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    {"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":"-8068:features","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"to_node_id":"-318:features_ds","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-24:data"},{"to_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-84:input_data","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-53:data"},{"to_node_id":"-329:data1","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":"-224:instruments","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-62:data"},{"to_node_id":"-1620:input_1","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-62:data"},{"to_node_id":"-470:input_data","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-84:data"},{"to_node_id":"-480:input_data","from_node_id":"-86:data"},{"to_node_id":"-411:input_data","from_node_id":"-404:data"},{"to_node_id":"-30120:input_data","from_node_id":"-411:data"},{"to_node_id":"-425:input_data","from_node_id":"-418:data"},{"to_node_id":"-30482:input_data","from_node_id":"-425:data"},{"to_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-60:model","from_node_id":"-8068:model"},{"to_node_id":"-224:features","from_node_id":"-148:data"},{"to_node_id":"-1620:input_2","from_node_id":"-184:data"},{"to_node_id":"-202:data2","from_node_id":"-189:data"},{"to_node_id":"-189:data2","from_node_id":"-196:data"},{"to_node_id":"-1626:input_ds","from_node_id":"-202:data"},{"to_node_id":"-189:data1","from_node_id":"-224:data"},{"to_node_id":"-418:features","from_node_id":"-318:data"},{"to_node_id":"-425:features","from_node_id":"-318:data"},{"to_node_id":"-404:features","from_node_id":"-318:data"},{"to_node_id":"-411:features","from_node_id":"-318:data"},{"to_node_id":"-329:data2","from_node_id":"-323:data"},{"to_node_id":"-202:data1","from_node_id":"-329:data"},{"to_node_id":"-196:input_ds","from_node_id":"-1620:data_1"},{"to_node_id":"-1918:options_data","from_node_id":"-1626:sorted_data"},{"to_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-53:data2","from_node_id":"-30120:data"},{"to_node_id":"-86:input_data","from_node_id":"-30482:data"},{"to_node_id":"-8068:training_ds","from_node_id":"-470:data"},{"to_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-60:data","from_node_id":"-480:data"},{"to_node_id":"-323:input_ds","from_node_id":"-480:data"}],"nodes":[{"node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-8","module_id":"BigQuantSpace.instruments.instruments-v2","parameters":[{"name":"start_date","value":"2019-01-01","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"2021-12-31","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":"# 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#当risk为1时，市场有风险，全部平仓，不再执行其它操作\n if benckmark_risk > 0:\n for instrument in positions.keys():\n context.order_target(context.symbol(instrument), 0)\n return\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 \n \n # 2. 根据需要加入固定天数卖出等模块\n stock_sold = [] # 记录卖出的股票，防止多次卖出出现空单\n \n #--------------------------START：持有固定天数卖出(不含建仓期)-----------\n current_stopdays_stock = []\n positions_lastdate = {e.symbol:p.last_sale_date for e,p in context.portfolio.positions.items()}\n # 不是建仓期（在前hold_days属于建仓期）\n if not is_staging:\n for instrument in positions.keys():\n #如果上面的止盈止损已经卖出过了，就不要重复卖出以防止产生空单\n if instrument in stock_sold:\n continue\n # 今天和上次交易的时间相隔hold_days就全部卖出 datetime.timedelta(context.options['hold_days'])也可以换成自己需要的天数，比如datetime.timedelta(5)\n #使用交易天数\n dt = pd.to_datetime(D.trading_days(end_date = today).iloc[-context.options['hold_days']].values[0])\n if pd.to_datetime(positions_lastdate[instrument].strftime('%Y-%m-%d')) <= dt and data.can_trade(context.symbol(instrument)):\n context.order_target_percent(context.symbol(instrument), 0)\n current_stopdays_stock.append(instrument)\n cash_for_sell -= positions[instrument]\n if len(current_stopdays_stock)>0: \n stock_sold += current_stopdays_stock\n #------------------------- END:持有固定天数卖出-----------------------\n \n #-------------------------- START: ST和退市股卖出 --------------------- \n st_stock_list = []\n for instrument in positions.keys():\n try:\n instrument_name = ranker_prediction[ranker_prediction.instrument==instrument].name.values[0]\n # 如果股票状态变为了st或者退市 则卖出\n if 'ST' in instrument_name or '退' in instrument_name:\n if instrument in stock_sold:\n continue\n if data.can_trade(context.symbol(instrument)):\n context.order_target(context.symbol(instrument), 0)\n st_stock_list.append(instrument)\n cash_for_sell -= positions[instrument]\n except:\n continue\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 stock_sold += st_stock_list\n\n #-------------------------- END: ST和退市股卖出 --------------------- \n \n \n # 3. 生成轮仓卖出订单：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 positions)])))\n for instrument in instruments:\n # 如果资金够了就不卖出了\n if cash_for_sell <= 0:\n break\n #防止多个止损条件同时满足，出现多次卖出产生空单\n if instrument in stock_sold:\n continue\n context.order_target(context.symbol(instrument), 0)\n cash_for_sell -= positions[instrument]\n stock_sold.append(instrument)\n\n # 4. 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    In [1]:
    # 本代码由可视化策略环境自动生成 2022年5月17日 10:15
# 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))
    # 股票数
    stock_count = 5
    # 每只的股票的权重，等权重
    context.stock_weights = [1 / stock_count 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

# 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):
    # 获取当前持仓
    positions = {e.symbol: p.amount * p.last_sale_price
                 for e, p in context.portfolio.positions.items()}
    
    today = data.current_dt.strftime('%Y-%m-%d')
    # 按日期过滤得到今日的预测数据
    ranker_prediction = context.ranker_prediction[
        context.ranker_prediction.date == today]
    if len(ranker_prediction)==0:
        return
    #大盘风控模块，读取风控数据 
    benckmark_risk=ranker_prediction['bm_0'].values[0]

    #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 positions.keys():
            context.order_target(context.symbol(instrument), 0)
        return
    
    # 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)
   
    
    # 2. 根据需要加入固定天数卖出等模块
    stock_sold = [] # 记录卖出的股票，防止多次卖出出现空单
    
    #--------------------------START：持有固定天数卖出(不含建仓期)-----------
    current_stopdays_stock = []
    positions_lastdate = {e.symbol:p.last_sale_date for e,p in context.portfolio.positions.items()}
    # 不是建仓期（在前hold_days属于建仓期）
    if not is_staging:
        for instrument in positions.keys():
            #如果上面的止盈止损已经卖出过了，就不要重复卖出以防止产生空单
            if instrument in stock_sold:
                continue
            # 今天和上次交易的时间相隔hold_days就全部卖出 datetime.timedelta(context.options['hold_days'])也可以换成自己需要的天数，比如datetime.timedelta(5)
            #使用交易天数
            dt = pd.to_datetime(D.trading_days(end_date = today).iloc[-context.options['hold_days']].values[0])
            if  pd.to_datetime(positions_lastdate[instrument].strftime('%Y-%m-%d')) <= dt and data.can_trade(context.symbol(instrument)):
                context.order_target_percent(context.symbol(instrument), 0)
                current_stopdays_stock.append(instrument)
                cash_for_sell -= positions[instrument]
        if len(current_stopdays_stock)>0:        
            stock_sold += current_stopdays_stock
    #-------------------------  END:持有固定天数卖出-----------------------
    
    #-------------------------- START: ST和退市股卖出 ---------------------  
    st_stock_list = []
    for instrument in positions.keys():
        try:
            instrument_name = ranker_prediction[ranker_prediction.instrument==instrument].name.values[0]
            # 如果股票状态变为了st或者退市 则卖出
            if 'ST' in instrument_name or '退' in instrument_name:
                if instrument in stock_sold:
                    continue
                if data.can_trade(context.symbol(instrument)):
                    context.order_target(context.symbol(instrument), 0)
                    st_stock_list.append(instrument)
                    cash_for_sell -= positions[instrument]
        except:
            continue
    if st_stock_list!=[]:
       # print(today,'持仓出现st股/退市股',st_stock_list,'进行卖出处理')    
        stock_sold += st_stock_list

    #-------------------------- END: ST和退市股卖出 --------------------- 
    
    
    # 3. 生成轮仓卖出订单：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 positions)])))
        for instrument in instruments:
            # 如果资金够了就不卖出了
            if cash_for_sell <= 0:
                break
            #防止多个止损条件同时满足，出现多次卖出产生空单
            if instrument in stock_sold:
                continue
            context.order_target(context.symbol(instrument), 0)
            cash_for_sell -= positions[instrument]
            stock_sold.append(instrument)

    # 4. 生成轮仓买入订单：按机器学习算法预测的排序，买入前面的stock_count只股票
    # 计算今日跌停的股票
    dt_list = list(ranker_prediction[ranker_prediction.price_limit_status_0==1].instrument)
    # 计算今日ST/退市的股票
    st_list = list(ranker_prediction[ranker_prediction.name.str.contains('ST')|ranker_prediction.name.str.contains('退')].instrument)
    # 计算所有禁止买入的股票池
    banned_list = stock_sold+dt_list+st_list
    buy_cash_weights = context.stock_weights
    buy_instruments=[k for k in list(ranker_prediction.instrument) if k not in banned_list][: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):
    pass
def m4_before_trading_start_bigquant_run(context, data):
    pass

g = T.Graph({

    'm1': 'M.instruments.v2',
    'm1.start_date': '2019-01-01',
    'm1.end_date': '2021-12-31',
    '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, -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)
""",
    '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
fs_deducted_profit_ttm_0
fs_roe_ttm_0
mf_net_pct_l_0
""",

    'm6': 'M.input_features.v1',
    'm6.features_ds': T.Graph.OutputPort('m3.data'),
    'm6.features': """price_limit_status_0
market_cap_float_0""",

    'm15': 'M.general_feature_extractor.v7',
    'm15.instruments': T.Graph.OutputPort('m1.data'),
    'm15.features': T.Graph.OutputPort('m6.data'),
    'm15.start_date': '',
    'm15.end_date': '',
    'm15.before_start_days': 60,

    'm16': 'M.derived_feature_extractor.v3',
    'm16.input_data': T.Graph.OutputPort('m15.data'),
    'm16.features': T.Graph.OutputPort('m6.data'),
    'm16.date_col': 'date',
    'm16.instrument_col': 'instrument',
    'm16.drop_na': False,
    'm16.remove_extra_columns': False,

    'm21': 'M.filter.v3',
    'm21.input_data': T.Graph.OutputPort('m16.data'),
    'm21.expr': 'market_cap_float_0<5000000000',
    'm21.output_left_data': False,

    'm7': 'M.join.v3',
    'm7.data1': T.Graph.OutputPort('m2.data'),
    'm7.data2': T.Graph.OutputPort('m21.data'),
    'm7.on': 'date,instrument',
    'm7.how': 'inner',
    'm7.sort': False,

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

    'm28': 'M.chinaa_stock_filter.v1',
    'm28.input_data': T.Graph.OutputPort('m13.data'),
    'm28.index_constituent_cond': ['全部'],
    'm28.board_cond': ['上证主板', '深证主板', '创业板'],
    'm28.industry_cond': ['全部'],
    'm28.st_cond': ['正常'],
    'm28.delist_cond': ['非退市'],
    'm28.output_left_data': False,

    'm5': 'M.stock_ranker_train.v6',
    'm5.training_ds': T.Graph.OutputPort('m28.data'),
    'm5.features': T.Graph.OutputPort('m3.data'),
    'm5.learning_algorithm': '排序',
    'm5.number_of_leaves': 30,
    'm5.minimum_docs_per_leaf': 1000,
    'm5.number_of_trees': 20,
    'm5.learning_rate': 0.1,
    'm5.max_bins': 1023,
    'm5.feature_fraction': 1,
    'm5.data_row_fraction': 1,
    'm5.plot_charts': True,
    'm5.ndcg_discount_base': 1,
    'm5.m_lazy_run': False,

    'm9': 'M.instruments.v2',
    'm9.start_date': T.live_run_param('trading_date', '2022-01-01'),
    'm9.end_date': T.live_run_param('trading_date', '2022-05-11'),
    '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('m6.data'),
    'm17.start_date': '',
    'm17.end_date': '',
    'm17.before_start_days': 60,

    'm18': 'M.derived_feature_extractor.v3',
    'm18.input_data': T.Graph.OutputPort('m17.data'),
    'm18.features': T.Graph.OutputPort('m6.data'),
    'm18.date_col': 'date',
    'm18.instrument_col': 'instrument',
    'm18.drop_na': False,
    'm18.remove_extra_columns': False,

    'm23': 'M.filter.v3',
    'm23.input_data': T.Graph.OutputPort('m18.data'),
    'm23.expr': 'market_cap_float_0<5000000000',
    'm23.output_left_data': False,

    'm14': 'M.dropnan.v1',
    'm14.input_data': T.Graph.OutputPort('m23.data'),

    'm29': 'M.chinaa_stock_filter.v1',
    'm29.input_data': T.Graph.OutputPort('m14.data'),
    'm29.index_constituent_cond': ['全部'],
    'm29.board_cond': ['上证主板', '深证主板', '创业板'],
    'm29.industry_cond': ['全部'],
    'm29.st_cond': ['正常'],
    'm29.delist_cond': ['非退市'],
    'm29.output_left_data': False,

    'm8': 'M.stock_ranker_predict.v5',
    'm8.model': T.Graph.OutputPort('m5.model'),
    'm8.data': T.Graph.OutputPort('m29.data'),
    'm8.m_lazy_run': False,

    'm10': 'M.select_columns.v3',
    'm10.input_ds': T.Graph.OutputPort('m29.data'),
    'm10.columns': 'date,instrument,price_limit_status_0',
    'm10.reverse_select': False,

    'm11': 'M.join.v3',
    'm11.data1': T.Graph.OutputPort('m8.predictions'),
    'm11.data2': T.Graph.OutputPort('m10.data'),
    'm11.on': 'date,instrument',
    'm11.how': 'left',
    'm11.sort': True,

    'm20': 'M.input_features.v1',
    'm20.features': """
# #号开始的表示注释，注释需单独一行
# 多个特征，每行一个，可以包含基础特征和衍生特征，特征须为本平台特征
name""",

    'm22': 'M.use_datasource.v1',
    'm22.instruments': T.Graph.OutputPort('m9.data'),
    'm22.features': T.Graph.OutputPort('m20.data'),
    'm22.datasource_id': 'instruments_CN_STOCK_A',
    'm22.start_date': '',
    'm22.end_date': '',

    'm24': 'M.input_features.v1',
    'm24.features': """
# #号开始的表示注释，注释需单独一行
# 多个特征，每行一个，可以包含基础特征和衍生特征，特征须为本平台特征
bm_0 = where(close/shift(close,5)-1<-0.05,1,0)""",

    'm12': 'M.index_feature_extract.v3',
    'm12.input_1': T.Graph.OutputPort('m9.data'),
    'm12.input_2': T.Graph.OutputPort('m24.data'),
    'm12.before_days': 100,
    'm12.index': '000300.HIX',

    'm26': 'M.select_columns.v3',
    'm26.input_ds': T.Graph.OutputPort('m12.data_1'),
    'm26.columns': 'date,bm_0',
    'm26.reverse_select': False,

    'm25': 'M.join.v3',
    'm25.data1': T.Graph.OutputPort('m22.data'),
    'm25.data2': T.Graph.OutputPort('m26.data'),
    'm25.on': 'date',
    'm25.how': 'left',
    'm25.sort': True,

    'm27': 'M.join.v3',
    'm27.data1': T.Graph.OutputPort('m11.data'),
    'm27.data2': T.Graph.OutputPort('m25.data'),
    'm27.on': 'date,instrument',
    'm27.how': 'left',
    'm27.sort': True,

    'm19': 'M.sort.v4',
    'm19.input_ds': T.Graph.OutputPort('m27.data'),
    'm19.sort_by': 'date,position',
    'm19.group_by': '--',
    'm19.keep_columns': '--',
    'm19.ascending': True,

    'm4': 'M.trade.v4',
    'm4.instruments': T.Graph.OutputPort('m9.data'),
    'm4.options_data': T.Graph.OutputPort('m19.sorted_data'),
    'm4.start_date': '',
    'm4.end_date': '',
    'm4.initialize': m4_initialize_bigquant_run,
    'm4.handle_data': m4_handle_data_bigquant_run,
    'm4.prepare': m4_prepare_bigquant_run,
    'm4.before_trading_start': m4_before_trading_start_bigquant_run,
    'm4.volume_limit': 0.025,
    'm4.order_price_field_buy': 'open',
    'm4.order_price_field_sell': 'close',
    'm4.capital_base': 1000000,
    'm4.auto_cancel_non_tradable_orders': True,
    'm4.data_frequency': 'daily',
    'm4.price_type': '真实价格',
    'm4.product_type': '股票',
    'm4.plot_charts': False,
    'm4.backtest_only': False,
    'm4.benchmark': '',
})

# g.run({})


def m30_run_bigquant_run(bq_graph, inputs):
     
    test_years = ['2014','2015','2016','2017','2018','2019','2020','2021','2022']
    parameters_list = []
     
    for i in test_years:
        train_start_date =  str(int(i) -3)+'-01'+'-01'
        train_end_date =  str(int(i) - 1)+'-12'+'-31'
        test_start_date = i+'-01'+'-01'
        if i == test_years[-1]:
            test_end_date = i+'-05'+'-11'
        else:
            test_end_date  =  i+'-12'+'-31'
        
        parameters = {'m1.start_date':train_start_date,
                      'm1.end_date':train_end_date,
                      'm9.start_date':test_start_date,
                      'm9.end_date':test_end_date,
                     }
        
        parameters_list.append({'parameters': parameters})
    print(len(parameters_list), parameters_list)

    def run(parameters):
        try:
            print(parameters)
            return g.run(parameters)
        except Exception as e:
            print('ERROR --------', e)
            return None
        
    results = T.parallel_map(run, parameters_list, max_workers=4, remote_run=True, silent=False, backend="threading")

    return results


m30 = M.hyper_run.v1(
    run=m30_run_bigquant_run,
    run_now=True,
    bq_graph=g
)

    9 [{'parameters': {'m1.start_date': '2011-01-01', 'm1.end_date': '2013-12-31', 'm9.start_date': '2014-01-01', 'm9.end_date': '2014-12-31'}}, {'parameters': {'m1.start_date': '2012-01-01', 'm1.end_date': '2014-12-31', 'm9.start_date': '2015-01-01', 'm9.end_date': '2015-12-31'}}, {'parameters': {'m1.start_date': '2013-01-01', 'm1.end_date': '2015-12-31', 'm9.start_date': '2016-01-01', 'm9.end_date': '2016-12-31'}}, {'parameters': {'m1.start_date': '2014-01-01', 'm1.end_date': '2016-12-31', 'm9.start_date': '2017-01-01', 'm9.end_date': '2017-12-31'}}, {'parameters': {'m1.start_date': '2015-01-01', 'm1.end_date': '2017-12-31', 'm9.start_date': '2018-01-01', 'm9.end_date': '2018-12-31'}}, {'parameters': {'m1.start_date': '2016-01-01', 'm1.end_date': '2018-12-31', 'm9.start_date': '2019-01-01', 'm9.end_date': '2019-12-31'}}, {'parameters': {'m1.start_date': '2017-01-01', 'm1.end_date': '2019-12-31', 'm9.start_date': '2020-01-01', 'm9.end_date': '2020-12-31'}}, {'parameters': {'m1.start_date': '2018-01-01', 'm1.end_date': '2020-12-31', 'm9.start_date': '2021-01-01', 'm9.end_date': '2021-12-31'}}, {'parameters': {'m1.start_date': '2019-01-01', 'm1.end_date': '2021-12-31', 'm9.start_date': '2022-01-01', 'm9.end_date': '2022-05-11'}}]
    In [2]:
    from datetime import datetime
print(datetime.now())
print(len(m30.result))
for i in range(len(m30.result)):
    try:
        print('==='*15, i)
        perf = m30.result[i]['m4'].raw_perf.read()
        T.render_perf(perf, buy_moment="buy", sell_moment="sell")
    except Exception as e:
        print(e)
        continue

    2022-05-17 10:06:45.055486
9
============================================= 0
    • 收益率78.18%
    • 年化收益率81.15%
    • 基准收益率51.66%
    • 阿尔法0.41
    • 贝塔0.59
    • 夏普比率2.53
    • 胜率0.62
    • 盈亏比-1.12
    • 收益波动率23.42%
    • 信息比率0.05
    • 最大回撤12.55%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-931254fbb1d046f0a11927c0f28f8479"}/bigcharts-data-end
    ============================================= 1
    • 收益率507.59%
    • 年化收益率544.61%
    • 基准收益率5.58%
    • 阿尔法5.38
    • 贝塔0.63
    • 夏普比率4.66
    • 胜率0.7
    • 盈亏比-1.07
    • 收益波动率41.37%
    • 信息比率0.32
    • 最大回撤19.67%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-c5782a85b6204289b0927abce4fa70d9"}/bigcharts-data-end
    ============================================= 2
    • 收益率63.46%
    • 年化收益率66.12%
    • 基准收益率-11.28%
    • 阿尔法0.88
    • 贝塔0.82
    • 夏普比率1.73
    • 胜率0.61
    • 盈亏比-1.08
    • 收益波动率30.33%
    • 信息比率0.17
    • 最大回撤18.39%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-585531a4541b47fbb61ee986fe5de0c6"}/bigcharts-data-end
    ============================================= 3
    • 收益率-21.63%
    • 年化收益率-22.26%
    • 基准收益率21.78%
    • 阿尔法-0.3
    • 贝塔0.56
    • 夏普比率-1.13
    • 胜率0.52
    • 盈亏比-0.76
    • 收益波动率22.65%
    • 信息比率-0.12
    • 最大回撤30.17%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-573fb868b55c41bc9812c9a4023920de"}/bigcharts-data-end
    ============================================= 4
    • 收益率-18.97%
    • 年化收益率-19.6%
    • 基准收益率-25.31%
    • 阿尔法0.03
    • 贝塔0.71
    • 夏普比率-0.58
    • 胜率0.48
    • 盈亏比-0.98
    • 收益波动率33.17%
    • 信息比率0.02
    • 最大回撤29.7%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-acef576d46ec45d59b2330a6aaf79d9d"}/bigcharts-data-end
    ============================================= 5
    • 收益率53.45%
    • 年化收益率55.62%
    • 基准收益率36.07%
    • 阿尔法0.24
    • 贝塔0.75
    • 夏普比率1.73
    • 胜率0.57
    • 盈亏比-1.19
    • 收益波动率25.75%
    • 信息比率0.04
    • 最大回撤13.18%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-60e281b99df34624bc77435d31f0c2f2"}/bigcharts-data-end
    ============================================= 6
    • 收益率13.87%
    • 年化收益率14.42%
    • 基准收益率27.21%
    • 阿尔法-0.05
    • 贝塔0.81
    • 夏普比率0.5
    • 胜率0.47
    • 盈亏比-1.24
    • 收益波动率29.35%
    • 信息比率-0.03
    • 最大回撤17.36%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-4aede7e8c1fa44029be9a6dd48624f52"}/bigcharts-data-end
    ============================================= 7
    • 收益率27.07%
    • 年化收益率28.21%
    • 基准收益率-5.2%
    • 阿尔法0.31
    • 贝塔0.24
    • 夏普比率0.95
    • 胜率0.53
    • 盈亏比-1.15
    • 收益波动率26.76%
    • 信息比率0.07
    • 最大回撤19.97%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-e205b9dcac6c4645a8907132336d66d3"}/bigcharts-data-end
    ============================================= 8
    • 收益率-21.4%
    • 年化收益率-52.28%
    • 基准收益率-19.51%
    • 阿尔法-0.25
    • 贝塔0.65
    • 夏普比率-2.33
    • 胜率0.46
    • 盈亏比-0.74
    • 收益波动率30.96%
    • 信息比率-0.01
    • 最大回撤27.1%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-a5eb3c67e4514ddb9a8f6ac0a8ea778e"}/bigcharts-data-end
    In [3]:
    df = pd.DataFrame() 
for i in range(len(m30.result)):
    tmp = m30.result[i]['m4'].raw_perf.read()
    df = df.append(tmp[['returns','benchmark_period_return']])
    
import empyrical
def get_stats(returns, benchmark_period_return):
    return_ratio  = empyrical.cum_returns_final(returns)
    annual_return_ratio  = empyrical.annual_return(returns)
    sharp_ratio = empyrical.sharpe_ratio(returns,0.035/252)
    return_volatility = empyrical.annual_volatility(returns)
    max_drawdown  = empyrical.max_drawdown(returns)
    benchmark_returns = (benchmark_period_return+1)/(benchmark_period_return+1).shift(1)-1
    alpha, beta =empyrical.alpha_beta_aligned(returns, benchmark_returns)
    
    return {
      'return_ratio': return_ratio,
      'annual_return_ratio': annual_return_ratio,
      'beta': beta,
      'alpha': alpha,
      'sharp_ratio': sharp_ratio,
      'return_volatility': return_volatility,
      'max_drawdown': max_drawdown,
      '收益回测比': abs(annual_return_ratio / max_drawdown)
    }
d=get_stats(df['returns'], df['benchmark_period_return'])
df1=pd.DataFrame.from_dict(d,orient='index')
df1.T
    Out[3]:
    return_ratio annual_return_ratio beta alpha sharp_ratio return_volatility max_drawdown 收益回测比
    0 18.612272 0.446418 0.354812 0.499667 1.267473 0.29935 -0.462402 0.965432
    In [4]:
    T.plot((df['returns']+1).cumprod())