克隆策略

    {"description":"实验创建于2017/8/26","graph":{"edges":[{"to_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-15:instruments","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-8:data"},{"to_node_id":"-404:instruments","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-8:data"},{"to_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-53:data1","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-15:data"},{"to_node_id":"-404:features","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"to_node_id":"-411:features","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"to_node_id":"-418:features","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"to_node_id":"-425:features","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"to_node_id":"-122:features","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"to_node_id":"-143:input_1","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-53:data"},{"to_node_id":"-1918:options_data","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-60:predictions"},{"to_node_id":"-418:instruments","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-62:data"},{"to_node_id":"-1918:instruments","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-62:data"},{"to_node_id":"-411:input_data","from_node_id":"-404:data"},{"to_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-53:data2","from_node_id":"-411:data"},{"to_node_id":"-425:input_data","from_node_id":"-418:data"},{"to_node_id":"-146:input_1","from_node_id":"-425:data"},{"to_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-60:model","from_node_id":"-122:model"},{"to_node_id":"-122:training_ds","from_node_id":"-136:data"},{"to_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-60:data","from_node_id":"-140:data"},{"to_node_id":"-136:input_data","from_node_id":"-143:data_1"},{"to_node_id":"-140:input_data","from_node_id":"-146:data_1"}],"nodes":[{"node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-8","module_id":"BigQuantSpace.instruments.instruments-v2","parameters":[{"name":"start_date","value":"2010-01-01","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"2015-01-01","type":"Literal","bound_global_parameter":null},{"name":"market","value":"CN_STOCK_A","type":"Literal","bound_global_parameter":null},{"name":"instrument_list","value":"","type":"Literal","bound_global_parameter":null},{"name":"max_count","value":"0","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"rolling_conf","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-8"}],"output_ports":[{"name":"data","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-8"}],"cacheable":true,"seq_num":1,"comment":"","comment_collapsed":true},{"node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-15","module_id":"BigQuantSpace.advanced_auto_labeler.advanced_auto_labeler-v2","parameters":[{"name":"label_expr","value":"# #号开始的表示注释\n# 0. 每行一个，顺序执行，从第二个开始，可以使用label字段\n# 1. 可用数据字段见 https://bigquant.com/docs/data_history_data.html\n# 添加benchmark_前缀，可使用对应的benchmark数据\n# 2. 可用操作符和函数见 `表达式引擎 <https://bigquant.com/docs/big_expr.html>`_\n\n# 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)\nshift(close, -5) / shift(open, -1)\n\n# 极值处理：用1%和99%分位的值做clip\nclip(label, all_quantile(label, 0.01), all_quantile(label, 0.99))\n\n# 将分数映射到分类，这里使用20个分类\nall_wbins(label, 20)\n\n# 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)\nwhere(shift(high, -1) == shift(low, -1), NaN, label)\n","type":"Literal","bound_global_parameter":null},{"name":"start_date","value":"","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"","type":"Literal","bound_global_parameter":null},{"name":"benchmark","value":"000300.SHA","type":"Literal","bound_global_parameter":null},{"name":"drop_na_label","value":"True","type":"Literal","bound_global_parameter":null},{"name":"cast_label_int","value":"True","type":"Literal","bound_global_parameter":null},{"name":"user_functions","value":"","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"instruments","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-15"}],"output_ports":[{"name":"data","node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-15"}],"cacheable":true,"seq_num":2,"comment":"","comment_collapsed":true},{"node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24","module_id":"BigQuantSpace.input_features.input_features-v1","parameters":[{"name":"features","value":"# #号开始的表示注释\n# 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回测引擎：初始化函数，只执行一次\ndef bigquant_run(context):\n # 加载预测数据\n context.ranker_prediction = context.options['data'].read_df()\n\n # 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\n context.set_commission(PerOrder(buy_cost=0.0003, sell_cost=0.0013, min_cost=5))\n # 预测数据，通过options传入进来，使用 read_df 函数，加载到内存 (DataFrame)\n # 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\n stock_count = 5\n # 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, ..]\n context.stock_weights = T.norm([1 / math.log(i + 2) for i in range(0, stock_count)])\n # 设置每只股票占用的最大资金比例\n context.max_cash_per_instrument = 0.2\n context.options['hold_days'] = 5\n\n from zipline.finance.slippage import SlippageModel\n class FixedPriceSlippage(SlippageModel):\n def process_order(self, data, order, bar_volume=0, trigger_check_price=0):\n if order.limit is None:\n price_field = self._price_field_buy if order.amount > 0 else self._price_field_sell\n price = data.current(order.asset, price_field)\n else:\n price = data.current(order.asset, self._price_field_buy)\n # 返回希望成交的价格和数量\n return (price, order.amount)\n # 设置price_field,默认是开盘买入，收盘卖出\n context.fix_slippage = FixedPriceSlippage(price_field_buy='open', price_field_sell='close')\n context.set_slippage(us_equities=context.fix_slippage)","type":"Literal","bound_global_parameter":null},{"name":"handle_data","value":"# 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 today = 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.portfolio.positions.items()}\n equities = {e.symbol: e for e, p in context.portfolio.positions.items()}\n\n # 记录持仓中st的股票\n st_stock_list = []\n name_df = context.name_df\n name_today = name_df[name_df.date==today]\n for instrument in equities:\n name_instrument = name_today[name_today.instrument==instrument]['name'].values[0]\n # 如果股票状态变为了st 则卖出\n if 'ST' in name_instrument or '退' in name_instrument:\n # 指定一个limit_price,此时会以开盘价成交，这是由于初始化函数中改写了下单价格\n context.order_target(context.symbol(instrument), 0, limit_price=1.0)\n st_stock_list.append(instrument)\n cash_for_sell -= positions[instrument]\n if st_stock_list!=[]:\n print(today,'持仓出现st股/退市股',st_stock_list,'进行卖出处理')\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 instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(\n lambda x: x in equities)])))\n price_limit_status = context.price_limit_status\n status_today = price_limit_status[price_limit_status.date==today]\n for instrument in instruments:\n # 如果是st股票已经卖过了，就跳过\n if instrument in st_stock_list:\n continue\n # 如果涨停就跳过股票\n status_instrument = status_today[status_today.instrument==instrument]['price_limit_status'].values[0]\n if status_instrument>2:\n continue\n context.order_target(context.symbol(instrument),0)\n cash_for_sell -= positions[instrument]\n if cash_for_sell <= 0:\n break\n\n # 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\n buy_cash_weights = context.stock_weights\n buy_instruments = list(ranker_prediction.instrument[:len(buy_cash_weights)])\n max_cash_per_instrument = context.portfolio.portfolio_value * context.max_cash_per_instrument\n for i, instrument in enumerate(buy_instruments):\n cash = cash_for_buy * buy_cash_weights[i]\n if cash > max_cash_per_instrument - positions.get(instrument, 0):\n # 确保股票持仓量不会超过每次股票最大的占用资金量\n cash = max_cash_per_instrument - positions.get(instrument, 0)\n if cash > 0:\n context.order_value(context.symbol(instrument), cash)\n\n","type":"Literal","bound_global_parameter":null},{"name":"prepare","value":"# 回测引擎：准备数据，只执行一次\ndef bigquant_run(context):\n # 获取股票名称 用于过滤st和退市股\n context.name_df = DataSource('instruments_CN_STOCK_A').read()\n # 获取涨跌停状态\n context.price_limit_status = DataSource('stock_status_CN_STOCK_A').read(fields=['price_limit_status'])\n","type":"Literal","bound_global_parameter":null},{"name":"before_trading_start","value":"def bigquant_run(context, data):\n df_price_limit_status=context.price_limit_status.set_index('date')\n today=data.current_dt.strftime('%Y-%m-%d')\n # 得到当前未完成订单\n for orders in get_open_orders().values():\n # 循环，撤销订单\n for _order in orders:\n ins=str(_order.sid.symbol)\n if data.can_trade(_order.sid):\n #判断一下如果当日涨停，则取消卖单\n if df_price_limit_status[df_price_limit_status.instrument==ins].price_limit_status.loc[today]>2 and _order.amount<0:\n cancel_order(_order)\n print(today,'尾盘涨停取消卖单',ins) 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    In [1]:
    # 本代码由可视化策略环境自动生成 2021年8月16日 20:56
# 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 m4_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.options['hold_days'] = 5

    from zipline.finance.slippage import SlippageModel
    class FixedPriceSlippage(SlippageModel):
        def process_order(self, data, order, bar_volume=0, trigger_check_price=0):
            if order.limit is None:
                price_field = self._price_field_buy if order.amount > 0 else self._price_field_sell
                price = data.current(order.asset, price_field)
            else:
                price = data.current(order.asset, self._price_field_buy)
            # 返回希望成交的价格和数量
            return (price, order.amount)
    # 设置price_field,默认是开盘买入，收盘卖出
    context.fix_slippage = FixedPriceSlippage(price_field_buy='open', price_field_sell='close')
    context.set_slippage(us_equities=context.fix_slippage)
# 回测引擎：每日数据处理函数，每天执行一次
def m4_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')]
    today = 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.portfolio.positions.items()}
    equities = {e.symbol: e for e, p in context.portfolio.positions.items()}

    # 记录持仓中st的股票
    st_stock_list = []
    name_df = context.name_df
    name_today = name_df[name_df.date==today]
    for instrument in equities:
        name_instrument = name_today[name_today.instrument==instrument]['name'].values[0]
        # 如果股票状态变为了st 则卖出
        if 'ST' in name_instrument or '退' in name_instrument:
            # 指定一个limit_price,此时会以开盘价成交，这是由于初始化函数中改写了下单价格
            context.order_target(context.symbol(instrument), 0, limit_price=1.0)
            st_stock_list.append(instrument)
            cash_for_sell -= positions[instrument]
    if st_stock_list!=[]:
        print(today,'持仓出现st股/退市股',st_stock_list,'进行卖出处理')
 
    # 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:
        instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(
            lambda x: x in equities)])))
        price_limit_status = context.price_limit_status
        status_today = price_limit_status[price_limit_status.date==today]
        for instrument in instruments:
            # 如果是st股票已经卖过了，就跳过
            if instrument in st_stock_list:
                continue
            # 如果涨停就跳过股票
            status_instrument = status_today[status_today.instrument==instrument]['price_limit_status'].values[0]
            if status_instrument>2:
                continue
            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 m4_prepare_bigquant_run(context):
    # 获取股票名称 用于过滤st和退市股
    context.name_df = DataSource('instruments_CN_STOCK_A').read()
    # 获取涨跌停状态
    context.price_limit_status = DataSource('stock_status_CN_STOCK_A').read(fields=['price_limit_status'])

def m4_before_trading_start_bigquant_run(context, data):
    df_price_limit_status=context.price_limit_status.set_index('date')
    today=data.current_dt.strftime('%Y-%m-%d')
    # 得到当前未完成订单
    for orders in get_open_orders().values():
        # 循环，撤销订单
        for _order in orders:
            ins=str(_order.sid.symbol)
            if data.can_trade(_order.sid):
                #判断一下如果当日涨停，则取消卖单
                if  df_price_limit_status[df_price_limit_status.instrument==ins].price_limit_status.loc[today]>2 and _order.amount<0:
                    cancel_order(_order)
                    print(today,'尾盘涨停取消卖单',ins)                     

m1 = M.instruments.v2(
    start_date='2010-01-01',
    end_date='2015-01-01',
    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)
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
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
)

m12 = M.filtet_st_stock.v7(
    input_1=m7.data
)

m10 = M.dropnan.v2(
    input_data=m12.data_1
)

m5 = M.stock_ranker_train.v6(
    training_ds=m10.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,
    data_row_fraction=1,
    ndcg_discount_base=1,
    m_lazy_run=False
)

m9 = M.instruments.v2(
    start_date=T.live_run_param('trading_date', '2015-01-01'),
    end_date=T.live_run_param('trading_date', '2017-01-01'),
    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
)

m19 = M.filtet_st_stock.v7(
    input_1=m18.data
)

m11 = M.dropnan.v2(
    input_data=m19.data_1
)

m8 = M.stock_ranker_predict.v5(
    model=m5.model,
    data=m11.data,
    m_lazy_run=False
)

m4 = M.trade.v4(
    instruments=m9.data,
    options_data=m8.predictions,
    start_date='',
    end_date='',
    initialize=m4_initialize_bigquant_run,
    handle_data=m4_handle_data_bigquant_run,
    prepare=m4_prepare_bigquant_run,
    before_trading_start=m4_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='后复权',
    product_type='股票',
    plot_charts=True,
    backtest_only=False,
    benchmark=''
)
    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-0e397aac770b48ad9911917409c2955b"}/bigcharts-data-end
    2015-01-13 尾盘涨停取消卖单 300380.SZA
2015-01-15 持仓出现st股/退市股 ['601918.SHA', '000693.SZA'] 进行卖出处理
2015-01-16 持仓出现st股/退市股 ['000693.SZA'] 进行卖出处理
2015-01-19 持仓出现st股/退市股 ['000693.SZA'] 进行卖出处理
2015-01-20 持仓出现st股/退市股 ['000693.SZA'] 进行卖出处理
2015-01-21 持仓出现st股/退市股 ['000693.SZA'] 进行卖出处理
2015-02-03 尾盘涨停取消卖单 002657.SZA
2015-02-11 尾盘涨停取消卖单 300378.SZA
2015-02-16 尾盘涨停取消卖单 300378.SZA
2015-03-05 尾盘涨停取消卖单 300166.SZA
2015-03-16 尾盘涨停取消卖单 000626.SZA
2015-03-23 尾盘涨停取消卖单 002537.SZA
2015-04-02 持仓出现st股/退市股 ['600306.SHA'] 进行卖出处理
2015-04-03 尾盘涨停取消卖单 300345.SZA
2015-04-03 尾盘涨停取消卖单 300266.SZA
2015-04-16 尾盘涨停取消卖单 002100.SZA
2015-04-17 持仓出现st股/退市股 ['000693.SZA'] 进行卖出处理
2015-05-13 持仓出现st股/退市股 ['600546.SHA'] 进行卖出处理
2015-05-21 尾盘涨停取消卖单 002275.SZA
2015-05-21 尾盘涨停取消卖单 002531.SZA
2015-06-01 尾盘涨停取消卖单 600006.SHA
2015-06-01 尾盘涨停取消卖单 600774.SHA
2015-06-05 持仓出现st股/退市股 ['000504.SZA'] 进行卖出处理
2015-06-08 尾盘涨停取消卖单 600679.SHA
2015-06-12 尾盘涨停取消卖单 600559.SHA
2015-06-15 尾盘涨停取消卖单 600198.SHA
2015-06-17 尾盘涨停取消卖单 300085.SZA
2015-06-23 尾盘涨停取消卖单 300248.SZA
2015-06-30 尾盘涨停取消卖单 300216.SZA
2015-07-07 持仓出现st股/退市股 ['600375.SHA'] 进行卖出处理
2015-07-08 持仓出现st股/退市股 ['600375.SHA'] 进行卖出处理
2015-07-09 尾盘涨停取消卖单 600375.SHA
2015-07-09 尾盘涨停取消卖单 000514.SZA
2015-07-09 尾盘涨停取消卖单 600893.SHA
2015-07-09 尾盘涨停取消卖单 601216.SHA
2015-07-09 尾盘涨停取消卖单 600783.SHA
2015-07-09 尾盘涨停取消卖单 600503.SHA
2015-07-09 持仓出现st股/退市股 ['600375.SHA'] 进行卖出处理
2015-07-10 尾盘涨停取消卖单 600375.SHA
2015-07-10 持仓出现st股/退市股 ['600375.SHA', '000913.SZA', '600721.SHA'] 进行卖出处理
2015-07-13 尾盘涨停取消卖单 600375.SHA
2015-07-13 尾盘涨停取消卖单 000913.SZA
2015-07-13 尾盘涨停取消卖单 600721.SHA
2015-07-13 持仓出现st股/退市股 ['600375.SHA', '000913.SZA', '600721.SHA'] 进行卖出处理
2015-07-17 尾盘涨停取消卖单 600119.SHA
2015-07-17 尾盘涨停取消卖单 603030.SHA
2015-07-17 尾盘涨停取消卖单 002197.SZA
2015-07-20 尾盘涨停取消卖单 600105.SHA
2015-07-21 尾盘涨停取消卖单 300222.SZA
2015-07-31 持仓出现st股/退市股 ['000933.SZA'] 进行卖出处理
2015-08-04 尾盘涨停取消卖单 000409.SZA
2015-08-04 尾盘涨停取消卖单 600986.SHA
2015-08-07 尾盘涨停取消卖单 300075.SZA
2015-08-07 尾盘涨停取消卖单 000948.SZA
2015-08-10 尾盘涨停取消卖单 002522.SZA
2015-08-27 尾盘涨停取消卖单 002375.SZA
2015-08-27 尾盘涨停取消卖单 300348.SZA
2015-08-27 尾盘涨停取消卖单 600790.SHA
2015-08-28 尾盘涨停取消卖单 002062.SZA
2015-08-28 尾盘涨停取消卖单 300012.SZA
2015-08-28 尾盘涨停取消卖单 000567.SZA
2015-08-28 尾盘涨停取消卖单 300053.SZA
2015-08-31 持仓出现st股/退市股 ['000504.SZA'] 进行卖出处理
2015-09-01 持仓出现st股/退市股 ['000504.SZA'] 进行卖出处理
2015-09-08 尾盘涨停取消卖单 300252.SZA
2015-09-09 尾盘涨停取消卖单 002229.SZA
2015-09-09 尾盘涨停取消卖单 600702.SHA
2015-09-09 尾盘涨停取消卖单 002268.SZA
2015-09-11 尾盘涨停取消卖单 002161.SZA
2015-09-16 尾盘涨停取消卖单 002161.SZA
2015-09-16 尾盘涨停取消卖单 300277.SZA
2015-09-16 尾盘涨停取消卖单 002229.SZA
2015-09-24 持仓出现st股/退市股 ['000693.SZA'] 进行卖出处理
2015-09-30 持仓出现st股/退市股 ['000693.SZA'] 进行卖出处理
2015-10-14 尾盘涨停取消卖单 002549.SZA
2015-10-14 尾盘涨停取消卖单 600355.SHA
2015-10-22 持仓出现st股/退市股 ['000693.SZA'] 进行卖出处理
2015-11-04 尾盘涨停取消卖单 002197.SZA
2015-11-06 尾盘涨停取消卖单 002197.SZA
2015-11-06 尾盘涨停取消卖单 300310.SZA
2015-12-09 持仓出现st股/退市股 ['000037.SZA'] 进行卖出处理
2015-12-14 尾盘涨停取消卖单 600257.SHA
2016-01-12 尾盘涨停取消卖单 002027.SZA
2016-01-14 尾盘涨停取消卖单 300149.SZA
2016-01-21 持仓出现st股/退市股 ['000633.SZA'] 进行卖出处理
2016-01-22 持仓出现st股/退市股 ['000629.SZA', '000856.SZA'] 进行卖出处理
2016-01-25 持仓出现st股/退市股 ['600866.SHA'] 进行卖出处理
2016-01-26 持仓出现st股/退市股 ['600866.SHA'] 进行卖出处理
2016-01-29 持仓出现st股/退市股 ['000408.SZA'] 进行卖出处理
2016-02-01 持仓出现st股/退市股 ['000693.SZA'] 进行卖出处理
2016-02-02 尾盘涨停取消卖单 300078.SZA
2016-02-04 持仓出现st股/退市股 ['000606.SZA', '000504.SZA'] 进行卖出处理
2016-02-05 持仓出现st股/退市股 ['000504.SZA'] 进行卖出处理
2016-02-16 尾盘涨停取消卖单 000566.SZA
2016-02-26 持仓出现st股/退市股 ['000693.SZA'] 进行卖出处理
2016-03-01 尾盘涨停取消卖单 600978.SHA
2016-03-21 尾盘涨停取消卖单 002388.SZA
2016-04-06 尾盘涨停取消卖单 300023.SZA
2016-04-15 尾盘涨停取消卖单 300028.SZA
2016-05-16 尾盘涨停取消卖单 300081.SZA
2016-06-15 尾盘涨停取消卖单 300236.SZA
2016-06-22 尾盘涨停取消卖单 300201.SZA
2016-07-08 持仓出现st股/退市股 ['600556.SHA'] 进行卖出处理
2016-07-11 持仓出现st股/退市股 ['600556.SHA'] 进行卖出处理
2016-07-12 持仓出现st股/退市股 ['600556.SHA'] 进行卖出处理
2016-07-13 持仓出现st股/退市股 ['600556.SHA'] 进行卖出处理
2016-07-14 持仓出现st股/退市股 ['600556.SHA'] 进行卖出处理
2016-07-15 尾盘涨停取消卖单 600556.SHA
2016-07-15 持仓出现st股/退市股 ['600556.SHA'] 进行卖出处理
2016-07-20 尾盘涨停取消卖单 002219.SZA
2016-07-28 持仓出现st股/退市股 ['600556.SHA'] 进行卖出处理
2016-08-09 尾盘涨停取消卖单 600084.SHA
2016-08-10 持仓出现st股/退市股 ['600556.SHA'] 进行卖出处理
2016-08-15 尾盘涨停取消卖单 300428.SZA
2016-09-05 尾盘涨停取消卖单 000652.SZA
2016-10-10 尾盘涨停取消卖单 300451.SZA
2016-10-21 尾盘涨停取消卖单 000935.SZA
2016-12-07 尾盘涨停取消卖单 600671.SHA
    • 收益率251.39%
    • 年化收益率91.36%
    • 基准收益率-6.33%
    • 阿尔法1.08
    • 贝塔1.0
    • 夏普比率1.61
    • 胜率0.61
    • 盈亏比0.91
    • 收益波动率44.78%
    • 信息比率0.15
    • 最大回撤47.0%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-d26b6759287a49b49b5e904b3f4ac342"}/bigcharts-data-end