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# 本代码由可视化策略环境自动生成 2022年3月27日 22:00
# 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 m6_initialize_bigquant_run(context):
    # 加载预测数据
    context.ranker_prediction = context.options['data'].read_df()

    # 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))
    # 预测数据，通过options传入进来，使用 read_df 函数，加载到内存 (DataFrame)
    # 设置买入的股票数量，这里买入预测股票列表排名靠前的5只
#     stock_count = 5
    # 每只的股票的权重，如下的权重分配会使得靠前的股票分配多一点的资金，[0.339160, 0.213986, 0.169580, ..]
#     context.stock_weights = T.norm([1 / math.log(i + 2) for i in range(0, stock_count)])
    # 设置每只股票占用的最大资金比例
#     context.max_cash_per_instrument = 0.2
#     context.hold_days = 5

import pandas as pd


# 回测引擎：每日数据处理函数，每天执行一次
def m6_handle_data_bigquant_run(context, data):
    
    # 按日期过滤得到今日的预测数据
    options_data = context.ranker_prediction[context.ranker_prediction.date == data.current_dt.strftime('%Y-%m-%d')]
    # 标的为字符串格式
    sid = context.symbol(context.instruments[0])# 标的为字符串格式
    price = data.current(sid, 'price') # 最新价格
    cash = context.portfolio.cash  # 现金
    cur_position = context.portfolio.positions[sid].amount # 持仓

#     rsi_short_0=options_data['ta_rsi_6'].values
#     rsi_long_0=options_data['ta_rsi_12'].values
    
#     rsi_short_1 = options_data['ta_rsi_6_1'].values
#     rsi_long_1 = options_data['ta_rsi_12_1'].values
    rsi_6 = options_data['rsi_6'].values
    rsi_14 = options_data['rsi_14'].values
    lagrsi_6=options_data['lagrsi_6'].values
    lagrsi_14=options_data['lagrsi_14'].values
    

    
    
    
        

    
    #交易逻辑
    # rsi_6 < 20--buy--signal=1, rsi>80--sell--signal=-1:
    sig1=[]
    for i in rsi_6:
        if i > 80:
            sig1.append(-1)
        elif i<20:
            sig1.append(1)
        else:
            sig1.append(0)
            

            
#     #rsi_6上穿rsi_14,buy--singal_2=1,rsi_6下穿rsi_14,sell--singal_2=-1   
    
    sig2=[]
    
 
    for i in range(0,len(rsi_14)):
        
        if (rsi_6[i] > rsi_14[i]) & (lagrsi_6[i]<lagrsi_14[i]):
            sig2.append(1)
        elif (rsi_6[i]< rsi_14[i] ) & (lagrsi_6[i]>lagrsi_14[i]):
            sig2.append(-1)
        else:
            sig2.append(0) 
            
#     print(sig2)
            
    signal = sig1 + sig2
    for i in signal:
        if i>=1:
            context.order(sid, int(cash/price/100)*100) # 买入
            print('{}全仓买入{}股票'.format(data.current_dt.strftime('%Y-%m-%d'),sid.symbol))
        elif i <=-1:
            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))
        else:
            return
            
            
#     for i in rsi_6:
#         if i < 20:
#             context.order(sid, int(cash/price/100)*100) # 买入
#             print('{}全仓买入{}股票'.format(data.current_dt.strftime('%Y-%m-%d'),sid.symbol))
#         elif i > 80:
#             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))
            
#         else:
#             return
  
        
        
    
        
        


# 回测引擎：准备数据，只执行一次
def m6_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 m6_before_trading_start_bigquant_run(context, data):
    pass


m1 = M.instruments.v2(
    start_date='2021-01-01',
    end_date='2022-03-25',
    market='CN_STOCK_A',
    instrument_list="""002665.SZA
# 603330.SHA""",
    max_count=0
)

m3 = M.input_features.v1(
    features="""
# #号开始的表示注释，注释需单独一行
# 多个特征，每行一个，可以包含基础特征和衍生特征，特征须为本平台特征

# high_0
# low_0
# close_0


k_9=ta_kdj_k(high, low, close, 9, 3)
d_9=ta_kdj_d(high, low, close, 9, 3)
# ta_kdj_j(hhigh_0, low_0, close_0, 18, 3)


# ta_kdj_k(high_0, low_0, close_0, 5, 3)

# ta_stoch_slowd_5_3_0_3_0_0

# ta_stoch_slowd_5_3_0_3_0_0













"""
)

m7 = M.use_datasource.v1(
    instruments=m1.data,
    features=m3.data,
    datasource_id='bar1d_CN_STOCK_A',
    start_date='',
    end_date=''
)

m4 = M.derived_feature_extractor.v3(
    input_data=m7.data,
    features=m3.data,
    date_col='date',
    instrument_col='instrument',
    drop_na=False,
    remove_extra_columns=False,
    user_functions={}
)

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

m6 = M.trade.v4(
    instruments=m1.data,
    options_data=m5.data,
    start_date='',
    end_date='',
    initialize=m6_initialize_bigquant_run,
    handle_data=m6_handle_data_bigquant_run,
    prepare=m6_prepare_bigquant_run,
    before_trading_start=m6_before_trading_start_bigquant_run,
    volume_limit=0.025,
    order_price_field_buy='open',
    order_price_field_sell='close',
    capital_base=10000,
    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='000300.HIX'
)

[2022-03-27 22:01:50.433788] INFO: moduleinvoker: instruments.v2 开始运行..

[2022-03-27 22:01:50.445503] INFO: moduleinvoker: 命中缓存

[2022-03-27 22:01:50.449208] INFO: moduleinvoker: instruments.v2 运行完成[0.015306s].

[2022-03-27 22:01:50.469536] INFO: moduleinvoker: input_features.v1 开始运行..

[2022-03-27 22:01:50.508485] INFO: moduleinvoker: input_features.v1 运行完成[0.038403s].

[2022-03-27 22:01:50.519428] INFO: moduleinvoker: use_datasource.v1 开始运行..

[2022-03-27 22:01:51.014303] INFO: moduleinvoker: use_datasource.v1 运行完成[0.494879s].

[2022-03-27 22:01:51.027224] INFO: moduleinvoker: derived_feature_extractor.v3 开始运行..

[2022-03-27 22:01:51.173026] INFO: derived_feature_extractor: 提取完成 k_9=ta_kdj_k(high, low, close, 9, 3), 0.073s

[2022-03-27 22:01:51.175018] INFO: derived_feature_extractor: 提取失败 d_9=ta_kdj_d(high, low, close, 9, 3): ta_kdj_d() takes exactly 7 positional arguments (6 given)

[2022-03-27 22:01:51.278129] INFO: derived_feature_extractor: /data, 297

[2022-03-27 22:01:51.333848] INFO: moduleinvoker: derived_feature_extractor.v3 运行完成[0.306614s].

[2022-03-27 22:01:51.379657] INFO: moduleinvoker: dropnan.v2 开始运行..

[2022-03-27 22:01:51.500927] INFO: dropnan: /data, 289/297

[2022-03-27 22:01:51.589288] INFO: dropnan: 行数: 289/297

[2022-03-27 22:01:51.594930] INFO: moduleinvoker: dropnan.v2 运行完成[0.21527s].

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