一个简单的策略

策略分享
标签: #<Tag:0x00007fcf6ab6c0d8>

(hugo) #1
克隆策略

市值因子+FFscores+限价止损做过一个传统的策略选股,不知道用特征因子会怎样所以做了这个
这个是算是做了玩的的吧,只是 市值因子+FFscores FFscores的模型选股标准本来因该是:

  • 1 资产收益率为正;
  • 2 经营现金流量为正;
  • 3 资产收益率同比增加;
  • 4 收益自然增长率为正;
  • 5 长期资产负债率同比减少;
  • 6 流动比率同比增加;
  • 7 股本等于或者小于同比的股本;
  • 8 毛利率同比增加;
  • 9 资产周转率同比增加 但是我懒的再去自己构建一些因子了,所以找了相似的....
In [1]:
# 基础参数配置
class conf:
    start_date = '2010-01-01'
    end_date='2017-11-21'
    split_date = '2015-01-01'

    instruments = D.instruments(start_date, split_date)


    label_expr = ['return * 100', 'where(label > {0}, {0}, where(label < -{0}, -{0}, label)) + {0}'.format(20)]
    hold_days = 5


    features = [
        'rank_market_cap_0',
        'fs_roa_0',
        'fs_net_cash_flow_0',
        'fs_net_cash_flow_0-fs_total_profit_0',
        'fs_non_current_liabilities_0/fs_non_current_assets_0',
        'fs_current_assets_0/fs_current_liabilities_0',
        'fs_gross_profit_margin_0',
        'fs_operating_revenue_0/(fs_operating_revenue_0+fs_non_current_assets_0)',
    ]


m1 = M.fast_auto_labeler.v8(
    instruments=conf.instruments, start_date=conf.start_date, end_date=conf.split_date,
    label_expr=conf.label_expr, hold_days=conf.hold_days,
    benchmark='000300.SHA', sell_at='open', buy_at='open')

m2 = M.general_feature_extractor.v5(
    instruments=conf.instruments, start_date=conf.start_date, end_date=conf.split_date,
    features=conf.features)

m3 = M.transform.v2(
    data=m2.data, transforms=T.get_stock_ranker_default_transforms(),
    drop_null=True, astype='int32', except_columns=['date', 'instrument'],
    clip_lower=0, clip_upper=200000000)

m4 = M.join.v2(data1=m1.data, data2=m3.data, on=['date', 'instrument'], sort=True)

m5 = M.stock_ranker_train.v3(training_ds=m4.data, features=conf.features)



def prepare(context):

    n1 = M.general_feature_extractor.v5(
        instruments=D.instruments(),
        start_date=context.start_date, end_date=context.end_date,
        model_id=context.options['model_id'])
    n2 = M.transform.v2(
        data=n1.data, transforms=T.get_stock_ranker_default_transforms(),
        drop_null=True, astype='int32', except_columns=['date', 'instrument'],
        clip_lower=0, clip_upper=200000000)
    n3 = M.stock_ranker_predict.v2(model_id=context.options['model_id'], data=n2.data)
    context.instruments = n3.instruments
    context.options['predictions'] = n3.predictions


def initialize(context):

    context.ranker_prediction = context.options['predictions'].read_df()
    context.set_commission(PerOrder(buy_cost=0.0003, sell_cost=0.0013, min_cost=5))
    stock_count = 5
    context.stock_weights = T.norm([1 / math.log(i + 2) for i in range(0, stock_count)])
    context.max_cash_per_instrument = 0.2


def handle_data(context, data):

    ranker_prediction = context.ranker_prediction[
        context.ranker_prediction.date == data.current_dt.strftime('%Y-%m-%d')]


    is_staging = context.trading_day_index < context.options['hold_days'] 
    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()}


    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


    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):

            cash = max_cash_per_instrument - positions.get(instrument, 0)
        if cash > 0:
            context.order_value(context.symbol(instrument), cash)



m6 = M.trade.v2(
    instruments=None,
    start_date=conf.split_date,
    end_date=conf.end_date,
    prepare=prepare,
    initialize=initialize,
    handle_data=handle_data,
    order_price_field_buy='open',       # 表示 开盘 时买入
    order_price_field_sell='close',     # 表示 收盘 前卖出
    capital_base=1000000,               # 初始资金
    benchmark='000300.SHA',             # 比较基准,不影响回测结果
    # 通过 options 参数传递预测数据和参数给回测引擎
    options={'hold_days': conf.hold_days, 'model_id': m5.model_id}
)
[2017-12-08 18:32:03.718811] INFO: bigquant: fast_auto_labeler.v8 开始运行..
[2017-12-08 18:32:03.740785] INFO: bigquant: 命中缓存
[2017-12-08 18:32:03.758037] INFO: bigquant: fast_auto_labeler.v8 运行完成[0.039242s].
[2017-12-08 18:32:03.805043] INFO: bigquant: general_feature_extractor.v5 开始运行..
[2017-12-08 18:32:14.071873] INFO: general_feature_extractor: year 2010, featurerows=431567
[2017-12-08 18:32:28.374085] INFO: general_feature_extractor: year 2011, featurerows=511455
[2017-12-08 18:32:58.795263] INFO: general_feature_extractor: year 2012, featurerows=565675
[2017-12-08 18:33:29.206950] INFO: general_feature_extractor: year 2013, featurerows=564168
[2017-12-08 18:33:59.827244] INFO: general_feature_extractor: year 2014, featurerows=569948
[2017-12-08 18:34:29.709223] INFO: general_feature_extractor: year 2015, featurerows=0
[2017-12-08 18:34:29.726874] INFO: general_feature_extractor: total feature rows: 2642813
[2017-12-08 18:34:29.728865] INFO: bigquant: general_feature_extractor.v5 运行完成[145.923823s].
[2017-12-08 18:34:29.743354] INFO: bigquant: transform.v2 开始运行..
[2017-12-08 18:34:31.744780] INFO: transform: transformed /y_2010, 405350/431567
[2017-12-08 18:34:33.722970] INFO: transform: transformed /y_2011, 485058/511455
[2017-12-08 18:34:35.923226] INFO: transform: transformed /y_2012, 546054/565675
[2017-12-08 18:34:38.597831] INFO: transform: transformed /y_2013, 554613/564168
[2017-12-08 18:34:40.701597] INFO: transform: transformed /y_2014, 554828/569948
[2017-12-08 18:34:40.736628] INFO: transform: transformed rows: 2545903/2642813
[2017-12-08 18:34:40.763489] INFO: bigquant: transform.v2 运行完成[11.020139s].
[2017-12-08 18:34:40.774428] INFO: bigquant: join.v2 开始运行..
[2017-12-08 18:34:54.627995] INFO: join: /y_2010, rows=404819/405350, timetaken=8.944864s
[2017-12-08 18:35:04.337689] INFO: join: /y_2011, rows=484565/485058, timetaken=9.681493s
[2017-12-08 18:35:13.982404] INFO: join: /y_2012, rows=544972/546054, timetaken=9.608476s
[2017-12-08 18:35:23.706753] INFO: join: /y_2013, rows=553574/554613, timetaken=9.678327s
[2017-12-08 18:35:34.437181] INFO: join: /y_2014, rows=538554/554828, timetaken=10.680748s
[2017-12-08 18:35:34.582627] INFO: join: total result rows: 2526484
[2017-12-08 18:35:34.584993] INFO: bigquant: join.v2 运行完成[53.810554s].
[2017-12-08 18:35:34.629839] INFO: bigquant: stock_ranker_train.v3 开始运行..
[2017-12-08 18:35:45.466400] INFO: df2bin: prepare data: training ..
[2017-12-08 18:36:22.463923] INFO: stock_ranker_train: 85d3466a training: 2526484 rows
[2017-12-08 18:38:53.505512] INFO: bigquant: stock_ranker_train.v3 运行完成[198.87565s].
[2017-12-08 18:38:53.784239] INFO: bigquant: backtest.v7 开始运行..
[2017-12-08 18:38:53.817175] INFO: bigquant: general_feature_extractor.v5 开始运行..
[2017-12-08 18:39:10.694018] INFO: general_feature_extractor: year 2015, featurerows=569698
[2017-12-08 18:39:38.297108] INFO: general_feature_extractor: year 2016, featurerows=641546
[2017-12-08 18:40:00.507796] INFO: general_feature_extractor: year 2017, featurerows=652594
[2017-12-08 18:40:00.533509] INFO: general_feature_extractor: total feature rows: 1863838
[2017-12-08 18:40:00.543409] INFO: bigquant: general_feature_extractor.v5 运行完成[66.726271s].
[2017-12-08 18:40:00.552993] INFO: bigquant: transform.v2 开始运行..
[2017-12-08 18:40:02.331195] INFO: transform: transformed /y_2015, 546200/569698
[2017-12-08 18:40:05.157616] INFO: transform: transformed /y_2016, 619388/641546
[2017-12-08 18:40:07.707303] INFO: transform: transformed /y_2017, 614391/652594
[2017-12-08 18:40:07.732555] INFO: transform: transformed rows: 1779979/1863838
[2017-12-08 18:40:07.758709] INFO: bigquant: transform.v2 运行完成[7.205691s].
[2017-12-08 18:40:07.778010] INFO: bigquant: stock_ranker_predict.v2 开始运行..
[2017-12-08 18:40:10.860710] INFO: df2bin: prepare data: prediction ..
[2017-12-08 18:40:31.031803] INFO: stock_ranker_predict: prediction: 1779979 rows
[2017-12-08 18:40:50.343649] INFO: bigquant: stock_ranker_predict.v2 运行完成[42.565594s].
[2017-12-08 18:41:38.780987] INFO: Performance: Simulated 704 trading days out of 704.
[2017-12-08 18:41:38.782324] INFO: Performance: first open: 2015-01-05 14:30:00+00:00
[2017-12-08 18:41:38.783377] INFO: Performance: last close: 2017-11-21 20:00:00+00:00
[注意] 有 11 笔卖出是在多天内完成的。当日卖出股票超过了当日股票交易的2.5%会出现这种情况。
  • 收益率225.68%
  • 年化收益率52.6%
  • 基准收益率19.36%
  • 阿尔法0.47
  • 贝塔0.65
  • 夏普比率1.68
  • 收益波动率28.75%
  • 信息比率1.88
  • 最大回撤32.97%
[2017-12-08 18:41:41.668754] INFO: bigquant: backtest.v7 运行完成[167.88449s].

(iQuant) #2

略作修改!因为输入的因子大多为财务因子,因此持仓时间和标注都进行了修改,把hold_days换成了60天。年化收益有所改善。


克隆策略

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    In [1]:
    # 本代码由可视化策略环境自动生成 2017年12月9日 10:20
    # 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。
    
    
    m1 = M.instruments.v2(
        start_date='2010-01-01',
        end_date='2015-01-01',
        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 https://bigquant.com/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 <https://bigquant.com/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, -60) / 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
    )
    
    m3 = M.input_features.v1(
        features="""rank_market_cap_0
    fs_roa_0
    fs_net_cash_flow_0
    fs_net_cash_flow_0-fs_total_profit_0
    fs_non_current_liabilities_0/fs_non_current_assets_0
    fs_current_assets_0/fs_current_liabilities_0
    fs_gross_profit_margin_0
    fs_operating_revenue_0/(fs_operating_revenue_0+fs_non_current_assets_0)"""
    )
    
    m4 = M.general_feature_extractor.v6(
        instruments=m1.data,
        features=m3.data,
        start_date='',
        end_date='',
        before_start_days=0
    )
    
    m5 = M.derived_feature_extractor.v2(
        input_data=m4.data,
        features=m3.data,
        date_col='date',
        instrument_col='instrument'
    )
    
    m7 = M.join.v3(
        data1=m2.data,
        data2=m5.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', '2015-01-01'),
        end_date=T.live_run_param('trading_date', '2017-01-01'),
        market='CN_STOCK_A',
        instrument_list='',
        max_count=0
    )
    
    m10 = M.general_feature_extractor.v6(
        instruments=m9.data,
        features=m3.data,
        start_date='',
        end_date='',
        before_start_days=0
    )
    
    m11 = M.derived_feature_extractor.v2(
        input_data=m10.data,
        features=m3.data,
        date_col='date',
        instrument_col='instrument'
    )
    
    m14 = M.dropnan.v1(
        input_data=m11.data
    )
    
    m8 = M.stock_ranker_predict.v5(
        model=m6.model,
        data=m14.data,
        m_lazy_run=False
    )
    
    # 回测引擎:每日数据处理函数,每天执行一次
    def m12_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 m12_prepare_bigquant_run(context):
        pass
    
    # 回测引擎:初始化函数,只执行一次
    def m12_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'] = 60
    
    m12 = M.trade.v3(
        instruments=m9.data,
        options_data=m8.predictions,
        start_date='',
        end_date='',
        handle_data=m12_handle_data_bigquant_run,
        prepare=m12_prepare_bigquant_run,
        initialize=m12_initialize_bigquant_run,
        volume_limit=0.025,
        order_price_field_buy='open',
        order_price_field_sell='close',
        capital_base=1000000,
        benchmark='000300.SHA',
        auto_cancel_non_tradable_orders=True,
        data_frequency='daily',
        plot_charts=True,
        backtest_only=False
    )
    
    [2017-12-09 10:18:23.139024] INFO: bigquant: instruments.v2 开始运行..
    [2017-12-09 10:18:23.171242] INFO: bigquant: 命中缓存
    [2017-12-09 10:18:23.172138] INFO: bigquant: instruments.v2 运行完成[0.033165s].
    [2017-12-09 10:18:23.441435] INFO: bigquant: advanced_auto_labeler.v2 开始运行..
    [2017-12-09 10:18:23.444925] INFO: bigquant: 命中缓存
    [2017-12-09 10:18:23.445807] INFO: bigquant: advanced_auto_labeler.v2 运行完成[0.00441s].
    [2017-12-09 10:18:23.536923] INFO: bigquant: input_features.v1 开始运行..
    [2017-12-09 10:18:23.539718] INFO: bigquant: 命中缓存
    [2017-12-09 10:18:23.540503] INFO: bigquant: input_features.v1 运行完成[0.003616s].
    [2017-12-09 10:18:23.641865] INFO: bigquant: general_feature_extractor.v6 开始运行..
    [2017-12-09 10:18:23.644220] INFO: bigquant: 命中缓存
    [2017-12-09 10:18:23.644944] INFO: bigquant: general_feature_extractor.v6 运行完成[0.003137s].
    [2017-12-09 10:18:23.656425] INFO: bigquant: derived_feature_extractor.v2 开始运行..
    [2017-12-09 10:18:23.658503] INFO: bigquant: 命中缓存
    [2017-12-09 10:18:23.659296] INFO: bigquant: derived_feature_extractor.v2 运行完成[0.002874s].
    [2017-12-09 10:18:23.741832] INFO: bigquant: join.v3 开始运行..
    [2017-12-09 10:18:23.745205] INFO: bigquant: 命中缓存
    [2017-12-09 10:18:23.746105] INFO: bigquant: join.v3 运行完成[0.004303s].
    [2017-12-09 10:18:23.838088] INFO: bigquant: dropnan.v1 开始运行..
    [2017-12-09 10:18:23.840676] INFO: bigquant: 命中缓存
    [2017-12-09 10:18:23.841424] INFO: bigquant: dropnan.v1 运行完成[0.003376s].
    [2017-12-09 10:18:23.941186] INFO: bigquant: stock_ranker_train.v5 开始运行..
    [2017-12-09 10:18:23.944373] INFO: bigquant: 命中缓存
    [2017-12-09 10:18:23.945098] INFO: bigquant: stock_ranker_train.v5 运行完成[0.003941s].
    [2017-12-09 10:18:23.948964] INFO: bigquant: instruments.v2 开始运行..
    [2017-12-09 10:18:23.953332] INFO: bigquant: 命中缓存
    [2017-12-09 10:18:23.954120] INFO: bigquant: instruments.v2 运行完成[0.005153s].
    [2017-12-09 10:18:23.959358] INFO: bigquant: general_feature_extractor.v6 开始运行..
    [2017-12-09 10:18:23.961237] INFO: bigquant: 命中缓存
    [2017-12-09 10:18:23.961958] INFO: bigquant: general_feature_extractor.v6 运行完成[0.002604s].
    [2017-12-09 10:18:23.967198] INFO: bigquant: derived_feature_extractor.v2 开始运行..
    [2017-12-09 10:18:23.968986] INFO: bigquant: 命中缓存
    [2017-12-09 10:18:23.969760] INFO: bigquant: derived_feature_extractor.v2 运行完成[0.002562s].
    [2017-12-09 10:18:23.975092] INFO: bigquant: dropnan.v1 开始运行..
    [2017-12-09 10:18:23.976854] INFO: bigquant: 命中缓存
    [2017-12-09 10:18:23.977576] INFO: bigquant: dropnan.v1 运行完成[0.002479s].
    [2017-12-09 10:18:24.040421] INFO: bigquant: stock_ranker_predict.v5 开始运行..
    [2017-12-09 10:18:24.046255] INFO: bigquant: 命中缓存
    [2017-12-09 10:18:24.047116] INFO: bigquant: stock_ranker_predict.v5 运行完成[0.006723s].
    [2017-12-09 10:18:24.268516] INFO: bigquant: backtest.v7 开始运行..
    [2017-12-09 10:18:24.270737] INFO: bigquant: 命中缓存
    
    • 收益率146.64%
    • 年化收益率59.39%
    • 基准收益率-6.33%
    • 阿尔法0.61
    • 贝塔0.81
    • 夏普比率1.52
    • 收益波动率36.84%
    • 信息比率2.34
    • 最大回撤44.44%
    [2017-12-09 10:18:25.543443] INFO: bigquant: backtest.v7 运行完成[1.27487s].