克隆策略

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回测引擎：每日数据处理函数，每天执行一次\ndef bigquant_run(context, data):\n # 按日期过滤得到今日的预测数据\n ranker_prediction = context.ranker_prediction[\n context.ranker_prediction.date == data.current_dt.strftime('%Y-%m-%d')]\n\n # 1. 资金分配\n # 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\n # 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）\n is_staging = context.trading_day_index < context.options['hold_days'] # 是否在建仓期间（前 hold_days 天）\n cash_avg = context.portfolio.portfolio_value / context.options['hold_days']\n cash_for_buy = min(context.portfolio.cash, (1 if is_staging else 1.5) * cash_avg)\n cash_for_sell = cash_avg - (context.portfolio.cash - cash_for_buy)\n positions = {e.symbol: p.amount * p.last_sale_price\n for e, p in context.perf_tracker.position_tracker.positions.items()}\n\n # 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\n if not is_staging and cash_for_sell > 0:\n equities = {e.symbol: e for e, p in context.perf_tracker.position_tracker.positions.items()}\n instruments = 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bigquant_run(\n bq_graph,\n inputs,\n trading_days_market='CN', # 使用那个市场的交易日历\n train_instruments_mid='m1', # 训练数据证券代码列表模块id\n test_instruments_mid='m9', # 测试数据证券代码列表模块id\n predict_mid='m8', # 预测模块id\n trade_mid='m19', # 回测模块id\n start_date='2016-01-01', # 数据开始日期\n end_date=T.live_run_param('trading_date', '2018-10-22'), # 数据结束日期\n train_update_days=250, # 更新周期，按交易日计算，每多少天更新一次\n train_update_days_for_live=None, #模拟实盘模式下的更新周期，按交易日计算，每多少天更新一次。如果需要在模拟实盘阶段使用不同的模型更新周期，可以设置这个参数\n train_data_min_days=250, # 最小数据天数，按交易日计算，所以第一个滚动的结束日期是从开始日期到开始日期+最小数据天数\n train_data_max_days=250, # 最大数据天数，按交易日计算，0，表示没有限制，否则每一个滚动的开始日期=max(此滚动的结束日期-最大数据天数, 开始日期\n rolling_count_for_live=1, #实盘模式下滚动次数，模拟实盘模式下，取最后多少次滚动。一般在模拟实盘模式下，只用到最后一次滚动训练的模型，这里可以设置为1；如果你的滚动训练数据时间段很短，以至于期间可能没有训练数据，这里可以设置大一点。0表示没有限制\n):\n def merge_datasources(input_1):\n df_list = [ds[0].read_df().set_index('date').ix[ds[1]:].reset_index() for ds in input_1]\n df = pd.concat(df_list)\n instrument_data = {\n 'start_date': 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In [1]:

# 本代码由可视化策略环境自动生成 2019年3月20日 09:44
# 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：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
    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.symbol: p.amount * p.last_sale_price
                 for e, p in context.perf_tracker.position_tracker.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.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]))])))
        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)

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

# 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 = 30
    # 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.options['hold_days'] = 22


g = T.Graph({

    'm1': 'M.instruments.v2',
    'm1.start_date': '2010-01-01',
    'm1.end_date': '2015-01-01',
    'm1.market': 'CN_STOCK_A',
    'm1.instrument_list': '',
    'm1.max_count': 0,

    'm2': 'M.advanced_auto_labeler.v2',
    'm2.instruments': T.Graph.OutputPort('m1.data'),
    'm2.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)
shift(close, -22) / 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)
""",
    'm2.start_date': '',
    'm2.end_date': '',
    'm2.benchmark': '000300.SHA',
    'm2.drop_na_label': True,
    'm2.cast_label_int': True,

    'm3': 'M.input_features.v1',
    'm3.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',
    'm15.instruments': T.Graph.OutputPort('m1.data'),
    'm15.features': T.Graph.OutputPort('m3.data'),
    'm15.start_date': '',
    'm15.end_date': '',
    'm15.before_start_days': 90,

    'm16': 'M.derived_feature_extractor.v3',
    'm16.input_data': T.Graph.OutputPort('m15.data'),
    'm16.features': T.Graph.OutputPort('m3.data'),
    'm16.date_col': 'date',
    'm16.instrument_col': 'instrument',
    'm16.drop_na': False,
    'm16.remove_extra_columns': False,

    'm7': 'M.join.v3',
    'm7.data1': T.Graph.OutputPort('m2.data'),
    'm7.data2': T.Graph.OutputPort('m16.data'),
    'm7.on': 'date,instrument',
    'm7.how': 'inner',
    'm7.sort': False,

    'm13': 'M.dropnan.v1',
    'm13.input_data': T.Graph.OutputPort('m7.data'),

    'm6': 'M.stock_ranker_train.v5',
    'm6.training_ds': T.Graph.OutputPort('m13.data'),
    'm6.features': T.Graph.OutputPort('m3.data'),
    'm6.learning_algorithm': '排序',
    'm6.number_of_leaves': 30,
    'm6.minimum_docs_per_leaf': 1000,
    'm6.number_of_trees': 20,
    'm6.learning_rate': 0.1,
    'm6.max_bins': 1023,
    'm6.feature_fraction': 1,
    'm6.m_lazy_run': False,

    'm9': 'M.instruments.v2',
    'm9.start_date': T.live_run_param('trading_date', '2017-01-01'),
    'm9.end_date': T.live_run_param('trading_date', '2018-10-22'),
    'm9.market': 'CN_STOCK_A',
    'm9.instrument_list': '',
    'm9.max_count': 0,

    'm17': 'M.general_feature_extractor.v7',
    'm17.instruments': T.Graph.OutputPort('m9.data'),
    'm17.features': T.Graph.OutputPort('m3.data'),
    'm17.start_date': '',
    'm17.end_date': '',
    'm17.before_start_days': 90,

    'm18': 'M.derived_feature_extractor.v3',
    'm18.input_data': T.Graph.OutputPort('m17.data'),
    'm18.features': T.Graph.OutputPort('m3.data'),
    'm18.date_col': 'date',
    'm18.instrument_col': 'instrument',
    'm18.drop_na': False,
    'm18.remove_extra_columns': False,

    'm14': 'M.dropnan.v1',
    'm14.input_data': T.Graph.OutputPort('m18.data'),

    'm8': 'M.stock_ranker_predict.v5',
    'm8.model': T.Graph.OutputPort('m6.model'),
    'm8.data': T.Graph.OutputPort('m14.data'),
    'm8.m_lazy_run': False,

    'm19': 'M.trade.v4',
    'm19.instruments': T.Graph.OutputPort('m9.data'),
    'm19.options_data': T.Graph.OutputPort('m8.predictions'),
    'm19.start_date': '',
    'm19.end_date': '',
    'm19.handle_data': m19_handle_data_bigquant_run,
    'm19.prepare': m19_prepare_bigquant_run,
    'm19.initialize': m19_initialize_bigquant_run,
    'm19.volume_limit': 0.025,
    'm19.order_price_field_buy': 'open',
    'm19.order_price_field_sell': 'close',
    'm19.capital_base': 1000000,
    'm19.auto_cancel_non_tradable_orders': True,
    'm19.data_frequency': 'daily',
    'm19.price_type': '后复权',
    'm19.product_type': '股票',
    'm19.plot_charts': True,
    'm19.backtest_only': False,
    'm19.benchmark': '',
})

# g.run({})


def m20_run_bigquant_run(
    bq_graph,
    inputs,
    trading_days_market='CN', # 使用那个市场的交易日历
    train_instruments_mid='m1', # 训练数据 证券代码列表 模块id
    test_instruments_mid='m9', # 测试数据 证券代码列表 模块id
    predict_mid='m8', # 预测 模块id
    trade_mid='m19', # 回测 模块id
    start_date='2016-01-01', # 数据开始日期
    end_date=T.live_run_param('trading_date', '2018-10-22'), # 数据结束日期
    train_update_days=250, # 更新周期，按交易日计算，每多少天更新一次
    train_update_days_for_live=None, #模拟实盘模式下的更新周期，按交易日计算，每多少天更新一次。如果需要在模拟实盘阶段使用不同的模型更新周期，可以设置这个参数
    train_data_min_days=250, # 最小数据天数，按交易日计算，所以第一个滚动的结束日期是 从开始日期到开始日期+最小数据天数
    train_data_max_days=250, # 最大数据天数，按交易日计算，0，表示没有限制，否则每一个滚动的开始日期=max(此滚动的结束日期-最大数据天数, 开始日期
    rolling_count_for_live=1, #实盘模式下滚动次数，模拟实盘模式下，取最后多少次滚动。一般在模拟实盘模式下，只用到最后一次滚动训练的模型，这里可以设置为1；如果你的滚动训练数据时间段很短，以至于期间可能没有训练数据，这里可以设置大一点。0表示没有限制
):
    def merge_datasources(input_1):
        df_list = [ds[0].read_df().set_index('date').ix[ds[1]:].reset_index() for ds in input_1]
        df = pd.concat(df_list)
        instrument_data = {
            'start_date': df['date'].min().strftime('%Y-%m-%d'),
            'end_date': df['date'].max().strftime('%Y-%m-%d'),
            'instruments': list(set(df['instrument'])),
        }
        return Outputs(data=DataSource.write_df(df), instrument_data=DataSource.write_pickle(instrument_data))

    def gen_rolling_dates(trading_days_market, start_date, end_date, train_update_days, train_update_days_for_live, train_data_min_days, train_data_max_days, rolling_count_for_live):
        # 是否实盘模式
        tdays = list(D.trading_days(market=trading_days_market, start_date=start_date, end_date=end_date)['date'])
        is_live_run = T.live_run_param('trading_date', None) is not None

        if is_live_run and train_update_days_for_live:
            train_update_days = train_update_days_for_live

        rollings = []
        train_end_date = train_data_min_days
        while train_end_date < len(tdays):
            if train_data_max_days is not None:
                train_start_date = max(train_end_date - train_data_max_days, 0)
            else:
                train_start_date = start_date
            rollings.append({
                'train_start_date': tdays[train_start_date].strftime('%Y-%m-%d'),
                'train_end_date': tdays[train_end_date - 1].strftime('%Y-%m-%d'),
                'test_start_date': tdays[train_end_date].strftime('%Y-%m-%d'),
                'test_end_date': tdays[min(train_end_date + train_update_days, len(tdays)) - 1].strftime('%Y-%m-%d'),
            })
            train_end_date += train_update_days

        if not rollings:
            raise Exception('没有滚动需要执行，请检查配置')

        if is_live_run and rolling_count_for_live:
            rollings = rollings[-rolling_count_for_live:]

        return rollings

    g = bq_graph

    rolling_dates = gen_rolling_dates(
        trading_days_market, start_date, end_date, train_update_days, train_update_days_for_live, train_data_min_days, train_data_max_days, rolling_count_for_live)

    # 训练和预测
    results = []
    for rolling in rolling_dates:
        parameters = {}
        # 先禁用回测
        parameters[trade_mid + '.__enabled__'] = False
        parameters[train_instruments_mid + '.start_date'] = rolling['train_start_date']
        parameters[train_instruments_mid + '.end_date'] = rolling['train_end_date']
        parameters[test_instruments_mid + '.start_date'] = rolling['test_start_date']
        parameters[test_instruments_mid + '.end_date'] = rolling['test_end_date']
        # print('------ rolling_train:', parameters)
        results.append(g.run(parameters))

    # 合并预测结果并回测
    mx = M.cached.v3(run=merge_datasources, input_1=[[result[predict_mid].predictions,result[test_instruments_mid].data.read_pickle()['start_date']] for result in results])
    parameters = {}
    parameters['*.__enabled__'] = False
    parameters[trade_mid + '.__enabled__'] = True
    parameters[trade_mid + '.instruments'] = mx.instrument_data
    parameters[trade_mid + '.options_data'] = mx.data

    trade = g.run(parameters)

    return {'rollings': results, 'trade': trade}


m20 = M.hyper_rolling_train.v1(
    run=m20_run_bigquant_run,
    run_now=True,
    bq_graph=g
)

[2019-03-20 09:34:40.111209] INFO: bigquant: input_features.v1 开始运行..

[2019-03-20 09:34:40.132939] INFO: bigquant: 命中缓存

[2019-03-20 09:34:40.134360] INFO: bigquant: input_features.v1 运行完成[0.023163s].

[2019-03-20 09:34:40.161272] INFO: bigquant: instruments.v2 开始运行..

[2019-03-20 09:34:40.168740] INFO: bigquant: 命中缓存

[2019-03-20 09:34:40.171053] INFO: bigquant: instruments.v2 运行完成[0.00977s].

[2019-03-20 09:34:40.175068] INFO: bigquant: instruments.v2 开始运行..

[2019-03-20 09:34:40.182037] INFO: bigquant: 命中缓存

[2019-03-20 09:34:40.183900] INFO: bigquant: instruments.v2 运行完成[0.008825s].

[2019-03-20 09:34:40.279573] INFO: bigquant: general_feature_extractor.v7 开始运行..

[2019-03-20 09:34:42.351597] INFO: bigquant: 命中缓存

[2019-03-20 09:34:42.353550] INFO: bigquant: general_feature_extractor.v7 运行完成[2.073977s].

[2019-03-20 09:34:42.402968] INFO: bigquant: advanced_auto_labeler.v2 开始运行..

[2019-03-20 09:34:42.865127] INFO: bigquant: 命中缓存

[2019-03-20 09:34:42.867189] INFO: bigquant: advanced_auto_labeler.v2 运行完成[0.464199s].

[2019-03-20 09:34:42.874792] INFO: bigquant: general_feature_extractor.v7 开始运行..

[2019-03-20 09:34:43.366124] INFO: bigquant: 命中缓存

[2019-03-20 09:34:43.367842] INFO: bigquant: general_feature_extractor.v7 运行完成[0.493049s].

[2019-03-20 09:34:43.389756] INFO: bigquant: derived_feature_extractor.v3 开始运行..

[2019-03-20 09:34:44.898358] INFO: bigquant: 命中缓存

[2019-03-20 09:34:44.900207] INFO: bigquant: derived_feature_extractor.v3 运行完成[1.510445s].

[2019-03-20 09:34:44.903319] INFO: bigquant: derived_feature_extractor.v3 开始运行..

[2019-03-20 09:34:45.064265] INFO: bigquant: 命中缓存

[2019-03-20 09:34:45.066014] INFO: bigquant: derived_feature_extractor.v3 运行完成[0.162689s].

[2019-03-20 09:34:45.072088] INFO: bigquant: dropnan.v1 开始运行..

[2019-03-20 09:34:46.164788] INFO: bigquant: 命中缓存

[2019-03-20 09:34:46.166930] INFO: bigquant: dropnan.v1 运行完成[1.094834s].

[2019-03-20 09:34:46.172588] INFO: bigquant: join.v3 开始运行..

[2019-03-20 09:34:46.670317] INFO: bigquant: 命中缓存

[2019-03-20 09:34:46.672381] INFO: bigquant: join.v3 运行完成[0.499786s].

[2019-03-20 09:34:46.675111] INFO: bigquant: dropnan.v1 开始运行..

[2019-03-20 09:34:46.954821] INFO: bigquant: 命中缓存

[2019-03-20 09:34:46.956829] INFO: bigquant: dropnan.v1 运行完成[0.281703s].

[2019-03-20 09:34:46.963645] INFO: bigquant: stock_ranker_train.v5 开始运行..

[2019-03-20 09:34:46.999898] INFO: bigquant: 命中缓存

[2019-03-20 09:34:47.002658] INFO: bigquant: stock_ranker_train.v5 运行完成[0.039004s].

[2019-03-20 09:34:47.008776] INFO: bigquant: stock_ranker_predict.v5 开始运行..

[2019-03-20 09:34:47.081233] INFO: bigquant: 命中缓存

[2019-03-20 09:34:47.083878] INFO: bigquant: stock_ranker_predict.v5 运行完成[0.075089s].

[2019-03-20 09:34:47.088651] INFO: bigquant: input_features.v1 开始运行..

[2019-03-20 09:34:47.094792] INFO: bigquant: 命中缓存

[2019-03-20 09:34:47.096968] INFO: bigquant: input_features.v1 运行完成[0.008313s].

[2019-03-20 09:34:47.100609] INFO: bigquant: instruments.v2 开始运行..

[2019-03-20 09:34:47.129238] INFO: bigquant: 命中缓存

[2019-03-20 09:34:47.131002] INFO: bigquant: instruments.v2 运行完成[0.030387s].

[2019-03-20 09:34:47.134572] INFO: bigquant: instruments.v2 开始运行..

[2019-03-20 09:34:47.140943] INFO: bigquant: 命中缓存

[2019-03-20 09:34:47.142829] INFO: bigquant: instruments.v2 运行完成[0.008257s].

[2019-03-20 09:34:47.150441] INFO: bigquant: general_feature_extractor.v7 开始运行..

[2019-03-20 09:34:47.183757] INFO: bigquant: 命中缓存

[2019-03-20 09:34:47.186103] INFO: bigquant: general_feature_extractor.v7 运行完成[0.035651s].

[2019-03-20 09:34:47.190130] INFO: bigquant: advanced_auto_labeler.v2 开始运行..

[2019-03-20 09:34:47.213921] INFO: bigquant: 命中缓存

[2019-03-20 09:34:47.216388] INFO: bigquant: advanced_auto_labeler.v2 运行完成[0.026248s].

[2019-03-20 09:34:47.223834] INFO: bigquant: general_feature_extractor.v7 开始运行..

[2019-03-20 09:34:47.229297] INFO: bigquant: 命中缓存

[2019-03-20 09:34:47.231235] INFO: bigquant: general_feature_extractor.v7 运行完成[0.007393s].

[2019-03-20 09:34:47.234654] INFO: bigquant: derived_feature_extractor.v3 开始运行..

[2019-03-20 09:34:47.241111] INFO: bigquant: 命中缓存

[2019-03-20 09:34:47.242928] INFO: bigquant: derived_feature_extractor.v3 运行完成[0.008266s].

[2019-03-20 09:34:47.246650] INFO: bigquant: derived_feature_extractor.v3 开始运行..

[2019-03-20 09:34:47.253069] INFO: bigquant: 命中缓存

[2019-03-20 09:34:47.255111] INFO: bigquant: derived_feature_extractor.v3 运行完成[0.008455s].

[2019-03-20 09:34:47.258614] INFO: bigquant: dropnan.v1 开始运行..

[2019-03-20 09:34:47.296333] INFO: bigquant: 命中缓存

[2019-03-20 09:34:47.298594] INFO: bigquant: dropnan.v1 运行完成[0.03997s].

[2019-03-20 09:34:47.302501] INFO: bigquant: join.v3 开始运行..

[2019-03-20 09:34:47.309010] INFO: bigquant: 命中缓存

[2019-03-20 09:34:47.311016] INFO: bigquant: join.v3 运行完成[0.008508s].

[2019-03-20 09:34:47.314471] INFO: bigquant: dropnan.v1 开始运行..

[2019-03-20 09:34:47.363830] INFO: bigquant: 命中缓存

[2019-03-20 09:34:47.366112] INFO: bigquant: dropnan.v1 运行完成[0.051635s].

[2019-03-20 09:34:47.369833] INFO: bigquant: stock_ranker_train.v5 开始运行..

[2019-03-20 09:34:47.446279] INFO: bigquant: 命中缓存

[2019-03-20 09:34:47.448988] INFO: bigquant: stock_ranker_train.v5 运行完成[0.079138s].

[2019-03-20 09:34:47.452347] INFO: bigquant: stock_ranker_predict.v5 开始运行..

[2019-03-20 09:34:47.525353] INFO: bigquant: 命中缓存

[2019-03-20 09:34:47.527876] INFO: bigquant: stock_ranker_predict.v5 运行完成[0.075522s].

[2019-03-20 09:34:47.668059] INFO: bigquant: cached.v3 开始运行..

[2019-03-20 09:34:52.319628] INFO: bigquant: cached.v3 运行完成[4.651571s].

[2019-03-20 09:34:52.660430] INFO: bigquant: backtest.v8 开始运行..

[2019-03-20 09:34:52.663201] INFO: bigquant: biglearning backtest:V8.1.11

[2019-03-20 09:34:52.664875] INFO: bigquant: product_type:stock by specified

[2019-03-20 09:35:03.496700] INFO: bigquant: 读取股票行情完成:2162671

[2019-03-20 09:35:25.231131] INFO: algo: TradingAlgorithm V1.4.8

[2019-03-20 09:35:36.453459] INFO: algo: trading transform...

[2019-03-20 09:36:06.533542] INFO: Performance: Simulated 432 trading days out of 432.

[2019-03-20 09:36:06.535807] INFO: Performance: first open: 2017-01-11 09:30:00+00:00

[2019-03-20 09:36:06.537663] INFO: Performance: last close: 2018-10-22 15:00:00+00:00

[2019-03-20 09:36:11.804816] INFO: bigquant: backtest.v8 运行完成[79.144362s].

收益率-44.67%
年化收益率-29.2%
基准收益率-2.62%
阿尔法-0.33
贝塔0.85
夏普比率-1.91
胜率0.47
盈亏比0.53
收益波动率18.7%
信息比率-0.16
最大回撤50.49%