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(iQuant) #1
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    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    In [11]:
    # 本代码由可视化策略环境自动生成 2018年6月13日 13:55
    # 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.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
    pb_lf_0
    pe_ttm_0
    amount_0"""
    )
    
    m2 = 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
    )
    
    m3 = M.general_feature_extractor.v6(
        instruments=m2.data,
        features=m1.data,
        start_date='',
        end_date='',
        before_start_days=30
    )
    
    m4 = M.derived_feature_extractor.v2(
        input_data=m3.data,
        features=m1.data,
        date_col='date',
        instrument_col='instrument'
    )
    
    m6 = M.dropnan.v1(
        input_data=m4.data
    )
    
    # 回测引擎:每日数据处理函数,每天执行一次
    def m5_handle_data_bigquant_run(context, data):
         #周期控制
        if (context.trading_day_index+1)%30!=0:#以30天换一次仓为例
            return
        # 当前的日期
        date = data.current_dt.strftime('%Y-%m-%d')
        # 买入股票列表
        stock_to_buy = context.daily_buy_stock.ix[date]
        # 目前持仓列表    
        stock_hold_now = [equity.symbol for equity in context.portfolio.positions]
        # 继续持有股票列表
        no_need_to_sell = [i for i in stock_hold_now  if i in stock_to_buy]
        # 卖出股票列表 
        stock_to_sell = [i for i in stock_hold_now if i not in no_need_to_sell]
        # 执行卖出
        for stock in stock_to_sell:
            if data.can_trade(context.symbol(stock)):
                context.order_target_percent(context.symbol(stock), 0)
                
        # 如果当天没有买入就返回
        if len(stock_to_buy) == 0:
            return
            # 等权重
        weight = 1 / len(stock_to_buy)
        # 执行买入
        for  cp in stock_to_buy:
            if data.can_trade(context.symbol(cp)):
                context.order_target_percent(context.symbol(cp), weight)
    # 回测引擎:准备数据,只执行一次
    def m5_prepare_bigquant_run(context):
        # 加载预测数据
        history_data = context.options['data'].read_df()
    
        def seek_symbol(df):
            selected = df[(df['pb_lf_0'] < 1.5)
                & (df['pe_ttm_0'] < 15) 
                & (df['amount_0'] > 0) 
                & (df['pb_lf_0'] > 0)
                & (df['pe_ttm_0'] > 0)]
    
            # 按pe_ttm和pb_lf 升序排列
            selected = selected.sort_values(['pe_ttm_0', 'pb_lf_0'])
            return list(selected.instrument)[:30] # 记得转化成list
        #获取每日买入股票列表
        context.daily_buy_stock = history_data.groupby('date').apply(seek_symbol)
    
    # 回测引擎:初始化函数,只执行一次
    def m5_initialize_bigquant_run(context):
    
        # 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))
    
    m5 = M.trade.v3(
        instruments=m2.data,
        options_data=m6.data,
        start_date='',
        end_date='',
        handle_data=m5_handle_data_bigquant_run,
        prepare=m5_prepare_bigquant_run,
        initialize=m5_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',
        price_type='后复权',
        plot_charts=True,
        backtest_only=False,
        amount_integer=False
    )
    
    [2018-06-01 12:03:25.383779] INFO: bigquant: input_features.v1 开始运行..
    [2018-06-01 12:03:25.388275] INFO: bigquant: 命中缓存
    [2018-06-01 12:03:25.389961] INFO: bigquant: input_features.v1 运行完成[0.006213s].
    [2018-06-01 12:03:25.400241] INFO: bigquant: instruments.v2 开始运行..
    [2018-06-01 12:03:25.422584] INFO: bigquant: 命中缓存
    [2018-06-01 12:03:25.437939] INFO: bigquant: instruments.v2 运行完成[0.037678s].
    [2018-06-01 12:03:25.500182] INFO: bigquant: general_feature_extractor.v6 开始运行..
    [2018-06-01 12:03:25.518303] INFO: bigquant: 命中缓存
    [2018-06-01 12:03:25.530167] INFO: bigquant: general_feature_extractor.v6 运行完成[0.03s].
    [2018-06-01 12:03:25.603881] INFO: bigquant: derived_feature_extractor.v2 开始运行..
    [2018-06-01 12:03:25.626463] INFO: bigquant: 命中缓存
    [2018-06-01 12:03:25.629830] INFO: bigquant: derived_feature_extractor.v2 运行完成[0.025972s].
    [2018-06-01 12:03:25.652660] INFO: bigquant: dropnan.v1 开始运行..
    [2018-06-01 12:03:25.659748] INFO: bigquant: 命中缓存
    [2018-06-01 12:03:25.663764] INFO: bigquant: dropnan.v1 运行完成[0.011143s].
    [2018-06-01 12:03:25.724218] INFO: bigquant: backtest.v7 开始运行..
    [2018-06-01 12:03:33.634779] INFO: algo: set price type:backward_adjusted
    [2018-06-01 12:04:32.490793] INFO: Performance: Simulated 488 trading days out of 488.
    [2018-06-01 12:04:32.493337] INFO: Performance: first open: 2015-01-05 01:30:00+00:00
    [2018-06-01 12:04:32.495228] INFO: Performance: last close: 2016-12-30 07:00:00+00:00
    
    • 收益率33.16%
    • 年化收益率15.94%
    • 基准收益率-6.33%
    • 阿尔法0.17
    • 贝塔0.63
    • 夏普比率0.49
    • 胜率0.611
    • 盈亏比3.057
    • 收益波动率25.39%
    • 信息比率0.99
    • 最大回撤33.77%
    [2018-06-01 12:04:39.016623] INFO: bigquant: backtest.v7 运行完成[73.292357s].
    

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