【模板案例】早盘买卖

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(iQuant) #1

10月15日模板案例:早盘买卖

克隆策略

    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    In [3]:
    # 本代码由可视化策略环境自动生成 2020年10月15日 16:19
    # 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=0.0018, sell_cost=0.0018, min_cost=5))
    
        # 设置持仓数量
        context.stock_count = 10
    # 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')].reset_index(drop=True)
    
        is_staging = context.trading_day_index < 1 # 是否在建仓期间(前 hold_days 天)
        cash_for_buy = context.portfolio.cash  # 获取账户现金
        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StockRanker预测的排序末位淘汰
    
        if not is_staging > 0:
            equities = {e.symbol: e for e, p in context.portfolio.positions.items()}
            stock_buy_num = context.stock_count - len(equities)
            for instrument in equities:
                try:
                    new_rank = ranker_prediction[ranker_prediction.instrument==instrument].index.tolist()[0]
                except:
                    new_rank = 50
                if data.can_trade(context.symbol(instrument)) and new_rank>40:
                    context.order_target(context.symbol(instrument), 0)
                    stock_buy_num += 1
                    cash_for_buy += positions[instrument]*0.9998  # 卖出一个股票就资金手动回笼(模拟手续费费率扣除处理万分之2)
                    
        else:
            stock_buy_num = context.stock_count
        # 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_instruments = list(ranker_prediction.instrument)
        stock_to_buy = [s for s in  buy_instruments if s not in positions][:stock_buy_num]
        buy_cash_weights =  T.norm([1.0 / math.log(i + 2) for i in range(stock_buy_num)])
        for i, instrument in enumerate(stock_to_buy):
            cash = cash_for_buy * buy_cash_weights[i]
            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='2010-01-01',
        end_date='2015-01-01',
        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/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.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
    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
    )
    
    m5 = M.dropnan.v2(
        input_data=m7.data
    )
    
    m4 = M.stock_ranker_train.v6(
        training_ds=m5.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,
        data_row_fraction=1,
        ndcg_discount_base=1,
        m_lazy_run=False
    )
    
    m9 = 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
    )
    
    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
    )
    
    m10 = M.dropnan.v2(
        input_data=m18.data
    )
    
    m8 = M.stock_ranker_predict.v5(
        model=m4.model,
        data=m10.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='close',
        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.SHA'
    )
    
    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-705a183c653c4f4cb8056745d946d554"}/bigcharts-data-end
    • 收益率95.94%
    • 年化收益率41.53%
    • 基准收益率-6.33%
    • 阿尔法0.44
    • 贝塔1.05
    • 夏普比率0.93
    • 胜率0.6
    • 盈亏比0.85
    • 收益波动率45.74%
    • 信息比率0.09
    • 最大回撤61.14%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-a1a3d2d0f16c4ce4a74121fc69cd2647"}/bigcharts-data-end

    BigQuant AI量化专家Meetup(更新至11月19日)