回测仓位问题

策略分享
标签: #<Tag:0x00007f6082f76018>

(iQuant) #1
克隆策略
In [1]:
# 本代码由可视化策略环境自动生成 2019年2月20日 14:44
# 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')]
    print('%s'%data.current_dt.strftime('%Y-%m-%d'))

    # 1. 资金分配
    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: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.perf_tracker.position_tracker.positions.items()}
    #print('ranker_prediction%s'%ranker_prediction.instrument[:10])
    print('equities%s'%equities)
    for e, p in context.perf_tracker.position_tracker.positions.items():
        print('%s'%e)
        print('%s'%p)
    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]))])))
    print('rank order for sell %s' % instruments)
    for instrument in instruments:
        context.order_target(context.symbol(instrument), 0)
        context.stocks_to_sell += 1
    print('stocks_to_sell %d' % context.stocks_to_sell)

    # 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
    cash_for_buy = context.portfolio.cash
    buy_instruments = []
    if context.is_init == 0 and context.stocks_to_sell > 0:
        buy_instruments = list(ranker_prediction.instrument[:context.stocks_to_sell])
        print('hello2')
        cash = cash_for_buy / context.stocks_to_sell
        print('rank order for buy %s' % buy_instruments)
    if context.is_init == 1 and context.run_times == 20:
        buy_instruments = list(ranker_prediction.instrument[:context.stock_count])
        context.is_init = 0
        print('is_init: %d'%context.is_init)
        cash = cash_for_buy / context.stock_count
        print('rank order for buy %s' % buy_instruments)

    max_cash_per_instrument = context.portfolio.portfolio_value * context.max_cash_per_instrument
    context.run_times += 1
    for i, instrument in enumerate(buy_instruments):
        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)
            print('buy %s'% context.symbol(instrument))
            context.stocks_to_sell = 0
            print('buy %d'% context.stocks_to_sell)


# 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
    context.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 = [0.2,0.2,0.2,0.2,0.2]
    # 设置每只股票占用的最大资金比例
    context.max_cash_per_instrument = 1
    context.options['hold_days'] = 5
    context.is_init = 1
    context.run_times = 0
    context.stocks_to_sell = 0
    print('init')


m1 = M.instruments.v2(
    start_date='2010-01-01',
    end_date='2016-01-01',
    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 {{web_host_url}}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 <{{web_host_url}}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, -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)
""",
    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
rank_market_cap_float_0
beta_csi500_5_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
)

m6 = M.stock_ranker_train.v5(
    training_ds=m13.data,
    features=m3.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,
    m_lazy_run=False
)

m9 = M.instruments.v2(
    start_date=T.live_run_param('trading_date', '2016-01-01'),
    end_date=T.live_run_param('trading_date', '2019-01-01'),
    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
)

m8 = M.stock_ranker_predict.v5(
    model=m6.model,
    data=m14.data,
    m_lazy_run=False
)

m19 = M.trade.v4(
    instruments=m9.data,
    options_data=m8.predictions,
    start_date='',
    end_date='',
    handle_data=m19_handle_data_bigquant_run,
    prepare=m19_prepare_bigquant_run,
    initialize=m19_initialize_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='forward_adjusted',
    product_type='股票',
    plot_charts=True,
    backtest_only=False,
    benchmark='000300.SHA'
)
!trade.v4 module price_type is pre_right
init
2016-01-04
equities{}
rank order for sell []
stocks_to_sell 0
2016-01-05
equities{}
rank order for sell []
stocks_to_sell 0
2016-01-06
equities{}
rank order for sell []
stocks_to_sell 0
2016-01-07
equities{}
rank order for sell []
stocks_to_sell 0
2016-01-08
equities{}
rank order for sell []
stocks_to_sell 0
2016-01-11
equities{}
rank order for sell []
stocks_to_sell 0
2016-01-12
equities{}
rank order for sell []
stocks_to_sell 0
2016-01-13
equities{}
rank order for sell []
stocks_to_sell 0
2016-01-14
equities{}
rank order for sell []
stocks_to_sell 0
2016-01-15
equities{}
rank order for sell []
stocks_to_sell 0
2016-01-18
equities{}
rank order for sell []
stocks_to_sell 0
2016-01-19
equities{}
rank order for sell []
stocks_to_sell 0
2016-01-20
equities{}
rank order for sell []
stocks_to_sell 0
2016-01-21
equities{}
rank order for sell []
stocks_to_sell 0
2016-01-22
equities{}
rank order for sell []
stocks_to_sell 0
2016-01-25
equities{}
rank order for sell []
stocks_to_sell 0
2016-01-26
equities{}
rank order for sell []
stocks_to_sell 0
2016-01-27
equities{}
rank order for sell []
stocks_to_sell 0
2016-01-28
equities{}
rank order for sell []
stocks_to_sell 0
2016-01-29
equities{}
rank order for sell []
stocks_to_sell 0
2016-02-01
equities{}
rank order for sell []
stocks_to_sell 0
is_init: 0
rank order for buy ['002759.SZA', '300371.SZA', '603066.SHA', '300488.SZA', '002767.SZA']
buy Equity(251 [002759.SZA])
buy 0
buy Equity(2368 [300371.SZA])
buy 0
buy Equity(986 [603066.SHA])
buy 0
buy Equity(1592 [300488.SZA])
buy 0
buy Equity(1376 [002767.SZA])
buy 0
2016-02-02
equities{'300371.SZA': Equity(2368 [300371.SZA]), '300488.SZA': Equity(1592 [300488.SZA]), '002759.SZA': Equity(251 [002759.SZA]), '002767.SZA': Equity(1376 [002767.SZA]), '603066.SHA': Equity(986 [603066.SHA])}
Equity(251 [002759.SZA])
asset: Equity(251 [002759.SZA]), amount: 6300, cost_basis: 31.400132308369496,             last_sale_price: 31.400132308369496
Equity(2368 [300371.SZA])
asset: Equity(2368 [300371.SZA]), amount: 9200, cost_basis: 21.760033441971512,             last_sale_price: 21.760033441971512
Equity(986 [603066.SHA])
asset: Equity(986 [603066.SHA]), amount: 6000, cost_basis: 33.870076905766375,             last_sale_price: 33.870076905766375
Equity(1592 [300488.SZA])
asset: Equity(1592 [300488.SZA]), amount: 3000, cost_basis: 65.2000861134622,             last_sale_price: 65.2000861134622
Equity(1376 [002767.SZA])
asset: Equity(1376 [002767.SZA]), amount: 6200, cost_basis: 32.10009041214542,             last_sale_price: 32.10009041214542
rank order for sell ['300488.SZA', '603066.SHA', '002767.SZA', '002759.SZA', '300371.SZA']
stocks_to_sell 5
hello2
rank order for buy ['002778.SZA', '603088.SHA', '300487.SZA', '002743.SZA', '300464.SZA']
buy Equity(2173 [002778.SZA])
buy 0
buy Equity(2543 [603088.SHA])
buy 0
buy Equity(160 [300487.SZA])
buy 0
buy Equity(1315 [002743.SZA])
buy 0
buy Equity(2242 [300464.SZA])
buy 0
2016-02-03
equities{}
rank order for sell []
stocks_to_sell 0
2016-02-04
equities{}
rank order for sell []
stocks_to_sell 0
2016-02-05
equities{}
rank order for sell []
stocks_to_sell 0
2016-02-15
equities{}
rank order for sell []
stocks_to_sell 0
2016-02-16
equities{}
rank order for sell []
stocks_to_sell 0
2016-02-17
equities{}
rank order for sell []
stocks_to_sell 0
2016-02-18
equities{}
rank order for sell []
stocks_to_sell 0
2016-02-19
equities{}
rank order for sell []
stocks_to_sell 0
2016-02-22
equities{}
rank order for sell []
stocks_to_sell 0
2016-02-23
equities{}
rank order for sell []
stocks_to_sell 0
2016-02-24
equities{}
rank order for sell []
stocks_to_sell 0
2016-02-25
equities{}
rank order for sell []
stocks_to_sell 0
2016-02-26
equities{}
rank order for sell []
stocks_to_sell 0
2016-02-29
equities{}
rank order for sell []
stocks_to_sell 0
2016-03-01
equities{}
rank order for sell []
stocks_to_sell 0
2016-03-02
equities{}
rank order for sell []
stocks_to_sell 0
2016-03-03
equities{}
rank order for sell []
stocks_to_sell 0
2016-03-04
equities{}
rank order for sell []
stocks_to_sell 0
2016-03-07
equities{}
rank order for sell []
stocks_to_sell 0
2016-03-08
equities{}
rank order for sell []
stocks_to_sell 0
2016-03-09
equities{}
rank order for sell []
stocks_to_sell 0
2016-03-10
equities{}
rank order for sell []
stocks_to_sell 0
2016-03-11
equities{}
rank order for sell []
stocks_to_sell 0
2016-03-14
equities{}
rank order for sell []
stocks_to_sell 0
2016-03-15
equities{}
rank order for sell []
stocks_to_sell 0
2016-03-16
equities{}
rank order for sell []
stocks_to_sell 0
2016-03-17
equities{}
rank order for sell []
stocks_to_sell 0
2016-03-18
equities{}
rank order for sell []
stocks_to_sell 0
2016-03-21
equities{}
rank order for sell []
stocks_to_sell 0
2016-03-22
equities{}
rank order for sell []
stocks_to_sell 0
2016-03-23
equities{}
rank order for sell []
stocks_to_sell 0
2016-03-24
equities{}
rank order for sell []
stocks_to_sell 0
2016-03-25
equities{}
rank order for sell []
stocks_to_sell 0
2016-03-28
equities{}
rank order for sell []
stocks_to_sell 0
2016-03-29
equities{}
rank order for sell []
stocks_to_sell 0
2016-03-30
equities{}
rank order for sell []
stocks_to_sell 0
2016-03-31
equities{}
rank order for sell []
stocks_to_sell 0
2016-04-01
equities{}
rank order for sell []
stocks_to_sell 0
2016-04-05
equities{}
rank order for sell []
stocks_to_sell 0
2016-04-06
equities{}
rank order for sell []
stocks_to_sell 0
2016-04-07
equities{}
rank order for sell []
stocks_to_sell 0
2016-04-08
equities{}
rank order for sell []
stocks_to_sell 0
2016-04-11
equities{}
rank order for sell []
stocks_to_sell 0
2016-04-12
equities{}
rank order for sell []
stocks_to_sell 0
2016-04-13
equities{}
rank order for sell []
stocks_to_sell 0
2016-04-14
equities{}
rank order for sell []
stocks_to_sell 0
2016-04-15
equities{}
rank order for sell []
stocks_to_sell 0
2016-04-18
equities{}
rank order for sell []
stocks_to_sell 0
2016-04-19
equities{}
rank order for sell []
stocks_to_sell 0
2016-04-20
equities{}
rank order for sell []
stocks_to_sell 0
2016-04-21
equities{}
rank order for sell []
stocks_to_sell 0
2016-04-22
equities{}
rank order for sell []
stocks_to_sell 0
2016-04-25
equities{}
rank order for sell []
stocks_to_sell 0
2016-04-26
equities{}
rank order for sell []
stocks_to_sell 0
2016-04-27
equities{}
rank order for sell []
stocks_to_sell 0
2016-04-28
equities{}
rank order for sell []
stocks_to_sell 0
2016-04-29
equities{}
rank order for sell []
stocks_to_sell 0
2016-05-03
equities{}
rank order for sell []
stocks_to_sell 0
2016-05-04
equities{}
rank order for sell []
stocks_to_sell 0
2016-05-05
equities{}
rank order for sell []
stocks_to_sell 0
2016-05-06
equities{}
rank order for sell []
stocks_to_sell 0
2016-05-09
equities{}
rank order for sell []
stocks_to_sell 0
2016-05-10
equities{}
rank order for sell []
stocks_to_sell 0
2016-05-11
equities{}
rank order for sell []
stocks_to_sell 0
2016-05-12
equities{}
rank order for sell []
stocks_to_sell 0
2016-05-13
equities{}
rank order for sell []
stocks_to_sell 0
2016-05-16
equities{}
rank order for sell []
stocks_to_sell 0
2016-05-17
equities{}
rank order for sell []
stocks_to_sell 0
2016-05-18
equities{}
rank order for sell []
stocks_to_sell 0
2016-05-19
equities{}
rank order for sell []
stocks_to_sell 0
2016-05-20
equities{}
rank order for sell []
stocks_to_sell 0
2016-05-23
equities{}
rank order for sell []
stocks_to_sell 0
2016-05-24
equities{}
rank order for sell []
stocks_to_sell 0
2016-05-25
equities{}
rank order for sell []
stocks_to_sell 0
2016-05-26
equities{}
rank order for sell []
stocks_to_sell 0
2016-05-27
equities{}
rank order for sell []
stocks_to_sell 0
2016-05-30
equities{}
rank order for sell []
stocks_to_sell 0
2016-05-31
equities{}
rank order for sell []
stocks_to_sell 0
2016-06-01
equities{}
rank order for sell []
stocks_to_sell 0
2016-06-02
equities{}
rank order for sell []
stocks_to_sell 0
2016-06-03
equities{}
rank order for sell []
stocks_to_sell 0
2016-06-06
equities{}
rank order for sell []
stocks_to_sell 0
2016-06-07
equities{}
rank order for sell []
stocks_to_sell 0
2016-06-08
equities{}
rank order for sell []
stocks_to_sell 0
2016-06-13
equities{}
rank order for sell []
stocks_to_sell 0
2016-06-14
equities{}
rank order for sell []
stocks_to_sell 0
2016-06-15
equities{}
rank order for sell []
stocks_to_sell 0
2016-06-16
equities{}
rank order for sell []
stocks_to_sell 0
2016-06-17
equities{}
rank order for sell []
stocks_to_sell 0
2016-06-20
equities{}
rank order for sell []
stocks_to_sell 0
2016-06-21
equities{}
rank order for sell []
stocks_to_sell 0
2016-06-22
equities{}
rank order for sell []
stocks_to_sell 0
2016-06-23
equities{}
rank order for sell []
stocks_to_sell 0
2016-06-24
equities{}
rank order for sell []
stocks_to_sell 0
2016-06-27
equities{}
rank order for sell []
stocks_to_sell 0
2016-06-28
equities{}
rank order for sell []
stocks_to_sell 0
2016-06-29
equities{}
rank order for sell []
stocks_to_sell 0
2016-06-30
equities{}
rank order for sell []
stocks_to_sell 0
2016-07-01
equities{}
rank order for sell []
stocks_to_sell 0
2016-07-04
equities{}
rank order for sell []
stocks_to_sell 0
2016-07-05
equities{}
rank order for sell []
stocks_to_sell 0
2016-07-06
equities{}
rank order for sell []
stocks_to_sell 0
2016-07-07
equities{}
rank order for sell []
stocks_to_sell 0
2016-07-08
equities{}
rank order for sell []
stocks_to_sell 0
2016-07-11
equities{}
rank order for sell []
stocks_to_sell 0
2016-07-12
equities{}
rank order for sell []
stocks_to_sell 0
2016-07-13
equities{}
rank order for sell []
stocks_to_sell 0
2016-07-14
equities{}
rank order for sell []
stocks_to_sell 0
2016-07-15
equities{}
rank order for sell []
stocks_to_sell 0
2016-07-18
equities{}
rank order for sell []
stocks_to_sell 0
2016-07-19
equities{}
rank order for sell []
stocks_to_sell 0
2016-07-20
equities{}
rank order for sell []
stocks_to_sell 0
2016-07-21
equities{}
rank order for sell []
stocks_to_sell 0
2016-07-22
equities{}
rank order for sell []
stocks_to_sell 0
2016-07-25
equities{}
rank order for sell []
stocks_to_sell 0
2016-07-26
equities{}
rank order for sell []
stocks_to_sell 0
2016-07-27
equities{}
rank order for sell []
stocks_to_sell 0
2016-07-28
equities{}
rank order for sell []
stocks_to_sell 0
2016-07-29
equities{}
rank order for sell []
stocks_to_sell 0
2016-08-01
equities{}
rank order for sell []
stocks_to_sell 0
2016-08-02
equities{}
rank order for sell []
stocks_to_sell 0
2016-08-03
equities{}
rank order for sell []
stocks_to_sell 0
2016-08-04
equities{}
rank order for sell []
stocks_to_sell 0
2016-08-05
equities{}
rank order for sell []
stocks_to_sell 0
2016-08-08
equities{}
rank order for sell []
stocks_to_sell 0
2016-08-09
equities{}
rank order for sell []
stocks_to_sell 0
2016-08-10
equities{}
rank order for sell []
stocks_to_sell 0
2016-08-11
equities{}
rank order for sell []
stocks_to_sell 0
2016-08-12
equities{}
rank order for sell []
stocks_to_sell 0
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equities{}
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equities{}
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equities{}
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equities{}
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equities{}
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2017-11-06
equities{}
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equities{}
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equities{}
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equities{}
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equities{}
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equities{}
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equities{}
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equities{}
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2017-11-20
equities{}
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2017-11-22
equities{}
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equities{}
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2017-11-24
equities{}
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2017-11-27
equities{}
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equities{}
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2017-11-29
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2017-11-30
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2017-12-01
equities{}
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equities{}
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equities{}
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equities{}
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equities{}
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equities{}
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equities{}
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equities{}
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equities{}
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equities{}
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equities{}
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equities{}
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equities{}
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equities{}
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equities{}
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equities{}
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equities{}
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equities{}
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equities{}
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equities{}
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equities{}
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equities{}
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equities{}
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equities{}
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equities{}
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equities{}
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equities{}
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equities{}
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equities{}
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equities{}
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equities{}
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equities{}
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equities{}
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equities{}
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equities{}
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equities{}
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equities{}
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equities{}
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equities{}
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equities{}
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equities{}
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equities{}
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2018-12-06
equities{}
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equities{}
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equities{}
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equities{}
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equities{}
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equities{}
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equities{}
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equities{}
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equities{}
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equities{}
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equities{}
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  • 收益率4.91%
  • 年化收益率1.67%
  • 基准收益率-19.31%
  • 阿尔法-0.01
  • 贝塔0.01
  • 夏普比率-0.49
  • 胜率1.0
  • 盈亏比0.0
  • 收益波动率2.57%
  • 信息比率0.02
  • 最大回撤0.0%