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In [3]:

# 本代码由可视化策略环境自动生成 2021年12月3日 14:51
# 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 = 0.05

    #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)
avg_turn_15/turn_0
mf_net_amount_xl_0
alpha4=close_0avg_turn_0+close_1avg_turn_1+close_2*avg_turn_2
#选择换手率因子，是为了找流动性较好的股票，股性较为活跃。 选取资金流超大单因子，是为了增加短期买点的爆发力。 因为超大单因子的ic均值很高，而且存在一定的方向性，在牛市中有较为稳健的风格收益"""
)

m15 = M.general_feature_extractor.v7(
    instruments=m1.data,
    features=m3.data,
    start_date='',
    end_date='',
    before_start_days=60
)

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=100000,
    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=''
)

[2021-12-03 14:50:00.040194] INFO: moduleinvoker: instruments.v2 开始运行..

[2021-12-03 14:50:00.123381] INFO: moduleinvoker: 命中缓存

[2021-12-03 14:50:00.125138] INFO: moduleinvoker: instruments.v2 运行完成[0.084958s].

[2021-12-03 14:50:00.135348] INFO: moduleinvoker: advanced_auto_labeler.v2 开始运行..

[2021-12-03 14:50:00.143261] INFO: moduleinvoker: 命中缓存

[2021-12-03 14:50:00.144722] INFO: moduleinvoker: advanced_auto_labeler.v2 运行完成[0.009369s].

[2021-12-03 14:50:00.148911] INFO: moduleinvoker: input_features.v1 开始运行..

[2021-12-03 14:50:00.161498] INFO: moduleinvoker: 命中缓存

[2021-12-03 14:50:00.162961] INFO: moduleinvoker: input_features.v1 运行完成[0.014047s].

[2021-12-03 14:50:00.180874] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2021-12-03 14:50:00.193263] INFO: moduleinvoker: 命中缓存

[2021-12-03 14:50:00.196611] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.015727s].

[2021-12-03 14:50:00.209440] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2021-12-03 14:50:00.220694] INFO: moduleinvoker: 命中缓存

[2021-12-03 14:50:00.222663] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.013242s].

[2021-12-03 14:50:00.234834] INFO: moduleinvoker: join.v3 开始运行..

[2021-12-03 14:50:00.245701] INFO: moduleinvoker: 命中缓存

[2021-12-03 14:50:00.247482] INFO: moduleinvoker: join.v3 运行完成[0.012648s].

[2021-12-03 14:50:00.256622] INFO: moduleinvoker: dropnan.v1 开始运行..

[2021-12-03 14:50:00.264918] INFO: moduleinvoker: 命中缓存

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[2021-12-03 14:50:00.279712] INFO: moduleinvoker: chinaa_stock_filter.v1 开始运行..

[2021-12-03 14:50:00.294120] INFO: moduleinvoker: 命中缓存

[2021-12-03 14:50:00.298032] INFO: moduleinvoker: chinaa_stock_filter.v1 运行完成[0.018307s].

[2021-12-03 14:50:00.309842] INFO: moduleinvoker: stock_ranker_train.v5 开始运行..

[2021-12-03 14:50:00.327033] INFO: moduleinvoker: 命中缓存

[2021-12-03 14:50:00.457374] INFO: moduleinvoker: stock_ranker_train.v5 运行完成[0.147529s].

[2021-12-03 14:50:00.464442] INFO: moduleinvoker: instruments.v2 开始运行..

[2021-12-03 14:50:00.482713] INFO: moduleinvoker: 命中缓存

[2021-12-03 14:50:00.484249] INFO: moduleinvoker: instruments.v2 运行完成[0.019806s].

[2021-12-03 14:50:00.497444] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2021-12-03 14:50:00.506906] INFO: moduleinvoker: 命中缓存

[2021-12-03 14:50:00.508804] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.011367s].

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[2021-12-03 14:50:00.536389] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.009993s].

[2021-12-03 14:50:00.544718] INFO: moduleinvoker: dropnan.v1 开始运行..

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[2021-12-03 14:50:00.559132] INFO: moduleinvoker: dropnan.v1 运行完成[0.014429s].

[2021-12-03 14:50:00.567414] INFO: moduleinvoker: chinaa_stock_filter.v1 开始运行..

[2021-12-03 14:50:00.573509] INFO: moduleinvoker: 命中缓存

[2021-12-03 14:50:00.574807] INFO: moduleinvoker: chinaa_stock_filter.v1 运行完成[0.007392s].

[2021-12-03 14:50:00.583122] INFO: moduleinvoker: stock_ranker_predict.v5 开始运行..

[2021-12-03 14:50:00.593829] INFO: moduleinvoker: 命中缓存

[2021-12-03 14:50:00.595289] INFO: moduleinvoker: stock_ranker_predict.v5 运行完成[0.012163s].

[2021-12-03 14:50:00.685882] INFO: moduleinvoker: backtest.v8 开始运行..

[2021-12-03 14:50:00.716046] INFO: backtest: biglearning backtest:V8.6.0

[2021-12-03 14:50:00.717898] INFO: backtest: product_type:stock by specified

[2021-12-03 14:50:00.896258] INFO: moduleinvoker: cached.v2 开始运行..

[2021-12-03 14:50:00.904994] INFO: moduleinvoker: 命中缓存

[2021-12-03 14:50:00.906511] INFO: moduleinvoker: cached.v2 运行完成[0.010307s].

[2021-12-03 14:50:05.126493] INFO: algo: TradingAlgorithm V1.8.5

[2021-12-03 14:50:07.227869] ERROR: moduleinvoker: module name: backtest, module version: v8, trackeback: NameError: name 'timedelta' is not defined

[2021-12-03 14:50:07.242014] ERROR: moduleinvoker: module name: trade, module version: v4, trackeback: NameError: name 'timedelta' is not defined

设置测试数据集，查看训练迭代过程的NDCG

bigcharts-data-start/{"__type":"tabs","__id":"bigchart-9e31b7d7d79944b0898c6e3191c2aeb2"}/bigcharts-data-end

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-3-11cea7d5fae0> in <module>
    293 )
    294 
--> 295 m4 = M.trade.v4(
    296     instruments=m9.data,
    297     options_data=m8.predictions,

<ipython-input-3-11cea7d5fae0> in m4_initialize_bigquant_run(context)
     31     #个股风控计算
     32     start_date = context.ranker_prediction.date.iloc[0]
---> 33     start_date = pd.to_datetime(start_date)-timedelta(days=30)
     34     end_date = context.ranker_prediction.date.iloc[-1]
     35     stocks = context.ranker_prediction.instrument.to_list()

NameError: name 'timedelta' is not defined