【宽客策略源码】海龟交易策略源码分享
本策略通过计算CZCE.FG801和SHFE.rb1801的ATR.唐奇安通道和MA线,并:上穿唐奇安通道且短MA在长MA上方则开多仓,下穿唐奇安通道且短MA在长MA下方则开空仓
若有 多/空 仓位则分别:
价格 跌/涨 破唐奇安平仓通道 上/下 轨则全平仓位,否则
根据 跌/涨 破持仓均价 -/+ x(x=0.5,1,1.5,2)倍ATR把仓位
回测数据为:CZCE.FG801和SHFE.rb1801的1min数据
回测时间为:2017-09-15 09:15:00到2017-10-01 15:00:00
# coding=utf-8
from __future__ import print_function, absolute_import, unicode_literals
import numpy as np
import pandas as pd
try:
import talib
except:
print('请安装TA-Lib库')
from gm.api import *
def init(context):
# context.parameter分别为唐奇安开仓通道.唐奇安平仓通道.短ma.长ma.ATR的参数
context.parameter =
context.tar = context.parameter
# context.goods交易的品种
context.goods = ['CZCE.FG801', 'SHFE.rb1801']
# context.ratio交易最大资金比率
context.ratio = 0.8
# 订阅context.goods里面的品种, bar频率为1min
subscribe(symbols=context.goods, frequency='60s', count=101)
# 止损的比例区间
def on_bar(context, bars):
bar = bars
symbol = bar['symbol']
recent_data = context.data(symbol=symbol, frequency='60s', count=101, fields='close,high,low')
close = recent_data['close'].values[-1]
# 计算ATR
atr = talib.ATR(recent_data['high'].values, recent_data['low'].values, recent_data['close'].values,
timeperiod=context.tar)[-1]
# 计算唐奇安开仓和平仓通道
context.don_open = context.parameter + 1
upper_band = talib.MAX(recent_data['close'].values[:-1], timeperiod=context.don_open)[-1]
context.don_close = context.parameter + 1
lower_band = talib.MIN(recent_data['close'].values[:-1], timeperiod=context.don_close)[-1]
# 计算开仓的资金比例
percent = context.ratio / float(len(context.goods))
# 若没有仓位则开仓
position_long = context.account().position(symbol=symbol, side=PositionSide_Long)
position_short = context.account().position(symbol=symbol, side=PositionSide_Short)
if not position_long and not position_short:
# 计算长短ma线.DIF
ma_short = talib.MA(recent_data['close'].values, timeperiod=(context.parameter + 1))[-1]
ma_long = talib.MA(recent_data['close'].values, timeperiod=(context.parameter + 1))[-1]
dif = ma_short - ma_long
# 获取当前价格
# 上穿唐奇安通道且短ma在长ma上方则开多仓
if close > upper_band and (dif > 0):
order_target_percent(symbol=symbol, percent=percent, order_type=OrderType_Market,
position_side=PositionSide_Long)
print(symbol, '市价单开多仓到比例: ', percent)
# 下穿唐奇安通道且短ma在长ma下方则开空仓
if close < lower_band and (dif < 0):
order_target_percent(symbol=symbol, percent=percent, order_type=OrderType_Market,
position_side=PositionSide_Short)
print(symbol, '市价单开空仓到比例: ', percent)
elif position_long:
# 价格跌破唐奇安平仓通道全平仓位止损
if close < lower_band:
order_close_all()
print(symbol, '市价单全平仓位')
else:
# 获取持仓均价
vwap = position_long['vwap']
# 获取持仓的资金
money = position_long['cost']
# 获取平仓的区间
band = vwap - np.array() * atr
grid_percent = float(pd.cut(, band, labels=)) * percent
# 选择现有百分比和区间百分比中较小的值(避免开仓)
target_percent = np.minimum(money / context.account().cash['nav'], grid_percent)
if target_percent != 1.0:
print(symbol, '市价单平多仓到比例: ', target_percent)
order_target_percent(symbol=symbol, percent=target_percent, order_type=OrderType_Market,
position_side=PositionSide_Long)
elif position_short:
# 价格涨破唐奇安平仓通道或价格涨破持仓均价加两倍ATR平空仓
if close > upper_band:
order_close_all()
print(symbol, '市价单全平仓位')
else:
# 获取持仓均价
vwap = position_short['vwap']
# 获取持仓的资金
money = position_short['cost']
# 获取平仓的区间
band = vwap + np.array([-100, 0.5, 1, 1.5, 2, 200]) * atr
grid_percent = float(pd.cut(, band, labels=)) * percent
# 选择现有百分比和区间百分比中较小的值(避免开仓)
target_percent = np.minimum(money / context.account().cash['nav'], grid_percent)
if target_percent != 1.0:
order_target_percent(symbol=symbol, percent=target_percent, order_type=OrderType_Market,
position_side=PositionSide_Short)
print(symbol, '市价单平空仓到比例: ', target_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='2017-09-15 09:15:00',
backtest_end_time='2017-10-01 15:00:00',
backtest_adjust=ADJUST_PREV,
backtest_initial_cash=10000000,
backtest_commission_ratio=0.0001,
backtest_slippage_ratio=0.0001)
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