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克隆策略

In [ ]:

# <b>StockRanker多因子选股策略</b>

In [7]:

import itertools
param_grid = {}

period_list = [5,20,60,100,120] 

# 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}""",
    """{"stock_count": 3, "hold_days": 1}""",
    """{"stock_count": 4, "hold_days": 2}"""
]
param_grid['m3.features'] = feature_list

In [8]:

param_grid

Out[8]:

{'m14.params': ['{"stock_count": 3, "hold_days": 3}',
  '{"stock_count": 4, "hold_days": 4}',
  '{"stock_count": 3, "hold_days": 1}',
  '{"stock_count": 4, "hold_days": 2}'],
 'm3.features': ['\ndouble_low = close + bond_prem_ratio\nremain_size\nrank_swing_volatility_5 = nanstd((high-low)/pre_close, 5)*sqrt(200)*100\n',
  '\ndouble_low = close + bond_prem_ratio\nremain_size\nrank_swing_volatility_5 = nanstd((high-low)/pre_close, 20)*sqrt(200)*100\n',
  '\ndouble_low = close + bond_prem_ratio\nremain_size\nrank_swing_volatility_5 = nanstd((high-low)/pre_close, 60)*sqrt(200)*100\n',
  '\ndouble_low = close + bond_prem_ratio\nremain_size\nrank_swing_volatility_5 = nanstd((high-low)/pre_close, 100)*sqrt(200)*100\n',
  '\ndouble_low = close + bond_prem_ratio\nremain_size\nrank_swing_volatility_5 = nanstd((high-low)/pre_close, 120)*sqrt(200)*100\n']}

In [7]:

def bigquant_run(context, data):
    # 按日期过滤得到今日的预测数据
    #time == data.current_dt.strftime('%Y-%m-%d%H%M%S')
    now = context.now()
    print(now)
    if str(now) == '14:33:30' :
        context.order_target('000001', 0.05)
        context.order_target('110041.HCB', 0.05)

In [5]:

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

In [7]:

Out[7]:

{'m14.params': ['{"stock_count": 3, "hold_days": 3}',
  '{"stock_count": 4, "hold_days": 4}'],
 'm3.features': ['\ndouble_low = close + bond_prem_ratio\nremain_size\nrank_swing_volatility_5 = nanstd((high-low)/pre_close, 5)*sqrt(200)*100\n',
  '\ndouble_low = close + bond_prem_ratio\nremain_size\nrank_swing_volatility_5 = nanstd((high-low)/pre_close, 6)*sqrt(200)*100\n',
  '\ndouble_low = close + bond_prem_ratio\nremain_size\nrank_swing_volatility_5 = nanstd((high-low)/pre_close, 120)*sqrt(200)*100\n']}

In [8]:

# 回测引擎：每日数据处理函数，每天执行一次
def bigquant_run(context, data):

    try:
        print('处理data',data)
        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

        # 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
                if context.order_target_percent(context.symbol(stock), 0) != 0:
                    print('sell_context.symbol',context.symbol(stock))

        # 如果当天没有买入的股票，就返回
        if len(stock_to_buy) == 0:
            return

        # 买入
        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:
                if context.order_value(context.symbol(instrument), cash) !=0:
                    print('fail_order_value',cash)            
    except Exception as e:
           logger.exception("限价单下买单失败%s,%s",context,e)

In [15]:

m21.read_raw_perf()['sharpe']

Out[15]:

2021-06-30     0.000000
2021-07-01   -12.135336
2021-07-02   -13.427960
2021-07-05    -6.247853
2021-07-06    -5.307045
                ...    
2021-12-24    -2.448882
2021-12-27    -2.453014
2021-12-28    -2.457153
2021-12-29    -2.461299
2021-12-30    -2.465450
Name: sharpe, Length: 125, dtype: float64

In [16]:

# 本代码由可视化策略环境自动生成 2022年6月2日 03:31
# 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):
    # 示例代码如下。在这里编写您的代码
    df = DataSource("bar1d_CN_CONBOND").read(start_date="2017-06-01", end_date="2020-06-30")
    df2 = df.drop(['close'],axis = 1)
    data_1 = DataSource.write_df(df)
    data_2 = DataSource.write_df(df2)
    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

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 m15_run_bigquant_run(input_1, input_2, input_3):
    # 示例代码如下。在这里编写您的代码
    df = DataSource("bar1d_CN_CONBOND").read(start_date="2020-06-30", end_date="2021-06-30")
    df = df.drop(['close'],axis = 1)
    data_1 = DataSource.write_df(df)
    data_2 = DataSource.write_pickle(df)
    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 m15_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 m13_run_bigquant_run(input_1, input_2, input_3):
    # 示例代码如下。在这里编写您的代码
    df = input_1.read_df()
    #print(df.columns)
    #df = df1[['date','instrument','close','label']]
    #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)
    data_2 = DataSource.write_pickle(df)
    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 m13_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 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
    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(context.options)
    # 加载预测数据
    context.ranker_prediction = context.options.get('data').read()['data']
    print(context.ranker_prediction)
    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=0.0013, 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"]

# 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
    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

# 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):

    try:
        print('处理data',data)
        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
        print("buy_cash_weights",buy_cash_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
                if context.order_target_percent(context.symbol(stock), 0) != 0:
                    print('sell_context.symbol',context.symbol(stock))

        # 如果当天没有买入的股票，就返回
        if len(stock_to_buy) == 0:
            return

        # 买入
        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:
                if context.order_value(context.symbol(instrument), cash) !=0:
                    print('fail_order_value',cash)            
    except Exception as e:
           logger.exception("限价单下买单失败%s,%s",context,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': '{}',
    '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': {},

    'm1': 'M.use_datasource.v1',
    'm1.datasource_id': 'market_performance_CN_CONBOND',
    'm1.start_date': '2020-01-01',
    'm1.end_date': '2020-06-30',

    'm5': 'M.use_datasource.v1',
    'm5.datasource_id': 'market_performance_CN_CONBOND',
    'm5.start_date': '2021-06-30',
    'm5.end_date': '2021-12-30',

    'm12': 'M.trade_data_generation.v1',
    'm12.input': T.Graph.OutputPort('m5.data'),
    'm12.category': 'CN_STOCK',
    'm12.m_cached': False,

    'm23': 'M.cached.v3',
    'm23.input_1': T.Graph.OutputPort('m1.data'),
    'm23.input_2': T.Graph.OutputPort('m5.data'),
    '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,

    'm4': 'M.join.v3',
    'm4.data1': T.Graph.OutputPort('m2.data_2'),
    'm4.data2': T.Graph.OutputPort('m23.data_1'),
    'm4.on': 'date,instrument',
    'm4.how': 'inner',
    'm4.sort': False,

    'm16': 'M.derived_feature_extractor.v3',
    'm16.input_data': T.Graph.OutputPort('m4.data'),
    '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': 2,
    '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,

    'm15': 'M.cached.v3',
    'm15.run': m15_run_bigquant_run,
    'm15.post_run': m15_post_run_bigquant_run,
    'm15.input_ports': '',
    'm15.params': '{}',
    'm15.output_ports': '',

    'm25': 'M.join.v3',
    'm25.data1': T.Graph.OutputPort('m23.data_2'),
    'm25.data2': T.Graph.OutputPort('m15.data_1'),
    'm25.on': 'date,instrument',
    'm25.how': 'inner',
    'm25.sort': False,

    'm18': 'M.derived_feature_extractor.v3',
    'm18.input_data': T.Graph.OutputPort('m25.data'),
    'm18.features': T.Graph.OutputPort('m3.data'),
    'm18.date_col': 'date',
    'm18.instrument_col': 'instrument',
    'm18.drop_na': False,
    'm18.remove_extra_columns': False,

    'm13': 'M.cached.v3',
    'm13.input_1': T.Graph.OutputPort('m18.data'),
    'm13.run': m13_run_bigquant_run,
    'm13.post_run': m13_post_run_bigquant_run,
    'm13.input_ports': '',
    'm13.params': '{}',
    'm13.output_ports': '',
    'm13.m_cached': False,

    'm8': 'M.stock_ranker_predict.v5',
    'm8.model': T.Graph.OutputPort('m6.model'),
    'm8.data': T.Graph.OutputPort('m13.data_1'),
    'm8.m_lazy_run': False,

    '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": 3,
    "hold_days": 4 
}""",
    'm14.output_ports': '',

    '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': '',

    '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': '8',
    'm21.order_price_field_buy': 'open',
    'm21.order_price_field_sell': 'close',
    'm21.benchmark': '000300.HIX',
    'm21.plot_charts': True,
    'm21.disable_cache': True,
    '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
)

[2022-06-02 10:48:53.252391] INFO: moduleinvoker: input_features.v1 开始运行..

[2022-06-02 10:48:53.262979] INFO: moduleinvoker: 命中缓存

[2022-06-02 10:48:53.268812] INFO: moduleinvoker: input_features.v1 运行完成[0.01643s].

[2022-06-02 10:48:53.285253] INFO: moduleinvoker: cached.v3 开始运行..

[2022-06-02 10:48:53.295924] INFO: moduleinvoker: 命中缓存

[2022-06-02 10:48:53.314808] INFO: moduleinvoker: cached.v3 运行完成[0.02957s].

[2022-06-02 10:48:53.341978] INFO: moduleinvoker: use_datasource.v1 开始运行..

[2022-06-02 10:48:53.359354] INFO: moduleinvoker: 命中缓存

[2022-06-02 10:48:53.361737] INFO: moduleinvoker: use_datasource.v1 运行完成[0.019766s].

[2022-06-02 10:48:53.369676] INFO: moduleinvoker: use_datasource.v1 开始运行..

[2022-06-02 10:48:53.403814] INFO: moduleinvoker: 命中缓存

[2022-06-02 10:48:53.406498] INFO: moduleinvoker: use_datasource.v1 运行完成[0.036837s].

[2022-06-02 10:48:53.419040] INFO: moduleinvoker: cached.v3 开始运行..

[2022-06-02 10:48:53.431972] INFO: moduleinvoker: 命中缓存

[2022-06-02 10:48:53.434207] INFO: moduleinvoker: cached.v3 运行完成[0.015187s].

[2022-06-02 10:48:53.457919] INFO: moduleinvoker: cached.v3 开始运行..

[2022-06-02 10:48:53.475502] INFO: moduleinvoker: 命中缓存

[2022-06-02 10:48:53.477780] INFO: moduleinvoker: cached.v3 运行完成[0.019887s].

[2022-06-02 10:48:53.487470] INFO: moduleinvoker: auto_labeler_on_datasource.v1 开始运行..

[2022-06-02 10:48:53.494522] INFO: moduleinvoker: 命中缓存

[2022-06-02 10:48:53.496999] INFO: moduleinvoker: auto_labeler_on_datasource.v1 运行完成[0.009526s].

[2022-06-02 10:48:53.529542] INFO: moduleinvoker: trade_data_generation.v1 开始运行..

[2022-06-02 10:48:53.894393] INFO: moduleinvoker: trade_data_generation.v1 运行完成[0.364845s].

[2022-06-02 10:48:53.939502] INFO: moduleinvoker: cached.v3 开始运行..

[2022-06-02 10:48:54.517013] INFO: moduleinvoker: cached.v3 运行完成[0.577527s].

[2022-06-02 10:48:54.540066] INFO: moduleinvoker: join.v3 开始运行..

[2022-06-02 10:48:57.952270] INFO: join: /data, 行数=1159/1170, 耗时=0.267118s

[2022-06-02 10:48:58.003602] INFO: join: 最终行数: 1159

[2022-06-02 10:48:58.031903] INFO: moduleinvoker: join.v3 运行完成[3.491832s].

[2022-06-02 10:48:58.064705] INFO: moduleinvoker: join.v3 开始运行..

[2022-06-02 10:48:59.869428] INFO: join: /data, 行数=10/268823, 耗时=1.600477s

[2022-06-02 10:48:59.964956] INFO: join: 最终行数: 10

[2022-06-02 10:48:59.986106] INFO: moduleinvoker: join.v3 运行完成[1.921404s].

[2022-06-02 10:49:00.017152] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-06-02 10:49:00.110910] INFO: derived_feature_extractor: 提取完成 double_low = close + bond_prem_ratio, 0.001s

[2022-06-02 10:49:00.163838] INFO: derived_feature_extractor: 提取完成 rank_swing_volatility_5 = nanstd((high-low)/pre_close, 5)*sqrt(200)*100, 0.031s

[2022-06-02 10:49:00.291812] INFO: derived_feature_extractor: /data, 1159

[2022-06-02 10:49:00.374211] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.35705s].

[2022-06-02 10:49:00.384654] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-06-02 10:49:00.536738] INFO: derived_feature_extractor: 提取完成 double_low = close + bond_prem_ratio, 0.001s

[2022-06-02 10:49:00.548495] INFO: derived_feature_extractor: 提取完成 rank_swing_volatility_5 = nanstd((high-low)/pre_close, 5)*sqrt(200)*100, 0.010s

[2022-06-02 10:49:00.672376] INFO: derived_feature_extractor: /data, 10

[2022-06-02 10:49:00.791586] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.406925s].

[2022-06-02 10:49:00.849984] INFO: moduleinvoker: join.v3 开始运行..

[2022-06-02 10:49:01.553463] INFO: join: /data, 行数=1100/1159, 耗时=0.111245s

[2022-06-02 10:49:01.584799] INFO: join: 最终行数: 1100

[2022-06-02 10:49:01.599125] INFO: moduleinvoker: join.v3 运行完成[0.749132s].

[2022-06-02 10:49:01.634453] INFO: moduleinvoker: cached.v3 开始运行..

[2022-06-02 10:49:01.819606] INFO: moduleinvoker: cached.v3 运行完成[0.185181s].

[2022-06-02 10:49:01.836806] INFO: moduleinvoker: stock_ranker_train.v6 开始运行..

[2022-06-02 10:49:01.932048] INFO: StockRanker: 特征预处理 ..

[2022-06-02 10:49:01.955249] INFO: StockRanker: prepare data: training ..

[2022-06-02 10:49:02.096876] INFO: StockRanker训练: 8f0bec52 准备训练: 1100 行数

[2022-06-02 10:49:02.106826] INFO: StockRanker训练: AI模型训练，将在1100*3=0.33万数据上对模型训练进行2轮迭代训练。预计将需要1～2分钟。请耐心等待。

[2022-06-02 10:49:02.499295] INFO: StockRanker训练: 正在训练 ..

[2022-06-02 10:49:02.578506] INFO: StockRanker训练: 任务状态: Pending

[2022-06-02 10:49:12.626076] INFO: StockRanker训练: 任务状态: Running

[2022-06-02 10:50:12.923927] INFO: StockRanker训练: 00:01:01.9751379, finished iteration 1

[2022-06-02 10:50:12.937330] INFO: StockRanker训练: 00:01:01.9779369, finished iteration 2

[2022-06-02 10:50:12.943680] INFO: StockRanker训练: 任务状态: Succeeded

[2022-06-02 10:50:13.207855] INFO: moduleinvoker: stock_ranker_train.v6 运行完成[71.371046s].

[2022-06-02 10:50:13.261678] INFO: moduleinvoker: stock_ranker_predict.v5 开始运行..

[2022-06-02 10:50:13.772556] INFO: StockRanker预测: /data ..

[2022-06-02 10:50:14.441479] INFO: moduleinvoker: stock_ranker_predict.v5 运行完成[1.179762s].

[2022-06-02 10:50:14.456480] INFO: moduleinvoker: cached.v3 开始运行..

[2022-06-02 10:50:14.614194] INFO: moduleinvoker: cached.v3 运行完成[0.157726s].

[2022-06-02 10:50:14.706480] INFO: moduleinvoker: hfbacktest.v1 开始运行..

[2022-06-02 10:50:14.720774] INFO: hfbacktest: passed-in daily_data_ds:None

[2022-06-02 10:50:14.722520] INFO: hfbacktest: passed-in minute_data_ds:None

[2022-06-02 10:50:14.726570] INFO: hfbacktest: passed-in tick_data_ds:None

[2022-06-02 10:50:14.730078] INFO: hfbacktest: passed-in each_data_ds:None

[2022-06-02 10:50:14.733208] INFO: hfbacktest: passed-in dominant_data_ds:None

[2022-06-02 10:50:14.736345] INFO: hfbacktest: passed-in benchmark_data_ds:None

[2022-06-02 10:50:14.739461] INFO: hfbacktest: passed-in trading_calendar_ds:None

[2022-06-02 10:50:14.746989] INFO: hfbacktest: biglearning V1.4.12

[2022-06-02 10:50:14.755276] INFO: hfbacktest: bigtrader v1.9.6 2022-05-31

[2022-06-02 10:50:14.759129] INFO: hfbacktest: strategy callbacks:{'on_init': , 'on_stop': , 'on_start': , 'handle_data': , 'handle_tick': , 'handle_trade': , 'handle_order': }

[2022-06-02 10:50:14.847538] INFO: hfbacktest: begin reading history data, 2021-06-30 00:00:00~2021-12-30, disable_cache:True, replay_bdb:1

[2022-06-02 10:50:14.849551] INFO: hfbacktest: reading benchmark data 2021-06-29 00:00:00~2021-12-30...

[2022-06-02 10:50:15.244885] INFO: hfbacktest: reading daily data 2020-06-24 00:00:00~2021-12-30...

[2022-06-02 10:50:15.860640] INFO: hfbacktest: cached_benchmark_ds:DataSource(eaf6509915e84102a631b2405dc9b3ffT)

[2022-06-02 10:50:15.862946] INFO: hfbacktest: cached_daily_ds:None

[2022-06-02 10:50:15.867550] INFO: hfbacktest: cached_minute_ds:None

[2022-06-02 10:50:15.871524] INFO: hfbacktest: cached_tick_ds:None

[2022-06-02 10:50:15.875519] INFO: hfbacktest: cached_each_ds:None

[2022-06-02 10:50:15.879470] INFO: hfbacktest: dominant_data_ds:None

[2022-06-02 10:50:15.883087] INFO: hfbacktest: read history data done, call run_backtest()

[2022-06-02 10:50:22.519061] INFO: hfbacktest: backtest done, raw_perf_ds:DataSource(f8101765288641198fdc2224417323bbT)

[2022-06-02 10:50:24.281929] INFO: moduleinvoker: hfbacktest.v1 运行完成[9.57547s].

[2022-06-02 10:50:24.283967] INFO: moduleinvoker: hftrade.v2 运行完成[9.617528s].

[2022-06-02 10:50:24.491446] INFO: moduleinvoker: input_features.v1 开始运行..

[2022-06-02 10:50:24.592364] INFO: moduleinvoker: input_features.v1 运行完成[0.100932s].

[2022-06-02 10:50:24.639309] INFO: moduleinvoker: cached.v3 开始运行..

[2022-06-02 10:50:24.648920] INFO: moduleinvoker: 命中缓存

[2022-06-02 10:50:24.651512] INFO: moduleinvoker: cached.v3 运行完成[0.012218s].

[2022-06-02 10:50:24.664066] INFO: moduleinvoker: use_datasource.v1 开始运行..

[2022-06-02 10:50:24.672879] INFO: moduleinvoker: 命中缓存

[2022-06-02 10:50:24.675226] INFO: moduleinvoker: use_datasource.v1 运行完成[0.011205s].

[2022-06-02 10:50:24.691115] INFO: moduleinvoker: use_datasource.v1 开始运行..

[2022-06-02 10:50:24.707034] INFO: moduleinvoker: 命中缓存

[2022-06-02 10:50:24.709146] INFO: moduleinvoker: use_datasource.v1 运行完成[0.018075s].

[2022-06-02 10:50:24.728619] INFO: moduleinvoker: cached.v3 开始运行..

[2022-06-02 10:50:24.738727] INFO: moduleinvoker: 命中缓存

[2022-06-02 10:50:24.740988] INFO: moduleinvoker: cached.v3 运行完成[0.012391s].

[2022-06-02 10:50:24.771260] INFO: moduleinvoker: cached.v3 开始运行..

[2022-06-02 10:50:24.780019] INFO: moduleinvoker: 命中缓存

[2022-06-02 10:50:24.782918] INFO: moduleinvoker: cached.v3 运行完成[0.011688s].

[2022-06-02 10:50:24.792591] INFO: moduleinvoker: auto_labeler_on_datasource.v1 开始运行..

[2022-06-02 10:50:24.800765] INFO: moduleinvoker: 命中缓存

[2022-06-02 10:50:24.803948] INFO: moduleinvoker: auto_labeler_on_datasource.v1 运行完成[0.011329s].

[2022-06-02 10:50:24.814128] INFO: moduleinvoker: trade_data_generation.v1 开始运行..

[2022-06-02 10:50:25.202367] INFO: moduleinvoker: trade_data_generation.v1 运行完成[0.388229s].

[2022-06-02 10:50:25.226760] INFO: moduleinvoker: cached.v3 开始运行..

[2022-06-02 10:50:26.073541] INFO: moduleinvoker: cached.v3 运行完成[0.846789s].

[2022-06-02 10:50:26.088263] INFO: moduleinvoker: join.v3 开始运行..

[2022-06-02 10:50:34.249938] INFO: join: /data, 行数=1159/1170, 耗时=0.28663s

[2022-06-02 10:50:34.365383] INFO: join: 最终行数: 1159

[2022-06-02 10:50:34.389469] INFO: moduleinvoker: join.v3 运行完成[8.301195s].

[2022-06-02 10:50:34.448853] INFO: moduleinvoker: join.v3 开始运行..

[2022-06-02 10:50:36.975644] INFO: join: /data, 行数=10/268823, 耗时=2.002701s

[2022-06-02 10:50:37.186506] INFO: join: 最终行数: 10

[2022-06-02 10:50:37.228157] INFO: moduleinvoker: join.v3 运行完成[2.779327s].

[2022-06-02 10:50:37.247338] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-06-02 10:50:37.378808] INFO: derived_feature_extractor: 提取完成 double_low = close + bond_prem_ratio, 0.001s

[2022-06-02 10:50:37.406223] INFO: derived_feature_extractor: 提取完成 rank_swing_volatility_5 = nanstd((high-low)/pre_close, 6)*sqrt(200)*100, 0.025s

[2022-06-02 10:50:37.592679] INFO: derived_feature_extractor: /data, 1159

[2022-06-02 10:50:37.685211] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.437859s].

[2022-06-02 10:50:37.704759] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-06-02 10:50:37.940298] INFO: derived_feature_extractor: 提取完成 double_low = close + bond_prem_ratio, 0.001s

[2022-06-02 10:50:37.970806] INFO: derived_feature_extractor: 提取完成 rank_swing_volatility_5 = nanstd((high-low)/pre_close, 6)*sqrt(200)*100, 0.019s

[2022-06-02 10:50:38.251278] INFO: derived_feature_extractor: /data, 10

[2022-06-02 10:50:38.426213] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.721452s].

[2022-06-02 10:50:38.458863] INFO: moduleinvoker: join.v3 开始运行..

[2022-06-02 10:50:39.729792] INFO: join: /data, 行数=1100/1159, 耗时=0.161345s

[2022-06-02 10:50:40.002328] INFO: join: 最终行数: 1100

[2022-06-02 10:50:40.032175] INFO: moduleinvoker: join.v3 运行完成[1.573309s].

[2022-06-02 10:50:40.050538] INFO: moduleinvoker: cached.v3 开始运行..

[2022-06-02 10:50:40.495318] INFO: moduleinvoker: cached.v3 运行完成[0.444793s].

[2022-06-02 10:50:40.591834] INFO: moduleinvoker: stock_ranker_train.v6 开始运行..

[2022-06-02 10:50:40.888069] INFO: StockRanker: 特征预处理 ..

[2022-06-02 10:50:40.949457] INFO: StockRanker: prepare data: training ..

[2022-06-02 10:50:41.163913] INFO: StockRanker训练: c9e95ada 准备训练: 1100 行数

[2022-06-02 10:50:41.166010] INFO: StockRanker训练: AI模型训练，将在1100*3=0.33万数据上对模型训练进行2轮迭代训练。预计将需要1～2分钟。请耐心等待。

[2022-06-02 10:50:41.408754] INFO: StockRanker训练: 正在训练 ..

[2022-06-02 10:50:41.472969] INFO: StockRanker训练: 任务状态: Pending

[2022-06-02 10:50:51.544103] INFO: StockRanker训练: 任务状态: Running

[2022-06-02 10:51:51.892511] INFO: StockRanker训练: 00:01:01.9873754, finished iteration 1

[2022-06-02 10:51:51.894334] INFO: StockRanker训练: 00:01:02.0090128, finished iteration 2

[2022-06-02 10:51:51.896117] INFO: StockRanker训练: 任务状态: Succeeded

[2022-06-02 10:51:52.240892] INFO: moduleinvoker: stock_ranker_train.v6 运行完成[71.649053s].

[2022-06-02 10:51:52.325479] INFO: moduleinvoker: stock_ranker_predict.v5 开始运行..

[2022-06-02 10:51:52.845417] INFO: StockRanker预测: /data ..

[2022-06-02 10:51:53.493041] INFO: moduleinvoker: stock_ranker_predict.v5 运行完成[1.167559s].

[2022-06-02 10:51:53.512999] INFO: moduleinvoker: cached.v3 开始运行..

[2022-06-02 10:51:53.589869] INFO: moduleinvoker: cached.v3 运行完成[0.076892s].

[2022-06-02 10:51:53.673748] INFO: moduleinvoker: hfbacktest.v1 开始运行..

[2022-06-02 10:51:53.680532] INFO: hfbacktest: passed-in daily_data_ds:None

[2022-06-02 10:51:53.682310] INFO: hfbacktest: passed-in minute_data_ds:None

[2022-06-02 10:51:53.683782] INFO: hfbacktest: passed-in tick_data_ds:None

[2022-06-02 10:51:53.685573] INFO: hfbacktest: passed-in each_data_ds:None

[2022-06-02 10:51:53.687031] INFO: hfbacktest: passed-in dominant_data_ds:None

[2022-06-02 10:51:53.688727] INFO: hfbacktest: passed-in benchmark_data_ds:None

[2022-06-02 10:51:53.690277] INFO: hfbacktest: passed-in trading_calendar_ds:None

[2022-06-02 10:51:53.691911] INFO: hfbacktest: biglearning V1.4.12

[2022-06-02 10:51:53.693455] INFO: hfbacktest: bigtrader v1.9.6 2022-05-31

[2022-06-02 10:51:53.694998] INFO: hfbacktest: strategy callbacks:{'on_init': , 'on_stop': , 'on_start': , 'handle_data': , 'handle_tick': , 'handle_trade': , 'handle_order': }

[2022-06-02 10:51:53.714353] INFO: hfbacktest: begin reading history data, 2021-06-30 00:00:00~2021-12-30, disable_cache:True, replay_bdb:1

[2022-06-02 10:51:53.716234] INFO: hfbacktest: reading benchmark data 2021-06-29 00:00:00~2021-12-30...

[2022-06-02 10:51:53.983780] INFO: hfbacktest: reading daily data 2020-06-24 00:00:00~2021-12-30...

[2022-06-02 10:51:56.758938] INFO: hfbacktest: cached_benchmark_ds:DataSource(3a91829a26644af880bef7122000a27bT)

[2022-06-02 10:51:56.761160] INFO: hfbacktest: cached_daily_ds:None

[2022-06-02 10:51:56.765576] INFO: hfbacktest: cached_minute_ds:None

[2022-06-02 10:51:56.769854] INFO: hfbacktest: cached_tick_ds:None

[2022-06-02 10:51:56.774563] INFO: hfbacktest: cached_each_ds:None

[2022-06-02 10:51:56.778907] INFO: hfbacktest: dominant_data_ds:None

[2022-06-02 10:51:56.783165] INFO: hfbacktest: read history data done, call run_backtest()

[2022-06-02 10:52:01.953043] INFO: hfbacktest: backtest done, raw_perf_ds:DataSource(a2378003e8af42f88ed5ced64e588437T)

[2022-06-02 10:52:03.280997] INFO: moduleinvoker: hfbacktest.v1 运行完成[9.607271s].

[2022-06-02 10:52:03.283104] INFO: moduleinvoker: hftrade.v2 运行完成[9.656191s].

[2022-06-02 10:52:03.376155] INFO: moduleinvoker: input_features.v1 开始运行..

[2022-06-02 10:52:03.432221] INFO: moduleinvoker: input_features.v1 运行完成[0.056068s].

[2022-06-02 10:52:03.550593] INFO: moduleinvoker: cached.v3 开始运行..

[2022-06-02 10:52:03.600628] INFO: moduleinvoker: 命中缓存

[2022-06-02 10:52:03.604058] INFO: moduleinvoker: cached.v3 运行完成[0.053477s].

[2022-06-02 10:52:03.611286] INFO: moduleinvoker: use_datasource.v1 开始运行..

[2022-06-02 10:52:03.620510] INFO: moduleinvoker: 命中缓存

[2022-06-02 10:52:03.622983] INFO: moduleinvoker: use_datasource.v1 运行完成[0.011689s].

[2022-06-02 10:52:03.653175] INFO: moduleinvoker: use_datasource.v1 开始运行..

[2022-06-02 10:52:03.672624] INFO: moduleinvoker: 命中缓存

[2022-06-02 10:52:03.674769] INFO: moduleinvoker: use_datasource.v1 运行完成[0.021611s].

[2022-06-02 10:52:03.688211] INFO: moduleinvoker: cached.v3 开始运行..

[2022-06-02 10:52:03.698316] INFO: moduleinvoker: 命中缓存

[2022-06-02 10:52:03.700482] INFO: moduleinvoker: cached.v3 运行完成[0.012282s].

[2022-06-02 10:52:03.741133] INFO: moduleinvoker: cached.v3 开始运行..

[2022-06-02 10:52:03.748275] INFO: moduleinvoker: 命中缓存

[2022-06-02 10:52:03.750352] INFO: moduleinvoker: cached.v3 运行完成[0.009242s].

[2022-06-02 10:52:03.795752] INFO: moduleinvoker: auto_labeler_on_datasource.v1 开始运行..

[2022-06-02 10:52:03.803983] INFO: moduleinvoker: 命中缓存

[2022-06-02 10:52:03.806161] INFO: moduleinvoker: auto_labeler_on_datasource.v1 运行完成[0.010414s].

[2022-06-02 10:52:03.887772] INFO: moduleinvoker: trade_data_generation.v1 开始运行..

[2022-06-02 10:52:04.486643] INFO: moduleinvoker: trade_data_generation.v1 运行完成[0.598865s].

[2022-06-02 10:52:04.529223] INFO: moduleinvoker: cached.v3 开始运行..

[2022-06-02 10:52:05.420857] INFO: moduleinvoker: cached.v3 运行完成[0.891641s].

[2022-06-02 10:52:05.453882] INFO: moduleinvoker: join.v3 开始运行..

[2022-06-02 10:52:09.514494] INFO: join: /data, 行数=1159/1170, 耗时=0.305324s

[2022-06-02 10:52:09.595866] INFO: join: 最终行数: 1159

[2022-06-02 10:52:09.627206] INFO: moduleinvoker: join.v3 运行完成[4.173316s].

[2022-06-02 10:52:09.658224] INFO: moduleinvoker: join.v3 开始运行..

[2022-06-02 10:52:11.446712] INFO: join: /data, 行数=10/268823, 耗时=1.503597s

[2022-06-02 10:52:11.494443] INFO: join: 最终行数: 10

[2022-06-02 10:52:11.545516] INFO: moduleinvoker: join.v3 运行完成[1.887286s].

[2022-06-02 10:52:11.557523] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-06-02 10:52:11.672694] INFO: derived_feature_extractor: 提取完成 double_low = close + bond_prem_ratio, 0.002s

[2022-06-02 10:52:11.693590] INFO: derived_feature_extractor: 提取完成 rank_swing_volatility_5 = nanstd((high-low)/pre_close, 120)*sqrt(200)*100, 0.019s

[2022-06-02 10:52:11.869578] INFO: derived_feature_extractor: /data, 1159

[2022-06-02 10:52:11.979933] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.422395s].

[2022-06-02 10:52:11.990606] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-06-02 10:52:12.084668] INFO: derived_feature_extractor: 提取完成 double_low = close + bond_prem_ratio, 0.001s

[2022-06-02 10:52:12.093004] INFO: derived_feature_extractor: 提取完成 rank_swing_volatility_5 = nanstd((high-low)/pre_close, 120)*sqrt(200)*100, 0.006s

[2022-06-02 10:52:12.183335] INFO: derived_feature_extractor: /data, 10

[2022-06-02 10:52:12.271273] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.280649s].

[2022-06-02 10:52:12.282141] INFO: moduleinvoker: join.v3 开始运行..

[2022-06-02 10:52:13.139501] INFO: join: /data, 行数=1100/1159, 耗时=0.110774s

[2022-06-02 10:52:13.170871] INFO: join: 最终行数: 1100

[2022-06-02 10:52:13.195202] INFO: moduleinvoker: join.v3 运行完成[0.913051s].

[2022-06-02 10:52:13.237012] INFO: moduleinvoker: cached.v3 开始运行..

[2022-06-02 10:52:13.546348] INFO: moduleinvoker: cached.v3 运行完成[0.309349s].

[2022-06-02 10:52:13.555935] INFO: moduleinvoker: stock_ranker_train.v6 开始运行..

[2022-06-02 10:52:13.757047] INFO: StockRanker: 特征预处理 ..

[2022-06-02 10:52:13.802735] INFO: StockRanker: prepare data: training ..

[2022-06-02 10:52:13.927782] INFO: StockRanker训练: 01532a6e 准备训练: 1100 行数

[2022-06-02 10:52:13.929668] INFO: StockRanker训练: AI模型训练，将在1100*3=0.33万数据上对模型训练进行2轮迭代训练。预计将需要1～2分钟。请耐心等待。

[2022-06-02 10:52:14.199750] INFO: StockRanker训练: 正在训练 ..

[2022-06-02 10:52:14.254817] INFO: StockRanker训练: 任务状态: Pending

[2022-06-02 10:52:24.303483] INFO: StockRanker训练: 任务状态: Running

[2022-06-02 10:53:24.614824] INFO: StockRanker训练: 00:01:01.7393880, finished iteration 1

[2022-06-02 10:53:24.617238] INFO: StockRanker训练: 00:01:01.7427592, finished iteration 2

[2022-06-02 10:53:24.619330] INFO: StockRanker训练: 任务状态: Succeeded

[2022-06-02 10:53:24.889803] INFO: moduleinvoker: stock_ranker_train.v6 运行完成[71.333862s].

[2022-06-02 10:53:24.899819] INFO: moduleinvoker: stock_ranker_predict.v5 开始运行..

[2022-06-02 10:53:25.570225] INFO: StockRanker预测: /data ..

[2022-06-02 10:53:26.105112] INFO: moduleinvoker: stock_ranker_predict.v5 运行完成[1.205282s].

[2022-06-02 10:53:26.150387] INFO: moduleinvoker: cached.v3 开始运行..

[2022-06-02 10:53:26.394879] INFO: moduleinvoker: cached.v3 运行完成[0.244507s].

[2022-06-02 10:53:26.566266] INFO: moduleinvoker: hfbacktest.v1 开始运行..

[2022-06-02 10:53:26.572580] INFO: hfbacktest: passed-in daily_data_ds:None

[2022-06-02 10:53:26.573926] INFO: hfbacktest: passed-in minute_data_ds:None

[2022-06-02 10:53:26.575479] INFO: hfbacktest: passed-in tick_data_ds:None

[2022-06-02 10:53:26.577102] INFO: hfbacktest: passed-in each_data_ds:None

[2022-06-02 10:53:26.578606] INFO: hfbacktest: passed-in dominant_data_ds:None

[2022-06-02 10:53:26.579952] INFO: hfbacktest: passed-in benchmark_data_ds:None

[2022-06-02 10:53:26.581387] INFO: hfbacktest: passed-in trading_calendar_ds:None

[2022-06-02 10:53:26.583143] INFO: hfbacktest: biglearning V1.4.12

[2022-06-02 10:53:26.584781] INFO: hfbacktest: bigtrader v1.9.6 2022-05-31

[2022-06-02 10:53:26.586241] INFO: hfbacktest: strategy callbacks:{'on_init': , 'on_stop': , 'on_start': , 'handle_data': , 'handle_tick': , 'handle_trade': , 'handle_order': }

[2022-06-02 10:53:26.610040] INFO: hfbacktest: begin reading history data, 2021-06-30 00:00:00~2021-12-30, disable_cache:True, replay_bdb:1

[2022-06-02 10:53:26.611915] INFO: hfbacktest: reading benchmark data 2021-06-29 00:00:00~2021-12-30...

[2022-06-02 10:53:26.953965] INFO: hfbacktest: reading daily data 2020-06-24 00:00:00~2021-12-30...

[2022-06-02 10:53:27.554539] INFO: hfbacktest: cached_benchmark_ds:DataSource(b8e96a50847f48e282601a3c6df6dc1cT)

[2022-06-02 10:53:27.556560] INFO: hfbacktest: cached_daily_ds:None

[2022-06-02 10:53:27.558643] INFO: hfbacktest: cached_minute_ds:None

[2022-06-02 10:53:27.560193] INFO: hfbacktest: cached_tick_ds:None

[2022-06-02 10:53:27.561585] INFO: hfbacktest: cached_each_ds:None

[2022-06-02 10:53:27.562910] INFO: hfbacktest: dominant_data_ds:None

[2022-06-02 10:53:27.564203] INFO: hfbacktest: read history data done, call run_backtest()

[2022-06-02 10:53:32.303172] INFO: hfbacktest: backtest done, raw_perf_ds:DataSource(46024ff650b145eabb7b8c7daa42109bT)

[2022-06-02 10:53:33.129998] INFO: moduleinvoker: hfbacktest.v1 运行完成[6.56375s].

[2022-06-02 10:53:33.132039] INFO: moduleinvoker: hftrade.v2 运行完成[6.683563s].

[2022-06-02 10:53:33.189978] INFO: moduleinvoker: input_features.v1 开始运行..

[2022-06-02 10:53:33.198398] INFO: moduleinvoker: 命中缓存

[2022-06-02 10:53:33.200646] INFO: moduleinvoker: input_features.v1 运行完成[0.010685s].

[2022-06-02 10:53:33.217157] INFO: moduleinvoker: cached.v3 开始运行..

[2022-06-02 10:53:33.225872] INFO: moduleinvoker: 命中缓存

[2022-06-02 10:53:33.231569] INFO: moduleinvoker: cached.v3 运行完成[0.014431s].

[2022-06-02 10:53:33.238379] INFO: moduleinvoker: use_datasource.v1 开始运行..

[2022-06-02 10:53:33.249330] INFO: moduleinvoker: 命中缓存

[2022-06-02 10:53:33.251704] INFO: moduleinvoker: use_datasource.v1 运行完成[0.013336s].

[2022-06-02 10:53:33.257947] INFO: moduleinvoker: use_datasource.v1 开始运行..

[2022-06-02 10:53:33.265282] INFO: moduleinvoker: 命中缓存

[2022-06-02 10:53:33.267652] INFO: moduleinvoker: use_datasource.v1 运行完成[0.009715s].

[2022-06-02 10:53:33.284592] INFO: moduleinvoker: cached.v3 开始运行..

[2022-06-02 10:53:33.296803] INFO: moduleinvoker: 命中缓存

[2022-06-02 10:53:33.299826] INFO: moduleinvoker: cached.v3 运行完成[0.015261s].

[2022-06-02 10:53:33.319540] INFO: moduleinvoker: cached.v3 开始运行..

[2022-06-02 10:53:33.396371] INFO: moduleinvoker: cached.v3 运行完成[0.076841s].

[2022-06-02 10:53:33.413250] INFO: moduleinvoker: auto_labeler_on_datasource.v1 开始运行..

[2022-06-02 10:53:33.421611] INFO: moduleinvoker: 命中缓存

[2022-06-02 10:53:33.423651] INFO: moduleinvoker: auto_labeler_on_datasource.v1 运行完成[0.010408s].

[2022-06-02 10:53:33.438897] INFO: moduleinvoker: trade_data_generation.v1 开始运行..

[2022-06-02 10:53:34.100149] INFO: moduleinvoker: trade_data_generation.v1 运行完成[0.661233s].

[2022-06-02 10:53:34.151710] INFO: moduleinvoker: cached.v3 开始运行..

[2022-06-02 10:53:35.315715] INFO: moduleinvoker: cached.v3 运行完成[1.164021s].

[2022-06-02 10:53:35.437569] INFO: moduleinvoker: join.v3 开始运行..

[2022-06-02 10:53:40.057340] INFO: join: /data, 行数=1159/1170, 耗时=0.371947s

[2022-06-02 10:53:40.121644] INFO: join: 最终行数: 1159

[2022-06-02 10:53:40.175396] INFO: moduleinvoker: join.v3 运行完成[4.737817s].

[2022-06-02 10:53:40.188018] INFO: moduleinvoker: join.v3 开始运行..

[2022-06-02 10:53:42.514826] INFO: join: /data, 行数=10/268823, 耗时=1.936725s

[2022-06-02 10:53:42.576995] INFO: join: 最终行数: 10

[2022-06-02 10:53:42.593270] INFO: moduleinvoker: join.v3 运行完成[2.405245s].

[2022-06-02 10:53:42.617704] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-06-02 10:53:42.755718] INFO: derived_feature_extractor: 提取完成 double_low = close + bond_prem_ratio, 0.002s

[2022-06-02 10:53:42.781692] INFO: derived_feature_extractor: 提取完成 rank_swing_volatility_5 = nanstd((high-low)/pre_close, 5)*sqrt(200)*100, 0.024s

[2022-06-02 10:53:42.939993] INFO: derived_feature_extractor: /data, 1159

[2022-06-02 10:53:43.038943] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.421229s].

[2022-06-02 10:53:43.057809] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-06-02 10:53:43.167204] INFO: derived_feature_extractor: 提取完成 double_low = close + bond_prem_ratio, 0.001s

[2022-06-02 10:53:43.177677] INFO: derived_feature_extractor: 提取完成 rank_swing_volatility_5 = nanstd((high-low)/pre_close, 5)*sqrt(200)*100, 0.009s

[2022-06-02 10:53:43.331780] INFO: derived_feature_extractor: /data, 10

[2022-06-02 10:53:43.398622] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.340804s].

[2022-06-02 10:53:43.432146] INFO: moduleinvoker: join.v3 开始运行..

[2022-06-02 10:53:44.309247] INFO: join: /data, 行数=1100/1159, 耗时=0.119317s

[2022-06-02 10:53:44.385429] INFO: join: 最终行数: 1100

[2022-06-02 10:53:44.420993] INFO: moduleinvoker: join.v3 运行完成[0.988834s].

[2022-06-02 10:53:44.490516] INFO: moduleinvoker: cached.v3 开始运行..

[2022-06-02 10:53:45.116095] INFO: moduleinvoker: cached.v3 运行完成[0.625585s].

[2022-06-02 10:53:45.192292] INFO: moduleinvoker: stock_ranker_train.v6 开始运行..

[2022-06-02 10:53:45.606904] INFO: StockRanker: 特征预处理 ..

[2022-06-02 10:53:45.655660] INFO: StockRanker: prepare data: training ..

[2022-06-02 10:53:45.885170] INFO: StockRanker训练: 37f4406c 准备训练: 1100 行数

[2022-06-02 10:53:45.887039] INFO: StockRanker训练: AI模型训练，将在1100*3=0.33万数据上对模型训练进行2轮迭代训练。预计将需要1～2分钟。请耐心等待。

[2022-06-02 10:53:46.206758] INFO: StockRanker训练: 正在训练 ..

[2022-06-02 10:53:46.288777] INFO: StockRanker训练: 任务状态: Pending

[2022-06-02 10:53:56.376017] INFO: StockRanker训练: 任务状态: Running

[2022-06-02 10:55:06.896752] INFO: StockRanker训练: 00:01:02.1288238, finished iteration 1

[2022-06-02 10:55:06.898860] INFO: StockRanker训练: 00:01:02.1342304, finished iteration 2

[2022-06-02 10:55:06.901315] INFO: StockRanker训练: 任务状态: Succeeded

[2022-06-02 10:55:07.478416] INFO: moduleinvoker: stock_ranker_train.v6 运行完成[82.286109s].

[2022-06-02 10:55:07.550156] INFO: moduleinvoker: stock_ranker_predict.v5 开始运行..

[2022-06-02 10:55:09.352189] INFO: StockRanker预测: /data ..

[2022-06-02 10:55:10.754213] INFO: moduleinvoker: stock_ranker_predict.v5 运行完成[3.204076s].

[2022-06-02 10:55:11.027392] INFO: moduleinvoker: cached.v3 开始运行..

[2022-06-02 10:55:11.579973] INFO: moduleinvoker: cached.v3 运行完成[0.552593s].

[2022-06-02 10:55:11.840431] INFO: moduleinvoker: hfbacktest.v1 开始运行..

[2022-06-02 10:55:11.851654] INFO: hfbacktest: passed-in daily_data_ds:None

[2022-06-02 10:55:11.853090] INFO: hfbacktest: passed-in minute_data_ds:None

[2022-06-02 10:55:11.854416] INFO: hfbacktest: passed-in tick_data_ds:None

[2022-06-02 10:55:11.855719] INFO: hfbacktest: passed-in each_data_ds:None

[2022-06-02 10:55:11.857118] INFO: hfbacktest: passed-in dominant_data_ds:None

[2022-06-02 10:55:11.858350] INFO: hfbacktest: passed-in benchmark_data_ds:None

[2022-06-02 10:55:11.859635] INFO: hfbacktest: passed-in trading_calendar_ds:None

[2022-06-02 10:55:11.860921] INFO: hfbacktest: biglearning V1.4.12

[2022-06-02 10:55:11.862293] INFO: hfbacktest: bigtrader v1.9.6 2022-05-31

[2022-06-02 10:55:11.863582] INFO: hfbacktest: strategy callbacks:{'on_init': , 'on_stop': , 'on_start': , 'handle_data': , 'handle_tick': , 'handle_trade': , 'handle_order': }

[2022-06-02 10:55:11.884466] INFO: hfbacktest: begin reading history data, 2021-06-30 00:00:00~2021-12-30, disable_cache:True, replay_bdb:1

[2022-06-02 10:55:11.886054] INFO: hfbacktest: reading benchmark data 2021-06-29 00:00:00~2021-12-30...

[2022-06-02 10:55:12.607788] INFO: hfbacktest: reading daily data 2020-06-24 00:00:00~2021-12-30...

[2022-06-02 10:55:14.629669] INFO: hfbacktest: cached_benchmark_ds:DataSource(013de5bc66394b1190adbcf24ccf4e7fT)

[2022-06-02 10:55:14.674027] INFO: hfbacktest: cached_daily_ds:None

[2022-06-02 10:55:14.680648] INFO: hfbacktest: cached_minute_ds:None

[2022-06-02 10:55:14.682572] INFO: hfbacktest: cached_tick_ds:None

[2022-06-02 10:55:14.684020] INFO: hfbacktest: cached_each_ds:None

[2022-06-02 10:55:14.685365] INFO: hfbacktest: dominant_data_ds:None

[2022-06-02 10:55:14.686681] INFO: hfbacktest: read history data done, call run_backtest()

[2022-06-02 10:55:21.799116] INFO: hfbacktest: backtest done, raw_perf_ds:DataSource(be1c12a6e97348aeb01d47e9edc6784aT)

[2022-06-02 10:55:23.606912] INFO: moduleinvoker: hfbacktest.v1 运行完成[11.766455s].

[2022-06-02 10:55:23.609000] INFO: moduleinvoker: hftrade.v2 运行完成[11.921983s].

[2022-06-02 10:55:23.835939] INFO: moduleinvoker: input_features.v1 开始运行..

[2022-06-02 10:55:23.845980] INFO: moduleinvoker: 命中缓存

[2022-06-02 10:55:23.848066] INFO: moduleinvoker: input_features.v1 运行完成[0.012143s].

[2022-06-02 10:55:24.138734] INFO: moduleinvoker: cached.v3 开始运行..

[2022-06-02 10:55:24.155306] INFO: moduleinvoker: 命中缓存

[2022-06-02 10:55:24.157583] INFO: moduleinvoker: cached.v3 运行完成[0.018723s].

[2022-06-02 10:55:24.281182] INFO: moduleinvoker: use_datasource.v1 开始运行..

[2022-06-02 10:55:24.290057] INFO: moduleinvoker: 命中缓存

[2022-06-02 10:55:24.292821] INFO: moduleinvoker: use_datasource.v1 运行完成[0.011654s].

[2022-06-02 10:55:24.354164] INFO: moduleinvoker: use_datasource.v1 开始运行..

[2022-06-02 10:55:24.378913] INFO: moduleinvoker: 命中缓存

[2022-06-02 10:55:24.382098] INFO: moduleinvoker: use_datasource.v1 运行完成[0.027951s].

[2022-06-02 10:55:24.417983] INFO: moduleinvoker: cached.v3 开始运行..

[2022-06-02 10:55:24.442980] INFO: moduleinvoker: 命中缓存

[2022-06-02 10:55:24.445206] INFO: moduleinvoker: cached.v3 运行完成[0.027249s].

[2022-06-02 10:55:24.490071] INFO: moduleinvoker: cached.v3 开始运行..

[2022-06-02 10:55:24.498839] INFO: moduleinvoker: 命中缓存

[2022-06-02 10:55:24.500675] INFO: moduleinvoker: cached.v3 运行完成[0.010631s].

[2022-06-02 10:55:24.508676] INFO: moduleinvoker: auto_labeler_on_datasource.v1 开始运行..

[2022-06-02 10:55:24.515629] INFO: moduleinvoker: 命中缓存

[2022-06-02 10:55:24.517186] INFO: moduleinvoker: auto_labeler_on_datasource.v1 运行完成[0.008507s].

[2022-06-02 10:55:24.532568] INFO: moduleinvoker: trade_data_generation.v1 开始运行..

[2022-06-02 10:55:25.795018] INFO: moduleinvoker: trade_data_generation.v1 运行完成[1.262434s].

[2022-06-02 10:55:25.847248] INFO: moduleinvoker: cached.v3 开始运行..

[2022-06-02 10:55:27.186078] INFO: moduleinvoker: cached.v3 运行完成[1.338845s].

[2022-06-02 10:55:27.334881] INFO: moduleinvoker: join.v3 开始运行..

[2022-06-02 10:55:43.296342] INFO: join: /data, 行数=1159/1170, 耗时=0.380063s

[2022-06-02 10:55:43.615865] INFO: join: 最终行数: 1159

[2022-06-02 10:55:43.654793] INFO: moduleinvoker: join.v3 运行完成[16.31991s].

[2022-06-02 10:55:43.764891] INFO: moduleinvoker: join.v3 开始运行..

[2022-06-02 10:55:48.756970] INFO: join: /data, 行数=10/268823, 耗时=4.58325s

[2022-06-02 10:55:49.009806] INFO: join: 最终行数: 10

[2022-06-02 10:55:49.065324] INFO: moduleinvoker: join.v3 运行完成[5.30042s].

[2022-06-02 10:55:49.150781] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-06-02 10:55:49.350767] INFO: derived_feature_extractor: 提取完成 double_low = close + bond_prem_ratio, 0.002s

[2022-06-02 10:55:49.378955] INFO: derived_feature_extractor: 提取完成 rank_swing_volatility_5 = nanstd((high-low)/pre_close, 6)*sqrt(200)*100, 0.026s

[2022-06-02 10:55:49.561820] INFO: derived_feature_extractor: /data, 1159

[2022-06-02 10:55:49.713192] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.562409s].

[2022-06-02 10:55:49.728485] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-06-02 10:55:49.883056] INFO: derived_feature_extractor: 提取完成 double_low = close + bond_prem_ratio, 0.001s

[2022-06-02 10:55:49.895651] INFO: derived_feature_extractor: 提取完成 rank_swing_volatility_5 = nanstd((high-low)/pre_close, 6)*sqrt(200)*100, 0.010s

[2022-06-02 10:55:50.161939] INFO: derived_feature_extractor: /data, 10

[2022-06-02 10:55:50.525194] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.79675s].

[2022-06-02 10:55:50.572706] INFO: moduleinvoker: join.v3 开始运行..

[2022-06-02 10:55:52.644066] INFO: join: /data, 行数=1100/1159, 耗时=0.142872s

[2022-06-02 10:55:52.869383] INFO: join: 最终行数: 1100

[2022-06-02 10:55:52.889810] INFO: moduleinvoker: join.v3 运行完成[2.317094s].

[2022-06-02 10:55:52.927975] INFO: moduleinvoker: cached.v3 开始运行..

[2022-06-02 10:55:53.676322] INFO: moduleinvoker: cached.v3 运行完成[0.748357s].

[2022-06-02 10:55:53.696453] INFO: moduleinvoker: stock_ranker_train.v6 开始运行..

[2022-06-02 10:55:53.951431] INFO: StockRanker: 特征预处理 ..

[2022-06-02 10:55:54.008725] INFO: StockRanker: prepare data: training ..

[2022-06-02 10:55:54.083868] INFO: StockRanker训练: 848f173a 准备训练: 1100 行数

[2022-06-02 10:55:54.085741] INFO: StockRanker训练: AI模型训练，将在1100*3=0.33万数据上对模型训练进行2轮迭代训练。预计将需要1～2分钟。请耐心等待。

[2022-06-02 10:55:54.340753] INFO: StockRanker训练: 正在训练 ..

[2022-06-02 10:55:54.403884] INFO: StockRanker训练: 任务状态: Pending

[2022-06-02 10:56:04.455477] INFO: StockRanker训练: 任务状态: Running

[2022-06-02 10:57:14.968802] INFO: StockRanker训练: 00:01:02.0743676, finished iteration 1

[2022-06-02 10:57:14.970784] INFO: StockRanker训练: 00:01:02.0845561, finished iteration 2

[2022-06-02 10:57:14.972449] INFO: StockRanker训练: 任务状态: Succeeded

[2022-06-02 10:57:15.378133] INFO: moduleinvoker: stock_ranker_train.v6 运行完成[81.681678s].

[2022-06-02 10:57:15.388276] INFO: moduleinvoker: stock_ranker_predict.v5 开始运行..

[2022-06-02 10:57:16.134839] INFO: StockRanker预测: /data ..

[2022-06-02 10:57:16.970260] INFO: moduleinvoker: stock_ranker_predict.v5 运行完成[1.581965s].

[2022-06-02 10:57:17.005800] INFO: moduleinvoker: cached.v3 开始运行..

[2022-06-02 10:57:17.118492] INFO: moduleinvoker: cached.v3 运行完成[0.112702s].

[2022-06-02 10:57:17.164754] INFO: moduleinvoker: hfbacktest.v1 开始运行..

[2022-06-02 10:57:17.170944] INFO: hfbacktest: passed-in daily_data_ds:None

[2022-06-02 10:57:17.177243] INFO: hfbacktest: passed-in minute_data_ds:None

[2022-06-02 10:57:17.180220] INFO: hfbacktest: passed-in tick_data_ds:None

[2022-06-02 10:57:17.184917] INFO: hfbacktest: passed-in each_data_ds:None

[2022-06-02 10:57:17.186930] INFO: hfbacktest: passed-in dominant_data_ds:None

[2022-06-02 10:57:17.192502] INFO: hfbacktest: passed-in benchmark_data_ds:None

[2022-06-02 10:57:17.194574] INFO: hfbacktest: passed-in trading_calendar_ds:None

[2022-06-02 10:57:17.196260] INFO: hfbacktest: biglearning V1.4.12

[2022-06-02 10:57:17.197830] INFO: hfbacktest: bigtrader v1.9.6 2022-05-31

[2022-06-02 10:57:17.199465] INFO: hfbacktest: strategy callbacks:{'on_init': , 'on_stop': , 'on_start': , 'handle_data': , 'handle_tick': , 'handle_trade': , 'handle_order': }

[2022-06-02 10:57:17.239053] INFO: hfbacktest: begin reading history data, 2021-06-30 00:00:00~2021-12-30, disable_cache:True, replay_bdb:1

[2022-06-02 10:57:17.241479] INFO: hfbacktest: reading benchmark data 2021-06-29 00:00:00~2021-12-30...

[2022-06-02 10:57:17.739747] INFO: hfbacktest: reading daily data 2020-06-24 00:00:00~2021-12-30...

[2022-06-02 10:57:18.221298] INFO: hfbacktest: cached_benchmark_ds:DataSource(6bda487a3cc342e487b427f0a9bdcda5T)

[2022-06-02 10:57:18.233752] INFO: hfbacktest: cached_daily_ds:None

[2022-06-02 10:57:18.243721] INFO: hfbacktest: cached_minute_ds:None

[2022-06-02 10:57:18.248867] INFO: hfbacktest: cached_tick_ds:None

[2022-06-02 10:57:18.251700] INFO: hfbacktest: cached_each_ds:None

[2022-06-02 10:57:18.254081] INFO: hfbacktest: dominant_data_ds:None

[2022-06-02 10:57:18.256271] INFO: hfbacktest: read history data done, call run_backtest()

[2022-06-02 10:57:23.653769] INFO: hfbacktest: backtest done, raw_perf_ds:DataSource(d837ac17f86342e98efb2334ad921401T)

[2022-06-02 10:57:24.689646] INFO: moduleinvoker: hfbacktest.v1 运行完成[7.524914s].

[2022-06-02 10:57:24.691816] INFO: moduleinvoker: hftrade.v2 运行完成[7.563441s].

[2022-06-02 10:57:24.823130] INFO: moduleinvoker: input_features.v1 开始运行..

[2022-06-02 10:57:24.845129] INFO: moduleinvoker: 命中缓存

[2022-06-02 10:57:24.847380] INFO: moduleinvoker: input_features.v1 运行完成[0.024269s].

[2022-06-02 10:57:24.878344] INFO: moduleinvoker: cached.v3 开始运行..

[2022-06-02 10:57:24.889285] INFO: moduleinvoker: 命中缓存

[2022-06-02 10:57:24.891192] INFO: moduleinvoker: cached.v3 运行完成[0.012892s].

[2022-06-02 10:57:24.907437] INFO: moduleinvoker: use_datasource.v1 开始运行..

[2022-06-02 10:57:24.915880] INFO: moduleinvoker: 命中缓存

[2022-06-02 10:57:24.918732] INFO: moduleinvoker: use_datasource.v1 运行完成[0.01133s].

[2022-06-02 10:57:24.939087] INFO: moduleinvoker: use_datasource.v1 开始运行..

[2022-06-02 10:57:24.949351] INFO: moduleinvoker: 命中缓存

[2022-06-02 10:57:24.951560] INFO: moduleinvoker: use_datasource.v1 运行完成[0.012501s].

[2022-06-02 10:57:24.964772] INFO: moduleinvoker: cached.v3 开始运行..

[2022-06-02 10:57:24.974970] INFO: moduleinvoker: 命中缓存

[2022-06-02 10:57:24.977037] INFO: moduleinvoker: cached.v3 运行完成[0.012288s].

[2022-06-02 10:57:24.991008] INFO: moduleinvoker: cached.v3 开始运行..

[2022-06-02 10:57:24.997368] INFO: moduleinvoker: 命中缓存

[2022-06-02 10:57:24.999359] INFO: moduleinvoker: cached.v3 运行完成[0.008361s].

[2022-06-02 10:57:25.050247] INFO: moduleinvoker: auto_labeler_on_datasource.v1 开始运行..

[2022-06-02 10:57:25.072721] INFO: moduleinvoker: 命中缓存

[2022-06-02 10:57:25.075080] INFO: moduleinvoker: auto_labeler_on_datasource.v1 运行完成[0.024838s].

[2022-06-02 10:57:25.124264] INFO: moduleinvoker: trade_data_generation.v1 开始运行..

[2022-06-02 10:57:26.045340] INFO: moduleinvoker: trade_data_generation.v1 运行完成[0.921072s].

[2022-06-02 10:57:26.077144] INFO: moduleinvoker: cached.v3 开始运行..

[2022-06-02 10:57:27.231191] INFO: moduleinvoker: cached.v3 运行完成[1.154063s].

[2022-06-02 10:57:27.276930] INFO: moduleinvoker: join.v3 开始运行..

[2022-06-02 10:57:43.000699] INFO: join: /data, 行数=1159/1170, 耗时=0.232644s

[2022-06-02 10:57:43.118794] INFO: join: 最终行数: 1159

[2022-06-02 10:57:43.234190] INFO: moduleinvoker: join.v3 运行完成[15.957246s].

[2022-06-02 10:57:43.292796] INFO: moduleinvoker: join.v3 开始运行..

[2022-06-02 10:57:47.266717] INFO: join: /data, 行数=10/268823, 耗时=3.53994s

[2022-06-02 10:57:47.325824] INFO: join: 最终行数: 10

[2022-06-02 10:57:47.377178] INFO: moduleinvoker: join.v3 运行完成[4.084382s].

[2022-06-02 10:57:47.386835] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-06-02 10:57:47.537115] INFO: derived_feature_extractor: 提取完成 double_low = close + bond_prem_ratio, 0.001s

[2022-06-02 10:57:47.562611] INFO: derived_feature_extractor: 提取完成 rank_swing_volatility_5 = nanstd((high-low)/pre_close, 120)*sqrt(200)*100, 0.024s

[2022-06-02 10:57:47.797748] INFO: derived_feature_extractor: /data, 1159

[2022-06-02 10:57:47.916264] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.529417s].

[2022-06-02 10:57:47.989293] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-06-02 10:57:48.154258] INFO: derived_feature_extractor: 提取完成 double_low = close + bond_prem_ratio, 0.001s

[2022-06-02 10:57:48.163664] INFO: derived_feature_extractor: 提取完成 rank_swing_volatility_5 = nanstd((high-low)/pre_close, 120)*sqrt(200)*100, 0.007s

[2022-06-02 10:57:48.286133] INFO: derived_feature_extractor: /data, 10

[2022-06-02 10:57:48.466228] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.47694s].

[2022-06-02 10:57:48.547791] INFO: moduleinvoker: join.v3 开始运行..

[2022-06-02 10:57:49.911460] INFO: join: /data, 行数=1100/1159, 耗时=0.225159s

[2022-06-02 10:57:49.948585] INFO: join: 最终行数: 1100

[2022-06-02 10:57:49.962279] INFO: moduleinvoker: join.v3 运行完成[1.414484s].

[2022-06-02 10:57:50.164735] INFO: moduleinvoker: cached.v3 开始运行..

[2022-06-02 10:57:50.402332] INFO: moduleinvoker: cached.v3 运行完成[0.237612s].

[2022-06-02 10:57:50.459072] INFO: moduleinvoker: stock_ranker_train.v6 开始运行..

[2022-06-02 10:57:50.628797] INFO: StockRanker: 特征预处理 ..

[2022-06-02 10:57:50.707806] INFO: StockRanker: prepare data: training ..

[2022-06-02 10:57:50.908795] INFO: StockRanker训练: ca23b8c8 准备训练: 1100 行数

[2022-06-02 10:57:50.910673] INFO: StockRanker训练: AI模型训练，将在1100*3=0.33万数据上对模型训练进行2轮迭代训练。预计将需要1～2分钟。请耐心等待。

[2022-06-02 10:57:51.191689] INFO: StockRanker训练: 正在训练 ..

[2022-06-02 10:57:51.285831] INFO: StockRanker训练: 任务状态: Pending

[2022-06-02 10:58:01.342984] INFO: StockRanker训练: 任务状态: Running

[2022-06-02 10:59:01.687307] INFO: StockRanker训练: 00:01:02.1883206, finished iteration 1

[2022-06-02 10:59:01.689909] INFO: StockRanker训练: 00:01:02.1943695, finished iteration 2

[2022-06-02 10:59:01.692135] INFO: StockRanker训练: 任务状态: Succeeded

[2022-06-02 10:59:01.906090] INFO: moduleinvoker: stock_ranker_train.v6 运行完成[71.44701s].

[2022-06-02 10:59:01.916727] INFO: moduleinvoker: stock_ranker_predict.v5 开始运行..

[2022-06-02 10:59:02.211939] INFO: StockRanker预测: /data ..

[2022-06-02 10:59:02.607115] INFO: moduleinvoker: stock_ranker_predict.v5 运行完成[0.690382s].

[2022-06-02 10:59:02.621970] INFO: moduleinvoker: cached.v3 开始运行..

[2022-06-02 10:59:02.739368] INFO: moduleinvoker: cached.v3 运行完成[0.117407s].

[2022-06-02 10:59:02.780021] INFO: moduleinvoker: hfbacktest.v1 开始运行..

[2022-06-02 10:59:02.786568] INFO: hfbacktest: passed-in daily_data_ds:None

[2022-06-02 10:59:02.788387] INFO: hfbacktest: passed-in minute_data_ds:None

[2022-06-02 10:59:02.790036] INFO: hfbacktest: passed-in tick_data_ds:None

[2022-06-02 10:59:02.791317] INFO: hfbacktest: passed-in each_data_ds:None

[2022-06-02 10:59:02.792359] INFO: hfbacktest: passed-in dominant_data_ds:None

[2022-06-02 10:59:02.794052] INFO: hfbacktest: passed-in benchmark_data_ds:None

[2022-06-02 10:59:02.795728] INFO: hfbacktest: passed-in trading_calendar_ds:None

[2022-06-02 10:59:02.797539] INFO: hfbacktest: biglearning V1.4.12

[2022-06-02 10:59:02.799404] INFO: hfbacktest: bigtrader v1.9.6 2022-05-31

[2022-06-02 10:59:02.801121] INFO: hfbacktest: strategy callbacks:{'on_init': , 'on_stop': , 'on_start': , 'handle_data': , 'handle_tick': , 'handle_trade': , 'handle_order': }

[2022-06-02 10:59:02.830877] INFO: hfbacktest: begin reading history data, 2021-06-30 00:00:00~2021-12-30, disable_cache:True, replay_bdb:1

[2022-06-02 10:59:02.832884] INFO: hfbacktest: reading benchmark data 2021-06-29 00:00:00~2021-12-30...

[2022-06-02 10:59:03.173697] INFO: hfbacktest: reading daily data 2020-06-24 00:00:00~2021-12-30...

[2022-06-02 10:59:03.485955] INFO: hfbacktest: cached_benchmark_ds:DataSource(6950b98dc41e4c40b71859159c7b37dfT)

[2022-06-02 10:59:03.487761] INFO: hfbacktest: cached_daily_ds:None

[2022-06-02 10:59:03.488934] INFO: hfbacktest: cached_minute_ds:None

[2022-06-02 10:59:03.490269] INFO: hfbacktest: cached_tick_ds:None

[2022-06-02 10:59:03.491458] INFO: hfbacktest: cached_each_ds:None

[2022-06-02 10:59:03.492711] INFO: hfbacktest: dominant_data_ds:None

[2022-06-02 10:59:03.494495] INFO: hfbacktest: read history data done, call run_backtest()

[2022-06-02 10:59:08.387081] INFO: hfbacktest: backtest done, raw_perf_ds:DataSource(aabcb8e6959b43e4b7a72f5393213d52T)

[2022-06-02 10:59:09.239350] INFO: moduleinvoker: hfbacktest.v1 运行完成[6.459339s].

[2022-06-02 10:59:09.242140] INFO: moduleinvoker: hftrade.v2 运行完成[6.494524s].

[2022-06-02 10:59:09.322784] INFO: moduleinvoker: input_features.v1 开始运行..

[2022-06-02 10:59:09.338066] INFO: moduleinvoker: 命中缓存

[2022-06-02 10:59:09.340085] INFO: moduleinvoker: input_features.v1 运行完成[0.017333s].

[2022-06-02 10:59:09.356013] INFO: moduleinvoker: cached.v3 开始运行..

[2022-06-02 10:59:09.366635] INFO: moduleinvoker: 命中缓存

[2022-06-02 10:59:09.368863] INFO: moduleinvoker: cached.v3 运行完成[0.012877s].

[2022-06-02 10:59:09.381126] INFO: moduleinvoker: use_datasource.v1 开始运行..

[2022-06-02 10:59:09.390308] INFO: moduleinvoker: 命中缓存

[2022-06-02 10:59:09.392589] INFO: moduleinvoker: use_datasource.v1 运行完成[0.011489s].

[2022-06-02 10:59:09.421846] INFO: moduleinvoker: use_datasource.v1 开始运行..

[2022-06-02 10:59:09.478773] INFO: moduleinvoker: 命中缓存

[2022-06-02 10:59:09.483443] INFO: moduleinvoker: use_datasource.v1 运行完成[0.061582s].

[2022-06-02 10:59:09.530342] INFO: moduleinvoker: cached.v3 开始运行..

[2022-06-02 10:59:09.540323] INFO: moduleinvoker: 命中缓存

[2022-06-02 10:59:09.542809] INFO: moduleinvoker: cached.v3 运行完成[0.012494s].

[2022-06-02 10:59:09.556387] INFO: moduleinvoker: cached.v3 开始运行..

[2022-06-02 10:59:09.563704] INFO: moduleinvoker: 命中缓存

[2022-06-02 10:59:09.565840] INFO: moduleinvoker: cached.v3 运行完成[0.009469s].

[2022-06-02 10:59:09.573718] INFO: moduleinvoker: auto_labeler_on_datasource.v1 开始运行..

[2022-06-02 10:59:09.593154] INFO: moduleinvoker: 命中缓存

[2022-06-02 10:59:09.595583] INFO: moduleinvoker: auto_labeler_on_datasource.v1 运行完成[0.021862s].

[2022-06-02 10:59:09.621629] INFO: moduleinvoker: trade_data_generation.v1 开始运行..

[2022-06-02 10:59:09.924743] INFO: moduleinvoker: trade_data_generation.v1 运行完成[0.30311s].

[2022-06-02 10:59:09.940689] INFO: moduleinvoker: cached.v3 开始运行..

[2022-06-02 10:59:10.465129] INFO: moduleinvoker: cached.v3 运行完成[0.524461s].

[2022-06-02 10:59:10.477720] INFO: moduleinvoker: join.v3 开始运行..

[2022-06-02 10:59:13.994380] INFO: join: /data, 行数=1159/1170, 耗时=0.648673s

[2022-06-02 10:59:14.031260] INFO: join: 最终行数: 1159

[2022-06-02 10:59:14.048888] INFO: moduleinvoker: join.v3 运行完成[3.571166s].

[2022-06-02 10:59:14.058215] INFO: moduleinvoker: join.v3 开始运行..

[2022-06-02 10:59:15.797177] INFO: join: /data, 行数=10/268823, 耗时=1.581613s

[2022-06-02 10:59:15.826474] INFO: join: 最终行数: 10

[2022-06-02 10:59:15.842422] INFO: moduleinvoker: join.v3 运行完成[1.784184s].

[2022-06-02 10:59:15.853202] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-06-02 10:59:15.940360] INFO: derived_feature_extractor: 提取完成 double_low = close + bond_prem_ratio, 0.001s

[2022-06-02 10:59:15.975847] INFO: derived_feature_extractor: 提取完成 rank_swing_volatility_5 = nanstd((high-low)/pre_close, 5)*sqrt(200)*100, 0.033s

[2022-06-02 10:59:16.086734] INFO: derived_feature_extractor: /data, 1159

[2022-06-02 10:59:16.163494] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.310296s].

[2022-06-02 10:59:16.174289] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-06-02 10:59:16.231652] INFO: derived_feature_extractor: 提取完成 double_low = close + bond_prem_ratio, 0.002s

[2022-06-02 10:59:16.243792] INFO: derived_feature_extractor: 提取完成 rank_swing_volatility_5 = nanstd((high-low)/pre_close, 5)*sqrt(200)*100, 0.010s

[2022-06-02 10:59:16.321423] INFO: derived_feature_extractor: /data, 10

[2022-06-02 10:59:16.456199] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.281908s].

[2022-06-02 10:59:16.469235] INFO: moduleinvoker: join.v3 开始运行..

[2022-06-02 10:59:17.374465] INFO: join: /data, 行数=1100/1159, 耗时=0.123245s

[2022-06-02 10:59:17.415128] INFO: join: 最终行数: 1100

[2022-06-02 10:59:17.462675] INFO: moduleinvoker: join.v3 运行完成[0.993432s].

[2022-06-02 10:59:17.475403] INFO: moduleinvoker: cached.v3 开始运行..

[2022-06-02 10:59:17.665475] INFO: moduleinvoker: cached.v3 运行完成[0.190091s].

[2022-06-02 10:59:17.676549] INFO: moduleinvoker: stock_ranker_train.v6 开始运行..

[2022-06-02 10:59:17.770331] INFO: StockRanker: 特征预处理 ..

[2022-06-02 10:59:17.804593] INFO: StockRanker: prepare data: training ..

[2022-06-02 10:59:17.900937] INFO: StockRanker训练: fe1de02c 准备训练: 1100 行数

[2022-06-02 10:59:17.903033] INFO: StockRanker训练: AI模型训练，将在1100*3=0.33万数据上对模型训练进行2轮迭代训练。预计将需要1～2分钟。请耐心等待。

[2022-06-02 10:59:18.096178] INFO: StockRanker训练: 正在训练 ..

[2022-06-02 10:59:18.172397] INFO: StockRanker训练: 任务状态: Pending

[2022-06-02 10:59:28.233976] INFO: StockRanker训练: 任务状态: Running

[2022-06-02 11:00:28.536491] INFO: StockRanker训练: 00:01:01.9443245, finished iteration 1

[2022-06-02 11:00:28.539091] INFO: StockRanker训练: 00:01:01.9498569, finished iteration 2

[2022-06-02 11:00:28.541124] INFO: StockRanker训练: 任务状态: Succeeded

[2022-06-02 11:00:28.844768] INFO: moduleinvoker: stock_ranker_train.v6 运行完成[71.168201s].

[2022-06-02 11:00:28.860172] INFO: moduleinvoker: stock_ranker_predict.v5 开始运行..

[2022-06-02 11:00:29.244212] INFO: StockRanker预测: /data ..

[2022-06-02 11:00:29.770223] INFO: moduleinvoker: stock_ranker_predict.v5 运行完成[0.910047s].

[2022-06-02 11:00:29.786593] INFO: moduleinvoker: cached.v3 开始运行..

[2022-06-02 11:00:29.988367] INFO: moduleinvoker: cached.v3 运行完成[0.201774s].

[2022-06-02 11:00:30.043616] INFO: moduleinvoker: hfbacktest.v1 开始运行..

[2022-06-02 11:00:30.059335] INFO: hfbacktest: passed-in daily_data_ds:None

[2022-06-02 11:00:30.061162] INFO: hfbacktest: passed-in minute_data_ds:None

[2022-06-02 11:00:30.062921] INFO: hfbacktest: passed-in tick_data_ds:None

[2022-06-02 11:00:30.064470] INFO: hfbacktest: passed-in each_data_ds:None

[2022-06-02 11:00:30.066006] INFO: hfbacktest: passed-in dominant_data_ds:None

[2022-06-02 11:00:30.067463] INFO: hfbacktest: passed-in benchmark_data_ds:None

[2022-06-02 11:00:30.068985] INFO: hfbacktest: passed-in trading_calendar_ds:None

[2022-06-02 11:00:30.070832] INFO: hfbacktest: biglearning V1.4.12

[2022-06-02 11:00:30.072189] INFO: hfbacktest: bigtrader v1.9.6 2022-05-31

[2022-06-02 11:00:30.073606] INFO: hfbacktest: strategy callbacks:{'on_init': , 'on_stop': , 'on_start': , 'handle_data': , 'handle_tick': , 'handle_trade': , 'handle_order': }

[2022-06-02 11:00:30.092478] INFO: hfbacktest: begin reading history data, 2021-06-30 00:00:00~2021-12-30, disable_cache:True, replay_bdb:1

[2022-06-02 11:00:30.094442] INFO: hfbacktest: reading benchmark data 2021-06-29 00:00:00~2021-12-30...

[2022-06-02 11:00:30.472533] INFO: hfbacktest: reading daily data 2020-06-24 00:00:00~2021-12-30...

[2022-06-02 11:00:30.978584] INFO: hfbacktest: cached_benchmark_ds:DataSource(2911c2589d5241fb97a051dc452a88edT)

[2022-06-02 11:00:30.981107] INFO: hfbacktest: cached_daily_ds:None

[2022-06-02 11:00:30.987279] INFO: hfbacktest: cached_minute_ds:None

[2022-06-02 11:00:30.989040] INFO: hfbacktest: cached_tick_ds:None

[2022-06-02 11:00:30.990470] INFO: hfbacktest: cached_each_ds:None

[2022-06-02 11:00:30.991864] INFO: hfbacktest: dominant_data_ds:None

[2022-06-02 11:00:30.993243] INFO: hfbacktest: read history data done, call run_backtest()

[2022-06-02 11:00:36.643330] INFO: hfbacktest: backtest done, raw_perf_ds:DataSource(24883e72afa744a38e82e34d3bb7c8a2T)

[2022-06-02 11:00:37.471171] INFO: moduleinvoker: hfbacktest.v1 运行完成[7.427574s].

[2022-06-02 11:00:37.475942] INFO: moduleinvoker: hftrade.v2 运行完成[7.476509s].

[2022-06-02 11:00:37.478009] INFO: moduleinvoker: hyper_parameter_search.v1 运行完成[704.23399s].

Fitting 1 folds for each of 6 candidates, totalling 6 fits
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[CV 1/1; 1/6] START m14.params={"stock_count": 3, "hold_days": 3}, m3.features=
    double_low = close + bond_prem_ratio
    remain_size
    rank_swing_volatility_5 = nanstd((high-low)/pre_close, 5)*sqrt(200)*100

Index(['remain_size', 'instrument', 'equ_trading_code', 'conversion_chg_pct',
       'date', 'conversion_price', 'name', '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'],
      dtype='object')
Index(['remain_size', 'instrument', 'equ_trading_code', 'conversion_chg_pct',
       'date', 'conversion_price', 'name', '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'],
      dtype='object')

Index(['instrument', 'date', 'pre_close', 'name_x', 'open', 'high', 'low',
       'deal_number', 'volume', 'amount', 'accrued_interest',
       'yield_to_maturity', 'vwap', 'gross_close', 'net_close',
       'bond_prem_ratio', 'close', 'close_equ', 'conversion_chg_pct',
       'conversion_chg_pct_week', 'conversion_price', 'equ_name',
       'equ_trading_code', 'name_y', 'pure_bond_prem_ratio', 'pure_bond_ratio',
       'redemption_price', 'remain_size', 'total_size', 'trading_code',
       'double_low', 'rank_swing_volatility_5'],
      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-87e976b2020e4f7d915cb4e2cade0bbb"}/bigcharts-data-end

2022-06-02 10:50:15.956918 init history datas... 
2022-06-02 10:50:15.958249 init history datas done. 
2022-06-02 10:50:15.986029 run_backtest() capital_base:1000000, frequency:1d, product_type:conbond, date:2021-06-30 ~ 2021-12-30 
2022-06-02 10:50:15.986389 run_backtest() running... 
2022-06-02 10:50:15.996010 initial contracts len=0 
2022-06-02 10:50:15.996346 backtest inited. 
{'data': DataSource(394b054dc70e498e9f7d35081d63b903T)}
        date  instrument     score  position
0 2021-06-30  123082.ZCB  0.134401         1
1 2021-06-30  128141.ZCB  0.134401         2
2 2021-06-30  128143.ZCB  0.134401         3
3 2021-06-30  128144.ZCB  0.134401         4
4 2021-06-30  123079.ZCB  0.134401         5
5 2021-06-30  123113.ZCB  0.134401         6
6 2021-06-30  127021.ZCB  0.029014         7
7 2021-06-30  123042.ZCB  0.029014         8
8 2021-06-30  123043.ZCB  0.029014         9
9 2021-06-30  110067.HCB -0.017028        10
2022-06-02 10:50:16.121258 backtest transforming 1d, bars=1... 
2022-06-02 10:50:16.121672 transform start_trading_day=2021-06-30 00:00:00, simulation period=2021-06-30 ~ 2021-12-30 
2022-06-02 10:50:16.121714 transform source=None, before_start_days=8 
2022-06-02 10:50:16.121746 transform replay_func=<cyfunction BacktestEngine.transform.<locals>.replay_bars_dt at 0x7f2e61fb3380> 
处理data BarDatas(current_dt:2021-06-30 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-07-01 15:00:00)
处理data BarDatas(current_dt:2021-07-02 15:00:00)
处理data BarDatas(current_dt:2021-07-05 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-07-06 15:00:00)
处理data BarDatas(current_dt:2021-07-07 15:00:00)
处理data BarDatas(current_dt:2021-07-08 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-07-09 15:00:00)
处理data BarDatas(current_dt:2021-07-12 15:00:00)
处理data BarDatas(current_dt:2021-07-13 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-07-14 15:00:00)
处理data BarDatas(current_dt:2021-07-15 15:00:00)
处理data BarDatas(current_dt:2021-07-16 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-07-19 15:00:00)
处理data BarDatas(current_dt:2021-07-20 15:00:00)
处理data BarDatas(current_dt:2021-07-21 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-07-22 15:00:00)
处理data BarDatas(current_dt:2021-07-23 15:00:00)
处理data BarDatas(current_dt:2021-07-26 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-07-27 15:00:00)
处理data BarDatas(current_dt:2021-07-28 15:00:00)
处理data BarDatas(current_dt:2021-07-29 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-07-30 15:00:00)
处理data BarDatas(current_dt:2021-08-02 15:00:00)
处理data BarDatas(current_dt:2021-08-03 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-08-04 15:00:00)
处理data BarDatas(current_dt:2021-08-05 15:00:00)
处理data BarDatas(current_dt:2021-08-06 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-08-09 15:00:00)
处理data BarDatas(current_dt:2021-08-10 15:00:00)
处理data BarDatas(current_dt:2021-08-11 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-08-12 15:00:00)
处理data BarDatas(current_dt:2021-08-13 15:00:00)
处理data BarDatas(current_dt:2021-08-16 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-08-17 15:00:00)
处理data BarDatas(current_dt:2021-08-18 15:00:00)
处理data BarDatas(current_dt:2021-08-19 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-08-20 15:00:00)
处理data BarDatas(current_dt:2021-08-23 15:00:00)
处理data BarDatas(current_dt:2021-08-24 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-08-25 15:00:00)
处理data BarDatas(current_dt:2021-08-26 15:00:00)
处理data BarDatas(current_dt:2021-08-27 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-08-30 15:00:00)
处理data BarDatas(current_dt:2021-08-31 15:00:00)
处理data BarDatas(current_dt:2021-09-01 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-09-02 15:00:00)
处理data BarDatas(current_dt:2021-09-03 15:00:00)
处理data BarDatas(current_dt:2021-09-06 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-09-07 15:00:00)
处理data BarDatas(current_dt:2021-09-08 15:00:00)
处理data BarDatas(current_dt:2021-09-09 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-09-10 15:00:00)
处理data BarDatas(current_dt:2021-09-13 15:00:00)
处理data BarDatas(current_dt:2021-09-14 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-09-15 15:00:00)
处理data BarDatas(current_dt:2021-09-16 15:00:00)
处理data BarDatas(current_dt:2021-09-17 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-09-22 15:00:00)
处理data BarDatas(current_dt:2021-09-23 15:00:00)
处理data BarDatas(current_dt:2021-09-24 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-09-27 15:00:00)
处理data BarDatas(current_dt:2021-09-28 15:00:00)
处理data BarDatas(current_dt:2021-09-29 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-09-30 15:00:00)
处理data BarDatas(current_dt:2021-10-08 15:00:00)
处理data BarDatas(current_dt:2021-10-11 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-10-12 15:00:00)
处理data BarDatas(current_dt:2021-10-13 15:00:00)
处理data BarDatas(current_dt:2021-10-14 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-10-15 15:00:00)
处理data BarDatas(current_dt:2021-10-18 15:00:00)
处理data BarDatas(current_dt:2021-10-19 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-10-20 15:00:00)
处理data BarDatas(current_dt:2021-10-21 15:00:00)
处理data BarDatas(current_dt:2021-10-22 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-10-25 15:00:00)
处理data BarDatas(current_dt:2021-10-26 15:00:00)
处理data BarDatas(current_dt:2021-10-27 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-10-28 15:00:00)
处理data BarDatas(current_dt:2021-10-29 15:00:00)
处理data BarDatas(current_dt:2021-11-01 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-11-02 15:00:00)
处理data BarDatas(current_dt:2021-11-03 15:00:00)
处理data BarDatas(current_dt:2021-11-04 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-11-05 15:00:00)
处理data BarDatas(current_dt:2021-11-08 15:00:00)
处理data BarDatas(current_dt:2021-11-09 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-11-10 15:00:00)
处理data BarDatas(current_dt:2021-11-11 15:00:00)
处理data BarDatas(current_dt:2021-11-12 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-11-15 15:00:00)
处理data BarDatas(current_dt:2021-11-16 15:00:00)
处理data BarDatas(current_dt:2021-11-17 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-11-18 15:00:00)
处理data BarDatas(current_dt:2021-11-19 15:00:00)
处理data BarDatas(current_dt:2021-11-22 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-11-23 15:00:00)
处理data BarDatas(current_dt:2021-11-24 15:00:00)
处理data BarDatas(current_dt:2021-11-25 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-11-26 15:00:00)
处理data BarDatas(current_dt:2021-11-29 15:00:00)
处理data BarDatas(current_dt:2021-11-30 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-12-01 15:00:00)
处理data BarDatas(current_dt:2021-12-02 15:00:00)
处理data BarDatas(current_dt:2021-12-03 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-12-06 15:00:00)
处理data BarDatas(current_dt:2021-12-07 15:00:00)
处理data BarDatas(current_dt:2021-12-08 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-12-09 15:00:00)
处理data BarDatas(current_dt:2021-12-10 15:00:00)
处理data BarDatas(current_dt:2021-12-13 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-12-14 15:00:00)
处理data BarDatas(current_dt:2021-12-15 15:00:00)
处理data BarDatas(current_dt:2021-12-16 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-12-17 15:00:00)
处理data BarDatas(current_dt:2021-12-20 15:00:00)
处理data BarDatas(current_dt:2021-12-21 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-12-22 15:00:00)
处理data BarDatas(current_dt:2021-12-23 15:00:00)
处理data BarDatas(current_dt:2021-12-24 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-12-27 15:00:00)
处理data BarDatas(current_dt:2021-12-28 15:00:00)
处理data BarDatas(current_dt:2021-12-29 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-12-30 15:00:00)
2022-06-02 10:50:19.889298 backtest run end! 
2022-06-02 10:50:20.476320 run_backtest() finished! time cost 4.49s!

2022-06-02 10:50:22.858629 perf_render raw_perf=DataSource(f8101765288641198fdc2224417323bbT), benchmark_data=DataSource(eaf6509915e84102a631b2405dc9b3ffT), process stats...
2022-06-02 10:50:23.587147 perf_render process transactions...
2022-06-02 10:50:23.748712 perf_render process positions...
2022-06-02 10:50:23.873460 perf_render process logs...
2022-06-02 10:50:24.270229 perf_render process plot...

收益率-0.98%
年化收益率-1.89%
基准收益率-5.79%
阿尔法-0.05
贝塔0.02
夏普比率-2.98
胜率0.0
盈亏比0.0
收益波动率1.65%
信息比率0.03
最大回撤1.33%

bigcharts-data-start/{"__type":"tabs","__id":"bigchart-8c458976292c43e0a1c46bdcbed6d357"}/bigcharts-data-end

[CV 1/1; 1/6] END m14.params={"stock_count": 3, "hold_days": 3}, m3.features=
    double_low = close + bond_prem_ratio
    remain_size
    rank_swing_volatility_5 = nanstd((high-low)/pre_close, 5)*sqrt(200)*100
    ; score: (test=-2.984) total time= 1.5min
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:  1.5min remaining:    0.0s
[CV 1/1; 2/6] START m14.params={"stock_count": 3, "hold_days": 3}, m3.features=
    double_low = close + bond_prem_ratio
    remain_size
    rank_swing_volatility_5 = nanstd((high-low)/pre_close, 6)*sqrt(200)*100

Index(['remain_size', 'instrument', 'equ_trading_code', 'conversion_chg_pct',
       'date', 'conversion_price', 'name', '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'],
      dtype='object')
Index(['remain_size', 'instrument', 'equ_trading_code', 'conversion_chg_pct',
       'date', 'conversion_price', 'name', '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'],
      dtype='object')

Index(['instrument', 'date', 'pre_close', 'name_x', 'open', 'high', 'low',
       'deal_number', 'volume', 'amount', 'accrued_interest',
       'yield_to_maturity', 'vwap', 'gross_close', 'net_close',
       'bond_prem_ratio', 'close', 'close_equ', 'conversion_chg_pct',
       'conversion_chg_pct_week', 'conversion_price', 'equ_name',
       'equ_trading_code', 'name_y', 'pure_bond_prem_ratio', 'pure_bond_ratio',
       'redemption_price', 'remain_size', 'total_size', 'trading_code',
       'double_low', 'rank_swing_volatility_5'],
      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-c99bc2bb9b60437bbef564e3a5dcd3d4"}/bigcharts-data-end

2022-06-02 10:51:56.845937 init history datas... 
2022-06-02 10:51:56.848510 init history datas done. 
2022-06-02 10:51:56.861246 run_backtest() capital_base:1000000, frequency:1d, product_type:conbond, date:2021-06-30 ~ 2021-12-30 
2022-06-02 10:51:56.861759 run_backtest() running... 
2022-06-02 10:51:56.870902 initial contracts len=0 
2022-06-02 10:51:56.871134 backtest inited. 
{'data': DataSource(c49ba232e26047fd96d2eb2d52affe21T)}
        date  instrument     score  position
0 2021-06-30  123082.ZCB  0.140023         1
1 2021-06-30  128143.ZCB  0.140023         2
2 2021-06-30  128144.ZCB  0.140023         3
3 2021-06-30  123079.ZCB  0.140023         4
4 2021-06-30  123113.ZCB  0.140023         5
5 2021-06-30  127021.ZCB  0.033559         6
6 2021-06-30  123042.ZCB  0.033559         7
7 2021-06-30  123043.ZCB  0.033559         8
8 2021-06-30  128141.ZCB -0.011513         9
9 2021-06-30  110067.HCB -0.011513        10
2022-06-02 10:51:56.933792 backtest transforming 1d, bars=1... 
2022-06-02 10:51:56.934176 transform start_trading_day=2021-06-30 00:00:00, simulation period=2021-06-30 ~ 2021-12-30 
2022-06-02 10:51:56.934225 transform source=None, before_start_days=8 
2022-06-02 10:51:56.934267 transform replay_func=<cyfunction BacktestEngine.transform.<locals>.replay_bars_dt at 0x7f2e61fb36c0> 
处理data BarDatas(current_dt:2021-06-30 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-07-01 15:00:00)
处理data BarDatas(current_dt:2021-07-02 15:00:00)
处理data BarDatas(current_dt:2021-07-05 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-07-06 15:00:00)
处理data BarDatas(current_dt:2021-07-07 15:00:00)
处理data BarDatas(current_dt:2021-07-08 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-07-09 15:00:00)
处理data BarDatas(current_dt:2021-07-12 15:00:00)
处理data BarDatas(current_dt:2021-07-13 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-07-14 15:00:00)
处理data BarDatas(current_dt:2021-07-15 15:00:00)
处理data BarDatas(current_dt:2021-07-16 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-07-19 15:00:00)
处理data BarDatas(current_dt:2021-07-20 15:00:00)
处理data BarDatas(current_dt:2021-07-21 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-07-22 15:00:00)
处理data BarDatas(current_dt:2021-07-23 15:00:00)
处理data BarDatas(current_dt:2021-07-26 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-07-27 15:00:00)
处理data BarDatas(current_dt:2021-07-28 15:00:00)
处理data BarDatas(current_dt:2021-07-29 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-07-30 15:00:00)
处理data BarDatas(current_dt:2021-08-02 15:00:00)
处理data BarDatas(current_dt:2021-08-03 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-08-04 15:00:00)
处理data BarDatas(current_dt:2021-08-05 15:00:00)
处理data BarDatas(current_dt:2021-08-06 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-08-09 15:00:00)
处理data BarDatas(current_dt:2021-08-10 15:00:00)
处理data BarDatas(current_dt:2021-08-11 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-08-12 15:00:00)
处理data BarDatas(current_dt:2021-08-13 15:00:00)
处理data BarDatas(current_dt:2021-08-16 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-08-17 15:00:00)
处理data BarDatas(current_dt:2021-08-18 15:00:00)
处理data BarDatas(current_dt:2021-08-19 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-08-20 15:00:00)
处理data BarDatas(current_dt:2021-08-23 15:00:00)
处理data BarDatas(current_dt:2021-08-24 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-08-25 15:00:00)
处理data BarDatas(current_dt:2021-08-26 15:00:00)
处理data BarDatas(current_dt:2021-08-27 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-08-30 15:00:00)
处理data BarDatas(current_dt:2021-08-31 15:00:00)
处理data BarDatas(current_dt:2021-09-01 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-09-02 15:00:00)
处理data BarDatas(current_dt:2021-09-03 15:00:00)
处理data BarDatas(current_dt:2021-09-06 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-09-07 15:00:00)
处理data BarDatas(current_dt:2021-09-08 15:00:00)
处理data BarDatas(current_dt:2021-09-09 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-09-10 15:00:00)
处理data BarDatas(current_dt:2021-09-13 15:00:00)
处理data BarDatas(current_dt:2021-09-14 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-09-15 15:00:00)
处理data BarDatas(current_dt:2021-09-16 15:00:00)
处理data BarDatas(current_dt:2021-09-17 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-09-22 15:00:00)
处理data BarDatas(current_dt:2021-09-23 15:00:00)
处理data BarDatas(current_dt:2021-09-24 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-09-27 15:00:00)
处理data BarDatas(current_dt:2021-09-28 15:00:00)
处理data BarDatas(current_dt:2021-09-29 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-09-30 15:00:00)
处理data BarDatas(current_dt:2021-10-08 15:00:00)
处理data BarDatas(current_dt:2021-10-11 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-10-12 15:00:00)
处理data BarDatas(current_dt:2021-10-13 15:00:00)
处理data BarDatas(current_dt:2021-10-14 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-10-15 15:00:00)
处理data BarDatas(current_dt:2021-10-18 15:00:00)
处理data BarDatas(current_dt:2021-10-19 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-10-20 15:00:00)
处理data BarDatas(current_dt:2021-10-21 15:00:00)
处理data BarDatas(current_dt:2021-10-22 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-10-25 15:00:00)
处理data BarDatas(current_dt:2021-10-26 15:00:00)
处理data BarDatas(current_dt:2021-10-27 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-10-28 15:00:00)
处理data BarDatas(current_dt:2021-10-29 15:00:00)
处理data BarDatas(current_dt:2021-11-01 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-11-02 15:00:00)
处理data BarDatas(current_dt:2021-11-03 15:00:00)
处理data BarDatas(current_dt:2021-11-04 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-11-05 15:00:00)
处理data BarDatas(current_dt:2021-11-08 15:00:00)
处理data BarDatas(current_dt:2021-11-09 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-11-10 15:00:00)
处理data BarDatas(current_dt:2021-11-11 15:00:00)
处理data BarDatas(current_dt:2021-11-12 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-11-15 15:00:00)
处理data BarDatas(current_dt:2021-11-16 15:00:00)
处理data BarDatas(current_dt:2021-11-17 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-11-18 15:00:00)
处理data BarDatas(current_dt:2021-11-19 15:00:00)
处理data BarDatas(current_dt:2021-11-22 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-11-23 15:00:00)
处理data BarDatas(current_dt:2021-11-24 15:00:00)
处理data BarDatas(current_dt:2021-11-25 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-11-26 15:00:00)
处理data BarDatas(current_dt:2021-11-29 15:00:00)
处理data BarDatas(current_dt:2021-11-30 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-12-01 15:00:00)
处理data BarDatas(current_dt:2021-12-02 15:00:00)
处理data BarDatas(current_dt:2021-12-03 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-12-06 15:00:00)
处理data BarDatas(current_dt:2021-12-07 15:00:00)
处理data BarDatas(current_dt:2021-12-08 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-12-09 15:00:00)
处理data BarDatas(current_dt:2021-12-10 15:00:00)
处理data BarDatas(current_dt:2021-12-13 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-12-14 15:00:00)
处理data BarDatas(current_dt:2021-12-15 15:00:00)
处理data BarDatas(current_dt:2021-12-16 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-12-17 15:00:00)
处理data BarDatas(current_dt:2021-12-20 15:00:00)
处理data BarDatas(current_dt:2021-12-21 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-12-22 15:00:00)
处理data BarDatas(current_dt:2021-12-23 15:00:00)
处理data BarDatas(current_dt:2021-12-24 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-12-27 15:00:00)
处理data BarDatas(current_dt:2021-12-28 15:00:00)
处理data BarDatas(current_dt:2021-12-29 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-12-30 15:00:00)
2022-06-02 10:51:59.719526 backtest run end! 
2022-06-02 10:52:00.266364 run_backtest() finished! time cost 3.405s!

2022-06-02 10:52:02.090264 perf_render raw_perf=DataSource(a2378003e8af42f88ed5ced64e588437T), benchmark_data=DataSource(3a91829a26644af880bef7122000a27bT), process stats...
2022-06-02 10:52:02.658655 perf_render process transactions...
2022-06-02 10:52:02.788066 perf_render process positions...
2022-06-02 10:52:02.917315 perf_render process logs...
2022-06-02 10:52:03.269500 perf_render process plot...

收益率-1.19%
年化收益率-2.29%
基准收益率-5.79%
阿尔法-0.05
贝塔0.02
夏普比率-2.89
胜率0.0
盈亏比0.0
收益波动率1.85%
信息比率0.03
最大回撤1.29%

bigcharts-data-start/{"__type":"tabs","__id":"bigchart-9eee3db811b047a9a35df50d06edd511"}/bigcharts-data-end

[CV 1/1; 2/6] END m14.params={"stock_count": 3, "hold_days": 3}, m3.features=
    double_low = close + bond_prem_ratio
    remain_size
    rank_swing_volatility_5 = nanstd((high-low)/pre_close, 6)*sqrt(200)*100
    ; score: (test=-2.891) total time= 1.6min
[Parallel(n_jobs=1)]: Done   2 out of   2 | elapsed:  3.2min remaining:    0.0s
[CV 1/1; 3/6] START m14.params={"stock_count": 3, "hold_days": 3}, m3.features=
    double_low = close + bond_prem_ratio
    remain_size
    rank_swing_volatility_5 = nanstd((high-low)/pre_close, 120)*sqrt(200)*100

Index(['remain_size', 'instrument', 'equ_trading_code', 'conversion_chg_pct',
       'date', 'conversion_price', 'name', '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'],
      dtype='object')
Index(['remain_size', 'instrument', 'equ_trading_code', 'conversion_chg_pct',
       'date', 'conversion_price', 'name', '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'],
      dtype='object')

Index(['instrument', 'date', 'pre_close', 'name_x', 'open', 'high', 'low',
       'deal_number', 'volume', 'amount', 'accrued_interest',
       'yield_to_maturity', 'vwap', 'gross_close', 'net_close',
       'bond_prem_ratio', 'close', 'close_equ', 'conversion_chg_pct',
       'conversion_chg_pct_week', 'conversion_price', 'equ_name',
       'equ_trading_code', 'name_y', 'pure_bond_prem_ratio', 'pure_bond_ratio',
       'redemption_price', 'remain_size', 'total_size', 'trading_code',
       'double_low', 'rank_swing_volatility_5'],
      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-433d3b796c6d477aaca832ca15f5944a"}/bigcharts-data-end

2022-06-02 10:53:27.589744 init history datas... 
2022-06-02 10:53:27.590714 init history datas done. 
2022-06-02 10:53:27.602265 run_backtest() capital_base:1000000, frequency:1d, product_type:conbond, date:2021-06-30 ~ 2021-12-30 
2022-06-02 10:53:27.602693 run_backtest() running... 
2022-06-02 10:53:27.610596 initial contracts len=0 
2022-06-02 10:53:27.611370 backtest inited. 
{'data': DataSource(a23486e43d84437696df984653403ac6T)}
        date  instrument     score  position
0 2021-06-30  123082.ZCB  0.164295         1
1 2021-06-30  128143.ZCB  0.164295         2
2 2021-06-30  128144.ZCB  0.164295         3
3 2021-06-30  123079.ZCB  0.164295         4
4 2021-06-30  123113.ZCB  0.164295         5
5 2021-06-30  127021.ZCB  0.028803         6
6 2021-06-30  123042.ZCB  0.028803         7
7 2021-06-30  123043.ZCB  0.028803         8
8 2021-06-30  128141.ZCB  0.024234         9
9 2021-06-30  110067.HCB  0.024234        10
2022-06-02 10:53:27.662341 backtest transforming 1d, bars=1... 
2022-06-02 10:53:27.662754 transform start_trading_day=2021-06-30 00:00:00, simulation period=2021-06-30 ~ 2021-12-30 
2022-06-02 10:53:27.662806 transform source=None, before_start_days=8 
2022-06-02 10:53:27.662846 transform replay_func=<cyfunction BacktestEngine.transform.<locals>.replay_bars_dt at 0x7f2e61c47520> 
处理data BarDatas(current_dt:2021-06-30 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-07-01 15:00:00)
处理data BarDatas(current_dt:2021-07-02 15:00:00)
处理data BarDatas(current_dt:2021-07-05 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-07-06 15:00:00)
处理data BarDatas(current_dt:2021-07-07 15:00:00)
处理data BarDatas(current_dt:2021-07-08 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-07-09 15:00:00)
处理data BarDatas(current_dt:2021-07-12 15:00:00)
处理data BarDatas(current_dt:2021-07-13 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-07-14 15:00:00)
处理data BarDatas(current_dt:2021-07-15 15:00:00)
处理data BarDatas(current_dt:2021-07-16 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-07-19 15:00:00)
处理data BarDatas(current_dt:2021-07-20 15:00:00)
处理data BarDatas(current_dt:2021-07-21 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-07-22 15:00:00)
处理data BarDatas(current_dt:2021-07-23 15:00:00)
处理data BarDatas(current_dt:2021-07-26 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-07-27 15:00:00)
处理data BarDatas(current_dt:2021-07-28 15:00:00)
处理data BarDatas(current_dt:2021-07-29 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-07-30 15:00:00)
处理data BarDatas(current_dt:2021-08-02 15:00:00)
处理data BarDatas(current_dt:2021-08-03 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-08-04 15:00:00)
处理data BarDatas(current_dt:2021-08-05 15:00:00)
处理data BarDatas(current_dt:2021-08-06 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-08-09 15:00:00)
处理data BarDatas(current_dt:2021-08-10 15:00:00)
处理data BarDatas(current_dt:2021-08-11 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-08-12 15:00:00)
处理data BarDatas(current_dt:2021-08-13 15:00:00)
处理data BarDatas(current_dt:2021-08-16 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-08-17 15:00:00)
处理data BarDatas(current_dt:2021-08-18 15:00:00)
处理data BarDatas(current_dt:2021-08-19 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-08-20 15:00:00)
处理data BarDatas(current_dt:2021-08-23 15:00:00)
处理data BarDatas(current_dt:2021-08-24 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-08-25 15:00:00)
处理data BarDatas(current_dt:2021-08-26 15:00:00)
处理data BarDatas(current_dt:2021-08-27 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-08-30 15:00:00)
处理data BarDatas(current_dt:2021-08-31 15:00:00)
处理data BarDatas(current_dt:2021-09-01 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-09-02 15:00:00)
处理data BarDatas(current_dt:2021-09-03 15:00:00)
处理data BarDatas(current_dt:2021-09-06 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-09-07 15:00:00)
处理data BarDatas(current_dt:2021-09-08 15:00:00)
处理data BarDatas(current_dt:2021-09-09 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-09-10 15:00:00)
处理data BarDatas(current_dt:2021-09-13 15:00:00)
处理data BarDatas(current_dt:2021-09-14 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-09-15 15:00:00)
处理data BarDatas(current_dt:2021-09-16 15:00:00)
处理data BarDatas(current_dt:2021-09-17 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-09-22 15:00:00)
处理data BarDatas(current_dt:2021-09-23 15:00:00)
处理data BarDatas(current_dt:2021-09-24 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-09-27 15:00:00)
处理data BarDatas(current_dt:2021-09-28 15:00:00)
处理data BarDatas(current_dt:2021-09-29 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-09-30 15:00:00)
处理data BarDatas(current_dt:2021-10-08 15:00:00)
处理data BarDatas(current_dt:2021-10-11 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-10-12 15:00:00)
处理data BarDatas(current_dt:2021-10-13 15:00:00)
处理data BarDatas(current_dt:2021-10-14 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-10-15 15:00:00)
处理data BarDatas(current_dt:2021-10-18 15:00:00)
处理data BarDatas(current_dt:2021-10-19 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-10-20 15:00:00)
处理data BarDatas(current_dt:2021-10-21 15:00:00)
处理data BarDatas(current_dt:2021-10-22 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-10-25 15:00:00)
处理data BarDatas(current_dt:2021-10-26 15:00:00)
处理data BarDatas(current_dt:2021-10-27 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-10-28 15:00:00)
处理data BarDatas(current_dt:2021-10-29 15:00:00)
处理data BarDatas(current_dt:2021-11-01 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-11-02 15:00:00)
处理data BarDatas(current_dt:2021-11-03 15:00:00)
处理data BarDatas(current_dt:2021-11-04 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-11-05 15:00:00)
处理data BarDatas(current_dt:2021-11-08 15:00:00)
处理data BarDatas(current_dt:2021-11-09 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-11-10 15:00:00)
处理data BarDatas(current_dt:2021-11-11 15:00:00)
处理data BarDatas(current_dt:2021-11-12 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-11-15 15:00:00)
处理data BarDatas(current_dt:2021-11-16 15:00:00)
处理data BarDatas(current_dt:2021-11-17 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-11-18 15:00:00)
处理data BarDatas(current_dt:2021-11-19 15:00:00)
处理data BarDatas(current_dt:2021-11-22 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-11-23 15:00:00)
处理data BarDatas(current_dt:2021-11-24 15:00:00)
处理data BarDatas(current_dt:2021-11-25 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-11-26 15:00:00)
处理data BarDatas(current_dt:2021-11-29 15:00:00)
处理data BarDatas(current_dt:2021-11-30 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-12-01 15:00:00)
处理data BarDatas(current_dt:2021-12-02 15:00:00)
处理data BarDatas(current_dt:2021-12-03 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-12-06 15:00:00)
处理data BarDatas(current_dt:2021-12-07 15:00:00)
处理data BarDatas(current_dt:2021-12-08 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-12-09 15:00:00)
处理data BarDatas(current_dt:2021-12-10 15:00:00)
处理data BarDatas(current_dt:2021-12-13 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-12-14 15:00:00)
处理data BarDatas(current_dt:2021-12-15 15:00:00)
处理data BarDatas(current_dt:2021-12-16 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-12-17 15:00:00)
处理data BarDatas(current_dt:2021-12-20 15:00:00)
处理data BarDatas(current_dt:2021-12-21 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-12-22 15:00:00)
处理data BarDatas(current_dt:2021-12-23 15:00:00)
处理data BarDatas(current_dt:2021-12-24 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-12-27 15:00:00)
处理data BarDatas(current_dt:2021-12-28 15:00:00)
处理data BarDatas(current_dt:2021-12-29 15:00:00)
buy_cash_weights [0.46927872602275644, 0.29608191096586517, 0.23463936301137822]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-12-30 15:00:00)
2022-06-02 10:53:30.233034 backtest run end! 
2022-06-02 10:53:30.716085 run_backtest() finished! time cost 3.113s!

2022-06-02 10:53:32.449161 perf_render raw_perf=DataSource(46024ff650b145eabb7b8c7daa42109bT), benchmark_data=DataSource(b8e96a50847f48e282601a3c6df6dc1cT), process stats...
2022-06-02 10:53:32.765301 perf_render process transactions...
2022-06-02 10:53:32.859262 perf_render process positions...
2022-06-02 10:53:32.966609 perf_render process logs...
2022-06-02 10:53:33.115646 perf_render process plot...

收益率-1.19%
年化收益率-2.29%
基准收益率-5.79%
阿尔法-0.05
贝塔0.02
夏普比率-2.89
胜率0.0
盈亏比0.0
收益波动率1.85%
信息比率0.03
最大回撤1.29%

bigcharts-data-start/{"__type":"tabs","__id":"bigchart-a5cc496bb9204085ad625f31373b14bb"}/bigcharts-data-end

[CV 1/1; 3/6] END m14.params={"stock_count": 3, "hold_days": 3}, m3.features=
    double_low = close + bond_prem_ratio
    remain_size
    rank_swing_volatility_5 = nanstd((high-low)/pre_close, 120)*sqrt(200)*100
    ; score: (test=-2.891) total time= 1.5min
[Parallel(n_jobs=1)]: Done   3 out of   3 | elapsed:  4.7min remaining:    0.0s
[CV 1/1; 4/6] START m14.params={"stock_count": 4, "hold_days": 4}, m3.features=
    double_low = close + bond_prem_ratio
    remain_size
    rank_swing_volatility_5 = nanstd((high-low)/pre_close, 5)*sqrt(200)*100

Index(['remain_size', 'instrument', 'equ_trading_code', 'conversion_chg_pct',
       'date', 'conversion_price', 'name', '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'],
      dtype='object')
Index(['remain_size', 'instrument', 'equ_trading_code', 'conversion_chg_pct',
       'date', 'conversion_price', 'name', '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'],
      dtype='object')

Index(['instrument', 'date', 'pre_close', 'name_x', 'open', 'high', 'low',
       'deal_number', 'volume', 'amount', 'accrued_interest',
       'yield_to_maturity', 'vwap', 'gross_close', 'net_close',
       'bond_prem_ratio', 'close', 'close_equ', 'conversion_chg_pct',
       'conversion_chg_pct_week', 'conversion_price', 'equ_name',
       'equ_trading_code', 'name_y', 'pure_bond_prem_ratio', 'pure_bond_ratio',
       'redemption_price', 'remain_size', 'total_size', 'trading_code',
       'double_low', 'rank_swing_volatility_5'],
      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-aecbd3fcf9ca433aac88e5f60e8ca162"}/bigcharts-data-end

2022-06-02 10:55:14.723804 init history datas... 
2022-06-02 10:55:14.726570 init history datas done. 
2022-06-02 10:55:14.736112 run_backtest() capital_base:1000000, frequency:1d, product_type:conbond, date:2021-06-30 ~ 2021-12-30 
2022-06-02 10:55:14.736533 run_backtest() running... 
2022-06-02 10:55:14.749905 initial contracts len=0 
2022-06-02 10:55:14.750111 backtest inited. 
{'data': DataSource(8cf679b5784446f3a48302df53b79785T)}
        date  instrument     score  position
0 2021-06-30  123082.ZCB  0.134401         1
1 2021-06-30  128141.ZCB  0.134401         2
2 2021-06-30  128143.ZCB  0.134401         3
3 2021-06-30  128144.ZCB  0.134401         4
4 2021-06-30  123079.ZCB  0.134401         5
5 2021-06-30  123113.ZCB  0.134401         6
6 2021-06-30  127021.ZCB  0.029014         7
7 2021-06-30  123042.ZCB  0.029014         8
8 2021-06-30  123043.ZCB  0.029014         9
9 2021-06-30  110067.HCB -0.017028        10
2022-06-02 10:55:14.932954 backtest transforming 1d, bars=1... 
2022-06-02 10:55:14.933368 transform start_trading_day=2021-06-30 00:00:00, simulation period=2021-06-30 ~ 2021-12-30 
2022-06-02 10:55:14.933425 transform source=None, before_start_days=8 
2022-06-02 10:55:14.933459 transform replay_func=<cyfunction BacktestEngine.transform.<locals>.replay_bars_dt at 0x7f2e61908110> 
处理data BarDatas(current_dt:2021-06-30 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-07-01 15:00:00)
处理data BarDatas(current_dt:2021-07-02 15:00:00)
处理data BarDatas(current_dt:2021-07-05 15:00:00)
处理data BarDatas(current_dt:2021-07-06 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-07-07 15:00:00)
处理data BarDatas(current_dt:2021-07-08 15:00:00)
处理data BarDatas(current_dt:2021-07-09 15:00:00)
处理data BarDatas(current_dt:2021-07-12 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-07-13 15:00:00)
处理data BarDatas(current_dt:2021-07-14 15:00:00)
处理data BarDatas(current_dt:2021-07-15 15:00:00)
处理data BarDatas(current_dt:2021-07-16 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-07-19 15:00:00)
处理data BarDatas(current_dt:2021-07-20 15:00:00)
处理data BarDatas(current_dt:2021-07-21 15:00:00)
处理data BarDatas(current_dt:2021-07-22 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-07-23 15:00:00)
处理data BarDatas(current_dt:2021-07-26 15:00:00)
处理data BarDatas(current_dt:2021-07-27 15:00:00)
处理data BarDatas(current_dt:2021-07-28 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-07-29 15:00:00)
处理data BarDatas(current_dt:2021-07-30 15:00:00)
处理data BarDatas(current_dt:2021-08-02 15:00:00)
处理data BarDatas(current_dt:2021-08-03 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-08-04 15:00:00)
处理data BarDatas(current_dt:2021-08-05 15:00:00)
处理data BarDatas(current_dt:2021-08-06 15:00:00)
处理data BarDatas(current_dt:2021-08-09 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-08-10 15:00:00)
处理data BarDatas(current_dt:2021-08-11 15:00:00)
处理data BarDatas(current_dt:2021-08-12 15:00:00)
处理data BarDatas(current_dt:2021-08-13 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-08-16 15:00:00)
处理data BarDatas(current_dt:2021-08-17 15:00:00)
处理data BarDatas(current_dt:2021-08-18 15:00:00)
处理data BarDatas(current_dt:2021-08-19 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-08-20 15:00:00)
处理data BarDatas(current_dt:2021-08-23 15:00:00)
处理data BarDatas(current_dt:2021-08-24 15:00:00)
处理data BarDatas(current_dt:2021-08-25 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-08-26 15:00:00)
处理data BarDatas(current_dt:2021-08-27 15:00:00)
处理data BarDatas(current_dt:2021-08-30 15:00:00)
处理data BarDatas(current_dt:2021-08-31 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-09-01 15:00:00)
处理data BarDatas(current_dt:2021-09-02 15:00:00)
处理data BarDatas(current_dt:2021-09-03 15:00:00)
处理data BarDatas(current_dt:2021-09-06 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-09-07 15:00:00)
处理data BarDatas(current_dt:2021-09-08 15:00:00)
处理data BarDatas(current_dt:2021-09-09 15:00:00)
处理data BarDatas(current_dt:2021-09-10 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-09-13 15:00:00)
处理data BarDatas(current_dt:2021-09-14 15:00:00)
处理data BarDatas(current_dt:2021-09-15 15:00:00)
处理data BarDatas(current_dt:2021-09-16 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-09-17 15:00:00)
处理data BarDatas(current_dt:2021-09-22 15:00:00)
处理data BarDatas(current_dt:2021-09-23 15:00:00)
处理data BarDatas(current_dt:2021-09-24 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-09-27 15:00:00)
处理data BarDatas(current_dt:2021-09-28 15:00:00)
处理data BarDatas(current_dt:2021-09-29 15:00:00)
处理data BarDatas(current_dt:2021-09-30 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-10-08 15:00:00)
处理data BarDatas(current_dt:2021-10-11 15:00:00)
处理data BarDatas(current_dt:2021-10-12 15:00:00)
处理data BarDatas(current_dt:2021-10-13 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-10-14 15:00:00)
处理data BarDatas(current_dt:2021-10-15 15:00:00)
处理data BarDatas(current_dt:2021-10-18 15:00:00)
处理data BarDatas(current_dt:2021-10-19 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-10-20 15:00:00)
处理data BarDatas(current_dt:2021-10-21 15:00:00)
处理data BarDatas(current_dt:2021-10-22 15:00:00)
处理data BarDatas(current_dt:2021-10-25 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-10-26 15:00:00)
处理data BarDatas(current_dt:2021-10-27 15:00:00)
处理data BarDatas(current_dt:2021-10-28 15:00:00)
处理data BarDatas(current_dt:2021-10-29 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-11-01 15:00:00)
处理data BarDatas(current_dt:2021-11-02 15:00:00)
处理data BarDatas(current_dt:2021-11-03 15:00:00)
处理data BarDatas(current_dt:2021-11-04 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-11-05 15:00:00)
处理data BarDatas(current_dt:2021-11-08 15:00:00)
处理data BarDatas(current_dt:2021-11-09 15:00:00)
处理data BarDatas(current_dt:2021-11-10 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-11-11 15:00:00)
处理data BarDatas(current_dt:2021-11-12 15:00:00)
处理data BarDatas(current_dt:2021-11-15 15:00:00)
处理data BarDatas(current_dt:2021-11-16 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-11-17 15:00:00)
处理data BarDatas(current_dt:2021-11-18 15:00:00)
处理data BarDatas(current_dt:2021-11-19 15:00:00)
处理data BarDatas(current_dt:2021-11-22 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-11-23 15:00:00)
处理data BarDatas(current_dt:2021-11-24 15:00:00)
处理data BarDatas(current_dt:2021-11-25 15:00:00)
处理data BarDatas(current_dt:2021-11-26 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-11-29 15:00:00)
处理data BarDatas(current_dt:2021-11-30 15:00:00)
处理data BarDatas(current_dt:2021-12-01 15:00:00)
处理data BarDatas(current_dt:2021-12-02 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-12-03 15:00:00)
处理data BarDatas(current_dt:2021-12-06 15:00:00)
处理data BarDatas(current_dt:2021-12-07 15:00:00)
处理data BarDatas(current_dt:2021-12-08 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-12-09 15:00:00)
处理data BarDatas(current_dt:2021-12-10 15:00:00)
处理data BarDatas(current_dt:2021-12-13 15:00:00)
处理data BarDatas(current_dt:2021-12-14 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-12-15 15:00:00)
处理data BarDatas(current_dt:2021-12-16 15:00:00)
处理data BarDatas(current_dt:2021-12-17 15:00:00)
处理data BarDatas(current_dt:2021-12-20 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-12-21 15:00:00)
处理data BarDatas(current_dt:2021-12-22 15:00:00)
处理data BarDatas(current_dt:2021-12-23 15:00:00)
处理data BarDatas(current_dt:2021-12-24 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-12-27 15:00:00)
处理data BarDatas(current_dt:2021-12-28 15:00:00)
处理data BarDatas(current_dt:2021-12-29 15:00:00)
处理data BarDatas(current_dt:2021-12-30 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
2022-06-02 10:55:19.653440 backtest run end! 
2022-06-02 10:55:20.159554 run_backtest() finished! time cost 5.423s!

2022-06-02 10:55:21.995855 perf_render raw_perf=DataSource(be1c12a6e97348aeb01d47e9edc6784aT), benchmark_data=DataSource(013de5bc66394b1190adbcf24ccf4e7fT), process stats...
2022-06-02 10:55:22.740652 perf_render process transactions...
2022-06-02 10:55:22.934826 perf_render process positions...
2022-06-02 10:55:23.240658 perf_render process logs...
2022-06-02 10:55:23.581423 perf_render process plot...

收益率-0.68%
年化收益率-1.32%
基准收益率-5.79%
阿尔法-0.04
贝塔0.02
夏普比率-2.6
胜率0.25
盈亏比0.33
收益波动率1.66%
信息比率0.03
最大回撤1.37%

bigcharts-data-start/{"__type":"tabs","__id":"bigchart-2ba510b41e224de29c16af7348a8dfed"}/bigcharts-data-end

[CV 1/1; 4/6] END m14.params={"stock_count": 4, "hold_days": 4}, m3.features=
    double_low = close + bond_prem_ratio
    remain_size
    rank_swing_volatility_5 = nanstd((high-low)/pre_close, 5)*sqrt(200)*100
    ; score: (test=-2.597) total time= 1.8min
[Parallel(n_jobs=1)]: Done   4 out of   4 | elapsed:  6.5min remaining:    0.0s
[CV 1/1; 5/6] START m14.params={"stock_count": 4, "hold_days": 4}, m3.features=
    double_low = close + bond_prem_ratio
    remain_size
    rank_swing_volatility_5 = nanstd((high-low)/pre_close, 6)*sqrt(200)*100

Index(['remain_size', 'instrument', 'equ_trading_code', 'conversion_chg_pct',
       'date', 'conversion_price', 'name', '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'],
      dtype='object')
Index(['remain_size', 'instrument', 'equ_trading_code', 'conversion_chg_pct',
       'date', 'conversion_price', 'name', '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'],
      dtype='object')

Index(['instrument', 'date', 'pre_close', 'name_x', 'open', 'high', 'low',
       'deal_number', 'volume', 'amount', 'accrued_interest',
       'yield_to_maturity', 'vwap', 'gross_close', 'net_close',
       'bond_prem_ratio', 'close', 'close_equ', 'conversion_chg_pct',
       'conversion_chg_pct_week', 'conversion_price', 'equ_name',
       'equ_trading_code', 'name_y', 'pure_bond_prem_ratio', 'pure_bond_ratio',
       'redemption_price', 'remain_size', 'total_size', 'trading_code',
       'double_low', 'rank_swing_volatility_5'],
      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-7b6bc837b0ca4d5ea4542522087a3a08"}/bigcharts-data-end

2022-06-02 10:57:18.282358 init history datas... 
2022-06-02 10:57:18.283604 init history datas done. 
2022-06-02 10:57:18.294270 run_backtest() capital_base:1000000, frequency:1d, product_type:conbond, date:2021-06-30 ~ 2021-12-30 
2022-06-02 10:57:18.294751 run_backtest() running... 
2022-06-02 10:57:18.302567 initial contracts len=0 
2022-06-02 10:57:18.303305 backtest inited. 
{'data': DataSource(8eac4e29df8d4ee58330e2de27a4fc6cT)}
        date  instrument     score  position
0 2021-06-30  123082.ZCB  0.140023         1
1 2021-06-30  128143.ZCB  0.140023         2
2 2021-06-30  128144.ZCB  0.140023         3
3 2021-06-30  123079.ZCB  0.140023         4
4 2021-06-30  123113.ZCB  0.140023         5
5 2021-06-30  127021.ZCB  0.033559         6
6 2021-06-30  123042.ZCB  0.033559         7
7 2021-06-30  123043.ZCB  0.033559         8
8 2021-06-30  128141.ZCB -0.011513         9
9 2021-06-30  110067.HCB -0.011513        10
2022-06-02 10:57:18.349460 backtest transforming 1d, bars=1... 
2022-06-02 10:57:18.349831 transform start_trading_day=2021-06-30 00:00:00, simulation period=2021-06-30 ~ 2021-12-30 
2022-06-02 10:57:18.349876 transform source=None, before_start_days=8 
2022-06-02 10:57:18.349911 transform replay_func=<cyfunction BacktestEngine.transform.<locals>.replay_bars_dt at 0x7f2e6189e5f0> 
处理data BarDatas(current_dt:2021-06-30 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-07-01 15:00:00)
处理data BarDatas(current_dt:2021-07-02 15:00:00)
处理data BarDatas(current_dt:2021-07-05 15:00:00)
处理data BarDatas(current_dt:2021-07-06 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-07-07 15:00:00)
处理data BarDatas(current_dt:2021-07-08 15:00:00)
处理data BarDatas(current_dt:2021-07-09 15:00:00)
处理data BarDatas(current_dt:2021-07-12 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-07-13 15:00:00)
处理data BarDatas(current_dt:2021-07-14 15:00:00)
处理data BarDatas(current_dt:2021-07-15 15:00:00)
处理data BarDatas(current_dt:2021-07-16 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-07-19 15:00:00)
处理data BarDatas(current_dt:2021-07-20 15:00:00)
处理data BarDatas(current_dt:2021-07-21 15:00:00)
处理data BarDatas(current_dt:2021-07-22 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-07-23 15:00:00)
处理data BarDatas(current_dt:2021-07-26 15:00:00)
处理data BarDatas(current_dt:2021-07-27 15:00:00)
处理data BarDatas(current_dt:2021-07-28 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-07-29 15:00:00)
处理data BarDatas(current_dt:2021-07-30 15:00:00)
处理data BarDatas(current_dt:2021-08-02 15:00:00)
处理data BarDatas(current_dt:2021-08-03 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-08-04 15:00:00)
处理data BarDatas(current_dt:2021-08-05 15:00:00)
处理data BarDatas(current_dt:2021-08-06 15:00:00)
处理data BarDatas(current_dt:2021-08-09 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-08-10 15:00:00)
处理data BarDatas(current_dt:2021-08-11 15:00:00)
处理data BarDatas(current_dt:2021-08-12 15:00:00)
处理data BarDatas(current_dt:2021-08-13 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-08-16 15:00:00)
处理data BarDatas(current_dt:2021-08-17 15:00:00)
处理data BarDatas(current_dt:2021-08-18 15:00:00)
处理data BarDatas(current_dt:2021-08-19 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-08-20 15:00:00)
处理data BarDatas(current_dt:2021-08-23 15:00:00)
处理data BarDatas(current_dt:2021-08-24 15:00:00)
处理data BarDatas(current_dt:2021-08-25 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-08-26 15:00:00)
处理data BarDatas(current_dt:2021-08-27 15:00:00)
处理data BarDatas(current_dt:2021-08-30 15:00:00)
处理data BarDatas(current_dt:2021-08-31 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-09-01 15:00:00)
处理data BarDatas(current_dt:2021-09-02 15:00:00)
处理data BarDatas(current_dt:2021-09-03 15:00:00)
处理data BarDatas(current_dt:2021-09-06 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-09-07 15:00:00)
处理data BarDatas(current_dt:2021-09-08 15:00:00)
处理data BarDatas(current_dt:2021-09-09 15:00:00)
处理data BarDatas(current_dt:2021-09-10 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-09-13 15:00:00)
处理data BarDatas(current_dt:2021-09-14 15:00:00)
处理data BarDatas(current_dt:2021-09-15 15:00:00)
处理data BarDatas(current_dt:2021-09-16 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-09-17 15:00:00)
处理data BarDatas(current_dt:2021-09-22 15:00:00)
处理data BarDatas(current_dt:2021-09-23 15:00:00)
处理data BarDatas(current_dt:2021-09-24 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-09-27 15:00:00)
处理data BarDatas(current_dt:2021-09-28 15:00:00)
处理data BarDatas(current_dt:2021-09-29 15:00:00)
处理data BarDatas(current_dt:2021-09-30 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-10-08 15:00:00)
处理data BarDatas(current_dt:2021-10-11 15:00:00)
处理data BarDatas(current_dt:2021-10-12 15:00:00)
处理data BarDatas(current_dt:2021-10-13 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-10-14 15:00:00)
处理data BarDatas(current_dt:2021-10-15 15:00:00)
处理data BarDatas(current_dt:2021-10-18 15:00:00)
处理data BarDatas(current_dt:2021-10-19 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-10-20 15:00:00)
处理data BarDatas(current_dt:2021-10-21 15:00:00)
处理data BarDatas(current_dt:2021-10-22 15:00:00)
处理data BarDatas(current_dt:2021-10-25 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-10-26 15:00:00)
处理data BarDatas(current_dt:2021-10-27 15:00:00)
处理data BarDatas(current_dt:2021-10-28 15:00:00)
处理data BarDatas(current_dt:2021-10-29 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-11-01 15:00:00)
处理data BarDatas(current_dt:2021-11-02 15:00:00)
处理data BarDatas(current_dt:2021-11-03 15:00:00)
处理data BarDatas(current_dt:2021-11-04 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-11-05 15:00:00)
处理data BarDatas(current_dt:2021-11-08 15:00:00)
处理data BarDatas(current_dt:2021-11-09 15:00:00)
处理data BarDatas(current_dt:2021-11-10 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-11-11 15:00:00)
处理data BarDatas(current_dt:2021-11-12 15:00:00)
处理data BarDatas(current_dt:2021-11-15 15:00:00)
处理data BarDatas(current_dt:2021-11-16 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-11-17 15:00:00)
处理data BarDatas(current_dt:2021-11-18 15:00:00)
处理data BarDatas(current_dt:2021-11-19 15:00:00)
处理data BarDatas(current_dt:2021-11-22 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-11-23 15:00:00)
处理data BarDatas(current_dt:2021-11-24 15:00:00)
处理data BarDatas(current_dt:2021-11-25 15:00:00)
处理data BarDatas(current_dt:2021-11-26 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-11-29 15:00:00)
处理data BarDatas(current_dt:2021-11-30 15:00:00)
处理data BarDatas(current_dt:2021-12-01 15:00:00)
处理data BarDatas(current_dt:2021-12-02 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-12-03 15:00:00)
处理data BarDatas(current_dt:2021-12-06 15:00:00)
处理data BarDatas(current_dt:2021-12-07 15:00:00)
处理data BarDatas(current_dt:2021-12-08 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-12-09 15:00:00)
处理data BarDatas(current_dt:2021-12-10 15:00:00)
处理data BarDatas(current_dt:2021-12-13 15:00:00)
处理data BarDatas(current_dt:2021-12-14 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-12-15 15:00:00)
处理data BarDatas(current_dt:2021-12-16 15:00:00)
处理data BarDatas(current_dt:2021-12-17 15:00:00)
处理data BarDatas(current_dt:2021-12-20 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-12-21 15:00:00)
处理data BarDatas(current_dt:2021-12-22 15:00:00)
处理data BarDatas(current_dt:2021-12-23 15:00:00)
处理data BarDatas(current_dt:2021-12-24 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-12-27 15:00:00)
处理data BarDatas(current_dt:2021-12-28 15:00:00)
处理data BarDatas(current_dt:2021-12-29 15:00:00)
处理data BarDatas(current_dt:2021-12-30 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
2022-06-02 10:57:21.589254 backtest run end! 
2022-06-02 10:57:22.095580 run_backtest() finished! time cost 3.801s!

2022-06-02 10:57:23.774105 perf_render raw_perf=DataSource(d837ac17f86342e98efb2334ad921401T), benchmark_data=DataSource(6bda487a3cc342e487b427f0a9bdcda5T), process stats...
2022-06-02 10:57:24.086418 perf_render process transactions...
2022-06-02 10:57:24.247016 perf_render process positions...
2022-06-02 10:57:24.467276 perf_render process logs...
2022-06-02 10:57:24.676912 perf_render process plot...

收益率-0.73%
年化收益率-1.41%
基准收益率-5.79%
阿尔法-0.04
贝塔0.02
夏普比率-2.7
胜率0.25
盈亏比0.3
收益波动率1.64%
信息比率0.03
最大回撤1.16%

bigcharts-data-start/{"__type":"tabs","__id":"bigchart-89c6a3718a02404384d4bb9214511fd1"}/bigcharts-data-end

[CV 1/1; 5/6] END m14.params={"stock_count": 4, "hold_days": 4}, m3.features=
    double_low = close + bond_prem_ratio
    remain_size
    rank_swing_volatility_5 = nanstd((high-low)/pre_close, 6)*sqrt(200)*100
    ; score: (test=-2.697) total time= 2.0min
[Parallel(n_jobs=1)]: Done   5 out of   5 | elapsed:  8.5min remaining:    0.0s
[CV 1/1; 6/6] START m14.params={"stock_count": 4, "hold_days": 4}, m3.features=
    double_low = close + bond_prem_ratio
    remain_size
    rank_swing_volatility_5 = nanstd((high-low)/pre_close, 120)*sqrt(200)*100

Index(['remain_size', 'instrument', 'equ_trading_code', 'conversion_chg_pct',
       'date', 'conversion_price', 'name', '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'],
      dtype='object')
Index(['remain_size', 'instrument', 'equ_trading_code', 'conversion_chg_pct',
       'date', 'conversion_price', 'name', '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'],
      dtype='object')

Index(['instrument', 'date', 'pre_close', 'name_x', 'open', 'high', 'low',
       'deal_number', 'volume', 'amount', 'accrued_interest',
       'yield_to_maturity', 'vwap', 'gross_close', 'net_close',
       'bond_prem_ratio', 'close', 'close_equ', 'conversion_chg_pct',
       'conversion_chg_pct_week', 'conversion_price', 'equ_name',
       'equ_trading_code', 'name_y', 'pure_bond_prem_ratio', 'pure_bond_ratio',
       'redemption_price', 'remain_size', 'total_size', 'trading_code',
       'double_low', 'rank_swing_volatility_5'],
      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-22369657a0be40638f9cd50f1ff1118d"}/bigcharts-data-end

2022-06-02 10:59:03.521905 init history datas... 
2022-06-02 10:59:03.523135 init history datas done. 
2022-06-02 10:59:03.536374 run_backtest() capital_base:1000000, frequency:1d, product_type:conbond, date:2021-06-30 ~ 2021-12-30 
2022-06-02 10:59:03.536767 run_backtest() running... 
2022-06-02 10:59:03.546646 initial contracts len=0 
2022-06-02 10:59:03.547237 backtest inited. 
{'data': DataSource(17b9485219f34a6db51916e59406da7dT)}
        date  instrument     score  position
0 2021-06-30  123082.ZCB  0.164295         1
1 2021-06-30  128143.ZCB  0.164295         2
2 2021-06-30  128144.ZCB  0.164295         3
3 2021-06-30  123079.ZCB  0.164295         4
4 2021-06-30  123113.ZCB  0.164295         5
5 2021-06-30  127021.ZCB  0.028803         6
6 2021-06-30  123042.ZCB  0.028803         7
7 2021-06-30  123043.ZCB  0.028803         8
8 2021-06-30  128141.ZCB  0.024234         9
9 2021-06-30  110067.HCB  0.024234        10
2022-06-02 10:59:03.588470 backtest transforming 1d, bars=1... 
2022-06-02 10:59:03.588898 transform start_trading_day=2021-06-30 00:00:00, simulation period=2021-06-30 ~ 2021-12-30 
2022-06-02 10:59:03.589320 transform source=None, before_start_days=8 
2022-06-02 10:59:03.589380 transform replay_func=<cyfunction BacktestEngine.transform.<locals>.replay_bars_dt at 0x7f2e6189e520> 
处理data BarDatas(current_dt:2021-06-30 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-07-01 15:00:00)
处理data BarDatas(current_dt:2021-07-02 15:00:00)
处理data BarDatas(current_dt:2021-07-05 15:00:00)
处理data BarDatas(current_dt:2021-07-06 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-07-07 15:00:00)
处理data BarDatas(current_dt:2021-07-08 15:00:00)
处理data BarDatas(current_dt:2021-07-09 15:00:00)
处理data BarDatas(current_dt:2021-07-12 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-07-13 15:00:00)
处理data BarDatas(current_dt:2021-07-14 15:00:00)
处理data BarDatas(current_dt:2021-07-15 15:00:00)
处理data BarDatas(current_dt:2021-07-16 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-07-19 15:00:00)
处理data BarDatas(current_dt:2021-07-20 15:00:00)
处理data BarDatas(current_dt:2021-07-21 15:00:00)
处理data BarDatas(current_dt:2021-07-22 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-07-23 15:00:00)
处理data BarDatas(current_dt:2021-07-26 15:00:00)
处理data BarDatas(current_dt:2021-07-27 15:00:00)
处理data BarDatas(current_dt:2021-07-28 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-07-29 15:00:00)
处理data BarDatas(current_dt:2021-07-30 15:00:00)
处理data BarDatas(current_dt:2021-08-02 15:00:00)
处理data BarDatas(current_dt:2021-08-03 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-08-04 15:00:00)
处理data BarDatas(current_dt:2021-08-05 15:00:00)
处理data BarDatas(current_dt:2021-08-06 15:00:00)
处理data BarDatas(current_dt:2021-08-09 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-08-10 15:00:00)
处理data BarDatas(current_dt:2021-08-11 15:00:00)
处理data BarDatas(current_dt:2021-08-12 15:00:00)
处理data BarDatas(current_dt:2021-08-13 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-08-16 15:00:00)
处理data BarDatas(current_dt:2021-08-17 15:00:00)
处理data BarDatas(current_dt:2021-08-18 15:00:00)
处理data BarDatas(current_dt:2021-08-19 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-08-20 15:00:00)
处理data BarDatas(current_dt:2021-08-23 15:00:00)
处理data BarDatas(current_dt:2021-08-24 15:00:00)
处理data BarDatas(current_dt:2021-08-25 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-08-26 15:00:00)
处理data BarDatas(current_dt:2021-08-27 15:00:00)
处理data BarDatas(current_dt:2021-08-30 15:00:00)
处理data BarDatas(current_dt:2021-08-31 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-09-01 15:00:00)
处理data BarDatas(current_dt:2021-09-02 15:00:00)
处理data BarDatas(current_dt:2021-09-03 15:00:00)
处理data BarDatas(current_dt:2021-09-06 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-09-07 15:00:00)
处理data BarDatas(current_dt:2021-09-08 15:00:00)
处理data BarDatas(current_dt:2021-09-09 15:00:00)
处理data BarDatas(current_dt:2021-09-10 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-09-13 15:00:00)
处理data BarDatas(current_dt:2021-09-14 15:00:00)
处理data BarDatas(current_dt:2021-09-15 15:00:00)
处理data BarDatas(current_dt:2021-09-16 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-09-17 15:00:00)
处理data BarDatas(current_dt:2021-09-22 15:00:00)
处理data BarDatas(current_dt:2021-09-23 15:00:00)
处理data BarDatas(current_dt:2021-09-24 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-09-27 15:00:00)
处理data BarDatas(current_dt:2021-09-28 15:00:00)
处理data BarDatas(current_dt:2021-09-29 15:00:00)
处理data BarDatas(current_dt:2021-09-30 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-10-08 15:00:00)
处理data BarDatas(current_dt:2021-10-11 15:00:00)
处理data BarDatas(current_dt:2021-10-12 15:00:00)
处理data BarDatas(current_dt:2021-10-13 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-10-14 15:00:00)
处理data BarDatas(current_dt:2021-10-15 15:00:00)
处理data BarDatas(current_dt:2021-10-18 15:00:00)
处理data BarDatas(current_dt:2021-10-19 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-10-20 15:00:00)
处理data BarDatas(current_dt:2021-10-21 15:00:00)
处理data BarDatas(current_dt:2021-10-22 15:00:00)
处理data BarDatas(current_dt:2021-10-25 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-10-26 15:00:00)
处理data BarDatas(current_dt:2021-10-27 15:00:00)
处理data BarDatas(current_dt:2021-10-28 15:00:00)
处理data BarDatas(current_dt:2021-10-29 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-11-01 15:00:00)
处理data BarDatas(current_dt:2021-11-02 15:00:00)
处理data BarDatas(current_dt:2021-11-03 15:00:00)
处理data BarDatas(current_dt:2021-11-04 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-11-05 15:00:00)
处理data BarDatas(current_dt:2021-11-08 15:00:00)
处理data BarDatas(current_dt:2021-11-09 15:00:00)
处理data BarDatas(current_dt:2021-11-10 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-11-11 15:00:00)
处理data BarDatas(current_dt:2021-11-12 15:00:00)
处理data BarDatas(current_dt:2021-11-15 15:00:00)
处理data BarDatas(current_dt:2021-11-16 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-11-17 15:00:00)
处理data BarDatas(current_dt:2021-11-18 15:00:00)
处理data BarDatas(current_dt:2021-11-19 15:00:00)
处理data BarDatas(current_dt:2021-11-22 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-11-23 15:00:00)
处理data BarDatas(current_dt:2021-11-24 15:00:00)
处理data BarDatas(current_dt:2021-11-25 15:00:00)
处理data BarDatas(current_dt:2021-11-26 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-11-29 15:00:00)
处理data BarDatas(current_dt:2021-11-30 15:00:00)
处理data BarDatas(current_dt:2021-12-01 15:00:00)
处理data BarDatas(current_dt:2021-12-02 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-12-03 15:00:00)
处理data BarDatas(current_dt:2021-12-06 15:00:00)
处理data BarDatas(current_dt:2021-12-07 15:00:00)
处理data BarDatas(current_dt:2021-12-08 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-12-09 15:00:00)
处理data BarDatas(current_dt:2021-12-10 15:00:00)
处理data BarDatas(current_dt:2021-12-13 15:00:00)
处理data BarDatas(current_dt:2021-12-14 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-12-15 15:00:00)
处理data BarDatas(current_dt:2021-12-16 15:00:00)
处理data BarDatas(current_dt:2021-12-17 15:00:00)
处理data BarDatas(current_dt:2021-12-20 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-12-21 15:00:00)
处理data BarDatas(current_dt:2021-12-22 15:00:00)
处理data BarDatas(current_dt:2021-12-23 15:00:00)
处理data BarDatas(current_dt:2021-12-24 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-12-27 15:00:00)
处理data BarDatas(current_dt:2021-12-28 15:00:00)
处理data BarDatas(current_dt:2021-12-29 15:00:00)
处理data BarDatas(current_dt:2021-12-30 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
2022-06-02 10:59:06.226564 backtest run end! 
2022-06-02 10:59:06.759032 run_backtest() finished! time cost 3.222s!

2022-06-02 10:59:08.512435 perf_render raw_perf=DataSource(aabcb8e6959b43e4b7a72f5393213d52T), benchmark_data=DataSource(6950b98dc41e4c40b71859159c7b37dfT), process stats...
2022-06-02 10:59:08.877781 perf_render process transactions...
2022-06-02 10:59:08.961433 perf_render process positions...
2022-06-02 10:59:09.062918 perf_render process logs...
2022-06-02 10:59:09.224773 perf_render process plot...

收益率-0.73%
年化收益率-1.41%
基准收益率-5.79%
阿尔法-0.04
贝塔0.02
夏普比率-2.7
胜率0.25
盈亏比0.3
收益波动率1.64%
信息比率0.03
最大回撤1.16%

bigcharts-data-start/{"__type":"tabs","__id":"bigchart-26778672a080461484db2a2ef7b11515"}/bigcharts-data-end

[CV 1/1; 6/6] END m14.params={"stock_count": 4, "hold_days": 4}, m3.features=
    double_low = close + bond_prem_ratio
    remain_size
    rank_swing_volatility_5 = nanstd((high-low)/pre_close, 120)*sqrt(200)*100
    ; score: (test=-2.697) total time= 1.7min
[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed: 10.3min remaining:    0.0s
[Parallel(n_jobs=1)]: Done   6 out of   6 | elapsed: 10.3min finished

Index(['remain_size', 'instrument', 'equ_trading_code', 'conversion_chg_pct',
       'date', 'conversion_price', 'name', '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'],
      dtype='object')
Index(['remain_size', 'instrument', 'equ_trading_code', 'conversion_chg_pct',
       'date', 'conversion_price', 'name', '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'],
      dtype='object')

Index(['instrument', 'date', 'pre_close', 'name_x', 'open', 'high', 'low',
       'deal_number', 'volume', 'amount', 'accrued_interest',
       'yield_to_maturity', 'vwap', 'gross_close', 'net_close',
       'bond_prem_ratio', 'close', 'close_equ', 'conversion_chg_pct',
       'conversion_chg_pct_week', 'conversion_price', 'equ_name',
       'equ_trading_code', 'name_y', 'pure_bond_prem_ratio', 'pure_bond_ratio',
       'redemption_price', 'remain_size', 'total_size', 'trading_code',
       'double_low', 'rank_swing_volatility_5'],
      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-be99ec584b6244388febd8341945cc18"}/bigcharts-data-end

2022-06-02 11:00:31.020203 init history datas... 
2022-06-02 11:00:31.021272 init history datas done. 
2022-06-02 11:00:31.034419 run_backtest() capital_base:1000000, frequency:1d, product_type:conbond, date:2021-06-30 ~ 2021-12-30 
2022-06-02 11:00:31.034908 run_backtest() running... 
2022-06-02 11:00:31.043086 initial contracts len=0 
2022-06-02 11:00:31.043251 backtest inited. 
{'data': DataSource(f1a0772d55a6474c9443e0376065866cT)}
        date  instrument     score  position
0 2021-06-30  123082.ZCB  0.134401         1
1 2021-06-30  128141.ZCB  0.134401         2
2 2021-06-30  128143.ZCB  0.134401         3
3 2021-06-30  128144.ZCB  0.134401         4
4 2021-06-30  123079.ZCB  0.134401         5
5 2021-06-30  123113.ZCB  0.134401         6
6 2021-06-30  127021.ZCB  0.029014         7
7 2021-06-30  123042.ZCB  0.029014         8
8 2021-06-30  123043.ZCB  0.029014         9
9 2021-06-30  110067.HCB -0.017028        10
2022-06-02 11:00:31.105599 backtest transforming 1d, bars=1... 
2022-06-02 11:00:31.105989 transform start_trading_day=2021-06-30 00:00:00, simulation period=2021-06-30 ~ 2021-12-30 
2022-06-02 11:00:31.106033 transform source=None, before_start_days=8 
2022-06-02 11:00:31.106616 transform replay_func=<cyfunction BacktestEngine.transform.<locals>.replay_bars_dt at 0x7f2e61fb3ad0> 
处理data BarDatas(current_dt:2021-06-30 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-07-01 15:00:00)
处理data BarDatas(current_dt:2021-07-02 15:00:00)
处理data BarDatas(current_dt:2021-07-05 15:00:00)
处理data BarDatas(current_dt:2021-07-06 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-07-07 15:00:00)
处理data BarDatas(current_dt:2021-07-08 15:00:00)
处理data BarDatas(current_dt:2021-07-09 15:00:00)
处理data BarDatas(current_dt:2021-07-12 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-07-13 15:00:00)
处理data BarDatas(current_dt:2021-07-14 15:00:00)
处理data BarDatas(current_dt:2021-07-15 15:00:00)
处理data BarDatas(current_dt:2021-07-16 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
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处理data BarDatas(current_dt:2021-07-19 15:00:00)
处理data BarDatas(current_dt:2021-07-20 15:00:00)
处理data BarDatas(current_dt:2021-07-21 15:00:00)
处理data BarDatas(current_dt:2021-07-22 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
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处理data BarDatas(current_dt:2021-07-23 15:00:00)
处理data BarDatas(current_dt:2021-07-26 15:00:00)
处理data BarDatas(current_dt:2021-07-27 15:00:00)
处理data BarDatas(current_dt:2021-07-28 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
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处理data BarDatas(current_dt:2021-07-29 15:00:00)
处理data BarDatas(current_dt:2021-07-30 15:00:00)
处理data BarDatas(current_dt:2021-08-02 15:00:00)
处理data BarDatas(current_dt:2021-08-03 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
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处理data BarDatas(current_dt:2021-08-04 15:00:00)
处理data BarDatas(current_dt:2021-08-05 15:00:00)
处理data BarDatas(current_dt:2021-08-06 15:00:00)
处理data BarDatas(current_dt:2021-08-09 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
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处理data BarDatas(current_dt:2021-08-10 15:00:00)
处理data BarDatas(current_dt:2021-08-11 15:00:00)
处理data BarDatas(current_dt:2021-08-12 15:00:00)
处理data BarDatas(current_dt:2021-08-13 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
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处理data BarDatas(current_dt:2021-08-16 15:00:00)
处理data BarDatas(current_dt:2021-08-17 15:00:00)
处理data BarDatas(current_dt:2021-08-18 15:00:00)
处理data BarDatas(current_dt:2021-08-19 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
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处理data BarDatas(current_dt:2021-08-20 15:00:00)
处理data BarDatas(current_dt:2021-08-23 15:00:00)
处理data BarDatas(current_dt:2021-08-24 15:00:00)
处理data BarDatas(current_dt:2021-08-25 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
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处理data BarDatas(current_dt:2021-08-26 15:00:00)
处理data BarDatas(current_dt:2021-08-27 15:00:00)
处理data BarDatas(current_dt:2021-08-30 15:00:00)
处理data BarDatas(current_dt:2021-08-31 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
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处理data BarDatas(current_dt:2021-09-01 15:00:00)
处理data BarDatas(current_dt:2021-09-02 15:00:00)
处理data BarDatas(current_dt:2021-09-03 15:00:00)
处理data BarDatas(current_dt:2021-09-06 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
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处理data BarDatas(current_dt:2021-09-07 15:00:00)
处理data BarDatas(current_dt:2021-09-08 15:00:00)
处理data BarDatas(current_dt:2021-09-09 15:00:00)
处理data BarDatas(current_dt:2021-09-10 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
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处理data BarDatas(current_dt:2021-09-13 15:00:00)
处理data BarDatas(current_dt:2021-09-14 15:00:00)
处理data BarDatas(current_dt:2021-09-15 15:00:00)
处理data BarDatas(current_dt:2021-09-16 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
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处理data BarDatas(current_dt:2021-09-17 15:00:00)
处理data BarDatas(current_dt:2021-09-22 15:00:00)
处理data BarDatas(current_dt:2021-09-23 15:00:00)
处理data BarDatas(current_dt:2021-09-24 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
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处理data BarDatas(current_dt:2021-09-27 15:00:00)
处理data BarDatas(current_dt:2021-09-28 15:00:00)
处理data BarDatas(current_dt:2021-09-29 15:00:00)
处理data BarDatas(current_dt:2021-09-30 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
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处理data BarDatas(current_dt:2021-10-08 15:00:00)
处理data BarDatas(current_dt:2021-10-11 15:00:00)
处理data BarDatas(current_dt:2021-10-12 15:00:00)
处理data BarDatas(current_dt:2021-10-13 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-10-14 15:00:00)
处理data BarDatas(current_dt:2021-10-15 15:00:00)
处理data BarDatas(current_dt:2021-10-18 15:00:00)
处理data BarDatas(current_dt:2021-10-19 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-10-20 15:00:00)
处理data BarDatas(current_dt:2021-10-21 15:00:00)
处理data BarDatas(current_dt:2021-10-22 15:00:00)
处理data BarDatas(current_dt:2021-10-25 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-10-26 15:00:00)
处理data BarDatas(current_dt:2021-10-27 15:00:00)
处理data BarDatas(current_dt:2021-10-28 15:00:00)
处理data BarDatas(current_dt:2021-10-29 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-11-01 15:00:00)
处理data BarDatas(current_dt:2021-11-02 15:00:00)
处理data BarDatas(current_dt:2021-11-03 15:00:00)
处理data BarDatas(current_dt:2021-11-04 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-11-05 15:00:00)
处理data BarDatas(current_dt:2021-11-08 15:00:00)
处理data BarDatas(current_dt:2021-11-09 15:00:00)
处理data BarDatas(current_dt:2021-11-10 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
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处理data BarDatas(current_dt:2021-11-11 15:00:00)
处理data BarDatas(current_dt:2021-11-12 15:00:00)
处理data BarDatas(current_dt:2021-11-15 15:00:00)
处理data BarDatas(current_dt:2021-11-16 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-11-17 15:00:00)
处理data BarDatas(current_dt:2021-11-18 15:00:00)
处理data BarDatas(current_dt:2021-11-19 15:00:00)
处理data BarDatas(current_dt:2021-11-22 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-11-23 15:00:00)
处理data BarDatas(current_dt:2021-11-24 15:00:00)
处理data BarDatas(current_dt:2021-11-25 15:00:00)
处理data BarDatas(current_dt:2021-11-26 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-11-29 15:00:00)
处理data BarDatas(current_dt:2021-11-30 15:00:00)
处理data BarDatas(current_dt:2021-12-01 15:00:00)
处理data BarDatas(current_dt:2021-12-02 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-12-03 15:00:00)
处理data BarDatas(current_dt:2021-12-06 15:00:00)
处理data BarDatas(current_dt:2021-12-07 15:00:00)
处理data BarDatas(current_dt:2021-12-08 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-12-09 15:00:00)
处理data BarDatas(current_dt:2021-12-10 15:00:00)
处理data BarDatas(current_dt:2021-12-13 15:00:00)
处理data BarDatas(current_dt:2021-12-14 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-12-15 15:00:00)
处理data BarDatas(current_dt:2021-12-16 15:00:00)
处理data BarDatas(current_dt:2021-12-17 15:00:00)
处理data BarDatas(current_dt:2021-12-20 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-12-21 15:00:00)
处理data BarDatas(current_dt:2021-12-22 15:00:00)
处理data BarDatas(current_dt:2021-12-23 15:00:00)
处理data BarDatas(current_dt:2021-12-24 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
处理data BarDatas(current_dt:2021-12-27 15:00:00)
处理data BarDatas(current_dt:2021-12-28 15:00:00)
处理data BarDatas(current_dt:2021-12-29 15:00:00)
处理data BarDatas(current_dt:2021-12-30 15:00:00)
buy_cash_weights [0.39038004999210163, 0.2463023887407299, 0.19519002499605081, 0.16812753627111746]
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
2022-06-02 11:00:34.433456 backtest run end! 
2022-06-02 11:00:34.997779 run_backtest() finished! time cost 3.963s!

2022-06-02 11:00:36.775892 perf_render raw_perf=DataSource(24883e72afa744a38e82e34d3bb7c8a2T), benchmark_data=DataSource(2911c2589d5241fb97a051dc452a88edT), process stats...
2022-06-02 11:00:37.095100 perf_render process transactions...
2022-06-02 11:00:37.164453 perf_render process positions...
2022-06-02 11:00:37.262288 perf_render process logs...
2022-06-02 11:00:37.456240 perf_render process plot...

收益率-0.68%
年化收益率-1.32%
基准收益率-5.79%
阿尔法-0.04
贝塔0.02
夏普比率-2.6
胜率0.25
盈亏比0.33
收益波动率1.66%
信息比率0.03
最大回撤1.37%

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