{"Description":"实验创建于2018/10/16","Summary":"","Graph":{"EdgesInternal":[{"DestinationInputPortId":"-1442:instruments","SourceOutputPortId":"-1483:data_1"},{"DestinationInputPortId":"-298:input_1","SourceOutputPortId":"-165:data"},{"DestinationInputPortId":"-1483:input_1","SourceOutputPortId":"-165:data"},{"DestinationInputPortId":"-307:features","SourceOutputPortId":"-173:data"},{"DestinationInputPortId":"-307:input_data","SourceOutputPortId":"-298:data_1"},{"DestinationInputPortId":"-1442:options_data","SourceOutputPortId":"-307:data"}],"ModuleNodes":[{"Id":"-1442","ModuleId":"BigQuantSpace.trade.trade-v4","ModuleParameters":[{"Name":"start_date","Value":"","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"end_date","Value":"","ValueType":"Literal","LinkedGlobalParameter":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 \n \n date_time = data.current_dt # 日期时间(timestamp格式)\n date = date_time.strftime('%Y-%m-%d') # 日期（str格式）\n \n if date not in context.DATE: # 用于判断是否是新的一天\n context.DATE.append(date)\n context.trading_counts = 0 # 每日开始的时候初始化该字段\n \n \n instrument_symbol = context.future_symbol(context.instruments[0]) # 交易标的\n curr_position = context.portfolio.positions[instrument_symbol].amount # 持仓数量\n price = data.current(instrument_symbol, 'price')\n \n try:\n upperline = context.indicator_df.ix[date]['upperline']\n lowerline = context.indicator_df.ix[date]['lowerline']\n except KeyError as e:\n return \n\n \n # 交易逻辑\n # 卖平\n if curr_position > 0 and price <= lowerline and date_time.hour<14 and context.trading_counts<context.max_trade_cnt:\n order(instrument_symbol, -1*abs(curr_position), position_effect='close')\n print(date_time, '卖平！','交易前持仓：',curr_position, price ,lowerline)\n \n # 买平\n elif curr_position < 0 and price >= upperline and date_time.hour<14 and context.trading_counts<context.max_trade_cnt:\n order(instrument_symbol, 1*abs(curr_position), position_effect='open') \n print(date_time, '买平！', '交易前持仓：',curr_position, price , upperline)\n \n # 买开\n if curr_position == 0 and price >= upperline and date_time.hour<14 and context.trading_counts<context.max_trade_cnt:\n order(instrument_symbol, 1, position_effect='open')\n context.trading_counts += 1\n print(date_time, '买开！','交易前持仓：',curr_position, price ,upperline)\n \n # 卖开 \n elif curr_position == 0 and price <= lowerline and date_time.hour<14 and context.trading_counts<context.max_trade_cnt:\n order(instrument_symbol, -1, position_effect='open') \n context.trading_counts += 1\n print(date_time, '卖开！','交易前持仓：',curr_position, price ,lowerline)\n \n \n \n \n # 每日收盘，日终平仓\n curr_position = context.portfolio.positions[instrument_symbol].amount # 持仓数量\n \n if date_time.hour == 14 and date_time.minute == 55:\n if curr_position >0:\n order(instrument_symbol, -1*abs(curr_position), position_effect='close')\n print(date_time, '收盘，卖平！', '交易前持仓：',curr_position)\n \n elif curr_position <0:\n order(instrument_symbol, 1*abs(curr_position), position_effect='close')\n print(date_time, '收盘，买平！', '交易前持仓：',curr_position)\n\n\n \n \n \n ","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"prepare","Value":"# 回测引擎：准备数据，只执行一次\ndef bigquant_run(context):\n pass","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"initialize","Value":"# 回测引擎：初始化函数，只执行一次\ndef bigquant_run(context):\n # 设置是否是结算模式\n context.set_need_settle(False)\n # 设置最大杠杆\n #context.set_max_leverage(1, 'fill_amap')\n \n # 设置手续费\n #context.set_commission(futures_commission=PerContract(cost={'RB':(0.000045, 0.000045, 0.000045)}))\n #context.set_margin('RB', 0.07)\n \n \n context.DATE = list()\n context.max_trade_cnt = 3 # 每日最大交易次数不超过3次\n context.indicator_df = context.options['data'].read_df().set_index('date')\n \n ","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"before_trading_start","Value":"# 回测引擎：每个单位时间开始前调用一次，即每日开盘前调用一次。\ndef bigquant_run(context, data):\n 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# 本代码由可视化策略环境自动生成 2019年6月15日 15:36
# 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 m6_run_bigquant_run(input_1, input_2, input_3,before_days):
    start_date=input_1.read_pickle()['start_date']
    end_date=input_1.read_pickle()['end_date']
    ins=input_1.read_pickle()['instruments']
    df = DataSource('bar1d_CN_FUTURE').read(instruments=ins,start_date=start_date,end_date=end_date,fields=['open','close','high','low','settle'])
    df['adjust_factor']=1.0
    data_1 = DataSource.write_df(df)
    return Outputs(data_1=data_1, data_2=None, data_3=None)

# 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 m6_post_run_bigquant_run(outputs):
    return outputs

# 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 m1_run_bigquant_run(input_1, input_2, input_3,before_days):
    
    
    data = input_1.read_pickle()
    
    start_date= data['start_date']
    end_date = data['end_date']
    contracts = data['instruments']
    
    ds1 = DataSource.write_pickle({'start_date':start_date, 'end_date':end_date, 'instruments':contracts})
 
    return Outputs(data_1=ds1)

# 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 m1_post_run_bigquant_run(outputs):
    return outputs

# 回测引擎：每日数据处理函数，每天执行一次
def m3_handle_data_bigquant_run(context, data):
     
    
    date_time = data.current_dt # 日期时间(timestamp格式)
    date = date_time.strftime('%Y-%m-%d') # 日期（str格式）
    
    if date not in context.DATE: # 用于判断是否是新的一天
        context.DATE.append(date)
        context.trading_counts = 0 # 每日开始的时候初始化该字段
        
     
    instrument_symbol = context.future_symbol(context.instruments[0]) # 交易标的
    curr_position = context.portfolio.positions[instrument_symbol].amount # 持仓数量
    price = data.current(instrument_symbol, 'price')
    
    try:
        upperline = context.indicator_df.ix[date]['upperline']
        lowerline = context.indicator_df.ix[date]['lowerline']
    except KeyError as e:
        return 

     
    # 交易逻辑
    # 卖平
    if curr_position > 0  and price <= lowerline and date_time.hour<14 and context.trading_counts<context.max_trade_cnt:
        order(instrument_symbol, -1*abs(curr_position), position_effect='close')
        print(date_time, '卖平！','交易前持仓：',curr_position, price ,lowerline)
   
     # 买平
    elif  curr_position < 0 and price >= upperline and date_time.hour<14 and context.trading_counts<context.max_trade_cnt:
        order(instrument_symbol, 1*abs(curr_position),  position_effect='open') 
        print(date_time, '买平！', '交易前持仓：',curr_position, price , upperline)
        
    # 买开
    if curr_position == 0 and price >= upperline and date_time.hour<14 and context.trading_counts<context.max_trade_cnt:
        order(instrument_symbol, 1, position_effect='open')
        context.trading_counts += 1
        print(date_time, '买开！','交易前持仓：',curr_position, price ,upperline)
    
    # 卖开 
    elif  curr_position == 0 and price <= lowerline and date_time.hour<14 and context.trading_counts<context.max_trade_cnt:
        order(instrument_symbol, -1, position_effect='open') 
        context.trading_counts += 1
        print(date_time, '卖开！','交易前持仓：',curr_position, price ,lowerline)
        
    
        
        
    # 每日收盘，日终平仓
    curr_position = context.portfolio.positions[instrument_symbol].amount # 持仓数量
     
    if date_time.hour == 14 and date_time.minute == 55:
        if curr_position >0:
            order(instrument_symbol, -1*abs(curr_position), position_effect='close')
            print(date_time, '收盘，卖平！', '交易前持仓：',curr_position)
            
        elif curr_position <0:
            order(instrument_symbol, 1*abs(curr_position), position_effect='close')
            print(date_time, '收盘，买平！', '交易前持仓：',curr_position)


        
        
        
        
# 回测引擎：准备数据，只执行一次
def m3_prepare_bigquant_run(context):
    pass
# 回测引擎：初始化函数，只执行一次
def m3_initialize_bigquant_run(context):
   # 设置是否是结算模式
    context.set_need_settle(False)
    # 设置最大杠杆
    #context.set_max_leverage(1, 'fill_amap')
    
    # 设置手续费
    #context.set_commission(futures_commission=PerContract(cost={'RB':(0.000045, 0.000045, 0.000045)}))
    #context.set_margin('RB', 0.07)
    
     
    context.DATE = list()
    context.max_trade_cnt = 3 # 每日最大交易次数不超过3次
    context.indicator_df = context.options['data'].read_df().set_index('date')
     
    
# 回测引擎：每个单位时间开始前调用一次，即每日开盘前调用一次。
def m3_before_trading_start_bigquant_run(context, data):
    pass


m2 = M.instruments.v2(
    start_date='2018-03-01',
    end_date='2018-06-01',
    market='CN_STOCK_A',
    instrument_list='RB1810.SHF',
    max_count=0
)

m6 = M.cached.v3(
    input_1=m2.data,
    run=m6_run_bigquant_run,
    post_run=m6_post_run_bigquant_run,
    input_ports='',
    params='{\'before_days\':1}',
    output_ports='',
    m_cached=False
)

m1 = M.cached.v3(
    input_1=m2.data,
    run=m1_run_bigquant_run,
    post_run=m1_post_run_bigquant_run,
    input_ports='',
    params='{\'before_days\':60}',
    output_ports=''
)

m4 = M.input_features.v1(
    features="""HH=ts_max(high, 10)
HC=ts_max(close, 10)
LC=ts_min(close,10)
LL=ts_min(low,10)
Range=max(HH-LC,HC-LL)
upperline=open+shift(0.1*Range,1)
lowerline=open-shift(0.1*Range,1)

"""
)

m5 = M.derived_feature_extractor.v3(
    input_data=m6.data_1,
    features=m4.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False,
    user_functions={}
)

m3 = M.trade.v4(
    instruments=m1.data_1,
    options_data=m5.data,
    start_date='',
    end_date='',
    handle_data=m3_handle_data_bigquant_run,
    prepare=m3_prepare_bigquant_run,
    initialize=m3_initialize_bigquant_run,
    before_trading_start=m3_before_trading_start_bigquant_run,
    volume_limit=0,
    order_price_field_buy='open',
    order_price_field_sell='open',
    capital_base=20000,
    auto_cancel_non_tradable_orders=True,
    data_frequency='minute',
    price_type='后复权',
    product_type='期货',
    plot_charts=True,
    backtest_only=False,
    benchmark=''
)

[2019-06-15 15:34:05.345937] INFO: bigquant: backtest.v8 运行完成[378.812597s].

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