克隆策略

目标¶

本示例旨在说明如何基于自定义的数据进行回测研究，例子以ETF回测为例，若是其他市场其他标的，同理即可。

策略逻辑¶

每日计算沪深300的5日均线和20日均线值

若5日均线大于20日均线，只持有a(注:a没有持仓就买入。b和c若有持仓就买出平仓)，建仓20%；

若5日均线小于20日均线，只持有b(注:b没有持仓就买入。a和c若有持仓就买出平仓)，建仓20%；

若5日均线小于20日均线，只持有c(注:c没有持仓就买入。a和b若有持仓就买出平仓)，建仓20%。

模块介绍¶

m2,数据源模块,读取基金日线行情数据

m1,自定义Python模块,过滤出三只ETF基金数据

m4,数据源模块,读取指数日线行情数据

m3,自定义Python模块,过滤出沪深300指数行情数据

m6,自定义Python模块,将沪深300指数数据和回测标的的历史行情数据整理到一起

m8,代码列表模块,指定回测标的、起始结束时间

m7,自定义Python模块,读取基准数据

m5,Trade(回测/模拟),回测执行模块

In [4]:

# 本代码由可视化策略环境自动生成 2021年5月8日20:38
# 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 m1_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()
    lt = ['518880.HOF','513600.HOF','512880.HOF']
    df = df[(df['instrument']==lt[0])|(df['instrument']==lt[1])|(df['instrument']==lt[2])]
    
    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 m1_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 m3_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()
    lt = '000300.HIX'
    df = df[df['instrument']==lt]
    
    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 m3_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 m6_run_bigquant_run(input_1, input_2, input_3):
    # 示例代码如下。在这里编写您的代码
    datadf = input_1.read()
    datadf_ind = input_2.read()   
    
    history_ds = DataSource.write_df(pd.concat([datadf,datadf_ind],axis=0))
    return Outputs(data_1=history_ds, 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 m6_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 m7_run_bigquant_run(input_1, input_2, input_3):
    # 示例代码如下。在这里编写您的代码
    start_date = '2017-11-27'
    end_date = '2018-09-05'
    bm_instruments = ['000300.HIX']
    start = pd.to_datetime(start_date)-datetime.timedelta(50)
    start = start.strftime('%Y-%m-%d')
    id_ohlc = 'bar1d_index_CN_STOCK_A'
    origin_fields=['open', 'high', 'low', 'close', 'volume', 'amount']
    ohlc_df = DataSource(id_ohlc).read(instruments=bm_instruments, start_date=start, end_date=end_date,
                                  fields=origin_fields)

    ohlc_df.rename({'s_dq_close':'close', 's_dq_high':'high', 's_dq_open':'open', 's_dq_low':'low', 's_dq_volume':'volume', 's_dq_amount':'amount'},axis=1, inplace=True)
    benchmark_ds = DataSource.write_df(ohlc_df)
    return Outputs(data_1=benchmark_ds, 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 m7_post_run_bigquant_run(outputs):
    return outputs

def m5_initialize_bigquant_run(context):
    set_commission(futures_commission=PerContract(cost={'IC':(0.0023, 0.0015, 0.0023)}))
    set_need_settle(False)
    context.length_1 = 5
    context.length_2 = 20

def m5_handle_data_bigquant_run(context, data):
    
    #instrument = ['IC0000.CFX','IF0000.CFX','IH0000.CFX']
    instrument = ['518880.HOF','513600.HOF','512880.HOF']

    today = data.current_dt.strftime('%Y-%m-%d')
    
    sid_ind = symbol('000300.HIX')
    sid_a = future_symbol(instrument[0])
    sid_b = future_symbol(instrument[1])
    sid_c = future_symbol(instrument[2])
    
    # 最新价格
    #price = data.current(sid, 'price')
    position_a = context.portfolio.positions[sid_a]
    position_b = context.portfolio.positions[sid_b]
    position_c = context.portfolio.positions[sid_c]
    curr_position_a = position_a.amount
    curr_position_b = position_b.amount
    curr_position_c = position_c.amount
    ma5_ind_price = data.history(sid_ind, 'price', context.length_1, '1d').mean()
    ma20_ind_price = data.history(sid_ind, 'price', context.length_2, '1d').mean() 


    if ma5_ind_price > ma20_ind_price:
        if data.can_trade(sid_a) and curr_position_a == 0 :
            order_target_percent(sid_a, 0.2)
            print(get_datetime(), 'buy_a')
            if data.can_trade(sid_b) and curr_position_b > 0:
                order_target_percent(sid_b, 0)
                print(get_datetime(), 'sell_b')
            elif data.can_trade(sid_c) and curr_position_c > 0:
                order_target_percent(sid_c, 0)
                print(get_datetime(), 'sell_c')
        
    elif ma5_ind_price < ma20_ind_price:
        if data.can_trade(sid_b) and curr_position_b == 0 :
            order_target_percent(sid_b, 0.2)
            print(get_datetime(), 'buy_b')
            if data.can_trade(sid_a) and curr_position_a > 0:
                order_target_percent(sid_a, 0)
                print('=='*10,curr_position_a)
                print(get_datetime(), 'sell_a')
            elif data.can_trade(sid_c) and curr_position_c > 0:
                order_target_percent(sid_c, 0)
                print(get_datetime(), 'sell_c')
            
    elif ma5_ind_price == ma20_ind_price:
        if data.can_trade(sid_c) and curr_position_c == 0 :
            order_target_percent(sid_c, 0.2)
            print(get_datetime(), 'buy_c')
            if data.can_trade(sid_a) and curr_position_a > 0:
                order_target_percent(sid_a, 0)
                print(get_datetime(), 'sell_a')
            elif data.can_trade(sid_b) and curr_position_b > 0:
                order_target_percent(sid_b, 0)
                print(get_datetime(), 'sell_b')
# 回测引擎：准备数据，只执行一次
def m5_prepare_bigquant_run(context):
    pass

# 回测引擎：每个单位时间开始前调用一次，即每日开盘前调用一次。
def m5_before_trading_start_bigquant_run(context, data):
    pass


m2 = M.use_datasource.v1(
    datasource_id='bar1d_CN_FUND',
    start_date='2017-11-27',
    end_date='2018-09-05'
)

m1 = M.cached.v3(
    input_1=m2.data,
    run=m1_run_bigquant_run,
    post_run=m1_post_run_bigquant_run,
    input_ports='',
    params='{}',
    output_ports=''
)

m4 = M.use_datasource.v1(
    datasource_id='bar1d_index_CN_STOCK_A',
    start_date='2017-11-27',
    end_date='2018-09-05'
)

m3 = M.cached.v3(
    input_1=m4.data,
    run=m3_run_bigquant_run,
    post_run=m3_post_run_bigquant_run,
    input_ports='',
    params='{}',
    output_ports=''
)

m6 = M.cached.v3(
    input_1=m1.data_1,
    input_2=m3.data_1,
    run=m6_run_bigquant_run,
    post_run=m6_post_run_bigquant_run,
    input_ports='',
    params='{}',
    output_ports=''
)

m7 = M.cached.v3(
    run=m7_run_bigquant_run,
    post_run=m7_post_run_bigquant_run,
    input_ports='',
    params='{}',
    output_ports=''
)

m8 = M.instruments.v2(
    start_date='2017-11-27',
    end_date='2018-09-05',
    market='CN_FUND',
    instrument_list='[\'518880.HOF\',\'513600.HOF\',\'512880.HOF\']',
    max_count=0
)

m5 = M.trade.v4(
    instruments=m8.data,
    history_ds=m6.data_1,
    benchmark_ds=m7.data_1,
    start_date='2018-01-16',
    end_date='2018-09-05',
    initialize=m5_initialize_bigquant_run,
    handle_data=m5_handle_data_bigquant_run,
    prepare=m5_prepare_bigquant_run,
    before_trading_start=m5_before_trading_start_bigquant_run,
    volume_limit=0.026,
    order_price_field_buy='open',
    order_price_field_sell='close',
    capital_base=2000,
    auto_cancel_non_tradable_orders=True,
    data_frequency='daily',
    price_type='真实价格',
    product_type='股票',
    plot_charts=True,
    backtest_only=False,
    benchmark=''
)

[2021-05-08 20:38:39.138530] INFO: moduleinvoker: use_datasource.v1 开始运行..

[2021-05-08 20:38:39.317898] INFO: moduleinvoker: 命中缓存

[2021-05-08 20:38:39.320180] INFO: moduleinvoker: use_datasource.v1 运行完成[0.181646s].

[2021-05-08 20:38:39.328362] INFO: moduleinvoker: cached.v3 开始运行..

[2021-05-08 20:38:39.341377] INFO: moduleinvoker: 命中缓存

[2021-05-08 20:38:39.343680] INFO: moduleinvoker: cached.v3 运行完成[0.015351s].

[2021-05-08 20:38:39.347977] INFO: moduleinvoker: use_datasource.v1 开始运行..

[2021-05-08 20:38:39.372724] INFO: moduleinvoker: 命中缓存

[2021-05-08 20:38:39.374858] INFO: moduleinvoker: use_datasource.v1 运行完成[0.026889s].

[2021-05-08 20:38:39.380463] INFO: moduleinvoker: cached.v3 开始运行..

[2021-05-08 20:38:39.389981] INFO: moduleinvoker: 命中缓存

[2021-05-08 20:38:39.391520] INFO: moduleinvoker: cached.v3 运行完成[0.011057s].

[2021-05-08 20:38:39.396939] INFO: moduleinvoker: cached.v3 开始运行..

[2021-05-08 20:38:39.414043] INFO: moduleinvoker: 命中缓存

[2021-05-08 20:38:39.415748] INFO: moduleinvoker: cached.v3 运行完成[0.018806s].

[2021-05-08 20:38:39.422019] INFO: moduleinvoker: cached.v3 开始运行..

[2021-05-08 20:38:39.434050] INFO: moduleinvoker: 命中缓存

[2021-05-08 20:38:39.437222] INFO: moduleinvoker: cached.v3 运行完成[0.015193s].

[2021-05-08 20:38:39.442870] INFO: moduleinvoker: instruments.v2 开始运行..

[2021-05-08 20:38:39.452569] INFO: moduleinvoker: 命中缓存

[2021-05-08 20:38:39.455454] INFO: moduleinvoker: instruments.v2 运行完成[0.012586s].

[2021-05-08 20:38:39.500333] INFO: moduleinvoker: backtest.v8 开始运行..

[2021-05-08 20:38:39.509659] INFO: backtest: biglearning backtest:V8.5.0

[2021-05-08 20:38:39.511607] INFO: backtest: product_type:stock by specified

[2021-05-08 20:38:39.513890] INFO: backtest: 其它市场:{"HOF','513600"}

[2021-05-08 20:38:39.653324] INFO: algo: TradingAlgorithm V1.8.2

[2021-05-08 20:38:39.798152] INFO: algo: trading transform...

[2021-05-08 20:38:41.699829] INFO: Performance: Simulated 157 trading days out of 157.

[2021-05-08 20:38:41.701628] INFO: Performance: first open: 2018-01-16 09:30:00+00:00

[2021-05-08 20:38:41.702667] INFO: Performance: last close: 2018-09-05 15:00:00+00:00

[2021-05-08 20:38:42.660633] INFO: moduleinvoker: backtest.v8 运行完成[3.160325s].

[2021-05-08 20:38:42.662822] INFO: moduleinvoker: trade.v4 运行完成[3.203625s].

2018-01-16 15:00:00+00:00 buy_a
2018-02-02 15:00:00+00:00 buy_b
==================== 100
2018-02-02 15:00:00+00:00 sell_a
2018-03-09 15:00:00+00:00 buy_a
2018-03-09 15:00:00+00:00 sell_b
2018-03-22 15:00:00+00:00 buy_b
==================== 100
2018-03-22 15:00:00+00:00 sell_a
2018-05-09 15:00:00+00:00 buy_a
2018-05-09 15:00:00+00:00 sell_b
2018-05-28 15:00:00+00:00 buy_b
==================== 100
2018-05-28 15:00:00+00:00 sell_a
2018-07-18 15:00:00+00:00 buy_a
2018-07-18 15:00:00+00:00 sell_b
2018-08-02 15:00:00+00:00 buy_b
==================== 100
2018-08-02 15:00:00+00:00 sell_a
2018-08-28 15:00:00+00:00 buy_a
2018-08-28 15:00:00+00:00 sell_b
2018-09-05 15:00:00+00:00 buy_b
==================== 100
2018-09-05 15:00:00+00:00 sell_a

收益率-4.93%
年化收益率-7.79%
基准收益率-21.94%
阿尔法-0.08
贝塔0.08
夏普比率-3.75
胜率0.5
盈亏比0.53
收益波动率2.94%
信息比率0.1
最大回撤4.93%

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