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# 本代码由可视化策略环境自动生成 2022年3月26日 21:52
# 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):
    print('初始化函数，只执行一次')
    # 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 = 10
    # 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 = 0.3
    context.options['hold_days'] = 10

# 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')]
    
    today = data.current_dt.strftime('%Y-%m-%d')
    #print('日期:',today)
    # 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']
    #print('context.portfolio.portfolio_value:',context.portfolio.portfolio_value)
    cash_for_buy = min(context.portfolio.cash, (1 if is_staging else 1.5) * cash_avg)
    #print('cash_for_buy:',cash_for_buy,' context.portfolio.cash:',context.portfolio.cash)
    cash_for_sell = cash_avg - (context.portfolio.cash - cash_for_buy)
    #print('context.portfolio.portfolio_value:',context.portfolio.portfolio_value)
    positions = {e.symbol: p.amount * p.last_sale_price
                 for e, p in context.portfolio.positions.items()}
    #print('is_staging:',is_staging,' cash_for_sell:',cash_for_sell)

    # 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: p for e, p in context.portfolio.positions.items() if p.amount>0}
                
        instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(
                lambda x: x in equities)])))

        print(today,' 选股: ',ranker_prediction[:10])
        print('sell instruments:',instruments)
        for instrument in instruments:
            context.order_target(context.symbol(instrument), 0)
            cash_for_sell -= positions[instrument]
            
            stock_market_today_high = data.current(context.symbol(instrument), 'high') #今日最高价      
            stock_market_today_close = data.current(context.symbol(instrument), 'close') #今日收盘价
            last_cost_price = equities[instrument].cost_basis # 上次交易金额   
            target_return = stock_market_today_close/last_cost_price

            
            print(today,' instrument 滚动卖出 :','收益： ',target_return, instrument,'context.symbol(instrument):',context.symbol(instrument))
            if cash_for_sell <= 0:
                break
                
         #加上持仓超过50天或者收益大于20%卖出
        if len(equities) > 0:
            for i in equities.keys():
               #print(today,' equities:',equities)
                #p = context.portfolio.positions.items(i)
                #cash_for_sell -= p.amount * p.last_sale_price
                
                stock_market_price = data.current(context.symbol(i), 'price')  # 最新市场价格
                stock_market_today_high = data.current(context.symbol(i), 'high') #今日最高价      
                stock_market_today_close = data.current(context.symbol(i), 'close') #今日收盘价
                last_sale_date = equities[i].last_sale_date   # 上次交易日期
                last_cost_price = equities[i].cost_basis # 上次交易金额
                delta_days = data.current_dt - last_sale_date  
                hold_days = delta_days.days # 持仓天数
        # 最高收益
        #high_return = (highclose_price_since_buy-last_cost_price)/last_cost_price
        
                target_return = stock_market_today_close/last_cost_price
               
                if hold_days>=100 :
                    context.order_target(context.symbol(i), 0)
                    print(today,'超期卖出 :','收益： ',target_return,equities[i], ' context.symbol(i):',context.symbol(i))
                    context.order_target(context.symbol(i), 0)
                #if target_return>=1.2 :
                #    context.order_target(context.symbol(i), 0)
                #    print(today,' 盈利卖出 :','收益： ',target_return,equities[i],' context.symbol(i):',context.symbol(i))
               # elif target_return<=0.9 :
               #     context.order_target(context.symbol(i), 0)
               #     print(today,' 止损卖出 :','收益： ',target_return,equities[i],' context.symbol(i):',context.symbol(i))
                

    # 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)])
    print(today,' buy_instruments:',buy_instruments,'  权重： ',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)
            print(today,' 买入 ',instrument)

# 回测引擎：准备数据，只执行一次
def m19_prepare_bigquant_run(context):
    # 加载预测数据
    print('准备数据，只执行一次')
    df = context.options['data'].read_df()
    # 函数：求满足开仓条件的股票列表
    def open_pos_con(df):
        return list(df[df['fantanbili']>0].instrument)
    # 函数：求满足平仓条件的股票列表
    def close_pos_con(df):
        return list(df[df['fantanbili']>0].instrument)
    
    # 每日卖出股票的数据框
    context.daily_sell_stock= df.groupby('date').apply(close_pos_con)  
    # 每日买入股票的数据框
    context.daily_buy_stock= df.groupby('date').apply(open_pos_con)  


m1 = M.instruments.v2(
    start_date='2019-01-01',
    end_date='2019-12-30',
    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, -100) / 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.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
#pe_ttm_0

#high_0/adjust_factor_0
#low_0/adjust_factor_0

#timeperiod移动平均线
#zhouma34=ta_ma(close_0, timeperiod=166)/adjust_factor_0
zhouma34=ta_ma(close_0, timeperiod=166)
#timeperiod移动平均线
zhouma55=ta_ma(close_0, timeperiod=258)
isup=zhouma34/zhouma55
sy=shift(close_0, -100)/shift(open_0, -1)

#ts_max(high_0,10)/adjust_factor_0
#ts_min(low_0,10)/adjust_factor_0

ts_argmax(high_0, 258*5)
#4年高点，1年按258个交易日算
isgaodian=where(ts_argmax(high_0, 258*5)<500.0,1,0)

#1年低点，1年按258个交易日算
ts_argmin(low_0, 258*1)
isdidian=where(ts_argmin(low_0, 258*1)<100.0,1,0)

#半年高点，1年按258个交易日算
isbanniangaodian=where(ts_argmax(high_0, 128)<80.0,1,0)
#在周34-55均线区间，日166-258
iszaiquejian=where((close_0<zhouma34) & (close_0>zhouma55),1,0)
#曾经前几天下过55线，触碰过日258线
isdiyu258=where(ts_min(low_0, 3)<zhouma55,1,0)

#tmax=ts_argmax(high_0, 258*5)

#反弹比例,选比例最高的10-20只交易，后面调试
yiniandidian=ts_min(low_0, 258*1)
banniangaodian=ts_max(high_0, 128*1)
fantanbili=banniangaodian/yiniandidian

#排除ST
st_status_0
#时间序列函数， d 天内的最大值
#ts_max(high_0, 258*4)
#时间序列函数， d 天内的最小值
#ts_min(low_0, 258*1)



#isxiadie=where(ts_max(high_0, 258*4)>ts_min(low_0, 258*1),1,0)
"""
)

m15 = M.general_feature_extractor.v7(
    instruments=m1.data,
    features=m3.data,
    start_date='',
    end_date='',
    before_start_days=2500
)

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

m5 = M.filter.v3(
    input_data=m16.data,
    expr='isgaodian==1&isdidian==1&isbanniangaodian==1&st_status_0==0&date>\'2019-01-01\'',
    output_left_data=True
)

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

m7 = M.join.v3(
    data1=m2.data,
    data2=m10.sorted_data,
    on='date,instrument',
    how='inner',
    sort=False
)

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

m6 = M.stock_ranker_train.v5(
    training_ds=m13.data,
    features=m3.data,
    learning_algorithm='排序',
    number_of_leaves=30,
    minimum_docs_per_leaf=1000,
    number_of_trees=20,
    learning_rate=0.1,
    max_bins=1023,
    feature_fraction=1,
    m_lazy_run=False
)

m9 = M.instruments.v2(
    start_date=T.live_run_param('trading_date', '2020-01-01'),
    end_date=T.live_run_param('trading_date', '2020-12-30'),
    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=2500
)

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

m12 = M.filter.v3(
    input_data=m18.data,
    expr='isgaodian==1&isdidian==1&isbanniangaodian==1&st_status_0==0&date>\'2020-01-01\'',
    output_left_data=True
)

m20 = M.sort.v5(
    input_ds=m12.data,
    sort_by='isup',
    group_by='date',
    keep_columns='--',
    ascending=False
)

m14 = M.dropnan.v1(
    input_data=m20.sorted_data
)

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

m19 = M.trade.v4(
    instruments=m9.data,
    options_data=m8.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='真实价格',
    product_type='股票',
    plot_charts=True,
    backtest_only=False,
    benchmark='000300.HIX'
)

[2022-03-26 19:48:38.383729] INFO: moduleinvoker: instruments.v2 开始运行..

[2022-03-26 19:48:38.402426] INFO: moduleinvoker: 命中缓存

[2022-03-26 19:48:38.404237] INFO: moduleinvoker: instruments.v2 运行完成[0.020539s].

[2022-03-26 19:48:38.418127] INFO: moduleinvoker: advanced_auto_labeler.v2 开始运行..

[2022-03-26 19:48:39.187061] INFO: 自动标注(股票): 加载历史数据: 881121 行

[2022-03-26 19:48:39.188750] INFO: 自动标注(股票): 开始标注 ..

[2022-03-26 19:48:40.216192] INFO: moduleinvoker: advanced_auto_labeler.v2 运行完成[1.798063s].

[2022-03-26 19:48:40.222625] INFO: moduleinvoker: input_features.v1 开始运行..

[2022-03-26 19:48:40.248748] INFO: moduleinvoker: input_features.v1 运行完成[0.026114s].

[2022-03-26 19:48:40.263802] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2022-03-26 19:48:41.777105] INFO: 基础特征抽取: 年份 2012, 特征行数=487249

[2022-03-26 19:48:43.030377] INFO: 基础特征抽取: 年份 2013, 特征行数=559456

[2022-03-26 19:48:44.452509] INFO: 基础特征抽取: 年份 2014, 特征行数=565910

[2022-03-26 19:48:45.605826] INFO: 基础特征抽取: 年份 2015, 特征行数=567320

[2022-03-26 19:48:47.367724] INFO: 基础特征抽取: 年份 2016, 特征行数=639455

[2022-03-26 19:48:49.430802] INFO: 基础特征抽取: 年份 2017, 特征行数=742144

[2022-03-26 19:48:51.515919] INFO: 基础特征抽取: 年份 2018, 特征行数=816396

[2022-03-26 19:48:53.795634] INFO: 基础特征抽取: 年份 2019, 特征行数=881126

[2022-03-26 19:48:53.874618] INFO: 基础特征抽取: 总行数: 5259056

[2022-03-26 19:48:53.888805] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[13.625015s].

[2022-03-26 19:48:53.903957] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-03-26 19:49:10.062576] INFO: derived_feature_extractor: 提取完成 zhouma34=ta_ma(close_0, timeperiod=166), 7.187s

[2022-03-26 19:49:17.040807] INFO: derived_feature_extractor: 提取完成 zhouma55=ta_ma(close_0, timeperiod=258), 6.977s

[2022-03-26 19:49:17.077126] INFO: derived_feature_extractor: 提取完成 isup=zhouma34/zhouma55, 0.034s

[2022-03-26 19:49:18.872943] INFO: derived_feature_extractor: 提取完成 sy=shift(close_0, -100)/shift(open_0, -1), 1.794s

[2022-03-26 19:49:28.433109] INFO: derived_feature_extractor: 提取完成 ts_argmax(high_0, 258*5), 9.559s

[2022-03-26 19:49:37.988602] INFO: derived_feature_extractor: 提取完成 isgaodian=where(ts_argmax(high_0, 258*5)<500.0,1,0), 9.554s

[2022-03-26 19:49:54.677620] INFO: derived_feature_extractor: 提取完成 ts_argmin(low_0, 258*1), 16.687s

[2022-03-26 19:50:11.076744] INFO: derived_feature_extractor: 提取完成 isdidian=where(ts_argmin(low_0, 258*1)<100.0,1,0), 16.397s

[2022-03-26 19:50:29.584117] INFO: derived_feature_extractor: 提取完成 isbanniangaodian=where(ts_argmax(high_0, 128)<80.0,1,0), 18.506s

[2022-03-26 19:50:29.682831] INFO: derived_feature_extractor: 提取完成 iszaiquejian=where((close_0zhouma55),1,0), 0.097s

[2022-03-26 19:50:33.976830] INFO: derived_feature_extractor: 提取完成 isdiyu258=where(ts_min(low_0, 3)[2022-03-26 19:50:38.189278] INFO: derived_feature_extractor: 提取完成 yiniandidian=ts_min(low_0, 258*1), 4.211s
[2022-03-26 19:50:42.483858] INFO: derived_feature_extractor: 提取完成 banniangaodian=ts_max(high_0, 128*1), 4.293s
[2022-03-26 19:50:42.599065] INFO: derived_feature_extractor: 提取完成 fantanbili=banniangaodian/yiniandidian, 0.113s
[2022-03-26 19:50:44.205313] INFO: derived_feature_extractor: /y_2012, 487249
[2022-03-26 19:50:45.319704] INFO: derived_feature_extractor: /y_2013, 559456
[2022-03-26 19:50:46.482267] INFO: derived_feature_extractor: /y_2014, 565910
[2022-03-26 19:50:47.549370] INFO: derived_feature_extractor: /y_2015, 567320
[2022-03-26 19:50:48.714632] INFO: derived_feature_extractor: /y_2016, 639455
[2022-03-26 19:50:50.159334] INFO: derived_feature_extractor: /y_2017, 742144
[2022-03-26 19:50:51.809174] INFO: derived_feature_extractor: /y_2018, 816396
[2022-03-26 19:50:53.751596] INFO: derived_feature_extractor: /y_2019, 881126
[2022-03-26 19:50:54.338911] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[120.434941s].
[2022-03-26 19:50:54.348898] INFO: moduleinvoker: filter.v3 开始运行..
[2022-03-26 19:50:54.370087] INFO: filter: 使用表达式 isgaodian==1&isdidian==1&isbanniangaodian==1&st_status_0==0&date>'2019-01-01' 过滤
[2022-03-26 19:50:54.503396] INFO: filter: 过滤 /y_2012, 0/0/0
[2022-03-26 19:50:54.535300] INFO: filter: 过滤 /y_2013, 0/0/0
[2022-03-26 19:50:54.564459] INFO: filter: 过滤 /y_2014, 0/0/0
[2022-03-26 19:50:54.597933] INFO: filter: 过滤 /y_2015, 0/0/0
[2022-03-26 19:50:54.634030] INFO: filter: 过滤 /y_2016, 0/0/0
[2022-03-26 19:50:54.823367] INFO: filter: 过滤 /y_2017, 0/134870/134870
[2022-03-26 19:50:55.331070] INFO: filter: 过滤 /y_2018, 0/487962/487962
[2022-03-26 19:50:55.711177] INFO: filter: 过滤 /y_2019, 41409/296154/337563
[2022-03-26 19:50:55.762756] INFO: moduleinvoker: filter.v3 运行完成[1.413842s].
[2022-03-26 19:50:55.772660] INFO: moduleinvoker: sort.v5 开始运行..
[2022-03-26 19:50:56.198534] INFO: moduleinvoker: sort.v5 运行完成[0.42586s].
[2022-03-26 19:50:56.210773] INFO: moduleinvoker: join.v3 开始运行..
[2022-03-26 19:50:57.412423] INFO: join: /data, 行数=41368/41409, 耗时=0.307458s
[2022-03-26 19:50:57.458340] INFO: join: 最终行数: 41368
[2022-03-26 19:50:57.467472] INFO: moduleinvoker: join.v3 运行完成[1.256701s].
[2022-03-26 19:50:57.478749] INFO: moduleinvoker: dropnan.v1 开始运行..
[2022-03-26 19:50:57.626878] INFO: dropnan: /data, 41368/41368
[2022-03-26 19:50:57.669886] INFO: dropnan: 行数: 41368/41368
[2022-03-26 19:50:57.676340] INFO: moduleinvoker: dropnan.v1 运行完成[0.197584s].
[2022-03-26 19:50:57.690105] INFO: moduleinvoker: stock_ranker_train.v5 开始运行..
[2022-03-26 19:50:57.844542] INFO: StockRanker: 特征预处理 ..
[2022-03-26 19:50:57.874316] INFO: StockRanker: prepare data: training ..
[2022-03-26 19:50:58.467028] INFO: StockRanker训练: ffea9d96 准备训练: 41368 行数
[2022-03-26 19:50:58.722675] INFO: StockRanker训练: 正在训练 ..
[2022-03-26 19:52:19.199459] INFO: moduleinvoker: stock_ranker_train.v5 运行完成[81.509359s].
[2022-03-26 19:52:19.204576] INFO: moduleinvoker: instruments.v2 开始运行..
[2022-03-26 19:52:19.215385] INFO: moduleinvoker: 命中缓存
[2022-03-26 19:52:19.216770] INFO: moduleinvoker: instruments.v2 运行完成[0.012183s].
[2022-03-26 19:52:19.229206] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..
[2022-03-26 19:52:20.469638] INFO: 基础特征抽取: 年份 2013, 特征行数=481423
[2022-03-26 19:52:21.736991] INFO: 基础特征抽取: 年份 2014, 特征行数=563554
[2022-03-26 19:52:22.949307] INFO: 基础特征抽取: 年份 2015, 特征行数=565314
[2022-03-26 19:52:24.333092] INFO: 基础特征抽取: 年份 2016, 特征行数=637231
[2022-03-26 19:52:26.188117] INFO: 基础特征抽取: 年份 2017, 特征行数=740053
[2022-03-26 19:52:28.231646] INFO: 基础特征抽取: 年份 2018, 特征行数=814800
[2022-03-26 19:52:30.418119] INFO: 基础特征抽取: 年份 2019, 特征行数=883658
[2022-03-26 19:52:32.496266] INFO: 基础特征抽取: 年份 2020, 特征行数=941844
[2022-03-26 19:52:32.582446] INFO: 基础特征抽取: 总行数: 5627877
[2022-03-26 19:52:32.591397] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[13.362192s].
[2022-03-26 19:52:32.599870] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..
[2022-03-26 19:52:48.842275] INFO: derived_feature_extractor: 提取完成 zhouma34=ta_ma(close_0, timeperiod=166), 7.278s
[2022-03-26 19:52:55.900374] INFO: derived_feature_extractor: 提取完成 zhouma55=ta_ma(close_0, timeperiod=258), 7.056s
[2022-03-26 19:52:55.909460] INFO: derived_feature_extractor: 提取完成 isup=zhouma34/zhouma55, 0.007s
[2022-03-26 19:52:57.522694] INFO: derived_feature_extractor: 提取完成 sy=shift(close_0, -100)/shift(open_0, -1), 1.612s
[2022-03-26 19:53:06.953954] INFO: derived_feature_extractor: 提取完成 ts_argmax(high_0, 258*5), 9.430s
[2022-03-26 19:53:16.477245] INFO: derived_feature_extractor: 提取完成 isgaodian=where(ts_argmax(high_0, 258*5)<500.0,1,0), 9.522s
[2022-03-26 19:53:35.586580] INFO: derived_feature_extractor: 提取完成 ts_argmin(low_0, 258*1), 19.108s
[2022-03-26 19:53:53.577895] INFO: derived_feature_extractor: 提取完成 isdidian=where(ts_argmin(low_0, 258*1)<100.0,1,0), 17.990s
[2022-03-26 19:54:12.177194] INFO: derived_feature_extractor: 提取完成 isbanniangaodian=where(ts_argmax(high_0, 128)<80.0,1,0), 18.598s
[2022-03-26 19:54:12.190914] INFO: derived_feature_extractor: 提取完成 iszaiquejian=where((close_0zhouma55),1,0), 0.012s
[2022-03-26 19:54:16.726689] INFO: derived_feature_extractor: 提取完成 isdiyu258=where(ts_min(low_0, 3)[2022-03-26 19:54:21.201262] INFO: derived_feature_extractor: 提取完成 yiniandidian=ts_min(low_0, 258*1), 4.473s
[2022-03-26 19:54:25.716021] INFO: derived_feature_extractor: 提取完成 banniangaodian=ts_max(high_0, 128*1), 4.513s
[2022-03-26 19:54:25.802293] INFO: derived_feature_extractor: 提取完成 fantanbili=banniangaodian/yiniandidian, 0.085s
[2022-03-26 19:54:27.442248] INFO: derived_feature_extractor: /y_2013, 481423
[2022-03-26 19:54:28.477036] INFO: derived_feature_extractor: /y_2014, 563554
[2022-03-26 19:54:29.537615] INFO: derived_feature_extractor: /y_2015, 565314
[2022-03-26 19:54:30.748522] INFO: derived_feature_extractor: /y_2016, 637231
[2022-03-26 19:54:32.129457] INFO: derived_feature_extractor: /y_2017, 740053
[2022-03-26 19:54:33.634678] INFO: derived_feature_extractor: /y_2018, 814800
[2022-03-26 19:54:35.379133] INFO: derived_feature_extractor: /y_2019, 883658
[2022-03-26 19:54:37.316085] INFO: derived_feature_extractor: /y_2020, 941844
[2022-03-26 19:54:37.887791] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[125.287903s].
[2022-03-26 19:54:37.897511] INFO: moduleinvoker: filter.v3 开始运行..
[2022-03-26 19:54:37.918351] INFO: filter: 使用表达式 isgaodian==1&isdidian==1&isbanniangaodian==1&st_status_0==0&date>'2020-01-01' 过滤
[2022-03-26 19:54:38.034063] INFO: filter: 过滤 /y_2013, 0/0/0
[2022-03-26 19:54:38.064283] INFO: filter: 过滤 /y_2014, 0/0/0
[2022-03-26 19:54:38.099034] INFO: filter: 过滤 /y_2015, 0/0/0
[2022-03-26 19:54:38.132221] INFO: filter: 过滤 /y_2016, 0/0/0
[2022-03-26 19:54:38.169817] INFO: filter: 过滤 /y_2017, 0/0/0
[2022-03-26 19:54:38.375563] INFO: filter: 过滤 /y_2018, 0/139092/139092
[2022-03-26 19:54:38.868945] INFO: filter: 过滤 /y_2019, 0/513204/513204
[2022-03-26 19:54:39.278238] INFO: filter: 过滤 /y_2020, 47691/299478/347169
[2022-03-26 19:54:39.328422] INFO: moduleinvoker: filter.v3 运行完成[1.430905s].
[2022-03-26 19:54:39.335351] INFO: moduleinvoker: sort.v5 开始运行..
[2022-03-26 19:54:40.013604] INFO: moduleinvoker: sort.v5 运行完成[0.678247s].
[2022-03-26 19:54:40.022792] INFO: moduleinvoker: dropnan.v1 开始运行..
[2022-03-26 19:54:40.154757] INFO: dropnan: /data, 47691/47691
[2022-03-26 19:54:40.201627] INFO: dropnan: 行数: 47691/47691
[2022-03-26 19:54:40.207084] INFO: moduleinvoker: dropnan.v1 运行完成[0.184285s].
[2022-03-26 19:54:40.218959] INFO: moduleinvoker: stock_ranker_predict.v5 开始运行..
[2022-03-26 19:54:40.392717] INFO: StockRanker预测: /data ..
[2022-03-26 19:54:40.523570] INFO: moduleinvoker: stock_ranker_predict.v5 运行完成[0.304586s].
[2022-03-26 19:54:42.402928] INFO: moduleinvoker: backtest.v8 开始运行..
[2022-03-26 19:54:42.409855] INFO: backtest: biglearning backtest:V8.6.2
[2022-03-26 19:54:42.948542] ERROR: moduleinvoker: module name: backtest, module version: v8, trackeback: KeyError: 'fantanbili'

The above exception was the direct cause of the following exception:

KeyError: 'fantanbili'
[2022-03-26 19:54:42.960299] ERROR: moduleinvoker: module name: trade, module version: v4, trackeback: KeyError: 'fantanbili'

The above exception was the direct cause of the following exception:

KeyError: 'fantanbili'

设置测试数据集，查看训练迭代过程的NDCG

bigcharts-data-start/{"__type":"tabs","__id":"bigchart-ba0fadd75f9c483da4895dd582850800"}/bigcharts-data-end

准备数据，只执行一次

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
KeyError: 'fantanbili'

The above exception was the direct cause of the following exception:

KeyError                                  Traceback (most recent call last)
<ipython-input-2-8c2d1e3333dc> in <module>
    323 )
    324 
--> 325 m19 = M.trade.v4(
    326     instruments=m9.data,
    327     options_data=m8.predictions,

<ipython-input-2-8c2d1e3333dc> in m19_prepare_bigquant_run(context)
    123 
    124     # 每日卖出股票的数据框
--> 125     context.daily_sell_stock= df.groupby('date').apply(close_pos_con)
    126     # 每日买入股票的数据框
    127     context.daily_buy_stock= df.groupby('date').apply(open_pos_con)

<ipython-input-2-8c2d1e3333dc> in close_pos_con(df)
    120     # 函数：求满足平仓条件的股票列表
    121     def close_pos_con(df):
--> 122         return list(df[df['fantanbili']>0].instrument)
    123 
    124     # 每日卖出股票的数据框

KeyError: 'fantanbili'

In [48]:

m10.sorted_data.read_df()
m2.plot_label_counts()