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# 本代码由可视化策略环境自动生成 2022年4月8日 14:59
# 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):

    # 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.set_commission(PerOrder(buy_cost=0.00001, sell_cost=0.0001, min_cost=1))
    
    # 设置买入的股票数量，这里买入预测股票列表排名靠前的5只
    context.stock_count = 1
    # 每只的股票的权重，如下的权重分配会使得靠前的股票分配多一点的资金，[0.339160, 0.213986, 0.169580, ..]
    #context.stock_weights = T.norm([1 / math.log(i + 2) for i in range(0, stock_count)])
    # 每只股票的权重平均分配
    context.stock_weights = 1/context.stock_count
    # 设置每只股票占用的最大资金比例
    context.max_cash_per_instrument = 1
    context.options['hold_days'] = 0


# 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):
    today = data.current_dt.strftime('%Y-%m-%d')
    equities = {e.symbol: p for e, p in context.portfolio.positions.items() if p.amount>0}
    stock_now = len(equities); #获取当前持仓股票数量
    stock_count = context.stock_count
    
    # 按日期过滤得到今日的预测数据
    # 加载预测数据
    df = context.options['data'].read_df()
    df_today = df[df.date == data.current_dt.strftime('%Y-%m-%d')]
    df_today.set_index('instrument')
    
    
    now_stock = []
    sell_stock = []
       
    try:
        buy_list = context.daily_buy_stock[today]
    except:
        buy_list = []

    
    # 1. 资金分配
    #is_staging = context.trading_day_index < context.options['hold_days'] # 是否在建仓期间（前 hold_days 天） 
    #stock_cash = context.portfolio.portfolio_value/stock_count
    #cash_avg = context.portfolio.portfolio_value
    #cash_for_buy = min(context.portfolio.cash,  stock_cash)
    #cash_for_sell = cash_avg - (context.portfolio.cash - cash_for_buy)
    
    positions = {e.symbol: p.amount * p.last_sale_price
                 for e, p in context.perf_tracker.position_tracker.positions.items()}
    
            
    #if not is_staging :
    if 1==1 :    
        if len(equities) > 0:
            for i in equities.keys():
                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
                if hold_days >= context.options['hold_days'] and i not in buy_list :
                    print('日期:',today,'卖出2:',i)
                    context.order_target(context.symbol(i), 0)
                    sell_stock.append(i)
                    stock_now = stock_now -1
                    #print('日期：', today, '股票：', i, ' 卖出')
                 
# 3. 生成买入订单
    buy_num = stock_count - stock_now
    #leiJiShouYi=context.portfolio.returns
    leiJiShouYi=context.portfolio.portfolio_value/100000-1
    print('日期：', today, '累计收益：', leiJiShouYi)
    print('日期：', today, '账户总资产：', context.portfolio.portfolio_value)  
    print('日期：', today, '账户初始金额：', context.portfolio. starting_cash)  
    if len(buy_list)>0 and buy_num>0:
        print('日期：', today, '选出股票数量：', len(buy_list))
        buy_instruments = [i for i in buy_list if i not in now_stock][:15]
        cash_for_buy = context.portfolio.cash
        #是否存在重仓股
        isHasZC=0
        for i, instrument in enumerate(buy_instruments):
            try :
                my_zcs=list(df_today[df_today.instrument==instrument]['my_zc'])
                my_zc=my_zcs[0]
            except :
                my_zc=0
            if my_zc==1:
                isHasZC=1
                break
        if isHasZC==1:
            print('日期：', today, '选出重仓股票数量：', len(buy_list))
        #循环买入
        for i, instrument in enumerate(buy_instruments):
        #是否为ZC股
            try :
                my_zcs=list(df_today[df_today.instrument==instrument]['my_zc'])
                my_zc=my_zcs[0]
            except :
                my_zc=0
            #累计收益小于30%
            if leiJiShouYi<-10 :
                if isHasZC==1 :
                    if my_zc==1:
                        current_price = data.current(context.symbol(instrument), 'price')            
                        if cash_for_buy>0 and data.can_trade(context.symbol(instrument)):
                            amount = math.floor(cash_for_buy / current_price / 100) * 100
                            context.order(context.symbol(instrument), amount)
                            print('日期:',today,'买入:',instrument)
                            break
                        else :
                            print('日期:',today,'无资金或不能交易未买入:',instrument)
                else :
                    current_price = data.current(context.symbol(instrument), 'price')
                    if cash_for_buy>0 and data.can_trade(context.symbol(instrument)):           
                        amount = math.floor(cash_for_buy / current_price / 100) * 100
                        context.order(context.symbol(instrument), amount)
                        print('日期:',today,'买入:',instrument)
                        break
                    else :
                        print('日期:',today,'无资金或不能交易未买入:',instrument)
            else :
                if my_zc==0 :
                    current_price = data.current(context.symbol(instrument), 'price')            
                    if cash_for_buy>0 and data.can_trade(context.symbol(instrument)):           
                        amount = math.floor(cash_for_buy / current_price / 100) * 100
                        context.order(context.symbol(instrument), amount)
                        print('日期:',today,'买入:',instrument)
                        break
                    else :
                        print('日期:',today,'无资金或不能交易未买入:',instrument)
    

# 回测引擎：准备数据，只执行一次
def m4_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)
    
    # 每日卖出股票的数据框
    context.daily_sell_stock= df.groupby('date').apply(close_pos_con)  
    # 每日买入股票的数据框
    context.daily_buy_stock= df.groupby('date').apply(open_pos_con)  



m1 = M.instruments.v2(
    start_date='2016-03-01',
    end_date='2020-03-31',
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m2 = M.advanced_auto_labeler.v2(
    instruments=m1.data,
    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：1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, -1) / 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)
""",
    start_date='',
    end_date='',
    benchmark='000300.SHA',
    drop_na_label=True,
    cast_label_int=True
)

m3 = M.input_features.v1(
    features="""return_5/return_20#43: 5天的收益率/20天的收益率
rank_amount_5#45：最近5日的成交额排名
avg_turn_10#46：平均10天的换手率
market_cap_float_0<280000000000#47：流通市值<280亿
pe_ttm_0>0#48：ttm pe市盈率要大于0
pb_lf_0#49:市净率
sum(mf_net_pct_main_0>0.12,30)>11#50：统计30天内主力流入占比大于12%的天数
fs_roa_ttm_0>5#51：总资产报酬率roa要大于5
fs_cash_ratio_0#52:现金流量
close_0>ts_max(close_0,56)#53：当日收盘价破56天最高价（创新高）
ta_sma_10_0/ta_sma_30_0#56:   10天的sma线/30天的sma线
ta_sar_0# 58：SAR抛物线指标
swing_volatility_10_0/swing_volatility_60_0 #59: 10天的波动率/60天的波动率
ta_cci_14_0 #60：CCI -14天的指标
rank_return_3  #61:   3天收益率的排名
mf_net_amount_0>mf_net_amount_1  #62:  判断当日的资金流入净额>昨日资金流入净额
mf_net_amount_xl_0>mean(mf_net_amount_xl_0, 30)# 64：当天的超大单流入净量>平均30天内的超大单流入净量（30天超大单MA线）
(close_0-close_30)/close_30>1.25#  66:30天内的涨幅大于125%
(close_0-close_5)/close_5>1.16#  67：5天内的涨幅>116%
list_days_0>365#  68：上市天数>365天
ta_bbands_middleband_28_0 #69：布林带28天均线
cond0=sum(price_limit_status_0==3,80)>5  #70：统计80天内涨停板的次数大于5
cond1=ta_trix(close_0, derive='long')#当日三重平滑平均线,多头
cond2=ta_dma(close_0, 'long')#当日平行线差,多头
cond3=low_0 > mean(close_0,20)#当日最低价，站稳20日均线
cond5=close_0>open_0#当日红柱收盘
cond6=st_status_0==0#过滤正常股票，滤除暂停上市股票
cond7=ta_macd(close_0,'long')#指数平滑移动平均线,多头
cond8=ta_ma(close_0,5, derive='long')#5日移动平均线,多头
cond9=sum(ta_macd_dif(close_0,2,4,4),5)>sum(ta_macd_dea(close_0,2,4,4),5)#MACD的红柱量能线要大于绿色量能线
#cond10= (close_0-close_1)/close_1 >0.04#当日涨幅大于4%
cond11=((avg_amount_5-avg_amount_21)/avg_amount_21)>0#5日成交均值大于21日均值
cond12=((avg_amount_5-avg_amount_28)/avg_amount_28)>0#5日成交均值大于28日均值
cond13=((amount_0-avg_amount_5)/avg_amount_5)>0#当日成交量大于5日成交均值"""
)

m15 = M.general_feature_extractor.v7(
    instruments=m1.data,
    features=m3.data,
    start_date='',
    end_date='',
    before_start_days=90
)

m16 = M.derived_feature_extractor.v3(
    input_data=m15.data,
    features=m3.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False
)

m7 = M.join.v3(
    data1=m2.data,
    data2=m16.data,
    on='date,instrument',
    how='inner',
    sort=False
)

m13 = M.dropnan.v1(
    input_data=m7.data
)

m6 = M.stock_ranker_train.v5(
    training_ds=m13.data,
    features=m3.data,
    learning_algorithm='排序',
    number_of_leaves=30,
    minimum_docs_per_leaf=1000,
    number_of_trees=20,
    learning_rate=0.1,
    max_bins=1023,
    feature_fraction=1,
    m_lazy_run=False
)

m9 = M.instruments.v2(
    start_date=T.live_run_param('trading_date', '2020-04-01'),
    end_date=T.live_run_param('trading_date', '2022-04-01'),
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m17 = M.general_feature_extractor.v7(
    instruments=m9.data,
    features=m3.data,
    start_date='',
    end_date='',
    before_start_days=90
)

m18 = M.derived_feature_extractor.v3(
    input_data=m17.data,
    features=m3.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False
)

m14 = M.dropnan.v1(
    input_data=m18.data
)

m8 = M.stock_ranker_predict.v5(
    model=m6.model,
    data=m14.data,
    m_lazy_run=False
)

m4 = M.trade.v4(
    instruments=m9.data,
    options_data=m8.predictions,
    start_date='',
    end_date='',
    initialize=m4_initialize_bigquant_run,
    handle_data=m4_handle_data_bigquant_run,
    prepare=m4_prepare_bigquant_run,
    before_trading_start=#判断一下如果当日涨停，则取消卖单
#      if  df_price_limit_status[df_price_limit_status.instrument==ins].price_limit_status_0.loc[today]>2 and _order.amount<0:
#          cancel_order(_order)
#          print(today,'尾盘涨停取消卖单',ins) ,
    volume_limit=0,
    order_price_field_buy='open',
    order_price_field_sell='close',
    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='000300.HIX'
)

  File "<ipython-input-19-28758254b2cc>", line 315
    volume_limit=0,
                ^
SyntaxError: invalid syntax