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# 本代码由可视化策略环境自动生成 2022年2月4日 22:10
# 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 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))
    context.stock_max_num = 5 # 最多同时持有20只股票

# 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):
    # 回测引擎：每日数据处理函数，每天执行一次
    today = data.current_dt.strftime('%Y-%m-%d') # 日期
    # 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和对应的最新市值
    positions = {e.symbol: p.amount * p.last_sale_price
                 for e, p in context.portfolio.positions.items()}
    stock_hold_now = {e.symbol: p.amount * p.last_sale_price
                 for e, p in context.perf_tracker.position_tracker.positions.items()}
    hold_num=len(stock_hold_now)
    
    # 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
    cash_for_buy = min(context.portfolio.cash, context.portfolio.portfolio_value*0.5)
    
    # 获取当日符合买入/卖出条件的股票列表
    try:
        buy_stock = context.daily_stock_buy[today]  # 当日符合买入条件的股票
    except:
        buy_stock=[]
    try:
        sell_stock = context.daily_stock_sell[today]  # 当日符合卖出条件的股票
    except:
        sell_stock = []

    # 需要卖出的股票:已有持仓中符合卖出条件的股票,且不在当日符合买入条件的股票
    stock_to_sell1 = [i for i in stock_hold_now if i in sell_stock]
    stock_to_sell = [i for i in stock_to_sell1 if i not in buy_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]

    # 2. ST股和退市股的卖出
    stock_sold = [] # 记录卖出的股票，防止多次卖出出现空单
    name_df = context.name_df
    name_today = name_df[name_df.date==today]
    #-------------------------- START: ST和退市股卖出 ---------------------  
    st_stock_list = []
    for instrument in positions.keys():
        try:
            instrument_name = name_today[name_today.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 or '*' 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 --------------------- 
    
    #------------------------------------------止损模块START--------------------------------------------
    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
    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*(1- 0.05)
            #计算当天return
            #!close_volume_data = data.history(context.symbol(i), fields=['close','volume'], bar_count=hold_days+6, frequency='1d')
            #close_1 = close_volume_data['close'][-2]
            #volume_rate=close_volume_data['volume'][-1]/close_volume_data['volume'][:-2].mean()
            #return_0 = stock_market_price/close_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(context.symbol(i), 0)    
                #stoploss_stock.append(i)
        #if len(stoploss_stock)>0:
            #print('日期：', today, '股票：', stoploss_stock, '出现跟踪止损状况')
    #-------------------------------------------止损模块END--------------------------------------------- 
    # 卖出

    for instrument in stock_to_sell:
        # 如果该股票停牌，则没法成交。因此需要用can_trade方法检查下该股票的状态
        # 如果返回真值，则可以正常下单，否则会出错
        # 因为stock是字符串格式，我们用symbol方法将其转化成平台可以接受的形式：Equity格式
        
        if data.can_trade(context.symbol(instrument)):
            
            # order_target_percent是平台的一个下单接口，表明下单使得该股票的权重为0，即卖出全部股票，可参考回测文档
            context.order_target(context.symbol(instrument), 0)
            # 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]
            #hold_num-=1
    
    #3Index = context.Index
    #Index_today = Index[Index.date == today]
    #if list(Index_today.price)[-1]<=0 and list(Index_today.amount)[-1] >=0:        
        #return
    # 当日还允许买入建仓的股票数目
    stock_can_buy_num = context.stock_max_num - hold_num
    stock_to_buy_num = min(stock_can_buy_num,len(stock_to_buy))
    
    # 如果当天没有买入的股票，就返回
    if stock_to_buy_num == 0:
        return
    
    """
    Index = context.Index
    Index_today = Index[Index.date == today]
    if list(Index_today.price)[-1]<=0 and list(Index_today.amount)[-1] >=0:        
        return
    """
    
    
    # 记录已经买入的股票数量
    buy_num = 0
    for instrument in stock_to_buy:
        # 使用当日可用现金等资金比例下单买入
        cash = cash_for_buy / stock_to_buy_num
        if data.can_trade(context.symbol(instrument)) and buy_num<stock_to_buy_num:
            # 整百下单
            today_price = data.current(symbol(instrument), ['close','adjust_factor'])
            buy_price =today_price['close'] / today_price['adjust_factor']
            buy_amount = int((cash/(buy_price*100)))
            if buy_amount > 0: 
                context.order_lots(symbol(instrument),buy_amount)
            
            #current_price = data.current(context.symbol(instrument), 'price')
            #amount = math.floor(cash / current_price / 100) * 100
            #context.order(context.symbol(instrument), amount)
            buy_num += 1


# 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)
    
    #大盘风控
    df = DataSource('bar1d_index_CN_STOCK_A').read(['000001.HIX'],start_date = '2005-11-01',end_date = None,fields = ['close','amount'])
    Index = pd.DataFrame()
    Index['date'],Index['price'],Index['amount'] = df.date,df.close.diff(4),df.amount.diff(2)
    context.Index = Index
    context.name_df = DataSource('instruments_CN_STOCK_A').read()

m1 = M.input_features.v1(
    features="""# #号开始的表示注释

buy_condition=where(rank(sum(amount_0*sign(close_0/close_1-close_0/open_0),5)/5)>0.99,1,0)*where(ts_min(close_0-ta_ma(close_0,10),10)>0,1,0)*where(amount_0/mean(amount_1,5)>1.5,1,0)*where(ts_max(st_status_0,10)<=0,1,0)


sell_condition=where(delta(ta_ma(close_0,5),1)<0,1,1)
"""
)

m2 = M.instruments.v2(
    start_date=T.live_run_param('trading_date', '2019-01-01'),
    end_date=T.live_run_param('trading_date', '2022-01-15'),
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m7 = M.general_feature_extractor.v7(
    instruments=m2.data,
    features=m1.data,
    start_date='',
    end_date='',
    before_start_days=200
)

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
)

m4 = M.dropnan.v2(
    input_data=m8.data
)

m5 = M.chinaa_stock_filter.v1(
    input_data=m4.data,
    index_constituent_cond=['全部'],
    board_cond=['上证主板', '深证主板', '创业板'],
    industry_cond=['全部'],
    st_cond=['正常'],
    delist_cond=['非退市'],
    output_left_data=False
)

m3 = M.trade.v4(
    instruments=m2.data,
    options_data=m5.data,
    start_date='',
    end_date='',
    initialize=m3_initialize_bigquant_run,
    handle_data=m3_handle_data_bigquant_run,
    prepare=m3_prepare_bigquant_run,
    volume_limit=0,
    order_price_field_buy='open',
    order_price_field_sell='open',
    capital_base=100000,
    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='000300.HIX'
)

[2022-02-04 22:10:04.198781] INFO: moduleinvoker: input_features.v1 开始运行..

[2022-02-04 22:10:04.237622] INFO: moduleinvoker: 命中缓存

[2022-02-04 22:10:04.239714] INFO: moduleinvoker: input_features.v1 运行完成[0.04099s].

[2022-02-04 22:10:04.249276] INFO: moduleinvoker: instruments.v2 开始运行..

[2022-02-04 22:10:04.256971] INFO: moduleinvoker: 命中缓存

[2022-02-04 22:10:04.258670] INFO: moduleinvoker: instruments.v2 运行完成[0.009379s].

[2022-02-04 22:10:04.301144] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2022-02-04 22:10:04.311452] INFO: moduleinvoker: 命中缓存

[2022-02-04 22:10:04.313528] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.012404s].

[2022-02-04 22:10:04.325025] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-02-04 22:10:04.335836] INFO: moduleinvoker: 命中缓存

[2022-02-04 22:10:04.337536] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.01252s].

[2022-02-04 22:10:04.349865] INFO: moduleinvoker: dropnan.v2 开始运行..

[2022-02-04 22:10:04.370775] INFO: moduleinvoker: 命中缓存

[2022-02-04 22:10:04.372991] INFO: moduleinvoker: dropnan.v2 运行完成[0.023123s].

[2022-02-04 22:10:04.388828] INFO: moduleinvoker: chinaa_stock_filter.v1 开始运行..

[2022-02-04 22:10:04.397683] INFO: moduleinvoker: 命中缓存

[2022-02-04 22:10:04.399528] INFO: moduleinvoker: chinaa_stock_filter.v1 运行完成[0.010705s].

[2022-02-04 22:10:11.154961] INFO: moduleinvoker: backtest.v8 开始运行..

[2022-02-04 22:10:11.169296] INFO: moduleinvoker: 命中缓存

[2022-02-04 22:10:13.457803] INFO: moduleinvoker: backtest.v8 运行完成[2.302848s].

[2022-02-04 22:10:13.459758] INFO: moduleinvoker: trade.v4 运行完成[9.046892s].

收益率-12.71%
年化收益率-4.53%
基准收益率57.0%
阿尔法-0.07
贝塔0.42
夏普比率-0.03
胜率0.48
盈亏比1.08
收益波动率36.22%
信息比率-0.03
最大回撤46.62%

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