复制链接

克隆策略

In [1]:

# 本代码由可视化策略环境自动生成 2022年5月6日 09:45
# 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。


# Python 代码入口函数，input_1/2/3 对应三个输入端，data_1/2/3 对应三个输出端
def m8_run_bigquant_run(input_1, input_index):
    # 示例代码如下。在这里编写您的代码
    start_date=input_1.read_pickle()['start_date']
    end_date=input_1.read_pickle()['end_date']
    df = DataSource('bar1d_index_CN_STOCK_A').read(instruments=[input_index],start_date=start_date,end_date=end_date,fields=['close'])
    data_1 = DataSource.write_df(df)
    return Outputs(data_1=data_1, data_2=None, data_3=None)

# 后处理函数，可选。输入是主函数的输出，可以在这里对数据做处理，或者返回更友好的outputs数据格式。此函数输出不会被缓存。
def m8_post_run_bigquant_run(outputs):
    return outputs

# Python 代码入口函数，input_1/2/3 对应三个输入端，data_1/2/3 对应三个输出端
def m22_run_bigquant_run(input_1, input_2, input_3):
    # 示例代码如下。在这里编写您的代码
    df = input_1.read_df()
    # 缺失值处理
    # if len(df)!=0:
    #     df.dropna(inplace=True)
        
    # 选股条件
    if len(df)!=0:
        df_filter1 = df[df['cond1']>0]
    else:
        df_filter1 = df
    
    # 指标排序
    if len(df_filter1)!=0:
        df_filter2 = df_filter1.groupby('date').apply(lambda x:x.sort_values(by=['cond2'],ascending=True))
    else:
        df_filter2 = df_filter1
    
    #输出条件过滤股票池
    data_1 = DataSource.write_df(df_filter2)

    
    # 进场条件
    if len(df)!=0:
        df_buy = df[df['cond3']>0]
    else:
        df_buy = df
    # 输出满足进场条件的股票池
    data_2 = DataSource.write_df(df_buy)

    
    # 出场条件
    if len(df)!=0:
        df_sell = df[df['cond4']>0]
    else:
        df_sell = df
    # 输出满足出场条件的股票池
    data_3 = DataSource.write_df(df_sell)    
    
    return Outputs(data_1=data_1, data_2=data_2, data_3=data_3)

# 后处理函数，可选。输入是主函数的输出，可以在这里对数据做处理，或者返回更友好的outputs数据格式。此函数输出不会被缓存。
def m22_post_run_bigquant_run(outputs):
    return outputs

# Python 代码入口函数，input_1/2/3 对应三个输入端，data_1/2/3 对应三个输出端
def m17_run_bigquant_run(input_1, input_2, input_3):
    df1 = input_1.read_df()
    df2 = input_2.read_df()
    df3 = input_3.read_df()

    if len(df1.index.names) == 2:
        df1.index.names = [None, None]
    else:
        df1.index.names = [None]
    
    df = {'df1':df1,'df2':df2,'df3':df3}
    ds = DataSource.write_pickle(df)
    return Outputs(data_1=ds)

# 后处理函数，可选。输入是主函数的输出，可以在这里对数据做处理，或者返回更友好的outputs数据格式。此函数输出不会被缓存。
def m17_post_run_bigquant_run(outputs):
    return outputs


def prepare_index_data(context):
    """准备指数数据"""
    if context.market_risk_conf != []:
        if len(context.market_risk_conf) == 1:
            index_code =  context.market_risk_conf[0]['params']['index_code']
            start_date = '2005-01-01'
            end_date = context.end_date
            index_data = DataSource('bar1d_index_CN_STOCK_A').read(instruments=[index_code], start_date=start_date, end_date=end_date).set_index('date')
            
            if  context.market_risk_conf[0]['method'] == 'market_ma_stoploss':
                ma_periods = int(context.market_risk_conf[0]['params']['ma_periods'])
                index_data['ma_%s'%ma_periods] = index_data['close'].rolling(ma_periods).mean()
                index_data['signal'] = np.where(index_data['close'] > index_data['ma_%s'%ma_periods], 'long', 'short')

            elif context.market_risk_conf[0]['method'] == 'market_fallrange_stoploss':
                days = context.market_risk_conf[0]['params']['days']
                fallrange = context.market_risk_conf[0]['params']['fallrange']
                index_data['signal'] = np.where(index_data['close']/index_data['close'].shift(days)-1 <= fallrange, 'long', 'short')
            context.index_signal_data = index_data 
            
        if  len(context.market_risk_conf) == 2:
            start_date = '2005-01-01'
            end_date = context.end_date 
            if context.market_risk_conf[0]['method'] == 'market_ma_stoploss':              
                index_code_1 = context.market_risk_conf[0]['params']['index_code']
                index_data_1 = DataSource('bar1d_index_CN_STOCK_A').read(instruments=[index_code_1], start_date=start_date, end_date=end_date).set_index('date')
                ma_periods = int(context.market_risk_conf[0]['params']['ma_periods'])
                
                index_code_2 =  context.market_risk_conf[1]['params']['index_code']
                index_data_2 = DataSource('bar1d_index_CN_STOCK_A').read(instruments=[index_code_2], start_date=start_date, end_date=end_date).set_index('date')
                days = context.market_risk_conf[1]['params']['days']
                fallrange = context.market_risk_conf[1]['params']['fallrange']
            else:
                index_code_1 = context.market_risk_conf[1]['params']['index_code']
                index_data_1 = DataSource('bar1d_index_CN_STOCK_A').read(instruments=[index_code_1], start_date=start_date, end_date=end_date).set_index('date')
                ma_periods = int(context.market_risk_conf[1]['params']['ma_periods'])
                
                index_code_2 =  context.market_risk_conf[0]['params']['index_code']
                index_data_2 = DataSource('bar1d_index_CN_STOCK_A').read(instruments=[index_code_2], start_date=start_date, end_date=end_date).set_index('date')
                days = context.market_risk_conf[0]['params']['days']
                fallrange = context.market_risk_conf[0]['params']['fallrange'] 
                
            index_data_1['ma_%s'%ma_periods] = index_data_1['close'].rolling(ma_periods).mean()
            index_data_1['signal_1'] = np.where(index_data_1['close'] > index_data_1['ma_%s'%ma_periods], 1, 0)
            signal_1 = index_data_1[['signal_1']].reset_index()                
            index_data_2['signal_2'] = np.where(index_data_2['close']/index_data_2['close'].shift(days)-1 <= fallrange, 1, 0)
            signal_2 = index_data_2[['signal_2']].reset_index()
            signal = pd.merge(signal_1,signal_2).set_index('date')
            signal['signal_sum'] = signal['signal_1'] + signal['signal_2']
            signal['signal'] = np.where(signal['signal_sum']>0,'long','short')     
            context.index_signal_data = signal
    else:
        context.index_signal_data = None  

def m4_initialize_bigquant_run(context):
    context.set_commission(PerOrder(buy_cost=0.0003, sell_cost=0.0013, min_cost=5))
    context.selected_stock = []
    context.trade_mode = '轮动'

    if context.trade_mode == '轮动':
        context.buy_frequency = 1
        context.sell_frequency = 1
        context.rebalance_periods = 1 # 调仓周期
        context.max_stock_count = 5 # 最大持仓股票数量
        context.order_weight_method = 'equal_weight' # 买入方式
        context.is_sell_willbuy_stock = False  # 卖出欲买进股票 
    else:
        # 买入条件参数
        context.stock_select_frequency = 1 # 选股频率
        context.order_weight_method = 'equal_weight' # 买入方式
        context.buy_frequency = 2 # 买入频率
        context.can_duplication_buy = False  # 是否可重复买入
        context.max_stock_count = 5 # 最大持仓股票数量
        context.max_stock_weight = 1 # 个股最大持仓比重

        # 卖出条件参数
        context.sell_frequency = 10 # 卖出频率
        context.is_sell_willbuy_stock = False  # 卖出欲买进股票 

    # 风控参数 
    context.stock_risk_conf = [{'method':'stock_percent_stoploss', 'params':{'percent': 0.05}}] # 支持多选  无：[]
    context.strategy_risk_conf = []  # 支持多选 无：[]
    context.market_risk_conf = [] # 支持多选, 无： []
    
    prepare_index_data(context)
    slippage_type = 'price'
    from zipline.finance.slippage import SlippageModel
    class FixedPriceSlippage(SlippageModel):
        # 指定初始化函数
        def __init__(self, spreads, price_field_buy, price_field_sell):
            # 存储spread的字典，用股票代码作为key
            self.spreads = spreads
            self._price_field_buy = price_field_buy
            self._price_field_sell = price_field_sell
        def process_order(self, data, order, bar_volume=0, trigger_check_price=0):
            if order.limit is None:
                price_field = self._price_field_buy if order.amount > 0 else self._price_field_sell
                price_base = data.current(order.asset, price_field)
                if slippage_type == 'price':
                    price = price_base + (self.spreads / 2) if order.amount > 0 else price_base - (self.spreads / 2)
                else:
                    price = price_base * (1.0 + self.spreads / 2) if order.amount > 0 else price_base * (1.0 - self.spreads / 2)
            else:
                price = order.limit
                # 返回希望成交的价格和数量
            return (price, order.amount)
    # 设置price_field
    fix_slippage = FixedPriceSlippage(price_field_buy='open', price_field_sell='open', spreads=0.02)
    context.set_slippage(us_equities=fix_slippage)
#--------------------------------------------------------------------
# 卖出条件
#--------------------------------------------------------------------     
def sell_action(context, data):
    date = data.current_dt.strftime('%Y-%m-%d')
    hit_stop_stock = context.stock_hit_stop 
    
    try:
        today_enter_stock = context.enter_daily_df.loc[date] 
    except KeyError as e:
        today_enter_stock = []
    try:
        today_exit_stock = context.exit_daily_df.loc[date] 
    except KeyError as e:
        today_exit_stock = []
        
    target_stock_to_buy = [i for i in  context.selected_stock  if i in today_enter_stock ]   
    stock_hold_now = [equity.symbol for equity in context.portfolio.positions] # 当前持仓股票
    
    if  context.trading_day_index % context.sell_frequency == 0:
        stock_to_sell = [i for i in stock_hold_now if i in today_exit_stock] # 要卖出的股票
        stock_buy_and_sell = [i for i in stock_to_sell if i in target_stock_to_buy]
        if context.is_sell_willbuy_stock == False: # 要买入的股票不卖出,但该票也不再买入
            stock_to_sell.extend(hit_stop_stock) # 将触发个股风控的股票融入到卖出票池
            stock_to_sell = [i for i in stock_to_sell if i not in stock_buy_and_sell] # 进行更新而已
        elif context.is_sell_willbuy_stock == True: # 要买入的股票依然要卖出,该票不再买入
            stock_to_sell.extend(hit_stop_stock)
        
        # 买入时需要过滤的股票
        context.cannot_buy_stock = stock_buy_and_sell
            
        for stock in stock_to_sell:
            if data.can_trade(context.symbol(stock)):
                context.order_target_percent(context.symbol(stock), 0)
                del context.portfolio.positions[context.symbol(stock)]


#--------------------------------------------------------------------
# 买入条件
#--------------------------------------------------------------------     
def buy_action(context, data):
    date = data.current_dt.strftime('%Y-%m-%d')
    
    try:
        today_enter_stock = context.enter_daily_df.loc[date] 
    except KeyError as e:
        today_enter_stock = []
    try:
        today_exit_stock = context.exit_daily_df.loc[date] 
    except KeyError as e:
        today_exit_stock = []
    
    target_stock_to_buy = [i for i in  context.selected_stock if i in today_enter_stock]  
    target_stock_to_buy = [s for s in target_stock_to_buy if s not in context.cannot_buy_stock] # 进行更新，不能买入的股票要过滤
    
    stock_hold_now = [equity.symbol for equity in context.portfolio.positions] # 当前持仓股票
    
    # 确定股票权重
    if context.order_weight_method == 'equal_weight':
        equal_weight =  1 / context.max_stock_count
        
    portfolio_value = context.portfolio.portfolio_value
    position_current_value = {pos.sid: pos.amount* pos.last_sale_price for i,pos in context.portfolio.positions.items()}
    
    # 买入
    if  context.trading_day_index % context.buy_frequency == 0:
        if len(stock_hold_now) >= context.max_stock_count:
            return 
        
        today_buy_count = 0
        if context.trade_mode == '轮动':
            for s in target_stock_to_buy:
                if today_buy_count + len(stock_hold_now) >= context.max_stock_count: # 超出最大持仓数量
                    break
                if data.can_trade(context.symbol(s)):
                    order_target_percent(context.symbol(s), equal_weight)
                    today_buy_count += 1
        else:
            if context.can_duplication_buy == True: # 可以重复买入，多一份买入
                for s in target_stock_to_buy:
                    if today_buy_count + len(stock_hold_now) >= context.max_stock_count: # 超出最大持仓数量
                        break
                        
                    if data.can_trade(context.symbol(s)):
                        if context.symbol(s) in position_current_weight:
                            curr_value = position_current_value.get(context.symbol(s)) 
                            order_value(context.symbol(s), min(context.max_stock_weight * portfolio_value - curr_value, equal_weight*portfolio_value))
                        else:
                            order_value(context.symbol(s), equal_weight*portfolio_value)
                        today_buy_count += 1

            elif context.can_duplication_buy == False: # 不可以重复买入，不买
                for s in target_stock_to_buy:
                    if today_buy_count + len(stock_hold_now) >= context.max_stock_count: # 超出最大持仓数量
                        break
                    if s in stock_hold_now:
                        continue
                    else:
                        if data.can_trade(context.symbol(s)):
                            order_target_percent(context.symbol(s), equal_weight)
                            today_buy_count += 1

                        
#--------------------------------------------------------------------
# 风控体系
#--------------------------------------------------------------------                         
def market_risk_manage(context, data):
    """大盘风控"""
    date = data.current_dt.strftime('%Y-%m-%d')
    if type(context.index_signal_data) == pd.DataFrame:
        current_signal = context.index_signal_data.loc[date]['signal']
        if current_signal == 'short': 
            stock_hold_now = [equity.symbol for equity in context.portfolio.positions]  
            # 平掉所有股票
            for stock in stock_hold_now:
                if data.can_trade(context.symbol(stock)):
                    context.order_target_percent(context.symbol(stock), 0) 
            print('大盘出现止损信号， 平掉全部仓位，并关闭交易！')
            context.market_risk_signal = 'short'
    else:
        context.market_risk_signal = 'long'

        
        
def strategy_risk_manage(context, data):
    """策略风控"""
    if context.strategy_risk_conf == []: # 没有设置策略风控
        context.strategy_risk_signal = 'long'
    
    else:
        for rm in context.strategy_risk_conf:
            if rm['method'] == 'strategy_percent_stopwin':
                pct = rm['params']['percent']
                portfolio_value = context.portfolio.portfolio_value 
                if  portfolio_value / context.capital_base - 1 > pct: 
                    stock_hold_now = [equity.symbol for equity in context.portfolio.positions]  
                    # 平掉所有股票
                    for stock in stock_hold_now:
                        if data.can_trade(context.symbol(stock)):
                            context.order_target_percent(context.symbol(stock), 0) 
                    print('策略出现止盈信号， 平掉全部仓位，并关闭交易！')
                    context.strategy_risk_signal = 'short'  
                
            
            if rm['method'] == 'strategy_percent_stoploss':
                pct = rm['params']['percent']
                portfolio_value = context.portfolio.portfolio_value 
                if  portfolio_value / context.capital_base -1 < pct:
                    stock_hold_now = [equity.symbol for equity in context.portfolio.positions]  
                    # 平掉所有股票
                    for stock in stock_hold_now:
                        if data.can_trade(context.symbol(stock)):
                            context.order_target_percent(context.symbol(stock), 0) 
                    print('策略出现止损信号， 平掉全部仓位，并关闭交易！')
                    context.strategy_risk_signal = 'short'

        
def stock_risk_manage(context, data):
    """个股风控"""
    position_current_pnl = {pos.sid: (pos.last_sale_price-pos.cost_basis)/pos.cost_basis for i,pos in context.portfolio.positions.items()}
    
    for rm in context.stock_risk_conf:
        params_pct = rm['params']['percent']
        if rm['method'] == 'stock_percent_stopwin':
            for sid,pnl_pct in position_current_pnl.items():  
                if pnl_pct > params_pct:
                    context.stock_hit_stop.append(sid.symbol)
          
        if rm['method'] == 'stock_percent_stoploss':
            for sid,pnl_pct in position_current_pnl.items():
                if pnl_pct < params_pct:
                    context.stock_hit_stop.append(sid.symbol)




# 回测引擎：每日数据处理函数，每天执行一次
def m4_handle_data_bigquant_run(context, data):
    """每日运行策略逻辑"""
    market_risk_manage(context, data)
    strategy_risk_manage(context, data)
    
    if context.market_risk_signal == 'short': return
    if context.strategy_risk_signal == 'short': return

    stock_risk_manage(context, data)
    if context.trading_day_index % context.rebalance_periods == 0:
        sell_action(context, data)
        buy_action(context, data)

# 回测引擎：准备数据，只执行一次
def m4_prepare_bigquant_run(context):
      
    load_data = context.options['data'].read_pickle()
    context.signal_daily_stock = load_data['df1'].groupby('date').apply(lambda x:list(x.instrument))
    context.enter_daily_df = load_data['df2'].groupby('date').apply(lambda x:list(x.instrument))
    context.exit_daily_df = load_data['df3'].groupby('date').apply(lambda x:list(x.instrument))

# 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 m4_before_trading_start_bigquant_run(context, data):
    
    """每日盘前更新股票池"""
    frequency = context.rebalance_periods if context.trade_mode == '轮动' else context.stock_select_frequency
    if context.trading_day_index % frequency == 0:
        date = data.current_dt.strftime('%Y-%m-%d')
        try:
            context.selected_stock = context.signal_daily_stock[date] 
        except KeyError as e:
            context.selected_stock = []
    
    """初始化风控参数"""
    context.strategy_risk_signal = 'long'
    context.market_risk_signal = 'long' 
    context.stock_hit_stop = []
    context.cannot_buy_stock = []


m1 = M.instruments.v2(
    start_date='2020-01-01',
    end_date='2022-05-01',
    market='CN_STOCK_A',
    instrument_list=''
)

m8 = M.cached.v3(
    input_1=m1.data,
    run=m8_run_bigquant_run,
    post_run=m8_post_run_bigquant_run,
    input_ports='input_1',
    params='{\'input_index\':\'000300.HIX\'}',
    output_ports='data_1'
)

m3 = M.input_features.v1(
    features="""
in_csi300_0
in_csi500_0
in_sse50_0
industry_sw_level1_0
st_status_0

# 选股条件
cond1=(ta_macd(close_0/adjust_factor_0, fastperiod=12, slowperiod=26, signalperiod=26, derive='golden_cross'))&\
(ta_cci(high_0/adjust_factor_0, low_0/adjust_factor_0, close_0/adjust_factor_0, timeperiod=14)>50)&\
(ta_rsi(close_0/adjust_factor_0, timeperiod=14)>50)

# 排序选股
cond2=1

# 进场条件
cond3=1
              
# 卖出条件
cond4=1
"""
)

m15 = M.general_feature_extractor.v7(
    instruments=m1.data,
    features=m3.data,
    start_date='',
    end_date='',
    before_start_days=300
)

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
)

m10 = M.input_features.v1(
    features="""concept
"""
)

m5 = M.use_datasource.v1(
    instruments=m1.data,
    features=m10.data,
    datasource_id='industry_CN_STOCK_A',
    start_date='',
    end_date=''
)

m7 = M.join.v3(
    data1=m5.data,
    data2=m16.data,
    on='date,instrument',
    how='inner',
    sort=False
)

m6 = M.input_features.v1(
    features='suspended'
)

m19 = M.use_datasource.v1(
    instruments=m1.data,
    features=m6.data,
    datasource_id='stock_status_CN_STOCK_A',
    start_date='',
    end_date=''
)

m20 = M.join.v3(
    data1=m7.data,
    data2=m19.data,
    on='date,instrument',
    how='inner',
    sort=False
)

m2 = M.stockpool_select.v6(
    input_1=m20.data,
    self_instruments=[],
    input_concepts=[],
    input_industrys=[360000,710000,220000,460000,370000,330000,340000,720000,240000,630000,280000,420000,510000,640000,610000,620000,650000,230000,410000,350000,490000,110000,210000,480000,730000,450000,270000,430000],
    input_indexs=['沪深300'],
    input_st='过滤',
    input_suspend='过滤'
)

m22 = M.cached.v3(
    input_1=m2.data,
    run=m22_run_bigquant_run,
    post_run=m22_post_run_bigquant_run,
    input_ports='',
    params='{}',
    output_ports=''
)

m17 = M.cached.v3(
    input_1=m22.data_1,
    input_2=m22.data_2,
    input_3=m22.data_3,
    run=m17_run_bigquant_run,
    post_run=m17_post_run_bigquant_run,
    input_ports='',
    params='{}',
    output_ports=''
)

m4 = M.trade.v4(
    instruments=m1.data,
    options_data=m17.data_1,
    benchmark_ds=m8.data_1,
    start_date='',
    end_date='',
    initialize=m4_initialize_bigquant_run,
    handle_data=m4_handle_data_bigquant_run,
    prepare=m4_prepare_bigquant_run,
    before_trading_start=m4_before_trading_start_bigquant_run,
    volume_limit=0.2,
    order_price_field_buy='open',
    order_price_field_sell='open',
    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.HIX'
)

[2022-05-06 09:19:31.306780] INFO: moduleinvoker: instruments.v2 开始运行..

[2022-05-06 09:19:31.451409] INFO: moduleinvoker: instruments.v2 运行完成[0.144636s].

[2022-05-06 09:19:31.472145] INFO: moduleinvoker: cached.v3 开始运行..

[2022-05-06 09:19:32.104732] INFO: moduleinvoker: cached.v3 运行完成[0.632567s].

[2022-05-06 09:19:32.120808] INFO: moduleinvoker: input_features.v1 开始运行..

[2022-05-06 09:19:32.186967] INFO: moduleinvoker: input_features.v1 运行完成[0.06616s].

[2022-05-06 09:19:32.217607] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2022-05-06 09:19:33.345354] INFO: 基础特征抽取: 年份 2009, 特征行数=313319

[2022-05-06 09:19:34.843158] INFO: 基础特征抽取: 年份 2010, 特征行数=431567

[2022-05-06 09:19:36.357881] INFO: 基础特征抽取: 年份 2011, 特征行数=511455

[2022-05-06 09:19:37.944204] INFO: 基础特征抽取: 年份 2012, 特征行数=565675

[2022-05-06 09:19:40.023012] INFO: 基础特征抽取: 年份 2013, 特征行数=564168

[2022-05-06 09:19:41.930581] INFO: 基础特征抽取: 年份 2014, 特征行数=569948

[2022-05-06 09:19:44.342696] INFO: 基础特征抽取: 年份 2015, 特征行数=569698

[2022-05-06 09:19:47.868262] INFO: 基础特征抽取: 年份 2016, 特征行数=641546

[2022-05-06 09:19:50.289173] INFO: 基础特征抽取: 年份 2017, 特征行数=743233

[2022-05-06 09:19:53.209433] INFO: 基础特征抽取: 年份 2018, 特征行数=816987

[2022-05-06 09:19:55.821109] INFO: 基础特征抽取: 年份 2019, 特征行数=884867

[2022-05-06 09:19:58.651259] INFO: 基础特征抽取: 年份 2020, 特征行数=945961

[2022-05-06 09:20:02.666431] INFO: 基础特征抽取: 年份 2021, 特征行数=1061527

[2022-05-06 09:20:03.679345] INFO: 基础特征抽取: 年份 2022, 特征行数=363977

[2022-05-06 09:20:03.821502] INFO: 基础特征抽取: 总行数: 8983928

[2022-05-06 09:20:03.830377] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[31.61277s].

[2022-05-06 09:20:03.854475] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-05-06 09:21:00.986667] INFO: derived_feature_extractor: 提取完成 cond1=(ta_macd(close_0/adjust_factor_0, fastperiod=12, slowperiod=26, signalperiod=26, derive='golden_cross'))&(ta_cci(high_0/adjust_factor_0, low_0/adjust_factor_0, close_0/adjust_factor_0, timeperiod=14)>50)&(ta_rsi(close_0/adjust_factor_0, timeperiod=14)>50), 41.140s

[2022-05-06 09:21:00.999646] INFO: derived_feature_extractor: 提取完成 cond2=1, 0.011s

[2022-05-06 09:21:01.025756] INFO: derived_feature_extractor: 提取完成 cond3=1, 0.025s

[2022-05-06 09:21:01.052530] INFO: derived_feature_extractor: 提取完成 cond4=1, 0.025s

[2022-05-06 09:21:02.061024] INFO: derived_feature_extractor: /y_2009, 313319

[2022-05-06 09:21:02.813574] INFO: derived_feature_extractor: /y_2010, 431567

[2022-05-06 09:21:03.700597] INFO: derived_feature_extractor: /y_2011, 511455

[2022-05-06 09:21:04.675274] INFO: derived_feature_extractor: /y_2012, 565675

[2022-05-06 09:21:05.687148] INFO: derived_feature_extractor: /y_2013, 564168

[2022-05-06 09:21:06.715288] INFO: derived_feature_extractor: /y_2014, 569948

[2022-05-06 09:21:07.819727] INFO: derived_feature_extractor: /y_2015, 569698

[2022-05-06 09:21:08.908134] INFO: derived_feature_extractor: /y_2016, 641546

[2022-05-06 09:21:10.272182] INFO: derived_feature_extractor: /y_2017, 743233

[2022-05-06 09:21:11.690063] INFO: derived_feature_extractor: /y_2018, 816987

[2022-05-06 09:21:13.231913] INFO: derived_feature_extractor: /y_2019, 884867

[2022-05-06 09:21:15.394870] INFO: derived_feature_extractor: /y_2020, 945961

[2022-05-06 09:21:17.475429] INFO: derived_feature_extractor: /y_2021, 1061527

[2022-05-06 09:21:18.704100] INFO: derived_feature_extractor: /y_2022, 363977

[2022-05-06 09:21:19.008439] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[75.153961s].

[2022-05-06 09:21:19.013514] INFO: moduleinvoker: input_features.v1 开始运行..

[2022-05-06 09:21:19.026677] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:21:19.028276] INFO: moduleinvoker: input_features.v1 运行完成[0.01477s].

[2022-05-06 09:21:19.038569] INFO: moduleinvoker: use_datasource.v1 开始运行..

[2022-05-06 09:21:27.466508] INFO: moduleinvoker: use_datasource.v1 运行完成[8.427938s].

[2022-05-06 09:21:27.489057] INFO: moduleinvoker: join.v3 开始运行..

[2022-05-06 09:22:03.403306] INFO: join: /y_2009, 行数=0/313319, 耗时=4.341606s

[2022-05-06 09:22:07.708945] INFO: join: /y_2010, 行数=431567/431567, 耗时=4.303091s

[2022-05-06 09:22:13.172494] INFO: join: /y_2011, 行数=511455/511455, 耗时=5.457941s

[2022-05-06 09:22:17.557473] INFO: join: /y_2012, 行数=565675/565675, 耗时=4.379337s

[2022-05-06 09:22:22.285088] INFO: join: /y_2013, 行数=564168/564168, 耗时=4.72124s

[2022-05-06 09:22:26.978697] INFO: join: /y_2014, 行数=569948/569948, 耗时=4.687507s

[2022-05-06 09:22:31.699074] INFO: join: /y_2015, 行数=569698/569698, 耗时=4.71427s

[2022-05-06 09:22:36.516353] INFO: join: /y_2016, 行数=641546/641546, 耗时=4.810868s

[2022-05-06 09:22:41.808613] INFO: join: /y_2017, 行数=743233/743233, 耗时=5.285436s

[2022-05-06 09:22:47.502123] INFO: join: /y_2018, 行数=816987/816987, 耗时=5.684907s

[2022-05-06 09:22:52.898047] INFO: join: /y_2019, 行数=884867/884867, 耗时=5.386509s

[2022-05-06 09:22:58.452002] INFO: join: /y_2020, 行数=945960/945961, 耗时=5.542411s

[2022-05-06 09:23:04.933197] INFO: join: /y_2021, 行数=1061527/1061527, 耗时=6.465447s

[2022-05-06 09:23:10.494839] INFO: join: /y_2022, 行数=363977/363977, 耗时=5.545842s

[2022-05-06 09:23:10.634185] INFO: join: 最终行数: 8670608

[2022-05-06 09:23:10.807346] INFO: moduleinvoker: join.v3 运行完成[103.318282s].

[2022-05-06 09:23:10.812881] INFO: moduleinvoker: input_features.v1 开始运行..

[2022-05-06 09:23:10.826013] INFO: moduleinvoker: 命中缓存

[2022-05-06 09:23:10.827642] INFO: moduleinvoker: input_features.v1 运行完成[0.014771s].

[2022-05-06 09:23:10.832532] INFO: moduleinvoker: use_datasource.v1 开始运行..

[2022-05-06 09:23:13.590093] INFO: moduleinvoker: use_datasource.v1 运行完成[2.757547s].

[2022-05-06 09:23:13.599188] INFO: moduleinvoker: join.v3 开始运行..

[2022-05-06 09:23:30.383154] INFO: join: /y_2009, 行数=0/0, 耗时=2.545489s

[2022-05-06 09:23:34.740839] INFO: join: /y_2010, 行数=431567/431567, 耗时=4.356109s

[2022-05-06 09:23:40.078588] INFO: join: /y_2011, 行数=511455/511455, 耗时=5.331383s

[2022-05-06 09:23:45.132239] INFO: join: /y_2012, 行数=565675/565675, 耗时=5.046864s

[2022-05-06 09:23:50.083506] INFO: join: /y_2013, 行数=564168/564168, 耗时=4.943123s

[2022-05-06 09:23:55.111615] INFO: join: /y_2014, 行数=569948/569948, 耗时=5.019088s

[2022-05-06 09:24:00.149793] INFO: join: /y_2015, 行数=569698/569698, 耗时=5.02861s

[2022-05-06 09:24:05.284119] INFO: join: /y_2016, 行数=641546/641546, 耗时=5.125708s

[2022-05-06 09:24:10.656817] INFO: join: /y_2017, 行数=743233/743233, 耗时=5.361616s

[2022-05-06 09:24:16.500483] INFO: join: /y_2018, 行数=816987/816987, 耗时=5.831628s

[2022-05-06 09:24:22.980140] INFO: join: /y_2019, 行数=884867/884867, 耗时=6.466431s

[2022-05-06 09:24:30.774146] INFO: join: /y_2020, 行数=945960/945960, 耗时=7.775277s

[2022-05-06 09:24:38.517425] INFO: join: /y_2021, 行数=1061527/1061527, 耗时=7.695269s

[2022-05-06 09:24:43.336104] INFO: join: /y_2022, 行数=363977/363977, 耗时=4.771877s

[2022-05-06 09:24:43.523794] INFO: join: 最终行数: 8670608

[2022-05-06 09:24:43.566360] INFO: moduleinvoker: join.v3 运行完成[89.967034s].

[2022-05-06 09:24:43.584777] INFO: moduleinvoker: stockpool_select.v6 开始运行..

[2022-05-06 09:42:42.985103] INFO: moduleinvoker: stockpool_select.v6 运行完成[1079.400319s].

[2022-05-06 09:42:43.003281] INFO: moduleinvoker: cached.v3 开始运行..

[2022-05-06 09:42:48.107996] INFO: moduleinvoker: cached.v3 运行完成[5.104732s].

[2022-05-06 09:42:48.124790] INFO: moduleinvoker: cached.v3 开始运行..

[2022-05-06 09:42:49.372632] INFO: moduleinvoker: cached.v3 运行完成[1.247849s].

[2022-05-06 09:42:51.497452] INFO: moduleinvoker: backtest.v8 开始运行..

[2022-05-06 09:42:51.503579] INFO: backtest: biglearning backtest:V8.6.2

[2022-05-06 09:42:53.468209] INFO: backtest: product_type:stock by specified

[2022-05-06 09:42:53.544412] INFO: moduleinvoker: cached.v2 开始运行..

[2022-05-06 09:43:40.275372] INFO: backtest: 读取股票行情完成:10393941

[2022-05-06 09:44:04.040411] INFO: moduleinvoker: cached.v2 运行完成[70.495953s].

[2022-05-06 09:44:13.622156] INFO: algo: TradingAlgorithm V1.8.7

[2022-05-06 09:44:17.511040] INFO: algo: trading transform...

[2022-05-06 09:44:22.817272] ERROR: moduleinvoker: module name: backtest, module version: v8, trackeback: KeyError: Equity(4423 [000060.SZA])

[2022-05-06 09:44:22.832341] ERROR: moduleinvoker: module name: trade, module version: v4, trackeback: KeyError: Equity(4423 [000060.SZA])

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
<ipython-input-1-50e4937832b6> in <module>
    537 )
    538 
--> 539 m4 = M.trade.v4(
    540     instruments=m1.data,
    541     options_data=m17.data_1,

<ipython-input-1-50e4937832b6> in m4_handle_data_bigquant_run(context, data)
    379     stock_risk_manage(context, data)
    380     if context.trading_day_index % context.rebalance_periods == 0:
--> 381         sell_action(context, data)
    382         buy_action(context, data)
    383 

<ipython-input-1-50e4937832b6> in sell_action(context, data)
    226             if data.can_trade(context.symbol(stock)):
    227                 context.order_target_percent(context.symbol(stock), 0)
--> 228                 del context.portfolio.positions[context.symbol(stock)]
    229 
    230 

KeyError: Equity(4423 [000060.SZA])