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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 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 = list(ranker_prediction.instrument[:len(buy_cash_weights)])\n max_cash_per_instrument = context.portfolio.portfolio_value * context.max_cash_per_instrument\n for i, instrument in enumerate(buy_instruments):\n cash = cash_for_buy * buy_cash_weights[i]\n if cash > max_cash_per_instrument - positions.get(instrument, 0):\n # 确保股票持仓量不会超过每次股票最大的占用资金量\n cash = max_cash_per_instrument - positions.get(instrument, 0)\n if cash > 0:\n context.order_value(context.symbol(instrument), cash)\n #----------------------------START：持有固定天数卖出---------------------------\n today = data.current_dt\n # 不是建仓期（在前hold_days属于建仓期）\n if not is_staging:\n equities = {e.symbol: p for e, p in context.portfolio.positions.items() if p.amount>0}\n for instrument in equities:\n# 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    In [4]:
    # 本代码由可视化策略环境自动生成 2022年3月9日 16:38
# 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 m24_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 = 1
    # 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 = 0.5
    context.options['hold_days'] = 0

# 回测引擎：每日数据处理函数，每天执行一次
def m24_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)
     #----------------------------START：持有固定天数卖出---------------------------
    today = data.current_dt
    # 不是建仓期（在前hold_days属于建仓期）
    if not is_staging:
        equities = {e.symbol: p for e, p in context.portfolio.positions.items() if p.amount>0}
        for instrument in equities:
#             print('last_sale_date: ', equities[instrument].last_sale_date)
            sid = equities[instrument].sid  # 交易标的
            # 今天和上次交易的时间相隔hold_days就全部卖出
            if today-equities[instrument].last_sale_date>=datetime.timedelta(context.options['hold_days']) and data.can_trade(context.symbol(instrument)):
                context.order_target_percent(sid, 0)
    #--------------------------------END:持有固定天数卖出---------------------------  

# 回测引擎：准备数据，只执行一次
def m24_prepare_bigquant_run(context):
    pass


m1 = M.instruments.v2(
    start_date='2015-01-01',
    end_date='2022-02-01',
    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 {{web_host_url}}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 <{{web_host_url}}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, -2) / shift(open, -1)
1000*label
# 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="""con=(return_1>1.095)|(return_0>1.095)|(return_2>1.095)|(return_3>1.095)
avg_mf_net_amount_5
avg_turn_0
avg_turn_13
avg_turn_5
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
(open_0/2+close_0/2-low_0)/open_0*10
(close_0-open_0)/open_0
(high_0-open_0/2-close_0/2)/(close_0-open_0)
rank((high_0-open_0/2-close_0/2)/(close_0-open_0))
(high_0+low_0)/close_1
((high_0+low_0)/close_1)/((high_1+low_1)/close_2)
alpha46=(mean(close_0,3)+mean(close_0,6)+mean(close_0,12)+mean(close_0,24))/(4*close_0)
alpha34=mean(close_0,12)/close_0
alpha65=mean(close_0,6)/close_0
alpha168=(-1*volume_0/mean(volume_0,20))
rank(std(volume_0,5))"""
)

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

m10 = M.chinaa_stock_filter.v1(
    input_data=m15.data,
    index_constituent_cond=['全部'],
    board_cond=['上证主板', '深证主板', '创业板'],
    industry_cond=['全部'],
    st_cond=['正常'],
    delist_cond=['非退市'],
    output_left_data=False
)

m16 = M.derived_feature_extractor.v3(
    input_data=m10.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
)

m4 = M.dropnan.v2(
    input_data=m7.data
)

m21 = M.filter.v3(
    input_data=m4.data,
    expr='con==1',
    output_left_data=False
)

m9 = M.rename_columns.v5(
    input_ds=m21.data,
    columns='label:ret',
    keep_old_columns=False
)

m6 = M.select_columns.v3(
    input_ds=m9.data,
    columns='date,instrument,ret',
    reverse_select=False
)

m8 = M.auto_labeler_on_datasource.v1(
    input_data=m6.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
# 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)
ret


# 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)
""",
    drop_na_label=True,
    cast_label_int=True,
    date_col='date',
    instrument_col='instrument',
    user_functions={}
)

m11 = M.join.v3(
    data1=m8.data,
    data2=m9.data,
    on='date,instrument',
    how='left',
    sort=False
)

m12 = M.filter.v3(
    input_data=m11.data,
    expr='date<\'2021-01-01\'',
    output_left_data=True
)

m13 = M.input_features.v1(
    features="""rank_return_10
alpha46
rank_return_0
rank(std(volume_0,5))
#317
alpha65
return_5
avg_turn_13
(close_0-open_0)/open_0
rank_return_5/rank_return_10
alpha168
rank_avg_amount_0/rank_avg_amount_5
(high_0+low_0)/close_1
avg_turn_5
pe_ttm_0
(high_0-open_0/2-close_0/2)/(close_0-open_0)
#avg_amount_0/avg_amount_5#304
avg_mf_net_amount_5
((high_0+low_0)/close_1)/((high_1+low_1)/close_2)
alpha34
return_10"""
)

m17 = M.stock_ranker_train.v6(
    training_ds=m12.data,
    features=m13.data,
    test_ds=m12.left_data,
    learning_algorithm='排序',
    number_of_leaves=30,
    minimum_docs_per_leaf=1000,
    number_of_trees=304,
    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
)

m5 = M.instruments.v2(
    start_date='2021-01-01',
    end_date='2022-02-18',
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m14 = M.general_feature_extractor.v7(
    instruments=m5.data,
    features=m3.data,
    start_date='',
    end_date='',
    before_start_days=60
)

m18 = M.chinaa_stock_filter.v1(
    input_data=m14.data,
    index_constituent_cond=['全部'],
    board_cond=['上证主板', '深证主板', '创业板'],
    industry_cond=['全部'],
    st_cond=['正常'],
    delist_cond=['非退市'],
    output_left_data=False
)

m19 = M.derived_feature_extractor.v3(
    input_data=m18.data,
    features=m3.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False
)

m20 = M.dropnan.v2(
    input_data=m19.data
)

m22 = M.filter.v3(
    input_data=m20.data,
    expr='con==1',
    output_left_data=False
)

m23 = M.stock_ranker_predict.v5(
    model=m17.model,
    data=m22.data,
    m_lazy_run=False
)

m24 = M.trade.v4(
    instruments=m5.data,
    options_data=m23.predictions,
    start_date='',
    end_date='',
    initialize=m24_initialize_bigquant_run,
    handle_data=m24_handle_data_bigquant_run,
    prepare=m24_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'
)

    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-fd3933669737443c9673ca7e709b4aef"}/bigcharts-data-end
                date  instrument     score  position
70698 2021-12-02  002068.SZA  3.794121         1
70942 2021-12-03  300311.SZA  3.040658         1
71185 2021-12-06  002011.SZA  2.692164         1
71358 2021-12-07  002232.SZA  3.062349         1
71530 2021-12-08  002350.SZA  3.040164         1
71759 2021-12-09  300675.SZA  3.778803         1
72018 2021-12-10  300264.SZA  3.392921         1
72301 2021-12-13  300707.SZA  3.016045         1
72643 2021-12-14  002232.SZA  4.016856         1
72968 2021-12-15  301128.SZA  4.284978         1
73286 2021-12-16  300860.SZA  2.938416         1
73618 2021-12-17  300612.SZA  4.113022         1
73885 2021-12-20  600854.SHA  3.512947         1
74107 2021-12-21  001213.SZA  3.569673         1
74385 2021-12-22  000795.SZA  5.201557         1
74651 2021-12-23  300148.SZA  3.061783         1
74931 2021-12-24  300987.SZA  5.172772         1
75160 2021-12-27  000812.SZA  2.696468         1
75388 2021-12-28  300436.SZA  3.623608         1
75579 2021-12-29  605089.SHA  4.921631         1
75804 2021-12-30  300467.SZA  3.365579         1
76091 2021-12-31  002565.SZA  4.292991         1
76353 2022-01-04  300818.SZA  3.766533         1
76683 2022-01-05  300026.SZA  3.974291         1
76963 2022-01-06  300389.SZA  4.602974         1
77237 2022-01-07  300094.SZA  3.437771         1
77405 2022-01-10  300967.SZA  3.963716         1
77606 2022-01-11  300534.SZA  3.387520         1
77814 2022-01-12  300261.SZA  3.992723         1
78053 2022-01-13  002750.SZA  4.259545         1
78274 2022-01-14  002316.SZA  3.236353         1
78510 2022-01-17  002584.SZA  2.504038         1
78886 2022-01-18  301089.SZA  4.665848         1
79126 2022-01-19  301089.SZA  5.010256         1
79391 2022-01-20  300043.SZA  4.587756         1
79567 2022-01-21  603716.SHA  4.633064         1
79676 2022-01-24  600847.SHA  6.702845         1
79797 2022-01-25  003042.SZA  3.882778         1
79873 2022-01-26  300399.SZA  4.052784         1
79988 2022-01-27  301089.SZA  4.792202         1
80032 2022-01-28  002348.SZA  4.230647         1
80133 2022-02-07  300546.SZA  4.213878         1
80385 2022-02-08  000622.SZA  4.812170         1
80928 2022-02-09  000812.SZA  2.802360         1
81786 2022-02-10  002197.SZA  4.516495         1
82400 2022-02-11  002721.SZA  3.816142         1
82660 2022-02-14  300468.SZA  4.343320         1
82848 2022-02-15  002761.SZA  5.109610         1
83017 2022-02-16  300541.SZA  3.385604         1
83250 2022-02-17  300387.SZA  4.202282         1
83523 2022-02-18  300921.SZA  4.268337         1
    • 收益率1613.98%
    • 年化收益率1290.82%
    • 基准收益率-10.75%
    • 阿尔法18.16
    • 贝塔0.58
    • 夏普比率3.78
    • 胜率0.52
    • 盈亏比1.61
    • 收益波动率76.7%
    • 信息比率0.25
    • 最大回撤33.7%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-69ae86f3ae98428591f8336c2a0294ab"}/bigcharts-data-end
    In [ ]: