策略出错 帮忙看一下 谢谢

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(yusheng361) #1
克隆策略
In [1]:
# 本代码由可视化策略环境自动生成 2019年12月30日 16:30
# 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=0.0003, 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 = 1
    # 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 = 0.5
    context.options['hold_days'] = 1

# 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):
    today = data.current_dt.strftime('%Y-%m-%d')
    
    #------------------------------------------止损模块START--------------------------------------------
    equities = {e.symbol: p for e, p in context.portfolio.positions.items() if p.amount>0}
    
    # 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
    stoploss_stock = [] 
    if len(equities) > 0:
        for i in equities.keys():
            stock_market_price = data.current(context.symbol(i), 'price')  # 最新市场价格
            last_sale_date = equities[i].last_sale_date   # 上次交易日期
            delta_days = data.current_dt - last_sale_date  
            hold_days = delta_days.days # 持仓天数
            # 建仓以来的最高价
            highest_price_since_buy = data.history(context.symbol(i), 'high', hold_days, '1d').max()
            # 确定止损位置
            stoploss_line = highest_price_since_buy - highest_price_since_buy * 0.10
            #record('止损位置', stoploss_line)
            # 如果价格下穿止损位置
            if stock_market_price < stoploss_line:
                context.order_target_percent(context.symbol(i), 0)     
                stoploss_stock.append(i)
        if len(stoploss_stock)>0:
            print('日期:', today, '股票:', stoploss_stock, '出现跟踪止损状况')
    #-------------------------------------------止损模块END---------------------------------------------    

    ranker_prediction = context.ranker_prediction[
        context.ranker_prediction.date == data.current_dt.strftime('%Y-%m-%d')]
    #print(ranker_prediction)
    # 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()}
    # 记录持仓中st的股票
    try:
        st_stock_list = []
        name_df = context.name_df
        name_today = name_df[name_df.date==today]
        for instrument in equities:
            name_instrument = name_today[name_today.instrument==instrument]['name'].values[0]
            # 如果股票状态变为了st 则卖出
            if 'ST' in name_instrument or '退' in name_instrument:
                # 指定一个limit_price,此时会以开盘价成交,这是由于初始化函数中改写了下单价格
                context.order_target(context.symbol(instrument), 0, limit_price=1.0)
                st_stock_list.append(instrument)
                cash_for_sell -= positions[instrument]
        if st_stock_list!=[]:
            print(today,'持仓出现st股/退市股',st_stock_list,'进行卖出处理')
    except:
        pass
    # 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StockRanker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[:1])[:]
    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:
            current_price = data.current(context.symbol(instrument), 'price')
            amount = math.floor(cash / current_price / 100) * 100
            context.order(context.symbol(instrument), amount)
# 回测引擎:准备数据,只执行一次
def m19_prepare_bigquant_run(context):
    context.name_df = DataSource('instruments_CN_STOCK_A').read()
    # 获取涨跌停状态
    context.price_limit_status = DataSource('stock_status_CN_STOCK_A').read(fields=['price_limit_status'])


m1 = M.instruments.v2(
    start_date='2008-01-01',
    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, -2) / 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_5-1
return_10-1
return_20-1
avg_amount_0/avg_amount_5-1
avg_amount_5/avg_amount_20-1
rank_avg_amount_0-rank_avg_amount_5
rank_avg_amount_5-rank_avg_amount_10
rank_return_0-rank_return_5
rank_return_5-rank_return_10
beta_csi300_30_0/10
beta_csi300_60_0/10
swing_volatility_5_0/swing_volatility_30_0-1
swing_volatility_30_0/swing_volatility_60_0-1
ta_atr_14_0/ta_atr_28_0-1
ta_sma_5_0/ta_sma_20_0-1
ta_sma_10_0/ta_sma_20_0-1
ta_sma_20_0/ta_sma_30_0-1
ta_sma_30_0/ta_sma_60_0-1
ta_rsi_14_0/100
ta_rsi_28_0/100
ta_cci_14_0/500
ta_cci_28_0/500
beta_industry_30_0/10
beta_industry_60_0/10
ta_sma(amount_0, 10)/ta_sma(amount_0, 20)-1
ta_sma(amount_0, 20)/ta_sma(amount_0, 30)-1
ta_sma(amount_0, 30)/ta_sma(amount_0, 60)-1
ta_sma(amount_0, 50)/ta_sma(amount_0, 100)-1
ta_sma(turn_0, 10)/ta_sma(turn_0, 20)-1
ta_sma(turn_0, 20)/ta_sma(turn_0, 30)-1
ta_sma(turn_0, 30)/ta_sma(turn_0, 60)-1
ta_sma(turn_0, 50)/ta_sma(turn_0, 100)-1
high_0/low_0-1
close_0/open_0-1
shift(close_0,1)/close_0-1
shift(close_0,2)/close_0-1
shift(close_0,3)/close_0-1
shift(close_0,4)/close_0-1
shift(close_0,5)/close_0-1
shift(close_0,10)/close_0-1
shift(close_0,20)/close_0-1
ta_sma(high_0-low_0, 5)/ta_sma(high_0-low_0, 20)-1
ta_sma(high_0-low_0, 10)/ta_sma(high_0-low_0, 20)-1
ta_sma(high_0-low_0, 20)/ta_sma(high_0-low_0, 30)-1
ta_sma(high_0-low_0, 30)/ta_sma(high_0-low_0, 60)-1
ta_sma(high_0-low_0, 50)/ta_sma(high_0-low_0, 100)-1
rank_avg_amount_5
rank_avg_turn_5
rank_volatility_5_0
rank_swing_volatility_5_0
rank_avg_mf_net_amount_5
rank_beta_industry_5_0
rank_return_5
rank_return_2
std(close_0,5)/std(close_0,20)-1
std(close_0,10)/std(close_0,20)-1
std(close_0,20)/std(close_0,30)-1
std(close_0,30)/std(close_0,60)-1
std(close_0,50)/std(close_0,100)-1"""
)

m15 = M.general_feature_extractor.v7(
    instruments=m1.data,
    features=m3.data,
    start_date='',
    end_date='',
    before_start_days=240
)

m4 = M.chinaa_stock_filter.v1(
    input_data=m15.data,
    index_constituent_cond=['全部'],
    board_cond=['全部'],
    industry_cond=['全部'],
    st_cond=['正常'],
    output_left_data=False
)

m16 = M.derived_feature_extractor.v3(
    input_data=m4.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
)

m10 = M.filter_delist_stock.v4(
    input_1=m7.data
)

m13 = M.dropnan.v1(
    input_data=m10.data_1
)

m20 = M.filter_stockcode.v2(
    input_1=m13.data,
    start='688'
)

m6 = M.stock_ranker_train.v5(
    training_ds=m20.data_1,
    features=m3.data,
    learning_algorithm='排序',
    number_of_leaves=30,
    minimum_docs_per_leaf=500,
    number_of_trees=200,
    learning_rate=0.25,
    max_bins=1023,
    feature_fraction=0.3,
    m_lazy_run=False
)

m9 = M.instruments.v2(
    start_date=T.live_run_param('trading_date', '2018-01-01'),
    end_date=T.live_run_param('trading_date', '2020-02-12'),
    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=240
)

m5 = M.chinaa_stock_filter.v1(
    input_data=m17.data,
    index_constituent_cond=['全部'],
    board_cond=['全部'],
    industry_cond=['全部'],
    st_cond=['正常'],
    output_left_data=False
)

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
)

m11 = M.filter_delist_stock.v4(
    input_1=m18.data
)

m14 = M.dropnan.v1(
    input_data=m11.data_1
)

m12 = M.filter_stockcode.v2(
    input_1=m14.data,
    start='688'
)

m8 = M.stock_ranker_predict.v5(
    model=m6.model,
    data=m12.data_1,
    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,
    order_price_field_buy='open',
    order_price_field_sell='close',
    capital_base=100000,
    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'
)
设置测试数据集,查看训练迭代过程的NDCG
bigcharts-data-start/{"__id":"bigchart-053d878baadf46418a94e0de3c1ff3ee","__type":"tabs"}/bigcharts-data-end

衍生特征抽取(derived_feature_extractor)使用错误,你可以:

1.一键查看文档

2.一键搜索答案

---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
<ipython-input-1-41844be5ae3f> in <module>()
    298     instrument_col='instrument',
    299     drop_na=False,
--> 300     remove_extra_columns=False
    301 )
    302 

IndexError: list index out of range

(iQuant) #2

收到,我们看下,稍后给您回复。


(达达) #3

A股股票过滤模块升级,模块需要添加参数
delist_cond=[‘全部’],

M.chinaa_stock_filter.v1(
    input_data=m17.data,
    index_constituent_cond=['全部'],
    board_cond=['全部'],
    industry_cond=['全部'],
    st_cond=['正常'],
    delist_cond=['全部'],
    output_left_data=False,

)

(yusheng361) #4

好的 谢谢 我试试


(iQuant) #5

也可以加这个参数:
delist_cond = [‘非退市’]