复制链接
克隆策略
In [12]:
df = m11.data.read()
#分箱
col_name = 'pe_ttm_bins'
def cal_bins(df):
    bins=5
    df[col_name] = np.array(pd.qcut(df.pe_ttm_0, bins, labels=range(0, bins)))
    return df
df = df.groupby('date').apply(cal_bins)
display("分箱结果：",df[['date','instrument','pe_ttm_0','pe_ttm_bins']])
#换成one-hot编码
df = pd.get_dummies(df,columns=[col_name])
display("one-hot编码结果：",df[['date','instrument','pe_ttm_0','pe_ttm_bins_0','pe_ttm_bins_1','pe_ttm_bins_2','pe_ttm_bins_3']])

'分箱结果：'
date instrument pe_ttm_0 pe_ttm_bins
0 2010-01-04 000001.SZA 78.793991 3
0 2011-01-04 000001.SZA 9.111742 0
0 2012-01-04 000001.SZA 8.408521 0
0 2013-01-04 000001.SZA 6.385547 0
0 2014-01-02 000001.SZA 6.746183 0
... ... ... ... ...
564577 2012-12-25 603993.SHA 35.913765 3
564578 2012-12-26 603993.SHA 37.037422 3
564579 2012-12-27 603993.SHA 35.568024 3
564580 2012-12-28 603993.SHA 35.654461 3
564581 2012-12-31 603993.SHA 36.000198 3

2606084 rows × 4 columns

'one-hot编码结果：'
date instrument pe_ttm_0 pe_ttm_bins_0 pe_ttm_bins_1 pe_ttm_bins_2 pe_ttm_bins_3
0 2010-01-04 000001.SZA 78.793991 0 0 0 1
0 2011-01-04 000001.SZA 9.111742 1 0 0 0
0 2012-01-04 000001.SZA 8.408521 1 0 0 0
0 2013-01-04 000001.SZA 6.385547 1 0 0 0
0 2014-01-02 000001.SZA 6.746183 1 0 0 0
... ... ... ... ... ... ... ...
564577 2012-12-25 603993.SHA 35.913765 0 0 0 1
564578 2012-12-26 603993.SHA 37.037422 0 0 0 1
564579 2012-12-27 603993.SHA 35.568024 0 0 0 1
564580 2012-12-28 603993.SHA 35.654461 0 0 0 1
564581 2012-12-31 603993.SHA 36.000198 0 0 0 1

2606084 rows × 7 columns

In [16]:
df = m15.data_1.read()
df[['date','instrument','pe_ttm_0','pe_ttm_bins_0','pe_ttm_bins_1','pe_ttm_bins_2','pe_ttm_bins_3']]
Out[16]:
date instrument pe_ttm_0 pe_ttm_bins_0 pe_ttm_bins_1 pe_ttm_bins_2 pe_ttm_bins_3
0 2010-01-04 000001.SZA 78.793991 0 0 0 1
0 2011-01-04 000001.SZA 9.111742 1 0 0 0
0 2012-01-04 000001.SZA 8.408521 1 0 0 0
0 2013-01-04 000001.SZA 6.385547 1 0 0 0
0 2014-01-02 000001.SZA 6.746183 1 0 0 0
... ... ... ... ... ... ... ...
564577 2012-12-25 603993.SHA 35.913765 0 0 0 1
564578 2012-12-26 603993.SHA 37.037422 0 0 0 1
564579 2012-12-27 603993.SHA 35.568024 0 0 0 1
564580 2012-12-28 603993.SHA 35.654461 0 0 0 1
564581 2012-12-31 603993.SHA 36.000198 0 0 0 1

2606084 rows × 7 columns

In [17]:
df = m15.data_2.read()
df
Out[17]:
['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_bins_0',
 'pe_ttm_bins_1',
 'pe_ttm_bins_2',
 'pe_ttm_bins_3',
 'pe_ttm_bins_4']

    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回测引擎：初始化函数，只执行一次\ndef bigquant_run(context):\n # 加载预测数据\n 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=0.0003, 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.hold_days = 5\n","type":"Literal","bound_global_parameter":null},{"name":"handle_data","value":"# 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.hold_days # 是否在建仓期间（前 hold_days 天）\n cash_avg = context.portfolio.portfolio_value / context.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StockRanker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 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bigquant_run(context, data):\n 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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\ndef bigquant_run(input_1, input_2, input_3):\n # 示例代码如下。在这里编写您的代码\n #分箱\n col_name = 'pe_ttm_bins'\n def cal_bins(df):\n bins=5\n df[col_name] = np.array(pd.qcut(df.pe_ttm_0, bins, labels=range(0, bins)))\n return df\n df = input_1.read().groupby('date').apply(cal_bins)\n #换成one-hot编码\n df = pd.get_dummies(df,columns=[col_name])\n cols = df.columns\n need_cols = [c for c in cols if col_name in c]\n features = input_2.read()\n features = features + need_cols\n features.remove('pe_ttm_0')\n features = DataSource.write_pickle(features)\n return Outputs(data_1=DataSource.write_df(df), data_2=features, data_3=None)\n","type":"Literal","bound_global_parameter":null},{"name":"post_run","value":"# 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。\ndef bigquant_run(outputs):\n return 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    In [2]:
    # 本代码由可视化策略环境自动生成 2022年11月5日 15:11
# 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 m15_run_bigquant_run(input_1, input_2, input_3):
    # 示例代码如下。在这里编写您的代码
    #分箱
    col_name = 'pe_ttm_bins'
    def cal_bins(df):
        bins=5
        df[col_name] = np.array(pd.qcut(df.pe_ttm_0, bins, labels=range(0, bins)))
        return df
    df = input_1.read().groupby('date').apply(cal_bins)
    #换成one-hot编码
    df = pd.get_dummies(df,columns=[col_name])
    cols = df.columns
    need_cols = [c for c in cols if col_name in c]
    features = input_2.read()
    features = features + need_cols
    features.remove('pe_ttm_0')
    features = DataSource.write_pickle(features)
    return Outputs(data_1=DataSource.write_df(df), data_2=features, 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 m15_post_run_bigquant_run(outputs):
    return outputs

# 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 m16_run_bigquant_run(input_1, input_2, input_3):
    # 示例代码如下。在这里编写您的代码
    col_name = 'pe_ttm_bins'
    def cal_bins(df):
        bins=5
        df[col_name] = np.array(pd.qcut(df.pe_ttm_0, bins, labels=range(0, bins)))
        return df
    df = input_1.read().groupby('date').apply(cal_bins)
    df = pd.get_dummies(df,columns=[col_name])
#     cols = df.columns
#     need_cols = [c for c in cols if col_name in c]
#     features = input_2.read()
#     features = features + need_cols
#     features = DataSource.write_pickle(features)
    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 m16_post_run_bigquant_run(outputs):
    return outputs

# 回测引擎：初始化函数，只执行一次
def m13_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.hold_days = 5

# 回测引擎：每日数据处理函数，每天执行一次
def m13_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.hold_days # 是否在建仓期间（前 hold_days 天）
    cash_avg = context.portfolio.portfolio_value / context.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StockRanker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StockRanker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 m13_prepare_bigquant_run(context):
    pass

# 回测引擎：每个单位时间开始前调用一次，即每日开盘前调用一次。
def m13_before_trading_start_bigquant_run(context, data):
    pass


m1 = M.instruments.v2(
    start_date='2010-01-01',
    end_date='2014-12-31',
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m8 = 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.SHA',
    drop_na_label=True,
    cast_label_int=False,
    user_functions={},
    m_cached=False
)

m2 = 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
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
"""
)

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

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

m9 = M.join.v3(
    data1=m8.data,
    data2=m6.data,
    on='date,instrument',
    how='inner',
    sort=False
)

m11 = M.dropnan.v1(
    input_data=m9.data
)

m15 = M.cached.v3(
    input_1=m11.data,
    input_2=m2.data,
    run=m15_run_bigquant_run,
    post_run=m15_post_run_bigquant_run,
    input_ports='',
    params='{}',
    output_ports=''
)

m4 = M.instruments.v2(
    start_date='2015-01-01',
    end_date='2017-01-01',
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m5 = M.general_feature_extractor.v7(
    instruments=m4.data,
    features=m2.data,
    start_date='',
    end_date='',
    before_start_days=90
)

m7 = M.derived_feature_extractor.v3(
    input_data=m5.data,
    features=m2.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False,
    user_functions={}
)

m10 = M.dropnan.v1(
    input_data=m7.data
)

m16 = M.cached.v3(
    input_1=m10.data,
    run=m16_run_bigquant_run,
    post_run=m16_post_run_bigquant_run,
    input_ports='',
    params='{}',
    output_ports=''
)

m12 = M.random_forest_regressor.v1(
    training_ds=m15.data_1,
    features=m15.data_2,
    predict_ds=m16.data_1,
    iterations=10,
    feature_fraction=1,
    max_depth=30,
    min_samples_per_leaf=200,
    key_cols='date,instrument',
    workers=1,
    random_state=0,
    other_train_parameters={"random_state":0}
)

m14 = M.sort.v4(
    input_ds=m12.predictions,
    sort_by='pred_label',
    group_by='date',
    keep_columns='--',
    ascending=False
)

m13 = M.trade.v4(
    instruments=m4.data,
    options_data=m14.sorted_data,
    start_date='',
    end_date='',
    initialize=m13_initialize_bigquant_run,
    handle_data=m13_handle_data_bigquant_run,
    prepare=m13_prepare_bigquant_run,
    before_trading_start=m13_before_trading_start_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=''
)
    • 收益率199.14%
    • 年化收益率76.09%
    • 基准收益率-6.33%
    • 阿尔法0.9
    • 贝塔0.99
    • 夏普比率1.45
    • 胜率0.6
    • 盈亏比0.85
    • 收益波动率43.53%
    • 信息比率0.14
    • 最大回撤48.37%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-d3dae2e7094646df8feb3af092d77ab0"}/bigcharts-data-end