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# 本代码由可视化策略环境自动生成 2020年4月29日 09:47
# 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.predictions = 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 = [1/stock_count for k in range(stock_count)]
    # 设置每只股票占用的最大资金比例
    context.max_cash_per_instrument = 0.2
    context.options['hold_days'] = 5

# 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):
    # 按日期过滤得到今日的预测数据
    daily_prediction = context.predictions[
        context.predictions.date == data.current_dt.strftime('%Y-%m-%d')]


    # 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
    equities = {e.symbol: e for e, p in context.portfolio.positions.items()}
    if equities!={}:
        instruments = [k for k in equities if daily_prediction[daily_prediction.instrument==k].classes_prob_0.values[0]>0.5]
        for instrument in instruments:
            context.order_target(context.symbol(instrument), 0)

    # 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(daily_prediction[daily_prediction.classes_prob_1>0.5].instrument[:len(buy_cash_weights)])
    for i, instrument in enumerate(buy_instruments):
        cash = context.portfolio.cash * buy_cash_weights[i]
        context.order_value(context.symbol(instrument), cash)

# 回测引擎：准备数据，只执行一次
def m19_prepare_bigquant_run(context):
    pass


m1 = M.instruments.v2(
    start_date='2010-01-01',
    end_date='2011-01-01',
    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/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)
where(shift(close, -5) / shift(open, -1)>0,1,0)
""",
    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
return_10
return_20
avg_amount_0/avg_amount_5
avg_amount_5/avg_amount_20
rank_avg_amount_0/rank_avg_amount_5
rank_avg_amount_5/rank_avg_amount_10
rank_return_0
rank_return_5
rank_return_10
rank_return_0/rank_return_5
rank_return_5/rank_return_10
pe_ttm_0
"""
)

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
)

m9 = M.instruments.v2(
    start_date=T.live_run_param('trading_date', '2015-01-01'),
    end_date=T.live_run_param('trading_date', '2015-05-01'),
    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.kneighbors_classifier.v1(
    training_ds=m13.data,
    features=m3.data,
    predict_ds=m14.data,
    n_neighbors=5,
    weights='uniform',
    algorithm='auto',
    leaf_size=30,
    metric='minkowski',
    key_cols='date,instrument',
    workers=1,
    other_train_parameters={}
)

m19 = M.trade.v4(
    instruments=m9.data,
    options_data=m4.predictions,
    start_date='',
    end_date='',
    initialize=m19_initialize_bigquant_run,
    handle_data=m19_handle_data_bigquant_run,
    prepare=m19_prepare_bigquant_run,
    volume_limit=0.025,
    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'
)

[2020-04-29 09:44:11.106835] INFO: moduleinvoker: instruments.v2 开始运行..

[2020-04-29 09:44:11.212942] INFO: moduleinvoker: instruments.v2 运行完成[0.106109s].

[2020-04-29 09:44:11.219164] INFO: moduleinvoker: advanced_auto_labeler.v2 开始运行..

[2020-04-29 09:44:14.856434] INFO: 自动标注(股票): 加载历史数据: 431567 行

[2020-04-29 09:44:14.859589] INFO: 自动标注(股票): 开始标注 ..

[2020-04-29 09:44:16.045518] INFO: moduleinvoker: advanced_auto_labeler.v2 运行完成[4.826362s].

[2020-04-29 09:44:16.051769] INFO: moduleinvoker: input_features.v1 开始运行..

[2020-04-29 09:44:16.056037] INFO: moduleinvoker: 命中缓存

[2020-04-29 09:44:16.057244] INFO: moduleinvoker: input_features.v1 运行完成[0.005476s].

[2020-04-29 09:44:16.065952] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2020-04-29 09:44:19.201122] INFO: 基础特征抽取: 年份 2010, 特征行数=431567

[2020-04-29 09:44:23.429769] INFO: 基础特征抽取: 年份 2011, 特征行数=0

[2020-04-29 09:44:24.156206] INFO: 基础特征抽取: 总行数: 431567

[2020-04-29 09:44:24.159622] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[8.093665s].

[2020-04-29 09:44:24.168657] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2020-04-29 09:44:24.379420] INFO: derived_feature_extractor: 提取完成 avg_amount_0/avg_amount_5, 0.002s

[2020-04-29 09:44:24.382695] INFO: derived_feature_extractor: 提取完成 avg_amount_5/avg_amount_20, 0.002s

[2020-04-29 09:44:24.387422] INFO: derived_feature_extractor: 提取完成 rank_avg_amount_0/rank_avg_amount_5, 0.004s

[2020-04-29 09:44:24.392247] INFO: derived_feature_extractor: 提取完成 rank_avg_amount_5/rank_avg_amount_10, 0.003s

[2020-04-29 09:44:24.680070] INFO: derived_feature_extractor: 提取完成 rank_return_0/rank_return_5, 0.286s

[2020-04-29 09:44:24.692822] INFO: derived_feature_extractor: 提取完成 rank_return_5/rank_return_10, 0.008s

[2020-04-29 09:44:24.884874] INFO: derived_feature_extractor: /y_2010, 431567

[2020-04-29 09:44:25.596779] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[1.428108s].

[2020-04-29 09:44:25.600915] INFO: moduleinvoker: join.v3 开始运行..

[2020-04-29 09:44:27.511606] INFO: join: /y_2010, 行数=431567/431567, 耗时=1.717995s

[2020-04-29 09:44:27.771835] INFO: join: 最终行数: 431567

[2020-04-29 09:44:27.775652] INFO: moduleinvoker: join.v3 运行完成[2.174726s].

[2020-04-29 09:44:27.799073] INFO: moduleinvoker: dropnan.v1 开始运行..

[2020-04-29 09:44:28.681144] INFO: dropnan: /y_2010, 424271/431567

[2020-04-29 09:44:29.034001] INFO: dropnan: 行数: 424271/431567

[2020-04-29 09:44:29.041684] INFO: moduleinvoker: dropnan.v1 运行完成[1.242603s].

[2020-04-29 09:44:29.044076] INFO: moduleinvoker: instruments.v2 开始运行..

[2020-04-29 09:44:29.128422] INFO: moduleinvoker: instruments.v2 运行完成[0.084311s].

[2020-04-29 09:44:29.134510] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2020-04-29 09:44:32.066708] INFO: 基础特征抽取: 年份 2015, 特征行数=182955

[2020-04-29 09:44:32.268628] INFO: 基础特征抽取: 总行数: 182955

[2020-04-29 09:44:32.276360] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[3.141847s].

[2020-04-29 09:44:32.279117] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2020-04-29 09:44:32.379667] INFO: derived_feature_extractor: 提取完成 avg_amount_0/avg_amount_5, 0.001s

[2020-04-29 09:44:32.382190] INFO: derived_feature_extractor: 提取完成 avg_amount_5/avg_amount_20, 0.001s

[2020-04-29 09:44:32.388575] INFO: derived_feature_extractor: 提取完成 rank_avg_amount_0/rank_avg_amount_5, 0.005s

[2020-04-29 09:44:32.394561] INFO: derived_feature_extractor: 提取完成 rank_avg_amount_5/rank_avg_amount_10, 0.002s

[2020-04-29 09:44:32.399711] INFO: derived_feature_extractor: 提取完成 rank_return_0/rank_return_5, 0.003s

[2020-04-29 09:44:32.402604] INFO: derived_feature_extractor: 提取完成 rank_return_5/rank_return_10, 0.001s

[2020-04-29 09:44:33.122058] INFO: derived_feature_extractor: /y_2015, 182955

[2020-04-29 09:44:34.562356] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[2.28321s].

[2020-04-29 09:44:34.565138] INFO: moduleinvoker: dropnan.v1 开始运行..

[2020-04-29 09:44:34.944732] INFO: dropnan: /y_2015, 181081/182955

[2020-04-29 09:44:36.182163] INFO: dropnan: 行数: 181081/182955

[2020-04-29 09:44:36.187827] INFO: moduleinvoker: dropnan.v1 运行完成[1.622684s].

[2020-04-29 09:44:36.748581] INFO: moduleinvoker: kneighbors_classifier.v1 开始运行..

[2020-04-29 09:45:33.160277] INFO: moduleinvoker: kneighbors_classifier.v1 运行完成[56.411699s].

[2020-04-29 09:45:34.551905] INFO: moduleinvoker: backtest.v8 开始运行..

[2020-04-29 09:45:34.557698] INFO: backtest: biglearning backtest:V8.3.4

[2020-04-29 09:45:34.559493] INFO: backtest: product_type:stock by specified

[2020-04-29 09:45:34.669284] INFO: moduleinvoker: cached.v2 开始运行..

[2020-04-29 09:45:41.867909] INFO: backtest: 读取股票行情完成:913770

[2020-04-29 09:45:45.393335] INFO: moduleinvoker: cached.v2 运行完成[10.724035s].

[2020-04-29 09:45:46.083097] INFO: algo: TradingAlgorithm V1.6.7

[2020-04-29 09:45:46.552377] INFO: algo: trading transform...

[2020-04-29 09:45:47.879397] INFO: Performance: Simulated 78 trading days out of 78.

[2020-04-29 09:45:47.880500] INFO: Performance: first open: 2015-01-05 09:30:00+00:00

[2020-04-29 09:45:47.881323] INFO: Performance: last close: 2015-04-30 15:00:00+00:00

[2020-04-29 09:45:51.046391] INFO: moduleinvoker: backtest.v8 运行完成[16.494505s].

[2020-04-29 09:45:51.048128] INFO: moduleinvoker: trade.v4 运行完成[17.880839s].

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