{"description":"实验创建于2017/8/26","graph":{"edges":[{"to_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-15:instruments","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-8:data"},{"to_node_id":"-215:instruments","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-8:data"},{"to_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-53:data1","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-15:data"},{"to_node_id":"-215:features","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"to_node_id":"-222:features","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"to_node_id":"-231:features","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"to_node_id":"-238:features","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"to_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-43:features","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"to_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-60:model","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-43:model"},{"to_node_id":"-4567:input_data","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-53:data"},{"to_node_id":"-1262:input_data","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-60:predictions"},{"to_node_id":"-231:instruments","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-62:data"},{"to_node_id":"-10865:instruments","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-62:data"},{"to_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-43:training_ds","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-84:data"},{"to_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-60:data","from_node_id":"-86:data"},{"to_node_id":"-222:input_data","from_node_id":"-215:data"},{"to_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-53:data2","from_node_id":"-222:data"},{"to_node_id":"-238:input_data","from_node_id":"-231:data"},{"to_node_id":"-4577:input_data","from_node_id":"-238:data"},{"to_node_id":"-10245:raw_perf","from_node_id":"-10865:raw_perf"},{"to_node_id":"-10865:options_data","from_node_id":"-1262:data"},{"to_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-84:input_data","from_node_id":"-4567:data"},{"to_node_id":"-86:input_data","from_node_id":"-4577:data"}],"nodes":[{"node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-8","module_id":"BigQuantSpace.instruments.instruments-v2","parameters":[{"name":"start_date","value":"2016-01-01","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"2020-12-31","type":"Literal","bound_global_parameter":null},{"name":"market","value":"CN_STOCK_A","type":"Literal","bound_global_parameter":null},{"name":"instrument_list","value":"","type":"Literal","bound_global_parameter":null},{"name":"max_count","value":"0","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"rolling_conf","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-8"}],"output_ports":[{"name":"data","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-8"}],"cacheable":true,"seq_num":1,"comment":"","comment_collapsed":true},{"node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-15","module_id":"BigQuantSpace.advanced_auto_labeler.advanced_auto_labeler-v2","parameters":[{"name":"label_expr","value":"# 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回测引擎：初始化函数，只执行一次\ndef bigquant_run(context):\n # 加载预测数据\n context.stock_pools = context.options['data'].read()\n context.show_debug_info = False\n # 系统已经设置了默认的交易手续费和滑点，要修改手续费可使用如下函数\n context.set_commission(PerOrder(buy_cost=0.0003, sell_cost=0.0013, min_cost=5))\n \n context.options['hold_days'] = 5\n context.stock_count = 100\n context.trade_index = 0\n context.opt = T.PORTFOLIO_OPTIMIZERS(context.stock_pools, context.start_date, context.end_date, model_type='daily',benchmark='000905.HIX')","type":"Literal","bound_global_parameter":null},{"name":"handle_data","value":"# 回测引擎：每日数据处理函数，每天执行一次\ndef bigquant_run(context, data):\n context.trade_index += 1 # 交易日历递增1\n today = data.current_dt.strftime(\"%Y-%m-%d\")\n print('current_date is:', today)\n context.stock_pool = context.stock_pools[context.stock_pools.date == today]\n if context.trade_index == 1: # 第一天建仓\n# try:\n context.opt.get_today_factor_data(context.stock_pool, today) # 当日数据初始化\n objective = context.opt.MaxScore()\n constraints = [context.opt.TotalWeightsConstraint(upper_limit=1), \n context.opt.Bounds(lower_limit=0, upper_limit=0.03),\n context.opt.ExcludeStyleConstraint(\"value\",lower_limit=-0.05,upper_limit=0.05,relative=False,priority=0),\n context.opt.VolatilityConstraint(upper_limit = 0.1,relative =False, priority = 1), \n ]\n weights_data = context.opt.optimize(objective, today, constraints, stock_count=context.stock_count, verbose=False, response=True, hard=False)\n def buy_1(df):\n target = df[\"instrument\"]\n weight = df[\"weight\"]\n sid = context.symbol(target)\n if data.can_trade(sid):\n context.order_target_percent(sid, weight)\n else:\n print(f\"{today} {target} 无法交易\")\n weights_data.apply(buy_1, axis=1)\n\n# except Exception as e:\n# print(today, \"当前日期建仓失败! except:\", e)\n# context.trade_index -= 1 # 交易日历索引保持不变，以便当日优化失败后次日接着优化\n\n if context.trade_index % context.options[\"hold_days\"] == 0 and context.trade_index != 1: # 每隔调仓日进行调仓\n positions_weight = {e.symbol: p.amount * p.last_sale_price / context.portfolio.portfolio_value for e, p in context.portfolio.positions.items()} # 持仓权重\n equities = [e.symbol for e, p in context.portfolio.positions.items()] # 持仓股票列表\n w0 = pd.Series(positions_weight, index=equities)\n w0 = pd.DataFrame({'pre_weight': w0.values, 'instrument': w0.index})\n context.stock_pool = pd.merge(context.stock_pool, w0, on=['instrument'], how='left').fillna(0)\n context.opt.get_today_factor_data(context.stock_pool, today)\n\n# try:\n objective = context.opt.MaxScore()\n constraints = [context.opt.TotalWeightsConstraint(upper_limit=1), \n context.opt.Bounds(lower_limit=0, upper_limit=0.03),\n context.opt.ExcludeStyleConstraint(\"value\",lower_limit=-0.05,upper_limit=0.05,relative=False,priority=0),\n context.opt.VolatilityConstraint(upper_limit = 0.1,relative =False, priority = 1),\n ]\n weights_data = context.opt.optimize(objective, today, constraints, stock_count=context.stock_count, verbose=False, response=True, hard=False)\n\n # 卖出逻辑\n need_hold_stocks = set(weights_data.instrument)\n for sx in equities:\n if sx not in need_hold_stocks: # 无法交易的持仓、优化股票之外的持仓 直接卖出\n order_target_percent(context.symbol(sx), 0)\n equities.remove(sx)\n positions_weight = {e:p for e, p in positions_weight.items() if e != sx}\n\n # 买入逻辑\n def buy_2(df):\n target = df[\"instrument\"]\n weight = df[\"weight\"]\n sid = context.symbol(target)\n if data.can_trade(sid):\n context.order_target_percent(sid, weight)\n else:\n print(f\"{today} {target} 无法交易\")\n weights_data.apply(buy_2, axis=1)\n \n# except Exception as e:\n# print(today, \"当前日期调仓失败! except:\", e)\n \n print('----------------------------------------------------------------------------------------date {} over----------------------------------------------------------------------------------------'.format(today))","type":"Literal","bound_global_parameter":null},{"name":"prepare","value":"# 回测引擎：准备数据，只执行一次\ndef bigquant_run(context):\n 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# 本代码由可视化策略环境自动生成 2023年4月29日 12:34
# 本代码单元只能在可视化模式下编辑。您也可以拷贝代码，粘贴到新建的代码单元或者策略，然后修改。


# 回测引擎：初始化函数，只执行一次
def m20_initialize_bigquant_run(context):
    # 加载预测数据
    context.stock_pools = context.options['data'].read()
    context.show_debug_info = False
    # 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))
    
    context.options['hold_days'] = 5
    context.stock_count = 100
    context.trade_index = 0
    context.opt = T.PORTFOLIO_OPTIMIZERS(context.stock_pools, context.start_date, context.end_date, model_type='daily',benchmark='000905.HIX')
# 回测引擎：每日数据处理函数，每天执行一次
def m20_handle_data_bigquant_run(context, data):
    context.trade_index += 1  # 交易日历递增1
    today = data.current_dt.strftime("%Y-%m-%d")
    print('current_date is:', today)
    context.stock_pool = context.stock_pools[context.stock_pools.date == today]
    if context.trade_index == 1:  # 第一天建仓
#         try:
        context.opt.get_today_factor_data(context.stock_pool, today) # 当日数据初始化
        objective = context.opt.MaxScore()
        constraints = [context.opt.TotalWeightsConstraint(upper_limit=1), 
                       context.opt.Bounds(lower_limit=0, upper_limit=0.03),
                       context.opt.ExcludeStyleConstraint("value",lower_limit=-0.05,upper_limit=0.05,relative=False,priority=0),
                       context.opt.VolatilityConstraint(upper_limit = 0.1,relative =False, priority  = 1), 
                      ]
        weights_data = context.opt.optimize(objective, today, constraints, stock_count=context.stock_count, verbose=False, response=True, hard=False)
        def buy_1(df):
            target = df["instrument"]
            weight = df["weight"]
            sid = context.symbol(target)
            if data.can_trade(sid):
                context.order_target_percent(sid, weight)
            else:
                print(f"{today} {target} 无法交易")
        weights_data.apply(buy_1, axis=1)

#         except Exception as e:
#         print(today, "当前日期建仓失败! except:", e)
#         context.trade_index -= 1  # 交易日历索引保持不变，以便当日优化失败后次日接着优化

    if context.trade_index % context.options["hold_days"] == 0 and context.trade_index != 1:  # 每隔调仓日进行调仓
        positions_weight = {e.symbol: p.amount * p.last_sale_price / context.portfolio.portfolio_value for e, p in context.portfolio.positions.items()} # 持仓权重
        equities = [e.symbol for e, p in context.portfolio.positions.items()]  # 持仓股票列表
        w0 = pd.Series(positions_weight, index=equities)
        w0 = pd.DataFrame({'pre_weight': w0.values, 'instrument': w0.index})
        context.stock_pool = pd.merge(context.stock_pool, w0, on=['instrument'], how='left').fillna(0)
        context.opt.get_today_factor_data(context.stock_pool, today)

#         try:
        objective = context.opt.MaxScore()
        constraints = [context.opt.TotalWeightsConstraint(upper_limit=1), 
                       context.opt.Bounds(lower_limit=0, upper_limit=0.03),
                       context.opt.ExcludeStyleConstraint("value",lower_limit=-0.05,upper_limit=0.05,relative=False,priority=0),
                       context.opt.VolatilityConstraint(upper_limit = 0.1,relative =False, priority  = 1),
                      ]
        weights_data = context.opt.optimize(objective, today, constraints, stock_count=context.stock_count, verbose=False, response=True, hard=False)

        # 卖出逻辑
        need_hold_stocks = set(weights_data.instrument)
        for sx in equities:
            if sx not in need_hold_stocks:  # 无法交易的持仓、优化股票之外的持仓 直接卖出
                order_target_percent(context.symbol(sx), 0)
                equities.remove(sx)
                positions_weight = {e:p for e, p in positions_weight.items() if e != sx}

        # 买入逻辑
        def buy_2(df):
            target = df["instrument"]
            weight = df["weight"]
            sid = context.symbol(target)
            if data.can_trade(sid):
                context.order_target_percent(sid, weight)
            else:
                print(f"{today} {target} 无法交易")
        weights_data.apply(buy_2, axis=1)
                
#         except Exception as e:
#             print(today, "当前日期调仓失败! except:", e)
            
        print('----------------------------------------------------------------------------------------date {} over----------------------------------------------------------------------------------------'.format(today))
# 回测引擎：准备数据，只执行一次
def m20_prepare_bigquant_run(context):
    pass


m1 = M.instruments.v2(
    start_date='2016-01-01',
    end_date='2020-12-31',
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m2 = M.advanced_auto_labeler.v2(
    instruments=m1.data,
    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
#   添加benchmark_前缀，可使用对应的benchmark数据
# 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, -2) / 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, 20)

# 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)
""",
    start_date='',
    end_date='',
    benchmark='000500.HIX',
    drop_na_label=True,
    cast_label_int=True
)

m3 = M.input_features.v1(
    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
return_5
return_10
return_20
avg_amount_0/avg_amount_5
avg_amount_5/avg_amount_20
rank_avg_amount_0/rank_avg_amount_5
rank_avg_amount_5/rank_avg_amount_10
rank_return_0
rank_return_5
rank_return_10
rank_return_0/rank_return_5
rank_return_5/rank_return_10
pe_ttm_0


hf_value_diff_large_order
hf_close_net_inflow_volume_rate
hf_close_net_inflow_value_rate
hf_volume_diff_large_order
hf_close_netinflow_rate_med_order_act
hf_close_sell_volume_med_order
hf_close_volume_ask
hf_close_netinflow_rate_small_order_act
hf_close_sell_volume_small_order
hf_close_buy_volume_small_order_act
hf_close_buy_volume_med_order_act
hf_close_sell_volume_large_order"""
)

m15 = M.general_feature_extractor.v7(
    instruments=m1.data,
    features=m3.data,
    start_date='',
    end_date='',
    before_start_days=90
)

m16 = M.derived_feature_extractor.v3(
    input_data=m15.data,
    features=m3.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False
)

m7 = M.join.v3(
    data1=m2.data,
    data2=m16.data,
    on='date,instrument',
    how='inner',
    sort=False
)

m5 = M.chinaa_stock_filter.v1(
    input_data=m7.data,
    index_constituent_cond=['中证500'],
    board_cond=['上证主板', '深证主板', '创业板'],
    industry_cond=['全部'],
    st_cond=['正常'],
    delist_cond=['非退市'],
    output_left_data=False
)

m13 = M.dropnan.v1(
    input_data=m5.data
)

m6 = M.stock_ranker_train.v6(
    training_ds=m13.data,
    features=m3.data,
    learning_algorithm='排序',
    number_of_leaves=40,
    minimum_docs_per_leaf=1000,
    number_of_trees=20,
    learning_rate=0.2,
    max_bins=1023,
    feature_fraction=1,
    data_row_fraction=1,
    plot_charts=True,
    ndcg_discount_base=1,
    m_lazy_run=False
)

m9 = M.instruments.v2(
    start_date=T.live_run_param('trading_date', '2021-06-07'),
    end_date=T.live_run_param('trading_date', '2022-11-28'),
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m17 = M.general_feature_extractor.v7(
    instruments=m9.data,
    features=m3.data,
    start_date='',
    end_date='',
    before_start_days=90
)

m18 = M.derived_feature_extractor.v3(
    input_data=m17.data,
    features=m3.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False
)

m10 = M.chinaa_stock_filter.v1(
    input_data=m18.data,
    index_constituent_cond=['中证500'],
    board_cond=['上证主板', '深证主板', '创业板'],
    industry_cond=['全部'],
    st_cond=['正常'],
    delist_cond=['非退市'],
    output_left_data=False
)

m14 = M.dropnan.v1(
    input_data=m10.data
)

m8 = M.stock_ranker_predict.v5(
    model=m6.model,
    data=m14.data,
    m_lazy_run=False
)

m4 = M.chinaa_stock_filter.v1(
    input_data=m8.predictions,
    index_constituent_cond=['中证500'],
    board_cond=['全部'],
    industry_cond=['全部'],
    st_cond=['正常'],
    delist_cond=['非退市'],
    output_left_data=False
)

m20 = M.trade.v4(
    instruments=m9.data,
    options_data=m4.data,
    start_date='',
    end_date='',
    initialize=m20_initialize_bigquant_run,
    handle_data=m20_handle_data_bigquant_run,
    prepare=m20_prepare_bigquant_run,
    volume_limit=0.025,
    order_price_field_buy='open',
    order_price_field_sell='close',
    capital_base=1000000,
    auto_cancel_non_tradable_orders=True,
    data_frequency='daily',
    price_type='真实价格',
    product_type='股票',
    plot_charts=True,
    backtest_only=False,
    benchmark='000905.HIX'
)

m11 = M.strategy_turn_analysis.v1(
    raw_perf=m20.raw_perf
)

[2023-04-29 11:52:52.500179] INFO: moduleinvoker: instruments.v2 开始运行..

[2023-04-29 11:52:52.516206] INFO: moduleinvoker: 命中缓存

[2023-04-29 11:52:52.518854] INFO: moduleinvoker: instruments.v2 运行完成[0.018698s].

[2023-04-29 11:52:52.531817] INFO: moduleinvoker: advanced_auto_labeler.v2 开始运行..

[2023-04-29 11:52:57.404803] INFO: 自动标注(股票): 加载历史数据: 4032588 行

[2023-04-29 11:52:57.407203] INFO: 自动标注(股票): 开始标注 ..

[2023-04-29 11:53:03.315342] INFO: moduleinvoker: advanced_auto_labeler.v2 运行完成[10.783518s].

[2023-04-29 11:53:03.322831] INFO: moduleinvoker: input_features.v1 开始运行..

[2023-04-29 11:53:03.332700] INFO: moduleinvoker: 命中缓存

[2023-04-29 11:53:03.335335] INFO: moduleinvoker: input_features.v1 运行完成[0.01251s].

[2023-04-29 11:53:03.357942] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2023-04-29 11:53:05.434204] INFO: 基础特征抽取: 年份 2015, 特征行数=148559

[2023-04-29 11:53:10.851918] INFO: 基础特征抽取: 年份 2016, 特征行数=638310

[2023-04-29 11:53:17.191035] INFO: 基础特征抽取: 年份 2017, 特征行数=743238

[2023-04-29 11:53:24.461357] INFO: 基础特征抽取: 年份 2018, 特征行数=814951

[2023-04-29 11:53:32.218871] INFO: 基础特征抽取: 年份 2019, 特征行数=884867

[2023-04-29 11:53:40.752287] INFO: 基础特征抽取: 年份 2020, 特征行数=945961

[2023-04-29 11:53:40.834414] INFO: 基础特征抽取: 总行数: 4175886

[2023-04-29 11:53:40.849731] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[37.491817s].

[2023-04-29 11:53:40.862126] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2023-04-29 11:53:51.861286] INFO: derived_feature_extractor: 提取完成 avg_amount_0/avg_amount_5, 0.016s

[2023-04-29 11:53:51.877005] INFO: derived_feature_extractor: 提取完成 avg_amount_5/avg_amount_20, 0.013s

[2023-04-29 11:53:51.885796] INFO: derived_feature_extractor: 提取完成 rank_avg_amount_0/rank_avg_amount_5, 0.007s

[2023-04-29 11:53:51.893782] INFO: derived_feature_extractor: 提取完成 rank_avg_amount_5/rank_avg_amount_10, 0.006s

[2023-04-29 11:53:51.903035] INFO: derived_feature_extractor: 提取完成 rank_return_0/rank_return_5, 0.007s

[2023-04-29 11:53:51.912451] INFO: derived_feature_extractor: 提取完成 rank_return_5/rank_return_10, 0.007s

[2023-04-29 11:53:54.079774] INFO: derived_feature_extractor: /y_2015, 148559

[2023-04-29 11:53:55.523867] INFO: derived_feature_extractor: /y_2016, 638310

[2023-04-29 11:53:57.919680] INFO: derived_feature_extractor: /y_2017, 743238

[2023-04-29 11:54:00.537651] INFO: derived_feature_extractor: /y_2018, 814951

[2023-04-29 11:54:03.461997] INFO: derived_feature_extractor: /y_2019, 884867

[2023-04-29 11:54:06.918501] INFO: derived_feature_extractor: /y_2020, 945961

[2023-04-29 11:54:08.462790] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[27.60065s].

[2023-04-29 11:54:08.474645] INFO: moduleinvoker: join.v3 开始运行..

[2023-04-29 11:54:27.294753] INFO: join: /y_2015, 行数=0/148559, 耗时=2.00445s

[2023-04-29 11:54:31.037472] INFO: join: /y_2016, 行数=634265/638310, 耗时=3.740522s

[2023-04-29 11:54:35.515753] INFO: join: /y_2017, 行数=738271/743238, 耗时=4.471875s

[2023-04-29 11:54:40.158624] INFO: join: /y_2018, 行数=811500/814951, 耗时=4.636026s

[2023-04-29 11:54:45.042515] INFO: join: /y_2019, 行数=881324/884867, 耗时=4.876461s

[2023-04-29 11:54:49.998100] INFO: join: /y_2020, 行数=931755/945961, 耗时=4.947474s

[2023-04-29 11:54:50.127038] INFO: join: 最终行数: 3997115

[2023-04-29 11:54:50.185021] INFO: moduleinvoker: join.v3 运行完成[41.710377s].

[2023-04-29 11:54:50.202534] INFO: moduleinvoker: chinaa_stock_filter.v1 开始运行..

[2023-04-29 11:54:56.722981] INFO: A股股票过滤: 过滤 /y_2016, 113666/0/634265

[2023-04-29 11:55:02.012940] INFO: A股股票过滤: 过滤 /y_2017, 115213/0/738271

[2023-04-29 11:55:07.793768] INFO: A股股票过滤: 过滤 /y_2018, 114877/0/811500

[2023-04-29 11:55:14.088681] INFO: A股股票过滤: 过滤 /y_2019, 121220/0/881324

[2023-04-29 11:55:20.713332] INFO: A股股票过滤: 过滤 /y_2020, 119670/0/931755

[2023-04-29 11:55:20.724372] INFO: A股股票过滤: 过滤完成, 584646 + 0

[2023-04-29 11:55:20.745766] INFO: moduleinvoker: chinaa_stock_filter.v1 运行完成[30.543214s].

[2023-04-29 11:55:20.760750] INFO: moduleinvoker: dropnan.v1 开始运行..

[2023-04-29 11:55:21.388974] INFO: dropnan: /y_2016, 56655/113666

[2023-04-29 11:55:21.736107] INFO: dropnan: /y_2017, 60342/115213

[2023-04-29 11:55:22.065978] INFO: dropnan: /y_2018, 59849/114877

[2023-04-29 11:55:22.389750] INFO: dropnan: /y_2019, 61321/121220

[2023-04-29 11:55:22.823564] INFO: dropnan: /y_2020, 60372/119670

[2023-04-29 11:55:22.862888] INFO: dropnan: 行数: 298539/584646

[2023-04-29 11:55:22.868376] INFO: moduleinvoker: dropnan.v1 运行完成[2.10762s].

[2023-04-29 11:55:22.886809] INFO: moduleinvoker: stock_ranker_train.v6 开始运行..

[2023-04-29 11:55:23.741047] INFO: StockRanker: 特征预处理 ..

[2023-04-29 11:55:24.575098] INFO: StockRanker: prepare data: training ..

[2023-04-29 11:55:25.567715] INFO: StockRanker: sort ..

[2023-04-29 11:55:30.670937] INFO: StockRanker训练: aaab894a 准备训练: 298539 行数

[2023-04-29 11:55:30.672710] INFO: StockRanker训练: AI模型训练，将在298539*25=746.35万数据上对模型训练进行20轮迭代训练。预计将需要3～6分钟。请耐心等待。

[2023-04-29 11:55:30.986211] INFO: StockRanker训练: 正在训练 ..

[2023-04-29 11:55:31.037522] INFO: StockRanker训练: 任务状态: Pending

[2023-04-29 11:55:41.082984] INFO: StockRanker训练: 任务状态: Running

[2023-04-29 11:56:41.339200] INFO: StockRanker训练: 00:01:02.7517952, finished iteration 1

[2023-04-29 11:56:41.341530] INFO: StockRanker训练: 00:01:03.0454400, finished iteration 2

[2023-04-29 11:56:41.343818] INFO: StockRanker训练: 00:01:03.3559600, finished iteration 3

[2023-04-29 11:56:41.345945] INFO: StockRanker训练: 00:01:03.6794417, finished iteration 4

[2023-04-29 11:56:41.347870] INFO: StockRanker训练: 00:01:04.0010198, finished iteration 5

[2023-04-29 11:56:41.349398] INFO: StockRanker训练: 00:01:04.2766346, finished iteration 6

[2023-04-29 11:56:41.351057] INFO: StockRanker训练: 00:01:04.6037617, finished iteration 7

[2023-04-29 11:56:41.352866] INFO: StockRanker训练: 00:01:04.9142953, finished iteration 8

[2023-04-29 11:56:41.354420] INFO: StockRanker训练: 00:01:05.2124136, finished iteration 9

[2023-04-29 11:56:41.355995] INFO: StockRanker训练: 00:01:05.5816520, finished iteration 10

[2023-04-29 11:56:41.357774] INFO: StockRanker训练: 00:01:05.9144599, finished iteration 11

[2023-04-29 11:56:41.359281] INFO: StockRanker训练: 00:01:06.2734558, finished iteration 12

[2023-04-29 11:56:41.360797] INFO: StockRanker训练: 00:01:06.6276366, finished iteration 13

[2023-04-29 11:56:51.415974] INFO: StockRanker训练: 00:01:06.9835931, finished iteration 14

[2023-04-29 11:56:51.417855] INFO: StockRanker训练: 00:01:07.2452847, finished iteration 15

[2023-04-29 11:56:51.419501] INFO: StockRanker训练: 00:01:07.5649308, finished iteration 16

[2023-04-29 11:56:51.421238] INFO: StockRanker训练: 00:01:07.8866882, finished iteration 17

[2023-04-29 11:56:51.423121] INFO: StockRanker训练: 00:01:08.2017857, finished iteration 18

[2023-04-29 11:56:51.424980] INFO: StockRanker训练: 00:01:08.5148644, finished iteration 19

[2023-04-29 11:56:51.426725] INFO: StockRanker训练: 00:01:08.8527135, finished iteration 20

[2023-04-29 11:56:51.428572] INFO: StockRanker训练: 任务状态: Succeeded

[2023-04-29 11:56:51.711907] INFO: moduleinvoker: stock_ranker_train.v6 运行完成[88.825092s].

[2023-04-29 11:56:51.719462] INFO: moduleinvoker: instruments.v2 开始运行..

[2023-04-29 11:56:51.825403] INFO: moduleinvoker: instruments.v2 运行完成[0.105939s].

[2023-04-29 11:56:51.863150] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2023-04-29 11:56:59.126639] INFO: 基础特征抽取: 年份 2021, 特征行数=888541

[2023-04-29 11:57:07.360648] INFO: 基础特征抽取: 年份 2022, 特征行数=1029063

[2023-04-29 11:57:07.440275] INFO: 基础特征抽取: 总行数: 1917604

[2023-04-29 11:57:07.449869] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[15.586747s].

[2023-04-29 11:57:07.462929] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2023-04-29 11:57:12.172824] INFO: derived_feature_extractor: 提取完成 avg_amount_0/avg_amount_5, 0.006s

[2023-04-29 11:57:12.179903] INFO: derived_feature_extractor: 提取完成 avg_amount_5/avg_amount_20, 0.005s

[2023-04-29 11:57:12.184781] INFO: derived_feature_extractor: 提取完成 rank_avg_amount_0/rank_avg_amount_5, 0.003s

[2023-04-29 11:57:12.189458] INFO: derived_feature_extractor: 提取完成 rank_avg_amount_5/rank_avg_amount_10, 0.003s

[2023-04-29 11:57:12.194046] INFO: derived_feature_extractor: 提取完成 rank_return_0/rank_return_5, 0.003s

[2023-04-29 11:57:12.198608] INFO: derived_feature_extractor: 提取完成 rank_return_5/rank_return_10, 0.003s

[2023-04-29 11:57:14.830870] INFO: derived_feature_extractor: /y_2021, 888541

[2023-04-29 11:57:18.074918] INFO: derived_feature_extractor: /y_2022, 1029063

[2023-04-29 11:57:19.551099] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[12.088173s].

[2023-04-29 11:57:19.560709] INFO: moduleinvoker: chinaa_stock_filter.v1 开始运行..

[2023-04-29 11:57:26.422026] INFO: A股股票过滤: 过滤 /y_2021, 99528/0/888541

[2023-04-29 11:57:33.472612] INFO: A股股票过滤: 过滤 /y_2022, 105327/0/1029063

[2023-04-29 11:57:33.478869] INFO: A股股票过滤: 过滤完成, 204855 + 0

[2023-04-29 11:57:33.498371] INFO: moduleinvoker: chinaa_stock_filter.v1 运行完成[13.937649s].

[2023-04-29 11:57:33.509920] INFO: moduleinvoker: dropnan.v1 开始运行..

[2023-04-29 11:57:33.879108] INFO: dropnan: /y_2021, 84783/99528

[2023-04-29 11:57:34.217188] INFO: dropnan: /y_2022, 105169/105327

[2023-04-29 11:57:34.289257] INFO: dropnan: 行数: 189952/204855

[2023-04-29 11:57:34.295530] INFO: moduleinvoker: dropnan.v1 运行完成[0.785593s].

[2023-04-29 11:57:34.316405] INFO: moduleinvoker: stock_ranker_predict.v5 开始运行..

[2023-04-29 11:57:34.665991] INFO: StockRanker预测: /y_2021 ..

[2023-04-29 11:57:35.055643] INFO: StockRanker预测: /y_2022 ..

[2023-04-29 11:57:35.518328] INFO: moduleinvoker: stock_ranker_predict.v5 运行完成[1.201909s].

[2023-04-29 11:57:35.527640] INFO: moduleinvoker: chinaa_stock_filter.v1 开始运行..

[2023-04-29 11:57:39.414452] INFO: A股股票过滤: 过滤 /data, 189952/0/189952

[2023-04-29 11:57:39.417312] INFO: A股股票过滤: 过滤完成, 189952 + 0

[2023-04-29 11:57:39.434502] INFO: moduleinvoker: chinaa_stock_filter.v1 运行完成[3.906865s].

[2023-04-29 11:57:42.905201] INFO: moduleinvoker: backtest.v8 开始运行..

[2023-04-29 11:57:42.911986] INFO: backtest: biglearning backtest:V8.6.3

[2023-04-29 11:57:42.913929] INFO: backtest: product_type:stock by specified

[2023-04-29 11:57:42.995881] INFO: moduleinvoker: cached.v2 开始运行..

[2023-04-29 11:57:51.527866] INFO: backtest: 读取股票行情完成:2984802

[2023-04-29 11:57:53.917887] INFO: moduleinvoker: cached.v2 运行完成[10.922034s].

[2023-04-29 11:58:07.414442] INFO: backtest: algo history_data=DataSource(72646b17622b44448c8dbc443c22edbcT)

[2023-04-29 11:58:07.416945] INFO: algo: TradingAlgorithm V1.8.9

[2023-04-29 11:58:16.153792] INFO: algo: trading transform...

[2023-04-29 12:25:08.985358] INFO: Performance: Simulated 360 trading days out of 360.

[2023-04-29 12:25:08.987405] INFO: Performance: first open: 2021-06-07 09:30:00+00:00

[2023-04-29 12:25:08.988758] INFO: Performance: last close: 2022-11-28 15:00:00+00:00

[2023-04-29 12:25:14.160118] INFO: moduleinvoker: backtest.v8 运行完成[1651.254926s].

[2023-04-29 12:25:14.161831] INFO: moduleinvoker: trade.v4 运行完成[1654.718507s].

[2023-04-29 12:25:14.175019] INFO: moduleinvoker: strategy_turn_analysis.v1 开始运行..

[2023-04-29 12:25:16.618891] INFO: moduleinvoker: strategy_turn_analysis.v1 运行完成[2.443863s].

bigcharts-data-start/{"__type":"tabs","__id":"bigchart-93308c0593ce47e3a898a555a42d660a"}/bigcharts-data-end

current_date is: 2021-06-07
amount of unique_stocks : 492, weights objective should be considered
Using Soft Constraints, and the Optimization result is optimal before the relaxation
current_date is: 2021-06-08
current_date is: 2021-06-09
current_date is: 2021-06-10
current_date is: 2021-06-11
amount of unique_stocks : 493, weights objective should be considered
Using Soft Constraints, and the Optimization result is optimal before the relaxation
----------------------------------------------------------------------------------------date 2021-06-11 over----------------------------------------------------------------------------------------
current_date is: 2021-06-15
current_date is: 2021-06-16
current_date is: 2021-06-17
current_date is: 2021-06-18
current_date is: 2021-06-21
amount of unique_stocks : 494, weights objective should be considered
Using Soft Constraints, and the Optimization result is optimal before the relaxation
----------------------------------------------------------------------------------------date 2021-06-21 over----------------------------------------------------------------------------------------
current_date is: 2021-06-22
current_date is: 2021-06-23
current_date is: 2021-06-24
current_date is: 2021-06-25
current_date is: 2021-06-28
amount of unique_stocks : 494, weights objective should be considered
Using Soft Constraints, and the Optimization result is optimal before the relaxation
----------------------------------------------------------------------------------------date 2021-06-28 over----------------------------------------------------------------------------------------
current_date is: 2021-06-29
current_date is: 2021-06-30
current_date is: 2021-07-01
current_date is: 2021-07-02
current_date is: 2021-07-05
amount of unique_stocks : 493, weights objective should be considered
Using Soft Constraints, and the Optimization result is optimal before the relaxation
----------------------------------------------------------------------------------------date 2021-07-05 over----------------------------------------------------------------------------------------
current_date is: 2021-07-06
current_date is: 2021-07-07
current_date is: 2021-07-08
current_date is: 2021-07-09
current_date is: 2021-07-12
amount of unique_stocks : 493, weights objective should be considered
Using Soft Constraints, and the Optimization result is optimal before the relaxation
----------------------------------------------------------------------------------------date 2021-07-12 over----------------------------------------------------------------------------------------
current_date is: 2021-07-13
current_date is: 2021-07-14
current_date is: 2021-07-15
current_date is: 2021-07-16
current_date is: 2021-07-19
amount of unique_stocks : 493, weights objective should be considered
Using Soft Constraints, and the Optimization result is optimal before the relaxation
----------------------------------------------------------------------------------------date 2021-07-19 over----------------------------------------------------------------------------------------
current_date is: 2021-07-20
current_date is: 2021-07-21
current_date is: 2021-07-22
current_date is: 2021-07-23
current_date is: 2021-07-26
amount of unique_stocks : 494, weights objective should be considered
Using Soft Constraints, and the Optimization result is optimal before the relaxation
----------------------------------------------------------------------------------------date 2021-07-26 over----------------------------------------------------------------------------------------
current_date is: 2021-07-27
current_date is: 2021-07-28
current_date is: 2021-07-29
current_date is: 2021-07-30
current_date is: 2021-08-02
amount of unique_stocks : 494, weights objective should be considered
Using Soft Constraints, and the Optimization result is optimal before the relaxation
----------------------------------------------------------------------------------------date 2021-08-02 over----------------------------------------------------------------------------------------
current_date is: 2021-08-03
current_date is: 2021-08-04
current_date is: 2021-08-05
current_date is: 2021-08-06
current_date is: 2021-08-09
amount of unique_stocks : 493, weights objective should be considered
Using Soft Constraints, and the Optimization result is optimal before the relaxation
----------------------------------------------------------------------------------------date 2021-08-09 over----------------------------------------------------------------------------------------
current_date is: 2021-08-10
current_date is: 2021-08-11
current_date is: 2021-08-12
current_date is: 2021-08-13
current_date is: 2021-08-16
amount of unique_stocks : 492, weights objective should be considered
Using Soft Constraints, and the Optimization result is optimal before the relaxation
----------------------------------------------------------------------------------------date 2021-08-16 over----------------------------------------------------------------------------------------
current_date is: 2021-08-17
current_date is: 2021-08-18
current_date is: 2021-08-19
current_date is: 2021-08-20
current_date is: 2021-08-23
amount of unique_stocks : 493, weights objective should be considered
Using Soft Constraints, and the Optimization result is optimal before the relaxation
----------------------------------------------------------------------------------------date 2021-08-23 over----------------------------------------------------------------------------------------
current_date is: 2021-08-24
current_date is: 2021-08-25
current_date is: 2021-08-26
current_date is: 2021-08-27
current_date is: 2021-08-30
amount of unique_stocks : 492, weights objective should be considered
Using Soft Constraints, and the Optimization result is optimal before the relaxation
----------------------------------------------------------------------------------------date 2021-08-30 over----------------------------------------------------------------------------------------
current_date is: 2021-08-31
current_date is: 2021-09-01
current_date is: 2021-09-02
current_date is: 2021-09-03
current_date is: 2021-09-06
amount of unique_stocks : 491, weights objective should be considered
Using Soft Constraints, and the Optimization result is optimal before the relaxation
----------------------------------------------------------------------------------------date 2021-09-06 over----------------------------------------------------------------------------------------
current_date is: 2021-09-07
current_date is: 2021-09-08
current_date is: 2021-09-09
current_date is: 2021-09-10
current_date is: 2021-09-13
amount of unique_stocks : 493, weights objective should be considered
Using Soft Constraints, and the Optimization result is optimal before the relaxation
----------------------------------------------------------------------------------------date 2021-09-13 over----------------------------------------------------------------------------------------
current_date is: 2021-09-14
current_date is: 2021-09-15
current_date is: 2021-09-16
current_date is: 2021-09-17
current_date is: 2021-09-22
amount of unique_stocks : 492, weights objective should be considered
Using Soft Constraints, and the Optimization result is optimal before the relaxation
----------------------------------------------------------------------------------------date 2021-09-22 over----------------------------------------------------------------------------------------
current_date is: 2021-09-23
current_date is: 2021-09-24
current_date is: 2021-09-27
current_date is: 2021-09-28
current_date is: 2021-09-29
amount of unique_stocks : 493, weights objective should be considered
Using Soft Constraints, and the Optimization result is optimal before the relaxation
----------------------------------------------------------------------------------------date 2021-09-29 over----------------------------------------------------------------------------------------
current_date is: 2021-09-30
current_date is: 2021-10-08
current_date is: 2021-10-11
current_date is: 2021-10-12
current_date is: 2021-10-13
amount of unique_stocks : 492, weights objective should be considered
Using Soft Constraints, and the Optimization result is optimal before the relaxation
----------------------------------------------------------------------------------------date 2021-10-13 over----------------------------------------------------------------------------------------
current_date is: 2021-10-14
current_date is: 2021-10-15
current_date is: 2021-10-18
current_date is: 2021-10-19
current_date is: 2021-10-20
amount of unique_stocks : 491, weights objective should be considered
Using Soft Constraints, and the Optimization result is optimal before the relaxation
----------------------------------------------------------------------------------------date 2021-10-20 over----------------------------------------------------------------------------------------
current_date is: 2021-10-21
current_date is: 2021-10-22
current_date is: 2021-10-25
current_date is: 2021-10-26
current_date is: 2021-10-27
amount of unique_stocks : 493, weights objective should be considered
Using Soft Constraints, and the Optimization result is optimal before the relaxation
----------------------------------------------------------------------------------------date 2021-10-27 over----------------------------------------------------------------------------------------
current_date is: 2021-10-28
current_date is: 2021-10-29
current_date is: 2021-11-01
current_date is: 2021-11-02
current_date is: 2021-11-03
amount of unique_stocks : 494, weights objective should be considered
Using Soft Constraints, and the Optimization result is optimal before the relaxation
----------------------------------------------------------------------------------------date 2021-11-03 over----------------------------------------------------------------------------------------
current_date is: 2021-11-04
current_date is: 2021-11-05
current_date is: 2021-11-08
current_date is: 2021-11-09
current_date is: 2021-11-10
amount of unique_stocks : 494, weights objective should be considered
Using Soft Constraints, and the Optimization result is optimal before the relaxation
----------------------------------------------------------------------------------------date 2021-11-10 over----------------------------------------------------------------------------------------
current_date is: 2021-11-11
current_date is: 2021-11-12
current_date is: 2021-11-15
current_date is: 2021-11-16
current_date is: 2021-11-17
amount of unique_stocks : 494, weights objective should be considered
Using Soft Constraints, and the Optimization result is optimal before the relaxation
----------------------------------------------------------------------------------------date 2021-11-17 over----------------------------------------------------------------------------------------
current_date is: 2021-11-18
current_date is: 2021-11-19
current_date is: 2021-11-22
current_date is: 2021-11-23
current_date is: 2021-11-24
amount of unique_stocks : 494, weights objective should be considered
Using Soft Constraints, and the Optimization result is optimal before the relaxation
----------------------------------------------------------------------------------------date 2021-11-24 over----------------------------------------------------------------------------------------
current_date is: 2021-11-25
current_date is: 2021-11-26
current_date is: 2021-11-29
current_date is: 2021-11-30
current_date is: 2021-12-01
amount of unique_stocks : 493, weights objective should be considered
Using Soft Constraints, and the Optimization result is optimal before the relaxation
----------------------------------------------------------------------------------------date 2021-12-01 over----------------------------------------------------------------------------------------
current_date is: 2021-12-02
current_date is: 2021-12-03
current_date is: 2021-12-06
current_date is: 2021-12-07
current_date is: 2021-12-08
amount of unique_stocks : 493, weights objective should be considered
Using Soft Constraints, and the Optimization result is optimal before the relaxation
----------------------------------------------------------------------------------------date 2021-12-08 over----------------------------------------------------------------------------------------
current_date is: 2021-12-09
current_date is: 2021-12-10
current_date is: 2021-12-13
current_date is: 2021-12-14
current_date is: 2021-12-15
amount of unique_stocks : 486, weights objective should be considered
Using Soft Constraints, and the Optimization result is optimal before the relaxation
----------------------------------------------------------------------------------------date 2021-12-15 over----------------------------------------------------------------------------------------
current_date is: 2021-12-16
current_date is: 2021-12-17
current_date is: 2021-12-20
current_date is: 2021-12-21
current_date is: 2021-12-22
amount of unique_stocks : 485, weights objective should be considered
Using Soft Constraints, and the Optimization result is optimal before the relaxation
----------------------------------------------------------------------------------------date 2021-12-22 over----------------------------------------------------------------------------------------
current_date is: 2021-12-23
current_date is: 2021-12-24
current_date is: 2021-12-27
current_date is: 2021-12-28
current_date is: 2021-12-29
amount of unique_stocks : 486, weights objective should be considered
Using Soft Constraints, and the Optimization result is optimal before the relaxation
----------------------------------------------------------------------------------------date 2021-12-29 over----------------------------------------------------------------------------------------
current_date is: 2021-12-30
current_date is: 2021-12-31
current_date is: 2022-01-04
current_date is: 2022-01-05
current_date is: 2022-01-06
amount of unique_stocks : 486, weights objective should be considered
Using Soft Constraints, and the Optimization result is optimal before the relaxation
----------------------------------------------------------------------------------------date 2022-01-06 over----------------------------------------------------------------------------------------
current_date is: 2022-01-07
current_date is: 2022-01-10
current_date is: 2022-01-11
current_date is: 2022-01-12
current_date is: 2022-01-13
amount of unique_stocks : 487, weights objective should be considered
Using Soft Constraints, and the Optimization result is optimal before the relaxation
----------------------------------------------------------------------------------------date 2022-01-13 over----------------------------------------------------------------------------------------
current_date is: 2022-01-14
current_date is: 2022-01-17
current_date is: 2022-01-18
current_date is: 2022-01-19
current_date is: 2022-01-20
amount of unique_stocks : 485, weights objective should be considered
Using Soft Constraints, and the Optimization result is optimal before the relaxation
----------------------------------------------------------------------------------------date 2022-01-20 over----------------------------------------------------------------------------------------
current_date is: 2022-01-21
current_date is: 2022-01-24
current_date is: 2022-01-25
current_date is: 2022-01-26
current_date is: 2022-01-27
amount of unique_stocks : 485, weights objective should be considered
Using Soft Constraints, and the Optimization result is optimal before the relaxation
----------------------------------------------------------------------------------------date 2022-01-27 over----------------------------------------------------------------------------------------
current_date is: 2022-01-28
current_date is: 2022-02-07
current_date is: 2022-02-08
current_date is: 2022-02-09
current_date is: 2022-02-10
amount of unique_stocks : 487, weights objective should be considered
Using Soft Constraints, and the Optimization result is optimal before the relaxation
----------------------------------------------------------------------------------------date 2022-02-10 over----------------------------------------------------------------------------------------
current_date is: 2022-02-11
current_date is: 2022-02-14
current_date is: 2022-02-15
current_date is: 2022-02-16
current_date is: 2022-02-17
amount of unique_stocks : 486, weights objective should be considered
Using Soft Constraints, and the Optimization result is optimal before the relaxation
----------------------------------------------------------------------------------------date 2022-02-17 over----------------------------------------------------------------------------------------
current_date is: 2022-02-18
current_date is: 2022-02-21
current_date is: 2022-02-22
current_date is: 2022-02-23
current_date is: 2022-02-24
amount of unique_stocks : 486, weights objective should be considered
Using Soft Constraints, and the Optimization result is optimal before the relaxation
----------------------------------------------------------------------------------------date 2022-02-24 over----------------------------------------------------------------------------------------
current_date is: 2022-02-25
current_date is: 2022-02-28
current_date is: 2022-03-01
current_date is: 2022-03-02
current_date is: 2022-03-03
amount of unique_stocks : 486, weights objective should be considered
Using Soft Constraints, and the Optimization result is optimal before the relaxation
----------------------------------------------------------------------------------------date 2022-03-03 over----------------------------------------------------------------------------------------
current_date is: 2022-03-04
current_date is: 2022-03-07
current_date is: 2022-03-08
current_date is: 2022-03-09
current_date is: 2022-03-10
amount of unique_stocks : 485, weights objective should be considered
Using Soft Constraints, and the Optimization result is optimal before the relaxation
----------------------------------------------------------------------------------------date 2022-03-10 over----------------------------------------------------------------------------------------
current_date is: 2022-03-11
current_date is: 2022-03-14
current_date is: 2022-03-15
current_date is: 2022-03-16
current_date is: 2022-03-17
amount of unique_stocks : 487, weights objective should be considered
Using Soft Constraints, and the Optimization result is optimal before the relaxation
----------------------------------------------------------------------------------------date 2022-03-17 over----------------------------------------------------------------------------------------
current_date is: 2022-03-18
current_date is: 2022-03-21
current_date is: 2022-03-22
current_date is: 2022-03-23
current_date is: 2022-03-24
amount of unique_stocks : 487, weights objective should be considered
Using Soft Constraints, and the Optimization result is optimal before the relaxation
----------------------------------------------------------------------------------------date 2022-03-24 over----------------------------------------------------------------------------------------
current_date is: 2022-03-25
current_date is: 2022-03-28
current_date is: 2022-03-29
current_date is: 2022-03-30
current_date is: 2022-03-31
amount of unique_stocks : 487, weights objective should be considered
Using Soft Constraints, and the Optimization result is optimal before the relaxation
----------------------------------------------------------------------------------------date 2022-03-31 over----------------------------------------------------------------------------------------
current_date is: 2022-04-01
current_date is: 2022-04-06
current_date is: 2022-04-07
current_date is: 2022-04-08
current_date is: 2022-04-11
amount of unique_stocks : 487, weights objective should be considered
Using Soft Constraints, and the Optimization result is optimal before the relaxation
----------------------------------------------------------------------------------------date 2022-04-11 over----------------------------------------------------------------------------------------
current_date is: 2022-04-12
current_date is: 2022-04-13
current_date is: 2022-04-14
current_date is: 2022-04-15
current_date is: 2022-04-18
amount of unique_stocks : 486, weights objective should be considered
Using Soft Constraints, and the Optimization result is optimal before the relaxation
----------------------------------------------------------------------------------------date 2022-04-18 over----------------------------------------------------------------------------------------
current_date is: 2022-04-19
current_date is: 2022-04-20
current_date is: 2022-04-21
current_date is: 2022-04-22
current_date is: 2022-04-25
amount of unique_stocks : 486, weights objective should be considered
Using Soft Constraints, and the Optimization result is optimal before the relaxation
----------------------------------------------------------------------------------------date 2022-04-25 over----------------------------------------------------------------------------------------
current_date is: 2022-04-26
current_date is: 2022-04-27
current_date is: 2022-04-28
current_date is: 2022-04-29
current_date is: 2022-05-05
amount of unique_stocks : 487, weights objective should be considered
Using Soft Constraints, and the Optimization result is optimal before the relaxation
----------------------------------------------------------------------------------------date 2022-05-05 over----------------------------------------------------------------------------------------
current_date is: 2022-05-06
current_date is: 2022-05-09
current_date is: 2022-05-10
current_date is: 2022-05-11
current_date is: 2022-05-12
amount of unique_stocks : 487, weights objective should be considered
Using Soft Constraints, and the Optimization result is optimal before the relaxation
----------------------------------------------------------------------------------------date 2022-05-12 over----------------------------------------------------------------------------------------
current_date is: 2022-05-13
current_date is: 2022-05-16
current_date is: 2022-05-17
current_date is: 2022-05-18
current_date is: 2022-05-19
amount of unique_stocks : 487, weights objective should be considered
Using Soft Constraints, and the Optimization result is optimal before the relaxation
----------------------------------------------------------------------------------------date 2022-05-19 over----------------------------------------------------------------------------------------
current_date is: 2022-05-20
current_date is: 2022-05-23
current_date is: 2022-05-24
current_date is: 2022-05-25
current_date is: 2022-05-26
amount of unique_stocks : 486, weights objective should be considered
Using Soft Constraints, and the Optimization result is optimal before the relaxation
----------------------------------------------------------------------------------------date 2022-05-26 over----------------------------------------------------------------------------------------
current_date is: 2022-05-27
current_date is: 2022-05-30
current_date is: 2022-05-31
current_date is: 2022-06-01
current_date is: 2022-06-02
amount of unique_stocks : 484, weights objective should be considered
Using Soft Constraints, and the Optimization result is optimal before the relaxation
----------------------------------------------------------------------------------------date 2022-06-02 over----------------------------------------------------------------------------------------
current_date is: 2022-06-06
current_date is: 2022-06-07
current_date is: 2022-06-08
current_date is: 2022-06-09
current_date is: 2022-06-10
amount of unique_stocks : 484, weights objective should be considered
Using Soft Constraints, and the Optimization result is optimal before the relaxation
----------------------------------------------------------------------------------------date 2022-06-10 over----------------------------------------------------------------------------------------
current_date is: 2022-06-13
current_date is: 2022-06-14
current_date is: 2022-06-15
current_date is: 2022-06-16
current_date is: 2022-06-17
amount of unique_stocks : 479, weights objective should be considered
Using Soft Constraints, and the Optimization result is optimal before the relaxation
----------------------------------------------------------------------------------------date 2022-06-17 over----------------------------------------------------------------------------------------
current_date is: 2022-06-20
current_date is: 2022-06-21
current_date is: 2022-06-22
current_date is: 2022-06-23
current_date is: 2022-06-24
amount of unique_stocks : 480, weights objective should be considered
Using Soft Constraints, and the Optimization result is optimal before the relaxation
----------------------------------------------------------------------------------------date 2022-06-24 over----------------------------------------------------------------------------------------
current_date is: 2022-06-27
current_date is: 2022-06-28
current_date is: 2022-06-29
current_date is: 2022-06-30
current_date is: 2022-07-01
amount of unique_stocks : 480, weights objective should be considered
Using Soft Constraints, and the Optimization result is optimal before the relaxation
----------------------------------------------------------------------------------------date 2022-07-01 over----------------------------------------------------------------------------------------
current_date is: 2022-07-04
current_date is: 2022-07-05
current_date is: 2022-07-06
current_date is: 2022-07-07
current_date is: 2022-07-08
amount of unique_stocks : 480, weights objective should be considered
Using Soft Constraints, and the Optimization result is optimal before the relaxation
----------------------------------------------------------------------------------------date 2022-07-08 over----------------------------------------------------------------------------------------
current_date is: 2022-07-11
current_date is: 2022-07-12
current_date is: 2022-07-13
current_date is: 2022-07-14
current_date is: 2022-07-15
amount of unique_stocks : 481, weights objective should be considered
Using Soft Constraints, and the Optimization result is optimal before the relaxation
----------------------------------------------------------------------------------------date 2022-07-15 over----------------------------------------------------------------------------------------
current_date is: 2022-07-18
current_date is: 2022-07-19
current_date is: 2022-07-20
current_date is: 2022-07-21
current_date is: 2022-07-22
amount of unique_stocks : 481, weights objective should be considered
Using Soft Constraints, and the Optimization result is optimal before the relaxation
----------------------------------------------------------------------------------------date 2022-07-22 over----------------------------------------------------------------------------------------
current_date is: 2022-07-25
current_date is: 2022-07-26
current_date is: 2022-07-27
current_date is: 2022-07-28
current_date is: 2022-07-29
amount of unique_stocks : 481, weights objective should be considered
Using Soft Constraints, and the Optimization result is optimal before the relaxation
----------------------------------------------------------------------------------------date 2022-07-29 over----------------------------------------------------------------------------------------
current_date is: 2022-08-01
current_date is: 2022-08-02
current_date is: 2022-08-03
current_date is: 2022-08-04
current_date is: 2022-08-05
amount of unique_stocks : 481, weights objective should be considered
Using Soft Constraints, and the Optimization result is optimal before the relaxation
----------------------------------------------------------------------------------------date 2022-08-05 over----------------------------------------------------------------------------------------
current_date is: 2022-08-08
current_date is: 2022-08-09
current_date is: 2022-08-10
current_date is: 2022-08-11
current_date is: 2022-08-12
amount of unique_stocks : 481, weights objective should be considered
Using Soft Constraints, and the Optimization result is optimal before the relaxation
----------------------------------------------------------------------------------------date 2022-08-12 over----------------------------------------------------------------------------------------
current_date is: 2022-08-15
current_date is: 2022-08-16
current_date is: 2022-08-17
current_date is: 2022-08-18
current_date is: 2022-08-19
amount of unique_stocks : 480, weights objective should be considered
Using Soft Constraints, and the Optimization result is optimal before the relaxation
----------------------------------------------------------------------------------------date 2022-08-19 over----------------------------------------------------------------------------------------
current_date is: 2022-08-22
current_date is: 2022-08-23
current_date is: 2022-08-24
current_date is: 2022-08-25
current_date is: 2022-08-26
amount of unique_stocks : 480, weights objective should be considered
Using Soft Constraints, and the Optimization result is optimal before the relaxation
----------------------------------------------------------------------------------------date 2022-08-26 over----------------------------------------------------------------------------------------
current_date is: 2022-08-29
current_date is: 2022-08-30
current_date is: 2022-08-31
current_date is: 2022-09-01
current_date is: 2022-09-02
amount of unique_stocks : 481, weights objective should be considered
Using Soft Constraints, and the Optimization result is optimal before the relaxation
----------------------------------------------------------------------------------------date 2022-09-02 over----------------------------------------------------------------------------------------
current_date is: 2022-09-05
current_date is: 2022-09-06
current_date is: 2022-09-07
current_date is: 2022-09-08
current_date is: 2022-09-09
amount of unique_stocks : 481, weights objective should be considered
Using Soft Constraints, and the Optimization result is optimal before the relaxation
----------------------------------------------------------------------------------------date 2022-09-09 over----------------------------------------------------------------------------------------
current_date is: 2022-09-13
current_date is: 2022-09-14
current_date is: 2022-09-15
current_date is: 2022-09-16
current_date is: 2022-09-19
amount of unique_stocks : 481, weights objective should be considered
Using Soft Constraints, and the Optimization result is optimal before the relaxation
----------------------------------------------------------------------------------------date 2022-09-19 over----------------------------------------------------------------------------------------
current_date is: 2022-09-20
current_date is: 2022-09-21
current_date is: 2022-09-22
current_date is: 2022-09-23
current_date is: 2022-09-26
amount of unique_stocks : 481, weights objective should be considered
Using Soft Constraints, and the Optimization result is optimal before the relaxation
----------------------------------------------------------------------------------------date 2022-09-26 over----------------------------------------------------------------------------------------
current_date is: 2022-09-27
current_date is: 2022-09-28
current_date is: 2022-09-29
current_date is: 2022-09-30
current_date is: 2022-10-10
amount of unique_stocks : 481, weights objective should be considered
Using Soft Constraints, and the Optimization result is optimal before the relaxation
----------------------------------------------------------------------------------------date 2022-10-10 over----------------------------------------------------------------------------------------
current_date is: 2022-10-11
current_date is: 2022-10-12
current_date is: 2022-10-13
current_date is: 2022-10-14
current_date is: 2022-10-17
amount of unique_stocks : 480, weights objective should be considered
Using Soft Constraints, and the Optimization result is optimal before the relaxation
----------------------------------------------------------------------------------------date 2022-10-17 over----------------------------------------------------------------------------------------
current_date is: 2022-10-18
current_date is: 2022-10-19
current_date is: 2022-10-20
current_date is: 2022-10-21
current_date is: 2022-10-24
amount of unique_stocks : 481, weights objective should be considered
Using Soft Constraints, and the Optimization result is optimal before the relaxation
----------------------------------------------------------------------------------------date 2022-10-24 over----------------------------------------------------------------------------------------
current_date is: 2022-10-25
current_date is: 2022-10-26
current_date is: 2022-10-27
current_date is: 2022-10-28
current_date is: 2022-10-31
amount of unique_stocks : 481, weights objective should be considered
Using Soft Constraints, and the Optimization result is optimal before the relaxation
----------------------------------------------------------------------------------------date 2022-10-31 over----------------------------------------------------------------------------------------
current_date is: 2022-11-01
current_date is: 2022-11-02
current_date is: 2022-11-03
current_date is: 2022-11-04
current_date is: 2022-11-07
amount of unique_stocks : 481, weights objective should be considered
Using Soft Constraints, and the Optimization result is optimal before the relaxation
----------------------------------------------------------------------------------------date 2022-11-07 over----------------------------------------------------------------------------------------
current_date is: 2022-11-08
current_date is: 2022-11-09
current_date is: 2022-11-10
current_date is: 2022-11-11
current_date is: 2022-11-14
amount of unique_stocks : 481, weights objective should be considered
Using Soft Constraints, and the Optimization result is optimal before the relaxation
----------------------------------------------------------------------------------------date 2022-11-14 over----------------------------------------------------------------------------------------
current_date is: 2022-11-15
current_date is: 2022-11-16
current_date is: 2022-11-17
current_date is: 2022-11-18
current_date is: 2022-11-21
amount of unique_stocks : 480, weights objective should be considered
Using Soft Constraints, and the Optimization result is optimal before the relaxation
----------------------------------------------------------------------------------------date 2022-11-21 over----------------------------------------------------------------------------------------
current_date is: 2022-11-22
current_date is: 2022-11-23
current_date is: 2022-11-24
current_date is: 2022-11-25
current_date is: 2022-11-28
amount of unique_stocks : 481, weights objective should be considered
Using Soft Constraints, and the Optimization result is optimal before the relaxation
----------------------------------------------------------------------------------------date 2022-11-28 over----------------------------------------------------------------------------------------

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