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    In [39]:
    # 本代码由可视化策略环境自动生成 2022年10月21日 18:01
# 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 m1_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))
    context.factor_data = context.options['data'].read_df()
# 回测引擎：每日数据处理函数，每天执行一次
def m1_handle_data_bigquant_run(context, data):
    # 按日期过滤得到今日的预测数据
    factor_data = context.factor_data[
        context.factor_data.date == data.current_dt.strftime('%Y-%m-%d')]
    for sid in context.instruments:
        factor = factor_data[factor_data.instrument==sid]
        # 标的为字符串格式
        sid = context.symbol(sid)# 标的为字符串格
        price = data.current(sid, 'price') # 最新价格
        rsrs = factor.rsrs.values
        cur_position =  context.portfolio.positions[sid].amount # 持仓数量 
        weight = 1 / len(context.instruments) # 等权重

        #交易逻辑
        # 如果短期均线大于长期均线形成金叉，并且没有持仓，并且该股票可以交易
        if rsrs>0.6 and cur_position == 0:  
            context.order_target_percent(sid, weight) # 买入
            print('{}全仓买入{}股票'.format(data.current_dt.strftime('%Y-%m-%d'),sid.symbol))
        # 如果短期均线小于长期均线形成死叉，并且有持仓，并且该股票可以交易
        elif rsrs<0.6 and cur_position > 0 :  
            context.order_target_percent(sid, 0) # 全部卖出
            print('{}卖出{}股票'.format(data.current_dt.strftime('%Y-%m-%d'),sid.symbol))
        
        

# 回测引擎：准备数据，只执行一次
def m1_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 m1_before_trading_start_bigquant_run(context, data):
    pass


m7 = M.input_features.v1(
    features='rsrs = ta_beta(high_0, low_0, 18)'
)

m2 = M.instruments.v2(
    start_date='2019-09-01',
    end_date='2021-09-01',
    market='CN_STOCK_A',
    instrument_list='000001.SZA',
    max_count=0
)

m3 = M.general_feature_extractor.v7(
    instruments=m2.data,
    features=m7.data,
    start_date='',
    end_date='',
    before_start_days=90
)

m8 = M.derived_feature_extractor.v3(
    input_data=m3.data,
    features=m7.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False,
    user_functions={}
)

m1 = M.trade.v4(
    instruments=m2.data,
    options_data=m8.data,
    start_date='',
    end_date='',
    initialize=m1_initialize_bigquant_run,
    handle_data=m1_handle_data_bigquant_run,
    prepare=m1_prepare_bigquant_run,
    before_trading_start=m1_before_trading_start_bigquant_run,
    volume_limit=0,
    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=''
)
    • 收益率31.91%
    • 年化收益率15.41%
    • 基准收益率28.16%
    • 阿尔法0.07
    • 贝塔0.71
    • 夏普比率0.54
    • 胜率0.67
    • 盈亏比0.72
    • 收益波动率28.47%
    • 信息比率0.01
    • 最大回撤31.85%
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