Alpha对冲策略–量化交易稳健代表(附Python完整代码)

# coding=utf-8from __future__ import print_function, absolute_import, unicode_literalsfrom gm.api import *'''本策略每隔1个月定时触发计算SHSE.000300成份股的过去一天EV/EBITDA值并选取30只EV/EBITDA值最小且大于零的股票对不在股票池的股票平仓并等权配置股票池的标的并用相应的CFFEX.IF对应的真实合约等额对冲回测数据为:SHSE.000300和他们的成份股和CFFEX.IF对应的真实合约回测时间为:2017-07-01 08:00:00到2017-10-01 16:00:00注意：本策略仅供参考，实际使用中要考虑到期货和股票处于两个不同的账户，需要人为的保证两个账户的资金相同。'''def init(context):    # 每月第一个交易日09:40:00的定时执行algo任务（仿真和实盘时不支持该频率）    schedule(schedule_func=algo, date_rule='1m', time_rule='09:40:00')    # 设置开仓在股票和期货的资金百分比(期货在后面自动进行杠杆相关的调整)    context.percentage_stock = 0.4    context.percentage_futures = 0.4def algo(context):    # 获取当前时刻    now = context.now    # 获取上一个交易日    last_day = get_previous_trading_date(exchange='SHSE', date=now)    # 获取沪深300成份股的股票代码    stock300 = get_history_constituents(index='SHSE.000300', start_date=last_day,                                                end_date=last_day)[0]['constituents'].keys()    # 获取上一个工作日的CFFEX.IF对应的合约    index_futures = get_continuous_contracts(csymbol='CFFEX.IF', start_date=last_day, end_date=last_day)[-1]['symbol']    # 获取当天有交易的股票    not_suspended_info = get_history_instruments(symbols=stock300, start_date=now, end_date=now)    not_suspended_symbols = [item['symbol'] for item in not_suspended_info if not item['is_suspended']]    # 获取成份股EV/EBITDA大于0并为最小的30个    fin = get_fundamentals(table='trading_derivative_indicator', symbols=not_suspended_symbols,                           start_date=now, end_date=now, fields='EVEBITDA',                           filter='EVEBITDA>0', order_by='EVEBITDA', limit=30, df=True)    fin.index = fin.symbol    # 获取当前仓位    positions = context.account().positions()    # 平不在标的池或不为当前股指期货主力合约对应真实合约的标的    for position in positions:        symbol = position['symbol']        sec_type = get_instrumentinfos(symbols=symbol)[0]['sec_type']        # 若类型为期货且不在标的池则平仓        if sec_type == SEC_TYPE_FUTURE and symbol != index_futures:            order_target_percent(symbol=symbol, percent=0, order_type=OrderType_Market,                                 position_side=PositionSide_Short)            print('市价单平不在标的池的', symbol)        elif symbol not in fin.index:            order_target_percent(symbol=symbol, percent=0, order_type=OrderType_Market,                                 position_side=PositionSide_Long)            print('市价单平不在标的池的', symbol)    # 获取股票的权重    percent = context.percentage_stock / len(fin.index)    # 买在标的池中的股票    for symbol in fin.index:        order_target_percent(symbol=symbol, percent=percent, order_type=OrderType_Market,                             position_side=PositionSide_Long)        print(symbol, '以市价单调多仓到仓位', percent)    # 获取股指期货的保证金比率    ratio = get_history_instruments(symbols=index_futures, start_date=last_day, end_date=last_day)[0]['margin_ratio']    # 更新股指期货的权重    percent = context.percentage_futures * ratio    # 买入股指期货对冲    # 注意：股指期货的percent参数是按照期货的保证金来算比例，不是按照合约价值， 比如说0.1就是用0.1的仓位的资金全部买入期货。    order_target_percent(symbol=index_futures, percent=percent, order_type=OrderType_Market,                         position_side=PositionSide_Short)    print(index_futures, '以市价单调空仓到仓位', percent)if __name__ == '__main__':    '''    strategy_id策略ID,由系统生成    filename文件名,请与本文件名保持一致    mode实时模式:MODE_LIVE回测模式:MODE_BACKTEST    token绑定计算机的ID,可在系统设置-密钥管理中生成    backtest_start_time回测开始时间    backtest_end_time回测结束时间    backtest_adjust股票复权方式不复权:ADJUST_NONE前复权:ADJUST_PREV后复权:ADJUST_POST    backtest_initial_cash回测初始资金    backtest_commission_ratio回测佣金比例    backtest_slippage_ratio回测滑点比例    '''    run(strategy_id='strategy_id',        filename='main.py',        mode=MODE_BACKTEST,        token='token_id',        backtest_start_time='2017-07-01 08:00:00',        backtest_end_time='2017-10-01 16:00:00',        backtest_adjust=ADJUST_PREV,        backtest_initial_cash=10000000,        backtest_commission_ratio=0.0001,        backtest_slippage_ratio=0.0001)