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# 本代码由可视化策略环境自动生成 2022年12月14日 14:48
# 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。


# 交易引擎：初始化函数，只执行一次
def m19_initialize_bigquant_run(context):
       # 加载股票指标数据,数据继承自m6模块
    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=0.00016, sell_cost=0.00116, 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)])
    context.stock_weights = [1/stock_count for i in range(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'] = 10
     #大盘数据获取
    bm_df = DataSource('bar1d_index_CN_STOCK_A').read(instruments=['000001.HIX'])
    bm_df["bm_ret"] = bm_df["close"]/bm_df["close"].shift(4)-1
    #bm_df["bm_ret"] = bm_df["bm_ret"].shift(1) #取昨日的收益情况
    context.bm_df = bm_df[['date','bm_ret']]
 
# 交易引擎：每个单位时间开盘前调用一次。
def m19_before_trading_start_bigquant_run(context, data):
    pass
# 交易引擎：tick数据处理函数，每个tick执行一次
def m19_handle_tick_bigquant_run(context, data):
    pass

# 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')
    positions = {e: p.amount * p.last_sale_price
                 for e, p in context.portfolio.positions.items()}
    positions_cost={e: p.cost_basis for e,p in context.portfolio.positions.items()} 
    now_bm = context.bm_df[context.bm_df.date==today]
    current_stoploss_stock = []
    current_stopwin_stock = []
    if len(positions) > 0:
        for i in positions.keys():
            stock_cost=positions_cost[i]  
            stock_market_price=data.current(context.symbol(i),'price') 
            # 亏5%就止损
            #if (stock_market_price - stock_cost) / stock_cost <= -0.1:   
                #context.order_target_percent(context.symbol(i),0)     
                #current_stoploss_stock.append(i)
                #print('日期：',date,i,'出现止损状况')
                #df = pd.DataFrame(current_stoploss_stock)
                #f=df.to_csv("loss.csv")
                #stop_loss = pd.read_csv("loss.csv")
                #stop_loss_stock = stop_loss.values.tolist()
                #print(stop_loss_stock
            if (stock_market_price - stock_cost ) / stock_cost >= 0.18:   
                context.order_target_percent(context.symbol(i),0)     
                current_stopwin_stock.append(i)
                #print('日期：',date,'股票：',i,'出现止盈状况')
    
    #context.bm_risk = 0
    if(now_bm.bm_ret.iloc[0]< -0.03):
            if len(positions)>0:
                for instrument in positions.keys():
                    stock_cost=positions_cost[instrument]  
                    stock_market_price=data.current(context.symbol(instrument),'price')  
                    #volume_since_buy = data.history(context.symbol(instrument), 'volume', 6, '1d')
            # 赚4%且为可交易状态就止盈
                    if stock_market_price/stock_cost-1>= 0.02 and data.can_trade(context.symbol(instrument)):
                         context.order_target_percent(context.symbol(instrument),0)
                         print("触发风控止赢！")
            print("触发大盘风控！")
            return      
        #if(context.bm_risk==1):
            #print("触发大盘风控！")
            #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')]

    # 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: 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: e for e, p in context.portfolio.positions.items()}
        instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(
                lambda x: x in equities)])))

        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)

# 交易引擎：成交回报处理函数，每个成交发生时执行一次
def m19_handle_trade_bigquant_run(context, data):
    pass

# 交易引擎：委托回报处理函数，每个委托变化时执行一次
def m19_handle_order_bigquant_run(context, data):
    pass

# 交易引擎：盘后处理函数，每日盘后执行一次
def m19_after_trading_bigquant_run(context, data):
    pass


m11 = M.input_features.v1(
    features="""return_1 = close/shift(close, 1)
return_3 = close/shift(close, 3)
return_5 = close/shift(close, 5)
amount/mean(amount,3)
mean(amount,3)/mean(amount,5)
rank(amount)/rank(mean(amount,5))
rank(amount)/rank(mean(amount,3))
rank(close/shift(close, 1))
rank(close/shift(close, 3))
rank(close/shift(close, 5))     
rank(close/shift(close, 1))/rank(close/shift(close, 5))    
rank(close/shift(close, 3))/rank(close/shift(close, 5))"""
)

m4 = M.instruments.v2(
    start_date='2017-01-03',
    end_date='2018-01-09',
    market='CN_CONBOND',
    instrument_list='',
    max_count=0
)

m12 = M.use_datasource.v1(
    instruments=m4.data,
    datasource_id='bar1d_CN_CONBOND',
    start_date='',
    end_date='',
    m_cached=False
)

m13 = M.derived_feature_extractor.v3(
    input_data=m12.data,
    features=m11.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False,
    user_functions={}
)

m14 = M.auto_labeler_on_datasource.v1(
    input_data=m12.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={}
)

m15 = M.join.v3(
    data1=m14.data,
    data2=m13.data,
    on='date,instrument',
    how='inner',
    sort=False
)

m22 = M.instruments.v2(
    start_date='2018-01-10',
    end_date='2022-12-12',
    market='CN_CONBOND',
    instrument_list='',
    max_count=0
)

m1 = M.use_datasource.v1(
    instruments=m22.data,
    datasource_id='market_performance_CN_CONBOND',
    start_date='',
    end_date='',
    m_cached=False
)

m2 = M.trade_data_generation.v1(
    input=m1.data,
    category='CN_STOCK',
    m_cached=False
)

m28 = M.use_datasource.v1(
    instruments=m2.instrument_list,
    datasource_id='bar1d_CN_CONBOND',
    start_date='',
    end_date=''
)

m30 = M.derived_feature_extractor.v3(
    input_data=m28.data,
    features=m11.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False,
    user_functions={}
)

m18 = M.input_features.v1(
    features="""return_1 
return_3 
return_5 
amount/mean(amount,3)
mean(amount,3)/mean(amount,5)
rank(amount)/rank(mean(amount,5))
rank(amount)/rank(mean(amount,3))
rank(close/shift(close, 1))
rank(close/shift(close, 3))
rank(close/shift(close, 5))     
rank(close/shift(close, 1))/rank(close/shift(close, 5))    
rank(close/shift(close, 3))/rank(close/shift(close, 5))"""
)

m23 = M.dropnan.v2(
    input_data=m15.data,
    features=m18.data
)

m16 = M.stock_ranker_train.v6(
    training_ds=m23.data,
    features=m18.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,
    plot_charts=True,
    ndcg_discount_base=1,
    m_lazy_run=False
)

m17 = M.stock_ranker_predict.v5(
    model=m16.model,
    data=m30.data,
    m_lazy_run=False
)

m19 = M.hftrade.v2(
    instruments=m2.instrument_list,
    options_data=m17.predictions,
    start_date='',
    end_date='',
    initialize=m19_initialize_bigquant_run,
    before_trading_start=m19_before_trading_start_bigquant_run,
    handle_tick=m19_handle_tick_bigquant_run,
    handle_data=m19_handle_data_bigquant_run,
    handle_trade=m19_handle_trade_bigquant_run,
    handle_order=m19_handle_order_bigquant_run,
    after_trading=m19_after_trading_bigquant_run,
    capital_base=1000000,
    frequency='daily',
    price_type='后复权',
    product_type='可转债',
    before_start_days='0',
    volume_limit=1,
    order_price_field_buy='open',
    order_price_field_sell='close',
    benchmark='000300.HIX',
    plot_charts=True,
    disable_cache=False,
    replay_bdb=False,
    show_debug_info=False,
    backtest_only=False
)

[2022-12-14 14:45:36.564640] INFO: moduleinvoker: input_features.v1 开始运行..

[2022-12-14 14:45:36.575031] INFO: moduleinvoker: 命中缓存

[2022-12-14 14:45:36.577693] INFO: moduleinvoker: input_features.v1 运行完成[0.013074s].

[2022-12-14 14:45:36.585565] INFO: moduleinvoker: instruments.v2 开始运行..

[2022-12-14 14:45:36.593487] INFO: moduleinvoker: 命中缓存

[2022-12-14 14:45:36.596118] INFO: moduleinvoker: instruments.v2 运行完成[0.010537s].

[2022-12-14 14:45:36.604207] INFO: moduleinvoker: use_datasource.v1 开始运行..

[2022-12-14 14:45:36.794376] INFO: moduleinvoker: use_datasource.v1 运行完成[0.190179s].

[2022-12-14 14:45:36.802522] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-12-14 14:45:36.865030] INFO: derived_feature_extractor: 提取完成 return_1 = close/shift(close, 1), 0.004s

[2022-12-14 14:45:36.870459] INFO: derived_feature_extractor: 提取完成 return_3 = close/shift(close, 3), 0.004s

[2022-12-14 14:45:36.875970] INFO: derived_feature_extractor: 提取完成 return_5 = close/shift(close, 5), 0.004s

[2022-12-14 14:45:36.887509] INFO: derived_feature_extractor: 提取完成 amount/mean(amount,3), 0.010s

[2022-12-14 14:45:36.927778] INFO: derived_feature_extractor: 提取完成 mean(amount,3)/mean(amount,5), 0.039s

[2022-12-14 14:45:36.947558] INFO: derived_feature_extractor: 提取完成 rank(amount)/rank(mean(amount,5)), 0.018s

[2022-12-14 14:45:36.968399] INFO: derived_feature_extractor: 提取完成 rank(amount)/rank(mean(amount,3)), 0.019s

[2022-12-14 14:45:36.978446] INFO: derived_feature_extractor: 提取完成 rank(close/shift(close, 1)), 0.008s

[2022-12-14 14:45:37.028291] INFO: derived_feature_extractor: 提取完成 rank(close/shift(close, 3)), 0.048s

[2022-12-14 14:45:37.042938] INFO: derived_feature_extractor: 提取完成 rank(close/shift(close, 5)), 0.012s

[2022-12-14 14:45:37.067983] INFO: derived_feature_extractor: 提取完成 rank(close/shift(close, 1))/rank(close/shift(close, 5)), 0.023s

[2022-12-14 14:45:37.121816] INFO: derived_feature_extractor: 提取完成 rank(close/shift(close, 3))/rank(close/shift(close, 5)), 0.051s

[2022-12-14 14:45:37.187412] INFO: derived_feature_extractor: /data, 5038

[2022-12-14 14:45:37.256288] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.453748s].

[2022-12-14 14:45:37.265142] INFO: moduleinvoker: auto_labeler_on_datasource.v1 开始运行..

[2022-12-14 14:45:37.332564] INFO: 自动标注(任意数据源): 开始标注 ..

[2022-12-14 14:45:37.420858] INFO: moduleinvoker: auto_labeler_on_datasource.v1 运行完成[0.155706s].

[2022-12-14 14:45:37.431349] INFO: moduleinvoker: join.v3 开始运行..

[2022-12-14 14:45:37.589008] INFO: join: /data, 行数=4627/5038, 耗时=0.077806s

[2022-12-14 14:45:37.616866] INFO: join: 最终行数: 4627

[2022-12-14 14:45:37.623832] INFO: moduleinvoker: join.v3 运行完成[0.192493s].

[2022-12-14 14:45:37.629534] INFO: moduleinvoker: instruments.v2 开始运行..

[2022-12-14 14:45:37.636711] INFO: moduleinvoker: 命中缓存

[2022-12-14 14:45:37.638970] INFO: moduleinvoker: instruments.v2 运行完成[0.00944s].

[2022-12-14 14:45:37.645013] INFO: moduleinvoker: use_datasource.v1 开始运行..

[2022-12-14 14:45:40.813138] INFO: moduleinvoker: use_datasource.v1 运行完成[3.168113s].

[2022-12-14 14:45:40.826504] INFO: moduleinvoker: trade_data_generation.v1 开始运行..

[2022-12-14 14:45:45.975357] INFO: moduleinvoker: trade_data_generation.v1 运行完成[5.148845s].

[2022-12-14 14:45:45.981459] INFO: moduleinvoker: use_datasource.v1 开始运行..

[2022-12-14 14:45:46.944241] INFO: moduleinvoker: use_datasource.v1 运行完成[0.962784s].

[2022-12-14 14:45:46.954868] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-12-14 14:45:48.293105] INFO: derived_feature_extractor: 提取完成 return_1 = close/shift(close, 1), 0.057s

[2022-12-14 14:45:48.351892] INFO: derived_feature_extractor: 提取完成 return_3 = close/shift(close, 3), 0.056s

[2022-12-14 14:45:48.397491] INFO: derived_feature_extractor: 提取完成 return_5 = close/shift(close, 5), 0.044s

[2022-12-14 14:45:48.744882] INFO: derived_feature_extractor: 提取完成 amount/mean(amount,3), 0.345s

[2022-12-14 14:45:49.441398] INFO: derived_feature_extractor: 提取完成 mean(amount,3)/mean(amount,5), 0.695s

[2022-12-14 14:45:50.057560] INFO: derived_feature_extractor: 提取完成 rank(amount)/rank(mean(amount,5)), 0.614s

[2022-12-14 14:45:50.730301] INFO: derived_feature_extractor: 提取完成 rank(amount)/rank(mean(amount,3)), 0.670s

[2022-12-14 14:45:50.940165] INFO: derived_feature_extractor: 提取完成 rank(close/shift(close, 1)), 0.207s

[2022-12-14 14:45:51.142089] INFO: derived_feature_extractor: 提取完成 rank(close/shift(close, 3)), 0.199s

[2022-12-14 14:45:51.347537] INFO: derived_feature_extractor: 提取完成 rank(close/shift(close, 5)), 0.203s

[2022-12-14 14:45:51.755335] INFO: derived_feature_extractor: 提取完成 rank(close/shift(close, 1))/rank(close/shift(close, 5)), 0.406s

[2022-12-14 14:45:52.165398] INFO: derived_feature_extractor: 提取完成 rank(close/shift(close, 3))/rank(close/shift(close, 5)), 0.408s

[2022-12-14 14:45:53.500364] INFO: derived_feature_extractor: /data, 305159

[2022-12-14 14:45:53.921190] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[6.966323s].

[2022-12-14 14:45:53.927109] INFO: moduleinvoker: input_features.v1 开始运行..

[2022-12-14 14:45:53.933959] INFO: moduleinvoker: 命中缓存

[2022-12-14 14:45:53.935887] INFO: moduleinvoker: input_features.v1 运行完成[0.008789s].

[2022-12-14 14:45:53.944790] INFO: moduleinvoker: dropnan.v2 开始运行..

[2022-12-14 14:45:54.058900] INFO: dropnan: /data, 4439/4627

[2022-12-14 14:45:54.097447] INFO: dropnan: 行数: 4439/4627

[2022-12-14 14:45:54.102618] INFO: moduleinvoker: dropnan.v2 运行完成[0.15782s].

[2022-12-14 14:45:54.111524] INFO: moduleinvoker: stock_ranker_train.v6 开始运行..

[2022-12-14 14:45:54.246330] INFO: StockRanker: 特征预处理 ..

[2022-12-14 14:45:54.275970] INFO: StockRanker: prepare data: training ..

[2022-12-14 14:45:54.437319] INFO: StockRanker训练: f4caa970 准备训练: 4439 行数

[2022-12-14 14:45:54.439653] INFO: StockRanker训练: AI模型训练，将在4439*12=5.33万数据上对模型训练进行20轮迭代训练。预计将需要1～2分钟。请耐心等待。

[2022-12-14 14:45:54.676251] INFO: StockRanker训练: 正在训练 ..

[2022-12-14 14:45:54.724920] INFO: StockRanker训练: 任务状态: Pending

[2022-12-14 14:46:04.776575] INFO: StockRanker训练: 任务状态: Running

[2022-12-14 14:47:05.161575] INFO: StockRanker训练: 00:01:01.3491826, finished iteration 1

[2022-12-14 14:47:05.163740] INFO: StockRanker训练: 00:01:01.3551678, finished iteration 2

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[2022-12-14 14:47:05.221534] INFO: StockRanker训练: 00:01:01.3809067, finished iteration 11

[2022-12-14 14:47:05.223658] INFO: StockRanker训练: 00:01:01.3836530, finished iteration 12

[2022-12-14 14:47:05.225522] INFO: StockRanker训练: 00:01:01.3867657, finished iteration 13

[2022-12-14 14:47:05.227708] INFO: StockRanker训练: 00:01:01.3894462, finished iteration 14

[2022-12-14 14:47:05.229419] INFO: StockRanker训练: 00:01:01.3927826, finished iteration 15

[2022-12-14 14:47:05.231307] INFO: StockRanker训练: 00:01:01.3955108, finished iteration 16

[2022-12-14 14:47:05.233836] INFO: StockRanker训练: 00:01:01.3983577, finished iteration 17

[2022-12-14 14:47:05.237010] INFO: StockRanker训练: 00:01:01.4014638, finished iteration 18

[2022-12-14 14:47:05.240169] INFO: StockRanker训练: 00:01:01.4045829, finished iteration 19

[2022-12-14 14:47:05.243190] INFO: StockRanker训练: 00:01:01.4070679, finished iteration 20

[2022-12-14 14:47:05.245843] INFO: StockRanker训练: 任务状态: Succeeded

[2022-12-14 14:47:05.623480] INFO: moduleinvoker: stock_ranker_train.v6 运行完成[71.51196s].

[2022-12-14 14:47:05.635504] INFO: moduleinvoker: stock_ranker_predict.v5 开始运行..

[2022-12-14 14:47:11.526208] INFO: StockRanker预测: /data ..

[2022-12-14 14:47:16.443507] INFO: moduleinvoker: stock_ranker_predict.v5 运行完成[10.807983s].

[2022-12-14 14:47:16.542992] INFO: moduleinvoker: hfbacktest.v1 开始运行..

[2022-12-14 14:47:16.549163] INFO: hfbacktest: biglearning V1.4.19

[2022-12-14 14:47:16.551165] INFO: hfbacktest: bigtrader v1.9.11 2022-12-09

[2022-12-14 14:47:16.634240] INFO: moduleinvoker: cached.v2 开始运行..

[2022-12-14 14:47:16.648385] INFO: moduleinvoker: 命中缓存

[2022-12-14 14:47:16.650765] INFO: moduleinvoker: cached.v2 运行完成[0.016547s].

[2022-12-14 14:47:16.842500] INFO: moduleinvoker: cached.v2 开始运行..

[2022-12-14 14:47:16.851891] INFO: moduleinvoker: 命中缓存

[2022-12-14 14:47:16.855529] INFO: moduleinvoker: cached.v2 运行完成[0.013043s].

[2022-12-14 14:47:39.936279] ERROR: moduleinvoker: module name: hfbacktest, module version: v1, trackeback: ZeroDivisionError: float division by zero

[2022-12-14 14:47:39.946309] ERROR: moduleinvoker: module name: hftrade, module version: v2, trackeback: ZeroDivisionError: float division by zero

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-42160908c3e94e0cbf892f1194e3c5a7"}/bigcharts-data-end

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2022-12-14 14:47:39.935205 strategy strategy exception:Traceback (most recent call last):
  File "bigtrader/strategy/engine.py", line 713, in bigtrader2.bigtrader.strategy.engine.StrategyEngine._call_strategy_func
  File "bigtrader/strategy/strategy_base.py", line 2275, in bigtrader2.bigtrader.strategy.strategy_base.StrategyBase.call_handle_data
  File "<ipython-input-7-309825713890>", line 58, in m19_handle_data_bigquant_run
    if (stock_market_price - stock_cost ) / stock_cost >= 0.18:
ZeroDivisionError: float division by zero

---------------------------------------------------------------------------
ZeroDivisionError                         Traceback (most recent call last)
<ipython-input-7-309825713890> in <module>
    285 )
    286 
--> 287 m19 = M.hftrade.v2(
    288     instruments=m2.instrument_list,
    289     options_data=m17.predictions,

<ipython-input-7-309825713890> in m19_handle_data_bigquant_run(context, data)
     56                 #stop_loss_stock = stop_loss.values.tolist()
     57                 #print(stop_loss_stock
---> 58             if (stock_market_price - stock_cost ) / stock_cost >= 0.18:
     59                 context.order_target_percent(context.symbol(i),0)
     60                 current_stopwin_stock.append(i)

ZeroDivisionError: float division by zero