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克隆策略

In [22]:

# #m7.data.read_df().columns
# #m17.predictions.read_df()
# #m8.predictions.read_df().head(100)
# #m8.predictions.read_df().tail(100)
# #m2.data_2.read_df()
# #m23.data_1.read_df()
# #m23.data_2.read_df()
# # df = m11.data_1.read()['data']
# # print(df[df['date']=='2021-01-26'])
# # print(df[df['date']=='2021-01-27'])

# # 计算n日涨幅
# def zhangfu(data, timeperiod=13):          
#     adj_close = data['close']- data['close'].shift(timeperiod)             
#     return adj_close

# # 计算动量
# def barAdjMon(data, timeperiod=2):          
#     adj_close = (data['close'] + data['high'] + data['low']) / 3               
#     return np.log(adj_close / adj_close.shift(timeperiod))

# def del_main(start_date_input,end_date_input):
#     instruments = ['510330.HOF','161017.ZOF','159949.ZOF']
#     df = DataSource("bar1d_CN_FUND").read(start_date=start_date_input,instruments = instruments, end_date=end_date_input)
#     groups = df.groupby(df['instrument'])
#     df_expend = pd.DataFrame()
#     for x in instruments:
#         tp = groups.get_group(x)
#         tp = tp.sort_values(by=['date'],na_position='first')
#         tp['pre_close'] = tp['close'].shift(1)
#         print(tp.head(2))
#         df_expend = df_expend.append(tp)
#     #df_159949 = groups.get_group('159949.ZOF')
#     df_expend.sort_values(['date'],na_position='first',inplace=True)
#     df_expend = df_expend.reset_index()
#     df_expend.drop('index',axis= 1,inplace = True)
    
#     #计算其他特征
#     df_expend['mom_20'] = barAdjMon(df_expend,timeperiod=20)
#     df_expend['mom_20'] = df_expend['mom_20'].fillna(value=0.0) #列向前填充
#     # 涨幅因子
#     df_expend['zhangfu'] = zhangfu(df_expend,timeperiod=13)
#     df_expend['zhangfu'] = df_expend['zhangfu'].fillna(value=0.0) #列向前填充
#     return df_expend

# # Python 代码入口函数，input_1/2/3 对应三个输入端，data_1/2/3 对应三个输出端
# def bigquant_run(input_1, input_2, input_3,start_date_input,end_date_input,pre_start_date,pre_end_date):
#     # 示例代码如下。在这里编写您的代码
#     # 读取数据  默认会返回全部证券代码数据, 通过指定参数 instruments 可以读取到指定的证券代码数据
#     df_expend = del_main(start_date_input,end_date_input)
#     data_1 = DataSource.write_df(df_expend)
#     data_2 = DataSource.write_df(df_expend)
#     df_pre = del_main(pre_start_date,pre_end_date)
#     data_3 = DataSource.write_df(df_pre)
#     return Outputs(data_1=data_1, data_2=data_2, data_3=data_3)

In [27]:

# m4.data.read()['start_date']

Out[27]:

'2021-06-01'

In [36]:

# 本代码由可视化策略环境自动生成 2022年7月11日 23:44
# 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。


# Python 代码入口函数，input_1/2/3 对应三个输入端，data_1/2/3 对应三个输出端
def m14_run_bigquant_run(input_1, input_2, input_3, stock_count, hold_days):
    # 示例代码如下。在这里编写您的代码
    param = {
        "stock_count": stock_count,
        "hold_days": hold_days
        }
    data_1 = DataSource.write_pickle(param)
    return Outputs(data_1=data_1, data_2=None, data_3=None)

# 后处理函数，可选。输入是主函数的输出，可以在这里对数据做处理，或者返回更友好的outputs数据格式。此函数输出不会被缓存。
def m14_post_run_bigquant_run(outputs):
    return outputs

# 计算n日涨幅
def zhangfu(data, timeperiod=13):          
    adj_close = data['close']- data['close'].shift(timeperiod)             
    return adj_close

# 计算动量
def barAdjMon(data, timeperiod=2):          
    adj_close = (data['close'] + data['high'] + data['low']) / 3               
    return np.log(adj_close / adj_close.shift(timeperiod))

def del_main(start_date_input,end_date_input):
    instruments = ['510330.HOF','161017.ZOF','159949.ZOF']
    df = DataSource("bar1d_CN_FUND").read(start_date=start_date_input,instruments = instruments, end_date=end_date_input)
    groups = df.groupby(df['instrument'])
    df_expend = pd.DataFrame()
    for x in instruments:
        tp = groups.get_group(x)
        tp = tp.sort_values(by=['date'],na_position='first')
        tp['pre_close'] = tp['close'].shift(1)
        print(tp.head(2))
        df_expend = df_expend.append(tp)
    #df_159949 = groups.get_group('159949.ZOF')
    df_expend.sort_values(['date'],na_position='first',inplace=True)
    df_expend = df_expend.reset_index()
    df_expend.drop('index',axis= 1,inplace = True)
    
    #计算其他特征
    df_expend['mom_20'] = barAdjMon(df_expend,timeperiod=20)
    df_expend['mom_20'] = df_expend['mom_20'].fillna(value=0.0) #列向前填充
    # 涨幅因子
    df_expend['zhangfu'] = zhangfu(df_expend,timeperiod=13)
    df_expend['zhangfu'] = df_expend['zhangfu'].fillna(value=0.0) #列向前填充
    return df_expend

# Python 代码入口函数，input_1/2/3 对应三个输入端，data_1/2/3 对应三个输出端
def m2_run_bigquant_run(input_1, input_2, input_3,start_date_input,end_date_input,pre_start_date,pre_end_date):
    pre_start_date = input_1.read()['start_date']
    pre_end_date = input_1.read()['end_date']
    # 示例代码如下。在这里编写您的代码
    # 读取数据  默认会返回全部证券代码数据, 通过指定参数 instruments 可以读取到指定的证券代码数据
    df_expend = del_main(start_date_input,end_date_input)
    data_1 = DataSource.write_df(df_expend)
    data_2 = DataSource.write_df(df_expend)
    print('pre_start_date,pre_end_date',pre_start_date,pre_end_date)
    df_pre = del_main(pre_start_date,pre_end_date)
    data_3 = DataSource.write_df(df_pre)
    return Outputs(data_1=data_1, data_2=data_2, data_3=data_3)

# 后处理函数，可选。输入是主函数的输出，可以在这里对数据做处理，或者返回更友好的outputs数据格式。此函数输出不会被缓存。
def m2_post_run_bigquant_run(outputs):
    return outputs

def del_input(input_1):
    df = input_1.read_df()
    data_1 = DataSource.write_df(df)
    return data_1
# Python 代码入口函数，input_1/2/3 对应三个输入端，data_1/2/3 对应三个输出端
def m23_run_bigquant_run(input_1, input_2, input_3):
    data_1 = del_input(input_1)
    data_2 = del_input(input_2)
    return Outputs(data_1=data_1, data_2=data_2, data_3=None)
# 后处理函数，可选。输入是主函数的输出，可以在这里对数据做处理，或者返回更友好的outputs数据格式。此函数输出不会被缓存。
def m23_post_run_bigquant_run(outputs):
    return outputs

# Python 代码入口函数，input_1/2/3 对应三个输入端，data_1/2/3 对应三个输出端
def m11_run_bigquant_run(input_1, input_2, input_3):
    # 示例代码如下。在这里编写您的代码
    print('m11.input1',input_1)
    df = input_1.read()
    param = input_2.read()
    
    data = {
        "param": param,
        "data": df
    }
    data_1 = DataSource.write_pickle(data)
    return Outputs(data_1=data_1, data_2=None, data_3=None)

# 后处理函数，可选。输入是主函数的输出，可以在这里对数据做处理，或者返回更友好的outputs数据格式。此函数输出不会被缓存。
def m11_post_run_bigquant_run(outputs):
    return outputs

# 回测引擎：初始化函数，只执行一次
def m21_initialize_bigquant_run(context):
    print('初始化函数开始：')
    print('context.options',context.options)
    # 加载预测数据
    context.ranker_prediction = context.options.get('data').read()['data']
    context.param = context.options['data'].read()["param"]
    # 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=0.0003, 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 = context.param["stock_count"]
    # 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 = 1.01
    context.hold_days = context.param["hold_days"]
    print('初始化函数结束')

# 交易引擎：每个单位时间开盘前调用一次。
def m21_before_trading_start_bigquant_run(context, data):
    # 盘前处理，订阅行情等
    print('订阅行情前',data)
    context.subscribe(context.instruments)
    print('订阅行情后',data)
    pass

# 交易引擎：tick数据处理函数，每个tick执行一次
def m21_handle_tick_bigquant_run(context, tick):
    pass

import math
# 回测引擎：每日数据处理函数，每天执行一次
def m21_handle_data_bigquant_run(context, data):
    print('当前日期data.current_dt',data.current_dt)
    print('data',data)
    try:
        context.ranker_prediction = context.options.get('data').read()['data']
        # 相隔几天（hold_days）进行一下换仓
        if context.trading_day_index % context.hold_days != 0:
            return 

        # 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')]
        # 目前持仓
        positions = {e: p.amount * p.last_sale_price for e, p in context.portfolio.positions.items()}
        # 权重
        buy_cash_weights = context.stock_weights
        # 今日买入股票列表
        stock_to_buy = list(ranker_prediction.instrument[:len(buy_cash_weights)])
        # 持仓上限
        max_cash_per_instrument = context.portfolio.portfolio_value * context.max_cash_per_instrument
        print("<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<")
        # 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 = [equity for equity in context.portfolio.positions ]
        # 继续持有的股票：调仓时，如果买入的股票已经存在于目前的持仓里，那么应继续持有
        no_need_to_sell = [i for i in stock_hold_now if i in stock_to_buy]
        # 需要卖出的股票
        stock_to_sell = [i for i in stock_hold_now if i not in no_need_to_sell]

        # 卖出
        for stock 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(stock)):
                # 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
                print('卖出order target percent',context.symbol(stock))
                print('卖出结果',context.order_target_percent(context.symbol(stock), 0))

        # 如果当天没有买入的股票，就返回
        if len(stock_to_buy) == 0:
            return

        # 买入
        print('买入列表',stock_to_buy)
        for i, instrument in enumerate(stock_to_buy):
            cash = context.portfolio.portfolio_value * 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 > 500:
                cash = int(math.floor(cash))
                print('买入order_value',context.symbol(instrument),' cash ',cash)
                print(context.order_value(context.symbol(instrument), cash))
    except Exception as e:
        print('抛出异常',e)
# 交易引擎：成交回报处理函数，每个成交发生时执行一次
def m21_handle_trade_bigquant_run(context, trade):
    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 m21_handle_order_bigquant_run(context, order):
    pass

# 交易引擎：盘后处理函数，每日盘后执行一次
def m21_after_trading_bigquant_run(context, data):
    pass


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

rank_swing_volatility_5 = nanstd((high-low)/pre_close, 5)*sqrt(200)*100
mom_20
zhangfu"""
)

m14 = M.cached.v3(
    run=m14_run_bigquant_run,
    post_run=m14_post_run_bigquant_run,
    input_ports='',
    params="""{
    "stock_count": 1,
    "hold_days": 1 
}""",
    output_ports=''
)

m4 = M.instruments.v2(
    start_date='2021-06-01',
    end_date=T.live_run_param('trading_date', '2022-06-01'),
    market='CN_FUND',
    instrument_list="""510330.HOF
161017.ZOF
159949.ZOF""",
    max_count=0
)

m2 = M.cached.v3(
    input_1=m4.data,
    run=m2_run_bigquant_run,
    post_run=m2_post_run_bigquant_run,
    input_ports='',
    params="""{"start_date_input":"2020-06-01",
"end_date_input":"2021-05-01",
"pre_start_date":"2021-06-01",
"pre_end_date":"2022-06-01"}""",
    output_ports=''
)

m10 = M.auto_labeler_on_datasource.v1(
    input_data=m2.data_1,
    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/develop/datasource/deprecated/history_data.html
# 2. 可用操作符和函数见 `表达式引擎 <https://bigquant.com/docs/develop/bigexpr/usage.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)
""",
    drop_na_label=True,
    cast_label_int=True,
    date_col='date',
    instrument_col='instrument',
    user_functions={}
)

m23 = M.cached.v3(
    input_1=m2.data_2,
    input_2=m2.data_3,
    run=m23_run_bigquant_run,
    post_run=m23_post_run_bigquant_run,
    input_ports='',
    params='{}',
    output_ports='',
    m_cached=False
)

m16 = M.derived_feature_extractor.v3(
    input_data=m23.data_1,
    features=m3.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False
)

m7 = M.join.v3(
    data1=m10.data,
    data2=m16.data,
    on='date,instrument',
    how='inner',
    sort=False
)

m6 = M.stock_ranker_train.v6(
    training_ds=m7.data,
    features=m3.data,
    learning_algorithm='排序',
    number_of_leaves=3,
    minimum_docs_per_leaf=100,
    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,
    m_cached=False
)

m18 = M.derived_feature_extractor.v3(
    input_data=m23.data_2,
    features=m3.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False
)

m8 = M.stock_ranker_predict.v5(
    model=m6.model,
    data=m18.data,
    m_lazy_run=False
)

m11 = M.cached.v3(
    input_1=m8.predictions,
    input_2=m14.data_1,
    run=m11_run_bigquant_run,
    post_run=m11_post_run_bigquant_run,
    input_ports='',
    params='{}',
    output_ports=''
)

m21 = M.hftrade.v2(
    instruments=m4.data,
    options_data=m11.data_1,
    start_date='',
    end_date='',
    initialize=m21_initialize_bigquant_run,
    before_trading_start=m21_before_trading_start_bigquant_run,
    handle_tick=m21_handle_tick_bigquant_run,
    handle_data=m21_handle_data_bigquant_run,
    handle_trade=m21_handle_trade_bigquant_run,
    handle_order=m21_handle_order_bigquant_run,
    after_trading=m21_after_trading_bigquant_run,
    capital_base=1000000,
    frequency='daily',
    price_type='真实价格',
    product_type='自动',
    before_start_days='80',
    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=True,
    backtest_only=False
)

[2022-07-06 14:33:19.157755] INFO: moduleinvoker: instruments.v2 开始运行..

[2022-07-06 14:33:19.171816] INFO: moduleinvoker: 命中缓存

[2022-07-06 14:33:19.174560] INFO: moduleinvoker: instruments.v2 运行完成[0.016801s].

[2022-07-06 14:33:19.318770] INFO: moduleinvoker: cached.v3 开始运行..

[2022-07-06 14:33:20.842842] INFO: moduleinvoker: cached.v3 运行完成[1.524079s].

   instrument       date    open   high     low   close       volume  \
2  510330.HOF 2020-06-01  4.5108  4.615  4.5108  4.6080  205954831.0   
5  510330.HOF 2020-06-02  4.6080  4.630  4.5965  4.6196  107216737.0   

        amount  adjust_factor  turn  pre_close  
2  813650964.0       1.157789   NaN        NaN  
5  426967604.0       1.157794   NaN      4.608  
   instrument       date    open    high     low   close     volume  \
1  161017.ZOF 2020-06-01  2.2009  2.2567  2.2009  2.2554  3000479.0   
4  161017.ZOF 2020-06-02  2.2567  2.2728  2.2468  2.2554  1403088.0   

        amount  adjust_factor  turn  pre_close  
1  5441987.193        1.23855   NaN        NaN  
4  2554776.191        1.23855   NaN     2.2554  
   instrument       date   open   high    low  close       volume  \
0  159949.ZOF 2020-06-01  0.793  0.818  0.793  0.816  754511227.0   
3  159949.ZOF 2020-06-02  0.818  0.818  0.805  0.811  645798345.0   

         amount  adjust_factor  turn  pre_close  
0  6.093608e+08            1.0   NaN        NaN  
3  5.231684e+08            1.0   NaN      0.816  
pre_start_date,pre_end_date 2021-06-01 2022-06-01
   instrument       date    open    high     low   close       volume  \
0  510330.HOF 2021-06-01  6.2829  6.3006  6.2099  6.2935  149795700.0   
3  510330.HOF 2021-06-02  6.3006  6.3100  6.2123  6.2452  119155700.0   

        amount  adjust_factor  turn  pre_close  
0  796794080.0       1.177676   NaN        NaN  
3  633040575.0       1.177673   NaN     6.2935  
   instrument       date    open    high     low   close    volume  \
2  161017.ZOF 2021-06-01  2.9106  2.9354  2.8859  2.9218  465328.0   
5  161017.ZOF 2021-06-02  2.9218  2.9230  2.8945  2.9044  510014.0   

        amount  adjust_factor  turn  pre_close  
2  1093198.557       1.238576   NaN        NaN  
5  1198952.227       1.238550   NaN     2.9218  
   instrument       date   open   high    low  close       volume  \
1  159949.ZOF 2021-06-01  1.435  1.442  1.411  1.439  643360309.0   
4  159949.ZOF 2021-06-02  1.441  1.445  1.403  1.408  744331379.0   

         amount  adjust_factor  turn  pre_close  
1  9.204310e+08            1.0   NaN        NaN  
4  1.056368e+09            1.0   NaN      1.439