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    {"description":"实验创建于2017/8/26","graph":{"edges":[{"to_node_id":"-549:instruments","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-8:data"},{"to_node_id":"-896:instruments","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-8:data"},{"to_node_id":"-741:instruments","from_node_id":"287d2cb0-f53c-4101-bdf8-104b137c8601-8:data"},{"to_node_id":"-32248:input_1","from_node_id":"-896:data"},{"to_node_id":"-32248:input_2","from_node_id":"-741:data"},{"to_node_id":"-549:options_data","from_node_id":"-32248:data_1"},{"to_node_id":"-32248:input_3","from_node_id":"-294:data"}],"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":"2022-06-30","type":"Literal","bound_global_parameter":null},{"name":"market","value":"CN_FUTURE","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":"-896","module_id":"BigQuantSpace.use_datasource.use_datasource-v2","parameters":[{"name":"datasource_id","value":"bar1d_CN_FUTURE","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":"before_start_days","value":"0","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"instruments","node_id":"-896"},{"name":"features","node_id":"-896"}],"output_ports":[{"name":"data","node_id":"-896"}],"cacheable":true,"seq_num":2,"comment":"","comment_collapsed":true},{"node_id":"-741","module_id":"BigQuantSpace.use_datasource.use_datasource-v2","parameters":[{"name":"datasource_id","value":"basic_info_CN_FUTURE","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":"before_start_days","value":"0","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"instruments","node_id":"-741"},{"name":"features","node_id":"-741"}],"output_ports":[{"name":"data","node_id":"-741"}],"cacheable":true,"seq_num":33,"comment":"","comment_collapsed":true},{"node_id":"-32248","module_id":"BigQuantSpace.cached.cached-v3","parameters":[{"name":"run","value":"# Python 代码入口函数，input_1/2/3 对应三个输入端，data_1/2/3 对应三个输出端\ndef bigquant_run(input_1, input_2, input_3,min_volume,N):\n from functools import reduce\n import pandas as pd \n import numpy as np \n \n df_raw = input_1.read()\n df_raw = df_raw[~(df_raw['instrument'].str.contains('IC|IF|IH|T|TF|TS|FB'))] #去除金融期货品种以及历史上数据有问题的品种FB\n df_basic = input_2.read()[['product_code','instrument','delist_date']]\n df = df_raw[~(df_raw['instrument'].str.contains('0000|8888|9999'))]\n df_index = df_raw[df_raw['instrument'].str.contains('0000')]\n df_dom = df_raw[df_raw['instrument'].str.contains('8888')]\n \n factor_ = input_3.read()[0]\n \n df = pd.merge(df,df_basic,how='left',on=['product_code','instrument'])\n df['maturity_days'] = (df['delist_date'] - df['date']).apply(lambda x:x.days)\n \n #去掉成交量少的品种\n def kill_min_volume_product(x):\n df_tp = x.groupby('product_code').sum()\n return df_tp[df_tp['volume'] >= min_volume]\n \n df_universe = df[['date','product_code','volume']].groupby('date').apply(kill_min_volume_product)\n df_universe.reset_index(inplace=True)\n df_universe = df_universe[['date','product_code']]\n \n #计算期限结构因子\n def calc_ts(x):\n factor_name = 'ts_mean'\n df_tp = x.sort_values(\"maturity_days\")\n if df_tp.shape[0] > 1:\n df_tp['ts'] = (df_tp['close'] - df_tp['close'].iloc[0])/df_tp['close'] * 365/(df_tp['maturity_days'] - df_tp['maturity_days'].iloc[0])\n df_tp[factor_name] = df_tp['ts'].sum() / (df_tp.shape[0] - 1)\n return df_tp.iloc[1]\n \n \n df_result = pd.DataFrame(df.groupby([\"date\",\"product_code\"]).apply(calc_ts))\n df_result['ts_mean'] = df_result['ts_mean'].rolling(N).mean()\n del df_result['instrument']\n df_result.reset_index(drop=True,inplace=True)\n\n\n #和样本空间\n df_result = pd.merge(df_universe,df_result,how='left',on=['date','product_code'])\n \n df_output = pd.merge(df_result,df_dom[['date','product_code','instrument']],how='left',on=['date','product_code'])\n del df_output['product_code']\n df_output['date'] = pd.to_datetime(df_output['date'])\n df_output.dropna(inplace=True)\n df_output.sort_values(\"date\",inplace=True)\n print(df_output)\n \n #signal \n def calc_signal(x,factor_name):\n x = x.sort_values(factor_name)\n length = x.shape[0]\n if length >= 10 :\n x['signal_'+factor_name] = [1]*5 + [0 for i in range(length-10)] + [-1]*5\n else :\n x['signal_' + factor_name] = [0] * length\n return x\n \n df_result = df_output.groupby(\"date\").apply(calc_signal,factor_)\n df_result.reset_index(drop=True,inplace=True)\n #产生信号\n df_result = df_result[['date','instrument','signal_'+factor_]] \n df_result['signal'] = df_result.iloc[:,2:].apply(lambda x:x.sum(),axis=1)\n df_result = df_result[~(df_result['signal'] == 0)]\n \n data_1 = DataSource.write_pickle(df_result[['date','instrument','signal']])\n return Outputs(data_1=data_1)\n\n","type":"Literal","bound_global_parameter":null},{"name":"post_run","value":"# 后处理函数，可选。输入是主函数的输出，可以在这里对数据做处理，或者返回更友好的outputs数据格式。此函数输出不会被缓存。\ndef bigquant_run(outputs):\n return outputs\n","type":"Literal","bound_global_parameter":null},{"name":"input_ports","value":"","type":"Literal","bound_global_parameter":null},{"name":"params","value":"{\n 'min_volume': 100000,\n 'N':1\n}","type":"Literal","bound_global_parameter":null},{"name":"output_ports","value":"","type":"Literal","bound_global_parameter":null}],"input_ports":[{"name":"input_1","node_id":"-32248"},{"name":"input_2","node_id":"-32248"},{"name":"input_3","node_id":"-32248"}],"output_ports":[{"name":"data_1","node_id":"-32248"},{"name":"data_2","node_id":"-32248"},{"name":"data_3","node_id":"-32248"}],"cacheable":true,"seq_num":27,"comment":"","comment_collapsed":true},{"node_id":"-549","module_id":"BigQuantSpace.hftrade.hftrade-v2","parameters":[{"name":"start_date","value":"","type":"Literal","bound_global_parameter":null},{"name":"end_date","value":"","type":"Literal","bound_global_parameter":null},{"name":"initialize","value":"# 回测引擎：初始化函数，只执行一次\ndef bigquant_run(context):\n outer_data = context.options['data'].read()\n context.stock_to_long = outer_data[(outer_data['signal'] > 0)]\n context.stock_to_short = outer_data[(outer_data['signal'] < 0)]\n context.freq = 5\n \n\n ","type":"Literal","bound_global_parameter":null},{"name":"before_trading_start","value":"# 交易引擎：每个单位时间开盘前调用一次。\ndef bigquant_run(context, data):\n # 盘前处理，订阅行情等\n context.position_check = context.get_positions()\n","type":"Literal","bound_global_parameter":null},{"name":"handle_tick","value":"# 交易引擎：tick数据处理函数，每个tick执行一次\ndef bigquant_run(context, tick):\n pass\n","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 date = data.current_dt.strftime('%Y-%m-%d')\n \n if (context.trading_day_index % context.freq == 0) :\n print('date'*5, date)\n \n stock_to_long = context.stock_to_long[context.stock_to_long['date'] == date]\n stock_to_long = stock_to_long[stock_to_long['signal'] > 0]['instrument']\n stock_to_short = context.stock_to_short[context.stock_to_short['date'] == date]\n stock_to_short = stock_to_short[stock_to_short['signal'] < 0]['instrument']\n\n \n # 平仓\n hold_symbols = context.get_account_positions()\n long_pos = {}\n short_pos = {}\n\n for i in hold_symbols.keys():\n if hold_symbols[i].long.current_qty > 0:\n long_pos[i] = hold_symbols[i].long.current_qty\n if hold_symbols[i].short.current_qty> 0:\n short_pos[i] = hold_symbols[i].short.current_qty\n \n for stock in long_pos.keys():\n pos = long_pos[stock]\n price = data.current(context.symbol(stock), \"close\")\n context.sell_close(context.symbol(stock), pos, price, order_type=OrderType.MARKET)\n for stock in short_pos.keys():\n pos = short_pos[stock]\n price = data.current(context.symbol(stock), \"close\")\n context.buy_close(context.symbol(stock), pos, price, order_type=OrderType.MARKET)\n \n \n # 开仓\n stock_num = len(stock_to_long) + len(stock_to_short)\n if stock_num != 0 :\n weight = 1 / stock_num\n portfolio_value = context.portfolio.portfolio_value\n book_value = portfolio_value * weight\n\n for i in range(len(stock_to_short)):\n stock_i = stock_to_short.iloc[i]\n stock = stock_i\n code = stock_i.split('.')[0][:-4]\n stock = context.get_dominant(code)\n if data.can_trade(stock):\n price = data.current(stock,\"close\")\n unit =context.get_contract(stock).multiplier\n lots = math.floor(book_value/(unit*price))\n if lots != 0:\n context.sell_open(stock, lots, price, order_type=OrderType.MARKET)\n\n for i in range(len(stock_to_long)):\n stock_i = stock_to_long.iloc[i]\n code = stock_i.split('.')[0][:-4]\n stock = context.get_dominant(code)\n if data.can_trade(stock):\n price = data.current(stock,\"close\")\n unit =context.get_contract(stock).multiplier\n lots = math.floor(book_value/(unit*price))\n if lots != 0:\n context.buy_open(stock, lots, price, order_type=OrderType.MARKET)\n \n\n \n ","type":"Literal","bound_global_parameter":null},{"name":"handle_trade","value":"# 交易引擎：成交回报处理函数，每个成交发生时执行一次\ndef bigquant_run(context, trade):\n pass\n","type":"Literal","bound_global_parameter":null},{"name":"handle_order","value":"# 交易引擎：委托回报处理函数，每个委托变化时执行一次\ndef bigquant_run(context, 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    In [10]:
    # 本代码由可视化策略环境自动生成 2022年9月24日 17:39
# 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。


# Python 代码入口函数，input_1/2/3 对应三个输入端，data_1/2/3 对应三个输出端
def m27_run_bigquant_run(input_1, input_2, input_3,min_volume,N):
    from functools import reduce
    import pandas as pd 
    import numpy as np 
    
    df_raw = input_1.read()
    df_raw = df_raw[~(df_raw['instrument'].str.contains('IC|IF|IH|T|TF|TS|FB'))]  #去除金融期货品种以及历史上数据有问题的品种FB
    df_basic = input_2.read()[['product_code','instrument','delist_date']]
    df = df_raw[~(df_raw['instrument'].str.contains('0000|8888|9999'))]
    df_index = df_raw[df_raw['instrument'].str.contains('0000')]
    df_dom = df_raw[df_raw['instrument'].str.contains('8888')]
    
    factor_ = input_3.read()[0]
    
    df = pd.merge(df,df_basic,how='left',on=['product_code','instrument'])
    df['maturity_days'] = (df['delist_date'] - df['date']).apply(lambda x:x.days)
    
    #去掉成交量少的品种
    def kill_min_volume_product(x):
        df_tp = x.groupby('product_code').sum()
        return df_tp[df_tp['volume'] >= min_volume]
    
    df_universe = df[['date','product_code','volume']].groupby('date').apply(kill_min_volume_product)
    df_universe.reset_index(inplace=True)
    df_universe = df_universe[['date','product_code']]
    
    #计算期限结构因子
    def calc_ts(x):
        factor_name = 'ts_mean'
        df_tp = x.sort_values("maturity_days")
        if df_tp.shape[0] > 1:
            df_tp['ts'] = (df_tp['close'] - df_tp['close'].iloc[0])/df_tp['close'] * 365/(df_tp['maturity_days'] - df_tp['maturity_days'].iloc[0])
            df_tp[factor_name] = df_tp['ts'].sum() / (df_tp.shape[0] - 1)
            return df_tp.iloc[1]
      
    
    df_result = pd.DataFrame(df.groupby(["date","product_code"]).apply(calc_ts))
    df_result['ts_mean'] = df_result['ts_mean'].rolling(N).mean()
    del df_result['instrument']
    df_result.reset_index(drop=True,inplace=True)


    #和样本空间
    df_result = pd.merge(df_universe,df_result,how='left',on=['date','product_code'])
    
    df_output = pd.merge(df_result,df_dom[['date','product_code','instrument']],how='left',on=['date','product_code'])
    del df_output['product_code']
    df_output['date'] = pd.to_datetime(df_output['date'])
    df_output.dropna(inplace=True)
    df_output.sort_values("date",inplace=True)
    print(df_output)
    
    #signal 
    def calc_signal(x,factor_name):
        x = x.sort_values(factor_name)
        length = x.shape[0]
        if length >= 10 :
            x['signal_'+factor_name] = [1]*5 + [0 for i in range(length-10)] + [-1]*5
        else :
            x['signal_' + factor_name] = [0] * length
        return x
    
    df_result = df_output.groupby("date").apply(calc_signal,factor_)
    df_result.reset_index(drop=True,inplace=True)
     #产生信号
    df_result =  df_result[['date','instrument','signal_'+factor_]]  
    df_result['signal'] = df_result.iloc[:,2:].apply(lambda x:x.sum(),axis=1)
    df_result = df_result[~(df_result['signal'] == 0)]
    
    data_1 = DataSource.write_pickle(df_result[['date','instrument','signal']])
    return Outputs(data_1=data_1)


# 后处理函数，可选。输入是主函数的输出，可以在这里对数据做处理，或者返回更友好的outputs数据格式。此函数输出不会被缓存。
def m27_post_run_bigquant_run(outputs):
    return outputs

# 回测引擎：初始化函数，只执行一次
def m4_initialize_bigquant_run(context):
    outer_data = context.options['data'].read()
    context.stock_to_long = outer_data[(outer_data['signal'] > 0)]
    context.stock_to_short = outer_data[(outer_data['signal'] < 0)]
    context.freq = 5
    

    
# 交易引擎：每个单位时间开盘前调用一次。
def m4_before_trading_start_bigquant_run(context, data):
    # 盘前处理，订阅行情等
    context.position_check = context.get_positions()

# 交易引擎：tick数据处理函数，每个tick执行一次
def m4_handle_tick_bigquant_run(context, tick):
    pass

# 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):
    date = data.current_dt.strftime('%Y-%m-%d')
        
    if (context.trading_day_index % context.freq == 0) :
        print('date'*5, date)
        
        stock_to_long = context.stock_to_long[context.stock_to_long['date'] == date]
        stock_to_long = stock_to_long[stock_to_long['signal'] > 0]['instrument']
        stock_to_short = context.stock_to_short[context.stock_to_short['date'] == date]
        stock_to_short = stock_to_short[stock_to_short['signal'] < 0]['instrument']

        
        # 平仓
        hold_symbols = context.get_account_positions()
        long_pos = {}
        short_pos = {}

        for i in hold_symbols.keys():
            if  hold_symbols[i].long.current_qty > 0:
                long_pos[i] = hold_symbols[i].long.current_qty
            if hold_symbols[i].short.current_qty> 0:
                short_pos[i] = hold_symbols[i].short.current_qty
        
        for stock in long_pos.keys():
            pos = long_pos[stock]
            price = data.current(context.symbol(stock), "close")
            context.sell_close(context.symbol(stock), pos, price, order_type=OrderType.MARKET)
        for stock in short_pos.keys():
            pos = short_pos[stock]
            price = data.current(context.symbol(stock), "close")
            context.buy_close(context.symbol(stock), pos, price, order_type=OrderType.MARKET)
            
 
        # 开仓
        stock_num = len(stock_to_long) + len(stock_to_short)
        if stock_num != 0 :
            weight =  1 / stock_num
            portfolio_value = context.portfolio.portfolio_value
            book_value = portfolio_value  * weight

            for i in range(len(stock_to_short)):
                stock_i = stock_to_short.iloc[i]
                stock = stock_i
                code = stock_i.split('.')[0][:-4]
                stock = context.get_dominant(code)
                if data.can_trade(stock):
                    price = data.current(stock,"close")
                    unit =context.get_contract(stock).multiplier
                    lots = math.floor(book_value/(unit*price))
                    if lots != 0:
                        context.sell_open(stock, lots, price, order_type=OrderType.MARKET)

            for i in range(len(stock_to_long)):
                stock_i = stock_to_long.iloc[i]
                code = stock_i.split('.')[0][:-4]
                stock = context.get_dominant(code)
                if data.can_trade(stock):
                    price = data.current(stock,"close")
                    unit =context.get_contract(stock).multiplier
                    lots = math.floor(book_value/(unit*price))
                    if lots != 0:
                        context.buy_open(stock, lots, price, order_type=OrderType.MARKET)
  

         
        
# 交易引擎：成交回报处理函数，每个成交发生时执行一次
def m4_handle_trade_bigquant_run(context, trade):
    pass

# 交易引擎：委托回报处理函数，每个委托变化时执行一次
def m4_handle_order_bigquant_run(context, order):
    pass

# 交易引擎：盘后处理函数，每日盘后执行一次
def m4_after_trading_bigquant_run(context, data):
    pass


m1 = M.instruments.v2(
    start_date='2010-01-01',
    end_date='2022-06-30',
    market='CN_FUTURE',
    instrument_list='',
    max_count=0
)

m2 = M.use_datasource.v2(
    instruments=m1.data,
    datasource_id='bar1d_CN_FUTURE',
    start_date='',
    end_date='',
    before_start_days=0
)

m33 = M.use_datasource.v2(
    instruments=m1.data,
    datasource_id='basic_info_CN_FUTURE',
    start_date='',
    end_date='',
    before_start_days=0
)

m6 = M.input_features.v1(
    features='ts_mean'
)

m27 = M.cached.v3(
    input_1=m2.data,
    input_2=m33.data,
    input_3=m6.data,
    run=m27_run_bigquant_run,
    post_run=m27_post_run_bigquant_run,
    input_ports='',
    params="""{
    'min_volume': 100000,
    'N':1
}""",
    output_ports=''
)

m4 = M.hftrade.v2(
    instruments=m1.data,
    options_data=m27.data_1,
    start_date='',
    end_date='',
    initialize=m4_initialize_bigquant_run,
    before_trading_start=m4_before_trading_start_bigquant_run,
    handle_tick=m4_handle_tick_bigquant_run,
    handle_data=m4_handle_data_bigquant_run,
    handle_trade=m4_handle_trade_bigquant_run,
    handle_order=m4_handle_order_bigquant_run,
    after_trading=m4_after_trading_bigquant_run,
    capital_base=100000000,
    frequency='daily',
    price_type='真实价格',
    product_type='期货',
    before_start_days='0',
    volume_limit=1,
    order_price_field_buy='close',
    order_price_field_sell='close',
    benchmark='000300.HIX',
    plot_charts=True,
    disable_cache=False,
    replay_bdb=False,
    show_debug_info=False,
    backtest_only=False
)

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2022-09-24 17:37:54.902100 handle_order_submit process req check position failed for order_req:OrderReq(bktfut,EB2102.DCE,'1','1',630,8911.0,U,0,strategy,2021-02-23 15:00:00) 
2022-09-24 17:37:54.902252 continous trading send order=OrderReq(bktfut,EB2102.DCE,'1','1',630,8911.0,U,0,strategy,2021-02-23 15:00:00) failed err=-113,无可用持仓 
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    • 收益率142.15%
    • 年化收益率7.31%
    • 基准收益率26.87%
    • 阿尔法0.05
    • 贝塔0.0
    • 夏普比率0.6
    • 胜率0.52
    • 盈亏比1.12
    • 收益波动率7.81%
    • 最大回撤18.44%
    bigcharts-data-start/{"__type":"tabs","__id":"bigchart-44d61ef2c87e42e6aff287e884e24f1e"}/bigcharts-data-end
    In [105]:
    import empyrical
start_date = ['2010-01-01', '2011-01-01', '2012-01-01', '2013-01-01', '2014-01-01', '2015-01-01', '2016-01-01', '2017-01-01', '2018-01-01', '2019-01-01', '2020-01-01', '2021-01-01', '2022-01-01']
end_date = ['2010-12-31', '2011-12-31', '2012-12-31', '2013-12-31', '2014-12-31', '2015-12-31', '2016-12-31', '2017-12-31', '2018-12-31', '2019-12-31', '2020-12-31', '2021-12-31', '2022-08-31']
raw_perf = m4.raw_perf.read()
df_result = []
for sd, ed in zip(start_date, end_date):
    df = raw_perf[raw_perf.date.between(sd, ed)]
    sharpe = empyrical.sharpe_ratio(df.returns)
    sortino = empyrical.sortino_ratio(df.returns)
    max_drawdown = empyrical.max_drawdown(df.returns)
    vol = empyrical.annual_volatility(df.returns)
    returns = empyrical.annual_return(df.returns)
    df_result.append({'start_date':sd,'end_date':ed,'returns':returns,'vol':vol,'max_drawdown':max_drawdown,'sharpe':sharpe,'sortino':sortino})
#     print(f"date between {sd} and {ed}, the sharpe is {sharpe}, sortino is {sortino}")
pd.DataFrame(df_result)[['returns','vol','max_drawdown','sharpe','sortino']]
    Out[105]:
    returns vol max_drawdown sharpe sortino
    0 0.188618 0.048466 -0.020677 3.590505 6.089683
    1 0.026929 0.050016 -0.041016 0.556220 0.811716
    2 0.075990 0.042782 -0.036324 1.733526 2.554126
    3 0.239387 0.062150 -0.021908 3.485567 6.484333
    4 0.225259 0.059449 -0.021533 3.448195 6.104840
    5 0.069663 0.062064 -0.028904 1.116071 1.738843
    6 -0.108153 0.086622 -0.111807 -1.277930 -1.708341
    7 0.106984 0.098963 -0.057324 1.076579 1.572197
    8 0.051304 0.065529 -0.072205 0.796175 1.212403
    9 -0.024243 0.068808 -0.111174 -0.322352 -0.434509
    10 0.135071 0.098118 -0.070653 1.340417 2.019233
    11 -0.110315 0.127175 -0.174831 -0.855346 -1.113550
    12 0.337974 0.108731 -0.041887 2.733126 4.609532