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    {"Description":"实验创建于2017/8/26","Summary":"","Graph":{"EdgesInternal":[{"DestinationInputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-15:instruments","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-8:data"},{"DestinationInputPortId":"-107:instruments","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-8:data"},{"DestinationInputPortId":"-779:data1","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-15:data"},{"DestinationInputPortId":"-107:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-161:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-819:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-837:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-5546:features","SourceOutputPortId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"DestinationInputPortId":"-819:input_data","SourceOutputPortId":"-107:data"},{"DestinationInputPortId":"-5546:training_ds","SourceOutputPortId":"-648:data"},{"DestinationInputPortId":"-161:instruments","SourceOutputPortId":"-152:data"},{"DestinationInputPortId":"-213:instruments","SourceOutputPortId":"-152:data"},{"DestinationInputPortId":"-142:instruments","SourceOutputPortId":"-152:data"},{"DestinationInputPortId":"-837:input_data","SourceOutputPortId":"-161:data"},{"DestinationInputPortId":"-5546:predict_ds","SourceOutputPortId":"-187:data"},{"DestinationInputPortId":"-2908:input_1","SourceOutputPortId":"-779:data"},{"DestinationInputPortId":"-779:data2","SourceOutputPortId":"-819:data"},{"DestinationInputPortId":"-2911:input_1","SourceOutputPortId":"-837:data"},{"DestinationInputPortId":"-224:input_data","SourceOutputPortId":"-207:data"},{"DestinationInputPortId":"-207:data1","SourceOutputPortId":"-213:data"},{"DestinationInputPortId":"-5546:test_ds","SourceOutputPortId":"-224:data"},{"DestinationInputPortId":"-648:input_data","SourceOutputPortId":"-2908:data_1"},{"DestinationInputPortId":"-187:input_data","SourceOutputPortId":"-2911:data_1"},{"DestinationInputPortId":"-207:data2","SourceOutputPortId":"-2911:data_1"},{"DestinationInputPortId":"-142:options_data","SourceOutputPortId":"-5546:predictions"}],"ModuleNodes":[{"Id":"287d2cb0-f53c-4101-bdf8-104b137c8601-8","ModuleId":"BigQuantSpace.instruments.instruments-v2","ModuleParameters":[{"Name":"start_date","Value":"2016-01-01","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"end_date","Value":"2019-01-01","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"market","Value":"CN_STOCK_A","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"instrument_list","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"max_count","Value":"0","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"rolling_conf","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-8"}],"OutputPortsInternal":[{"Name":"data","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-8","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":1,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"287d2cb0-f53c-4101-bdf8-104b137c8601-15","ModuleId":"BigQuantSpace.advanced_auto_labeler.advanced_auto_labeler-v2","ModuleParameters":[{"Name":"label_expr","Value":"# 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10)\n############################3rank_swing_volatility_60_0\n############################3swing_volatility_30_0\n############################3ta_rsi_14_0","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features_ds","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24"}],"OutputPortsInternal":[{"Name":"data","NodeId":"287d2cb0-f53c-4101-bdf8-104b137c8601-24","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":3,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-107","ModuleId":"BigQuantSpace.general_feature_extractor.general_feature_extractor-v7","ModuleParameters":[{"Name":"start_date","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"end_date","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"before_start_days","Value":"300","ValueType":"Literal","LinkedGlobalParameter":null}],"InputPortsInternal":[{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"instruments","NodeId":"-107"},{"DataSourceId":null,"TrainedModelId":null,"TransformModuleId":null,"Name":"features","NodeId":"-107"}],"OutputPortsInternal":[{"Name":"data","NodeId":"-107","OutputType":null}],"UsePreviousResults":true,"moduleIdForCode":15,"IsPartOfPartialRun":null,"Comment":"","CommentCollapsed":true},{"Id":"-142","ModuleId":"BigQuantSpace.trade.trade-v4","ModuleParameters":[{"Name":"start_date","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"end_date","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"initialize","Value":"# 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benckmark_risk > 0:\n # for instrument in stock_hold_now:\n # context.order_target(symbol(instrument), 0)\n # print(today,'大盘风控止损触发,全仓卖出')\n # return\n\n \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 positions = {e.symbol: p.amount * p.last_sale_price\n for e, p in context.portfolio.positions.items()}\n \n #----------------------------START：持有固定交易日天数卖出---------------------------\n today = data.current_dt.strftime('%Y-%m-%d')\n # 不是建仓期（在前hold_days属于建仓期）\n if not is_staging:\n equities = {e.symbol: p for e, p in context.portfolio.positions.items() if p.amount>0}\n for instrument in equities:\n sid = equities[instrument].sid # 交易标的\n # 今天和上次交易的时间相隔hold_days就全部卖出\n dt = pd.to_datetime(D.trading_days(end_date = today).iloc[-context.options['hold_days']].values[0])\n if pd.to_datetime(equities[instrument].last_sale_date.strftime('%Y-%m-%d')) <= dt and data.can_trade(context.symbol(instrument)):\n context.order_target_percent(sid, 0)\n cash_for_buy += positions[instrument]\n #--------------------------------END:持有固定天数卖出--------------------------- \n \n\n # 3. 生成买入订单：按StockRanker预测的排序，买入前面的stock_count只股票\n buy_cash_weights = context.stock_weights\n counto = 1\n buy_instruments = list(ranker_prediction.instrument[:len(buy_cash_weights)])\n while (' '.join(buy_instruments)) in positions.keys():\n buy_instruments = list(ranker_prediction.instrument[counto:len(buy_cash_weights)+counto])\n counto += 1\n \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 #print(context.symbol(instrument), cash)\n","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"prepare","Value":"def bigquant_run(context):\n #在数据准备函数中一次性计算每日的大盘风控条件相比于在handle中每日计算风控条件可以提高回测速度\n # 多取50天的数据便于计算均值(保证回测的第一天均值不为Nan值)，其中context.start_date和context.end_date是回测指定的起始时间和终止时间\n start_date= (pd.to_datetime(context.start_date) - datetime.timedelta(days=50)).strftime('%Y-%m-%d') \n df=DataSource('bar1d_index_CN_STOCK_A').read(start_date=start_date,end_date=context.end_date,fields=['close'])\n benckmark_data=df[df.instrument=='000001.HIX']\n #计算上证指数5日涨幅\n 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    In [1]:
    # 本代码由可视化策略环境自动生成 2020年9月22日 11:10
# 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.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 = 3
    # 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 = 0.07
    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):
    today = data.current_dt.strftime('%Y-%m-%d')
    stock_hold_now = [equity.symbol for equity in context.portfolio.positions ]
    ##大盘风控模块，读取风控数据    
    #benckmark_risk=context.benckmark_risk[today]
    #context.symbol
    ##当risk为1时，市场有风险，全部平仓，不再执行其它操作
    #if benckmark_risk > 0:
    #    for instrument in stock_hold_now:
    #        context.order_target(symbol(instrument), 0)
    #    print(today,'大盘风控止损触发,全仓卖出')
    #    return

    
    # 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)
    positions = {e.symbol: p.amount * p.last_sale_price
                 for e, p in context.portfolio.positions.items()}
   
    #----------------------------START：持有固定交易日天数卖出---------------------------
    today = data.current_dt.strftime('%Y-%m-%d')
    # 不是建仓期（在前hold_days属于建仓期）
    if not is_staging:
        equities = {e.symbol: p for e, p in context.portfolio.positions.items() if p.amount>0}
        for instrument in equities:
            sid = equities[instrument].sid  # 交易标的
            # 今天和上次交易的时间相隔hold_days就全部卖出
            dt = pd.to_datetime(D.trading_days(end_date = today).iloc[-context.options['hold_days']].values[0])
            if  pd.to_datetime(equities[instrument].last_sale_date.strftime('%Y-%m-%d')) <= dt and data.can_trade(context.symbol(instrument)):
                context.order_target_percent(sid, 0)
                cash_for_buy += positions[instrument]
    #--------------------------------END:持有固定天数卖出--------------------------- 
          

    # 3. 生成买入订单：按StockRanker预测的排序，买入前面的stock_count只股票
    buy_cash_weights = context.stock_weights
    counto = 1
    buy_instruments = list(ranker_prediction.instrument[:len(buy_cash_weights)])
    while (' '.join(buy_instruments)) in positions.keys():
        buy_instruments = list(ranker_prediction.instrument[counto:len(buy_cash_weights)+counto])
        counto += 1
        
    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)
            #print(context.symbol(instrument), cash)

def m19_prepare_bigquant_run(context):
    #在数据准备函数中一次性计算每日的大盘风控条件相比于在handle中每日计算风控条件可以提高回测速度
    # 多取50天的数据便于计算均值(保证回测的第一天均值不为Nan值)，其中context.start_date和context.end_date是回测指定的起始时间和终止时间
    start_date= (pd.to_datetime(context.start_date) - datetime.timedelta(days=50)).strftime('%Y-%m-%d') 
    df=DataSource('bar1d_index_CN_STOCK_A').read(start_date=start_date,end_date=context.end_date,fields=['close'])
    benckmark_data=df[df.instrument=='000001.HIX']
    #计算上证指数5日涨幅
    benckmark_data['ret5']=benckmark_data['close']/benckmark_data['close'].shift(1)-1
    #计算大盘风控条件，如果5日涨幅小于-4%则设置风险状态risk为1，否则为0
    benckmark_data['risk'] = np.where(benckmark_data['ret5']<-0.04,1,0)
    #修改日期格式为字符串(便于在handle中使用字符串日期索引来查看每日的风险状态)
    benckmark_data['date']=benckmark_data['date'].apply(lambda x:x.strftime('%Y-%m-%d'))
    #设置日期为索引
    benckmark_data.set_index('date',inplace=True)
    #把风控序列输出给全局变量context.benckmark_risk
    context.benckmark_risk=benckmark_data['risk']


m1 = M.instruments.v2(
    start_date='2016-01-01',
    end_date='2019-01-01',
    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, -6)/shift(close, -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
rank_swing_volatility_60_0
swing_volatility_30_0
#ta_rsi_14_0
#########ta_cci_14_0
#avg_turn_5
#########rank_return_20
#avg_amount_5
#rank_avg_turn_5
#mean(turn_0*return_0, 15)
#rank_swing_volatility_120_0
#########mean(return_0*turn_0, 180)
#rank_swing_volatility_60_0
#std(return_0, 180)
#volatility_120_0
#rank_swing_volatility_5_0
#ta_aroon_up_28_0
#swing_volatility_60_0
#std(turn_0, 10)
#std(return_0, 60)
##########mean(turn_0*return_0, 90)
##########std(avg_amount_0, 60)
##########std(mean(deal_number_0, 180), 180)
#avg_amount_5
##(-1 * correlation(high_0, rank(volume_0), 5))
##((-1 * rank(std(high_0, 10))) * correlation(high_0, volume_0, 10))
##(-1 * rank(covariance(rank(close_0), rank(volume_0), 5)))
#std(avg_turn_0, 10)
############################3rank_swing_volatility_60_0
############################3swing_volatility_30_0
############################3ta_rsi_14_0"""
)

m15 = M.general_feature_extractor.v7(
    instruments=m1.data,
    features=m3.data,
    start_date='',
    end_date='',
    before_start_days=300
)

m24 = 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,
    user_functions={}
)

m10 = M.join.v3(
    data1=m2.data,
    data2=m24.data,
    on='date,instrument',
    how='inner',
    sort=False
)

m6 = M.filtet_st_stock.v7(
    input_1=m10.data
)

m5 = M.dropnan.v2(
    input_data=m6.data_1
)

m16 = M.instruments.v2(
    start_date='2019-01-01',
    end_date='2020-09-21',
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m18 = M.general_feature_extractor.v7(
    instruments=m16.data,
    features=m3.data,
    start_date='',
    end_date='',
    before_start_days=300
)

m26 = M.derived_feature_extractor.v3(
    input_data=m18.data,
    features=m3.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False,
    user_functions={}
)

m7 = M.filtet_st_stock.v7(
    input_1=m26.data
)

m22 = M.dropnan.v2(
    input_data=m7.data_1
)

m9 = M.advanced_auto_labeler.v2(
    instruments=m16.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, -6)/shift(close, -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
)

m8 = M.join.v3(
    data1=m9.data,
    data2=m7.data_1,
    on='date,instrument',
    how='inner',
    sort=False
)

m11 = M.dropnan.v2(
    input_data=m8.data
)

m4 = M.stock_ranker.v2(
    training_ds=m5.data,
    features=m3.data,
    test_ds=m11.data,
    predict_ds=m22.data,
    learning_algorithm='排序',
    number_of_leaves=5,
    minimum_docs_per_leaf=1000,
    number_of_trees=10,
    learning_rate=0.1,
    max_bins=1023,
    feature_fraction=1,
    data_row_fraction=1,
    ndcg_discount_base=1,
    slim_data=True
)

m19 = M.trade.v4(
    instruments=m16.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,
    order_price_field_buy='close',
    order_price_field_sell='close',
    capital_base=500000,
    auto_cancel_non_tradable_orders=False,
    data_frequency='daily',
    price_type='真实价格',
    product_type='股票',
    plot_charts=True,
    backtest_only=False,
    benchmark=''
)

    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-d3dfe83950ed46b2aead3acd2c989556"}/bigcharts-data-end
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-916f3f9596c24606976fa9cffd7bea27"}/bigcharts-data-end
    • 收益率10.44%
    • 年化收益率6.14%
    • 基准收益率55.83%
    • 阿尔法-0.05
    • 贝塔0.34
    • 夏普比率0.35
    • 胜率0.54
    • 盈亏比0.99
    • 收益波动率9.89%
    • 信息比率-0.09
    • 最大回撤8.95% 
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-aabcf3cecc264956aa7a1b2b69c6906f"}/bigcharts-data-end
    In [2]:
    #m13.result.best_params_
    In [3]:
    #dt = m4.predictions.read_df()[-30000:]
#dt.to_csv('3.csv')