分钟策略研究------高手看看为什么跑不通

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克隆策略

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1\n else:\n context.sell_flag = 0\n \n if context.buy_flag==0 and context.sell_flag==0:\n return\n \n # 按日期过滤得到今日的预测数据\n ranker_prediction = context.ranker_prediction[\n context.ranker_prediction.date == data.current_dt.strftime('%Y-%m-%d %H:%M:%S')]\n\n # 1. 资金分配\n # 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\n # 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)\n is_staging = context.trading_day_index < context.hold_days # 是否在建仓期间(前 hold_days 天)\n cash_avg = context.portfolio.portfolio_value / context.hold_days\n cash_for_buy = min(context.portfolio.cash, (1 if is_staging else 1.5) * cash_avg)\n cash_for_sell = cash_avg - (context.portfolio.cash - cash_for_buy)\n positions = {e.symbol: p.amount * p.last_sale_price\n for e, p in context.perf_tracker.position_tracker.positions.items()}\n\n # 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\n if context.sell_flag>0:\n if not is_staging and cash_for_sell > 0:\n equities = {e.symbol: e for e, p in 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    In [3]:
    # 本代码由可视化策略环境自动生成 2020年8月30日 20:50
    # 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 m10_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 = 0.2
        context.hold_days = 5
        context.buy_flag = 0
        context.sell_flag = 1
    # 回测引擎:每日数据处理函数,每天执行一次
    def m10_handle_data_bigquant_run(context, data):
        # 每天只在固定时间买入轮仓
        if data.current_dt.strftime('%H:%M:%S')=='09:40:00':
            context.buy_flag = 1
        else:
            context.buy_flag = 0
    
        # 每天只在固定时间卖出轮仓
        if data.current_dt.strftime('%H:%M:%S')=='14:50:00':
            context.sell_flag = 1
        else:
            context.sell_flag = 0
       
        if context.buy_flag==0 and context.sell_flag==0:
            return
        
        # 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 %H:%M:%S')]
    
        # 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.hold_days # 是否在建仓期间(前 hold_days 天)
        cash_avg = context.portfolio.portfolio_value / context.hold_days
        cash_for_buy = min(context.portfolio.cash, (1 if is_staging else 1.5) * 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()}
    
        # 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 context.sell_flag>0:
            if not is_staging and cash_for_sell > 0:
                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]))])))
                # 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
        if context.buy_flag>0:
            buy_cash_weights = context.stock_weights
            buy_instruments = list(ranker_prediction.instrument[:len(buy_cash_weights)])
            max_cash_per_instrument = context.portfolio.portfolio_value * context.max_cash_per_instrument
            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)
                if cash > 0:
                    context.order_value(context.symbol(instrument), cash)
    
    # 回测引擎:准备数据,只执行一次
    def m10_prepare_bigquant_run(context):
        pass
    
    # 回测引擎:每个单位时间开始前调用一次,即每日开盘前调用一次。
    def m10_before_trading_start_bigquant_run(context, data):
        pass
    
    
    m1 = M.instruments.v2(
        start_date='2018-01-01',
        end_date='2019-12-31',
        market='CN_STOCK_A',
        instrument_list='',
        max_count=0
    )
    
    m13 = M.use_datasource.v1(
        instruments=m1.data,
        datasource_id='bar60m_CN_STOCK_A',
        start_date='',
        end_date=''
    )
    
    m11 = M.auto_labeler_on_datasource.v1(
        input_data=m13.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
    # 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)
    """,
        drop_na_label=True,
        cast_label_int=True,
        date_col='date',
        instrument_col='instrument',
        user_functions={}
    )
    
    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
    ta_rsi(close,14)/60
    ta_rsi(close,14)/40"""
    )
    
    m16 = M.derived_feature_extractor.v3(
        input_data=m13.data,
        features=m3.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=False,
        remove_extra_columns=False
    )
    
    m7 = M.join.v3(
        data1=m11.data,
        data2=m16.data,
        on='date,instrument',
        how='inner',
        sort=False
    )
    
    m12 = M.dropnan.v2(
        input_data=m7.data
    )
    
    m4 = M.stock_ranker_train.v6(
        training_ds=m12.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', '2020-01-01'),
        end_date=T.live_run_param('trading_date', '2020-08-28'),
        market='CN_STOCK_A',
        instrument_list='',
        max_count=0
    )
    
    m5 = M.use_datasource.v1(
        instruments=m9.data,
        datasource_id='bar60m_CN_STOCK_A',
        start_date='',
        end_date=''
    )
    
    m18 = M.derived_feature_extractor.v3(
        input_data=m5.data,
        features=m3.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=False,
        remove_extra_columns=False
    )
    
    m2 = M.dropnan.v2(
        input_data=m18.data
    )
    
    m8 = M.stock_ranker_predict.v5(
        model=m4.model,
        data=m2.data,
        m_lazy_run=False
    )
    
    m10 = M.trade.v4(
        instruments=m9.data,
        options_data=m8.predictions,
        start_date='',
        end_date='',
        initialize=m10_initialize_bigquant_run,
        handle_data=m10_handle_data_bigquant_run,
        prepare=m10_prepare_bigquant_run,
        before_trading_start=m10_before_trading_start_bigquant_run,
        volume_limit=0.025,
        order_price_field_buy='open',
        order_price_field_sell='close',
        capital_base=1000001,
        auto_cancel_non_tradable_orders=True,
        data_frequency='minute',
        price_type='真实价格',
        product_type='股票',
        plot_charts=True,
        backtest_only=False,
        benchmark=''
    )
    
    ---------------------------------------------------------------------------
    Exception                                 Traceback (most recent call last)
    <ipython-input-3-79abdaa541a3> in <module>()
         35 
         36 m2 = M.dropnan.v2(
    ---> 37     input_data=m18.data
         38 )
    
    Exception: no data left after dropnan

    Exception Traceback (most recent call last)
    in ()
    35
    36 m2 = M.dropnan.v2(
    —> 37 input_data=m18.data
    38 )

    Exception: no data left after dropnan
    为什么会没有数据了呢?


    (adhaha111) #2

    您好,现在您再试下呢


    (albertech) #3

    又出现新的问题

    [2020-09-01 09:51:18.998940] ERROR: moduleinvoker: module name: cached, module version: v2, trackeback: Traceback (most recent call last):
    tables.exceptions.HDF5ExtError: HDF5 error back trace

    File “H5Dio.c”, line 216, in H5Dread
    can’t read data
    File “H5Dio.c”, line 587, in H5D__read
    can’t read data
    File “H5Dchunk.c”, line 2304, in H5D__chunk_read
    unable to read raw data chunk
    File “H5Dchunk.c”, line 3659, in H5D__chunk_lock
    data pipeline read failed
    File “H5Z.c”, line 1279, in H5Z_pipeline
    filter returned failure during read
    File “hdf5-blosc/src/blosc_filter.c”, line 254, in blosc_filter
    Blosc decompression error

    End of HDF5 error back trace

    Problems reading the array data.
    [2020-09-01 09:51:19.251615] ERROR: moduleinvoker: module name: backtest, module version: v8, trackeback: Traceback (most recent call last):
    tables.exceptions.HDF5ExtError: HDF5 error back trace

    File “H5Dio.c”, line 216, in H5Dread
    can’t read data
    File “H5Dio.c”, line 587, in H5D__read
    can’t read data
    File “H5Dchunk.c”, line 2304, in H5D__chunk_read
    unable to read raw data chunk
    File “H5Dchunk.c”, line 3659, in H5D__chunk_lock
    data pipeline read failed
    File “H5Z.c”, line 1279, in H5Z_pipeline
    filter returned failure during read
    File “hdf5-blosc/src/blosc_filter.c”, line 254, in blosc_filter
    Blosc decompression error

    End of HDF5 error back trace

    Problems reading the array data.
    [2020-09-01 09:51:19.256412] ERROR: moduleinvoker: module name: trade, module version: v4, trackeback: Traceback (most recent call last):
    tables.exceptions.HDF5ExtError: HDF5 error back trace

    File “H5Dio.c”, line 216, in H5Dread
    can’t read data
    File “H5Dio.c”, line 587, in H5D__read
    can’t read data
    File “H5Dchunk.c”, line 2304, in H5D__chunk_read
    unable to read raw data chunk
    File “H5Dchunk.c”, line 3659, in H5D__chunk_lock
    data pipeline read failed
    File “H5Z.c”, line 1279, in H5Z_pipeline
    filter returned failure during read
    File “hdf5-blosc/src/blosc_filter.c”, line 254, in blosc_filter
    Blosc decompression error

    End of HDF5 error back trace

    Problems reading the array data.


    (albertech) #4

    没有下文了吗?问题还没解决哦!!!!!!!!!!!!!!!