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# 本代码由可视化策略环境自动生成 2019年6月20日 15:59
# 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：每日数据处理函数，每天执行一次
def m3_handle_data_bigquant_run(context, data):
    # 1. 获取今日的日期
    today = data.current_dt.strftime('%Y-%m-%d')
    
    # 2. 获取目前持仓的股票和最新市值
    stock_hold_now = {e.symbol: p.amount * p.last_sale_price for e, p in context.portfolio.positions.items()} 
    
    # 3. 获取当前账户可用现金
    cash_for_buy = context.portfolio.cash
    
    # 4. 获取当日的买卖信号股票列表
    try:
        buy_stock = context.daily_stock_buy[today]
    except:
        buy_stock=[]  # 如果没有符合条件的股票，就设置为空
    
    try:
        sell_stock = context.daily_stock_sell[today]
    except:
        sell_stock=[] # 如果没有符合条件的股票，就设置为空 
        
    # 5. 确认需要卖出的股票:已有持仓中符合卖出条件的股票
    stock_to_sell = [ i for i in stock_hold_now if i in sell_stock ]
    
    # 6. 执行卖出操作
    if len(stock_to_sell)>0:
        for instrument in stock_to_sell:
            sid = context.symbol(instrument) # 将标的转化为equity格式
            cur_position = context.portfolio.positions[sid].amount # 当前持仓
            if cur_position > 0 and data.can_trade(sid):
                context.order_target_percent(sid, 0) 
                # 根据卖出的股票市值更新可用现金；
                cash_for_buy += stock_hold_now[instrument]
    
    # 7. 执行买入操作
    if len(buy_stock)>0:
        weight = 1/len(buy_stock)  # 每只股票的比重为等资金比例持有
        for instrument in buy_stock:
            sid = context.symbol(instrument) # 将标的转化为equity格式
            cur_position = context.portfolio.positions[sid].amount # 当前持仓
            if  data.can_trade(sid) and cur_position==0:
                context.order_target_value(sid, weight*cash_for_buy) # 按可用现金等比例买入
# 回测引擎：准备数据，只执行一次
def m3_prepare_bigquant_run(context):
    # 加载预测数据
    df = context.options['data'].read_df()

    # 函数：求满足开仓条件的股票列表
    def open_pos_con(df):
        return list(df[df['buy_condition']>0].instrument)

    # 函数：求满足平仓条件的股票列表
    def close_pos_con(df):
        return list(df[df['sell_condition']>0].instrument)

    # 每日买入股票的数据框
    context.daily_stock_buy= df.groupby('date').apply(open_pos_con)
    # 每日卖出股票的数据框
    context.daily_stock_sell= df.groupby('date').apply(close_pos_con)
# 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))

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
buy_condition=where(mean(close_0,5)>mean(close_0,20),1,0)
sell_condition=where(mean(close_0,5)<mean(close_0,20),1,0)""",
    m_cached=False
)

m2 = M.instruments.v2(
    start_date=T.live_run_param('trading_date', '2016-01-01'),
    end_date=T.live_run_param('trading_date', '2017-01-01'),
    market='CN_STOCK_A',
    instrument_list='600519.SHA',
    max_count=0
)

m7 = M.general_feature_extractor.v7(
    instruments=m2.data,
    features=m1.data,
    start_date='',
    end_date='',
    before_start_days=200,
    m_cached=False
)

m8 = 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
)

m6 = M.dropnan.v1(
    input_data=m8.data
)

m3 = M.trade.v4(
    instruments=m2.data,
    options_data=m6.data,
    start_date='',
    end_date='',
    handle_data=m3_handle_data_bigquant_run,
    prepare=m3_prepare_bigquant_run,
    initialize=m3_initialize_bigquant_run,
    volume_limit=0.025,
    order_price_field_buy='close',
    order_price_field_sell='close',
    capital_base=1000000,
    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=''
)

[2019-06-20 13:14:10.005262] INFO: bigquant: input_features.v1 开始运行..

[2019-06-20 13:14:10.063670] INFO: bigquant: input_features.v1 运行完成[0.058405s].

[2019-06-20 13:14:10.067459] INFO: bigquant: instruments.v2 开始运行..

[2019-06-20 13:14:10.094217] INFO: bigquant: 命中缓存

[2019-06-20 13:14:10.095947] INFO: bigquant: instruments.v2 运行完成[0.028497s].

[2019-06-20 13:14:10.128575] INFO: bigquant: general_feature_extractor.v7 开始运行..

[2019-06-20 13:14:10.246481] INFO: 基础特征抽取: 年份 2015, 特征行数=136

[2019-06-20 13:14:10.273475] INFO: 基础特征抽取: 年份 2016, 特征行数=244

[2019-06-20 13:14:10.285415] INFO: 基础特征抽取: 年份 2017, 特征行数=0

[2019-06-20 13:14:10.345540] INFO: 基础特征抽取: 总行数: 380

[2019-06-20 13:14:10.347480] INFO: bigquant: general_feature_extractor.v7 运行完成[0.218905s].

[2019-06-20 13:14:10.350536] INFO: bigquant: derived_feature_extractor.v3 开始运行..

[2019-06-20 13:14:10.436890] INFO: derived_feature_extractor: 提取完成 buy_condition=where(mean(close_0,5)>mean(close_0,20),1,0), 0.007s

[2019-06-20 13:14:10.446629] INFO: derived_feature_extractor: 提取完成 sell_condition=where(mean(close_0,5)
[2019-06-20 13:14:10.486996] INFO: derived_feature_extractor: /y_2015, 136
[2019-06-20 13:14:10.513525] INFO: derived_feature_extractor: /y_2016, 244
[2019-06-20 13:14:10.613676] INFO: bigquant: derived_feature_extractor.v3 运行完成[0.263128s].
[2019-06-20 13:14:10.616840] INFO: bigquant: dropnan.v1 开始运行..
[2019-06-20 13:14:10.695606] INFO: dropnan: /y_2015, 136/136
[2019-06-20 13:14:10.726307] INFO: dropnan: /y_2016, 244/244
[2019-06-20 13:14:10.813265] INFO: dropnan: 行数: 380/380
[2019-06-20 13:14:10.816473] INFO: bigquant: dropnan.v1 运行完成[0.199615s].
[2019-06-20 13:14:10.860465] INFO: bigquant: backtest.v8 开始运行..
[2019-06-20 13:14:10.864171] INFO: bigquant: biglearning backtest:V8.2.2
[2019-06-20 13:14:11.500200] INFO: bigquant: product_type:stock by specified
[2019-06-20 13:14:11.687216] INFO: bigquant: cached.v2 开始运行..
[2019-06-20 13:14:11.726424] INFO: bigquant: 命中缓存
[2019-06-20 13:14:11.729266] INFO: bigquant: cached.v2 运行完成[0.04205s].
[2019-06-20 13:14:11.768565] INFO: algo: TradingAlgorithm V1.5.0
[2019-06-20 13:14:11.812191] INFO: algo: trading transform...
[2019-06-20 13:14:12.482313] INFO: Performance: Simulated 244 trading days out of 244.
[2019-06-20 13:14:12.484003] INFO: Performance: first open: 2016-01-04 09:30:00+00:00
[2019-06-20 13:14:12.485730] INFO: Performance: last close: 2016-12-30 15:00:00+00:00
[2019-06-20 13:14:13.484915] INFO: bigquant: backtest.v8 运行完成[2.624447s].

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