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(iQuant) #1
克隆策略

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    In [9]:
    # 本代码由可视化策略环境自动生成 2018年6月13日 13:57
    # 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
    market_cap_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')
        # 根据日期获取调仓需要买入的股票的列表
        print(date)
        
        stock_to_buy = list(context.daily_buy_stock.ix[date].instrument)
        # 通过positions对象,使用列表生成式的方法获取目前持仓的股票列表
        stock_hold_now = [equity.symbol for equity in context.portfolio.positions]
        # 继续持有的股票:调仓时,如果买入的股票已经存在于目前的持仓里,那么应继续持有
        no_need_to_sell = [i for i in stock_hold_now if i in stock_to_buy]
        # 需要卖出的股票
        stock_to_sell = [i for i in stock_hold_now if i not in no_need_to_sell]
    
        # 卖出
        for stock in stock_to_sell:
            # 如果该股票停牌,则没法成交。因此需要用can_trade方法检查下该股票的状态
            # 如果返回真值,则可以正常下单,否则会出错
            # 因为stock是字符串格式,我们用symbol方法将其转化成平台可以接受的形式:Equity格式
    
            if data.can_trade(context.symbol(stock)):
                # order_target_percent是平台的一个下单接口,表明下单使得该股票的权重为0,
                #   即卖出全部股票,可参考回测文档
                context.order_target_percent(context.symbol(stock), 0)
        
        # 如果当天没有买入的股票,就返回
        if len(stock_to_buy) == 0:
            return
    
        # 等权重买入 
        weight =  1 / len(stock_to_buy)
        
        # 买入
        for stock in stock_to_buy:
            if data.can_trade(context.symbol(stock)):
                # 下单使得某只股票的持仓权重达到weight,因为
                # weight大于0,因此是等权重买入
                context.order_target_percent(context.symbol(stock), 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
        df = context.options['data'].read_df()
    
        # 获取股票总市值数据,返回DataFrame数据格式  
        market_cap_data=df[['date','instrument','amount_0','market_cap_0']]
        
        # 获取每日按小市值排序 (从低到高)的前三十只股票
        daily_buy_stock = market_cap_data.groupby('date').apply(lambda df:df[(df['amount_0'] > 0)].sort_values('market_cap_0')[:30])
        context.daily_buy_stock = daily_buy_stock
    
    # 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
    )
    
    [2018-05-31 20:08:40.616820] INFO: bigquant: input_features.v1 开始运行..
    [2018-05-31 20:08:40.621087] INFO: bigquant: 命中缓存
    [2018-05-31 20:08:40.622182] INFO: bigquant: input_features.v1 运行完成[0.005379s].
    [2018-05-31 20:08:40.630141] INFO: bigquant: instruments.v2 开始运行..
    [2018-05-31 20:08:40.644899] INFO: bigquant: 命中缓存
    [2018-05-31 20:08:40.646322] INFO: bigquant: instruments.v2 运行完成[0.016154s].
    [2018-05-31 20:08:40.671136] INFO: bigquant: general_feature_extractor.v6 开始运行..
    [2018-05-31 20:08:42.031163] INFO: 基础特征抽取: 年份 2014, 特征行数=51091
    [2018-05-31 20:08:43.853423] INFO: 基础特征抽取: 年份 2015, 特征行数=569698
    [2018-05-31 20:08:45.231281] INFO: 基础特征抽取: 年份 2016, 特征行数=641546
    [2018-05-31 20:08:48.561640] INFO: 基础特征抽取: 年份 2017, 特征行数=0
    [2018-05-31 20:08:48.586317] INFO: 基础特征抽取: 总行数: 1262335
    [2018-05-31 20:08:48.593777] INFO: bigquant: general_feature_extractor.v6 运行完成[7.922669s].
    [2018-05-31 20:08:48.603164] INFO: bigquant: derived_feature_extractor.v2 开始运行..
    [2018-05-31 20:08:49.190918] INFO: derived_feature_extractor: /y_2014, 51091
    [2018-05-31 20:08:49.403570] INFO: derived_feature_extractor: /y_2015, 569698
    [2018-05-31 20:08:50.085420] INFO: derived_feature_extractor: /y_2016, 641546
    [2018-05-31 20:08:50.500778] INFO: bigquant: derived_feature_extractor.v2 运行完成[1.897583s].
    [2018-05-31 20:08:50.512732] INFO: bigquant: dropnan.v1 开始运行..
    [2018-05-31 20:08:50.653675] INFO: dropnan: /y_2014, 51091/51091
    [2018-05-31 20:08:51.200852] INFO: dropnan: /y_2015, 569698/569698
    [2018-05-31 20:08:51.891284] INFO: dropnan: /y_2016, 641546/641546
    [2018-05-31 20:08:51.924928] INFO: dropnan: 行数: 1262335/1262335
    [2018-05-31 20:08:51.962388] INFO: bigquant: dropnan.v1 运行完成[1.449625s].
    [2018-05-31 20:08:52.012339] INFO: bigquant: backtest.v7 开始运行..
    [2018-05-31 20:08:54.817467] INFO: algo: set price type:backward_adjusted
    2015-02-13
    2015-04-03
    2015-05-19
    2015-07-01
    2015-08-12
    2015-09-25
    2015-11-13
    2015-12-25
    2016-02-15
    2016-03-28
    2016-05-11
    2016-06-24
    2016-08-05
    2016-09-20
    2016-11-08
    2016-12-20
    [2018-05-31 20:10:47.641487] INFO: Performance: Simulated 488 trading days out of 488.
    [2018-05-31 20:10:47.648309] INFO: Performance: first open: 2015-01-05 01:30:00+00:00
    [2018-05-31 20:10:47.654329] INFO: Performance: last close: 2016-12-30 07:00:00+00:00
    
    • 收益率236.31%
    • 年化收益率87.07%
    • 基准收益率-6.33%
    • 阿尔法0.88
    • 贝塔0.59
    • 夏普比率2.99
    • 胜率0.9
    • 盈亏比1.967
    • 收益波动率28.02%
    • 信息比率3.7
    • 最大回撤34.52%
    [2018-05-31 20:10:51.577533] INFO: bigquant: backtest.v7 运行完成[119.565207s].
    

    传统策略的可视化开发