小号分享的策略-2020年6,7两月累计收益率415%

策略分享
标签: #<Tag:0x00007fc576bef238>

(zhrh888) #1
克隆策略

    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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 = 1\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 = 0.5\n context.options['hold_days'] = 0\n","ValueType":"Literal","LinkedGlobalParameter":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.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. 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# print('last_sale_date: ', equities[instrument].last_sale_date)\n sid = equities[instrument].sid # 交易标的\n # 今天和上次交易的时间相隔hold_days就全部卖出\n if 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    In [88]:
    # 本代码由可视化策略环境自动生成 2020年7月31日 10:35
    # 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 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 = 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 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)
     #----------------------------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 m19_prepare_bigquant_run(context):
        pass
    
    
    m1 = M.instruments.v2(
        start_date='2019-01-01',
        end_date='2020-07-30',
        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, -1) /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='399006.ZIX',
        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
    
    
    #max(turn_0,turn_1)-max(turn_2,turn_3,turn_4,turn_5,turn_6,turn_7,turn_8,turn_9)
    #avg_turn_0
    #avg_turn_13
    #avg_turn_5
    #sum(where(return_0>1,where(turn_0>turn_1,1,-1),where(turn_0<turn_1,1,-1)),10)
    daily_return_0
    
    daily_return_1
    daily_return_2
    #mto=100*shift(open_0, -1)/close_0-100
    
    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
    
    (high_0-open_0/2-close_0/2)/open_0*10
    (high_1-open_1/2-close_1/2)/open_1*10
    (open_0/2+close_0/2-low_0)/open_0*10
    (open_1/2+close_1/2-low_1)/open_1*10
    (close_0-open_0)/open_0
    (close_1-open_1)/open_1
    (high_0-open_0/2-close_0/2)/(close_0-open_0)
    (high_1-open_1/2-close_1/2)/(close_1-open_1)
    rank((high_0-open_0/2-close_0/2)/(close_0-open_0))
    rank((high_1-open_1/2-close_1/2)/(close_1-open_1))
    (high_0+low_0)/close_1
    (high_1+low_1)/close_2
    ((high_0+low_0)/close_1)/((high_1+low_1)/close_2)
    #((high_1+low_1)/close_2)/((high_2+low_2)/close_3)
    
    rank_avg_mf_net_amount_3
    #rank_avg_mf_net_amount_0
    rank_avg_mf_net_amount_10
    rank_avg_mf_net_amount_20"""
    )
    
    m15 = M.general_feature_extractor.v7(
        instruments=m1.data,
        features=m3.data,
        start_date='',
        end_date='',
        before_start_days=90
    )
    
    m11 = 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=m11.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
    )
    
    m10 = M.filter.v3(
        input_data=m13.data,
        expr='daily_return_0>1.095',
        output_left_data=False
    )
    
    m4 = M.stock_ranker_train.v6(
        training_ds=m10.data,
        features=m3.data,
        learning_algorithm='排序',
        number_of_leaves=30,
        minimum_docs_per_leaf=1000,
        number_of_trees=450,
        learning_rate=0.3,
        max_bins=1023,
        feature_fraction=1,
        data_row_fraction=1,
        ndcg_discount_base=1,
        m_lazy_run=False
    )
    
    m9 = M.instruments.v2(
        start_date=T.live_run_param('trading_date', '2020-06-01'),
        end_date=T.live_run_param('trading_date', '2020-07-31'),
        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
    )
    
    m12 = M.chinaa_stock_filter.v1(
        input_data=m17.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
    )
    
    m18 = M.derived_feature_extractor.v3(
        input_data=m12.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
    )
    
    m20 = M.filter.v3(
        input_data=m14.data,
        expr='daily_return_0>1.095',
        output_left_data=False
    )
    
    m8 = M.stock_ranker_predict.v5(
        model=m4.model,
        data=m20.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='open',
        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'
    )
    
    m6 = 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
    
    zf=(shift(close_0, -1)/shift(open_0, -1)-1)*100
    zfo=(shift(open_0, -2)/shift(open_0, -1)-1)*100
    zfc=(shift(close_0, -2)/shift(open_0, -1)-1)*100
    zfh=(shift(high_0, -2)/shift(open_0, -1)-1)*100
    mto=100*shift(open_0, -1)/close_0-100"""
    )
    
    m21 = M.general_feature_extractor.v7(
        instruments=m9.data,
        features=m6.data,
        start_date='',
        end_date='',
        before_start_days=90
    )
    
    m22 = M.derived_feature_extractor.v3(
        input_data=m21.data,
        features=m6.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=False,
        remove_extra_columns=False
    )
    
    m5 = M.join.v3(
        data1=m8.predictions,
        data2=m22.data,
        on='date,instrument',
        how='inner',
        sort=True,
        m_cached=False
    )
    
    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-3baef3de87d248d2823a0b4dac0c20b8"}/bigcharts-data-end
    • 收益率415.24%
    • 年化收益率1488183.83%
    • 基准收益率20.41%
    • 阿尔法9.58
    • 贝塔0.22
    • 夏普比率27.14
    • 胜率0.97
    • 盈亏比12.05
    • 收益波动率36.2%
    • 信息比率1.32
    • 最大回撤0.0%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-dcb06d21bc444248b5ea05c61f20777f"}/bigcharts-data-end
    In [89]:
    #### a=m8.predictions.read_all_df()
    #a=a[a.position.isin([1,2,3])].tail(12)
    #score =m19.read_raw_perf()
    #score.returns.sum()
    #b=a["instrument"].tail(10)
    c=m1.data.read()
    c=c['end_date']
    b=m5.data.read_all_df()
    b=b[b.position.isin([1,2,3])]
    b=b[b.date==c]
    
    #b.to_csv('450tree.csv',header=False,mode='a')
    
    b
    
    Out[89]:
    close_0 date high_0 instrument open_0 zf zfo zfc zfh mto score position
    5520 48.777054 2020-07-30 48.777054 000524.SZA 44.194965 NaN NaN NaN NaN NaN 1.694678 1
    5549 67.092667 2020-07-30 67.092667 002852.SZA 60.682728 NaN NaN NaN NaN NaN 0.913595 3
    5574 20.447901 2020-07-30 20.447901 600178.SHA 18.360950 NaN NaN NaN NaN NaN 1.177682 2

    (aaqing) #3

    这个可能实盘可以实现吗?


    (user404) #4

    你的训练数据时间到7月31日,然后回测6月到7月数据?什么路数?


    (user4310) #5

    别在这误导别人,啥都不懂的sb


    (caoweii) #6

    未来数据而已,看着未来预测未来有啥用


    (youmin) #7

    吓我一跳,哥们,你训练数据和回测数据不能用同一个时间段的数据,建议看下视频


    (jack920) #8

    这个疯了啊,哈哈哈


    (iitcat) #9

    被这个策略坑了16个点了