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# 本代码由可视化策略环境自动生成 2021年8月17日 14:38
# 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。


def m4_func_bigquant_run(df, close, op):
    res = close.pct_change()
    res.iloc[0] = close.iloc[0] / op.iloc[0] - 1
    return res

def m11_initialize_bigquant_run(context):
    
    context.ranker_prediction = context.options['data'].read_df()
    factor_name = [i for i in context.ranker_prediction.columns if i not in ['date','instrument','in_csi500_0']]
    print('因子名称:', factor_name)
     
    context.ranker_prediction.sort_values(['date',factor_name[0]], ascending=[True,False], inplace=True)
    context.ranker_prediction.rename(columns={factor_name[0]:'prediction'}, inplace=True)
    
    # 股票数量        
    context.stock_count = 100
    # 组合权重
    #context.stock_weights = T.norm([1 / math.log(i + 2) for i in range(0, context.stock_count)])
    context.stock_weights = [1/ context.stock_count]* context.stock_count
    # 换仓频率
    context.rebalance_days = 1
    
    # 设置交易费用，买入是万三，卖出是千分之1.3，如果不足5元按5元算
    context.set_commission(PerOrder(buy_cost=0.00015,   
                                        sell_cost=0.00015, min_cost=5))
 
    if 'index' not in context.extension:
         context.extension['index'] = 0
# 回测引擎：每日数据处理函数，每天执行一次
def m11_handle_data_bigquant_run(context, data):
    # 日期
    date = data.current_dt.strftime('%Y-%m-%d')
    context.extension['index'] += 1
    
    if  context.extension['index'] % context.rebalance_days != 0:
        return 

   
    cur_df = context.ranker_prediction[context.ranker_prediction['date'] == date]
    stock_to_buy = cur_df.instrument.tolist()[: context.stock_count]
        
    # 目前持仓列表    
    stock_hold_now = [equity.symbol for equity in context.portfolio.positions]
    # 继续持有股票列表
    no_need_to_sell = [i for i in stock_hold_now  if i in stock_to_buy]
    # 卖出股票列表 
    stock_to_sell = [i for i in stock_hold_now if i not in no_need_to_sell]
    
    # 执行卖出
    for stock in stock_to_sell:
        if data.can_trade(context.symbol(stock)):
            context.order_target_percent(context.symbol(stock), 0)
    
    if len(stock_to_buy) == 0:
        return
    
    # 执行买入
    for  i in np.arange(len(stock_to_buy)):
        cp = stock_to_buy[i]
        weight =  context.stock_weights[i]
        if data.can_trade(context.symbol(cp)):
            context.order_target_percent(context.symbol(cp), weight)
# 回测引擎：准备数据，只执行一次
def m11_prepare_bigquant_run(context):
    pass

# 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 m5_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() 
    f = input_2.read()[0]
    df[f] = df[f] * -1
    data_1 = DataSource.write_df(df)
    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 m5_post_run_bigquant_run(outputs):
    return outputs


m1 = M.input_features.v1(
    features="""_amt = amount
_ret = ret_sim(close, open)
big_order_ret = np.prod(1 + _ret*where(_amt >= _amt.sort_values(ascending=False).iloc[np.int(240*0.4-1)], 1, 0))
"""
)

m2 = M.instruments.v2(
    start_date='2019-01-01',
    end_date='2021-08-01',
    market='CN_STOCK_A',
    instrument_list='',
    max_count=0
)

m9 = M.input_features.v1(
    features='big_order_ret',
    m_cached=False
)

m12 = M.input_features.v1(
    features='in_csi500_0'
)

m13 = M.general_feature_extractor.v7(
    instruments=m2.data,
    features=m12.data,
    start_date='',
    end_date='',
    before_start_days=90
)

m14 = M.filter.v3(
    input_data=m13.data,
    expr='in_csi500_0 == 1',
    output_left_data=False
)

m4 = M.feature_extractor_user_function.v1(
    name='ret_sim',
    func=m4_func_bigquant_run
)

m7 = M.feature_extractor_1m.v2(
    instruments=m2.data,
    features=m1.data,
    user_functions=m4.functions,
    start_date='',
    end_date='',
    before_start_days=20,
    workers=20,
    parallel_mode='集群',
    table_1m='level2_bar1m_CN_STOCK_A'
)

m15 = M.data_join.v3(
    input_1=m7.data,
    input_2=m14.data,
    on='date,instrument',
    how='inner',
    sort=False
)

m11 = M.trade.v4(
    instruments=m2.data,
    options_data=m15.data,
    start_date='',
    end_date='',
    initialize=m11_initialize_bigquant_run,
    handle_data=m11_handle_data_bigquant_run,
    prepare=m11_prepare_bigquant_run,
    volume_limit=0.025,
    order_price_field_buy='open',
    order_price_field_sell='close',
    capital_base=10000000,
    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='000905.HIX'
)

m5 = M.cached.v3(
    input_1=m7.data,
    input_2=m9.data,
    run=m5_run_bigquant_run,
    post_run=m5_post_run_bigquant_run,
    input_ports='',
    params='{}',
    output_ports=''
)

m8 = M.factorlens.v1(
    features=m9.data,
    user_factor_data=m5.data_1,
    title='因子分析: {factor_name}',
    start_date='2019-01-01',
    end_date='2021-08-01',
    rebalance_period=2,
    delay_rebalance_days=0,
    rebalance_price='close_0',
    stock_pool='全市场',
    quantile_count=10,
    commission_rate=0.0002,
    returns_calculation_method='累乘',
    benchmark='无',
    drop_new_stocks=60,
    drop_price_limit_stocks=False,
    drop_st_stocks=False,
    drop_suspended_stocks=False,
    normalization=False,
    neutralization=[],
    metrics=['因子表现概览', '因子分布', '因子行业分布', '因子市值分布', 'IC分析', '买入信号重合分析', '因子估值分析', '因子拥挤度分析', '因子值最大/最小股票', '表达式因子值', '多因子相关性分析'],
    factor_coverage=0.5,
    user_data_merge='inner'
)

[2021-08-17 14:28:48.971882] INFO: moduleinvoker: input_features.v1 开始运行..

[2021-08-17 14:28:48.980348] INFO: moduleinvoker: 命中缓存

[2021-08-17 14:28:48.981827] INFO: moduleinvoker: input_features.v1 运行完成[0.009964s].

[2021-08-17 14:28:48.984023] INFO: moduleinvoker: instruments.v2 开始运行..

[2021-08-17 14:28:48.990261] INFO: moduleinvoker: 命中缓存

[2021-08-17 14:28:48.991435] INFO: moduleinvoker: instruments.v2 运行完成[0.007412s].

[2021-08-17 14:28:48.993396] INFO: moduleinvoker: input_features.v1 开始运行..

[2021-08-17 14:28:49.003238] INFO: moduleinvoker: 命中缓存

[2021-08-17 14:28:49.004664] INFO: moduleinvoker: input_features.v1 运行完成[0.011265s].

[2021-08-17 14:28:49.016623] INFO: moduleinvoker: general_feature_extractor.v7 开始运行..

[2021-08-17 14:28:49.029690] INFO: moduleinvoker: 命中缓存

[2021-08-17 14:28:49.031008] INFO: moduleinvoker: general_feature_extractor.v7 运行完成[0.01439s].

[2021-08-17 14:28:49.033772] INFO: moduleinvoker: filter.v3 开始运行..

[2021-08-17 14:28:49.045170] INFO: moduleinvoker: 命中缓存

[2021-08-17 14:28:49.046411] INFO: moduleinvoker: filter.v3 运行完成[0.01264s].

[2021-08-17 14:28:49.051344] INFO: moduleinvoker: feature_extractor_user_function.v1 运行完成[0.000107s].

[2021-08-17 14:28:49.057011] INFO: moduleinvoker: feature_extractor_1m.v2 开始运行..

[2021-08-17 14:28:49.072155] INFO: moduleinvoker: 命中缓存

[2021-08-17 14:28:49.073476] INFO: moduleinvoker: feature_extractor_1m.v2 运行完成[0.016467s].

[2021-08-17 14:28:49.090338] INFO: moduleinvoker: data_join.v3 开始运行..

[2021-08-17 14:28:49.098333] INFO: moduleinvoker: 命中缓存

[2021-08-17 14:28:49.099643] INFO: moduleinvoker: data_join.v3 运行完成[0.009307s].

[2021-08-17 14:28:49.149667] INFO: moduleinvoker: backtest.v8 开始运行..

[2021-08-17 14:28:49.166114] INFO: moduleinvoker: 命中缓存

[2021-08-17 14:28:55.273017] INFO: moduleinvoker: backtest.v8 运行完成[6.123343s].

[2021-08-17 14:28:55.274833] INFO: moduleinvoker: trade.v4 运行完成[6.171044s].

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