克隆策略

In [83]:

# 本代码由可视化策略环境自动生成 2021年8月31日 10:59
# 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。


def cal_bm_return(df):
    bm_df = DataSource('bar1d_index_CN_STOCK_A').read(instruments=['000001.HIX'])
    bm_df["bm_ret"] = bm_df["close"]/bm_df["close"].shift(10)-1
    merge_df = pd.merge(df, bm_df[['date','bm_ret']], on='date', how='left')
    return merge_df['bm_ret']


m16_user_functions_bigquant_run = {
    'cal_bm_return': cal_bm_return,
}

# 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))
    # 设置每只股票占用的最大资金比例
    context.order_pct = 0.1

    #大盘数据获取
    bm_df = DataSource('bar1d_index_CN_STOCK_A').read(instruments=['000001.HIX'])
    bm_df["bm_ret"] = bm_df["close"]/bm_df["close"].shift(10)-1
    bm_df["bm_ret"] = bm_df["bm_ret"].shift(1) #取昨日的收益情况
    context.bm_df = bm_df[['date','bm_ret']]
    
    #个股风控计算
    start_date = context.ranker_prediction.date.iloc[0]
    start_date = pd.to_datetime(start_date)-timedelta(days=30)
    end_date = context.ranker_prediction.date.iloc[-1]
    stocks = context.ranker_prediction.instrument.to_list()
    data = DataSource("bar1d_CN_STOCK_A").read(instruments=stocks,start_date=start_date.strftime("%Y-%m-%d"),end_date=end_date)
    #计算个股风控，小于20日均线
    def cal_risk(df):
        df = df.sort_values("date")
        df["ma"] = df.close.rolling(20).mean()
        df["risk"] = np.where(df.close.shift(1)<df.ma.shift(1),1,0)
        return df
    context.stock_risk_data = data.groupby("instrument").apply(cal_risk).reset_index(drop=True)




# 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):
    # 每10天执行一次
#     if context.trading_day_index % 2 != 0:
#         return
    
    if(context.bm_risk==1):
        print("触发大盘风控，每日处理函数直接返回！")
        return
    today = data.current_dt.strftime('%Y-%m-%d')  

    #====卖出股票
    stock_hold_now = [e.symbol for e, _ in context.perf_tracker.position_tracker.positions.items()]
    print(today,"=======卖出的股票:",stock_hold_now)
    for instr in stock_hold_now:
        context.order_target(context.symbol(instr), 0) #卖出
    
    #买入股票
    ranker_prediction = context.ranker_prediction[context.ranker_prediction.date == today]
    #取排名靠前的前5只
    today_to_buy = list(ranker_prediction.instrument[:5])
    print(today,"=======买入的股票 {}".format(today_to_buy))
    
    # 获取账户资金
    total_portfolio = context.portfolio.portfolio_value
        
    for instr in today_to_buy:
        #最新价格
        price = data.current(context.symbol(instr), 'close')
        #计算买入此股票的数量，不要超过总资金的某个比例
        order_num = int(total_portfolio*context.order_pct/price/100)*100
        context.order_target(context.symbol(instr), order_num) # 买入
        print("{} 买入{}  最新价={} 下单量={}".format(today,instr,str(price),order_num))

# 回测引擎：准备数据，只执行一次
def m4_prepare_bigquant_run(context):
    pass

from zipline.finance.order import Order

#插入定单
def insert_order(context,date,instr,amount):
    order = Order(
        dt = pd.to_datetime(date+" 09:30:00"),
        asset=context.symbol(instr),
        amount=-amount,
        stop=None,
        limit=None,
        price_field='open')

    try:
        context.blotter.open_orders[order.asset].append(order)
    except Exception:
        context.blotter.open_orders[order.asset] = [order]

    context.blotter.orders[order.id] = order
    context.blotter.new_orders.append(order) 
    
#个股风控判断
def stock_risk(context, data):
    today=data.current_dt.strftime('%Y-%m-%d')
    #====卖出股票
    stock_hold_now = {e.symbol:p.amount for e, p in context.perf_tracker.position_tracker.positions.items()}
    stocks = stock_hold_now.keys()

    for instr,amount in stock_hold_now.items():
        nowdata = context.stock_risk_data[(context.stock_risk_data.instrument==instr)&(context.stock_risk_data.date==today)]
        #触发个股风控，早盘卖出
        if nowdata.risk.iloc[0] == 1 and amount>0:
            print(today,'个股风控卖出：',instr) 
            insert_order(context,today,instr,amount)
            
#主函数
def m4_before_trading_start_bigquant_run(context, data):
    today=data.current_dt.strftime('%Y-%m-%d')
    now_bm = context.bm_df[context.bm_df.date==today]
    #个股风控
    stock_risk(context,data)
    context.bm_risk = 0
    #大盘风控判断
    if(now_bm.bm_ret.iloc[0]<-0.01):
        context.bm_risk = 1
        # 得到当前未完成订单
        for orders in get_open_orders().values():
            # 循环，撤销订单
            for _order in orders:
                ins=str(_order.sid.symbol)
                if data.can_trade(_order.sid) and _order.amount>0:
                    #大盘风控取消买单
                    cancel_order(_order)
                    print(today,'大盘风控取消买单',ins) 
                if data.can_trade(_order.sid) and _order.amount<0:#卖单由后续统一处理，先取消
                    #大盘风控取消卖单
                    cancel_order(_order)
                    print(today,'大盘风控取消卖单',ins) 
                    
        #====卖出股票
        stock_hold_now = {e.symbol:p.amount for e, p in context.perf_tracker.position_tracker.positions.items()}
        print(today,"=======风控卖出所有的股票:",stock_hold_now)
        for instr,amount in stock_hold_now.items():
            #插入定单
            insert_order(context,today,instr,amount)
                   

m1 = M.instruments.v2(
    start_date='2020-10-01',
    end_date='2020-12-31',
    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：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
# ts_rank(turn_0, 5)

return_0
bm_ret=cal_bm_return()
"""
)

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,
    user_functions=m16_user_functions_bigquant_run
)

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
)

m5 = M.chinaa_stock_filter.v1(
    input_data=m13.data,
    index_constituent_cond=['全部'],
    board_cond=['全部'],
    industry_cond=['全部'],
    st_cond=['正常'],
    delist_cond=['非退市'],
    output_left_data=False
)

m6 = M.stock_ranker_train.v5(
    training_ds=m5.data,
    features=m3.data,
    learning_algorithm='排序',
    number_of_leaves=30,
    minimum_docs_per_leaf=1000,
    number_of_trees=20,
    learning_rate=0.5,
    max_bins=1023,
    feature_fraction=1,
    m_lazy_run=False
)

m9 = M.instruments.v2(
    start_date=T.live_run_param('trading_date', '2021-01-21'),
    end_date=T.live_run_param('trading_date', '2021-02-01'),
    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=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
)

m10 = M.chinaa_stock_filter.v1(
    input_data=m14.data,
    index_constituent_cond=['全部'],
    board_cond=['全部'],
    industry_cond=['全部'],
    st_cond=['正常'],
    delist_cond=['非退市'],
    output_left_data=False
)

m8 = M.stock_ranker_predict.v5(
    model=m6.model,
    data=m10.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=m4_before_trading_start_bigquant_run,
    volume_limit=0,
    order_price_field_buy='open',
    order_price_field_sell='close',
    capital_base=100000000,
    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=''
)

[2021-08-31 10:58:46.753523] INFO: moduleinvoker: instruments.v2 开始运行..

[2021-08-31 10:58:46.923459] INFO: moduleinvoker: 命中缓存

[2021-08-31 10:58:46.926229] INFO: moduleinvoker: instruments.v2 运行完成[0.172702s].

[2021-08-31 10:58:46.929913] INFO: moduleinvoker: advanced_auto_labeler.v2 开始运行..

[2021-08-31 10:58:46.945444] INFO: moduleinvoker: 命中缓存

[2021-08-31 10:58:46.947218] INFO: moduleinvoker: advanced_auto_labeler.v2 运行完成[0.01731s].

[2021-08-31 10:58:46.949412] INFO: moduleinvoker: input_features.v1 开始运行..

[2021-08-31 10:58:46.957643] INFO: moduleinvoker: 命中缓存

[2021-08-31 10:58:46.959972] INFO: moduleinvoker: input_features.v1 运行完成[0.010558s].

[2021-08-31 10:58:46.977293] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2021-08-31 10:58:46.984064] INFO: moduleinvoker: 命中缓存

[2021-08-31 10:58:46.985793] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.008537s].

[2021-08-31 10:58:46.991797] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2021-08-31 10:58:46.997559] INFO: moduleinvoker: 命中缓存

[2021-08-31 10:58:46.999379] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.007597s].

[2021-08-31 10:58:47.002776] INFO: moduleinvoker: join.v3 开始运行..

[2021-08-31 10:58:47.009378] INFO: moduleinvoker: 命中缓存

[2021-08-31 10:58:47.011289] INFO: moduleinvoker: join.v3 运行完成[0.008513s].

[2021-08-31 10:58:47.015289] INFO: moduleinvoker: dropnan.v1 开始运行..

[2021-08-31 10:58:47.022494] INFO: moduleinvoker: 命中缓存

[2021-08-31 10:58:47.024353] INFO: moduleinvoker: dropnan.v1 运行完成[0.009065s].

[2021-08-31 10:58:47.028057] INFO: moduleinvoker: chinaa_stock_filter.v1 开始运行..

[2021-08-31 10:58:47.037244] INFO: moduleinvoker: 命中缓存

[2021-08-31 10:58:47.039343] INFO: moduleinvoker: chinaa_stock_filter.v1 运行完成[0.01127s].

[2021-08-31 10:58:47.042552] INFO: moduleinvoker: stock_ranker_train.v5 开始运行..

[2021-08-31 10:58:47.053506] INFO: moduleinvoker: 命中缓存

[2021-08-31 10:58:47.164576] INFO: moduleinvoker: stock_ranker_train.v5 运行完成[0.122014s].

[2021-08-31 10:58:47.168125] INFO: moduleinvoker: instruments.v2 开始运行..

[2021-08-31 10:58:47.178157] INFO: moduleinvoker: 命中缓存

[2021-08-31 10:58:47.180089] INFO: moduleinvoker: instruments.v2 运行完成[0.011962s].

[2021-08-31 10:58:47.188532] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2021-08-31 10:58:47.196135] INFO: moduleinvoker: 命中缓存

[2021-08-31 10:58:47.198282] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.009766s].

[2021-08-31 10:58:47.201462] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2021-08-31 10:58:47.210662] INFO: moduleinvoker: 命中缓存

[2021-08-31 10:58:47.212347] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.010881s].

[2021-08-31 10:58:47.215800] INFO: moduleinvoker: dropnan.v1 开始运行..

[2021-08-31 10:58:47.221821] INFO: moduleinvoker: 命中缓存

[2021-08-31 10:58:47.223089] INFO: moduleinvoker: dropnan.v1 运行完成[0.007293s].

[2021-08-31 10:58:47.226882] INFO: moduleinvoker: chinaa_stock_filter.v1 开始运行..

[2021-08-31 10:58:47.234778] INFO: moduleinvoker: 命中缓存

[2021-08-31 10:58:47.236189] INFO: moduleinvoker: chinaa_stock_filter.v1 运行完成[0.009332s].

[2021-08-31 10:58:47.239847] INFO: moduleinvoker: stock_ranker_predict.v5 开始运行..

[2021-08-31 10:58:47.249230] INFO: moduleinvoker: 命中缓存

[2021-08-31 10:58:47.251381] INFO: moduleinvoker: stock_ranker_predict.v5 运行完成[0.011522s].

[2021-08-31 10:58:47.317399] INFO: moduleinvoker: backtest.v8 开始运行..

[2021-08-31 10:58:47.322741] INFO: backtest: biglearning backtest:V8.5.0

[2021-08-31 10:58:47.324070] INFO: backtest: product_type:stock by specified

[2021-08-31 10:58:47.822261] INFO: moduleinvoker: cached.v2 开始运行..

[2021-08-31 10:58:47.831469] INFO: moduleinvoker: 命中缓存

[2021-08-31 10:58:47.834379] INFO: moduleinvoker: cached.v2 运行完成[0.012132s].

[2021-08-31 10:58:49.417974] INFO: algo: TradingAlgorithm V1.8.5

[2021-08-31 10:59:14.382426] INFO: algo: trading transform...

[2021-08-31 10:59:16.322255] INFO: Performance: Simulated 8 trading days out of 8.

[2021-08-31 10:59:16.324449] INFO: Performance: first open: 2021-01-21 09:30:00+00:00

[2021-08-31 10:59:16.325479] INFO: Performance: last close: 2021-02-01 15:00:00+00:00

[2021-08-31 10:59:19.994404] INFO: moduleinvoker: backtest.v8 运行完成[32.677012s].

[2021-08-31 10:59:19.996005] INFO: moduleinvoker: trade.v4 运行完成[32.741496s].

设置测试数据集，查看训练迭代过程的NDCG

bigcharts-data-start/{"__type":"tabs","__id":"bigchart-5ca536854c54438c88fcf4442aeacb1b"}/bigcharts-data-end

2021-01-21 =======卖出的股票: []
2021-01-21 =======买入的股票 ['600496.SHA', '600650.SHA', '688089.SHA', '002978.SZA', '002980.SZA']
2021-01-21 买入600496.SHA  最新价=53.81652 下单量=185800
2021-01-21 买入600650.SHA  最新价=36.925148 下单量=270800
2021-01-21 买入688089.SHA  最新价=39.786358 下单量=251300
2021-01-21 买入002978.SZA  最新价=45.525936 下单量=219600
2021-01-21 买入002980.SZA  最新价=45.70997 下单量=218700
2021-01-22 =======卖出的股票: ['600496.SHA', '600650.SHA', '688089.SHA', '002978.SZA', '002980.SZA']
2021-01-22 =======买入的股票 ['300708.SZA', '603233.SHA', '603613.SHA', '002100.SZA', '002797.SZA']
2021-01-22 买入300708.SZA  最新价=24.31698 下单量=410500
2021-01-22 买入603233.SHA  最新价=158.9186 下单量=62800
2021-01-22 买入603613.SHA  最新价=238.07945 下单量=41900
2021-01-22 买入002100.SZA  最新价=130.10913 下单量=76700
2021-01-22 买入002797.SZA  最新价=13.357279 下单量=747400
2021-01-25 个股风控卖出： 600650.SHA
2021-01-25 =======卖出的股票: ['600650.SHA', '300708.SZA', '603233.SHA', '603613.SHA', '002100.SZA', '002797.SZA']
2021-01-25 =======买入的股票 ['600163.SHA', '600292.SHA', '688557.SHA', '600835.SHA', '002221.SZA']
2021-01-25 买入600163.SHA  最新价=10.061796 下单量=988700
2021-01-25 买入600292.SHA  最新价=26.495672 下单量=375400
2021-01-25 买入688557.SHA  最新价=37.04 下单量=268500
2021-01-25 买入600835.SHA  最新价=114.81151 下单量=86600
2021-01-25 买入002221.SZA  最新价=46.613247 下单量=213400
2021-01-26 个股风控卖出： 300708.SZA
2021-01-26 个股风控卖出： 002100.SZA
2021-01-26 个股风控卖出： 002797.SZA
2021-01-26 =======卖出的股票: ['300708.SZA', '002100.SZA', '002797.SZA', '600163.SHA', '600292.SHA', '688557.SHA', '600835.SHA', '002221.SZA']
2021-01-26 =======买入的股票 ['002274.SZA', '603408.SHA', '600624.SHA', '605169.SHA', '600128.SHA']
2021-01-26 买入002274.SZA  最新价=15.133812 下单量=648800
2021-01-26 买入603408.SHA  最新价=15.49673 下单量=633600
2021-01-26 买入600624.SHA  最新价=140.63109 下单量=69800
2021-01-26 买入605169.SHA  最新价=20.52 下单量=478500
2021-01-26 买入600128.SHA  最新价=18.40585 下单量=533400
2021-01-27 个股风控卖出： 600292.SHA
2021-01-27 个股风控卖出： 688557.SHA
2021-01-27 个股风控卖出： 600835.SHA
2021-01-27 大盘风控取消买单 300708.SZA
2021-01-27 大盘风控取消买单 002100.SZA
2021-01-27 大盘风控取消买单 002797.SZA
2021-01-27 大盘风控取消卖单 600163.SHA
2021-01-27 大盘风控取消卖单 600292.SHA
2021-01-27 大盘风控取消卖单 600292.SHA
2021-01-27 大盘风控取消卖单 688557.SHA
2021-01-27 大盘风控取消卖单 688557.SHA
2021-01-27 大盘风控取消卖单 600835.SHA
2021-01-27 大盘风控取消卖单 600835.SHA
2021-01-27 大盘风控取消卖单 002221.SZA
2021-01-27 大盘风控取消买单 002274.SZA
2021-01-27 大盘风控取消买单 603408.SHA
2021-01-27 大盘风控取消买单 600624.SHA
2021-01-27 大盘风控取消买单 605169.SHA
2021-01-27 大盘风控取消买单 600128.SHA
2021-01-27 =======风控卖出所有的股票: {'300708.SZA': -410500.0, '002100.SZA': -76700.0, '002797.SZA': -747400.0, '600163.SHA': 988700.0, '600292.SHA': 375400.0, '688557.SHA': 268500.0, '600835.SHA': 86600.0, '002221.SZA': 213400.0}
触发大盘风控，每日处理函数直接返回！
2021-01-28 =======卖出的股票: ['002797.SZA']
2021-01-28 =======买入的股票 ['002919.SZA', '300325.SZA', '300823.SZA', '300703.SZA', '300061.SZA']
2021-01-28 买入002919.SZA  最新价=60.395344 下单量=161700
2021-01-28 买入300325.SZA  最新价=20.03712 下单量=487400
2021-01-28 买入300823.SZA  最新价=27.12691 下单量=360000
2021-01-28 买入300703.SZA  最新价=22.520224 下单量=433700
2021-01-28 买入300061.SZA  最新价=22.050161 下单量=442900
2021-01-29 大盘风控取消买单 002797.SZA
2021-01-29 大盘风控取消买单 002919.SZA
2021-01-29 大盘风控取消买单 300325.SZA
2021-01-29 大盘风控取消买单 300823.SZA
2021-01-29 大盘风控取消买单 300703.SZA
2021-01-29 大盘风控取消买单 300061.SZA
2021-01-29 =======风控卖出所有的股票: {'002797.SZA': -563960.7820365988}
触发大盘风控，每日处理函数直接返回！
2021-02-01 =======风控卖出所有的股票: {}
触发大盘风控，每日处理函数直接返回！

收益率-2.4%
年化收益率-53.44%
基准收益率-1.07%
阿尔法-0.53
贝塔0.12
夏普比率-11.39
胜率0.21
盈亏比1.2
收益波动率6.94%
信息比率-0.13
最大回撤2.4%

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