StockRanker多因子选股策略¶

In [9]:

# 本代码由可视化策略环境自动生成 2022年10月23日 22:14
# 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。


# 交易引擎：初始化函数，只执行一次
def m6_initialize_bigquant_run(context):
    df = DataSource("bar1d_index_CN_STOCK_A").read(instruments="000300.HIX",start_date="2021-01-01",end_date="2021-08-01")
    df["ma"] = df.close.rolling(5).mean()
    df["signal"] = df.apply(lambda x:1 if x.close>x.ma else 0,axis=1)
    df["signal"] = df["signal"].shift(1) #取昨日的收盘信号
    df=df[["date","signal"]]
    #信号数据
    context.signal_df = df
    #每支股票占比
    context.order_pct = 0.01
    #获取预测股票集
    context.to_buy = context.options['data'].read()

# 交易引擎：每个单位时间开盘前调用一次。
def m6_before_trading_start_bigquant_run(context, data):
    now = data.current_dt.strftime('%Y-%m-%d')
    context.today = data.current_dt.strftime('%Y-%m-%d')
    context.signal = context.signal_df[context.signal_df.date==now]["signal"].iloc[0]
    context.handle_flag = 0 #由于是分钟回测，每天只需要处理一次买卖
    context.sold_stock_list = []
    context.position_check = context.get_positions()
    print('日期{} 持仓 {} -----------'.format(now, context.position_check))

# 交易引擎：bar数据处理函数，每个单位执行一次
def m6_handle_tick_bigquant_run(context, data):
    pass

#卖出函数
def sell_stock(context,data,msg):
    #获取当前所有持仓
    stock_hold_now = context.get_account_positions()
    for instr in stock_hold_now:
        if instr not in context.sold_stock_list:
            #卖出可用仓位（可能有今仓）
            position = context.get_position(instr).avail_qty
            if(position>0):
                #最新价格
                price = data.current(instr, 'close')
                context.order(instr, -position, price, order_type=OrderType.MARKET)
                context.sold_stock_list.append(instr)
                print("{}卖出{} {}".format(msg,instr,position))

# 交易引擎：bar数据处理函数，每个单位执行一次
def m6_handle_data_bigquant_run(context, data):
    
    #signal为0开盘卖
    if context.signal == 0:
        msg = context.today+" 开盘"
        sell_stock(context,data,msg)
        
    current_stopwin_stock = []
    current_stoploss_stock = []
    if len(context.position_check) > 0:
        #------------------------START:止赢止损模块(含建仓期)---------------
        positions_cost={e:p.cost_price for e,p in context.get_positions().items()}
        avail_positions = {e: p.avail_qty for e, p in context.get_positions().items()}
        for instrument in positions_cost.keys():
            s = context.get_position(instrument).cost_price
            stock_cost=positions_cost[instrument]
            stock_market_price=data.current(context.symbol(instrument),'price')
            if stock_market_price/stock_cost-1>=0.05 and avail_positions[instrument] != 0:
                context.order_target(instrument, 0, order_type=OrderType.MARKET)
                print('止盈成功, 止盈标的{}'.format(instrument))
                current_stopwin_stock.append(instrument)
            elif stock_market_price/stock_cost-1 <= -0.02 and avail_positions[instrument] != 0:
                context.order_target(instrument, 0, order_type=OrderType.MARKET)
                print('止损成功, 止损标的{}'.format(instrument))
                current_stoploss_stock.append(instrument)
        if len(current_stopwin_stock)>0:
#             print(context.today,'止盈股票列表',current_stopwin_stock)
            context.sold_stock_list += current_stopwin_stock
        if len(current_stoploss_stock)>0:
#             print(context.today,'止损股票列表',current_stoploss_stock)
            context.sold_stock_list += current_stoploss_stock
        #--------------------------END: 止赢止损模块--------------------------
    

    #signal为1尾盘卖
    if context.signal == 1:
        cur_date = data.current_dt
        cur_hm =  cur_date.strftime('%H:%M')
        if(cur_hm=="14:55"):
            msg = str(cur_date)+" 尾盘"
            sell_stock(context,data,msg)
        
    #每天只处理一次
    if context.handle_flag==1:
        return
    
    #买入预测集的前5只股票
    now_data = context.to_buy[context.to_buy['date']==context.today]
    today_to_buy = []
    if not now_data.empty:
        today_to_buy = now_data.instrument[:5].to_list()
    print(context.today,"=======早盘计划买入股票 {}".format(today_to_buy))
    
    # 获取账户资金
    total_portfolio = context.portfolio.portfolio_value

    for instr in today_to_buy:
        if instr not in context.sold_stock_list:
            #最新价格
            price = data.current(instr, 'close')

            #计算买入此股票的数量，不要超过总资金的某个比例
            context.order_value(instr, total_portfolio*context.order_pct, price, order_type=OrderType.MARKET)
            print("买入{}".format(instr))
            
    context.handle_flag = 1


    


# 交易引擎：成交回报处理函数，每个成交发生时执行一次
def m6_handle_trade_bigquant_run(context, data):
    pass

# 交易引擎：委托回报处理函数，每个委托变化时执行一次
def m6_handle_order_bigquant_run(context, data):
    pass

# 交易引擎：盘后处理函数，每日盘后执行一次
def m6_after_trading_bigquant_run(context, data):
    pass



m1 = M.instruments.v2(
    start_date='2021-01-01',
    end_date='2021-02-01',
    market='CN_STOCK_A',
    instrument_list="""000021.SZA
002460.SZA
000027.SZA
000050.SZA
000333.SZA
000338.SZA
000521.SZA
000538.SZA
000615.SZA
000625.SZA
000626.SZA
000651.SZA
000661.SZA
000713.SZA
000733.SZA
000768.SZA
000798.SZA
000858.SZA
000928.SZA
000963.SZA
000995.SZA
002007.SZA
002055.SZA
002074.SZA
002092.SZA
002125.SZA
002151.SZA
002156.SZA
002162.SZA
002163.SZA
002179.SZA
002184.SZA
002185.SZA
002190.SZA
002192.SZA
002221.SZA
002237.SZA
002240.SZA
002245.SZA
002271.SZA
002340.SZA
002352.SZA
002371.SZA
002386.SZA
002389.SZA
002407.SZA
002415.SZA
002430.SZA
002466.SZA
002497.SZA
002529.SZA
002557.SZA
002594.SZA
002601.SZA
002607.SZA
002625.SZA
002668.SZA
002709.SZA
002756.SZA
002799.SZA
002812.SZA
002813.SZA
002821.SZA
002906.SZA
002920.SZA
300003.SZA
300014.SZA
300015.SZA
300034.SZA
300037.SZA
300059.SZA
300122.SZA
300124.SZA
300142.SZA
300244.SZA
300251.SZA
300274.SZA
300316.SZA
300339.SZA
300340.SZA
300347.SZA
300357.SZA
300433.SZA
300450.SZA
300477.SZA
300496.SZA
300529.SZA
300558.SZA
300567.SZA
300576.SZA
300581.SZA
300595.SZA
300601.SZA
300604.SZA
300623.SZA
300648.SZA
300661.SZA
300685.SZA
300690.SZA
300699.SZA
300719.SZA
300722.SZA
300726.SZA
300750.SZA
300759.SZA
300760.SZA
300763.SZA
300769.SZA
600016.SHA
600031.SHA
600089.SHA
600110.SHA
600183.SHA
600196.SHA
600198.SHA
600237.SHA
600256.SHA
600276.SHA
600295.SHA
600305.SHA
600309.SHA
600316.SHA
600325.SHA
600392.SHA
600398.SHA
600418.SHA
600438.SHA
600460.SHA
600499.SHA
600516.SHA
600570.SHA
600584.SHA
600585.SHA
600596.SHA
600660.SHA
600685.SHA
600690.SHA
600699.SHA
600703.SHA
600733.SHA
600745.SHA
600760.SHA
600763.SHA
600805.SHA
600809.SHA
600862.SHA
600864.SHA
600882.SHA
600886.SHA
600887.SHA
600893.SHA
600977.SHA
600988.SHA
601012.SHA
601100.SHA
601611.SHA
601633.SHA
601865.SHA
601866.SHA
601869.SHA
601888.SHA
601899.SHA
601901.SHA
601919.SHA
603005.SHA
603019.SHA
603025.SHA
603027.SHA
603185.SHA
603197.SHA
603259.SHA
603260.SHA
603267.SHA
603501.SHA
603517.SHA
603605.SHA
603650.SHA
603678.SHA
603707.SHA
603799.SHA
603806.SHA
603882.SHA""",
    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：5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, -5) / 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="""# #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
pe_ttm_0
"""
)

m15 = M.general_feature_extractor.v7(
    instruments=m1.data,
    features=m3.data,
    start_date='',
    end_date='',
    before_start_days=0
)

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
)

m4 = M.stock_ranker_train.v6(
    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,
    data_row_fraction=1,
    plot_charts=True,
    ndcg_discount_base=1,
    m_lazy_run=False
)

m9 = M.instruments.v2(
    start_date=T.live_run_param('trading_date', '2021-02-01'),
    end_date=T.live_run_param('trading_date', '2021-03-01'),
    market='CN_STOCK_A',
    instrument_list="""000021.SZA
002460.SZA
000027.SZA
000050.SZA
000333.SZA
000338.SZA
000521.SZA
000538.SZA
000615.SZA
000625.SZA
000626.SZA
000651.SZA
000661.SZA
000713.SZA
000733.SZA
000768.SZA
000798.SZA
000858.SZA
000928.SZA
000963.SZA
000995.SZA
002007.SZA
002055.SZA
002074.SZA
002092.SZA
002125.SZA
002151.SZA
002156.SZA
002162.SZA
002163.SZA
002179.SZA
002184.SZA
002185.SZA
002190.SZA
002192.SZA
002221.SZA
002237.SZA
002240.SZA
002245.SZA
002271.SZA
002340.SZA
002352.SZA
002371.SZA
002386.SZA
002389.SZA
002407.SZA
002415.SZA
002430.SZA
002466.SZA
002497.SZA
002529.SZA
002557.SZA
002594.SZA
002601.SZA
002607.SZA
002625.SZA
002668.SZA
002709.SZA
002756.SZA
002799.SZA
002812.SZA
002813.SZA
002821.SZA
002906.SZA
002920.SZA
300003.SZA
300014.SZA
300015.SZA
300034.SZA
300037.SZA
300059.SZA
300122.SZA
300124.SZA
300142.SZA
300244.SZA
300251.SZA
300274.SZA
300316.SZA
300339.SZA
300340.SZA
300347.SZA
300357.SZA
300433.SZA
300450.SZA
300477.SZA
300496.SZA
300529.SZA
300558.SZA
300567.SZA
300576.SZA
300581.SZA
300595.SZA
300601.SZA
300604.SZA
300623.SZA
300648.SZA
300661.SZA
300685.SZA
300690.SZA
300699.SZA
300719.SZA
300722.SZA
300726.SZA
300750.SZA
300759.SZA
300760.SZA
300763.SZA
300769.SZA
600016.SHA
600031.SHA
600089.SHA
600110.SHA
600183.SHA
600196.SHA
600198.SHA
600237.SHA
600256.SHA
600276.SHA
600295.SHA
600305.SHA
600309.SHA
600316.SHA
600325.SHA
600392.SHA
600398.SHA
600418.SHA
600438.SHA
600460.SHA
600499.SHA
600516.SHA
600570.SHA
600584.SHA
600585.SHA
600596.SHA
600660.SHA
600685.SHA
600690.SHA
600699.SHA
600703.SHA
600733.SHA
600745.SHA
600760.SHA
600763.SHA
600805.SHA
600809.SHA
600862.SHA
600864.SHA
600882.SHA
600886.SHA
600887.SHA
600893.SHA
600977.SHA
600988.SHA
601012.SHA
601100.SHA
601611.SHA
601633.SHA
601865.SHA
601866.SHA
601869.SHA
601888.SHA
601899.SHA
601901.SHA
601919.SHA
603005.SHA
603019.SHA
603025.SHA
603027.SHA
603185.SHA
603197.SHA
603259.SHA
603260.SHA
603267.SHA
603501.SHA
603517.SHA
603605.SHA
603650.SHA
603678.SHA
603707.SHA
603799.SHA
603806.SHA
603882.SHA""",
    max_count=0
)

m17 = M.general_feature_extractor.v7(
    instruments=m9.data,
    features=m3.data,
    start_date='',
    end_date='',
    before_start_days=60
)

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=m4.model,
    data=m14.data,
    m_lazy_run=False,
    m_cached=False
)

m6 = M.hftrade.v2(
    instruments=m9.data,
    options_data=m8.predictions,
    start_date='',
    end_date='',
    initialize=m6_initialize_bigquant_run,
    before_trading_start=m6_before_trading_start_bigquant_run,
    handle_tick=m6_handle_tick_bigquant_run,
    handle_data=m6_handle_data_bigquant_run,
    handle_trade=m6_handle_trade_bigquant_run,
    handle_order=m6_handle_order_bigquant_run,
    after_trading=m6_after_trading_bigquant_run,
    capital_base=1000000,
    frequency='minute',
    price_type='真实价格',
    product_type='股票',
    before_start_days='0',
    volume_limit=1,
    order_price_field_buy='open',
    order_price_field_sell='close',
    benchmark='000300.HIX',
    plot_charts=True,
    disable_cache=False,
    replay_bdb=False,
    show_debug_info=False,
    backtest_only=False
)

[2022-10-23 22:12:44.874218] INFO: moduleinvoker: instruments.v2 开始运行..

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[2022-10-23 22:12:45.735529] INFO: StockRanker预测: /y_2020 ..

[2022-10-23 22:12:46.215389] INFO: StockRanker预测: /y_2021 ..

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[2022-10-23 22:12:46.813661] INFO: moduleinvoker: hfbacktest.v1 开始运行..

[2022-10-23 22:12:46.820819] INFO: hfbacktest: biglearning V1.4.19

[2022-10-23 22:12:46.823367] INFO: hfbacktest: bigtrader v1.9.8 2022-10-10

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[2022-10-23 22:12:46.967030] INFO: moduleinvoker: 命中缓存

[2022-10-23 22:12:46.968752] INFO: moduleinvoker: cached.v2 运行完成[0.009186s].

[2022-10-23 22:12:47.495910] ERROR: moduleinvoker: module name: hfbacktest, module version: v1, trackeback: AttributeError: 'NoneType' object has no attribute 'get_value'

[2022-10-23 22:12:47.504464] ERROR: moduleinvoker: module name: hftrade, module version: v2, trackeback: AttributeError: 'NoneType' object has no attribute 'get_value'

设置评估测试数据集，查看训练曲线
[视频教程]StockRanker训练曲线

bigcharts-data-start/{"__type":"tabs","__id":"bigchart-0766af1a164f4f5584a6e3b2ed950468"}/bigcharts-data-end

日期2021-02-01 持仓 {} -----------
2021-02-01 =======早盘计划买入股票 ['000661.SZA', '600584.SHA', '600660.SHA', '603019.SHA', '002179.SZA']
2022-10-23 22:12:47.495129 strategy strategy exception:Traceback (most recent call last):
  File "bigtrader/strategy/engine.py", line 713, in bigtrader2.bigtrader.strategy.engine.StrategyEngine._call_strategy_func
  File "bigtrader/strategy/strategy_base.py", line 2251, in bigtrader2.bigtrader.strategy.strategy_base.StrategyBase.call_handle_data
  File "<ipython-input-9-ee34edabea57>", line 108, in m6_handle_data_bigquant_run
    price = data.current(instr, 'close')
  File "/var/app/enabled/bigtrader2/bigtrader/protocol.py", line 285, in current
    return self.__data_engine.get_current_value(asset, curr_dt, fields)
  File "bigtrader/mdata/engine.py", line 690, in bigtrader2.bigtrader.mdata.engine.DataEngine._get_minute_value_from_df
AttributeError: 'NoneType' object has no attribute 'get_value'

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-9-ee34edabea57> in <module>
    617 )
    618 
--> 619 m6 = M.hftrade.v2(
    620     instruments=m9.data,
    621     options_data=m8.predictions,

<ipython-input-9-ee34edabea57> in m6_handle_data_bigquant_run(context, data)
    106         if instr not in context.sold_stock_list:
    107             #最新价格
--> 108             price = data.current(instr, 'close')
    109 
    110             #计算买入此股票的数量，不要超过总资金的某个比例

AttributeError: 'NoneType' object has no attribute 'get_value'