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# 本代码由可视化策略环境自动生成 2021年9月2日 09:01
# 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 m5_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

# 回测引擎：每日数据处理函数，每天执行一次
def m5_handle_data_bigquant_run(context, data):
    # 按日期过滤得到今日的预测数据
    if context.trading_day_index % 22 != 0:
        return

    # 按日期过滤得到当日的数据
    cur_data = context.data[context.data.date == data.current_dt.strftime('%Y-%m-%d')]
    buy_stock = list(cur_data['instrument'])

    # 买入股票
    for i in buy_stock:
        if data.can_trade(context.symbol(i)):
            context.order_target_percent(context.symbol(i), 1/len(buy_stock))  # 等权重买入
            
    # 持仓股票列表，为字符串
    is_staging = context.trading_day_index < context.hold_days # 是否在建仓期间（前 hold_days 天）
    equities = {e.symbol: p for e,p in context.portfolio.positions.items() if p.amount>0}
    for instrument in equities:
        if data.can_trade(context.symbol(instrument)):
            context.order_target_percent(context.symbol(instrument), 0)
# 回测引擎：准备数据，只执行一次
def m5_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 m5_before_trading_start_bigquant_run(context, data):
    pass


m1 = M.instruments.v2(
    start_date='2013-01-01',
    end_date='2017-11-07',
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m2 = M.input_features.v1(
    features="""
# #号开始的表示注释，注释需单独一行
# 多个特征，每行一个，可以包含基础特征和衍生特征，特征须为本平台特征
rank_pb_lf_0"""
)

m3 = M.general_feature_extractor.v7(
    instruments=m1.data,
    features=m2.data,
    start_date='',
    end_date='',
    before_start_days=90
)

m4 = M.filter.v3(
    input_data=m3.data,
    expr='rank_pb_lf_0 < 0.1',
    output_left_data=False
)

m5 = M.trade.v4(
    instruments=m1.data,
    options_data=m4.data,
    start_date='',
    end_date='',
    initialize=m5_initialize_bigquant_run,
    handle_data=m5_handle_data_bigquant_run,
    prepare=m5_prepare_bigquant_run,
    before_trading_start=m5_before_trading_start_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='真实价格',
    product_type='股票',
    plot_charts=True,
    backtest_only=False,
    benchmark=''
)

[2021-09-02 08:59:57.745716] INFO: moduleinvoker: instruments.v2 开始运行..

[2021-09-02 08:59:57.756256] INFO: moduleinvoker: 命中缓存

[2021-09-02 08:59:57.757740] INFO: moduleinvoker: instruments.v2 运行完成[0.012032s].

[2021-09-02 08:59:57.761019] INFO: moduleinvoker: input_features.v1 开始运行..

[2021-09-02 08:59:57.769944] INFO: moduleinvoker: 命中缓存

[2021-09-02 08:59:57.771230] INFO: moduleinvoker: input_features.v1 运行完成[0.010212s].

[2021-09-02 08:59:57.784566] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2021-09-02 08:59:57.793993] INFO: moduleinvoker: 命中缓存

[2021-09-02 08:59:57.795281] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.010721s].

[2021-09-02 08:59:57.801448] INFO: moduleinvoker: filter.v3 开始运行..

[2021-09-02 08:59:57.819076] INFO: filter: 使用表达式 rank_pb_lf_0 < 0.1 过滤

[2021-09-02 08:59:57.984250] INFO: filter: 过滤 /y_2012, 14647/0/146839

[2021-09-02 08:59:58.165188] INFO: filter: 过滤 /y_2013, 56291/0/564168

[2021-09-02 08:59:58.352889] INFO: filter: 过滤 /y_2014, 56855/0/569948

[2021-09-02 08:59:58.526832] INFO: filter: 过滤 /y_2015, 56837/0/569698

[2021-09-02 08:59:58.726267] INFO: filter: 过滤 /y_2016, 64018/0/641546

[2021-09-02 08:59:58.920768] INFO: filter: 过滤 /y_2017, 61910/0/620177

[2021-09-02 08:59:58.957733] INFO: moduleinvoker: filter.v3 运行完成[1.156271s].

[2021-09-02 09:00:00.486252] INFO: moduleinvoker: backtest.v8 开始运行..

[2021-09-02 09:00:00.490819] INFO: backtest: biglearning backtest:V8.5.0

[2021-09-02 09:00:00.493560] INFO: backtest: product_type:stock by specified

[2021-09-02 09:00:01.653877] INFO: moduleinvoker: cached.v2 开始运行..

[2021-09-02 09:00:42.726071] INFO: backtest: 读取股票行情完成:4256462

[2021-09-02 09:00:54.416085] INFO: moduleinvoker: cached.v2 运行完成[52.762214s].

[2021-09-02 09:01:07.544274] INFO: algo: TradingAlgorithm V1.8.5

[2021-09-02 09:01:13.388334] INFO: algo: trading transform...

[2021-09-02 09:01:14.795155] ERROR: moduleinvoker: module name: backtest, module version: v8, trackeback: AttributeError: 'TradingAlgorithm' object has no attribute 'data'

[2021-09-02 09:01:14.807123] ERROR: moduleinvoker: module name: trade, module version: v4, trackeback: AttributeError: 'TradingAlgorithm' object has no attribute 'data'

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-9-9835637ac2df> in <module>
     78 )
     79 
---> 80 m5 = M.trade.v4(
     81     instruments=m1.data,
     82     options_data=m4.data,

<ipython-input-9-9835637ac2df> in m5_handle_data_bigquant_run(context, data)
     26 
     27     # 按日期过滤得到当日的数据
---> 28     cur_data = context.data[context.data.date == data.current_dt.strftime('%Y-%m-%d')]
     29     buy_stock = list(cur_data['instrument'])
     30 

AttributeError: 'TradingAlgorithm' object has no attribute 'data'