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# 本代码由可视化策略环境自动生成 2022年3月29日 11:01
# 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。


# 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 m28_run_bigquant_run(input_1, input_2, input_3):
    # 分别读取两个模型的数据
    df1 = input_1.read().rename(columns={'score':'score1','position':'position1'})
    df2 = input_2.read().rename(columns={'score':'score2','position':'position2'})
    
    #合并重新计算得分
    df = pd.merge(left=df1,right=df2,on=['date','instrument'],how='inner')
    df['score'] = df.score1 + df.score2
    #排序
    df = df.groupby('date').apply(lambda x:x.sort_values('score',ascending=False)).reset_index(drop=True)

    return Outputs(data_1=DataSource.write_df(df), data_2=None, 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 m28_post_run_bigquant_run(outputs):
    return outputs

# 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 = 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'] = 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.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)])
#     print(data.current_dt.strftime('%Y-%m-%d'),buy_instruments)
    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, -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.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.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', '2021-01-01'),
    end_date=T.live_run_param('trading_date', '2021-12-31'),
    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
)

m4 = M.instruments.v2(
    start_date='2018-01-01',
    end_date='2020-12-31',
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m5 = M.advanced_auto_labeler.v2(
    instruments=m4.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, -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.HIX',
    drop_na_label=True,
    cast_label_int=True
)

m10 = 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
fs_net_profit_ttm_0
"""
)

m24 = M.general_feature_extractor.v7(
    instruments=m4.data,
    features=m10.data,
    start_date='',
    end_date='',
    before_start_days=90
)

m25 = M.derived_feature_extractor.v3(
    input_data=m24.data,
    features=m10.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False
)

m12 = M.join.v3(
    data1=m5.data,
    data2=m25.data,
    on='date,instrument',
    how='inner',
    sort=False
)

m22 = M.dropnan.v1(
    input_data=m12.data
)

m11 = M.stock_ranker_train.v5(
    training_ds=m22.data,
    features=m10.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
)

m21 = M.instruments.v2(
    start_date='2021-01-01',
    end_date='2021-12-31',
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m26 = M.general_feature_extractor.v7(
    instruments=m21.data,
    features=m10.data,
    start_date='',
    end_date='',
    before_start_days=90
)

m27 = M.derived_feature_extractor.v3(
    input_data=m26.data,
    features=m10.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False
)

m23 = M.dropnan.v1(
    input_data=m27.data
)

m20 = M.stock_ranker_predict.v5(
    model=m11.model,
    data=m23.data,
    m_lazy_run=False
)

m28 = M.cached.v3(
    input_1=m8.predictions,
    input_2=m20.predictions,
    run=m28_run_bigquant_run,
    post_run=m28_post_run_bigquant_run,
    input_ports='',
    params='{}',
    output_ports=''
)

m19 = M.trade.v4(
    instruments=m9.data,
    options_data=m28.data_1,
    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'
)

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[2022-03-23 14:24:47.441241] INFO: moduleinvoker: cached.v3 运行完成[0.00856s].

[2022-03-23 14:24:47.493271] INFO: moduleinvoker: backtest.v8 开始运行..

[2022-03-23 14:24:47.498042] INFO: backtest: biglearning backtest:V8.6.2

[2022-03-23 14:24:47.499296] INFO: backtest: product_type:stock by specified

[2022-03-23 14:24:47.593212] INFO: moduleinvoker: cached.v2 开始运行..

[2022-03-23 14:24:47.603269] INFO: moduleinvoker: 命中缓存

[2022-03-23 14:24:47.605172] INFO: moduleinvoker: cached.v2 运行完成[0.011979s].

[2022-03-23 14:24:49.699786] INFO: algo: TradingAlgorithm V1.8.7

[2022-03-23 14:24:50.810100] INFO: algo: trading transform...

[2022-03-23 14:24:53.732143] INFO: algo: handle_splits get splits [dt:2021-05-21 00:00:00+00:00] [asset:Equity(5090 [688060.SHA]), ratio:0.9935547709465027]

[2022-03-23 14:24:53.733707] INFO: Position: position stock handle split[sid:5090, orig_amount:2500, new_amount:2516.0, orig_cost:57.43890568338266, new_cost:57.0687, ratio:0.9935547709465027, last_sale_price:57.03997802734375]

[2022-03-23 14:24:53.734777] INFO: Position: after split: PositionStock(asset:Equity(5090 [688060.SHA]), amount:2516.0, cost_basis:57.0687, last_sale_price:57.40999984741211)

[2022-03-23 14:24:53.735723] INFO: Position: returning cash: 12.4118

[2022-03-23 14:24:54.440250] INFO: algo: handle_splits get splits [dt:2021-05-25 00:00:00+00:00] [asset:Equity(3562 [300342.SZA]), ratio:0.9861623644828796]

[2022-03-23 14:24:54.442194] INFO: Position: position stock handle split[sid:3562, orig_amount:4700, new_amount:4765.0, orig_cost:10.900001206774633, new_cost:10.7492, ratio:0.9861623644828796, last_sale_price:10.690001487731934]

[2022-03-23 14:24:54.443950] INFO: Position: after split: PositionStock(asset:Equity(3562 [300342.SZA]), amount:4765.0, cost_basis:10.7492, last_sale_price:10.840001106262207)

[2022-03-23 14:24:54.445344] INFO: Position: returning cash: 10.1499

[2022-03-23 14:24:54.542719] INFO: algo: handle_splits get splits [dt:2021-05-28 00:00:00+00:00] [asset:Equity(3652 [300552.SZA]), ratio:0.981345534324646]

[2022-03-23 14:24:54.544159] INFO: algo: handle_splits get splits [dt:2021-05-28 00:00:00+00:00] [asset:Equity(5383 [300462.SZA]), ratio:0.9876540899276733]

[2022-03-23 14:24:54.545368] INFO: Position: position stock handle split[sid:3652, orig_amount:2900, new_amount:2955.0, orig_cost:32.110004419775976, new_cost:31.511, ratio:0.981345534324646, last_sale_price:32.09000015258789]

[2022-03-23 14:24:54.546661] INFO: Position: after split: PositionStock(asset:Equity(3652 [300552.SZA]), amount:2955.0, cost_basis:31.511, last_sale_price:32.70000076293945)

[2022-03-23 14:24:54.547708] INFO: Position: returning cash: 4.053

[2022-03-23 14:24:54.548729] INFO: Position: position stock handle split[sid:5383, orig_amount:12700, new_amount:12858.0, orig_cost:11.576145534584922, new_cost:11.4332, ratio:0.9876540899276733, last_sale_price:11.999998092651367]

[2022-03-23 14:24:54.549783] INFO: Position: after split: PositionStock(asset:Equity(5383 [300462.SZA]), amount:12858.0, cost_basis:11.4332, last_sale_price:12.15000057220459)

[2022-03-23 14:24:54.550883] INFO: Position: returning cash: 9.0361

[2022-03-23 14:24:54.732630] INFO: algo: handle_splits get splits [dt:2021-06-08 00:00:00+00:00] [asset:Equity(4979 [002730.SZA]), ratio:0.9940072894096375]

[2022-03-23 14:24:55.022341] INFO: algo: handle_splits get splits [dt:2021-06-24 00:00:00+00:00] [asset:Equity(1975 [300165.SZA]), ratio:0.9979838132858276]

[2022-03-23 14:24:55.023802] INFO: Position: position stock handle split[sid:1975, orig_amount:12700, new_amount:12725.0, orig_cost:5.040000746643777, new_cost:5.0298, ratio:0.9979838132858276, last_sale_price:4.950000286102295]

[2022-03-23 14:24:55.024887] INFO: Position: after split: PositionStock(asset:Equity(1975 [300165.SZA]), amount:12725.0, cost_basis:5.0298, last_sale_price:4.960000514984131)

[2022-03-23 14:24:55.026023] INFO: Position: returning cash: 3.2536

[2022-03-23 14:24:55.105682] INFO: algo: handle_splits get splits [dt:2021-06-29 00:00:00+00:00] [asset:Equity(830 [002981.SZA]), ratio:0.9980751872062683]

[2022-03-23 14:24:55.107225] INFO: Position: position stock handle split[sid:830, orig_amount:5200, new_amount:5210.0, orig_cost:25.890069343720512, new_cost:25.8402, ratio:0.9980751872062683, last_sale_price:25.929994583129883]

[2022-03-23 14:24:55.108424] INFO: Position: after split: PositionStock(asset:Equity(830 [002981.SZA]), amount:5210.0, cost_basis:25.8402, last_sale_price:25.98000144958496)

[2022-03-23 14:24:55.109503] INFO: Position: returning cash: 0.7346

[2022-03-23 14:24:59.394670] INFO: Performance: Simulated 243 trading days out of 243.

[2022-03-23 14:24:59.396260] INFO: Performance: first open: 2021-01-04 09:30:00+00:00

[2022-03-23 14:24:59.397404] INFO: Performance: last close: 2021-12-31 15:00:00+00:00

[2022-03-23 14:25:02.245034] INFO: moduleinvoker: backtest.v8 运行完成[14.75174s].

[2022-03-23 14:25:02.246893] INFO: moduleinvoker: trade.v4 运行完成[14.799393s].

设置测试数据集，查看训练迭代过程的NDCG

bigcharts-data-start/{"__type":"tabs","__id":"bigchart-ed880febe9dd42bc8cc5d5667e2dc3b4"}/bigcharts-data-end

设置测试数据集，查看训练迭代过程的NDCG

bigcharts-data-start/{"__type":"tabs","__id":"bigchart-95a147ad0ae34325a3753219ca661ebd"}/bigcharts-data-end

收益率66.25%
年化收益率69.41%
基准收益率-5.2%
阿尔法0.73
贝塔0.28
夏普比率2.13
胜率0.51
盈亏比1.5
收益波动率24.86%
信息比率0.14
最大回撤15.9%

bigcharts-data-start/{"__type":"tabs","__id":"bigchart-d98f2ab1a2474234ba8d7c8bfc87bac9"}/bigcharts-data-end