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In [6]:

# 本代码由可视化策略环境自动生成 2022年8月22日 17:02
# 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 m8_initialize_bigquant_run(context):
    # 加载预测数据
    context.ranker_prediction = context.options['data'].read_df()

    # 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))
    # 预测数据，通过options传入进来，使用 read_df 函数，加载到内存 (DataFrame)
    # 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
    stock_count = 3
    # 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)])
    # 设置每只股票占用的最大资金比例
    context.max_cash_per_instrument = 0.3
    context.hold_days = 10

# 回测引擎：每日数据处理函数，每天执行一次
def m8_handle_data_bigquant_run(context, data):
    ranker_prediction = context.ranker_prediction[
        context.ranker_prediction.date == data.current_dt.strftime('%Y-%m-%d')]
 
    #------------------------------------------止赢模块START--------------------------------------------
    date = data.current_dt.strftime('%Y-%m-%d')
    positions = {e.symbol: p.cost_basis  for e, p in context.portfolio.positions.items()}
    # 新建当日止赢股票列表是为了handle_data 策略逻辑部分不再对该股票进行判断
    current_stopwin_stock = [] 
    if len(positions) > 0:
        for i in positions.keys():
            stock_cost = positions[i] 
            stock_market_price = data.current(context.symbol(i), 'price') 
            # 赚30%就止赢
            if (stock_market_price - stock_cost ) / stock_cost>= 0.25:   
                context.order_target_percent(context.symbol(i),0)     
                current_stopwin_stock.append(i)
                print('日期：',date,'股票：',i,'出现止盈状况')
    #-------------------------------------------止赢模块END---------------------------------------------
    #------------------------------------------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 * 0.20
            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--------------------------------------------------
     #-------------------------- START: ST和退市股卖出 ---------------------  
    st_stock_list = []
    for instrument in positions.keys():
        try:
            instrument_name = ranker_prediction[ranker_prediction.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:
                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 --------------------- 
    # 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
        # 如果有卖出信号
    if len(stock_to_sell)>0:
        for instrument in stock_to_sell:

            #----------这里加入股票判断，如果已经止盈/止损了就跳过此股票，避免二次卖出--------
            if instrument in current_stopwin_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 
                cash_for_buy += stock_hold_now[instrument]
    
    # 1. 资金分配
    # 平均持仓时间是hold_days，每日都将买入股票，每日预期使用 1/hold_days 的资金
    # 实际操作中，会存在一定的买入误差，所以在前hold_days天，等量使用资金；之后，尽量使用剩余资金（这里设置最多用等量的1.5倍）
    is_staging = context.trading_day_index < context.hold_days # 是否在建仓期间（前 hold_days 天）
    cash_avg = context.portfolio.portfolio_value / context.hold_days
    cash_for_buy = min(context.portfolio.cash, (1 if is_staging else 1.5) * cash_avg)
    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()}
    

    
    # 3. 生成买入订单：按StockRanker预测的排序，买入前面的stock_count只股票
    ranker_prediction = ranker_prediction[ranker_prediction.buy_condition>=1]
    buy_cash_weights = context.stock_weights
    buy_instruments = list(ranker_prediction.instrument[:len(buy_cash_weights)])
    max_cash_per_instrument = context.portfolio.portfolio_value * context.max_cash_per_instrument
    for i, instrument in enumerate(buy_instruments):
        cash = cash_for_buy * buy_cash_weights[i]
        if cash > max_cash_per_instrument - positions.get(instrument, 0):
            # 确保股票持仓量不会超过每次股票最大的占用资金量
            cash = max_cash_per_instrument - positions.get(instrument, 0)
        if cash > 0:
            context.order_value(context.symbol(instrument), cash)

# 回测引擎：准备数据，只执行一次
def m8_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)

m1 = M.input_features.v1(
    features="""# #号开始的表示注释，注释需单独一行
rps = group_rank(industry_sw_level2_0,return_10)
#dif
#nn_dif=ta_macd_macd_12_26_9_0/adjust_factor_0
#dea
#nn_dea=ta_macd_macdsignal_12_26_9_0/adjust_factor_0
#macd
#nn_macd=ta_macd_macdhist_12_26_9_0
#ma60
ma60=mean(close_0,60)
sc60=where((close_1<ma60)&(close_0>ma60),1,0)
dy60=where(close_0<ma60,1,0)
#30天首次上穿60
scsc60=sum(sc60,40)
dydy60=sum(dy60,40)
#N天内站住60
zz60=where(close_0>ma60,1,0)
tzz60=sum(zz60,7)
#
my1=where(scsc60==1,1,0)
my2=where(tzz60>3,1,0)
my3=where((close_0>open_0)&(close_0>ma60)&(volume_0/volume_1>=2)&(volume_0/volume_1<3),1,0)
my4=where(close_0/ma60<1.07,1,0)
my5=where(dydy60>30,1,0)
my=my1*my2*my3*my4*my5
buy_condition=where(my>0,1,0)
sell_condition=where(close_0/ma60>1.3,1,0)"""
)

m2 = M.instruments.v2(
    start_date='2018-01-01',
    end_date='2022-08-16',
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m3 = M.general_feature_extractor.v7(
    instruments=m2.data,
    features=m1.data,
    start_date='',
    end_date='',
    before_start_days=120
)

m5 = M.derived_feature_extractor.v3(
    input_data=m3.data,
    features=m1.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False,
    user_functions={}
)

m4 = M.chinaa_stock_filter.v1(
    input_data=m5.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
)

m10 = M.filter.v3(
    input_data=m4.data,
    expr='my>0',
    output_left_data=False
)

m9 = M.use_datasource.v1(
    instruments=m2.data,
    datasource_id='instruments_CN_STOCK_A',
    start_date='',
    end_date=''
)

m7 = M.join.v3(
    data1=m10.data,
    data2=m9.data,
    on='date,instrument',
    how='left',
    sort=False
)

m6 = M.sort.v5(
    input_ds=m7.data,
    sort_by='rps',
    group_by='date',
    keep_columns='--',
    ascending=False
)

m8 = M.trade.v4(
    instruments=m2.data,
    options_data=m6.sorted_data,
    start_date='',
    end_date='',
    initialize=m8_initialize_bigquant_run,
    handle_data=m8_handle_data_bigquant_run,
    prepare=m8_prepare_bigquant_run,
    volume_limit=0.025,
    order_price_field_buy='twap_1',
    order_price_field_sell='twap_8',
    capital_base=1000000,
    auto_cancel_non_tradable_orders=True,
    data_frequency='daily',
    price_type='真实价格',
    product_type='股票',
    plot_charts=True,
    backtest_only=False,
    benchmark='000300.HIX'
)

[2022-08-22 11:33:15.079707] INFO: moduleinvoker: input_features.v1 开始运行..

[2022-08-22 11:33:15.090512] INFO: moduleinvoker: 命中缓存

[2022-08-22 11:33:15.092372] INFO: moduleinvoker: input_features.v1 运行完成[0.012678s].

[2022-08-22 11:33:15.099205] INFO: moduleinvoker: instruments.v2 开始运行..

[2022-08-22 11:33:15.110608] INFO: moduleinvoker: 命中缓存

[2022-08-22 11:33:15.112608] INFO: moduleinvoker: instruments.v2 运行完成[0.013409s].

[2022-08-22 11:33:15.126899] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2022-08-22 11:33:15.139254] INFO: moduleinvoker: 命中缓存

[2022-08-22 11:33:15.141259] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.014372s].

[2022-08-22 11:33:15.150655] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-08-22 11:33:15.161624] INFO: moduleinvoker: 命中缓存

[2022-08-22 11:33:15.164167] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.01351s].

[2022-08-22 11:33:15.190472] INFO: moduleinvoker: chinaa_stock_filter.v1 开始运行..

[2022-08-22 11:33:15.203148] INFO: moduleinvoker: 命中缓存

[2022-08-22 11:33:15.205840] INFO: moduleinvoker: chinaa_stock_filter.v1 运行完成[0.015381s].

[2022-08-22 11:33:15.215652] INFO: moduleinvoker: filter.v3 开始运行..

[2022-08-22 11:33:15.227528] INFO: moduleinvoker: 命中缓存

[2022-08-22 11:33:15.229779] INFO: moduleinvoker: filter.v3 运行完成[0.014147s].

[2022-08-22 11:33:15.236418] INFO: moduleinvoker: use_datasource.v1 开始运行..

[2022-08-22 11:33:15.246511] INFO: moduleinvoker: 命中缓存

[2022-08-22 11:33:15.249704] INFO: moduleinvoker: use_datasource.v1 运行完成[0.013294s].

[2022-08-22 11:33:15.261303] INFO: moduleinvoker: join.v3 开始运行..

[2022-08-22 11:33:15.274988] INFO: moduleinvoker: 命中缓存

[2022-08-22 11:33:15.277449] INFO: moduleinvoker: join.v3 运行完成[0.016151s].

[2022-08-22 11:33:15.284850] INFO: moduleinvoker: sort.v5 开始运行..

[2022-08-22 11:33:15.296310] INFO: moduleinvoker: 命中缓存

[2022-08-22 11:33:15.298638] INFO: moduleinvoker: sort.v5 运行完成[0.013785s].

[2022-08-22 11:33:15.365493] INFO: moduleinvoker: backtest.v8 开始运行..

[2022-08-22 11:33:15.380407] INFO: moduleinvoker: 命中缓存

[2022-08-22 11:33:17.930978] INFO: moduleinvoker: backtest.v8 运行完成[2.565478s].

[2022-08-22 11:33:17.933322] INFO: moduleinvoker: trade.v4 运行完成[2.625668s].

[2022-08-22 11:33:17.998232] INFO: moduleinvoker: backtest.v8 开始运行..

[2022-08-22 11:33:18.003242] INFO: backtest: biglearning backtest:V8.6.2

[2022-08-22 11:33:18.004624] INFO: backtest: product_type:stock by specified

[2022-08-22 11:33:18.178685] INFO: moduleinvoker: cached.v2 开始运行..

[2022-08-22 11:33:18.188679] INFO: moduleinvoker: 命中缓存

[2022-08-22 11:33:18.190530] INFO: moduleinvoker: cached.v2 运行完成[0.011868s].

[2022-08-22 11:33:26.394158] INFO: algo: TradingAlgorithm V1.8.8

[2022-08-22 11:33:28.750709] INFO: algo: trading transform...

[2022-08-22 11:33:31.264095] INFO: algo: handle_splits get splits [dt:2020-07-16 00:00:00+00:00] [asset:Equity(2355 [600640.SHA]), ratio:0.9975621700286865]

[2022-08-22 11:33:31.265778] INFO: Position: position stock handle split[sid:2355, orig_amount:4400, new_amount:4410.0, orig_cost:18.89266586303711, new_cost:18.8466, ratio:0.9975621700286865, last_sale_price:20.459999084472656]

[2022-08-22 11:33:31.267413] INFO: Position: after split: PositionStock(asset:Equity(2355 [600640.SHA]), amount:4410.0, cost_basis:18.8466, last_sale_price:20.509998321533203)

[2022-08-22 11:33:31.269241] INFO: Position: returning cash: 15.3995

[2022-08-22 11:33:31.853025] INFO: algo: handle_splits get splits [dt:2021-06-28 00:00:00+00:00] [asset:Equity(5200 [300265.SZA]), ratio:0.9978564381599426]

[2022-08-22 11:33:31.855971] INFO: Position: position stock handle split[sid:5200, orig_amount:8100, new_amount:8117.0, orig_cost:9.36199951171875, new_cost:9.3419, ratio:0.9978564381599426, last_sale_price:9.3100004196167]

[2022-08-22 11:33:31.858224] INFO: Position: after split: PositionStock(asset:Equity(5200 [300265.SZA]), amount:8117.0, cost_basis:9.3419, last_sale_price:9.329999923706055)

[2022-08-22 11:33:31.860072] INFO: Position: returning cash: 3.7254

[2022-08-22 11:33:32.680697] INFO: Performance: Simulated 1123 trading days out of 1123.

[2022-08-22 11:33:32.682899] INFO: Performance: first open: 2018-01-02 09:30:00+00:00

[2022-08-22 11:33:32.684597] INFO: Performance: last close: 2022-08-16 15:00:00+00:00

[2022-08-22 11:33:41.838127] INFO: moduleinvoker: backtest.v8 运行完成[23.839879s].

[2022-08-22 11:33:41.840131] INFO: moduleinvoker: trade.v4 运行完成[23.899742s].

收益率64.92%
年化收益率11.88%
基准收益率3.65%
阿尔法0.1
贝塔0.55
夏普比率0.57
胜率0.41
盈亏比1.78
收益波动率17.17%
信息比率0.04
最大回撤28.36%

bigcharts-data-start/{"__type":"tabs","__id":"bigchart-a3822ad4eef64a4596f193c5821b39fc"}/bigcharts-data-end

66666.66666666666
66666.66666666666
66666.66666666666
66666.66666666666
66666.66666666666
66666.66666666666
66666.66666666666
66176.1789666748
67954.97933649698
59306.82262673378
59054.98703821898
日期： 2019-02-22 股票： 600643.SHA 出现止盈状况
66395.60688915013
66395.60688915013
66395.60688915013
66395.60688915013
65046.38968275468
65324.5838579154
64340.53448009253
64641.96608287573
日期： 2020-01-08 股票： 002160.SZA 出现止盈状况
日期： 2020-02-05 股票： 600400.SHA 出现止盈状况
83699.06868697883
83699.06868697883
83699.06868697883
83699.06868697883

85088.95910268929
85009.19776684143
85009.19776684143
85009.19776684143
72084.99109259623
70543.8066721759
日期： 2021-03-25 股票： 601028.SHA 出现止盈状况
76716.82098619797
76807.14605402964
76273.973405053

75963.5353985853
78424.45833644754
80110.90463707174
83337.83154784725
82107.69714342321
83212.34691318081
83101.6545388735
83101.6545388735
83101.6545388735
日期： 2022-07-20 股票： 002669.SZA 出现止盈状况
90045.83107802755

收益率36.57%
年化收益率7.24%
基准收益率3.65%
阿尔法0.05
贝塔0.13
夏普比率0.36
胜率0.53
盈亏比1.62
收益波动率13.93%
信息比率0.01
最大回撤24.58%

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