使用沪深300数据进行可视化策略

策略分享
标签: #<Tag:0x00007ff195ee9970>
(zxm) #1
克隆策略
In [30]:
DataSource("index_constituent_CN_STOCK_A").read().head()
Out[30]:
date in_csi100 in_csi300 in_csi500 in_csi800 in_sse180 in_sse50 in_szse100 instrument
0 2005-01-04 0 0 0 0 1 1 0 600000.SHA
1 2005-01-04 0 0 0 0 1 0 0 600333.SHA
2 2005-01-04 0 0 0 0 1 1 0 600002.SHA
3 2005-01-04 0 0 0 0 1 1 0 600004.SHA
4 2005-01-04 0 0 0 0 1 1 0 600005.SHA

    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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.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 context.portfolio.positions.items()}\n instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(\n lambda x: x in equities)])))\n\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. 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    In [31]:
    # 本代码由可视化策略环境自动生成 2020年12月29日 15:15
# 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 m11_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 = DataSource("index_constituent_CN_STOCK_A").read() # 读取A股指数成分表，具体信息见 数据文档-A股-其他常规数据-A股指数成分
    df_csi300 = df[df.in_csi300==1] #选取 沪深300 的记录
    ins_list = df_csi300.instrument.tolist() # 获取对应的 instruments列表
    data_1 = {'end_date': '2015-01-01','instruments':ins_list,'start_date':'2010-01-01'} #输出格式和原代码列表模块输出格式保持一致，代码列表输出格式可通过 m1.data.read()看到
    return Outputs(data_1=data_1, data_2=None, data_3=None) #只需要用到一个输出端，可以把另外两个设为 None。 最后在可视化模块里用输出端口1分别连接 自动标注模块(m2), 基础特征抽取模块(m15)，即可。

# 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 m11_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)])
    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


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
"""
)

m9 = M.instruments.v2(
    start_date=T.live_run_param('trading_date', '2015-01-01'),
    end_date=T.live_run_param('trading_date', '2017-01-01'),
    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
)

m10 = M.dropnan.v2(
    input_data=m18.data
)

m11 = M.cached.v3(
    run=m11_run_bigquant_run,
    post_run=m11_post_run_bigquant_run,
    input_ports='',
    params='{}',
    output_ports=''
)

m2 = M.advanced_auto_labeler.v2(
    instruments=m11.data_1,
    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=True
)

m15 = M.general_feature_extractor.v7(
    instruments=m11.data_1,
    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
)

m5 = M.dropnan.v2(
    input_data=m7.data
)

m4 = M.stock_ranker_train.v6(
    training_ds=m5.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,
    ndcg_discount_base=1,
    m_lazy_run=False
)

m8 = M.stock_ranker_predict.v5(
    model=m4.model,
    data=m10.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.SHA'
)
    设置评估测试数据集，查看训练曲线
    [视频教程]StockRanker训练曲线 
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-ec87cce110c8459e82e1f59f290e26b2"}/bigcharts-data-end
    • 收益率133.22%
    • 年化收益率54.85%
    • 基准收益率-6.33%
    • 阿尔法0.51
    • 贝塔1.02
    • 夏普比率1.17
    • 胜率0.59
    • 盈亏比0.84
    • 收益波动率42.55%
    • 信息比率0.12
    • 最大回撤57.93% 
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-a3623e638190412c84bf04582f5bc52d"}/bigcharts-data-end
    In [32]:
    m1.data.read()
    Out[32]:
    {'end_date': '2015-01-01',
 'instruments': ['000001.SZA',
  '000002.SZA',
  '000004.SZA',
  '000005.SZA',
  '000006.SZA',
  '000007.SZA',
  '000008.SZA',
  '000009.SZA',
  '000010.SZA',
  '000011.SZA',
  '000012.SZA',
  '000014.SZA',
  '000016.SZA',
  '000017.SZA',
  '000018.SZA',
  '000019.SZA',
  '000020.SZA',
  '000021.SZA',
  '000022.SZA',
  '000023.SZA',
  '000024.SZA',
  '000025.SZA',
  '000026.SZA',
  '000027.SZA',
  '000028.SZA',
  '000029.SZA',
  '000030.SZA',
  '000031.SZA',
  '000032.SZA',
  '000033.SZA',
  '000034.SZA',
  '000035.SZA',
  '000036.SZA',
  '000037.SZA',
  '000038.SZA',
  '000039.SZA',
  '000040.SZA',
  '000042.SZA',
  '000043.SZA',
  '000045.SZA',
  '000046.SZA',
  '000048.SZA',
  '000049.SZA',
  '000050.SZA',
  '000055.SZA',
  '000056.SZA',
  '000058.SZA',
  '000059.SZA',
  '000060.SZA',
  '000061.SZA',
  '000062.SZA',
  '000063.SZA',
  '000065.SZA',
  '000066.SZA',
  '000068.SZA',
  '000069.SZA',
  '000070.SZA',
  '000078.SZA',
  '000088.SZA',
  '000089.SZA',
  '000090.SZA',
  '000096.SZA',
  '000099.SZA',
  '000100.SZA',
  '000150.SZA',
  '000151.SZA',
  '000153.SZA',
  '000155.SZA',
  '000156.SZA',
  '000157.SZA',
  '000158.SZA',
  '000159.SZA',
  '000301.SZA',
  '000333.SZA',
  '000338.SZA',
  '000400.SZA',
  '000401.SZA',
  '000402.SZA',
  '000403.SZA',
  '000404.SZA',
  '000407.SZA',
  '000408.SZA',
  '000409.SZA',
  '000410.SZA',
  '000411.SZA',
  '000413.SZA',
  '000415.SZA',
  '000416.SZA',
  '000417.SZA',
  '000418.SZA',
  '000419.SZA',
  '000420.SZA',
  '000421.SZA',
  '000422.SZA',
  '000423.SZA',
  '000425.SZA',
  '000426.SZA',
  '000428.SZA',
  '000429.SZA',
  '000430.SZA',
  '000488.SZA',
  '000498.SZA',
  '000501.SZA',
  '000502.SZA',
  '000503.SZA',
  '000504.SZA',
  '000505.SZA',
  '000506.SZA',
  '000507.SZA',
  '000509.SZA',
  '000510.SZA',
  '000511.SZA',
  '000513.SZA',
  '000514.SZA',
  '000516.SZA',
  '000517.SZA',
  '000518.SZA',
  '000519.SZA',
  '000520.SZA',
  '000521.SZA',
  '000522.SZA',
  '000523.SZA',
  '000524.SZA',
  '000525.SZA',
  '000526.SZA',
  '000527.SZA',
  '000528.SZA',
  '000529.SZA',
  '000530.SZA',
  '000531.SZA',
  '000532.SZA',
  '000533.SZA',
  '000534.SZA',
  '000536.SZA',
  '000537.SZA',
  '000538.SZA',
  '000539.SZA',
  '000540.SZA',
  '000541.SZA',
  '000543.SZA',
  '000544.SZA',
  '000545.SZA',
  '000546.SZA',
  '000547.SZA',
  '000548.SZA',
  '000550.SZA',
  '000551.SZA',
  '000552.SZA',
  '000553.SZA',
  '000554.SZA',
  '000555.SZA',
  '000557.SZA',
  '000558.SZA',
  '000559.SZA',
  '000560.SZA',
  '000561.SZA',
  '000562.SZA',
  '000563.SZA',
  '000564.SZA',
  '000565.SZA',
  '000566.SZA',
  '000567.SZA',
  '000568.SZA',
  '000570.SZA',
  '000571.SZA',
  '000572.SZA',
  '000573.SZA',
  '000576.SZA',
  '000578.SZA',
  '000581.SZA',
  '000582.SZA',
  '000584.SZA',
  '000585.SZA',
  '000586.SZA',
  '000587.SZA',
  '000589.SZA',
  '000590.SZA',
  '000591.SZA',
  '000592.SZA',
  '000593.SZA',
  '000594.SZA',
  '000595.SZA',
  '000596.SZA',
  '000597.SZA',
  '000598.SZA',
  '000599.SZA',
  '000600.SZA',
  '000601.SZA',
  '000602.SZA',
  '000603.SZA',
  '000605.SZA',
  '000606.SZA',
  '000607.SZA',
  '000608.SZA',
  '000609.SZA',
  '000610.SZA',
  '000611.SZA',
  '000612.SZA',
  '000613.SZA',
  '000615.SZA',
  '000616.SZA',
  '000617.SZA',
  '000619.SZA',
  '000620.SZA',
  '000622.SZA',
  '000623.SZA',
  '000625.SZA',
  '000626.SZA',
  '000627.SZA',
  '000628.SZA',
  '000629.SZA',
  '000630.SZA',
  '000631.SZA',
  '000632.SZA',
  '000633.SZA',
  '000635.SZA',
  '000636.SZA',
  '000637.SZA',
  '000638.SZA',
  '000639.SZA',
  '000650.SZA',
  '000651.SZA',
  '000652.SZA',
  '000655.SZA',
  '000656.SZA',
  '000657.SZA',
  '000659.SZA',
  '000661.SZA',
  '000662.SZA',
  '000663.SZA',
  '000665.SZA',
  '000666.SZA',
  '000667.SZA',
  '000668.SZA',
  '000669.SZA',
  '000670.SZA',
  '000671.SZA',
  '000672.SZA',
  '000673.SZA',
  '000676.SZA',
  '000677.SZA',
  '000678.SZA',
  '000679.SZA',
  '000680.SZA',
  '000681.SZA',
  '000682.SZA',
  '000683.SZA',
  '000685.SZA',
  '000686.SZA',
  '000687.SZA',
  '000688.SZA',
  '000690.SZA',
  '000691.SZA',
  '000692.SZA',
  '000693.SZA',
  '000695.SZA',
  '000697.SZA',
  '000698.SZA',
  '000700.SZA',
  '000701.SZA',
  '000702.SZA',
  '000703.SZA',
  '000705.SZA',
  '000707.SZA',
  '000708.SZA',
  '000709.SZA',
  '000710.SZA',
  '000711.SZA',
  '000712.SZA',
  '000713.SZA',
  '000715.SZA',
  '000716.SZA',
  '000717.SZA',
  '000718.SZA',
  '000719.SZA',
  '000720.SZA',
  '000721.SZA',
  '000722.SZA',
  '000723.SZA',
  '000725.SZA',
  '000726.SZA',
  '000727.SZA',
  '000728.SZA',
  '000729.SZA',
  '000731.SZA',
  '000732.SZA',
  '000733.SZA',
  '000735.SZA',
  '000736.SZA',
  '000737.SZA',
  '000738.SZA',
  '000739.SZA',
  '000748.SZA',
  '000750.SZA',
  '000751.SZA',
  '000752.SZA',
  '000753.SZA',
  '000755.SZA',
  '000756.SZA',
  '000757.SZA',
  '000758.SZA',
  '000759.SZA',
  '000760.SZA',
  '000761.SZA',
  '000762.SZA',
  '000766.SZA',
  '000767.SZA',
  '000768.SZA',
  '000776.SZA',
  '000777.SZA',
  '000778.SZA',
  '000779.SZA',
  '000780.SZA',
  '000782.SZA',
  '000783.SZA',
  '000785.SZA',
  '000786.SZA',
  '000787.SZA',
  '000788.SZA',
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  '002490.SZA',
  '002491.SZA',
  '002492.SZA',
  '002493.SZA',
  '002494.SZA',
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  '002496.SZA',
  '002497.SZA',
  '002498.SZA',
  '002499.SZA',
  '002500.SZA',
  '002501.SZA',
  '002502.SZA',
  '002503.SZA',
  '002504.SZA',
  '002505.SZA',
  '002506.SZA',
  '002507.SZA',
  '002508.SZA',
  '002509.SZA',
  '002510.SZA',
  '002511.SZA',
  '002512.SZA',
  '002513.SZA',
  '002514.SZA',
  '002515.SZA',
  '002516.SZA',
  '002517.SZA',
  '002518.SZA',
  '002519.SZA',
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  ...],
 'start_date': '2010-01-01'}