策略名称¶

可转债双低策略

策略思路¶

建仓条件：市场双低值均值低于170可进行建仓。¶

选择范围：在双低值排位（升序）前30位的可转债中进行筛选。¶

筛选条件：规模大于1.5亿元；剩余期限大于半年；上市超过1个月。¶

最终确定：如果数量大于10支，则选择转债价格最低的10支，等权重买入；如果不足10支，则双低排位范围扩大，直凑够10支。¶

轮动规则: 每月调仓 version 20230419¶

In [4]:

# 本代码由可视化策略环境自动生成 2023年5月24日 18:28
# 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。
 
# 显式导入 BigQuant 相关 SDK 模块
import datetime
import pandas as pd
from bigdatasource.api import DataSource
from zipline.finance.commission import PerOrder
from biglearning.api import M
from biglearning.api import tools as T
from biglearning.module2.common.data import 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 m8_run_bigquant_run(input_1, input_2, input_3):
    # 示例代码如下。在这里编写您的代码
    df = input_1.read()
    df['list_date'] = df['list_date'].astype(str)
    df['delist_date'] = df['delist_date'].astype(str)
    df = df.loc[:,['instrument','list_date','delist_date']]
    
    data_1 = DataSource.write_df(df)
    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 m8_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 m9_run_bigquant_run(input_1, input_2, input_3):
    # 示例代码如下。在这里编写您的代码
    df = input_1.read()
    
    df = df.loc[:,['date','instrument','close','bond_prem_ratio','double_low','remain_size']]
    
    data_1 = DataSource.write_df(df)
    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 m9_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 m10_run_bigquant_run(input_1, input_2, input_3):
    # 示例代码如下。在这里编写您的代码
    df = input_1.read()
    
    df['date'] = pd.to_datetime(df['date'])
    df['list_date'] = pd.to_datetime(df['list_date'])
    df['delist_date'] = pd.to_datetime(df['delist_date'])
    
    #买入规则：上市满30天才能买入
    df['can_buy'] = df['date'] > (df['list_date'] + datetime.timedelta(days=30))

    #卖出规则：离退市日期差6个月时清仓
    df['to_clean'] = (df['date'] + datetime.timedelta(days=180)) > df['delist_date']
    
    data_1 = DataSource.write_df(df)
    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 m10_post_run_bigquant_run(outputs):
    return outputs

# 交易引擎：初始化函数，只执行一次
def m7_initialize_bigquant_run(context):
    print("initializing")
       # 加载股票指标数据,数据继承自m6模块
    context.indicator_data = 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))
    
    # 设置股票数量
    context.stock_num = 20
    
    # 调仓天数,22个交易日大概就是一个月。可以理解为一个月换仓一次
    context.rebalance_days = 22
    
    # 如果策略运行中，需要将数据进行保存，可以借用extension这个对象，类型为dict
    # 比如当前运行的k线的索引，比如个股持仓天数、买入均价
    try:
        print(f"check idx: {context.extension.idx}")
    except AttributeError as e:
        context.extension.idx = 0
 
# 交易引擎：每个单位时间开盘前调用一次。
def m7_before_trading_start_bigquant_run(context, data):
    context.subscribe(context.instruments)
    pass
# 交易引擎：tick数据处理函数，每个tick执行一次
def m7_handle_tick_bigquant_run(context, data):
    pass

# 回测引擎：每日数据处理函数，每天执行一次
def m7_handle_data_bigquant_run(context, data):
    
    context.extension.idx += 1
    # 不在换仓日就return,相当于后面的代码只会一个月运行一次，买入的股票会持有1个月
    if  context.extension.idx % context.rebalance_days != 0:
        return
    
    # 当前的日期
    date = data.current_dt.strftime('%Y-%m-%d')
    cur_data = context.indicator_data[context.indicator_data['date'] == date]
    
    #建仓条件判断：市场双低均值达到170可进行建仓
    if cur_data['double_low'].mean() > 170:
        print(date,'：该调仓日市场双低均值为',cur_data['double_low'].mean(),",无需建仓")
        return
    
    
    # 设定股票池
    # 条件1：上市超过1个月
    stock_can_buy = cur_data[cur_data['can_buy'] == 1]
    # 条件2：剩余期限小于180个自然日
    stock_to_clean = cur_data[cur_data['to_clean'] == 1]
    
    symbols = list(set(stock_can_buy['instrument'].values).difference(set(stock_to_clean['instrument'].values)))
    cur_data = cur_data.set_index('instrument').loc[symbols,:].reset_index().sort_values('double_low')
    stock_to_buy = list(cur_data.instrument[:context.stock_num])
   
    # 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:
        context.order_target_percent(stock, 0)
    
    # 如果当天没有买入的股票，就返回
    if len(stock_to_buy) == 0:
        print(date,'当天没有买入的股票')
        return

    # 等权重买入 
    weight =  1 / len(stock_to_buy)
    
    # 买入
    for stock in stock_to_buy:
        context.order_target_percent(stock, weight)
 
# 交易引擎：成交回报处理函数，每个成交发生时执行一次
def m7_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 m7_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 m7_after_trading_bigquant_run(context, data):
    pass


m1 = M.use_datasource.v1(
    datasource_id='market_performance_CN_CONBOND',
    start_date='2018-01-01',
    end_date='2020-12-31'
)

m6 = M.trade_data_generation.v1(
    input=m1.data,
    category='CN_STOCK',
    m_cached=False
)

m2 = M.use_datasource.v1(
    datasource_id='basic_info_CN_CONBOND',
    start_date='2018-01-01',
    end_date='2020-12-31'
)

m8 = M.cached.v3(
    input_1=m2.data,
    run=m8_run_bigquant_run,
    post_run=m8_post_run_bigquant_run,
    input_ports='',
    params='{}',
    output_ports=''
)

m4 = M.input_features.v1(
    features="""
# #号开始的表示注释，注释需单独一行
# 多个特征，每行一个，可以包含基础特征和衍生特征，特征须为本平台特征
double_low = close + bond_prem_ratio
remain_size > 1.5
"""
)

m5 = M.derived_feature_extractor.v3(
    input_data=m1.data,
    features=m4.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False,
    user_functions={}
)

m9 = M.cached.v3(
    input_1=m5.data,
    run=m9_run_bigquant_run,
    post_run=m9_post_run_bigquant_run,
    input_ports='',
    params='{}',
    output_ports=''
)

m3 = M.join.v3(
    data1=m9.data_1,
    data2=m8.data_1,
    on='instrument',
    how='inner',
    sort=False
)

m10 = M.cached.v3(
    input_1=m3.data,
    run=m10_run_bigquant_run,
    post_run=m10_post_run_bigquant_run,
    input_ports='',
    params='{}',
    output_ports=''
)

m7 = M.hftrade.v2(
    instruments=m6.instrument_list,
    options_data=m10.data_1,
    start_date='',
    end_date='',
    initialize=m7_initialize_bigquant_run,
    before_trading_start=m7_before_trading_start_bigquant_run,
    handle_tick=m7_handle_tick_bigquant_run,
    handle_data=m7_handle_data_bigquant_run,
    handle_trade=m7_handle_trade_bigquant_run,
    handle_order=m7_handle_order_bigquant_run,
    after_trading=m7_after_trading_bigquant_run,
    capital_base=1000001,
    frequency='daily',
    price_type='真实价格',
    product_type='可转债',
    before_start_days='0',
    volume_limit=1,
    order_price_field_buy='close',
    order_price_field_sell='close',
    benchmark='000300.HIX',
    plot_charts=True,
    disable_cache=False,
    replay_bdb=False,
    show_debug_info=False,
    backtest_only=False
)

[2023-05-24 18:29:15.181265] INFO moduleinvoker: use_datasource.v1 开始运行..
[2023-05-24 18:29:15.186877] INFO moduleinvoker: 命中缓存
[2023-05-24 18:29:15.188429] INFO moduleinvoker: use_datasource.v1 运行完成[0.007216s].
[2023-05-24 18:29:15.198955] INFO moduleinvoker: trade_data_generation.v1 开始运行..
[2023-05-24 18:29:16.887663] INFO moduleinvoker: trade_data_generation.v1 运行完成[1.688636s].
[2023-05-24 18:29:16.896052] INFO moduleinvoker: use_datasource.v1 开始运行..
[2023-05-24 18:29:16.902982] INFO moduleinvoker: 命中缓存
[2023-05-24 18:29:16.904568] INFO moduleinvoker: use_datasource.v1 运行完成[0.008536s].
[2023-05-24 18:29:16.924144] INFO moduleinvoker: cached.v3 开始运行..
[2023-05-24 18:29:16.930098] INFO moduleinvoker: 命中缓存
[2023-05-24 18:29:16.931703] INFO moduleinvoker: cached.v3 运行完成[0.0076s].
[2023-05-24 18:29:16.936875] INFO moduleinvoker: input_features.v1 开始运行..
[2023-05-24 18:29:16.942212] INFO moduleinvoker: 命中缓存
[2023-05-24 18:29:16.943893] INFO moduleinvoker: input_features.v1 运行完成[0.007039s].
[2023-05-24 18:29:16.956891] INFO moduleinvoker: derived_feature_extractor.v3 开始运行..
[2023-05-24 18:29:16.962102] INFO moduleinvoker: 命中缓存
[2023-05-24 18:29:16.964207] INFO moduleinvoker: derived_feature_extractor.v3 运行完成[0.007361s].
[2023-05-24 18:29:16.977156] INFO moduleinvoker: cached.v3 开始运行..
[2023-05-24 18:29:16.987667] INFO moduleinvoker: 命中缓存
[2023-05-24 18:29:16.989301] INFO moduleinvoker: cached.v3 运行完成[0.012178s].
[2023-05-24 18:29:17.000334] INFO moduleinvoker: join.v3 开始运行..
[2023-05-24 18:29:17.006278] INFO moduleinvoker: 命中缓存
[2023-05-24 18:29:17.008191] INFO moduleinvoker: join.v3 运行完成[0.007869s].
[2023-05-24 18:29:17.021609] INFO moduleinvoker: cached.v3 开始运行..
[2023-05-24 18:29:17.028136] INFO moduleinvoker: 命中缓存
[2023-05-24 18:29:17.029640] INFO moduleinvoker: cached.v3 运行完成[0.00805s].
[2023-05-24 18:29:17.053817] INFO moduleinvoker: hfbacktest.v1 开始运行..
[2023-05-24 18:29:17.057654] INFO hfbacktest: biglearning V1.4.21
[2023-05-24 18:29:17.059095] INFO hfbacktest: bigtrader v1.10.2 2023-05-18
[2023-05-24 18:29:17.074195] INFO moduleinvoker: cached.v2 开始运行..
[2023-05-24 18:29:17.079298] INFO moduleinvoker: 命中缓存
[2023-05-24 18:29:17.080666] INFO moduleinvoker: cached.v2 运行完成[0.006478s].
[2023-05-24 18:29:17.351430] INFO moduleinvoker: cached.v2 开始运行..
[2023-05-24 18:29:17.357949] INFO moduleinvoker: 命中缓存
[2023-05-24 18:29:17.360752] INFO moduleinvoker: cached.v2 运行完成[0.009342s].
initializing

/usr/local/python3/lib/python3.8/site-packages/empyrical/stats.py:710: RuntimeWarning: divide by zero encountered in divide
  np.divide(
/usr/local/python3/lib/python3.8/site-packages/empyrical/stats.py:799: RuntimeWarning: invalid value encountered in divide
  np.divide(average_annual_return, annualized_downside_risk, out=out)
/usr/local/python3/lib/python3.8/site-packages/pandas/core/generic.py:2605: PerformanceWarning: 
your performance may suffer as PyTables will pickle object types that it cannot
map directly to c-types [inferred_type->mixed,key->block3_values] [items->Index(['orders', 'transactions', 'positions', 'POS_FAC', 'TRA_FAC', 'LOG'], dtype='object')]

  pytables.to_hdf(

[2023-05-24 18:29:48.576407] INFO hfbacktest: backtest done, raw_perf_ds:DataSource(b1b65011c15a4a228edaddc8be3eeff2T)

/usr/local/python3/lib/python3.8/site-packages/pandas/core/indexing.py:1637: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  self._setitem_single_block(indexer, value, name)
[2023-05-24 18:29:53.958569] INFO: bigcharts.impl.render:render.py:407:render_chart Data is None, skip loading it to chart.

收益率36.93%
年化收益率10.98%
基准收益率27.5%
阿尔法0.06
贝塔0.38
夏普比率0.67
胜率0.55
盈亏比2.56
收益波动率12.96%
信息比率0.0
最大回撤14.77%

日期	时间	股票代码	股票名称	买/卖	数量	成交价	总成本	交易佣金
Loading... (need help?)

日期	标的代码	标的名称	持仓均价	收盘价	数量	持仓价值	收益
Loading... (need help?)

时间	级别	内容
Loading... (need help?)

[2023-05-24 18:29:59.653334] INFO moduleinvoker: hfbacktest.v1 运行完成[42.599457s].

INFO:moduleinvoker:hfbacktest.v1 运行完成[42.599457s].

[2023-05-24 18:29:59.658044] INFO moduleinvoker: hftrade.v2 运行完成[42.620044s].

INFO:moduleinvoker:hftrade.v2 运行完成[42.620044s].