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    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    In [1]:
    # 本代码由可视化策略环境自动生成 2022年5月21日 09:48
    # 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 = 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
    
    
    m1 = M.instruments.v2(
        start_date='2018-01-01',
        end_date='2020-12-31',
        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, -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.HIX',
        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
    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
    """
    )
    
    m15 = M.general_feature_extractor.v7(
        instruments=m1.data,
        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
    )
    
    m13 = M.dropnan.v1(
        input_data=m7.data
    )
    
    m6 = M.stock_ranker_train.v6(
        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', '2021-01-01'),
        end_date=T.live_run_param('trading_date', '2021-12-31'),
        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
    )
    
    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
    )
    
    m14 = M.dropnan.v1(
        input_data=m18.data
    )
    
    m8 = M.stock_ranker_predict.v5(
        model=m6.model,
        data=m14.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.HIX'
    )
    
    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-9493af562ea04486a4a4bd587f2b1736"}/bigcharts-data-end
    • 收益率58.34%
    • 年化收益率61.06%
    • 基准收益率-5.2%
    • 阿尔法0.66
    • 贝塔0.39
    • 夏普比率1.87
    • 胜率0.51
    • 盈亏比1.42
    • 收益波动率25.72%
    • 信息比率0.13
    • 最大回撤15.12%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-6f8fbc7d5c8d4cf598832743aeb18ff1"}/bigcharts-data-end
    In [14]:
    def transaction_money(trade):
        '''
        输入trade
        输出前十个和后十个收益率的股票数据
        '''
        #交易详情
        df=trade.read_raw_perf()
        transactions=df.transactions.iloc[1:].values
        tras=None
        #链接成dataframe
        for i in transactions:
            if isinstance(tras,pd.DataFrame):        
                tras=pd.concat([tras,pd.DataFrame(i)])
            else:
                tras=pd.DataFrame(i)
    
        #改变sid形式
        tras.sid=tras.sid.apply(lambda x:x.symbol)
        #获取需要的数据
        tras=tras[['dt','sid','transaction_money']]
        
        #找到收益最大的10和最少的十个,这个是错误的 。
        transaction_money_sum=tras.groupby('sid').apply(lambda x:x.transaction_money.sum()).sort_values(ascending=False)
        top=transaction_money_sum.head(10)
        bottom=transaction_money_sum.tail(10)
        return top,bottom,tras
    
    def get_bars(ins,start_date,end_date,signal):
        # 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 m5_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 = input_1.read()
            df=df.rename({'open_0':'open','close_0':'close',"low_0":'low','high_0':'high'},axis='columns').set_index('date')
            ins=df.instrument.iloc[0]
            del df['instrument']
            data_1 = DataSource.write_df(df)
            return Outputs(data_1=data_1, data_2=ins, 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 m5_post_run_bigquant_run(outputs):
            return outputs
    
        m1 = M.instruments.v2(
            start_date=start_date,
            end_date=end_date,
            market='CN_STOCK_A',
            instrument_list=[ins],
            max_count=0
        )
    
        m3 = 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
        high_0
        close_0
        low_0
        open_0
        ta_sma_30_0   
        """+signal
            
        )
    
        m2 = M.general_feature_extractor.v7(
            instruments=m1.data,
            features=m3.data,
            start_date='',
            end_date='',
            before_start_days=90
        )
    
        m6 = M.derived_feature_extractor.v3(
            input_data=m2.data,
            features=m3.data,
            date_col='date',
            instrument_col='instrument',
            drop_na=False,
            remove_extra_columns=False,
            user_functions={}
        )
    
        m5 = M.cached.v3(
            input_1=m6.data,
            run=m5_run_bigquant_run,
            post_run=m5_post_run_bigquant_run,
            input_ports='',
            params='{}',
            output_ports=''
        )
    
        df=m5.data_1.read()    
        df=df[df.index>=start_date]
        return df
    
    def bar_tras(bar,tras):
        '''
        bar:日线数据
        tras:交易记录
        链接两个数据,并且设置sell,buy列
        
        '''
        #合并两个数据
        bar.reset_index(inplace=True)
        bar=bar.merge(tras,on='date',how='left')
    
        #添加两列
        bar['sell']=np.nan
        bar['buy']=np.nan
        #设置买入卖出点
        def buy_or_sell(x):
            if x.transaction_money>0:
                x.buy=x.low
            if x.transaction_money<0:
                x.sell=x.high
            return x
        #应用
        bar=bar.apply(lambda x: buy_or_sell(x),axis=1)
        #设置日期
        bar.set_index('date',drop=True,inplace=True)
        return bar
    
    top,bottom,tras=transaction_money(m19)
    
    # 循环画图
    for sid in top.index:
        
        sel_tras=tras[tras.sid==sid]    
        sel_tras['date']=sel_tras.dt.apply(lambda x :pd.Timestamp(x.strftime('%Y-%m-%d')))
        #设置日期
        start_date= ( sel_tras.date.iloc[0]- datetime.timedelta(days=50)).strftime('%Y-%m-%d')
        end_date= (sel_tras.date.iloc[-1] + datetime.timedelta(days=50)).strftime('%Y-%m-%d')
    
        signal='''ta_atr_28_0/close_0*100
        '''
        #读取数据
        bar=get_bars(sid,start_date,end_date,signal)
        df=bar_tras(bar,sel_tras)
        #选择画图数据
        select=['open', 'high', 'low', 'close','ta_sma_30_0','sell','buy']
        #把 signal 加入到select
        for i in signal.strip().split('\n'):
            select.append(i.strip())
            
        print(sid,'-'*40)
        T.plot(df[select],  options={'chart': {'title': sid, 'height': 800}, 'series': [{},{'type': 'line','yAxis':0},{'type': 'scatter','yAxis':0,'color':'green'},{'type': 'scatter','yAxis':0,'color':'red'}]},stock=True, candlestick=True)
        print('--------------\n'*2)
        
    
    688037.SHA ----------------------------------------
    
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    In [ ]: