ATR突破择时策略¶

# 1. 策略基本参数
def prepare(context):
    # 策略比较参考标准，以沪深300为例
    benchmark = '000300.INDX'

# 初始化虚拟账户状态，只在第一个交易日运行
def initialize(context):
    # 设置是否是结算模式
    context.set_need_settle(False)
    # 设置手续费，PerContract 说明是每手手续费多少，分别是开仓、平昨、平今
    context.set_commission(equities_commission=None, futures_commission=None) # 手续费按系统默认手续费设置
    # 设置最大杠杆
    context.set_max_leverage(0.6, 'fill_amap') # 这里能够设置最大杠杆
    context.observation = 30
    context.width = 2
    context.ma_length = 25
    
def handle_data(context, data):
    
    if context.trading_day_index < context.observation: # 在30个交易日以后才开始真正运行
        return
    today = data.current_dt.strftime('%Y-%m-%d') # 当前交易日期
    instrument = context.future_symbol(context.instruments[0]) # 交易标的
    curr_po=context.portfolio.positions[instrument] # 持仓
    curr_position = curr_po.amount  # 持仓数量
     
    price = data.current(instrument, 'price')  # 当前价格
    high_price= np.array(data.history(instrument, 'high', context.observation, '1d')) # high
    low_price= np.array(data.history(instrument, 'low', context.observation, '1d')) # low
    close_price= np.array(data.history(instrument, 'close', context.observation, '1d')) # close_price

    # 判断获取的数据中多少天是缺失数据，如果全是缺失数据，是不能计算ATR指标的
    nan_num = [k for k in [np.isnan(i) for i in close_price] if k == True]
    if len(nan_num) == context.observation:
        return

    # 创建ATR买卖信号，包括最高价，最低价，收盘价和参数timeperiod
    # 注意：ATR函数使用的price必须是narray
    import talib
    atr = talib.ATR(high_price,low_price,close_price, timeperiod=context.ma_length)[-1]
    ma = data.history(instrument, 'high', context.ma_length, '1d').mean()
    high_line = ma + context.width * atr # 上轨
    low_line = ma - context.width * atr # 下轨
 
    # 交易逻辑
    if price >= high_line and curr_position <=0 and data.can_trade(instrument): # 开多
        order_target(instrument,  5) # order_target的含义是本次下单使得最终的仓位为目标仓位，如果是正数，那么就是多头仓位
        print(today, '买入开仓')

    elif price <= low_line and curr_position >= 0 and data.can_trade(instrument): # 开空
        order_target(instrument,  -5) # 下单后使得仓位为5手空单
        print(today, '卖出开仓')

# 3. 启动回测
# 策略回测接口: https://v2.bigquant.com/docs/module_trade.html
m = M.trade.v4(
    instruments= ['RU1809.SHF'],
    start_date='2017-11-03',
    end_date='2018-07-01',
    prepare=prepare,
    initialize=initialize,
    handle_data=handle_data,
    # 买入订单以开盘价成交
    order_price_field_buy='open',
    # 卖出订单以开盘价成交
    order_price_field_sell='open',
    capital_base=1000000,
    benchmark='000300.SHA',
    product_type = 'future',
    m_deps=np.random.rand()
)