├── README.md ├── attach.png └── example.py /README.md: -------------------------------------------------------------------------------- 1 | # 日内回转交易(股票) 2 | 基于股票日内偏离度回归的日内回转策略 3 | 4 | ## 源码 5 | ```python 6 | # coding=utf-8 7 | from __future__ import print_function, absolute_import, unicode_literals 8 | try: 9 | import talib 10 | except: 11 | print('请安装TA-Lib库') 12 | from gm.api import * 13 | ''' 14 | 本策略首先买入SHSE.600000股票10000股 15 | 随后根据60s的数据来计算MACD(12,26,9)线,并在MACD>0的时候买入100股,MACD<0的时候卖出100股 16 | 但每日操作的股票数不超过原有仓位,并于收盘前把仓位调整至开盘前的仓位 17 | 回测数据为:SHSE.600000的60s数据 18 | 回测时间为:2017-09-01 08:00:00到2017-10-01 16:00:00 19 | ''' 20 | def init(context): 21 | # 设置标的股票 22 | context.symbol = 'SHSE.600000' 23 | # 用于判定第一个仓位是否成功开仓 24 | context.first = 0 25 | # 订阅浦发银行, bar频率为1min 26 | subscribe(symbols=context.symbol, frequency='60s', count=35) 27 | # 日内回转每次交易100股 28 | context.trade_n = 100 29 | # 获取昨今天的时间 30 | context.day = [0, 0] 31 | # 用于判断是否触发了回转逻辑的计时 32 | context.ending = 0 33 | def on_bar(context, bars): 34 | bar = bars[0] 35 | if context.first == 0: 36 | # 最开始配置仓位 37 | # 需要保持的总仓位 38 | context.total = 10000 39 | # 购买10000股浦发银行股票 40 | order_volume(symbol=context.symbol, volume=context.total, side=PositionSide_Long, 41 | order_type=OrderType_Market, position_effect=PositionEffect_Open) 42 | print(context.symbol, '以市价单开多仓10000股') 43 | context.first = 1. 44 | day = bar.bob.strftime('%Y-%m-%d') 45 | context.day[-1] = day[-2:] 46 | # 每天的仓位操作 47 | context.turnaround = [0, 0] 48 | return 49 | # 更新最新的日期 50 | day = bar.bob.strftime('%Y-%m-%d %H:%M:%S') 51 | context.day[0] = bar.bob.day 52 | # 若为新的一天,获取可用于回转的昨仓 53 | if context.day[0] != context.day[-1]: 54 | context.ending = 0 55 | context.turnaround = [0, 0] 56 | if context.ending == 1: 57 | return 58 | # 若有可用的昨仓则操作 59 | if context.total >= 0: 60 | # 获取时间序列数据 61 | symbol = bar['symbol'] 62 | recent_data = context.data(symbol=symbol, frequency='60s', count=35, fields='close') 63 | # 计算MACD线 64 | macd = talib.MACD(recent_data['close'].values)[0][-1] 65 | # 根据MACD>0则开仓,小于0则平仓 66 | if macd > 0: 67 | # 多空单向操作都不能超过昨仓位,否则最后无法调回原仓位 68 | if context.turnaround[0] + context.trade_n < context.total: 69 | # 计算累计仓位 70 | context.turnaround[0] += context.trade_n 71 | order_volume(symbol=context.symbol, volume=context.trade_n, side=PositionSide_Long, 72 | order_type=OrderType_Market, position_effect=PositionEffect_Open) 73 | print(symbol, '市价单开多仓', context.trade_n, '股') 74 | elif macd < 0: 75 | if context.turnaround[1] + context.trade_n < context.total: 76 | context.turnaround[1] += context.trade_n 77 | order_volume(symbol=context.symbol, volume=context.trade_n, side=PositionSide_Short, 78 | order_type=OrderType_Market, position_effect=PositionEffect_Close) 79 | print(symbol, '市价单平多仓', context.trade_n, '股') 80 | # 临近收盘时若仓位数不等于昨仓则回转所有仓位 81 | if day[11:16] == '14:55' or day[11:16] == '14:57': 82 | position = context.account().position(symbol=context.symbol, side=PositionSide_Long) 83 | if position['volume'] != context.total: 84 | order_target_volume(symbol=context.symbol, volume=context.total, order_type=OrderType_Market, 85 | position_side=PositionSide_Long) 86 | print('市价单回转仓位操作...') 87 | context.ending = 1 88 | # 更新过去的日期数据 89 | context.day[-1] = context.day[0] 90 | if __name__ == '__main__': 91 | ''' 92 | strategy_id策略ID,由系统生成 93 | filename文件名,请与本文件名保持一致 94 | mode实时模式:MODE_LIVE回测模式:MODE_BACKTEST 95 | token绑定计算机的ID,可在系统设置-密钥管理中生成 96 | backtest_start_time回测开始时间 97 | backtest_end_time回测结束时间 98 | backtest_adjust股票复权方式不复权:ADJUST_NONE前复权:ADJUST_PREV后复权:ADJUST_POST 99 | backtest_initial_cash回测初始资金 100 | backtest_commission_ratio回测佣金比例 101 | backtest_slippage_ratio回测滑点比例 102 | ''' 103 | run(strategy_id='strategy_id', 104 | filename='main.py', 105 | mode=MODE_BACKTEST, 106 | token='token_id', 107 | backtest_start_time='2017-09-01 08:00:00', 108 | backtest_end_time='2017-10-01 16:00:00', 109 | backtest_adjust=ADJUST_PREV, 110 | backtest_initial_cash=2000000, 111 | backtest_commission_ratio=0.0001, 112 | backtest_slippage_ratio=0.0001) 113 | ``` 114 | 115 | ## 绩效 116 | ![绩效](attach.png) 117 | -------------------------------------------------------------------------------- /attach.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/TruthHun/day-wing-transaction/6c7e0bdbbc0a3ae62e9612c0f8b61a1ed11ee469/attach.png -------------------------------------------------------------------------------- /example.py: -------------------------------------------------------------------------------- 1 | # coding=utf-8 2 | from __future__ import print_function, absolute_import, unicode_literals 3 | try: 4 | import talib 5 | except: 6 | print('请安装TA-Lib库') 7 | from gm.api import * 8 | ''' 9 | 本策略首先买入SHSE.600000股票10000股 10 | 随后根据60s的数据来计算MACD(12,26,9)线,并在MACD>0的时候买入100股,MACD<0的时候卖出100股 11 | 但每日操作的股票数不超过原有仓位,并于收盘前把仓位调整至开盘前的仓位 12 | 回测数据为:SHSE.600000的60s数据 13 | 回测时间为:2017-09-01 08:00:00到2017-10-01 16:00:00 14 | ''' 15 | def init(context): 16 | # 设置标的股票 17 | context.symbol = 'SHSE.600000' 18 | # 用于判定第一个仓位是否成功开仓 19 | context.first = 0 20 | # 订阅浦发银行, bar频率为1min 21 | subscribe(symbols=context.symbol, frequency='60s', count=35) 22 | # 日内回转每次交易100股 23 | context.trade_n = 100 24 | # 获取昨今天的时间 25 | context.day = [0, 0] 26 | # 用于判断是否触发了回转逻辑的计时 27 | context.ending = 0 28 | def on_bar(context, bars): 29 | bar = bars[0] 30 | if context.first == 0: 31 | # 最开始配置仓位 32 | # 需要保持的总仓位 33 | context.total = 10000 34 | # 购买10000股浦发银行股票 35 | order_volume(symbol=context.symbol, volume=context.total, side=PositionSide_Long, 36 | order_type=OrderType_Market, position_effect=PositionEffect_Open) 37 | print(context.symbol, '以市价单开多仓10000股') 38 | context.first = 1. 39 | day = bar.bob.strftime('%Y-%m-%d') 40 | context.day[-1] = day[-2:] 41 | # 每天的仓位操作 42 | context.turnaround = [0, 0] 43 | return 44 | # 更新最新的日期 45 | day = bar.bob.strftime('%Y-%m-%d %H:%M:%S') 46 | context.day[0] = bar.bob.day 47 | # 若为新的一天,获取可用于回转的昨仓 48 | if context.day[0] != context.day[-1]: 49 | context.ending = 0 50 | context.turnaround = [0, 0] 51 | if context.ending == 1: 52 | return 53 | # 若有可用的昨仓则操作 54 | if context.total >= 0: 55 | # 获取时间序列数据 56 | symbol = bar['symbol'] 57 | recent_data = context.data(symbol=symbol, frequency='60s', count=35, fields='close') 58 | # 计算MACD线 59 | macd = talib.MACD(recent_data['close'].values)[0][-1] 60 | # 根据MACD>0则开仓,小于0则平仓 61 | if macd > 0: 62 | # 多空单向操作都不能超过昨仓位,否则最后无法调回原仓位 63 | if context.turnaround[0] + context.trade_n < context.total: 64 | # 计算累计仓位 65 | context.turnaround[0] += context.trade_n 66 | order_volume(symbol=context.symbol, volume=context.trade_n, side=PositionSide_Long, 67 | order_type=OrderType_Market, position_effect=PositionEffect_Open) 68 | print(symbol, '市价单开多仓', context.trade_n, '股') 69 | elif macd < 0: 70 | if context.turnaround[1] + context.trade_n < context.total: 71 | context.turnaround[1] += context.trade_n 72 | order_volume(symbol=context.symbol, volume=context.trade_n, side=PositionSide_Short, 73 | order_type=OrderType_Market, position_effect=PositionEffect_Close) 74 | print(symbol, '市价单平多仓', context.trade_n, '股') 75 | # 临近收盘时若仓位数不等于昨仓则回转所有仓位 76 | if day[11:16] == '14:55' or day[11:16] == '14:57': 77 | position = context.account().position(symbol=context.symbol, side=PositionSide_Long) 78 | if position['volume'] != context.total: 79 | order_target_volume(symbol=context.symbol, volume=context.total, order_type=OrderType_Market, 80 | position_side=PositionSide_Long) 81 | print('市价单回转仓位操作...') 82 | context.ending = 1 83 | # 更新过去的日期数据 84 | context.day[-1] = context.day[0] 85 | if __name__ == '__main__': 86 | ''' 87 | strategy_id策略ID,由系统生成 88 | filename文件名,请与本文件名保持一致 89 | mode实时模式:MODE_LIVE回测模式:MODE_BACKTEST 90 | token绑定计算机的ID,可在系统设置-密钥管理中生成 91 | backtest_start_time回测开始时间 92 | backtest_end_time回测结束时间 93 | backtest_adjust股票复权方式不复权:ADJUST_NONE前复权:ADJUST_PREV后复权:ADJUST_POST 94 | backtest_initial_cash回测初始资金 95 | backtest_commission_ratio回测佣金比例 96 | backtest_slippage_ratio回测滑点比例 97 | ''' 98 | run(strategy_id='strategy_id', 99 | filename='main.py', 100 | mode=MODE_BACKTEST, 101 | token='token_id', 102 | backtest_start_time='2017-09-01 08:00:00', 103 | backtest_end_time='2017-10-01 16:00:00', 104 | backtest_adjust=ADJUST_PREV, 105 | backtest_initial_cash=2000000, 106 | backtest_commission_ratio=0.0001, 107 | backtest_slippage_ratio=0.0001) --------------------------------------------------------------------------------