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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 #------------------------------------------止损模块START--------------------------------------------\n date = data.current_dt.strftime('%Y-%m-%d') \n equities = {e.symbol: p for e, p in context.portfolio.positions.items() if p.amount>0}\n \n # 新建当日止损股票列表是为了handle_data 策略逻辑部分不再对该股票进行判断\n current_stoploss_stock = [] \n if len(equities) > 0:\n for i in equities.keys():\n stock_market_price = data.current(context.symbol(i), 'price') # 最新市场价格\n last_sale_date = equities[i].last_sale_date # 上次交易日期\n delta_days = data.current_dt - last_sale_date \n hold_days = delta_days.days # 持仓天数\n # 建仓以来的最高价\n highest_price_since_buy = data.history(context.symbol(i), 'high', hold_days, '1d').max()\n # 确定止损位置\n stoploss_line = highest_price_since_buy - highest_price_since_buy * 0.1\n record('止损位置', stoploss_line)\n # 如果价格下穿止损位置\n if stock_market_price < stoploss_line:\n context.order_target_percent(context.symbol(i), 0) \n current_stoploss_stock.append(i)\n print('日期：', date , '股票：', i, '出现止损状况')\n #-------------------------------------------止损模块END--------------------------------------------------\n\n \n # 获取今日的日期\n today = data.current_dt.strftime('%Y-%m-%d') \n # 通过positions对象，使用列表生成式的方法获取目前持仓的股票列表\n stock_hold_now = {e.symbol: p.amount * p.last_sale_price\n for e, p in context.portfolio.positions.items()}\n\n # 记录用于买入股票的可用现金,因为是早盘卖股票，需要记录卖出的股票市值并在买入下单前更新可用现金；\n # 如果是早盘买尾盘卖，则卖出时不需更新可用现金，因为尾盘卖出股票所得现金无法使用\n cash_for_buy = context.portfolio.cash \n \n try:\n buy_stock = context.daily_stock_buy[today] # 当日符合买入条件的股票\n except:\n buy_stock=[] # 如果没有符合条件的股票，就设置为空\n \n try:\n sell_stock = context.daily_stock_sell[today] # 当日符合卖出条件的股票\n except:\n sell_stock=[] # 如果没有符合条件的股票，就设置为空\n \n # 需要卖出的股票:已有持仓中符合卖出条件的股票\n stock_to_sell = [ i for i in stock_hold_now if i in sell_stock ]\n # 需要买入的股票:没有持仓且符合买入条件的股票\n stock_to_buy = [ i for i in buy_stock if i not in stock_hold_now ] \n # 需要调仓的股票：已有持仓且不符合卖出条件的股票\n 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# 本代码由可视化策略环境自动生成 2019年1月24日 10:29
# 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：每日数据处理函数，每天执行一次
def m3_handle_data_bigquant_run(context, data):

    #------------------------------------------止损模块START--------------------------------------------
    date = data.current_dt.strftime('%Y-%m-%d')  
    equities = {e.symbol: p for e, p in context.portfolio.positions.items() if p.amount>0}
    
    # 新建当日止损股票列表是为了handle_data 策略逻辑部分不再对该股票进行判断
    current_stoploss_stock = [] 
    if len(equities) > 0:
        for i in equities.keys():
            stock_market_price = data.current(context.symbol(i), 'price')  # 最新市场价格
            last_sale_date = equities[i].last_sale_date   # 上次交易日期
            delta_days = data.current_dt - last_sale_date  
            hold_days = delta_days.days # 持仓天数
            # 建仓以来的最高价
            highest_price_since_buy = data.history(context.symbol(i), 'high', hold_days, '1d').max()
            # 确定止损位置
            stoploss_line = highest_price_since_buy - highest_price_since_buy * 0.1
            record('止损位置', stoploss_line)
            # 如果价格下穿止损位置
            if stock_market_price < stoploss_line:
                context.order_target_percent(context.symbol(i), 0)     
                current_stoploss_stock.append(i)
                print('日期：', date , '股票：', i, '出现止损状况')
    #-------------------------------------------止损模块END--------------------------------------------------

       
    # 获取今日的日期
    today = data.current_dt.strftime('%Y-%m-%d')  
    # 通过positions对象，使用列表生成式的方法获取目前持仓的股票列表
    stock_hold_now = {e.symbol: p.amount * p.last_sale_price
                 for e, p in context.portfolio.positions.items()}

    # 记录用于买入股票的可用现金,因为是早盘卖股票，需要记录卖出的股票市值并在买入下单前更新可用现金；
    # 如果是早盘买尾盘卖，则卖出时不需更新可用现金，因为尾盘卖出股票所得现金无法使用
    cash_for_buy = context.portfolio.cash    
    
    try:
        buy_stock = context.daily_stock_buy[today]  # 当日符合买入条件的股票
    except:
        buy_stock=[]  # 如果没有符合条件的股票，就设置为空
    
    try:
        sell_stock = context.daily_stock_sell[today]  # 当日符合卖出条件的股票
    except:
        sell_stock=[] # 如果没有符合条件的股票，就设置为空
    
    # 需要卖出的股票:已有持仓中符合卖出条件的股票
    stock_to_sell = [ i for i in stock_hold_now if i in sell_stock ]
    # 需要买入的股票:没有持仓且符合买入条件的股票
    stock_to_buy = [ i for i in buy_stock if i not in stock_hold_now ]  
    # 需要调仓的股票：已有持仓且不符合卖出条件的股票
    stock_to_adjust=[ i for i in stock_hold_now if i not in sell_stock ]
    
    # 如果有卖出信号
    if len(stock_to_sell)>0:
        for instrument in stock_to_sell:
            if instrument in current_stoploss_stock:
                continue
            sid = context.symbol(instrument) # 将标的转化为equity格式
            cur_position = context.portfolio.positions[sid].amount # 持仓
            if cur_position > 0 and data.can_trade(sid):
                context.order_target_percent(sid, 0) # 全部卖出 
                # 因为设置的是早盘卖出早盘买入，需要根据卖出的股票更新可用现金；如果设置尾盘卖出早盘买入，则不需更新可用现金(可以删除下面的语句)
                cash_for_buy += stock_hold_now[instrument]
    
    # 如果有买入信号/有持仓
    if len(stock_to_buy)>0:
        weight = 1/len(set(stock_to_buy+stock_to_adjust)) # 每只股票的比重为等资金比例持有
        for instrument in set(stock_to_buy+stock_to_adjust):
            sid = context.symbol(instrument) # 将标的转化为equity格式
            if  data.can_trade(sid):
                context.order_target_value(sid, weight*cash_for_buy) # 买入
# 回测引擎：准备数据，只执行一次
def m3_prepare_bigquant_run(context):
    # 加载预测数据
    df = context.options['data'].read_df()

    # 函数：求满足开仓条件的股票列表
    def open_pos_con(df):
        return list(df[df['buy_condition']>0].instrument)

    # 函数：求满足平仓条件的股票列表
    def close_pos_con(df):
        return list(df[df['sell_condition']>0].instrument)

    # 每日买入股票的数据框
    context.daily_stock_buy= df.groupby('date').apply(open_pos_con)
    # 每日卖出股票的数据框
    context.daily_stock_sell= df.groupby('date').apply(close_pos_con)
# 回测引擎：初始化函数，只执行一次
def m3_initialize_bigquant_run(context):

    # 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))


m1 = 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
buy_condition=where(mean(close_0,5)>mean(close_0,50),1,0)
sell_condition=where(mean(close_0,5)<mean(close_0,50),1,0)""",
    m_cached=False
)

m2 = M.instruments.v2(
    start_date=T.live_run_param('trading_date', '2012-05-29'),
    end_date=T.live_run_param('trading_date', '2017-11-08'),
    market='CN_STOCK_A',
    instrument_list='600519.SHA',
    max_count=0
)

m7 = M.general_feature_extractor.v7(
    instruments=m2.data,
    features=m1.data,
    start_date='',
    end_date='',
    before_start_days=30
)

m8 = M.derived_feature_extractor.v3(
    input_data=m7.data,
    features=m1.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False
)

m6 = M.dropnan.v1(
    input_data=m8.data
)

m3 = M.trade.v4(
    instruments=m2.data,
    options_data=m6.data,
    start_date='',
    end_date='',
    handle_data=m3_handle_data_bigquant_run,
    prepare=m3_prepare_bigquant_run,
    initialize=m3_initialize_bigquant_run,
    volume_limit=0.025,
    order_price_field_buy='open',
    order_price_field_sell='open',
    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=''
)

[2019-01-24 10:28:38.829416] INFO: bigquant: input_features.v1 开始运行..
[2019-01-24 10:28:38.835972] INFO: bigquant: input_features.v1 运行完成[0.006565s].
[2019-01-24 10:28:38.838659] INFO: bigquant: instruments.v2 开始运行..
[2019-01-24 10:28:38.842732] INFO: bigquant: 命中缓存
[2019-01-24 10:28:38.843744] INFO: bigquant: instruments.v2 运行完成[0.005082s].
[2019-01-24 10:28:38.849113] INFO: bigquant: general_feature_extractor.v7 开始运行..
[2019-01-24 10:28:38.853107] INFO: bigquant: 命中缓存
[2019-01-24 10:28:38.853925] INFO: bigquant: general_feature_extractor.v7 运行完成[0.004821s].
[2019-01-24 10:28:38.856613] INFO: bigquant: derived_feature_extractor.v3 开始运行..
[2019-01-24 10:28:38.934132] INFO: derived_feature_extractor: 提取完成 buy_condition=where(mean(close_0,5)>mean(close_0,50),1,0), 0.008s
[2019-01-24 10:28:38.943099] INFO: derived_feature_extractor: 提取完成 sell_condition=where(mean(close_0,5)<mean(close_0,50),1,0), 0.008s
[2019-01-24 10:28:38.963278] INFO: derived_feature_extractor: /y_2012, 165
[2019-01-24 10:28:38.989117] INFO: derived_feature_extractor: /y_2013, 238
[2019-01-24 10:28:39.025541] INFO: derived_feature_extractor: /y_2014, 245
[2019-01-24 10:28:39.054356] INFO: derived_feature_extractor: /y_2015, 244
[2019-01-24 10:28:39.082826] INFO: derived_feature_extractor: /y_2016, 244
[2019-01-24 10:28:39.106189] INFO: derived_feature_extractor: /y_2017, 207
[2019-01-24 10:28:39.155087] INFO: bigquant: derived_feature_extractor.v3 运行完成[0.298437s].
[2019-01-24 10:28:39.158915] INFO: bigquant: dropnan.v1 开始运行..
[2019-01-24 10:28:39.240877] INFO: dropnan: /y_2012, 165/165
[2019-01-24 10:28:39.268540] INFO: dropnan: /y_2013, 238/238
[2019-01-24 10:28:39.298469] INFO: dropnan: /y_2014, 245/245
[2019-01-24 10:28:39.341826] INFO: dropnan: /y_2015, 244/244
[2019-01-24 10:28:39.388205] INFO: dropnan: /y_2016, 244/244
[2019-01-24 10:28:39.436448] INFO: dropnan: /y_2017, 207/207
[2019-01-24 10:28:39.451129] INFO: dropnan: 行数: 1343/1343
[2019-01-24 10:28:39.454232] INFO: bigquant: dropnan.v1 运行完成[0.295272s].
[2019-01-24 10:28:39.478837] INFO: bigquant: backtest.v8 开始运行..
[2019-01-24 10:28:39.481688] INFO: bigquant: biglearning backtest:V8.1.6
[2019-01-24 10:28:42.204641] INFO: bigquant: product_type:stock by specified
[2019-01-24 10:28:53.958079] INFO: bigquant: 读取股票行情完成:1568
[2019-01-24 10:28:54.006757] INFO: algo: TradingAlgorithm V1.4.2
[2019-01-24 10:28:54.341509] INFO: algo: trading transform...
日期： 2012-08-16 股票： 600519.SHA 出现止损状况
日期： 2014-03-26 股票： 600519.SHA 出现止损状况
日期： 2014-05-05 股票： 600519.SHA 出现止损状况
日期： 2015-01-14 股票： 600519.SHA 出现止损状况
日期： 2015-02-02 股票： 600519.SHA 出现止损状况
日期： 2015-05-05 股票： 600519.SHA 出现止损状况
日期： 2015-06-04 股票： 600519.SHA 出现止损状况
日期： 2015-06-18 股票： 600519.SHA 出现止损状况
[2019-01-24 10:29:01.727346] INFO: Performance: Simulated 1327 trading days out of 1327.
[2019-01-24 10:29:01.729245] INFO: Performance: first open: 2012-05-29 09:30:00+00:00
[2019-01-24 10:29:01.739814] INFO: Performance: last close: 2017-11-08 15:00:00+00:00

[2019-01-24 10:29:04.332712] INFO: bigquant: backtest.v8 运行完成[24.853869s].