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# 本代码由可视化策略环境自动生成 2020年9月23日 14:42
# 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。


# 回测引擎：初始化函数，只执行一次
def m3_initialize_bigquant_run(context):
    # 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))
# 回测引擎：每日数据处理函数，每天执行一次
def m3_handle_data_bigquant_run(context, data):
    today = data.current_dt.strftime('%Y-%m-%d')
    zscore_today =context.zscore.ix[today]
    #获取股票的列表
    stocklist=context.instruments
    # 转换成回测引擎所需要的symbol格式
    symbol_1 = context.symbol(stocklist[0]) 
    symbol_2 = context.symbol(stocklist[1])  

    # 持仓
    cur_position_1 = context.portfolio.positions[symbol_1].amount
    cur_position_2 = context.portfolio.positions[symbol_2].amount
       
    # 交易逻辑
    # 如果zscore大于上轨（>1），则价差会向下回归均值，因此需要买入股票x，卖出股票y
    if zscore_today > 1 and cur_position_1 == 0 and data.can_trade(symbol_1) and data.can_trade(symbol_2):  
        context.order_target_percent(symbol_2, 0)
        context.order_target_percent(symbol_1, 1)
        print(today, '全仓买入：',stocklist[0])
        
    # 如果zscore小于下轨（<-1），则价差会向上回归均值，因此需要买入股票y，卖出股票x
    elif zscore_today < -1 and cur_position_2 == 0 and data.can_trade(symbol_1) and data.can_trade(symbol_2):  
        context.order_target_percent(symbol_1, 0)  
        context.order_target_percent(symbol_2, 1)
        print(today, '全仓买入：',stocklist[1])
 
          

# 回测引擎：准备数据，只执行一次
def m3_prepare_bigquant_run(context):
    pass
# 回测引擎：准备数据，只执行一次
def m3_before_trading_start_bigquant_run(context,data):
    # 加载股票历史数据
    df = context.options['data'].read_df()
    df['date'] = df['date'].apply(lambda x:x.strftime('%Y-%m-%d'))
    today = data.current_dt.strftime('%Y-%m-%d')
    # 获取前240个自然日的数据
    start_date = (pd.to_datetime(data.current_dt)-datetime.timedelta(days=240)).strftime('%Y-%m-%d')
    stock_data = df[df.date <= today]
    # 由于可能上市天数不同，对缺失值填充处理
    prices_df=pd.pivot_table(stock_data, values='close_0', index=['date'], columns=['instrument'])
    prices_df.fillna(method='ffill',inplace=True)
    
    x = prices_df[context.instruments[0]] # 股票1
    y = prices_df[context.instruments[1]] # 股票2
    
    # 线性回归两个股票的股价 y=ax+b
    from pyfinance import ols
    model = ols.OLS(y=y, x=x)
 
    def zscore(series):
        return (series - series.mean()) / np.std(series)
    
    # 计算 y-a*x 序列的zscore值序列
    zscore_calcu = zscore(y-model.beta*x)
    context.zscore=zscore_calcu

m1 = 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
close_0
"""
)

m2 = M.instruments.v2(
    start_date=T.live_run_param('trading_date', '2015-01-01'),
    end_date=T.live_run_param('trading_date', '2018-10-28'),
    market='CN_STOCK_A',
    instrument_list="""601328.SHA
601998.SHA""",
    max_count=0
)

m7 = M.general_feature_extractor.v7(
    instruments=m2.data,
    features=m1.data,
    start_date='',
    end_date='',
    before_start_days=300
)

m4 = M.derived_feature_extractor.v3(
    input_data=m7.data,
    features=m1.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False,
    user_functions={}
)

m5 = M.dropnan.v2(
    input_data=m4.data
)

m3 = M.trade.v4(
    instruments=m2.data,
    options_data=m5.data,
    start_date='',
    end_date='',
    initialize=m3_initialize_bigquant_run,
    handle_data=m3_handle_data_bigquant_run,
    prepare=m3_prepare_bigquant_run,
    before_trading_start=m3_before_trading_start_bigquant_run,
    volume_limit=0.025,
    order_price_field_buy='open',
    order_price_field_sell='open',
    capital_base=1000000,
    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=''
)

[2019-06-21 09:53:38.503017] INFO: bigquant: input_features.v1 开始运行..

[2019-06-21 09:53:38.545842] INFO: bigquant: 命中缓存

[2019-06-21 09:53:38.556901] INFO: bigquant: input_features.v1 运行完成[0.053881s].

[2019-06-21 09:53:38.560006] INFO: bigquant: instruments.v2 开始运行..

[2019-06-21 09:53:38.602468] INFO: bigquant: 命中缓存

[2019-06-21 09:53:38.604658] INFO: bigquant: instruments.v2 运行完成[0.044642s].

[2019-06-21 09:53:38.635206] INFO: bigquant: general_feature_extractor.v7 开始运行..

[2019-06-21 09:53:38.665744] INFO: bigquant: 命中缓存

[2019-06-21 09:53:38.667802] INFO: bigquant: general_feature_extractor.v7 运行完成[0.032597s].

[2019-06-21 09:53:38.670660] INFO: bigquant: derived_feature_extractor.v3 开始运行..

[2019-06-21 09:53:38.711715] INFO: bigquant: 命中缓存

[2019-06-21 09:53:38.713990] INFO: bigquant: derived_feature_extractor.v3 运行完成[0.04332s].

[2019-06-21 09:53:38.716530] INFO: bigquant: dropnan.v1 开始运行..

[2019-06-21 09:53:38.742758] INFO: bigquant: 命中缓存

[2019-06-21 09:53:38.744626] INFO: bigquant: dropnan.v1 运行完成[0.028085s].

[2019-06-21 09:53:38.791453] INFO: bigquant: backtest.v8 开始运行..

[2019-06-21 09:53:38.794461] INFO: bigquant: biglearning backtest:V8.2.2

[2019-06-21 09:53:38.795752] INFO: bigquant: product_type:stock by specified

[2019-06-21 09:53:39.084577] INFO: bigquant: cached.v2 开始运行..

[2019-06-21 09:53:39.128779] INFO: bigquant: 命中缓存

[2019-06-21 09:53:39.130639] INFO: bigquant: cached.v2 运行完成[0.046066s].

[2019-06-21 09:53:39.184503] INFO: algo: TradingAlgorithm V1.5.0

[2019-06-21 09:53:39.373587] INFO: algo: trading transform...

[2019-06-21 09:54:29.600489] INFO: algo: handle_splits get splits [dt:2016-07-25 00:00:00+00:00] [asset:Equity(0 [601998.SHA]), ratio:0.9657426445424234]

[2019-06-21 09:54:29.602455] INFO: Position: position stock handle split[sid:0, orig_amount:183300, new_amount:189802.0, orig_cost:5.690047724566808, new_cost:5.5, ratio:0.9657426445424234, last_sale_price:5.9200025215655305]

[2019-06-21 09:54:29.604078] INFO: Position: after split: asset: Equity(0 [601998.SHA]), amount: 189802.0, cost_basis: 5.5,             last_sale_price: 6.130000114440918

[2019-06-21 09:54:29.605697] INFO: Position: returning cash: 0.7

[2019-06-21 09:55:01.107468] INFO: algo: handle_splits get splits [dt:2017-07-24 00:00:00+00:00] [asset:Equity(0 [601998.SHA]), ratio:0.9700427168666901]

[2019-06-21 09:55:01.110080] INFO: Position: position stock handle split[sid:0, orig_amount:218600, new_amount:225350.0, orig_cost:6.1500281882553605, new_cost:5.97, ratio:0.9700427168666901, last_sale_price:6.799999667260655]

[2019-06-21 09:55:01.112385] INFO: Position: after split: asset: Equity(0 [601998.SHA]), amount: 225350.0, cost_basis: 5.97,             last_sale_price: 7.010000228881836

[2019-06-21 09:55:01.114301] INFO: Position: returning cash: 6.13

[2019-06-21 09:55:33.612472] INFO: algo: handle_splits get splits [dt:2018-07-16 00:00:00+00:00] [asset:Equity(1 [601328.SHA]), ratio:0.9517470390385913]

[2019-06-21 09:55:33.614314] INFO: Position: position stock handle split[sid:1, orig_amount:254600, new_amount:267508.0, orig_cost:7.0600000818200925, new_cost:6.72, ratio:0.9517470390385913, last_sale_price:5.7199994686311895]

[2019-06-21 09:55:33.615812] INFO: Position: after split: asset: Equity(1 [601328.SHA]), amount: 267508.0, cost_basis: 6.72,             last_sale_price: 6.009999752044678

[2019-06-21 09:55:33.617095] INFO: Position: returning cash: 0.32

[2019-06-21 09:55:42.568981] INFO: Performance: Simulated 930 trading days out of 930.

[2019-06-21 09:55:42.571150] INFO: Performance: first open: 2015-01-05 09:30:00+00:00

[2019-06-21 09:55:42.573583] INFO: Performance: last close: 2018-10-26 15:00:00+00:00

[2019-06-21 09:55:44.571635] INFO: bigquant: backtest.v8 运行完成[125.780166s].

2015-01-05 全仓买入： 601328.SHA
2015-06-01 全仓买入： 601998.SHA
2015-07-08 全仓买入： 601328.SHA
2015-08-31 全仓买入： 601998.SHA
2015-11-18 全仓买入： 601328.SHA
2016-06-06 全仓买入： 601998.SHA

2016-11-28 全仓买入： 601328.SHA
2017-04-19 全仓买入： 601998.SHA

2018-02-05 全仓买入： 601328.SHA

2018-09-14 全仓买入： 601998.SHA

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