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context.trading_days_str[context.trading_days_str.index(date)-1]\n elif context.trading_days_str.index(date) == 0:\n pre_date = None\n \n if pre_date == None:\n return \n \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 == pre_date]\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\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 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# 本代码由可视化策略环境自动生成 2022年7月1日 20:53
# 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()

    pred_df = context.ranker_prediction
    trading_days = pred_df.date.unique() 
    context.trading_days_str = [str(i)[:10] for i in trading_days]
    
    # 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 = 3
    # 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'] = 1

# 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):
    
    
    date = data.current_dt.strftime('%Y-%m-%d')

    if context.trading_days_str.index(date) > 0:
        pre_date = context.trading_days_str[context.trading_days_str.index(date)-1]
    elif  context.trading_days_str.index(date) == 0:
        pre_date = None
    
    if  pre_date == None:
        return 
 
    # 按日期过滤得到今日的预测数据
    ranker_prediction = context.ranker_prediction[
        context.ranker_prediction.date == pre_date]

    # 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()}

    # 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.portfolio.positions.items()}
        instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(
                lambda x: x in equities)])))

        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='2018-01-01',
    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='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
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
"""
)

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
)

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

m6 = M.stock_ranker_train.v6(
    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,
    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', '2021-01-01'),
    end_date=T.live_run_param('trading_date', '2022-05-31'),
    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=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
)

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='',
    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'
)

[2022-07-01 20:36:10.485575] INFO: moduleinvoker: instruments.v2 开始运行..

[2022-07-01 20:36:10.547105] INFO: moduleinvoker: 命中缓存

[2022-07-01 20:36:10.552143] INFO: moduleinvoker: instruments.v2 运行完成[0.066564s].

[2022-07-01 20:36:10.573107] INFO: moduleinvoker: advanced_auto_labeler.v2 开始运行..

[2022-07-01 20:36:15.238343] INFO: 自动标注(股票): 加载历史数据: 2647809 行

[2022-07-01 20:36:15.242286] INFO: 自动标注(股票): 开始标注 ..

[2022-07-01 20:36:20.501321] INFO: moduleinvoker: advanced_auto_labeler.v2 运行完成[9.928237s].

[2022-07-01 20:36:20.548025] INFO: moduleinvoker: input_features.v1 开始运行..

[2022-07-01 20:36:20.566586] INFO: moduleinvoker: 命中缓存

[2022-07-01 20:36:20.572382] INFO: moduleinvoker: input_features.v1 运行完成[0.024351s].

[2022-07-01 20:36:20.657391] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2022-07-01 20:36:20.670712] INFO: moduleinvoker: 命中缓存

[2022-07-01 20:36:20.674563] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.017164s].

[2022-07-01 20:36:20.749029] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-07-01 20:36:20.772723] INFO: moduleinvoker: 命中缓存

[2022-07-01 20:36:20.777673] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.028641s].

[2022-07-01 20:36:20.852666] INFO: moduleinvoker: join.v3 开始运行..

[2022-07-01 20:36:32.732872] INFO: join: /y_2017, 行数=0/193398, 耗时=2.852548s

[2022-07-01 20:36:41.725945] INFO: join: /y_2018, 行数=813532/816987, 耗时=8.990199s

[2022-07-01 20:36:50.980533] INFO: join: /y_2019, 行数=881324/884867, 耗时=9.234367s

[2022-07-01 20:37:00.249832] INFO: join: /y_2020, 行数=931755/945961, 耗时=9.259155s

[2022-07-01 20:37:01.540961] INFO: join: 最终行数: 2626611

[2022-07-01 20:37:01.570060] INFO: moduleinvoker: join.v3 运行完成[40.717395s].

[2022-07-01 20:37:01.642753] INFO: moduleinvoker: dropnan.v1 开始运行..

[2022-07-01 20:37:02.259608] INFO: dropnan: /y_2017, 0/0

[2022-07-01 20:37:07.086733] INFO: dropnan: /y_2018, 811852/813532

[2022-07-01 20:37:12.386408] INFO: dropnan: /y_2019, 877982/881324

[2022-07-01 20:37:18.143130] INFO: dropnan: /y_2020, 923310/931755

[2022-07-01 20:37:18.485506] INFO: dropnan: 行数: 2613144/2626611

[2022-07-01 20:37:18.548367] INFO: moduleinvoker: dropnan.v1 运行完成[16.905612s].

[2022-07-01 20:37:18.562017] INFO: moduleinvoker: stock_ranker_train.v6 开始运行..

[2022-07-01 20:37:24.967381] INFO: StockRanker: 特征预处理 ..

[2022-07-01 20:37:30.165318] INFO: StockRanker: prepare data: training ..

[2022-07-01 20:37:36.323972] INFO: StockRanker: sort ..

[2022-07-01 20:38:47.043369] INFO: StockRanker训练: 8b877b86 准备训练: 2613144 行数

[2022-07-01 20:38:47.046814] INFO: StockRanker训练: AI模型训练，将在2613144*13=3397.09万数据上对模型训练进行20轮迭代训练。预计将需要11～21分钟。请耐心等待。

[2022-07-01 20:38:47.309936] INFO: StockRanker训练: 正在训练 ..

[2022-07-01 20:38:47.374870] INFO: StockRanker训练: 任务状态: Pending

[2022-07-01 20:38:57.474517] INFO: StockRanker训练: 任务状态: Running

[2022-07-01 20:40:28.977702] INFO: StockRanker训练: 00:01:36.5632019, finished iteration 1

[2022-07-01 20:41:10.243503] INFO: StockRanker训练: 00:02:08.4362585, finished iteration 2

[2022-07-01 20:41:40.550145] INFO: StockRanker训练: 00:02:40.3688340, finished iteration 3

[2022-07-01 20:42:11.121943] INFO: StockRanker训练: 00:03:10.6488830, finished iteration 4

[2022-07-01 20:42:41.269685] INFO: StockRanker训练: 00:03:42.0127285, finished iteration 5

[2022-07-01 20:43:11.413206] INFO: StockRanker训练: 00:04:13.1737721, finished iteration 6

[2022-07-01 20:43:41.584213] INFO: StockRanker训练: 00:04:48.2001756, finished iteration 7

[2022-07-01 20:44:21.768243] INFO: StockRanker训练: 00:05:24.3633002, finished iteration 8

[2022-07-01 20:45:02.035111] INFO: StockRanker训练: 00:05:59.8515164, finished iteration 9

[2022-07-01 20:45:32.197657] INFO: StockRanker训练: 00:06:30.1762252, finished iteration 10

[2022-07-01 20:46:02.417851] INFO: StockRanker训练: 00:07:00.5552530, finished iteration 11

[2022-07-01 20:46:32.557040] INFO: StockRanker训练: 00:07:31.0610074, finished iteration 12

[2022-07-01 20:47:02.720998] INFO: StockRanker训练: 00:08:01.9655382, finished iteration 13

[2022-07-01 20:47:32.963774] INFO: StockRanker训练: 00:08:33.5881708, finished iteration 14

[2022-07-01 20:48:03.117238] INFO: StockRanker训练: 00:09:04.3242046, finished iteration 15

[2022-07-01 20:48:33.318994] INFO: StockRanker训练: 00:09:36.4106240, finished iteration 16

[2022-07-01 20:49:03.479834] INFO: StockRanker训练: 00:10:07.6673987, finished iteration 17

[2022-07-01 20:49:33.674478] INFO: StockRanker训练: 00:10:40.4816154, finished iteration 18

[2022-07-01 20:50:13.899986] INFO: StockRanker训练: 00:11:12.3990673, finished iteration 19

[2022-07-01 20:50:44.067662] INFO: StockRanker训练: 00:11:44.1773833, finished iteration 20

[2022-07-01 20:50:44.070337] INFO: StockRanker训练: 任务状态: Succeeded

[2022-07-01 20:50:44.522697] INFO: moduleinvoker: stock_ranker_train.v6 运行完成[805.96067s].

[2022-07-01 20:50:44.542331] INFO: moduleinvoker: instruments.v2 开始运行..

[2022-07-01 20:50:44.585390] INFO: moduleinvoker: 命中缓存

[2022-07-01 20:50:44.588864] INFO: moduleinvoker: instruments.v2 运行完成[0.046549s].

[2022-07-01 20:50:44.623433] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2022-07-01 20:50:44.641806] INFO: moduleinvoker: 命中缓存

[2022-07-01 20:50:44.645631] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.022173s].

[2022-07-01 20:50:44.677422] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-07-01 20:50:44.705776] INFO: moduleinvoker: 命中缓存

[2022-07-01 20:50:44.708382] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.03098s].

[2022-07-01 20:50:44.735114] INFO: moduleinvoker: dropnan.v1 开始运行..

[2022-07-01 20:50:44.746744] INFO: moduleinvoker: 命中缓存

[2022-07-01 20:50:44.749722] INFO: moduleinvoker: dropnan.v1 运行完成[0.014604s].

[2022-07-01 20:50:44.774257] INFO: moduleinvoker: stock_ranker_predict.v5 开始运行..

[2022-07-01 20:50:45.784940] INFO: StockRanker预测: /y_2020 ..

[2022-07-01 20:50:47.598270] INFO: StockRanker预测: /y_2021 ..

[2022-07-01 20:50:54.132018] INFO: StockRanker预测: /y_2022 ..

[2022-07-01 20:50:58.729461] INFO: moduleinvoker: stock_ranker_predict.v5 运行完成[13.955212s].

[2022-07-01 20:50:58.898314] INFO: moduleinvoker: backtest.v8 开始运行..

[2022-07-01 20:50:58.905710] INFO: backtest: biglearning backtest:V8.6.2

[2022-07-01 20:50:58.908374] INFO: backtest: product_type:stock by specified

[2022-07-01 20:50:59.698777] INFO: moduleinvoker: cached.v2 开始运行..

[2022-07-01 20:50:59.715132] INFO: moduleinvoker: 命中缓存

[2022-07-01 20:50:59.719646] INFO: moduleinvoker: cached.v2 运行完成[0.020889s].

[2022-07-01 20:51:05.750916] INFO: algo: TradingAlgorithm V1.8.8

[2022-07-01 20:51:07.714806] INFO: algo: trading transform...

[2022-07-01 20:51:22.618757] INFO: Performance: Simulated 339 trading days out of 339.

[2022-07-01 20:51:22.621277] INFO: Performance: first open: 2021-01-04 09:30:00+00:00

[2022-07-01 20:51:22.625910] INFO: Performance: last close: 2022-05-31 15:00:00+00:00

[2022-07-01 20:51:31.669822] INFO: moduleinvoker: backtest.v8 运行完成[32.771529s].

[2022-07-01 20:51:31.671969] INFO: moduleinvoker: trade.v4 运行完成[32.894466s].

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