模拟交易的时候报错Error怎么整


(h4527) #1

模拟交易的时候报错Error怎么整,回测的实收没有问题。

# 本代码由可视化策略环境自动生成 2019年2月12日 21:43
# 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:每日数据处理函数,每天执行一次
def m10_handle_data_bigquant_run(context, data):
    #------------------------------------------止损模块START--------------------------------------------
    positions = {e.symbol: p.cost_basis  for e, p in context.portfolio.positions.items()}
    # 新建当日止损股票列表是为了handle_data 策略逻辑部分不再对该股票进行判断
    current_stoploss_stock = []  # 将当天止损的股票整理到一个集合
    if len(positions) > 0:
        for i in positions.keys():
            stock_cost = positions[i] 
            stock_market_price = data.current(context.symbol(i), 'price') 
            # 亏5%就止损
            if (stock_market_price - stock_cost) / stock_cost <= -0.03:   
                context.order_target_percent(context.symbol(i),0)     
                current_stoploss_stock.append(i)
            #    print('日期:',date,'股票:',i,'出现止损状况')
    #-------------------------------------------止损模块END---------------------------------------------
    # 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')]
    # 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.options['hold_days'] # 是否在建仓期间(前 hold_days 天)
    cash_avg = context.portfolio.portfolio_value / context.options['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,按机器学习算法预测的排序末位淘汰
    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:按机器学习算法预测的排序,买入前面的stock_count只股票
    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: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 = 15
    # 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.options['hold_days'] = 5


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
rank_turn_3 #过去i个交易日的换手率 (turn_i) 排名,=从小到大排名序号/总数
return_3
return_10 #10日累计收益
return_360 #360日累计收益
return_360/market_cap_float_0 #360日累计收益与流通市值比
west_netprofit_ftm_0 #一致预测净利润(未来12个月)
west_eps_ftm_0	#一致预测每股收益(未来12个月)
west_avgcps_ftm_0	#一致预测每股现金流(未来12个月)
list_board_0 #所在版块
ta_sma_5_0 
ta_sma_10_0
ta_sma_20_0
ta_sma_30_0
ta_sma_60_0
fs_eps_yoy_0	#每股收益同比增长率
rank_fs_cash_ratio_0	#现金比率,升序百分比排名 
fs_roe_ttm_0	#净资产收益率 (TTM)
fs_net_profit_yoy_0	#归属母公司股东的净利润同比增长率
ta_macd_macd_12_26_9_0 #MACD
swing_volatility_5_0 #波动率
price_limit_status_1+price_limit_status_2+price_limit_status_3+price_limit_status_4+price_limit_status_5+price_limit_status_6+price_limit_status_7+price_limit_status_8+price_limit_status_9+price_limit_status_10
avg_amount_0/avg_amount_5
rank_avg_amount_0/rank_avg_amount_5
pe_ttm_0
rank_avg_mf_net_amount_3
avg_mf_net_amount_3/market_cap_float_0
# BOLL、成交量相关
(avg_amount_10/avg_amount_5) * sum((ta_sma_20_0 + 2 * std(close_0, 20)),5) / 5
(avg_amount_10/avg_amount_5) * sum((ta_sma_20_0 - 2 * std(close_0, 20)),5) / 5

"""
)

m9 = M.instruments.v2(
    start_date=T.live_run_param('trading_date', '2018-01-01'),
    end_date=T.live_run_param('trading_date', '2019-02-12'),
    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=0
)

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
)

m14 = M.dropnan.v1(
    input_data=m18.data
)

m4 = M.rolling_conf.v1(
    start_date='2010-01-01',
    end_date='2019-01-31',
    rolling_update_days=365,
    rolling_min_days=730,
    rolling_max_days=0,
    rolling_count_for_live=1
)

m1 = M.instruments.v2(
    rolling_conf=m4.data,
    start_date='',
    end_date='',
    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/data_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/big_expr.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
)

m15 = M.general_feature_extractor.v7(
    instruments=m1.data,
    features=m3.data,
    start_date='',
    end_date='',
    before_start_days=0
)

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
)

m13 = M.dropnan.v1(
    input_data=m7.data
)

m6 = M.stock_ranker_train.v5(
    training_ds=m13.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,
    m_lazy_run=True
)

m5 = M.rolling_run.v1(
    run=m6.m_lazy_run,
    input_list=m4.data,
    param_name='rolling_input'
)

m8 = M.stock_ranker_predict.v5(
    model=m5.data,
    data=m14.data,
    m_lazy_run=False
)

m10 = M.trade.v4(
    instruments=m9.data,
    options_data=m8.predictions,
    start_date='',
    end_date='',
    handle_data=m10_handle_data_bigquant_run,
    prepare=m10_prepare_bigquant_run,
    initialize=m10_initialize_bigquant_run,
    volume_limit=0.025,
    order_price_field_buy='open',
    order_price_field_sell='close',
    capital_base=1000000,
    auto_cancel_non_tradable_orders=True,
    data_frequency='daily',
    price_type='后复权',
    product_type='股票',
    plot_charts=True,
    backtest_only=False,
    benchmark='000300.SHA'
)

(netsophier) #2

你得贴出来是什么错误信息。
如果是那个no data left after dropna,搜一下社区或者看下我发过的贴子,有解决办法。


(小Q) #3

可否将报错的截图贴一下呢?


(h4527) #4


(yusheng361) #5

你点这个日志进去 翻到末尾 看报的错误是什么


(h4527) #6

Exception Traceback (most recent call last)
in ()
139
140 m14 = M.dropnan.v1(
–> 141 input_data=m18.data
142 )
143

/var/app/enabled/biglearning/module2/common/modulemanagerv2.cpython-35m-x86_64-linux-gnu.so in biglearning.module2.common.modulemanagerv2.BigQuantModuleVersion.call()

/var/app/enabled/biglearning/module2/common/moduleinvoker.cpython-35m-x86_64-linux-gnu.so in biglearning.module2.common.moduleinvoker.module_invoke()

/var/app/enabled/biglearning/module2/common/moduleinvoker.cpython-35m-x86_64-linux-gnu.so in biglearning.module2.common.moduleinvoker._invoke_with_cache()

/var/app/enabled/biglearning/module2/common/moduleinvoker.cpython-35m-x86_64-linux-gnu.so in biglearning.module2.common.moduleinvoker._invoke_with_cache()

/var/app/enabled/biglearning/module2/common/moduleinvoker.cpython-35m-x86_64-linux-gnu.so in biglearning.module2.common.moduleinvoker._module_run()

/var/app/enabled/biglearning/module2/modules/dropnan/v1/init.py in run(self)
42
43 if after_drop_row_count == 0:
—> 44 raise Exception(‘no data left after dropnan’)
45 DisplayLog(log).info(‘行数: %s/%s’ % (after_drop_row_count, before_drop_row_count))
46

Exception: no data left after dropnan


运行报错是这样的


(iQuant) #7

收到您的提问,我们帮您看一下,稍后给您回复。


(iQuant) #8

您好,问题已经解决,您现在再看一下呢