可视化均线金叉死叉策略

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(iQuant) #1
克隆策略

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    In [4]:
    # 本代码由可视化策略环境自动生成 2018年6月13日 14:00
    # 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
    mean(close_0,5)
    mean(close_0,10)"""
    )
    
    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='600006.SHA',
        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):
        today=data.current_dt.strftime('%Y-%m-%d')
        # 长期均线值要有意义,需要在50根k线之后
        k = context.instruments[0] # 标的为字符串格式
        sid = context.symbol(k) # 将标的转化为equity格式
        price = data.current(sid, 'price') # 最新价格
        #如果今天有均线值
        if today in context.short_mavg.index:
            short_mavg =context.short_mavg.ix[today]  # 短期均线值
            long_mavg = context.long_mavg.ix[today]   # 长期均线值
        else:
            return
          
        cash = context.portfolio.cash  # 现金
        cur_position = context.portfolio.positions[sid].amount # 持仓
        
        # 交易逻辑
        # 如果短期均线大于长期均线形成金叉,并且没有持仓,并且该股票可以交易
        if short_mavg > long_mavg and cur_position == 0 and data.can_trade(sid):  
            context.order(sid, int(cash/price/100)*100) # 买入
        # 如果短期均线小于长期均线形成死叉,并且有持仓,并且该股票可以交易
        elif short_mavg < long_mavg and cur_position > 0 and data.can_trade(sid):  
            context.order_target_percent(sid, 0) # 全部卖出
    
    # 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().set_index('date')
        context.instruments=df.instrument.unique()
        context.short_mavg=df['mean(close_0,5)']
        context.long_mavg=df['mean(close_0,10)']
    
    # 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 14:18:42.656265] INFO: bigquant: input_features.v1 开始运行..
    [2018-06-01 14:18:42.663262] INFO: bigquant: 命中缓存
    [2018-06-01 14:18:42.665081] INFO: bigquant: input_features.v1 运行完成[0.008852s].
    [2018-06-01 14:18:42.684597] INFO: bigquant: instruments.v2 开始运行..
    [2018-06-01 14:18:42.691773] INFO: bigquant: 命中缓存
    [2018-06-01 14:18:42.693520] INFO: bigquant: instruments.v2 运行完成[0.008969s].
    [2018-06-01 14:18:42.723468] INFO: bigquant: general_feature_extractor.v6 开始运行..
    [2018-06-01 14:18:42.746640] INFO: bigquant: 命中缓存
    [2018-06-01 14:18:42.765848] INFO: bigquant: general_feature_extractor.v6 运行完成[0.042357s].
    [2018-06-01 14:18:43.149590] INFO: bigquant: derived_feature_extractor.v2 开始运行..
    [2018-06-01 14:18:43.159442] INFO: bigquant: 命中缓存
    [2018-06-01 14:18:43.164396] INFO: bigquant: derived_feature_extractor.v2 运行完成[0.014784s].
    [2018-06-01 14:18:43.209687] INFO: bigquant: dropnan.v1 开始运行..
    [2018-06-01 14:18:43.215080] INFO: bigquant: 命中缓存
    [2018-06-01 14:18:43.216760] INFO: bigquant: dropnan.v1 运行完成[0.007114s].
    [2018-06-01 14:18:43.451548] INFO: bigquant: backtest.v7 开始运行..
    [2018-06-01 14:18:43.545643] INFO: bigquant: 命中缓存
    
    • 收益率16.9%
    • 年化收益率8.4%
    • 基准收益率-6.33%
    • 阿尔法0.09
    • 贝塔0.63
    • 夏普比率0.11
    • 胜率0.458
    • 盈亏比1.446
    • 收益波动率46.74%
    • 信息比率0.27
    • 最大回撤44.04%
    [2018-06-01 14:18:47.830872] INFO: bigquant: backtest.v7 运行完成[4.379307s].
    

    [量化学堂-策略开发]金叉死叉策略
    传统策略的可视化开发
    (richewan) #2

    instrument_list=‘600006.SHA’
    把这条改成instrument_list=’ ’ 基于全部的股票就报错了 请问要怎么调整下,对市场上所有的股票做选择


    (神龙斗士) #3

    这个示例策略主要是用单只股票测试的,要支持所有股票:

    1. 去掉filter模块
    2. 交易模块那里的代码配置,需要修改一下,来支持多只股票