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- # coding=utf-8
- from __future__ import print_function, absolute_import
- from gm.api import *
- import talib
- import time
- '''
- 本策略以DCE.i1801为交易标的，根据其一分钟(即60s频度）bar数据建立双均线模型，
- 短周期为30，长周期为60，当短期均线由上向下穿越长期均线时做空，
- 当短期均线由下向上穿越长期均线时做多,每次开仓前先平掉所持仓位，再开仓。
- 回测数据为:DCE.i1801的60s频度bar数据
- 回测时间为:2017-09-01 09:00:00到2017-09-30 15:00:00
- '''
- def init(context):
- context.FAST = 30 # 短周期
- context.SLOW = 60 # 长周期
- context.symbol = 'DCE.i1801' # 订阅&交易标的
- context.period = context.SLOW + 1 # 订阅数据滑窗长度
- subscribe(context.symbol, '60s', count=context.period) # 订阅行情
- def on_bar(context, bars):
- print (bars[0].bob)
- # 获取数据
- prices = context.data('DCE.i1801', '60s', context.period, fields='close')
- # 计算长短周期均线
- fast_avg = talib.SMA(prices.values.reshape(context.period), context.FAST)
- slow_avg = talib.SMA(prices.values.reshape(context.period), context.SLOW)
- # 均线下穿，做空
- if slow_avg[-2] < fast_avg[-2] and slow_avg[-1] >= fast_avg[-1]:
- # 平多仓
- order_target_percent(symbol=context.symbol, percent=0, position_side=1, order_type=2)
- # 开空仓
- order_target_percent(symbol=context.symbol, percent=0.1, position_side=2, order_type=2)
- # 均线上穿，做多
- if fast_avg[-2] < slow_avg[-2] and fast_avg[-1] >= slow_avg[-1]:
- # 平空仓
- order_target_percent(symbol=context.symbol, percent=0, position_side=2,order_type=2)
- # 开多仓
- order_target_percent(symbol=context.symbol, percent=0.1, position_side=1,order_type=2)
- def on_execution_report(context, execrpt):
- # 打印委托执行回报
- print(execrpt)
- 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='2017-09-01 09:00:00',
- backtest_end_time='2017-09-30 15:00:00',
- backtest_adjust=ADJUST_NONE,
- backtest_initial_cash=10000000,
- backtest_commission_ratio=0.0001,
- backtest_slippage_ratio=0.0001)
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