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# 本代码由可视化策略环境自动生成 2022年9月23日 09:44
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# 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
    from datetime import datetime,timedelta
    df_data = input_1.read()
    df_data['start_date'] = (datetime.strptime(df_data['start_date'],'%Y-%m-%d') - timedelta(days=150)).strftime('%Y-%m-%d')
    return Outputs(data_1=df_data)

# 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 m27_run_bigquant_run(input_1, input_2, input_3,min_volume,N):
    from functools import reduce
    import pandas as pd 
    import numpy as np 
    
    df_raw = input_1.read()
    df_raw = df_raw[~(df_raw['instrument'].str.contains('IC|IF|IH|T|TF|TS'))]
    df_basic = input_2.read()[['product_code','instrument','delist_date']]
    df = df_raw[~(df_raw['instrument'].str.contains('0000|8888|9999'))]
    df_index = df_raw[df_raw['instrument'].str.contains('0000')]
    df_dom = df_raw[df_raw['instrument'].str.contains('9999')]
    
    df = pd.merge(df,df_basic,how='left',on=['product_code','instrument'])
    df['maturity_days'] = (df['delist_date'] - df['date']).apply(lambda x:x.days)
    
    #去掉成交量少的品种
    def kill_min_volume_product(x):
        df_tp = x.groupby('product_code').sum()
        return df_tp[df_tp['volume'] >= min_volume]
    
    df_universe = df[['date','product_code','volume']].groupby('date').apply(kill_min_volume_product)
    df_universe.reset_index(inplace=True)
    df_universe = df_universe[['date','product_code']]
    
    #因子加工函数
    def calc_t_mom(x,N):
        factor_name = 't_mom'
        x.sort_values("date",inplace=True)
        x['returns'] = x['close'].pct_change()
        x[factor_name] = x['returns'].rolling(N).sum() * -1
        del x['product_code']
        x.set_index('date',inplace=True)
        return x

    def calc_t_cumstep(x,N):
        factor_name = 't_cum_step'
        x.sort_values('date',inplace=True)
        x[factor_name] = (2 * (x['high'] - x['low']) * np.sign(x['close'] - x['open']) - (x['close'] - x['open'])) / x['close']
        x[factor_name] = x[factor_name].rolling(N).mean() * -1
        del x['product_code']
        x.set_index('date',inplace=True)
        return x

    def calc_t_stds(x,N):
        factor_name = 't_stds'
        x.sort_values('date',inplace=True)
        x['h_hat'] = np.log(x['high']) - np.log(x['open'])
        x['l_hat'] = np.log(x['low']) - np.log(x['open'])
        x['c_hat'] = np.log(x['close']) - np.log(x['open'])
        x['gk'] = 0.5 * (x['h_hat'] - x['l_hat']) * (x['h_hat'] - x['l_hat']) - (2 * np.log(2) - 1) * x['c_hat'] * x['c_hat']
        x['gk'] = np.sqrt(x['gk'].rolling(5).sum() * 252 / 5)
        del x['product_code']
        del x['h_hat']
        del x['l_hat']
        del x['c_hat']
        x[factor_name] = (x['gk'] * (x['close'] - x['open']).apply(lambda x:np.sign(x)) ).rolling(N).mean() * -1
        x.set_index('date',inplace=True)
        return x
    
    df_result = pd.DataFrame(df_dom.groupby("product_code").apply(calc_t_cumstep,N))
    df_result = df_result.reset_index()
    del df_result['instrument']
    
    #和样本空间
    df_result = pd.merge(df_universe,df_result,how='left',on=['date','product_code'])
    
    df_output = pd.merge(df_result,df_index[['date','product_code','instrument']],how='left',on=['date','product_code'])
    del df_output['product_code']
    df_output['date'] = pd.to_datetime(df_output['date'])
    df_output.dropna(inplace=True)
    df_output.sort_values("date",inplace=True)
    data_1 = DataSource.write_df(df_output)
    return Outputs(data_1=data_1)



# 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 m27_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
    # 期限结构因子
    df1 = input_1.read()
    data_1 = DataSource.write_pickle(df1)
    return Outputs(data_1=data_1)

# 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

# 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
    # 期限结构因子
    df = input_1.read()
    factor_name = input_2.read()[0]
    #signal 
    def calc_signal(x,factor_name):
        x = x.sort_values(factor_name)
        length = x.shape[0]
        if length >= 10 :
            x['signal_'+factor_name] = [1]*5 + [0 for i in range(length-10)] + [-1]*5
        else :
            x['signal_' + factor_name] = [0] * length
        return x
    
    df_result = df.groupby("date").apply(calc_signal,factor_name)
    df_result.reset_index(drop=True,inplace=True)
     #产生信号
    df_result =  df_result[['date','instrument','signal_'+factor_name]]  
    df_result['signal'] = df_result.iloc[:,2:].apply(lambda x:x.sum(),axis=1)
    df_result = df_result[~(df_result['signal'] == 0)]
    
    data_1 = DataSource.write_pickle(df_result[['date','instrument','signal']])
    return Outputs(data_1=data_1)

# 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

# 回测引擎：初始化函数，只执行一次
def m4_initialize_bigquant_run(context):
    outer_data = context.options['data'].read()
    context.stock_to_long = outer_data[(outer_data['signal'] > 0)]
    context.stock_to_short = outer_data[(outer_data['signal'] < 0)]
    context.freq = 5
    

    
# 交易引擎：每个单位时间开盘前调用一次。
def m4_before_trading_start_bigquant_run(context, data):
    # 盘前处理，订阅行情等
    context.position_check = context.get_positions()

# 交易引擎：tick数据处理函数，每个tick执行一次
def m4_handle_tick_bigquant_run(context, tick):
    pass

# 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):
    date = data.current_dt.strftime('%Y-%m-%d')
        
    if (context.trading_day_index % context.freq == 0) :
        print('date'*5, date)
        
        stock_to_long = context.stock_to_long[context.stock_to_long['date'] == date]
        stock_to_long = stock_to_long[stock_to_long['signal'] > 0]['instrument']
        stock_to_short = context.stock_to_short[context.stock_to_short['date'] == date]
        stock_to_short = stock_to_short[stock_to_short['signal'] < 0]['instrument']

        
        # 平仓
        hold_symbols = context.get_account_positions()
        long_pos = {}
        short_pos = {}

        for i in hold_symbols.keys():
            if  hold_symbols[i].long.current_qty > 0:
                long_pos[i] = hold_symbols[i].long.current_qty
            if hold_symbols[i].short.current_qty> 0:
                short_pos[i] = hold_symbols[i].short.current_qty
        
        for stock in long_pos.keys():
            pos = long_pos[stock]
            price = data.current(context.symbol(stock), "close")
            context.sell_close(context.symbol(stock), pos, price, order_type=OrderType.MARKET)
        for stock in short_pos.keys():
            pos = short_pos[stock]
            price = data.current(context.symbol(stock), "close")
            context.buy_close(context.symbol(stock), pos, price, order_type=OrderType.MARKET)
            
 
        # 开仓
        stock_num = len(stock_to_long) + len(stock_to_short)
        if stock_num != 0 :
            weight =  1 / stock_num
            portfolio_value = context.portfolio.portfolio_value
            book_value = portfolio_value  * weight

            for i in range(len(stock_to_short)):
                stock_i = stock_to_short.iloc[i]
                stock = stock_i
                code = stock_i.split('.')[0][:-4]
                stock = context.get_dominant(code)
                if data.can_trade(stock):
                    price = data.current(stock,"close")
                    unit =context.get_contract(stock).multiplier
                    lots = math.floor(book_value/(unit*price))
                    if lots != 0:
                        context.sell_open(stock, lots, price, order_type=OrderType.MARKET)

            for i in range(len(stock_to_long)):
                stock_i = stock_to_long.iloc[i]
                code = stock_i.split('.')[0][:-4]
                stock = context.get_dominant(code)
                if data.can_trade(stock):
                    price = data.current(stock,"close")
                    unit =context.get_contract(stock).multiplier
                    lots = math.floor(book_value/(unit*price))
                    if lots != 0:
                        context.buy_open(stock, lots, price, order_type=OrderType.MARKET)
  

         
        
# 交易引擎：成交回报处理函数，每个成交发生时执行一次
def m4_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 m4_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 m4_after_trading_bigquant_run(context, data):
    pass


m1 = M.instruments.v2(
    start_date='2010-01-01',
    end_date='2022-06-30',
    market='CN_FUTURE',
    instrument_list='',
    max_count=0
)

m8 = M.cached.v3(
    input_1=m1.data,
    run=m8_run_bigquant_run,
    post_run=m8_post_run_bigquant_run,
    input_ports='',
    params='{}',
    output_ports=''
)

m2 = M.use_datasource.v2(
    instruments=m8.data_1,
    datasource_id='bar1d_CN_FUTURE',
    start_date='',
    end_date='',
    before_start_days=0
)

m33 = M.use_datasource.v2(
    instruments=m8.data_1,
    datasource_id='basic_info_CN_FUTURE',
    start_date='',
    end_date='',
    before_start_days=0
)

m27 = M.cached.v3(
    input_1=m2.data,
    input_2=m33.data,
    run=m27_run_bigquant_run,
    post_run=m27_post_run_bigquant_run,
    input_ports='',
    params="""{
    'min_volume': 100000,
    'N':10
}""",
    output_ports=''
)

m7 = M.cached.v3(
    input_1=m27.data_1,
    input_2=m2.data,
    run=m7_run_bigquant_run,
    post_run=m7_post_run_bigquant_run,
    input_ports='',
    params='{}',
    output_ports=''
)

m6 = M.input_features.v1(
    features='t_cum_step'
)

m5 = M.factorlens_custom.v2(
    features=m6.data,
    factor_data=m7.data_1,
    start_date='2010-01-01',
    end_date='2022-06-30',
    instrument_list='',
    expr='',
    rebalance_period=5,
    quantile_num=5,
    buy_commission_rate=0.0005,
    sell_commission_rate=0.0005,
    ic_method='Rank_IC',
    is_winsorize=True,
    is_standardlize=False
)

m3 = M.cached.v3(
    input_1=m27.data_1,
    input_2=m6.data,
    run=m3_run_bigquant_run,
    post_run=m3_post_run_bigquant_run,
    input_ports='',
    params='{}',
    output_ports=''
)

m4 = M.hftrade.v2(
    instruments=m1.data,
    options_data=m3.data_1,
    start_date='',
    end_date='',
    initialize=m4_initialize_bigquant_run,
    before_trading_start=m4_before_trading_start_bigquant_run,
    handle_tick=m4_handle_tick_bigquant_run,
    handle_data=m4_handle_data_bigquant_run,
    handle_trade=m4_handle_trade_bigquant_run,
    handle_order=m4_handle_order_bigquant_run,
    after_trading=m4_after_trading_bigquant_run,
    capital_base=100000000,
    frequency='daily',
    price_type='真实价格',
    product_type='期货',
    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
)

[2022-09-22 18:55:20.954361] INFO: moduleinvoker: instruments.v2 开始运行..

[2022-09-22 18:55:21.181506] INFO: moduleinvoker: 命中缓存

[2022-09-22 18:55:21.216177] INFO: moduleinvoker: instruments.v2 运行完成[0.261864s].

[2022-09-22 18:55:21.268949] INFO: moduleinvoker: cached.v3 开始运行..

[2022-09-22 18:55:21.280763] INFO: moduleinvoker: 命中缓存

[2022-09-22 18:55:21.282803] INFO: moduleinvoker: cached.v3 运行完成[0.013888s].

[2022-09-22 18:55:21.301135] INFO: moduleinvoker: use_datasource.v2 开始运行..

[2022-09-22 18:55:21.309187] INFO: moduleinvoker: 命中缓存

[2022-09-22 18:55:21.336286] INFO: moduleinvoker: use_datasource.v2 运行完成[0.035158s].

[2022-09-22 18:55:21.343237] INFO: moduleinvoker: use_datasource.v2 开始运行..

[2022-09-22 18:55:21.353698] INFO: moduleinvoker: 命中缓存

[2022-09-22 18:55:21.355686] INFO: moduleinvoker: use_datasource.v2 运行完成[0.012472s].

[2022-09-22 18:55:21.399371] INFO: moduleinvoker: cached.v3 开始运行..

[2022-09-22 18:55:58.600675] INFO: moduleinvoker: cached.v3 运行完成[37.201344s].

[2022-09-22 18:55:58.627925] INFO: moduleinvoker: cached.v3 开始运行..

[2022-09-22 18:55:58.941332] INFO: moduleinvoker: cached.v3 运行完成[0.313413s].

[2022-09-22 18:55:58.951090] INFO: moduleinvoker: input_features.v1 开始运行..

[2022-09-22 18:55:58.962177] INFO: moduleinvoker: 命中缓存

[2022-09-22 18:55:58.964393] INFO: moduleinvoker: input_features.v1 运行完成[0.013324s].

[2022-09-22 18:55:59.082950] INFO: moduleinvoker: factorlens_custom.v2 开始运行..

[2022-09-22 18:56:07.836477] INFO: AI: data_processing start ...

[2022-09-22 18:56:51.453480] INFO: AI: data_processing process 43.617s

[2022-09-22 18:56:51.459274] INFO: AI: ic_processing start ...

[2022-09-22 18:56:54.752218] INFO: AI: ic_processing process  3.293s

[2022-09-22 18:56:54.754606] INFO: AI: ols_stats_processing start ...

[2022-09-22 18:56:56.119131] INFO: AI: ols_stats_processing process  1.365s

[2022-09-22 18:56:56.121056] INFO: AI: group_processing start ...

[2022-09-22 19:03:13.110791] INFO: AI: group_processing process  376.990s

[2022-09-22 19:03:13.998813] INFO: moduleinvoker: factorlens_custom.v2 运行完成[434.915855s].

[2022-09-22 19:03:14.031887] INFO: moduleinvoker: cached.v3 开始运行..

[2022-09-22 19:03:26.284291] INFO: moduleinvoker: cached.v3 运行完成[12.252416s].

[2022-09-22 19:03:26.325980] INFO: moduleinvoker: hfbacktest.v1 开始运行..

[2022-09-22 19:03:26.333098] INFO: hfbacktest: biglearning V1.4.19

[2022-09-22 19:03:26.334576] INFO: hfbacktest: bigtrader v1.9.7_sp8 2022-09-16

[2022-09-22 19:03:26.386101] INFO: moduleinvoker: cached.v2 开始运行..

[2022-09-22 19:03:26.395750] INFO: moduleinvoker: 命中缓存

[2022-09-22 19:03:26.398694] INFO: moduleinvoker: cached.v2 运行完成[0.012612s].

[2022-09-22 19:03:26.537072] INFO: moduleinvoker: cached.v2 开始运行..

[2022-09-22 19:03:26.549517] INFO: moduleinvoker: 命中缓存

[2022-09-22 19:03:26.551344] INFO: moduleinvoker: cached.v2 运行完成[0.014299s].

[2022-09-22 19:03:28.137473] INFO: moduleinvoker: cached.v2 开始运行..

[2022-09-22 19:03:28.145735] INFO: moduleinvoker: 命中缓存

[2022-09-22 19:03:28.148319] INFO: moduleinvoker: cached.v2 运行完成[0.010899s].

[2022-09-22 19:05:04.336034] INFO: hfbacktest: backtest done, raw_perf_ds:DataSource(e833d0736c5c4fa9b11f14d0059b17aeT)

[2022-09-22 19:05:09.459261] INFO: moduleinvoker: hfbacktest.v1 运行完成[103.133294s].

[2022-09-22 19:05:09.465478] INFO: moduleinvoker: hftrade.v2 运行完成[103.172528s].

t_cum_step: IC分析

IC均值0.0032
IC标准差0.283
ICIR0.0113
IC正值次数298次
IC负值次数307次
IC偏度-0.0468
IC峰度-0.1976

t_cum_step: 因子收益率分析

因子收益均值-0.078
因子收益标准差2.583
因子收益为正比率49.830000000000005%
t值绝对值的均值1.2664
t值绝对值大于2的比率0.1997
因子收益t检验p值小于0.05的比率0.1848

t_cum_step: 因子绩效分析

top0_ret top4_ret LS_ret
收益率 -0.3307 -0.1429 -0.1416
近1日收益率 -0.0011 0.001 -0.001
近1周收益率 -0.0022 -0.0076 0.0026
近1月收益率 -0.0754 -0.083 0.004
年化收益率 -0.0328 -0.0127 -0.0126
夏普比率 -0.2876 -0.1981 -0.5344
收益波动率 0.1808 0.1691 0.0828
最大回撤 -0.7022 -0.7704 -0.4288

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datedatedatedatedate 2017-09-26
datedatedatedatedate 2017-10-31
datedatedatedatedate 2017-11-28
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datedatedatedatedate 2018-01-24
datedatedatedatedate 2018-02-28
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datedatedatedatedate 2018-04-27
datedatedatedatedate 2018-05-29
datedatedatedatedate 2018-06-27
datedatedatedatedate 2018-07-25
datedatedatedatedate 2018-08-22
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datedatedatedatedate 2020-01-15
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datedatedatedatedate 2020-12-14
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datedatedatedatedate 2021-06-15
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datedatedatedatedate 2021-08-10
datedatedatedatedate 2021-09-07
datedatedatedatedate 2021-10-14
datedatedatedatedate 2021-11-11
datedatedatedatedate 2021-12-09
datedatedatedatedate 2022-01-07
datedatedatedatedate 2022-02-11
datedatedatedatedate 2022-03-11
datedatedatedatedate 2022-04-12
datedatedatedatedate 2022-05-13
datedatedatedatedate 2022-06-13

收益率26.88%
年化收益率1.92%
基准收益率26.87%
阿尔法-0.01
贝塔-0.01
夏普比率-0.07
胜率0.53
盈亏比1.06
收益波动率8.61%
最大回撤23.27%

bigcharts-data-start/{"__type":"tabs","__id":"bigchart-295869b43b17497c92213cce6ffd3c66"}/bigcharts-data-end

In [5]:

import empyrical
start_date = ['2010-01-01', '2011-01-01', '2012-01-01', '2013-01-01', '2014-01-01', '2015-01-01', '2016-01-01', '2017-01-01', '2018-01-01', '2019-01-01', '2020-01-01', '2021-01-01', '2022-01-01']
end_date = ['2010-12-31', '2011-12-31', '2012-12-31', '2013-12-31', '2014-12-31', '2015-12-31', '2016-12-31', '2017-12-31', '2018-12-31', '2019-12-31', '2020-12-31', '2021-12-31', '2022-08-31']
raw_perf = m4.raw_perf.read()
df_result = []
for sd, ed in zip(start_date, end_date):
    df = raw_perf[raw_perf.date.between(sd, ed)]
    sharpe = empyrical.sharpe_ratio(df.returns)
    sortino = empyrical.sortino_ratio(df.returns)
    max_drawdown = empyrical.max_drawdown(df.returns)
    vol = empyrical.annual_volatility(df.returns)
    returns = empyrical.annual_return(df.returns)
    df_result.append({'start_date':sd,'end_date':ed,'returns':returns,'vol':vol,'max_drawdown':max_drawdown,'sharpe':sharpe,'sortino':sortino})
#     print(f"date between {sd} and {ed}, the sharpe is {sharpe}, sortino is {sortino}")
pd.DataFrame(df_result)[['returns','vol','max_drawdown','sharpe','sortino']]

Out[5]:

	returns	vol	max_drawdown	sharpe	sortino
0	-0.003174	0.078035	-0.076142	-0.001876	-0.002610
1	0.145042	0.072891	-0.058363	1.894949	2.887791
2	0.120870	0.077576	-0.038748	1.509760	2.476620
3	0.079012	0.070575	-0.056094	1.112800	1.666135
4	-0.036534	0.075730	-0.075938	-0.453740	-0.644220
5	0.113790	0.078001	-0.064545	1.420765	2.145643
6	0.153066	0.141398	-0.108166	1.077808	1.673547
7	-0.066750	0.124550	-0.111116	-0.492476	-0.665048
8	0.147776	0.097805	-0.067818	1.458223	2.286790
9	0.101444	0.095580	-0.078425	1.058654	1.617385
10	-0.049555	0.140734	-0.189198	-0.291059	-0.414395
11	0.129588	0.171734	-0.141043	0.795118	1.197187
12	-0.131828	0.144301	-0.081542	-0.907353	-1.134813

In [26]:

Out[26]:

-0.013927574430000478

In [ ]: