AI选股策略_因子过滤jundada01

策略分享
标签: #<Tag:0x00007fc827cae710>

(jundada) #1
克隆策略

条件过滤——因子过滤

    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回测引擎:初始化函数,只执行一次\ndef bigquant_run(context):\n # 加载预测数据\n context.ranker_prediction = context.options['data'].read_df()\n\n # 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\n context.set_commission(PerOrder(buy_cost=0.0003, sell_cost=0.0013, min_cost=5))\n # 预测数据,通过options传入进来,使用 read_df 函数,加载到内存 (DataFrame)\n # 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\n stock_count = 1\n # 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, ..]\n context.stock_weights = T.norm([1 / math.log(i + 2) for i in range(0, stock_count)])\n # 设置每只股票占用的最大资金比例\n context.max_cash_per_instrument = 0.6\n context.options['hold_days'] = 1\n","ValueType":"Literal","LinkedGlobalParameter":null},{"Name":"handle_data","Value":"# 回测引擎:每日数据处理函数,每天执行一次\ndef bigquant_run(context, data):\n # 按日期过滤得到今日的预测数据\n ranker_prediction = context.ranker_prediction[\n context.ranker_prediction.date == data.current_dt.strftime('%Y-%m-%d')]\n\n # 1. 资金分配\n # 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\n # 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)\n is_staging = context.trading_day_index < context.options['hold_days'] # 是否在建仓期间(前 hold_days 天)\n cash_avg = context.portfolio.portfolio_value / context.options['hold_days']\n cash_for_buy = min(context.portfolio.cash, (1 if is_staging else 1.5) * cash_avg)\n cash_for_sell = cash_avg - (context.portfolio.cash - cash_for_buy)\n positions = {e.symbol: p.amount * p.last_sale_price\n for e, p in context.perf_tracker.position_tracker.positions.items()}\n #print (context.perf_tracker.position_tracker.positions.items())\n #print (positions)\n #context.portfolio.positions\n # 2. 生成卖出订单:hold_days天之后才开始卖出;对持仓的股票,按机器学习算法预测的排序末位淘汰\n if not is_staging and cash_for_sell > 0:\n #equities = {e.symbol: e for e, p in context.perf_tracker.position_tracker.positions.items()}\n #instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(\n # lambda x: x in equities and not context.has_unfinished_sell_order(equities[x]))])))\n instruments = []\n for key in positions:\n instruments.append(key)\n #print('rank order for sell %s' % instruments)\n for instrument in instruments:\n context.order_target(context.symbol(instrument), 0)\n cash_for_sell -= positions[instrument]\n if cash_for_sell <= 0:\n break\n\n # 3. 生成买入订单:按机器学习算法预测的排序,买入前面的stock_count只股票\n buy_cash_weights = context.stock_weights\n #print(buy_cash_weights)\n buy_instruments = list(ranker_prediction.instrument[:len(buy_cash_weights)])\n \n '''buy_instruments_20 = list(ranker_prediction.instrument[:30])\n stock_to_buy = []\n for stock in buy_instruments_20:\n df = get_history_data_by_days(stock, data.current_dt.strftime('%Y-%m-%d'), 1, 0,['name','price_limit_status','open','close'])\n df2 = get_history_data_by_days(stock, data.current_dt.strftime('%Y-%m-%d'), 2, 0,['name','price_limit_status','open','close'])\n for indexs in df.index:\n if (df.loc[indexs]['price_limit_status'] == 3 and df.loc[indexs]['open'] != df.loc[indexs]['close']) or (df2.loc[indexs]['price_limit_status'] == 3 and df.loc[indexs]['open'] != df.loc[indexs]['close']) :\n stock_to_buy.append(df.loc[indexs]['instrument'])\n else:\n pass\n #if len(stock_to_buy)>=3:\n # break\n if len(stock_to_buy)>=len(buy_cash_weights):\n stock_to_buy = stock_to_buy[:len(buy_cash_weights)]\n '''\n '''if len(stock_to_buy) == 0:\n buy_instruments = list(ranker_prediction.instrument[:len(buy_cash_weights)])\n else:\n for stock in buy_instruments:\n if len(stock_to_buy)<3 and stock not in stock_to_buy:\n stock_to_buy.append(stock)\n buy_instruments = stock_to_buy[:len(buy_cash_weights)]\n '''\n #buy_instruments = stock_to_buy\n #print(data.current_dt.strftime('%Y-%m-%d'))\n #print(':')\n #print(buy_instruments)\n #print(',')\n \n '''\n buy_instruments_20 = list(ranker_prediction.instrument[:20])\n data = D.history_data(instruments=buy_instruments_20, start_date=start_time, end_date=data.current_dt.strftime('%Y-%m-%d'),\n fields=['name','price_limit_status','open','close'])\n stock_to_buy = []\n for indexs in data.index:\n if data.loc[indexs]['price_limit_status']:\n stock_to_buy.append(data.loc[indexs]['instrument'])\n #print(data.loc[indexs]['instrument'])\n stock_to_buy = stock_to_buy[:len(buy_cash_weights)]\n '''\n #print(buy_instruments)\n max_cash_per_instrument = context.portfolio.portfolio_value * context.max_cash_per_instrument\n for i, instrument in enumerate(buy_instruments):\n cash = cash_for_buy * buy_cash_weights[i]\n if cash > max_cash_per_instrument - positions.get(instrument, 0):\n # 确保股票持仓量不会超过每次股票最大的占用资金量\n cash = max_cash_per_instrument - positions.get(instrument, 0)\n if cash > 0:\n context.order_value(context.symbol(instrument), cash)\n\ndef get_history_data_by_days(instruments,date,days,direction,fields):\n ix = get_date_index(date, direction)\n start_date = get_date_range(ix,direction,days)[0]\n end_date = get_date_range(ix,direction,days)[1]\n return 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    In [19]:
    # 本代码由可视化策略环境自动生成 2020年2月13日 15:02
    # 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 m4_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 = 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)])
        # 设置每只股票占用的最大资金比例
        context.max_cash_per_instrument = 0.6
        context.options['hold_days'] = 1
    
    # 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):
        # 按日期过滤得到今日的预测数据
        ranker_prediction = context.ranker_prediction[
            context.ranker_prediction.date == data.current_dt.strftime('%Y-%m-%d')]
    
        # 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.options['hold_days'] # 是否在建仓期间(前 hold_days 天)
        cash_avg = context.portfolio.portfolio_value / context.options['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()}
        #print (context.perf_tracker.position_tracker.positions.items())
        #print (positions)
        #context.portfolio.positions
        # 2. 生成卖出订单:hold_days天之后才开始卖出;对持仓的股票,按机器学习算法预测的排序末位淘汰
        if not is_staging and cash_for_sell > 0:
            #equities = {e.symbol: e for e, p in context.perf_tracker.position_tracker.positions.items()}
            #instruments = list(reversed(list(ranker_prediction.instrument[ranker_prediction.instrument.apply(
            #        lambda x: x in equities and not context.has_unfinished_sell_order(equities[x]))])))
            instruments = []
            for key in positions:
                instruments.append(key)
            #print('rank order for sell %s' % instruments)
            for instrument in instruments:
                context.order_target(context.symbol(instrument), 0)
                cash_for_sell -= positions[instrument]
                if cash_for_sell <= 0:
                    break
    
        # 3. 生成买入订单:按机器学习算法预测的排序,买入前面的stock_count只股票
        buy_cash_weights = context.stock_weights
        #print(buy_cash_weights)
        buy_instruments = list(ranker_prediction.instrument[:len(buy_cash_weights)])
        
        '''buy_instruments_20 = list(ranker_prediction.instrument[:30])
        stock_to_buy = []
        for stock in buy_instruments_20:
            df = get_history_data_by_days(stock, data.current_dt.strftime('%Y-%m-%d'), 1, 0,['name','price_limit_status','open','close'])
            df2 = get_history_data_by_days(stock, data.current_dt.strftime('%Y-%m-%d'), 2, 0,['name','price_limit_status','open','close'])
            for indexs in df.index:
                if (df.loc[indexs]['price_limit_status'] == 3 and df.loc[indexs]['open'] != df.loc[indexs]['close']) or (df2.loc[indexs]['price_limit_status'] == 3 and df.loc[indexs]['open'] != df.loc[indexs]['close']) :
                    stock_to_buy.append(df.loc[indexs]['instrument'])
                else:
                    pass
                    #if len(stock_to_buy)>=3:
                    #    break
        if len(stock_to_buy)>=len(buy_cash_weights):
            stock_to_buy = stock_to_buy[:len(buy_cash_weights)]
        '''
        '''if len(stock_to_buy) == 0:
            buy_instruments = list(ranker_prediction.instrument[:len(buy_cash_weights)])
        else:
            for stock in buy_instruments:
                if len(stock_to_buy)<3 and stock not in stock_to_buy:
                    stock_to_buy.append(stock)
            buy_instruments = stock_to_buy[:len(buy_cash_weights)]
        '''
        #buy_instruments = stock_to_buy
        #print(data.current_dt.strftime('%Y-%m-%d'))
        #print(':')
        #print(buy_instruments)
        #print(',')
        
        '''
        buy_instruments_20 = list(ranker_prediction.instrument[:20])
        data = D.history_data(instruments=buy_instruments_20, start_date=start_time, end_date=data.current_dt.strftime('%Y-%m-%d'),
                   fields=['name','price_limit_status','open','close'])
        stock_to_buy = []
        for indexs in data.index:
            if data.loc[indexs]['price_limit_status']:
                stock_to_buy.append(data.loc[indexs]['instrument'])
                #print(data.loc[indexs]['instrument'])
        stock_to_buy = stock_to_buy[:len(buy_cash_weights)]
        '''
        #print(buy_instruments)
        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)
    
    def get_history_data_by_days(instruments,date,days,direction,fields):
        ix = get_date_index(date, direction)
        start_date = get_date_range(ix,direction,days)[0]
        end_date = get_date_range(ix,direction,days)[1]
        return D.history_data(instruments=instruments,start_date=start_date,end_date=end_date,fields=fields)
    
    def get_near_date(date,direction):
        td = D.trading_days(market='CN', start_date=None, end_date=None)
        td['date'] = td['date'].apply(lambda x:x.strftime('%Y-%m-%d'))
        while True:
            if direction == 0:
                pre_date = datetime.datetime.strptime(date,'%Y-%m-%d') - datetime.timedelta(1)
            elif direction == 1:
                pre_date = datetime.datetime.strptime(date,'%Y-%m-%d') + datetime.timedelta(1)
            pre_date = pre_date.strftime('%Y-%m-%d')
            date = pre_date
            if pre_date in list(td['date']):
                break
        return date
    
    def get_date_index(date,direction):
        td = D.trading_days(market='CN', start_date=None, end_date=None)
        td['date'] = td['date'].apply(lambda x:x.strftime('%Y-%m-%d'))
        try:
            ix = td[td['date']== date].index[0]
        except IndexError as e:
            date = get_near_date(date,direction)
            ix = td[td['date']== date].index[0]
        return ix
    
    def get_date_range(ix,direction,days):
        td = D.trading_days(market='CN', start_date=None, end_date=None)
        if direction == 0: 
            start_ix = ix-days+1
            assert len(td.ix[start_ix:ix]) == days
            start_date = td.ix[start_ix:ix]['date'].min()
            end_date = td.ix[start_ix:ix]['date'].max()
        elif direction ==1:
            end_ix = ix+days-1
            assert len(td.ix[ix:end_ix]) == days
            start_date = td.ix[ix:end_ix]['date'].min()
            end_date = td.ix[ix:end_ix]['date'].max()
        return (start_date, end_date)
    # 回测引擎:准备数据,只执行一次
    def m4_prepare_bigquant_run(context):
        pass
    
    
    m1 = M.instruments.v2(
        start_date='2010-01-01',
        end_date='2015-01-01',
        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:2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, -2) / 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
    return_5
    return_10
    return_20
    avg_amount_0/avg_amount_5
    avg_amount_5/avg_amount_20
    rank_avg_amount_0/rank_avg_amount_5
    rank_avg_amount_5/rank_avg_amount_10
    rank_return_0
    rank_return_5
    rank_return_10
    rank_return_0/rank_return_5
    rank_return_5/rank_return_10
    pe_ttm_0
    """
    )
    
    m11 = M.input_features.v1(
        features_ds=m3.data,
        features="""price_limit_status_0
    price_limit_status_1
    open_0
    close_0
    open_1
    close_1"""
    )
    
    m15 = M.general_feature_extractor.v7(
        instruments=m1.data,
        features=m11.data,
        start_date='',
        end_date='',
        before_start_days=60
    )
    
    m16 = M.derived_feature_extractor.v3(
        input_data=m15.data,
        features=m11.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
    )
    
    m19 = M.filter_delist_stocks.v3(
        input_1=m7.data
    )
    
    m20 = M.filter_stockcode.v2(
        input_1=m19.data,
        start='688'
    )
    
    m21 = M.filtet_st_stock.v2(
        input_1=m20.data_1
    )
    
    m5 = M.filter.v3(
        input_data=m21.data_1,
        expr='price_limit_status_0 == 3 & open_0 < close_0',
        output_left_data=False
    )
    
    m13 = M.dropnan.v1(
        input_data=m5.data
    )
    
    m12 = 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,
        ndcg_discount_base=1,
        m_lazy_run=False
    )
    
    m9 = M.instruments.v2(
        start_date=T.live_run_param('trading_date', '2015-01-01'),
        end_date=T.live_run_param('trading_date', '2017-01-01'),
        market='CN_STOCK_A',
        instrument_list='',
        max_count=0
    )
    
    m17 = M.general_feature_extractor.v7(
        instruments=m9.data,
        features=m11.data,
        start_date='',
        end_date='',
        before_start_days=60 
    )
    
    m18 = M.derived_feature_extractor.v3(
        input_data=m17.data,
        features=m11.data,
        date_col='date',
        instrument_col='instrument',
        drop_na=False,
        remove_extra_columns=False
    )
    
    m23 = M.filter_delist_stocks.v3(
        input_1=m18.data
    )
    
    m24 = M.filter_stockcode.v2(
        input_1=m23.data,
        start='688'
    )
    
    m22 = M.filtet_st_stock.v2(
        input_1=m24.data_1
    )
    
    m10 = M.filter.v3(
        input_data=m22.data_1,
        expr='price_limit_status_0 == 3 & open_0 < close_0',
        output_left_data=False
    )
    
    m14 = M.dropnan.v1(
        input_data=m10.data
    )
    
    m8 = M.stock_ranker_predict.v5(
        model=m12.model,
        data=m14.data,
        m_lazy_run=False
    )
    
    m4 = M.trade.v4(
        instruments=m9.data,
        options_data=m8.predictions,
        start_date='2018-01-01',
        end_date='2019-12-31',
        initialize=m4_initialize_bigquant_run,
        handle_data=m4_handle_data_bigquant_run,
        prepare=m4_prepare_bigquant_run,
        volume_limit=0.025,
        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=''
    )
    
    设置测试数据集,查看训练迭代过程的NDCG
    bigcharts-data-start/{"__id":"bigchart-62f870da935540f5be53ca1069ec0b1d","__type":"tabs"}/bigcharts-data-end
    • 收益率0.0%
    • 年化收益率0.0%
    • 基准收益率1.63%
    • 阿尔法-0.03
    • 贝塔0.0
    • 夏普比率n/a
    • 胜率0.0
    • 盈亏比0.0
    • 收益波动率0.0%
    • 信息比率-0.01
    • 最大回撤0.0%
    bigcharts-data-start/{"__id":"bigchart-a78ad7ff43dc4a0081ec05efb19352c2","__type":"tabs"}/bigcharts-data-end