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    In [1]:
    # 本代码由可视化策略环境自动生成 2019年1月21日 21:20
    # 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。
    
    
    # 回测引擎:每日数据处理函数,每天执行一次
    def m4_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 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()}
        #print(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 True:
            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]))])))
            instruments12 = list(ranker_prediction.instrument[ranker_prediction.instrument.apply(
                    lambda x: x in equities )])
            position_prediction = ranker_prediction[ranker_prediction.instrument.isin(instruments12)]
            instruments = list(position_prediction.instrument[position_prediction.score <0.5])
            #instruments = list(position_prediction.instrument[position_prediction.score < 0.2])
            #print(instruments)
            #print(instruments12,list(ranker_prediction.date)[0])
            #print(context.has_unfinished_sell_order)
            # 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
        rank_buy = ranker_prediction[ranker_prediction.score>1.2]
        #rank_buy = ranker_prediction[ranker_prediction.score>1.12]
        buy_instruments1 = list(rank_buy.instrument)
        #print(list(ranker_prediction.date)[0],ranker_prediction.score)
        #buy_instruments1 = list(ranker_prediction.instrument[ranker_prediction.score>1.16])
        buy_scores1 = list(rank_buy.score)
        buy_instruments = buy_instruments1[:np.where(len(buy_instruments1)>1,1,len(buy_instruments1))]
        buy_scores = buy_scores1[:len(buy_instruments)]
        buy_cash_weights = buy_scores/np.sum(buy_scores)
        #buy_cash_weights = T.norm([1/math.log(i+2) for i in range(0,len(buy_instruments))])
        max_cash_per_instrument = context.portfolio.portfolio_value * context.max_cash_per_instrument
        if any(buy_instruments):
            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)
                today_price = data.current(symbol(instrument), ['amount', 'volume'])
                buy_price =today_price['amount'] / today_price['volume']
                buy_amount = int(round(cash/(buy_price*100)))
                #print(buy_amount)
            #context.order_lots(symbol(instrument),1)
                if buy_amount > 0: 
                    context.order_lots(symbol(instrument),buy_amount)
           # if cash > 0:
             #   context.order_value(context.symbol(instrument), cash)
    # 回测引擎:准备数据,只执行一次
    def m4_prepare_bigquant_run(context):
        pass
    # 回测引擎:初始化函数,只执行一次
    def m4_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 = 1
        context.options['hold_days'] = 5
    
    
    m1 = M.instruments.v2(
        start_date='2010-01-01',
        end_date='2016-01-01',
        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)
    #signedpower((shift(close, -5) / shift(open, -1)-1),log10(market_cap_float)/pe_ttm)
    #where((shift(close, -3) / shift(open, -1) > 0) & (correlation(close, amount, -3)>0),correlation(close, amount, -3) ,0)
    (shift(close, -5) / shift(open, -1)-1)*100
    # 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, 60)
    
    # 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="""# #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
    avg_turn_10
    return_10
    rank_return_10
    return_15
    rank_return_15
    
    
    
    """
    )
    
    m15 = M.general_feature_extractor.v7(
        instruments=m1.data,
        features=m3.data,
        start_date='',
        end_date='',
        before_start_days=0
    )
    
    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.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', '2016-01-02'),
        end_date=T.live_run_param('trading_date', '2018-12-21'),
        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=0
    )
    
    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
    )
    
    m4 = M.trade.v4(
        instruments=m9.data,
        options_data=m8.predictions,
        start_date='',
        end_date='',
        handle_data=m4_handle_data_bigquant_run,
        prepare=m4_prepare_bigquant_run,
        initialize=m4_initialize_bigquant_run,
        volume_limit=0.005,
        order_price_field_buy='open',
        order_price_field_sell='close',
        capital_base=10000000,
        auto_cancel_non_tradable_orders=True,
        data_frequency='daily',
        price_type='后复权',
        product_type='股票',
        plot_charts=True,
        backtest_only=False,
        benchmark=''
    )
    
    [2019-01-21 20:33:35.543026] INFO: bigquant: instruments.v2 开始运行..
    [2019-01-21 20:33:35.565195] INFO: bigquant: 命中缓存
    [2019-01-21 20:33:35.566610] INFO: bigquant: instruments.v2 运行完成[0.023659s].
    [2019-01-21 20:33:35.572698] INFO: bigquant: advanced_auto_labeler.v2 开始运行..
    [2019-01-21 20:33:35.579294] INFO: bigquant: 命中缓存
    [2019-01-21 20:33:35.580781] INFO: bigquant: advanced_auto_labeler.v2 运行完成[0.008067s].
    [2019-01-21 20:33:35.584736] INFO: bigquant: input_features.v1 开始运行..
    [2019-01-21 20:33:35.593862] INFO: bigquant: input_features.v1 运行完成[0.009088s].
    [2019-01-21 20:33:35.662018] INFO: bigquant: general_feature_extractor.v7 开始运行..
    [2019-01-21 20:33:42.442981] INFO: 基础特征抽取: 年份 2010, 特征行数=431567
    [2019-01-21 20:33:48.876503] INFO: 基础特征抽取: 年份 2011, 特征行数=511455
    [2019-01-21 20:33:53.643541] INFO: 基础特征抽取: 年份 2012, 特征行数=565675
    [2019-01-21 20:33:57.731471] INFO: 基础特征抽取: 年份 2013, 特征行数=564168
    [2019-01-21 20:34:00.209422] INFO: 基础特征抽取: 年份 2014, 特征行数=569948
    [2019-01-21 20:34:02.091113] INFO: 基础特征抽取: 年份 2015, 特征行数=569698
    [2019-01-21 20:34:05.950662] INFO: 基础特征抽取: 年份 2016, 特征行数=0
    [2019-01-21 20:34:06.335629] INFO: 基础特征抽取: 总行数: 3212511
    [2019-01-21 20:34:06.339405] INFO: bigquant: general_feature_extractor.v7 运行完成[30.67739s].
    [2019-01-21 20:34:06.344359] INFO: bigquant: derived_feature_extractor.v3 开始运行..
    [2019-01-21 20:34:07.841875] INFO: derived_feature_extractor: /y_2010, 431567
    [2019-01-21 20:34:08.190283] INFO: derived_feature_extractor: /y_2011, 511455
    [2019-01-21 20:34:08.582040] INFO: derived_feature_extractor: /y_2012, 565675
    [2019-01-21 20:34:09.128856] INFO: derived_feature_extractor: /y_2013, 564168
    [2019-01-21 20:34:09.540204] INFO: derived_feature_extractor: /y_2014, 569948
    [2019-01-21 20:34:09.973769] INFO: derived_feature_extractor: /y_2015, 569698
    [2019-01-21 20:34:10.305283] INFO: bigquant: derived_feature_extractor.v3 运行完成[3.960899s].
    [2019-01-21 20:34:10.309726] INFO: bigquant: join.v3 开始运行..
    [2019-01-21 20:34:14.978285] INFO: join: /y_2010, 行数=431030/431567, 耗时=2.055588s
    [2019-01-21 20:34:16.888317] INFO: join: /y_2011, 行数=510922/511455, 耗时=1.892157s
    [2019-01-21 20:34:18.782715] INFO: join: /y_2012, 行数=564582/565675, 耗时=1.869201s
    [2019-01-21 20:34:20.661970] INFO: join: /y_2013, 行数=563137/564168, 耗时=1.849598s
    [2019-01-21 20:34:22.619865] INFO: join: /y_2014, 行数=567866/569948, 耗时=1.917074s
    [2019-01-21 20:34:24.529228] INFO: join: /y_2015, 行数=546721/569698, 耗时=1.87601s
    [2019-01-21 20:34:24.682484] INFO: join: 最终行数: 3184258
    [2019-01-21 20:34:24.684974] INFO: bigquant: join.v3 运行完成[14.375218s].
    [2019-01-21 20:34:24.690301] INFO: bigquant: dropnan.v1 开始运行..
    [2019-01-21 20:34:25.185243] INFO: dropnan: /y_2010, 425464/431030
    [2019-01-21 20:34:25.703600] INFO: dropnan: /y_2011, 506344/510922
    [2019-01-21 20:34:26.283076] INFO: dropnan: /y_2012, 561970/564582
    [2019-01-21 20:34:26.839777] INFO: dropnan: /y_2013, 563112/563137
    [2019-01-21 20:34:27.475827] INFO: dropnan: /y_2014, 566585/567866
    [2019-01-21 20:34:28.096689] INFO: dropnan: /y_2015, 545355/546721
    [2019-01-21 20:34:28.146851] INFO: dropnan: 行数: 3168830/3184258
    [2019-01-21 20:34:28.166719] INFO: bigquant: dropnan.v1 运行完成[3.476392s].
    [2019-01-21 20:34:28.172045] INFO: bigquant: stock_ranker_train.v5 开始运行..
    [2019-01-21 20:34:30.096297] INFO: StockRanker: 特征预处理 ..
    [2019-01-21 20:34:31.822232] INFO: StockRanker: prepare data: training ..
    [2019-01-21 20:34:34.091925] INFO: StockRanker: sort ..
    [2019-01-21 20:35:04.780965] INFO: StockRanker训练: e4b391dc 准备训练: 3168830 行数
    [2019-01-21 20:35:04.833081] INFO: StockRanker训练: 正在训练 ..
    [2019-01-21 20:39:38.481568] INFO: bigquant: stock_ranker_train.v5 运行完成[310.309461s].
    [2019-01-21 20:39:38.485275] INFO: bigquant: instruments.v2 开始运行..
    [2019-01-21 20:39:38.506782] INFO: bigquant: instruments.v2 运行完成[0.021465s].
    [2019-01-21 20:39:38.516180] INFO: bigquant: general_feature_extractor.v7 开始运行..
    [2019-01-21 20:39:46.929483] INFO: 基础特征抽取: 年份 2016, 特征行数=641546
    [2019-01-21 20:39:56.664468] INFO: 基础特征抽取: 年份 2017, 特征行数=743233
    [2019-01-21 20:40:02.060004] INFO: 基础特征抽取: 年份 2018, 特征行数=799263
    [2019-01-21 20:40:02.105463] INFO: 基础特征抽取: 总行数: 2184042
    [2019-01-21 20:40:02.116067] INFO: bigquant: general_feature_extractor.v7 运行完成[23.599917s].
    [2019-01-21 20:40:02.120336] INFO: bigquant: derived_feature_extractor.v3 开始运行..
    [2019-01-21 20:40:03.013593] INFO: derived_feature_extractor: /y_2016, 641546
    [2019-01-21 20:40:03.574139] INFO: derived_feature_extractor: /y_2017, 743233
    [2019-01-21 20:40:04.370790] INFO: derived_feature_extractor: /y_2018, 799263
    [2019-01-21 20:40:04.988070] INFO: bigquant: derived_feature_extractor.v3 运行完成[2.867724s].
    [2019-01-21 20:40:04.990907] INFO: bigquant: dropnan.v1 开始运行..
    [2019-01-21 20:40:05.643601] INFO: dropnan: /y_2016, 637985/641546
    [2019-01-21 20:40:06.443761] INFO: dropnan: /y_2017, 736106/743233
    [2019-01-21 20:40:07.420320] INFO: dropnan: /y_2018, 797471/799263
    [2019-01-21 20:40:07.470115] INFO: dropnan: 行数: 2171562/2184042
    [2019-01-21 20:40:07.525445] INFO: bigquant: dropnan.v1 运行完成[2.534475s].
    [2019-01-21 20:40:07.530487] INFO: bigquant: stock_ranker_predict.v5 开始运行..
    [2019-01-21 20:40:08.724287] INFO: StockRanker: prepare data: prediction ..
    [2019-01-21 20:40:28.600864] INFO: stock_ranker_predict: 准备预测: 2171562 行
    [2019-01-21 20:40:28.601910] INFO: stock_ranker_predict: 正在预测 ..
    [2019-01-21 20:40:59.044750] INFO: bigquant: stock_ranker_predict.v5 运行完成[51.514231s].
    [2019-01-21 20:40:59.099410] INFO: bigquant: backtest.v8 开始运行..
    [2019-01-21 20:40:59.101678] INFO: bigquant: biglearning backtest:V8.1.6
    [2019-01-21 20:40:59.102566] INFO: bigquant: product_type:stock by specified
    [2019-01-21 20:41:21.177021] INFO: bigquant: 读取股票行情完成:3006629
    [2019-01-21 20:41:55.343300] INFO: algo: TradingAlgorithm V1.4.2
    [2019-01-21 20:42:08.701766] INFO: algo: trading transform...
    [2019-01-21 20:42:31.460927] INFO: Performance: Simulated 726 trading days out of 726.
    [2019-01-21 20:42:31.462040] INFO: Performance: first open: 2016-01-04 09:30:00+00:00
    [2019-01-21 20:42:31.463745] INFO: Performance: last close: 2018-12-21 15:00:00+00:00
    
    • 收益率4.96%
    • 年化收益率1.69%
    • 基准收益率-18.8%
    • 阿尔法0.06
    • 贝塔0.39
    • 夏普比率0.09
    • 胜率0.47
    • 盈亏比1.18
    • 收益波动率27.41%
    • 信息比率0.02
    • 最大回撤42.77%
    [2019-01-21 20:42:35.113849] INFO: bigquant: backtest.v8 运行完成[96.014379s].
    
    In [ ]:
    m6.feature_gains.read_df()
    

    (达达) #2

    成交率限制可以设置小点比如 0.00002就能看出效果,这个限制是针对个股全天成交额的。