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回测引擎：每日数据处理函数，每天执行一次\ndef bigquant_run(context, data):\n # 按日期过滤得到今日的预测数据\n ranker_prediction = context.ranker_prediction[\n context.ranker_prediction.date == data.current_dt.strftime('%Y-%m-%d')]\n\n # 1. 资金分配\n # 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\n # 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.portfolio.positions.items()}\n equities = {e.symbol: e for e, p in context.portfolio.positions.items()}\n\n # 记录持仓中st的股票\n st_stock_list = []\n name_df = context.name_df\n name_today = name_df[name_df.date==data.current_dt.strftime('%Y-%m-%d')]\n for instrument in equities:\n try:\n name_instrument = name_today[name_today.instrument==instrument]['name'].values[0]\n except:\n continue\n # 如果股票名称中含有ST或退等字样则卖出\n if '退' in name_instrument:\n context.order_target(context.symbol(instrument), 0)\n st_stock_list.append(instrument)\n cash_for_sell -= positions[instrument]\n if st_stock_list!=[]:\n print(data.current_dt.strftime('%Y-%m-%d'),'持仓出现退市股',st_stock_list,'进行卖出处理')\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 instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(\n lambda x: x in equities)])))\n for instrument in instruments:\n # st/退市股票上面已经卖过了，此处跳过防止出现空单\n if instrument in st_stock_list:\n continue\n context.order_target(context.symbol(instrument),0)\n cash_for_sell -= positions[instrument]\n if cash_for_sell <= 0:\n break\n\n # 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\n buy_cash_weights = context.stock_weights\n buy_instruments = 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context.ranker_prediction = context.options['data'].read_df()\n\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=0.0002, sell_cost=0.0013, min_cost=5))\n # 预测数据，通过options传入进来，使用 read_df 函数，加载到内存 (DataFrame)\n # 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\n stock_count = 5\n # 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, ..]\n context.stock_weights = T.norm([1 / math.log(i + 2) for i in range(0, stock_count)])\n # 设置每只股票占用的最大资金比例\n context.max_cash_per_instrument = 0.2\n context.options['hold_days'] = 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In [3]:

# 本代码由可视化策略环境自动生成 2023年1月4日 14:46
# 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：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.portfolio.positions.items()}
    equities = {e.symbol: e for e, p in context.portfolio.positions.items()}

    # 记录持仓中st的股票
    st_stock_list = []
    name_df = context.name_df
    name_today = name_df[name_df.date==data.current_dt.strftime('%Y-%m-%d')]
    for instrument in equities:
        try:
            name_instrument = name_today[name_today.instrument==instrument]['name'].values[0]
        except:
            continue
        # 如果股票名称中含有ST或退等字样则卖出
        if  '退' in name_instrument:
            context.order_target(context.symbol(instrument), 0)
            st_stock_list.append(instrument)
            cash_for_sell -= positions[instrument]
    if st_stock_list!=[]:
        print(data.current_dt.strftime('%Y-%m-%d'),'持仓出现退市股',st_stock_list,'进行卖出处理')
 
    # 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:
        instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(
            lambda x: x in equities)])))
        for instrument in instruments:
            # st/退市股票上面已经卖过了，此处跳过防止出现空单
            if instrument in st_stock_list:
                continue
            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:
            try:
                name_instrument = name_today[name_today.instrument==instrument]['name'].values[0]
            except:
                continue
            if  '退' in name_instrument:
                pass
            else:
                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):
    # 获取股票名称 用于过滤st和退市股
    context.name_df = DataSource('instruments_CN_STOCK_A').read()

# 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=0.0002, 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 = 5
    # 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'] = 10


m1 = M.instruments.v2(
    start_date='2012-01-01',
    end_date='2018-01-01',
    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/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, -10) / shift(open, -1)

# 极值处理：用1%和99%分位的值做clip
clip(label, all_quantile(label, 0.03), all_quantile(label, 0.97))

# 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="""ta_atr_28_0/close_0*100

"""
)

m15 = M.general_feature_extractor.v7(
    instruments=m1.data,
    features=m3.data,
    start_date='',
    end_date='',
    before_start_days=350
)

m5 = M.chinaa_stock_filter.v1(
    input_data=m15.data,
    index_constituent_cond=['全部'],
    board_cond=['全部'],
    industry_cond=['全部'],
    st_cond=['正常'],
    output_left_data=False
)

m16 = M.derived_feature_extractor.v3(
    input_data=m5.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
)

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', '2018-01-01'),
    end_date=T.live_run_param('trading_date', '2019-04-04'),
    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=350
)

m4 = M.chinaa_stock_filter.v1(
    input_data=m17.data,
    index_constituent_cond=['全部'],
    board_cond=['全部'],
    industry_cond=['全部'],
    st_cond=['正常'],
    output_left_data=False
)

m18 = M.derived_feature_extractor.v3(
    input_data=m4.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
)

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='',
    handle_data=m19_handle_data_bigquant_run,
    prepare=m19_prepare_bigquant_run,
    initialize=m19_initialize_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'
)

[2019-04-15 16:54:38.294005] INFO: bigquant: instruments.v2 开始运行..

[2019-04-15 16:54:38.392025] INFO: bigquant: 命中缓存

[2019-04-15 16:54:38.393764] INFO: bigquant: instruments.v2 运行完成[0.099754s].

[2019-04-15 16:54:38.396966] INFO: bigquant: advanced_auto_labeler.v2 开始运行..

[2019-04-15 16:54:43.404793] INFO: 自动标注(股票): 加载历史数据: 3654268 行

[2019-04-15 16:54:43.406885] INFO: 自动标注(股票): 开始标注 ..

[2019-04-15 16:54:52.072108] INFO: bigquant: advanced_auto_labeler.v2 运行完成[13.675123s].

[2019-04-15 16:54:52.076193] INFO: bigquant: input_features.v1 开始运行..

[2019-04-15 16:54:52.083792] INFO: bigquant: 命中缓存

[2019-04-15 16:54:52.087408] INFO: bigquant: input_features.v1 运行完成[0.011206s].

[2019-04-15 16:54:52.093600] INFO: bigquant: general_feature_extractor.v7 开始运行..

[2019-04-15 16:54:53.768792] INFO: 基础特征抽取: 年份 2011, 特征行数=493616

[2019-04-15 16:54:55.483486] INFO: 基础特征抽取: 年份 2012, 特征行数=565675

[2019-04-15 16:54:57.607077] INFO: 基础特征抽取: 年份 2013, 特征行数=564168

[2019-04-15 16:54:59.523396] INFO: 基础特征抽取: 年份 2014, 特征行数=569948

[2019-04-15 16:55:01.730389] INFO: 基础特征抽取: 年份 2015, 特征行数=569698

[2019-04-15 16:55:04.166669] INFO: 基础特征抽取: 年份 2016, 特征行数=641546

[2019-04-15 16:55:14.745100] INFO: 基础特征抽取: 年份 2017, 特征行数=743233

[2019-04-15 16:55:23.386204] INFO: 基础特征抽取: 年份 2018, 特征行数=0

[2019-04-15 16:55:23.424989] INFO: 基础特征抽取: 总行数: 4147884

[2019-04-15 16:55:23.428416] INFO: bigquant: general_feature_extractor.v7 运行完成[31.334821s].

[2019-04-15 16:55:23.431802] INFO: bigquant: chinaa_stock_filter.v1 开始运行..

[2019-04-15 16:55:25.890439] INFO: A股股票过滤: 过滤 /y_2011, 465517/0/493616

[2019-04-15 16:55:27.511254] INFO: A股股票过滤: 过滤 /y_2012, 541178/0/565675

[2019-04-15 16:55:29.222969] INFO: A股股票过滤: 过滤 /y_2013, 550859/0/564168

[2019-04-15 16:55:30.973540] INFO: A股股票过滤: 过滤 /y_2014, 561221/0/569948

[2019-04-15 16:55:33.213077] INFO: A股股票过滤: 过滤 /y_2015, 561179/0/569698

[2019-04-15 16:55:35.140665] INFO: A股股票过滤: 过滤 /y_2016, 630635/0/641546

[2019-04-15 16:55:37.413884] INFO: A股股票过滤: 过滤 /y_2017, 730744/0/743233

[2019-04-15 16:55:37.418546] INFO: A股股票过滤: 过滤完成, 4041333 + 0

[2019-04-15 16:55:37.438033] INFO: bigquant: chinaa_stock_filter.v1 运行完成[14.006215s].

[2019-04-15 16:55:37.441515] INFO: bigquant: derived_feature_extractor.v3 开始运行..

[2019-04-15 16:55:39.011658] INFO: general_feature_extractor: 提取完成 ta_atr_28_0/close_0*100, 0.042s

[2019-04-15 16:55:39.371732] INFO: general_feature_extractor: /y_2011, 465517

[2019-04-15 16:55:39.717890] INFO: general_feature_extractor: /y_2012, 541178

[2019-04-15 16:55:40.318603] INFO: general_feature_extractor: /y_2013, 550859

[2019-04-15 16:55:40.758618] INFO: general_feature_extractor: /y_2014, 561221

[2019-04-15 16:55:41.166396] INFO: general_feature_extractor: /y_2015, 561179

[2019-04-15 16:55:41.629748] INFO: general_feature_extractor: /y_2016, 630635

[2019-04-15 16:55:42.099065] INFO: general_feature_extractor: /y_2017, 730744

[2019-04-15 16:55:42.646282] INFO: bigquant: derived_feature_extractor.v3 运行完成[5.204648s].

[2019-04-15 16:55:42.649814] INFO: bigquant: join.v3 开始运行..

[2019-04-15 16:55:45.894538] INFO: join: /y_2011, 行数=0/465517, 耗时=2.041143s

[2019-04-15 16:55:48.397375] INFO: join: /y_2012, 行数=540639/541178, 耗时=2.48579s

[2019-04-15 16:55:50.794836] INFO: join: /y_2013, 行数=550011/550859, 耗时=2.372891s

[2019-04-15 16:55:53.184893] INFO: join: /y_2014, 行数=559260/561221, 耗时=2.370925s

[2019-04-15 16:55:55.892898] INFO: join: /y_2015, 行数=552405/561179, 耗时=2.684594s

[2019-04-15 16:55:58.237435] INFO: join: /y_2016, 行数=626713/630635, 耗时=2.316059s

[2019-04-15 16:56:00.695425] INFO: join: /y_2017, 行数=692336/730744, 耗时=2.436434s

[2019-04-15 16:56:00.910682] INFO: join: 最终行数: 3521364

[2019-04-15 16:56:00.913691] INFO: bigquant: join.v3 运行完成[18.263859s].

[2019-04-15 16:56:00.916675] INFO: bigquant: dropnan.v1 开始运行..

[2019-04-15 16:56:00.989493] INFO: dropnan: /y_2011, 0/0

[2019-04-15 16:56:01.577655] INFO: dropnan: /y_2012, 535948/540639

[2019-04-15 16:56:02.205023] INFO: dropnan: /y_2013, 549974/550011

[2019-04-15 16:56:02.797803] INFO: dropnan: /y_2014, 556690/559260

[2019-04-15 16:56:03.361553] INFO: dropnan: /y_2015, 548695/552405

[2019-04-15 16:56:04.085070] INFO: dropnan: /y_2016, 623420/626713

[2019-04-15 16:56:04.832383] INFO: dropnan: /y_2017, 683731/692336

[2019-04-15 16:56:04.931832] INFO: dropnan: 行数: 3498458/3521364

[2019-04-15 16:56:04.976725] INFO: bigquant: dropnan.v1 运行完成[4.060029s].

[2019-04-15 16:56:04.980988] INFO: bigquant: stock_ranker_train.v5 开始运行..

[2019-04-15 16:56:06.924629] INFO: StockRanker: 特征预处理 ..

[2019-04-15 16:56:07.326495] INFO: StockRanker: prepare data: training ..

[2019-04-15 16:56:07.962632] INFO: StockRanker: sort ..

[2019-04-15 16:56:40.479249] INFO: StockRanker训练: 4d4a612e 准备训练: 3498458 行数

[2019-04-15 16:56:40.497034] INFO: StockRanker训练: 正在训练 ..

[2019-04-15 17:02:42.181072] INFO: bigquant: stock_ranker_train.v5 运行完成[397.200062s].

[2019-04-15 17:02:42.187295] INFO: bigquant: instruments.v2 开始运行..

[2019-04-15 17:02:42.200396] INFO: bigquant: 命中缓存

[2019-04-15 17:02:42.202248] INFO: bigquant: instruments.v2 运行完成[0.014945s].

[2019-04-15 17:02:42.214591] INFO: bigquant: general_feature_extractor.v7 开始运行..

[2019-04-15 17:02:50.599676] INFO: 基础特征抽取: 年份 2017, 特征行数=717475

[2019-04-15 17:03:00.419683] INFO: 基础特征抽取: 年份 2018, 特征行数=816987

[2019-04-15 17:03:02.094552] INFO: 基础特征抽取: 年份 2019, 特征行数=221089

[2019-04-15 17:03:02.123949] INFO: 基础特征抽取: 总行数: 1755551

[2019-04-15 17:03:02.131383] INFO: bigquant: general_feature_extractor.v7 运行完成[19.916779s].

[2019-04-15 17:03:02.135756] INFO: bigquant: chinaa_stock_filter.v1 开始运行..

[2019-04-15 17:03:04.889766] INFO: A股股票过滤: 过滤 /y_2017, 705492/0/717475

[2019-04-15 17:03:07.685838] INFO: A股股票过滤: 过滤 /y_2018, 800353/0/816987

[2019-04-15 17:03:08.270735] INFO: A股股票过滤: 过滤 /y_2019, 216092/0/221089

[2019-04-15 17:03:08.275989] INFO: A股股票过滤: 过滤完成, 1721937 + 0

[2019-04-15 17:03:08.288239] INFO: bigquant: chinaa_stock_filter.v1 运行完成[6.152467s].

[2019-04-15 17:03:08.292947] INFO: bigquant: derived_feature_extractor.v3 开始运行..

[2019-04-15 17:03:08.949741] INFO: general_feature_extractor: 提取完成 ta_atr_28_0/close_0*100, 0.007s

[2019-04-15 17:03:09.250994] INFO: general_feature_extractor: /y_2017, 705492

[2019-04-15 17:03:10.325627] INFO: general_feature_extractor: /y_2018, 800353

[2019-04-15 17:03:10.816912] INFO: general_feature_extractor: /y_2019, 216092

[2019-04-15 17:03:10.975099] INFO: bigquant: derived_feature_extractor.v3 运行完成[2.682147s].

[2019-04-15 17:03:10.978506] INFO: bigquant: dropnan.v1 开始运行..

[2019-04-15 17:03:11.581638] INFO: dropnan: /y_2017, 693393/705492

[2019-04-15 17:03:12.371728] INFO: dropnan: /y_2018, 797068/800353

[2019-04-15 17:03:12.598180] INFO: dropnan: /y_2019, 215215/216092

[2019-04-15 17:03:12.621493] INFO: dropnan: 行数: 1705676/1721937

[2019-04-15 17:03:12.631347] INFO: bigquant: dropnan.v1 运行完成[1.652825s].

[2019-04-15 17:03:12.635028] INFO: bigquant: stock_ranker_predict.v5 开始运行..

[2019-04-15 17:03:13.339565] INFO: StockRanker: prepare data: prediction ..

[2019-04-15 17:03:30.661843] INFO: stock_ranker_predict: 准备预测: 1705676 行

[2019-04-15 17:03:30.664312] INFO: stock_ranker_predict: 正在预测 ..

[2019-04-15 17:04:01.077631] INFO: bigquant: stock_ranker_predict.v5 运行完成[48.442582s].

[2019-04-15 17:04:01.136316] INFO: bigquant: backtest.v8 开始运行..

[2019-04-15 17:04:01.139833] INFO: bigquant: biglearning backtest:V8.1.11

[2019-04-15 17:04:11.557328] INFO: bigquant: product_type:stock by specified

[2019-04-15 17:04:20.094145] INFO: bigquant: 读取股票行情完成:1877599

[2019-04-15 17:04:39.997990] INFO: algo: TradingAlgorithm V1.4.11

[2019-04-15 17:04:52.969025] INFO: algo: trading transform...

[2019-04-15 17:05:13.242624] INFO: Performance: Simulated 305 trading days out of 305.

[2019-04-15 17:05:13.244343] INFO: Performance: first open: 2018-01-02 09:30:00+00:00

[2019-04-15 17:05:13.245920] INFO: Performance: last close: 2019-04-04 15:00:00+00:00

[2019-04-15 17:05:16.797462] INFO: bigquant: backtest.v8 运行完成[75.661128s].

StockRanker模型训练明细

设置测试数据集，查看训练迭代过程的NDCG

收益率35.46%
年化收益率28.5%
基准收益率0.78%
阿尔法0.27
贝塔0.61
夏普比率0.88
胜率0.56
盈亏比1.25
收益波动率30.45%
信息比率0.06
最大回撤32.56%

如果可以画出交易点可能会对改进算法有一定的帮助¶

In [4]:

def transaction_money(trade):
    '''
    输入trade
    输出前十个和后十个收益率的股票数据
    '''
    #交易详情
    df=trade.read_raw_perf()
    transactions=df.transactions.iloc[1:].values
    tras=None
    #链接成dataframe
    for i in transactions:
        if isinstance(tras,pd.DataFrame):        
            tras=pd.concat([tras,pd.DataFrame(i)])
        else:
            tras=pd.DataFrame(i)

    #改变sid形式
    tras.sid=tras.sid.apply(lambda x:x.symbol)
    #获取需要的数据
    tras=tras[['dt','sid','transaction_money']]
    
    #找到收益最大的10和最少的十个，这个是错误的 。
    transaction_money_sum=tras.groupby('sid').apply(lambda x:x.transaction_money.sum()).sort_values()
    top=transaction_money_sum.head(10)
    bottom=transaction_money_sum.tail(10)
    return top,bottom,tras

def get_bars(ins,start_date,end_date,signal):
    # Python 代码入口函数，input_1/2/3 对应三个输入端，data_1/2/3 对应三个输出端
    def m5_run_bigquant_run(input_1, input_2, input_3):
        # 示例代码如下。在这里编写您的代码
        df = input_1.read()
        df=df.rename_axis({'open_0':'open','close_0':'close',"low_0":'low','high_0':'high'},axis='columns').set_index('date')
        ins=df.instrument.iloc[0]
        del df['instrument']    
        data_1 = DataSource.write_df(df)       
        return Outputs(data_1=data_1, data_2=ins, data_3=None)

    # 后处理函数，可选。输入是主函数的输出，可以在这里对数据做处理，或者返回更友好的outputs数据格式。此函数输出不会被缓存。
    def m5_post_run_bigquant_run(outputs):
        return outputs


    m1 = M.instruments.v2(
        start_date=start_date,
        end_date=end_date,
        market='CN_STOCK_A',
        instrument_list=[ins],
        max_count=0
    )

    m3 = M.input_features.v1(
        features="""
    # #号开始的表示注释，注释需单独一行
    # 多个特征，每行一个，可以包含基础特征和衍生特征，特征须为本平台特征
    high_0
    close_0
    low_0
    open_0
    ta_sma_30_0   
    """+signal
        
    )

    m2 = M.general_feature_extractor.v7(
        instruments=m1.data,
        features=m3.data,
        start_date='',
        end_date='',
        before_start_days=90
    )

    m6 = M.derived_feature_extractor.v3(
        input_data=m2.data,
        features=m3.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=False,
        remove_extra_columns=False,
        user_functions={}
    )

    m5 = M.cached.v3(
        input_1=m6.data,
        run=m5_run_bigquant_run,
        post_run=m5_post_run_bigquant_run,
        input_ports='',
        params='{}',
        output_ports=''
    )

    df=m5.data_1.read()    
    df=df[df.index>=start_date]
    return df    
####
def bar_tras(bar,tras):
    '''
    bar：日线数据
    tras：交易记录
    链接两个数据，并且设置sell，buy列
    
    '''
    #合并两个数据
    bar.reset_index(inplace=True)
    bar=bar.merge(tras,on='date',how='left')

    #添加两列
    bar['sell']=np.nan
    bar['buy']=np.nan
    #设置买入卖出点
    def buy_or_sell(x):
        if x.transaction_money>0:
            x.buy=x.low
        if x.transaction_money<0:
            x.sell=x.high
        return x
    #应用
    bar=bar.apply(lambda x: buy_or_sell(x),axis=1)
    #设置日期
    bar.set_index('date',drop=True,inplace=True)
    return bar

top,bottom,tras=transaction_money(m19)

# 循环画图
for sid in bottom.index:
    
    sel_tras=tras[tras.sid==sid]    
    sel_tras['date']=sel_tras.dt.apply(lambda x :pd.Timestamp(x.strftime('%Y-%m-%d')))
    #设置日期
    start_date= ( sel_tras.date.iloc[0]- datetime.timedelta(days=50)).strftime('%Y-%m-%d') 
    end_date= (sel_tras.date.iloc[-1] + datetime.timedelta(days=50)).strftime('%Y-%m-%d') 

    signal='''ta_atr_28_0/close_0*100
    '''
    #读取数据
    bar=get_bars(sid,start_date,end_date,signal)
    df=bar_tras(bar,sel_tras)
    #选择画图数据
    select=['open', 'high', 'low', 'close','ta_sma_30_0','sell','buy']    
    #把 signal 加入到select 
    for i in signal.strip().split('\n'):
        select.append(i.strip())
        
    print(sid,'-'*40)
    T.plot(df[select],  options={'chart': {'title': sid, 'height': 800}, 'series': [{},{'type': 'line','yAxis':0},{'type': 'scatter','yAxis':0,'color':'green'},{'type': 'scatter','yAxis':0,'color':'red'}]},stock=True, candlestick=True)
    print('--------------\n'*2)

[2019-04-15 17:05:19.085617] INFO: bigquant: instruments.v2 开始运行..

[2019-04-15 17:05:19.098958] INFO: bigquant: instruments.v2 运行完成[0.013309s].

[2019-04-15 17:05:19.103250] INFO: bigquant: input_features.v1 开始运行..

[2019-04-15 17:05:19.115241] INFO: bigquant: input_features.v1 运行完成[0.011984s].

[2019-04-15 17:05:19.124721] INFO: bigquant: general_feature_extractor.v7 开始运行..

[2019-04-15 17:05:19.279403] INFO: 基础特征抽取: 年份 2018, 特征行数=75

[2019-04-15 17:05:19.286604] INFO: 基础特征抽取: 总行数: 75

[2019-04-15 17:05:19.290985] INFO: bigquant: general_feature_extractor.v7 运行完成[0.166494s].

[2019-04-15 17:05:19.294797] INFO: bigquant: derived_feature_extractor.v3 开始运行..

[2019-04-15 17:05:19.317410] INFO: general_feature_extractor: 提取完成 ta_atr_28_0/close_0*100, 0.001s

[2019-04-15 17:05:19.337676] INFO: general_feature_extractor: /y_2018, 75

[2019-04-15 17:05:19.361835] INFO: bigquant: derived_feature_extractor.v3 运行完成[0.067023s].

[2019-04-15 17:05:19.369347] INFO: bigquant: cached.v3 开始运行..

[2019-04-15 17:05:19.419621] INFO: bigquant: cached.v3 运行完成[0.050269s].

[2019-04-15 17:05:19.631059] INFO: bigquant: instruments.v2 开始运行..

[2019-04-15 17:05:19.641532] INFO: bigquant: instruments.v2 运行完成[0.010468s].

[2019-04-15 17:05:19.643912] INFO: bigquant: input_features.v1 开始运行..

[2019-04-15 17:05:19.648978] INFO: bigquant: 命中缓存

[2019-04-15 17:05:19.650641] INFO: bigquant: input_features.v1 运行完成[0.006721s].

[2019-04-15 17:05:19.658263] INFO: bigquant: general_feature_extractor.v7 开始运行..

[2019-04-15 17:05:19.840745] INFO: 基础特征抽取: 年份 2017, 特征行数=93

[2019-04-15 17:05:19.887682] INFO: 基础特征抽取: 年份 2018, 特征行数=243

[2019-04-15 17:05:19.925902] INFO: 基础特征抽取: 年份 2019, 特征行数=67

[2019-04-15 17:05:19.937042] INFO: 基础特征抽取: 总行数: 403

[2019-04-15 17:05:19.940341] INFO: bigquant: general_feature_extractor.v7 运行完成[0.282128s].

[2019-04-15 17:05:19.943398] INFO: bigquant: derived_feature_extractor.v3 开始运行..

[2019-04-15 17:05:19.998789] INFO: general_feature_extractor: 提取完成 ta_atr_28_0/close_0*100, 0.001s

[2019-04-15 17:05:20.022865] INFO: general_feature_extractor: /y_2017, 93

[2019-04-15 17:05:20.047953] INFO: general_feature_extractor: /y_2018, 243

[2019-04-15 17:05:20.074610] INFO: general_feature_extractor: /y_2019, 67

[2019-04-15 17:05:20.103838] INFO: bigquant: derived_feature_extractor.v3 运行完成[0.160424s].

[2019-04-15 17:05:20.108920] INFO: bigquant: cached.v3 开始运行..

[2019-04-15 17:05:20.187960] INFO: bigquant: cached.v3 运行完成[0.079025s].

[2019-04-15 17:05:20.467569] INFO: bigquant: instruments.v2 开始运行..

[2019-04-15 17:05:20.478508] INFO: bigquant: instruments.v2 运行完成[0.010932s].

[2019-04-15 17:05:20.481414] INFO: bigquant: input_features.v1 开始运行..

[2019-04-15 17:05:20.486852] INFO: bigquant: 命中缓存

[2019-04-15 17:05:20.488884] INFO: bigquant: input_features.v1 运行完成[0.007459s].

[2019-04-15 17:05:20.496198] INFO: bigquant: general_feature_extractor.v7 开始运行..

[2019-04-15 17:05:20.636944] INFO: 基础特征抽取: 年份 2018, 特征行数=91

[2019-04-15 17:05:20.642956] INFO: 基础特征抽取: 总行数: 91

[2019-04-15 17:05:20.645522] INFO: bigquant: general_feature_extractor.v7 运行完成[0.149319s].

[2019-04-15 17:05:20.648664] INFO: bigquant: derived_feature_extractor.v3 开始运行..

[2019-04-15 17:05:20.689770] INFO: general_feature_extractor: 提取完成 ta_atr_28_0/close_0*100, 0.001s

[2019-04-15 17:05:20.708544] INFO: general_feature_extractor: /y_2018, 91

[2019-04-15 17:05:20.731678] INFO: bigquant: derived_feature_extractor.v3 运行完成[0.083004s].

[2019-04-15 17:05:20.736634] INFO: bigquant: cached.v3 开始运行..

[2019-04-15 17:05:20.782980] INFO: bigquant: cached.v3 运行完成[0.046334s].

[2019-04-15 17:05:21.019190] INFO: bigquant: instruments.v2 开始运行..

[2019-04-15 17:05:21.351801] INFO: bigquant: instruments.v2 运行完成[0.332619s].

[2019-04-15 17:05:21.355946] INFO: bigquant: input_features.v1 开始运行..

[2019-04-15 17:05:21.388380] INFO: bigquant: 命中缓存

[2019-04-15 17:05:21.390825] INFO: bigquant: input_features.v1 运行完成[0.034865s].

[2019-04-15 17:05:21.399415] INFO: bigquant: general_feature_extractor.v7 开始运行..

[2019-04-15 17:05:21.649154] INFO: 基础特征抽取: 年份 2018, 特征行数=140

[2019-04-15 17:05:21.657893] INFO: 基础特征抽取: 总行数: 140

[2019-04-15 17:05:21.661766] INFO: bigquant: general_feature_extractor.v7 运行完成[0.262336s].

[2019-04-15 17:05:21.665443] INFO: bigquant: derived_feature_extractor.v3 开始运行..

[2019-04-15 17:05:21.689601] INFO: general_feature_extractor: 提取完成 ta_atr_28_0/close_0*100, 0.001s

[2019-04-15 17:05:21.713286] INFO: general_feature_extractor: /y_2018, 140

[2019-04-15 17:05:21.738532] INFO: bigquant: derived_feature_extractor.v3 运行完成[0.073071s].

[2019-04-15 17:05:21.743224] INFO: bigquant: cached.v3 开始运行..

[2019-04-15 17:05:21.790869] INFO: bigquant: cached.v3 运行完成[0.04764s].

[2019-04-15 17:05:22.043149] INFO: bigquant: instruments.v2 开始运行..

[2019-04-15 17:05:22.051001] INFO: bigquant: instruments.v2 运行完成[0.007845s].

[2019-04-15 17:05:22.053781] INFO: bigquant: input_features.v1 开始运行..

[2019-04-15 17:05:22.070176] INFO: bigquant: 命中缓存

[2019-04-15 17:05:22.071795] INFO: bigquant: input_features.v1 运行完成[0.018005s].

[2019-04-15 17:05:22.079730] INFO: bigquant: general_feature_extractor.v7 开始运行..

[2019-04-15 17:05:22.248417] INFO: 基础特征抽取: 年份 2018, 特征行数=64

[2019-04-15 17:05:22.278787] INFO: 基础特征抽取: 年份 2019, 特征行数=68

[2019-04-15 17:05:22.289887] INFO: 基础特征抽取: 总行数: 132

[2019-04-15 17:05:22.292975] INFO: bigquant: general_feature_extractor.v7 运行完成[0.213251s].

[2019-04-15 17:05:22.296297] INFO: bigquant: derived_feature_extractor.v3 开始运行..

[2019-04-15 17:05:22.341408] INFO: general_feature_extractor: 提取完成 ta_atr_28_0/close_0*100, 0.001s

[2019-04-15 17:05:22.365889] INFO: general_feature_extractor: /y_2018, 64

[2019-04-15 17:05:22.396478] INFO: general_feature_extractor: /y_2019, 68

[2019-04-15 17:05:22.423948] INFO: bigquant: derived_feature_extractor.v3 运行完成[0.127638s].

[2019-04-15 17:05:22.428783] INFO: bigquant: cached.v3 开始运行..

[2019-04-15 17:05:22.474539] INFO: bigquant: cached.v3 运行完成[0.045747s].

[2019-04-15 17:05:22.720924] INFO: bigquant: instruments.v2 开始运行..

[2019-04-15 17:05:22.731013] INFO: bigquant: instruments.v2 运行完成[0.010081s].

[2019-04-15 17:05:22.734791] INFO: bigquant: input_features.v1 开始运行..

[2019-04-15 17:05:22.744666] INFO: bigquant: 命中缓存

[2019-04-15 17:05:22.746618] INFO: bigquant: input_features.v1 运行完成[0.011818s].

[2019-04-15 17:05:22.772240] INFO: bigquant: general_feature_extractor.v7 开始运行..

[2019-04-15 17:05:22.928823] INFO: 基础特征抽取: 年份 2018, 特征行数=118

[2019-04-15 17:05:22.935483] INFO: 基础特征抽取: 总行数: 118

[2019-04-15 17:05:22.938556] INFO: bigquant: general_feature_extractor.v7 运行完成[0.166321s].

[2019-04-15 17:05:22.941519] INFO: bigquant: derived_feature_extractor.v3 开始运行..

[2019-04-15 17:05:22.963886] INFO: general_feature_extractor: 提取完成 ta_atr_28_0/close_0*100, 0.001s

[2019-04-15 17:05:22.983662] INFO: general_feature_extractor: /y_2018, 118

[2019-04-15 17:05:23.005667] INFO: bigquant: derived_feature_extractor.v3 运行完成[0.06413s].

[2019-04-15 17:05:23.012108] INFO: bigquant: cached.v3 开始运行..

[2019-04-15 17:05:23.052834] INFO: bigquant: cached.v3 运行完成[0.040712s].

[2019-04-15 17:05:23.343049] INFO: bigquant: instruments.v2 开始运行..

[2019-04-15 17:05:23.352867] INFO: bigquant: instruments.v2 运行完成[0.009805s].

[2019-04-15 17:05:23.356148] INFO: bigquant: input_features.v1 开始运行..

[2019-04-15 17:05:23.373964] INFO: bigquant: 命中缓存

[2019-04-15 17:05:23.378014] INFO: bigquant: input_features.v1 运行完成[0.021854s].

[2019-04-15 17:05:23.390906] INFO: bigquant: general_feature_extractor.v7 开始运行..

[2019-04-15 17:05:23.567352] INFO: 基础特征抽取: 年份 2018, 特征行数=103

[2019-04-15 17:05:23.601266] INFO: 基础特征抽取: 年份 2019, 特征行数=68

[2019-04-15 17:05:23.610682] INFO: 基础特征抽取: 总行数: 171

[2019-04-15 17:05:23.613908] INFO: bigquant: general_feature_extractor.v7 运行完成[0.223001s].

[2019-04-15 17:05:23.616471] INFO: bigquant: derived_feature_extractor.v3 开始运行..

[2019-04-15 17:05:23.652632] INFO: general_feature_extractor: 提取完成 ta_atr_28_0/close_0*100, 0.001s

[2019-04-15 17:05:23.676088] INFO: general_feature_extractor: /y_2018, 103

[2019-04-15 17:05:23.705260] INFO: general_feature_extractor: /y_2019, 68

[2019-04-15 17:05:23.733278] INFO: bigquant: derived_feature_extractor.v3 运行完成[0.116792s].

[2019-04-15 17:05:23.738467] INFO: bigquant: cached.v3 开始运行..

[2019-04-15 17:05:23.785339] INFO: bigquant: cached.v3 运行完成[0.046867s].

[2019-04-15 17:05:23.983139] INFO: bigquant: instruments.v2 开始运行..

[2019-04-15 17:05:23.991388] INFO: bigquant: instruments.v2 运行完成[0.008246s].

[2019-04-15 17:05:23.993964] INFO: bigquant: input_features.v1 开始运行..

[2019-04-15 17:05:23.998239] INFO: bigquant: 命中缓存

[2019-04-15 17:05:23.999681] INFO: bigquant: input_features.v1 运行完成[0.005716s].

[2019-04-15 17:05:24.006103] INFO: bigquant: general_feature_extractor.v7 开始运行..

[2019-04-15 17:05:24.243646] INFO: 基础特征抽取: 年份 2017, 特征行数=93

[2019-04-15 17:05:24.275594] INFO: 基础特征抽取: 年份 2018, 特征行数=243

[2019-04-15 17:05:24.302072] INFO: 基础特征抽取: 年份 2019, 特征行数=68

[2019-04-15 17:05:24.311861] INFO: 基础特征抽取: 总行数: 404

[2019-04-15 17:05:24.314673] INFO: bigquant: general_feature_extractor.v7 运行完成[0.308568s].

[2019-04-15 17:05:24.317414] INFO: bigquant: derived_feature_extractor.v3 开始运行..

[2019-04-15 17:05:24.365150] INFO: general_feature_extractor: 提取完成 ta_atr_28_0/close_0*100, 0.001s

[2019-04-15 17:05:24.391247] INFO: general_feature_extractor: /y_2017, 93

[2019-04-15 17:05:24.416895] INFO: general_feature_extractor: /y_2018, 243

[2019-04-15 17:05:24.441423] INFO: general_feature_extractor: /y_2019, 68

[2019-04-15 17:05:24.468210] INFO: bigquant: derived_feature_extractor.v3 运行完成[0.15074s].

[2019-04-15 17:05:24.473026] INFO: bigquant: cached.v3 开始运行..

[2019-04-15 17:05:24.528081] INFO: bigquant: cached.v3 运行完成[0.055049s].

[2019-04-15 17:05:24.793602] INFO: bigquant: instruments.v2 开始运行..

[2019-04-15 17:05:24.804850] INFO: bigquant: instruments.v2 运行完成[0.011239s].

[2019-04-15 17:05:24.807748] INFO: bigquant: input_features.v1 开始运行..

[2019-04-15 17:05:24.813352] INFO: bigquant: 命中缓存

[2019-04-15 17:05:24.814757] INFO: bigquant: input_features.v1 运行完成[0.007005s].

[2019-04-15 17:05:24.821380] INFO: bigquant: general_feature_extractor.v7 开始运行..

[2019-04-15 17:05:25.070678] INFO: 基础特征抽取: 年份 2018, 特征行数=160

[2019-04-15 17:05:25.104516] INFO: 基础特征抽取: 年份 2019, 特征行数=68

[2019-04-15 17:05:25.117401] INFO: 基础特征抽取: 总行数: 228

[2019-04-15 17:05:25.121544] INFO: bigquant: general_feature_extractor.v7 运行完成[0.300146s].

[2019-04-15 17:05:25.124686] INFO: bigquant: derived_feature_extractor.v3 开始运行..

[2019-04-15 17:05:25.157178] INFO: general_feature_extractor: 提取完成 ta_atr_28_0/close_0*100, 0.001s

[2019-04-15 17:05:25.179995] INFO: general_feature_extractor: /y_2018, 160

[2019-04-15 17:05:25.203299] INFO: general_feature_extractor: /y_2019, 68

[2019-04-15 17:05:25.225781] INFO: bigquant: derived_feature_extractor.v3 运行完成[0.101083s].

[2019-04-15 17:05:25.229978] INFO: bigquant: cached.v3 开始运行..

[2019-04-15 17:05:25.338759] INFO: bigquant: cached.v3 运行完成[0.108738s].

[2019-04-15 17:05:25.655065] INFO: bigquant: instruments.v2 开始运行..

[2019-04-15 17:05:25.684618] INFO: bigquant: 命中缓存

[2019-04-15 17:05:25.687535] INFO: bigquant: instruments.v2 运行完成[0.032463s].

[2019-04-15 17:05:25.690802] INFO: bigquant: input_features.v1 开始运行..

[2019-04-15 17:05:25.699303] INFO: bigquant: 命中缓存

[2019-04-15 17:05:25.702031] INFO: bigquant: input_features.v1 运行完成[0.011214s].

[2019-04-15 17:05:25.710129] INFO: bigquant: general_feature_extractor.v7 开始运行..

[2019-04-15 17:05:25.877865] INFO: 基础特征抽取: 年份 2018, 特征行数=113

[2019-04-15 17:05:25.980732] INFO: 基础特征抽取: 总行数: 113

[2019-04-15 17:05:25.983777] INFO: bigquant: general_feature_extractor.v7 运行完成[0.273636s].

[2019-04-15 17:05:25.986971] INFO: bigquant: derived_feature_extractor.v3 开始运行..

[2019-04-15 17:05:26.016915] INFO: general_feature_extractor: 提取完成 ta_atr_28_0/close_0*100, 0.001s

[2019-04-15 17:05:26.039288] INFO: general_feature_extractor: /y_2018, 113

[2019-04-15 17:05:26.063690] INFO: bigquant: derived_feature_extractor.v3 运行完成[0.076703s].

[2019-04-15 17:05:26.069145] INFO: bigquant: cached.v3 开始运行..

[2019-04-15 17:05:26.119630] INFO: bigquant: cached.v3 运行完成[0.050479s].

600666.SHA ----------------------------------------

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600269.SHA ----------------------------------------

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002445.SZA ----------------------------------------

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002072.SZA ----------------------------------------

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601857.SHA ----------------------------------------

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002622.SZA ----------------------------------------

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601288.SHA ----------------------------------------

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600377.SHA ----------------------------------------

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601988.SHA ----------------------------------------

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300028.SZA ----------------------------------------

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