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跨品种套利(期货)Python策略源码模板【东方财富Python量化】
[code]# coding=utf-8
from __future__ import print_function, absolute_import, unicode_literals
from gm.api import *
import numpy as np
'''
本策略首先滚动计算过去30个1min收盘价的均值,然后用均值加减2个标准差得到布林线.
若无仓位,在最新价差上穿上轨时做空价差;下穿下轨时做多价差
若有仓位则在最新价差回归至上下轨水平内时平仓
回测数据为:DCE.j1901和DCE.jm1901的1min数据
回测时间为:2018-02-01 08:00:00到2018-12-31 08:00:00
'''
def init(context):
# 选择的两个合约
context.symbol = ['DCE.j1901', 'DCE.jm1901']
# 订阅历史数据
subscribe(symbols=context.symbol, frequency='1d', count=11, wait_group=True)
def on_bar(context, bars):
# 数据提取
j_close = context.data(symbol=context.symbol[0],frequency='1d',fields='close',count=31).values
jm_close = context.data(symbol=context.symbol[1],frequency='1d',fields='close',count=31).values
# 提取最新价差
new_price = j_close[-1] - jm_close[-1]
# 计算历史价差,上下限，止损点
spread_history = j_close[:-2] - jm_close[:-2]
context.spread_history_mean = np.mean(spread_history)
context.spread_history_std = np.std(spread_history)
context.up = context.spread_history_mean + 0.75 * context.spread_history_std
context.down = context.spread_history_mean - 0.75 * context.spread_history_std
context.up_stoppoint = context.spread_history_mean + 2 * context.spread_history_std
context.down_stoppoint = context.spread_history_mean - 2 * context.spread_history_std
# 查持仓
position_jm_long = context.account().position(symbol=context.symbol[0], side=1)
position_jm_short = context.account().position(symbol=context.symbol[0], side=2)
# 设计买卖信号
# 设计开仓信号
if not position_jm_short and not position_jm_long:
if new_price > context.up:
print('做空价差组合')
order_volume(symbol=context.symbol[0],side=OrderSide_Sell,volume=1,order_type=OrderType_Market, position_effect=1)
order_volume(symbol=context.symbol[1], side=OrderSide_Buy, volume=1, order_type=OrderType_Market, position_effect=PositionEffect_Open)
if new_price < context.down:
print('做多价差组合')
order_volume(symbol=context.symbol[0], side=OrderSide_Buy, volume=1, order_type=OrderType_Market, position_effect=PositionEffect_Open)
order_volume(symbol=context.symbol[1], side=OrderSide_Sell, volume=1, order_type=OrderType_Market, position_effect=PositionEffect_Open)
# 设计平仓信号
# 持jm多仓时
if position_jm_long:
if new_price >= context.spread_history_mean:
# 价差回归到均值水平时，平仓
print('价差回归到均衡水平，平仓')
order_volume(symbol=context.symbol[0], side=OrderSide_Sell, volume=1, order_type=OrderType_Market, position_effect=PositionEffect_Close)
order_volume(symbol=context.symbol[1], side=OrderSide_Buy, volume=1, order_type=OrderType_Market, position_effect=PositionEffect_Close)
if new_price < context.down_stoppoint:
# 价差达到止损位，平仓止损
print('价差超过止损点，平仓止损')
order_volume(symbol=context.symbol[0], side=OrderSide_Sell, volume=1, order_type=OrderType_Market, position_effect=PositionEffect_Close)
order_volume(symbol=context.symbol[1], side=OrderSide_Buy, volume=1, order_type=OrderType_Market, position_effect=PositionEffect_Close)
# 持jm空仓时
if position_jm_short:
if new_price <= context.spread_history_mean:
# 价差回归到均值水平时，平仓
print('价差回归到均衡水平，平仓')
order_volume(symbol=context.symbol[0], side=OrderSide_Buy, volume=1, order_type=OrderType_Market, position_effect=PositionEffect_Close)
order_volume(symbol=context.symbol[1], side=OrderSide_Sell, volume=1, order_type=OrderType_Market, position_effect=PositionEffect_Close)
if new_price > context.up_stoppoint:
# 价差达到止损位，平仓止损
print('价差超过止损点，平仓止损')
order_volume(symbol=context.symbol[0], side=OrderSide_Buy, volume=1, order_type=OrderType_Market, position_effect=PositionEffect_Close)
order_volume(symbol=context.symbol[1], side=OrderSide_Sell, volume=1, order_type=OrderType_Market, position_effect=PositionEffect_Close)
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}}',
backtest_start_time='2018-02-01 08:00:00',
backtest_end_time='2018-12-31 16:00:00',
backtest_adjust=ADJUST_PREV,
backtest_initial_cash=2000000,
backtest_commission_ratio=0.0001,
backtest_slippage_ratio=0.0001)
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