_days != 0:
            return 

        # 按日期过滤得到今日的预测数据
        ranker_prediction = context.ranker_prediction[
            context.ranker_prediction.date == data.current_dt.strftime('%Y-%m-%d')]
        # 目前持仓
        positions = {e: p.amount * p.last_sale_price for e, p in context.portfolio.positions.items()}
        # 权重
        buy_cash_weights = context.stock_weights
        # 今日买入股票列表
        stock_to_buy = list(ranker_prediction.instrument[:len(buy_cash_weights)])
        # 持仓上限
        max_cash_per_instrument = context.portfolio.portfolio_value * context.max_cash_per_instrument
        print("<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<")
        # 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 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
                print('卖出order target percent',context.symbol(stock))
                print('卖出结果',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

        # 买入
        print('买入列表',stock_to_buy)
        for i, instrument in enumerate(stock_to_buy):
            cash = context.portfolio.portfolio_value * buy_cash_weights[i]
            if cash > max_cash_per_instrument - positions.get(instrument, 0):
                # 确保股票持仓量不会超过每次股票最大的占用资金量
                cash = max_cash_per_instrument - positions.get(instrument, 0)
            if cash > 500:
                cash = int(math.floor(cash))
                print('买入order_value',context.symbol(instrument),' cash ',cash)
                print(context.order_value(context.symbol(instrument), cash))
    except Exception as e:
        print('抛出异常',e)
# 交易引擎：成交回报处理函数，每个成交发生时执行一次
def m21_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 m21_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 m21_after_trading_bigquant_run(context, data):
    pass


g = T.Graph({

    'm3': 'M.input_features.v1',
    'm3.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
double_low = close + bond_prem_ratio
remain_size
rank_swing_volatility_5 = nanstd((high-low)/pre_close, 5)*sqrt(200)*100""",

    'm2': 'M.cached.v3',
    'm2.run': m2_run_bigquant_run,
    'm2.post_run': m2_post_run_bigquant_run,
    'm2.input_ports': '',
    'm2.params': """{"start_date_input":"2017-06-01",
"end_date_input":"2019-11-01"}""",
    'm2.output_ports': '',

    'm10': 'M.auto_labeler_on_datasource.v1',
    'm10.input_data': T.Graph.OutputPort('m2.data_1'),
    'm10.label_expr': """# #号开始的表示注释
# 0. 每行一个，顺序执行，从第二个开始，可以使用label字段
# 1. 可用数据字段见 https://bigquant.com/docs/develop/datasource/deprecated/history_data.html
# 2. 可用操作符和函数见 `表达式引擎 <https://bigquant.com/docs/develop/bigexpr/usage.html>`_

# 计算收益：5日收盘价(作为卖出价格)除以明日开盘价(作为买入价格)
shift(close, -5) / shift(open, -1)

# 极值处理：用1%和99%分位的值做clip
clip(label, all_quantile(label, 0.01), all_quantile(label, 0.99))

# 将分数映射到分类，这里使用20个分类
all_wbins(label, 10)

# 过滤掉一字涨停的情况 (设置label为NaN，在后续处理和训练中会忽略NaN的label)
where(shift(high, -1) == shift(low, -1), NaN, label)
""",
    'm10.drop_na_label': True,
    'm10.cast_label_int': True,
    'm10.date_col': 'date',
    'm10.instrument_col': 'instrument',
    'm10.user_functions': {},

    'm14': 'M.cached.v3',
    'm14.run': m14_run_bigquant_run,
    'm14.post_run': m14_post_run_bigquant_run,
    'm14.input_ports': '',
    'm14.params': """{
    "stock_count": 4,
    "hold_days": 5 
}""",
    'm14.output_ports': '',

    'm1': 'M.cached.v3',
    'm1.run': m1_run_bigquant_run,
    'm1.post_run': m1_post_run_bigquant_run,
    'm1.input_ports': '',
    'm1.params': """{"start_date_input":"2021-01-05",
"end_date_input":"2021-06-19"}""",
    'm1.output_ports': '',

    'm23': 'M.cached.v3',
    'm23.input_1': T.Graph.OutputPort('m2.data_2'),
    'm23.input_2': T.Graph.OutputPort('m1.data_2'),
    'm23.run': m23_run_bigquant_run,
    'm23.post_run': m23_post_run_bigquant_run,
    'm23.input_ports': '',
    'm23.params': '{}',
    'm23.output_ports': '',
    'm23.m_cached': False,

    'm16': 'M.derived_feature_extractor.v3',
    'm16.input_data': T.Graph.OutputPort('m23.data_1'),
    'm16.features': T.Graph.OutputPort('m3.data'),
    'm16.date_col': 'date',
    'm16.instrument_col': 'instrument',
    'm16.drop_na': False,
    'm16.remove_extra_columns': False,

    'm7': 'M.join.v3',
    'm7.data1': T.Graph.OutputPort('m10.data'),
    'm7.data2': T.Graph.OutputPort('m16.data'),
    'm7.on': 'date,instrument',
    'm7.how': 'inner',
    'm7.sort': False,

    'm6': 'M.stock_ranker_train.v6',
    'm6.training_ds': T.Graph.OutputPort('m7.data'),
    'm6.features': T.Graph.OutputPort('m3.data'),
    'm6.learning_algorithm': '排序',
    'm6.number_of_leaves': 3,
    'm6.minimum_docs_per_leaf': 100,
    'm6.number_of_trees': 20,
    'm6.learning_rate': 0.1,
    'm6.max_bins': 1023,
    'm6.feature_fraction': 1,
    'm6.data_row_fraction': 1,
    'm6.plot_charts': True,
    'm6.ndcg_discount_base': 1,
    'm6.m_lazy_run': False,
    'm6.m_cached': False,

    'm18': 'M.derived_feature_extractor.v3',
    'm18.input_data': T.Graph.OutputPort('m23.data_2'),
    'm18.features': T.Graph.OutputPort('m3.data'),
    'm18.date_col': 'date',
    'm18.instrument_col': 'instrument',
    'm18.drop_na': False,
    'm18.remove_extra_columns': False,

    'm8': 'M.stock_ranker_predict.v5',
    'm8.model': T.Graph.OutputPort('m6.model'),
    'm8.data': T.Graph.OutputPort('m18.data'),
    'm8.m_lazy_run': False,

    'm11': 'M.cached.v3',
    'm11.input_1': T.Graph.OutputPort('m8.predictions'),
    'm11.input_2': T.Graph.OutputPort('m14.data_1'),
    'm11.run': m11_run_bigquant_run,
    'm11.post_run': m11_post_run_bigquant_run,
    'm11.input_ports': '',
    'm11.params': '{}',
    'm11.output_ports': '',

    'm12': 'M.trade_data_generation.v1',
    'm12.input': T.Graph.OutputPort('m1.data_2'),
    'm12.category': 'CN_STOCK',
    'm12.m_cached': False,

    'm21': 'M.hftrade.v2',
    'm21.instruments': T.Graph.OutputPort('m12.instrument_list'),
    'm21.options_data': T.Graph.OutputPort('m11.data_1'),
    'm21.start_date': '',
    'm21.end_date': '',
    'm21.initialize': m21_initialize_bigquant_run,
    'm21.before_trading_start': m21_before_trading_start_bigquant_run,
    'm21.handle_tick': m21_handle_tick_bigquant_run,
    'm21.handle_data': m21_handle_data_bigquant_run,
    'm21.handle_trade': m21_handle_trade_bigquant_run,
    'm21.handle_order': m21_handle_order_bigquant_run,
    'm21.after_trading': m21_after_trading_bigquant_run,
    'm21.capital_base': 1000000,
    'm21.frequency': 'daily',
    'm21.price_type': '真实价格',
    'm21.product_type': '可转债',
    'm21.before_start_days': '80',
    'm21.order_price_field_buy': 'open',
    'm21.order_price_field_sell': 'close',
    'm21.benchmark': '000300.HIX',
    'm21.plot_charts': True,
    'm21.disable_cache': False,
    'm21.replay_bdb': True,
    'm21.show_debug_info': True,
    'm21.backtest_only': False,
})

# g.run({})


def m9_param_grid_builder_bigquant_run():
    import itertools
    param_grid = {}
    
    period_list = [5,6,120] 
    
    # 在这里设置需要调优的参数备选
    feature_list = [
    '''
    double_low = close + bond_prem_ratio
    remain_size
    rank_swing_volatility_5 = nanstd((high-low)/pre_close, {0})*sqrt(200)*100
    '''.format(period) for period in period_list
    ]
    param_grid["m14.params"] = [
        """{"stock_count": 3, "hold_days": 3}""",
        """{"stock_count": 4, "hold_days": 4}""",
    ]
    param_grid['m3.features'] = feature_list
    return param_grid
def m9_scoring_bigquant_run(result):
    # 评分：收益/最大回撤
    score = result.get('m21').read_raw_perf()['sharpe'].tail(1)[0]
    return {'score': score}


m9 = M.hyper_parameter_search.v1(
    param_grid_builder=m9_param_grid_builder_bigquant_run,
    scoring=m9_scoring_bigquant_run,
    search_algorithm='网格搜索',
    search_iterations=10,
    workers=1,
    worker_distributed_run=False,
    worker_silent=False,
    run_now=True,
    bq_graph=g
)

[2022-06-28 17:54:50.928381] INFO: moduleinvoker: input_features.v1 开始运行..

[2022-06-28 17:54:50.954108] INFO: moduleinvoker: 命中缓存

[2022-06-28 17:54:50.957877] INFO: moduleinvoker: input_features.v1 运行完成[0.029559s].

[2022-06-28 17:54:50.989344] INFO: moduleinvoker: cached.v3 开始运行..

[2022-06-28 17:54:51.000331] INFO: moduleinvoker: 命中缓存

[2022-06-28 17:54:51.003732] INFO: moduleinvoker: cached.v3 运行完成[0.014398s].

[2022-06-28 17:54:51.445158] INFO: moduleinvoker: auto_labeler_on_datasource.v1 开始运行..

[2022-06-28 17:54:51.457386] INFO: moduleinvoker: 命中缓存

[2022-06-28 17:54:51.461420] INFO: moduleinvoker: auto_labeler_on_datasource.v1 运行完成[0.016281s].

[2022-06-28 17:54:51.554686] INFO: moduleinvoker: cached.v3 开始运行..

[2022-06-28 17:54:51.567388] INFO: moduleinvoker: 命中缓存

[2022-06-28 17:54:51.570383] INFO: moduleinvoker: cached.v3 运行完成[0.015712s].

[2022-06-28 17:54:51.601451] INFO: moduleinvoker: cached.v3 开始运行..

[2022-06-28 17:54:51.612702] INFO: moduleinvoker: 命中缓存

[2022-06-28 17:54:51.615771] INFO: moduleinvoker: cached.v3 运行完成[0.014335s].

[2022-06-28 17:54:51.646880] INFO: moduleinvoker: cached.v3 开始运行..

[2022-06-28 17:54:53.850822] INFO: moduleinvoker: cached.v3 运行完成[2.203974s].

[2022-06-28 17:54:53.897484] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-06-28 17:54:54.146913] INFO: derived_feature_extractor: 提取完成 double_low = close + bond_prem_ratio, 0.014s

[2022-06-28 17:54:54.190920] INFO: derived_feature_extractor: 提取完成 rank_swing_volatility_5 = nanstd((high-low)/pre_close, 5)*sqrt(200)*100, 0.041s

[2022-06-28 17:54:54.598745] INFO: derived_feature_extractor: /data, 5920

[2022-06-28 17:54:54.715034] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.817549s].

[2022-06-28 17:54:54.738837] INFO: moduleinvoker: join.v3 开始运行..

[2022-06-28 17:54:56.547992] INFO: join: /data, 行数=5789/5920, 耗时=0.282459s

[2022-06-28 17:54:56.611722] INFO: join: 最终行数: 5789

[2022-06-28 17:54:56.625762] INFO: moduleinvoker: join.v3 运行完成[1.886904s].

[2022-06-28 17:54:56.665213] INFO: moduleinvoker: stock_ranker_train.v6 开始运行..

[2022-06-28 17:54:56.893365] INFO: StockRanker: 特征预处理 ..

[2022-06-28 17:54:56.923495] INFO: StockRanker: prepare data: training ..

[2022-06-28 17:54:57.106965] INFO: StockRanker训练: 5da83076 准备训练: 5789 行数

[2022-06-28 17:54:57.110202] INFO: StockRanker训练: AI模型训练，将在5789*3=1.74万数据上对模型训练进行20轮迭代训练。预计将需要1～2分钟。请耐心等待。

[2022-06-28 17:54:57.306747] INFO: StockRanker训练: 正在训练 ..

[2022-06-28 17:54:57.389332] INFO: StockRanker训练: 任务状态: Pending

[2022-06-28 17:55:17.573363] INFO: StockRanker训练: 任务状态: Running

[2022-06-28 17:56:19.391977] INFO: StockRanker训练: 00:01:03.2561508, finished iteration 1

[2022-06-28 17:56:29.843745] INFO: StockRanker训练: 00:01:03.3316824, finished iteration 2

[2022-06-28 17:56:29.845889] INFO: StockRanker训练: 00:01:03.3663429, finished iteration 3

[2022-06-28 17:56:29.848445] INFO: StockRanker训练: 00:01:03.3746432, finished iteration 4

[2022-06-28 17:56:29.852394] INFO: StockRanker训练: 00:01:03.3807765, finished iteration 5

[2022-06-28 17:56:29.854703] INFO: StockRanker训练: 00:01:03.4012624, finished iteration 6

[2022-06-28 17:56:29.856846] INFO: StockRanker训练: 00:01:03.5083817, finished iteration 7

[2022-06-28 17:56:29.858928] INFO: StockRanker训练: 00:01:03.5199912, finished iteration 8

[2022-06-28 17:56:29.861173] INFO: StockRanker训练: 00:01:03.5718622, finished iteration 9

[2022-06-28 17:56:29.870043] INFO: StockRanker训练: 00:01:03.5801816, finished iteration 10

[2022-06-28 17:56:29.872375] INFO: StockRanker训练: 00:01:03.6637460, finished iteration 11

[2022-06-28 17:56:29.874714] INFO: StockRanker训练: 00:01:03.6694159, finished iteration 12

[2022-06-28 17:56:29.876893] INFO: StockRanker训练: 00:01:03.6738881, finished iteration 13

[2022-06-28 17:56:29.878913] INFO: StockRanker训练: 00:01:03.6785059, finished iteration 14

[2022-06-28 17:56:29.880851] INFO: StockRanker训练: 00:01:03.7238582, finished iteration 15

[2022-06-28 17:56:29.882726] INFO: StockRanker训练: 00:01:03.7677311, finished iteration 16

[2022-06-28 17:56:29.884585] INFO: StockRanker训练: 00:01:03.7739511, finished iteration 17

[2022-06-28 17:56:29.886451] INFO: StockRanker训练: 00:01:03.7817651, finished iteration 18

[2022-06-28 17:56:29.888264] INFO: StockRanker训练: 00:01:03.8647270, finished iteration 19

[2022-06-28 17:56:29.890158] INFO: StockRanker训练: 00:01:03.8758093, finished iteration 20

[2022-06-28 17:56:39.960580] INFO: StockRanker训练: 任务状态: Succeeded

[2022-06-28 17:56:40.162552] INFO: moduleinvoker: stock_ranker_train.v6 运行完成[103.497351s].

[2022-06-28 17:56:40.184763] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-06-28 17:56:40.300313] INFO: derived_feature_extractor: 提取完成 double_low = close + bond_prem_ratio, 0.002s

[2022-06-28 17:56:40.348739] INFO: derived_feature_extractor: 提取完成 rank_swing_volatility_5 = nanstd((high-low)/pre_close, 5)*sqrt(200)*100, 0.046s

[2022-06-28 17:56:40.465049] INFO: derived_feature_extractor: /data, 1090

[2022-06-28 17:56:40.558057] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.373282s].

[2022-06-28 17:56:40.581841] INFO: moduleinvoker: stock_ranker_predict.v5 开始运行..

[2022-06-28 17:56:44.177462] INFO: StockRanker预测: /data ..

[2022-06-28 17:56:46.972778] INFO: moduleinvoker: stock_ranker_predict.v5 运行完成[6.390933s].

[2022-06-28 17:56:47.057738] INFO: moduleinvoker: cached.v3 开始运行..

[2022-06-28 17:56:47.720057] INFO: moduleinvoker: cached.v3 运行完成[0.662316s].

[2022-06-28 17:56:47.740299] INFO: moduleinvoker: trade_data_generation.v1 开始运行..

[2022-06-28 17:56:51.238005] INFO: moduleinvoker: trade_data_generation.v1 运行完成[3.497681s].

[2022-06-28 17:56:51.323682] INFO: moduleinvoker: hfbacktest.v1 开始运行..

[2022-06-28 17:56:51.410846] INFO: hfbacktest: passed-in daily_data_ds:None

[2022-06-28 17:56:51.414203] INFO: hfbacktest: passed-in minute_data_ds:None

[2022-06-28 17:56:51.424340] INFO: hfbacktest: passed-in tick_data_ds:None

[2022-06-28 17:56:51.428012] INFO: hfbacktest: passed-in each_data_ds:None

[2022-06-28 17:56:51.430941] INFO: hfbacktest: passed-in dominant_data_ds:None

[2022-06-28 17:56:51.433748] INFO: hfbacktest: passed-in benchmark_data_ds:None

[2022-06-28 17:56:51.436546] INFO: hfbacktest: passed-in trading_calendar_ds:None

[2022-06-28 17:56:51.998482] INFO: hfbacktest: biglearning V1.4.14

[2022-06-28 17:56:52.001242] INFO: hfbacktest: bigtrader v1.9.6 2022-06-22

[2022-06-28 17:56:52.071063] INFO: hfbacktest: strategy callbacks:{'on_init': , 'on_stop': , 'on_start': , 'handle_data': , 'handle_tick': , 'handle_trade': , 'handle_order': }

[2022-06-28 17:56:52.101256] INFO: hfbacktest: begin reading history data, 2021-01-05 00:00:00~2021-06-18, disable_cache:False, replay_bdb:1

[2022-06-28 17:56:52.104933] INFO: hfbacktest: reading benchmark data 2021-01-04 00:00:00~2021-06-18...

[2022-06-28 17:56:52.127657] INFO: moduleinvoker: cached.v2 开始运行..

[2022-06-28 17:56:52.137542] INFO: moduleinvoker: 命中缓存

[2022-06-28 17:56:52.140669] INFO: moduleinvoker: cached.v2 运行完成[0.013085s].

[2022-06-28 17:56:52.267910] INFO: hfbacktest: reading daily data 2019-12-30 00:00:00~2021-06-18...

[2022-06-28 17:56:52.313123] INFO: moduleinvoker: cached.v2 开始运行..

[2022-06-28 17:56:52.322462] INFO: moduleinvoker: 命中缓存

[2022-06-28 17:56:52.325438] INFO: moduleinvoker: cached.v2 运行完成[0.012329s].

[2022-06-28 17:56:52.553882] INFO: hfbacktest: cached_benchmark_ds:DataSource(c5e0f20f865940969d5feb2d2b887b9cT)

[2022-06-28 17:56:52.561787] INFO: hfbacktest: cached_daily_ds:DataSource(1b262c256b01402a85c38ceb31b2c10cT)

[2022-06-28 17:56:52.571329] INFO: hfbacktest: cached_minute_ds:None

[2022-06-28 17:56:52.581808] INFO: hfbacktest: cached_tick_ds:None

[2022-06-28 17:56:52.589866] INFO: hfbacktest: cached_each_ds:None

[2022-06-28 17:56:52.594396] INFO: hfbacktest: dominant_data_ds:None

[2022-06-28 17:56:52.596596] INFO: hfbacktest: read history data done, call run_backtest()

[2022-06-28 17:56:53.920753] ERROR: moduleinvoker: module name: hfbacktest, module version: v1, trackeback: OverflowError: cannot convert float infinity to integer

[2022-06-28 17:56:53.930572] ERROR: moduleinvoker: module name: hftrade, module version: v2, trackeback: OverflowError: cannot convert float infinity to integer

Index(['remain_size', 'instrument', 'equ_trading_code', 'conversion_chg_pct',
       'date', 'conversion_price', 'name_x', 'trading_code',
       'conversion_chg_pct_week', 'bond_prem_ratio', 'equ_name',
       'redemption_price', 'close_equ', 'total_size', 'pure_bond_prem_ratio',
       'pure_bond_ratio', 'close', 'pre_close', 'name_y', 'open', 'high',
       'low', 'deal_number', 'volume', 'amount', 'accrued_interest',
       'yield_to_maturity', 'vwap', 'gross_close', 'net_close'],
      dtype='object')
Index(['remain_size', 'instrument', 'equ_trading_code', 'conversion_chg_pct',
       'date', 'conversion_price', 'name_x', 'trading_code',
       'conversion_chg_pct_week', 'bond_prem_ratio', 'equ_name',
       'redemption_price', 'close_equ', 'total_size', 'pure_bond_prem_ratio',
       'pure_bond_ratio', 'close', 'pre_close', 'name_y', 'open', 'high',
       'low', 'deal_number', 'volume', 'amount', 'accrued_interest',
       'yield_to_maturity', 'vwap', 'gross_close', 'net_close'],
      dtype='object')

设置评估测试数据集，查看训练曲线
[视频教程]StockRanker训练曲线

bigcharts-data-start/{"__type":"tabs","__id":"bigchart-b8b6cf04959544cc8bc5354cc67c9330"}/bigcharts-data-end

m11.input1 DataSource(5fd7deb0d55041118e59a9f9c082f8e3T)

2022-06-28 17:56:52.649920 init history datas... 
2022-06-28 17:56:52.651108 init history datas done. 
2022-06-28 17:56:52.663777 run_backtest() capital_base:1000000, frequency:1d, product_type:conbond, date:2021-01-05 ~ 2021-06-18 
2022-06-28 17:56:52.664184 run_backtest() running... 
2022-06-28 17:56:52.682326 initial contracts len=0 
2022-06-28 17:56:52.682565 backtest inited. 
初始化函数开始：
context.options {'data': DataSource(c8440a59ad074d12b3311532baf0f382T)}
初始化函数结束
2022-06-28 17:56:52.742436 backtest transforming 1d, bars=1... 
2022-06-28 17:56:52.742685 transform start_trading_day=2021-01-05 00:00:00, simulation period=2021-01-05 ~ 2021-06-18 
2022-06-28 17:56:52.742730 transform source=None, before_start_days=80 
2022-06-28 17:56:52.742761 transform replay_func=<cyfunction BacktestEngine.transform.<locals>.replay_bars_dt at 0x7fe2890fb110> 
订阅行情前 BarDatas(current_dt:2021-01-05 08:30:00)
订阅行情后 BarDatas(current_dt:2021-01-05 08:30:00)
当前日期data.current_dt 2021-01-05 15:00:00
data BarDatas(current_dt:2021-01-05 15:00:00)
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
买入列表 ['110047.HCB', '110033.HCB', '128042.ZCB', '128129.ZCB']
买入order_value Equity(340 [110047.HCB])  cash  390380
0
买入order_value Equity(213 [110033.HCB])  cash  246302
0
买入order_value Equity(347 [128042.ZCB])  cash  195190
0
买入order_value Equity(105 [128129.ZCB])  cash  168127
0
订阅行情前 BarDatas(current_dt:2021-01-06 08:30:00)
订阅行情后 BarDatas(current_dt:2021-01-06 08:30:00)
当前日期data.current_dt 2021-01-06 15:00:00
data BarDatas(current_dt:2021-01-06 15:00:00)
订阅行情前 BarDatas(current_dt:2021-01-07 08:30:00)
订阅行情后 BarDatas(current_dt:2021-01-07 08:30:00)
当前日期data.current_dt 2021-01-07 15:00:00
data BarDatas(current_dt:2021-01-07 15:00:00)
订阅行情前 BarDatas(current_dt:2021-01-08 08:30:00)
订阅行情后 BarDatas(current_dt:2021-01-08 08:30:00)
当前日期data.current_dt 2021-01-08 15:00:00
data BarDatas(current_dt:2021-01-08 15:00:00)
订阅行情前 BarDatas(current_dt:2021-01-11 08:30:00)
订阅行情后 BarDatas(current_dt:2021-01-11 08:30:00)
当前日期data.current_dt 2021-01-11 15:00:00
data BarDatas(current_dt:2021-01-11 15:00:00)
订阅行情前 BarDatas(current_dt:2021-01-12 08:30:00)
订阅行情后 BarDatas(current_dt:2021-01-12 08:30:00)
当前日期data.current_dt 2021-01-12 15:00:00
data BarDatas(current_dt:2021-01-12 15:00:00)
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
卖出order target percent Equity(340 [110047.HCB])
卖出结果 0
卖出order target percent Equity(213 [110033.HCB])
卖出结果 0
卖出order target percent Equity(347 [128042.ZCB])
卖出结果 0
卖出order target percent Equity(105 [128129.ZCB])
卖出结果 0
买入列表 ['110065.HCB', '110034.HCB', '110051.HCB', '110056.HCB']
买入order_value Equity(245 [110065.HCB])  cash  388693
0
买入order_value Equity(249 [110034.HCB])  cash  245238
0
买入order_value Equity(272 [110051.HCB])  cash  194346
0
买入order_value Equity(389 [110056.HCB])  cash  167401
0
订阅行情前 BarDatas(current_dt:2021-01-13 08:30:00)
订阅行情后 BarDatas(current_dt:2021-01-13 08:30:00)
当前日期data.current_dt 2021-01-13 15:00:00
data BarDatas(current_dt:2021-01-13 15:00:00)
订阅行情前 BarDatas(current_dt:2021-01-14 08:30:00)
订阅行情后 BarDatas(current_dt:2021-01-14 08:30:00)
当前日期data.current_dt 2021-01-14 15:00:00
data BarDatas(current_dt:2021-01-14 15:00:00)
订阅行情前 BarDatas(current_dt:2021-01-15 08:30:00)
订阅行情后 BarDatas(current_dt:2021-01-15 08:30:00)
当前日期data.current_dt 2021-01-15 15:00:00
data BarDatas(current_dt:2021-01-15 15:00:00)
订阅行情前 BarDatas(current_dt:2021-01-18 08:30:00)
订阅行情后 BarDatas(current_dt:2021-01-18 08:30:00)
当前日期data.current_dt 2021-01-18 15:00:00
data BarDatas(current_dt:2021-01-18 15:00:00)
订阅行情前 BarDatas(current_dt:2021-01-19 08:30:00)
订阅行情后 BarDatas(current_dt:2021-01-19 08:30:00)
当前日期data.current_dt 2021-01-19 15:00:00
data BarDatas(current_dt:2021-01-19 15:00:00)
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
卖出order target percent Equity(245 [110065.HCB])
卖出结果 0
卖出order target percent Equity(272 [110051.HCB])
卖出结果 0
卖出order target percent Equity(389 [110056.HCB])
卖出结果 0
买入列表 ['110034.HCB', '113036.HCB', '110038.HCB', '110045.HCB']
买入order_value Equity(249 [110034.HCB])  cash  145528
0
买入order_value Equity(197 [113036.HCB])  cash  243631
0
买入order_value Equity(290 [110038.HCB])  cash  193073
0
买入order_value Equity(49 [110045.HCB])  cash  166304
0
订阅行情前 BarDatas(current_dt:2021-01-20 08:30:00)
订阅行情后 BarDatas(current_dt:2021-01-20 08:30:00)
当前日期data.current_dt 2021-01-20 15:00:00
data BarDatas(current_dt:2021-01-20 15:00:00)
订阅行情前 BarDatas(current_dt:2021-01-21 08:30:00)
订阅行情后 BarDatas(current_dt:2021-01-21 08:30:00)
当前日期data.current_dt 2021-01-21 15:00:00
data BarDatas(current_dt:2021-01-21 15:00:00)
订阅行情前 BarDatas(current_dt:2021-01-22 08:30:00)
订阅行情后 BarDatas(current_dt:2021-01-22 08:30:00)
当前日期data.current_dt 2021-01-22 15:00:00
data BarDatas(current_dt:2021-01-22 15:00:00)
订阅行情前 BarDatas(current_dt:2021-01-25 08:30:00)
订阅行情后 BarDatas(current_dt:2021-01-25 08:30:00)
当前日期data.current_dt 2021-01-25 15:00:00
data BarDatas(current_dt:2021-01-25 15:00:00)
订阅行情前 BarDatas(current_dt:2021-01-26 08:30:00)
订阅行情后 BarDatas(current_dt:2021-01-26 08:30:00)
当前日期data.current_dt 2021-01-26 15:00:00
data BarDatas(current_dt:2021-01-26 15:00:00)
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
卖出order target percent Equity(197 [113036.HCB])
卖出结果 0
卖出order target percent Equity(290 [110038.HCB])
卖出结果 0
卖出order target percent Equity(49 [110045.HCB])
卖出结果 0
买入列表 ['110047.HCB', '110077.HCB', '113024.HCB', '110034.HCB']
买入order_value Equity(340 [110047.HCB])  cash  381043
0
买入order_value Equity(319 [110077.HCB])  cash  240411
0
买入order_value Equity(155 [113024.HCB])  cash  190521
0
订阅行情前 BarDatas(current_dt:2021-01-27 08:30:00)
订阅行情后 BarDatas(current_dt:2021-01-27 08:30:00)
2022-06-28 17:56:53.919955 strategy strategy exception:Traceback (most recent call last):
  File "bigtrader/strategy/engine.py", line 713, in bigtrader2.bigtrader.strategy.engine.StrategyEngine._call_strategy_func
  File "bigtrader/strategy/strategy_base.py", line 2188, in bigtrader2.bigtrader.strategy.strategy_base.StrategyBase.call_handle_continous_trading
  File "bigtrader/finance/account_engine.py", line 2457, in bigtrader2.bigtrader.finance.account_engine.AccountEngine.send_order
  File "bigtrader/finance/account_engine.py", line 2355, in bigtrader2.bigtrader.finance.account_engine.AccountEngine._check_order_cash_or_adjust
OverflowError: cannot convert float infinity to integer

---------------------------------------------------------------------------
OverflowError                             Traceback (most recent call last)
<ipython-input-2-87948ab8b5c7> in <module>
    327 )
    328 
--> 329 m21 = M.hftrade.v2(
    330     instruments=m12.instrument_list,
    331     options_data=m11.data_1,

OverflowError: cannot convert float infinity to integer