海龟交易法(期货)

基于海龟交易法则的交易策略

源码

# coding=utf-8
from __future__ import print_function, absolute_import, unicode_literals

import sys

import numpy as np
import pandas as pd

try:
    import talib
except:
    print('请安装TA-Lib库')
    sys.exit(-1)
from gm.api import *

'''
本策略通过计算CZCE.FG801和SHFE.rb1801的ATR.唐奇安通道和MA线,
当价格上穿唐奇安通道且短MA在长MA上方时开多仓;当价格下穿唐奇安通道且短MA在长MA下方时开空仓(8手)
若有多仓则在价格跌破唐奇安平仓通道下轨的时候全平仓位,否则根据跌破
持仓均价 - x(x=0.5,1,1.5,2)倍ATR把仓位平至6/4/2/0手
若有空仓则在价格涨破唐奇安平仓通道上轨的时候全平仓位,否则根据涨破
持仓均价 + x(x=0.5,1,1.5,2)倍ATR把仓位平至6/4/2/0手
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
'''


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 = [55, 20, 10, 60, 20]
    context.tar = context.parameter[4]
    # context.goods交易的品种
    context.goods = ['CZCE.FG801', 'SHFE.rb1801']
    # 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[0]
    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[0] + 1
    upper_band = talib.MAX(recent_data['close'].values[:-1], timeperiod=context.don_open)[-1]
    context.don_close = context.parameter[1] + 1
    lower_band = talib.MIN(recent_data['close'].values[:-1], timeperiod=context.don_close)[-1]
    # 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[2] + 1))[-1]
        ma_long = talib.MA(recent_data['close'].values, timeperiod=(context.parameter[3] + 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_volume(symbol=symbol, volume=8, position_side=PositionSide_Long, order_type=OrderType_Market)
            print(symbol, '市价单开多仓8手')
        # 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_volume(symbol=symbol, volume=8, position_side=PositionSide_Short, order_type=OrderType_Market)
            print(symbol, '市价单开空仓8手')
    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
            band = vwap - np.array([200, 2, 1.5, 1, 0.5, -100]) * atr
            # 计算最新应持仓位
            grid_volume = int(pd.cut([close], band, labels=[0, 1, 2, 3, 4])[0]) * 2
            order_target_volume(symbol=symbol, volume=grid_volume, position_side=PositionSide_Long,
                                order_type=OrderType_Market)
            print(symbol, '市价单平多仓到', grid_volume, '手')
    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']
            # 获取平仓的区间
            band = vwap + np.array([-100, 0.5, 1, 1.5, 2, 200]) * atr
            # 计算最新应持仓位
            grid_volume = int(pd.cut([close], band, labels=[0, 1, 2, 3, 4])[0]) * 2
            order_target_volume(symbol=symbol, volume=grid_volume, position_side=PositionSide_Short,
                                order_type=OrderType_Market)
            print(symbol, '市价单平空仓到', grid_volume, '手')


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)

绩效