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label)\n","type":"Literal","bound_global_parameter":null},{"name":"start_date","value":"","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"","type":"Literal","bound_global_parameter":null},{"name":"benchmark","value":"000300.SHA","type":"Literal","bound_global_parameter":null},{"name":"drop_na_label","value":"True","type":"Literal","bound_global_parameter":null},{"name":"cast_label_int","value":"True","type":"Literal","bound_global_parameter":null},{"name":"user_functions","value":"","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"instruments","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-15"}],"output_ports":[{"name":"data","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-15"}],"cacheable":true,"seq_num":2,"comment":"","comment_collapsed":true},{"node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24","module_id":"BigQuantSpace.input_features.input_features-v1","parameters":[{"name":"features","value":"# #号开始的表示注释\n# 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实际操作中，会存在一定的买入误差，所以在前hold_days天，等量使用资金；之后，尽量使用剩余资金（这里设置最多用等量的1.5倍）\n is_staging = context.trading_day_index < context.options['hold_days'] # 是否在建仓期间（前 hold_days 天）\n cash_avg = context.portfolio.portfolio_value / context.options['hold_days']\n cash_for_buy = min(context.portfolio.cash, (1 if is_staging else 1.5) * cash_avg)\n cash_for_sell = cash_avg - (context.portfolio.cash - cash_for_buy)\n positions = {e.symbol: p.amount * p.last_sale_price\n for e, p in context.perf_tracker.position_tracker.positions.items()}\n\n # 2. 生成卖出订单：hold_days天之后才开始卖出；对持仓的股票，按机器学习算法预测的排序末位淘汰\n if not is_staging and cash_for_sell > 0:\n equities = {e.symbol: e for e, p in context.perf_tracker.position_tracker.positions.items()}\n instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(\n lambda x: x in equities and not context.has_unfinished_sell_order(equities[x]))])))\n # print('rank order for sell %s' % instruments)\n for instrument in instruments:\n 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# 本代码由可视化策略环境自动生成 2023年5月9日 14:14
# 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 m19_initialize_bigquant_run(context):
    # 加载预测数据
    context.ranker_prediction = context.options['data'].read_df()

    # 系统已经设置了默认的交易手续费和滑点，要修改手续费可使用如下函数
    context.set_commission(PerOrder(buy_cost=0.0003, 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 = 30
    # 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 = 0.2
    context.options['hold_days'] = 5

# 回测引擎：每日数据处理函数，每天执行一次
def m19_handle_data_bigquant_run(context, data):
    # 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')]

    # 1. 资金分配
    # 平均持仓时间是hold_days，每日都将买入股票，每日预期使用 1/hold_days 的资金
    # 实际操作中，会存在一定的买入误差，所以在前hold_days天，等量使用资金；之后，尽量使用剩余资金（这里设置最多用等量的1.5倍）
    is_staging = context.trading_day_index < context.options['hold_days'] # 是否在建仓期间（前 hold_days 天）
    cash_avg = context.portfolio.portfolio_value / context.options['hold_days']
    cash_for_buy = min(context.portfolio.cash, (1 if is_staging else 1.5) * cash_avg)
    cash_for_sell = cash_avg - (context.portfolio.cash - cash_for_buy)
    positions = {e.symbol: p.amount * p.last_sale_price
                 for e, p in context.perf_tracker.position_tracker.positions.items()}

    # 2. 生成卖出订单：hold_days天之后才开始卖出；对持仓的股票，按机器学习算法预测的排序末位淘汰
    if not is_staging and cash_for_sell > 0:
        equities = {e.symbol: e for e, p in context.perf_tracker.position_tracker.positions.items()}
        instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(
                lambda x: x in equities and not context.has_unfinished_sell_order(equities[x]))])))
        # print('rank order for sell %s' % instruments)
        for instrument in instruments:
            context.order_target(context.symbol(instrument), 0)
            cash_for_sell -= positions[instrument]
            if cash_for_sell <= 0:
                break

    # 3. 生成买入订单：按机器学习算法预测的排序，买入前面的stock_count只股票
    buy_cash_weights = context.stock_weights
    buy_instruments = list(ranker_prediction.instrument[:len(buy_cash_weights)])
    max_cash_per_instrument = context.portfolio.portfolio_value * context.max_cash_per_instrument
    for i, instrument in enumerate(buy_instruments):
        cash = cash_for_buy * 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 > 0:
            context.order_value(context.symbol(instrument), cash)

# 回测引擎：准备数据，只执行一次
def m19_prepare_bigquant_run(context):
    pass


m1 = M.instruments.v2(
    start_date='2015-01-01',
    end_date='2021-01-01',
    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/data_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/big_expr.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, 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='000300.SHA',
    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
(close_0-mean(close_0,12))/mean(close_0,12)*100
rank(std(amount_0,15))
rank_avg_amount_0/rank_avg_amount_8
ts_argmin(low_0,20)
rank_return_30
(low_1-close_0)/close_0
ta_bbands_lowerband_14_0
mean(mf_net_pct_s_0,4)
amount_0/avg_amount_3
return_0/return_5
return_1/return_5
rank_avg_amount_7/rank_avg_amount_10
ta_sma_10_0/close_0
sqrt(high_0*low_0)-amount_0/volume_0*adjust_factor_0
avg_turn_15/(turn_0+1e-5)
return_10
mf_net_pct_s_0
(close_0-open_0)/close_1
"""
)

m15 = M.general_feature_extractor.v7(
    instruments=m1.data,
    features=m3.data,
    start_date='',
    end_date='',
    before_start_days=0
)

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
)

m13 = M.dropnan.v1(
    input_data=m7.data
)

m9 = M.instruments.v2(
    start_date=T.live_run_param('trading_date', '2021-01-01'),
    end_date=T.live_run_param('trading_date', '2023-03-31'),
    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=0
)

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
)

m14 = M.dropnan.v1(
    input_data=m18.data
)

m20 = M.xgboost.v1(
    training_ds=m13.data,
    features=m3.data,
    predict_ds=m14.data,
    num_boost_round=30,
    objective='排序(pairwise)',
    booster='gbtree',
    max_depth=6,
    key_cols='date,instrument',
    group_col='date',
    nthread=1,
    n_gpus=-1,
    other_train_parameters={}
)

m4 = M.ml_model_explanation.v1(
    input_model=m20.output_model,
    features=m3.data,
    plot_kind=['总览图'],
    class_ind=0,
    feature_name=''
)

m19 = M.trade.v4(
    instruments=m9.data,
    options_data=m20.predictions,
    start_date='',
    end_date='',
    initialize=m19_initialize_bigquant_run,
    handle_data=m19_handle_data_bigquant_run,
    prepare=m19_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='后复权',
    product_type='股票',
    plot_charts=True,
    backtest_only=False,
    benchmark='000300.SHA'
)

[2023-05-09 13:49:35.963106] INFO: moduleinvoker: instruments.v2 开始运行..

[2023-05-09 13:49:35.988147] INFO: moduleinvoker: 命中缓存

[2023-05-09 13:49:35.989473] INFO: moduleinvoker: instruments.v2 运行完成[0.026375s].

[2023-05-09 13:49:35.999808] INFO: moduleinvoker: advanced_auto_labeler.v2 开始运行..

[2023-05-09 13:49:36.006369] INFO: moduleinvoker: 命中缓存

[2023-05-09 13:49:36.921966] INFO: moduleinvoker: advanced_auto_labeler.v2 运行完成[0.92215s].

[2023-05-09 13:49:36.937664] INFO: moduleinvoker: input_features.v1 开始运行..

[2023-05-09 13:49:36.945405] INFO: moduleinvoker: 命中缓存

[2023-05-09 13:49:36.947135] INFO: moduleinvoker: input_features.v1 运行完成[0.009457s].

[2023-05-09 13:49:36.974506] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2023-05-09 13:49:39.789003] INFO: 基础特征抽取: 年份 2015, 特征行数=569698

[2023-05-09 13:49:43.172676] INFO: 基础特征抽取: 年份 2016, 特征行数=641546

[2023-05-09 13:49:46.521941] INFO: 基础特征抽取: 年份 2017, 特征行数=743233

[2023-05-09 13:49:50.353936] INFO: 基础特征抽取: 年份 2018, 特征行数=816987

[2023-05-09 13:49:55.187052] INFO: 基础特征抽取: 年份 2019, 特征行数=884867

[2023-05-09 13:49:59.806216] INFO: 基础特征抽取: 年份 2020, 特征行数=945961

[2023-05-09 13:50:00.263547] INFO: 基础特征抽取: 年份 2021, 特征行数=0

[2023-05-09 13:50:00.336764] INFO: 基础特征抽取: 总行数: 4602292

[2023-05-09 13:50:00.340431] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[23.365925s].

[2023-05-09 13:50:00.349865] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2023-05-09 13:50:16.188232] INFO: derived_feature_extractor: 提取完成 (close_0-mean(close_0,12))/mean(close_0,12)*100, 5.417s

[2023-05-09 13:50:21.397397] INFO: derived_feature_extractor: 提取完成 rank(std(amount_0,15)), 5.208s

[2023-05-09 13:50:21.404577] INFO: derived_feature_extractor: 提取完成 rank_avg_amount_0/rank_avg_amount_8, 0.006s

[2023-05-09 13:50:34.653649] INFO: derived_feature_extractor: 提取完成 ts_argmin(low_0,20), 13.248s

[2023-05-09 13:50:34.661433] INFO: derived_feature_extractor: 提取完成 (low_1-close_0)/close_0, 0.006s

[2023-05-09 13:50:38.040750] INFO: derived_feature_extractor: 提取完成 mean(mf_net_pct_s_0,4), 3.378s

[2023-05-09 13:50:38.062614] INFO: derived_feature_extractor: 提取完成 amount_0/avg_amount_3, 0.020s

[2023-05-09 13:50:38.069807] INFO: derived_feature_extractor: 提取完成 return_0/return_5, 0.006s

[2023-05-09 13:50:38.077181] INFO: derived_feature_extractor: 提取完成 return_1/return_5, 0.006s

[2023-05-09 13:50:38.086093] INFO: derived_feature_extractor: 提取完成 rank_avg_amount_7/rank_avg_amount_10, 0.008s

[2023-05-09 13:50:38.095882] INFO: derived_feature_extractor: 提取完成 ta_sma_10_0/close_0, 0.008s

[2023-05-09 13:50:38.153132] INFO: derived_feature_extractor: 提取完成 sqrt(high_0*low_0)-amount_0/volume_0*adjust_factor_0, 0.056s

[2023-05-09 13:50:38.176734] INFO: derived_feature_extractor: 提取完成 avg_turn_15/(turn_0+1e-5), 0.022s

[2023-05-09 13:50:38.189103] INFO: derived_feature_extractor: 提取完成 (close_0-open_0)/close_1, 0.011s

[2023-05-09 13:50:45.877509] INFO: derived_feature_extractor: /y_2015, 569698

[2023-05-09 13:50:47.323808] INFO: derived_feature_extractor: /y_2016, 641546

[2023-05-09 13:50:49.047231] INFO: derived_feature_extractor: /y_2017, 743233

[2023-05-09 13:50:51.054303] INFO: derived_feature_extractor: /y_2018, 816987

[2023-05-09 13:50:53.356612] INFO: derived_feature_extractor: /y_2019, 884867

[2023-05-09 13:50:57.817965] INFO: derived_feature_extractor: /y_2020, 945961

[2023-05-09 13:51:00.508794] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[60.158912s].

[2023-05-09 13:51:00.525089] INFO: moduleinvoker: join.v3 开始运行..

[2023-05-09 13:51:15.810730] INFO: join: /y_2015, 行数=560428/569698, 耗时=5.54038s

[2023-05-09 13:51:20.236556] INFO: join: /y_2016, 行数=637478/641546, 耗时=4.421528s

[2023-05-09 13:51:24.593227] INFO: join: /y_2017, 行数=738259/743233, 耗时=4.349656s

[2023-05-09 13:51:29.603625] INFO: join: /y_2018, 行数=813508/816987, 耗时=5.004431s

[2023-05-09 13:51:34.816684] INFO: join: /y_2019, 行数=881288/884867, 耗时=5.206719s

[2023-05-09 13:51:39.953533] INFO: join: /y_2020, 行数=919362/945961, 耗时=5.128901s

[2023-05-09 13:51:40.026982] INFO: join: 最终行数: 4550323

[2023-05-09 13:51:40.085512] INFO: moduleinvoker: join.v3 运行完成[39.560417s].

[2023-05-09 13:51:40.097775] INFO: moduleinvoker: dropnan.v1 开始运行..

[2023-05-09 13:51:43.542498] INFO: dropnan: /y_2015, 508264/560428

[2023-05-09 13:51:45.853216] INFO: dropnan: /y_2016, 633538/637478

[2023-05-09 13:51:56.341704] INFO: dropnan: /y_2017, 727999/738259

[2023-05-09 13:51:59.052029] INFO: dropnan: /y_2018, 810444/813508

[2023-05-09 13:52:05.248244] INFO: dropnan: /y_2019, 876118/881288

[2023-05-09 13:52:09.817202] INFO: dropnan: /y_2020, 908889/919362

[2023-05-09 13:52:09.938775] INFO: dropnan: 行数: 4465252/4550323

[2023-05-09 13:52:09.956949] INFO: moduleinvoker: dropnan.v1 运行完成[29.859178s].

[2023-05-09 13:52:09.962978] INFO: moduleinvoker: instruments.v2 开始运行..

[2023-05-09 13:52:10.028670] INFO: moduleinvoker: instruments.v2 运行完成[0.065684s].

[2023-05-09 13:52:10.054342] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2023-05-09 13:52:14.583222] INFO: 基础特征抽取: 年份 2021, 特征行数=1061527

[2023-05-09 13:52:19.204263] INFO: 基础特征抽取: 年份 2022, 特征行数=1171038

[2023-05-09 13:52:20.809452] INFO: 基础特征抽取: 年份 2023, 特征行数=300168

[2023-05-09 13:52:20.945611] INFO: 基础特征抽取: 总行数: 2532733

[2023-05-09 13:52:20.950680] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[10.896341s].

[2023-05-09 13:52:20.958566] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2023-05-09 13:52:27.718253] INFO: derived_feature_extractor: 提取完成 (close_0-mean(close_0,12))/mean(close_0,12)*100, 2.632s

[2023-05-09 13:52:30.044488] INFO: derived_feature_extractor: 提取完成 rank(std(amount_0,15)), 2.325s

[2023-05-09 13:52:30.049752] INFO: derived_feature_extractor: 提取完成 rank_avg_amount_0/rank_avg_amount_8, 0.004s

[2023-05-09 13:52:36.730138] INFO: derived_feature_extractor: 提取完成 ts_argmin(low_0,20), 6.679s

[2023-05-09 13:52:36.736548] INFO: derived_feature_extractor: 提取完成 (low_1-close_0)/close_0, 0.005s

[2023-05-09 13:52:38.326938] INFO: derived_feature_extractor: 提取完成 mean(mf_net_pct_s_0,4), 1.589s

[2023-05-09 13:52:38.336162] INFO: derived_feature_extractor: 提取完成 amount_0/avg_amount_3, 0.008s

[2023-05-09 13:52:38.341148] INFO: derived_feature_extractor: 提取完成 return_0/return_5, 0.004s

[2023-05-09 13:52:38.345495] INFO: derived_feature_extractor: 提取完成 return_1/return_5, 0.003s

[2023-05-09 13:52:38.350032] INFO: derived_feature_extractor: 提取完成 rank_avg_amount_7/rank_avg_amount_10, 0.004s

[2023-05-09 13:52:38.354675] INFO: derived_feature_extractor: 提取完成 ta_sma_10_0/close_0, 0.003s

[2023-05-09 13:52:38.367390] INFO: derived_feature_extractor: 提取完成 sqrt(high_0*low_0)-amount_0/volume_0*adjust_factor_0, 0.012s

[2023-05-09 13:52:38.377033] INFO: derived_feature_extractor: 提取完成 avg_turn_15/(turn_0+1e-5), 0.009s

[2023-05-09 13:52:38.382041] INFO: derived_feature_extractor: 提取完成 (close_0-open_0)/close_1, 0.004s

[2023-05-09 13:52:43.360271] INFO: derived_feature_extractor: /y_2021, 1061527

[2023-05-09 13:52:46.358399] INFO: derived_feature_extractor: /y_2022, 1171038

[2023-05-09 13:52:49.403021] INFO: derived_feature_extractor: /y_2023, 300168

[2023-05-09 13:52:49.832073] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[28.873499s].

[2023-05-09 13:52:49.841731] INFO: moduleinvoker: dropnan.v1 开始运行..

[2023-05-09 13:52:55.017828] INFO: dropnan: /y_2021, 966048/1061527

[2023-05-09 13:52:59.952277] INFO: dropnan: /y_2022, 1135226/1171038

[2023-05-09 13:53:00.647551] INFO: dropnan: /y_2023, 287312/300168

[2023-05-09 13:53:00.769411] INFO: dropnan: 行数: 2388586/2532733

[2023-05-09 13:53:00.774492] INFO: moduleinvoker: dropnan.v1 运行完成[10.932781s].

[2023-05-09 13:53:00.799960] INFO: moduleinvoker: xgboost.v1 开始运行..

[2023-05-09 14:12:25.794628] INFO: moduleinvoker: xgboost.v1 运行完成[1164.994667s].

[2023-05-09 14:12:25.804722] ERROR: moduleinvoker: module name: ml_model_explanation, module version: v1, trackeback: TypeError: bigquant_run() takes at least 3 positional arguments (2 given)

[13:53:45] WARNING: /workspace/src/learner.cc:686: Tree method is automatically selected to be 'approx' for faster speed. To use old behavior (exact greedy algorithm on single machine), set tree_method to 'exact'.

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-1-cc67cd0a27e3> in <module>
    148 )
    149 
--> 150 m4 = M.ml_model_explanation.v1(
    151     input_model=m20.output_model,
    152     features=m3.data,

TypeError: bigquant_run() takes at least 3 positional arguments (2 given)