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# 本代码由可视化策略环境自动生成 2018年6月13日 14:00
# 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。


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
mean(close_0,5)
mean(close_0,10)"""
)

m2 = M.instruments.v2(
    start_date=T.live_run_param('trading_date', '2015-01-01'),
    end_date=T.live_run_param('trading_date', '2017-01-01'),
    market='CN_STOCK_A',
    instrument_list='600006.SHA',
    max_count=0
)

m3 = M.general_feature_extractor.v6(
    instruments=m2.data,
    features=m1.data,
    start_date='',
    end_date='',
    before_start_days=30
)

m4 = M.derived_feature_extractor.v2(
    input_data=m3.data,
    features=m1.data,
    date_col='date',
    instrument_col='instrument'
)

m6 = M.dropnan.v1(
    input_data=m4.data
)

# 回测引擎：每日数据处理函数，每天执行一次
def m5_handle_data_bigquant_run(context, data):
    today=data.current_dt.strftime('%Y-%m-%d')
    # 长期均线值要有意义，需要在50根k线之后
    k = context.instruments[0] # 标的为字符串格式
    sid = context.symbol(k) # 将标的转化为equity格式
    price = data.current(sid, 'price') # 最新价格
    #如果今天有均线值
    if today in context.short_mavg.index:
        short_mavg =context.short_mavg.ix[today]  # 短期均线值
        long_mavg = context.long_mavg.ix[today]   # 长期均线值
    else:
        return
      
    cash = context.portfolio.cash  # 现金
    cur_position = context.portfolio.positions[sid].amount # 持仓
    
    # 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 short_mavg > long_mavg and cur_position == 0 and data.can_trade(sid):  
        context.order(sid, int(cash/price/100)*100) # 买入
    # 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 short_mavg < long_mavg and cur_position > 0 and data.can_trade(sid):  
        context.order_target_percent(sid, 0) # 全部卖出

# 回测引擎：准备数据，只执行一次
def m5_prepare_bigquant_run(context):
    # 加载预测数据
    df = context.options['data'].read_df().set_index('date')
    context.instruments=df.instrument.unique()
    context.short_mavg=df['mean(close_0,5)']
    context.long_mavg=df['mean(close_0,10)']

# 回测引擎：初始化函数，只执行一次
def m5_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))

m5 = M.trade.v3(
    instruments=m2.data,
    options_data=m6.data,
    start_date='',
    end_date='',
    handle_data=m5_handle_data_bigquant_run,
    prepare=m5_prepare_bigquant_run,
    initialize=m5_initialize_bigquant_run,
    volume_limit=0.025,
    order_price_field_buy='open',
    order_price_field_sell='close',
    capital_base=1000000,
    benchmark='000300.SHA',
    auto_cancel_non_tradable_orders=True,
    data_frequency='daily',
    price_type='后复权',
    plot_charts=True,
    backtest_only=False,
    amount_integer=False
)

[2018-06-01 14:18:42.656265] INFO: bigquant: input_features.v1 开始运行..
[2018-06-01 14:18:42.663262] INFO: bigquant: 命中缓存
[2018-06-01 14:18:42.665081] INFO: bigquant: input_features.v1 运行完成[0.008852s].
[2018-06-01 14:18:42.684597] INFO: bigquant: instruments.v2 开始运行..
[2018-06-01 14:18:42.691773] INFO: bigquant: 命中缓存
[2018-06-01 14:18:42.693520] INFO: bigquant: instruments.v2 运行完成[0.008969s].
[2018-06-01 14:18:42.723468] INFO: bigquant: general_feature_extractor.v6 开始运行..
[2018-06-01 14:18:42.746640] INFO: bigquant: 命中缓存
[2018-06-01 14:18:42.765848] INFO: bigquant: general_feature_extractor.v6 运行完成[0.042357s].
[2018-06-01 14:18:43.149590] INFO: bigquant: derived_feature_extractor.v2 开始运行..
[2018-06-01 14:18:43.159442] INFO: bigquant: 命中缓存
[2018-06-01 14:18:43.164396] INFO: bigquant: derived_feature_extractor.v2 运行完成[0.014784s].
[2018-06-01 14:18:43.209687] INFO: bigquant: dropnan.v1 开始运行..
[2018-06-01 14:18:43.215080] INFO: bigquant: 命中缓存
[2018-06-01 14:18:43.216760] INFO: bigquant: dropnan.v1 运行完成[0.007114s].
[2018-06-01 14:18:43.451548] INFO: bigquant: backtest.v7 开始运行..
[2018-06-01 14:18:43.545643] INFO: bigquant: 命中缓存

[2018-06-01 14:18:47.830872] INFO: bigquant: backtest.v7 运行完成[4.379307s].