海龟交易法(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
import numpy as np
import pandas as pd
from gm.api import *
'''
以短期为例：20日线
第一步：获取历史数据，计算唐奇安通道和ATR
第二步：当突破唐奇安通道时，开仓。
第三步：计算加仓和止损信号。
'''
def init(context):
# 设置计算唐奇安通道的参数
context.n = 20
# 设置合约标的
context.symbol = 'DCE.i2012'
# 设置交易最大资金比率
context.ratio = 0.8
# 订阅数据
subscribe(symbols=context.symbol, frequency='60s', count=2)
# 获取当前时间
time = context.now.strftime('%H:%M:%S')
# 如果策略执行时间点是交易时间段，则直接执行algo定义atr等参数，以防直接进入on_bar()导致atr等未定义
if '09:00:00' < time < '15:00:00' or '21:00:00' < time < '23:00:00':
algo(context)
# 如果是交易时间段，等到开盘时间确保进入algo()
schedule(schedule_func=algo, date_rule='1d', time_rule='09:00:00')
schedule(schedule_func=algo, date_rule='1d', time_rule='21:00:00')
def algo(context):
# 计算通道的数据:当日最低、最高、上一交易日收盘
# 注：由于talib库计算ATR的结果与公式求得的结果不符，所以这里利用公式计算ATR
# 如果是回测模式,当天的数据直接用history取到
if context.mode == 2:
data = history_n(symbol=context.symbol, frequency='1d', count=context.n+1, end_time=context.now, fields='close,high,low,bob', df=True) # 计算ATR
tr_list = []
for i in range(0, len(data)-1):
tr = max((data['high'].iloc[i] - data['low'].iloc[i]), data['close'].shift(-1).iloc[i] - data['high'].iloc[i],
data['close'].shift(-1).iloc[i] - data['low'].iloc[i])
tr_list.append(tr)
context.atr = int(np.floor(np.mean(tr_list)))
context.atr_half = int(np.floor(0.5 * context.atr))
# 计算唐奇安通道
context.don_open = np.max(data['high'].values[-context.n:])
context.don_close = np.min(data['low'].values[-context.n:])
# 如果是实时模式，当天的数据需要用current取到
if context.mode == 1:
data = history_n(symbol=context.symbol, frequency='1d', count=context.n, end_time=context.now, fields='close,high,low',
df=True) # 计算ATR
current_data = current(symbols=context.symbol) # 最新一个交易日的最高、最低
tr_list = []
for i in range(1, len(data)):
tr = max((data['high'].iloc[i] - data['low'].iloc[i]),
data['close'].shift(-1).iloc[i] - data['high'].iloc[i],
data['close'].shift(-1).iloc[i] - data['low'].iloc[i])
tr_list.append(tr)
# 把最新一期tr加入列表中
tr_new = max((current_data[0]['high'] - current_data[0]['low']),
data['close'].iloc[-1] - current_data[0]['high'],
data['close'].iloc[-1] - current_data[0]['low'])
tr_list.append(tr_new)
context.atr = int(np.floor(np.mean(tr_list)))
context.atr_half = int(np.floor(0.5 * context.atr))
# 计算唐奇安通道
context.don_open = np.max(data['high'].values[-context.n:])
context.don_close = np.min(data['low'].values[-context.n:])
# 计算加仓点和止损点
context.long_add_point = context.don_open + context.atr_half
context.long_stop_loss = context.don_open - context.atr_half
context.short_add_point = context.don_close - context.atr_half
context.short_stop_loss = context.don_close + context.atr_half
def on_bar(context, bars):
# 提取数据
symbol = bars[0]['symbol']
recent_data = context.data(symbol=context.symbol, frequency='60s', count=2, fields='close,high,low')
close = recent_data['close'].values[-1]
# 账户仓位情况
position_long = context.account().position(symbol=symbol, side=PositionSide_Long)
position_short = context.account().position(symbol=symbol, side=PositionSide_Short)
# 当无持仓时
if not position_long and not position_short:
# 如果向上突破唐奇安通道，则开多
if close > context.don_open:
order_volume(symbol=symbol, side=OrderSide_Buy, volume=context.atr, order_type=OrderType_Market, position_effect=PositionEffect_Open)
print('开多仓atr')
# 如果向下突破唐奇安通道，则开空
if close < context.don_close:
order_volume(symbol=symbol, side=OrderSide_Sell, volume=context.atr, order_type=OrderType_Market, position_effect=PositionEffect_Open)
print('开空仓atr')
# 有持仓时
# 持多仓，继续突破（加仓）
if position_long:
# 当突破1/2atr时加仓
if close > context.long_add_point:
order_volume(symbol=symbol, volume=context.atr_half, side=OrderSide_Buy, order_type=OrderType_Market,position_effect=PositionEffect_Open)
print('继续加仓0.5atr')
context.long_add_point += context.atr_half
context.long_stop_loss += context.atr_half
# 持多仓，止损位计算
if close < context.long_stop_loss:
volume_hold = position_long['volume']
if volume_hold >= context.atr_half:
order_volume(symbol=symbol, volume=context.atr_half, side=OrderSide_Sell, order_type=OrderType_Market, position_effect=PositionEffect_Close)
else:
order_volume(symbol=symbol, volume=volume_hold, side=OrderSide_Sell, order_type=OrderType_Market,position_effect=PositionEffect_Close)
print('平多仓0.5atr')
context.long_add_point -= context.atr_half
context.long_stop_loss -= context.atr_half
# 持空仓，继续突破（加仓）
if position_short:
# 当跌破加仓点时加仓
if close < context.short_add_point:
order_volume(symbol = symbol, volume=context.atr_half, side=OrderSide_Sell, order_type=OrderType_Market, position_effect=PositionEffect_Open)
print('继续加仓0.5atr')
context.short_add_point -= context.atr_half
context.short_stop_loss -= context.atr_half
# 持多仓，止损位计算
if close > context.short_stop_loss:
volume_hold = position_short['volume']
if volume_hold >= context.atr_half:
order_volume(symbol=symbol, volume=context.atr_half, side=OrderSide_Buy, order_type=OrderType_Market, position_effect=PositionEffect_Close)
else:
order_volume(symbol=symbol, volume=volume_hold, side=OrderSide_Buy, order_type=OrderType_Market,position_effect=PositionEffect_Close)
print('平空仓0.5atr')
context.short_add_point += context.atr_half
context.short_stop_loss += context.atr_half
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='2020-02-15 09:15:00',
backtest_end_time='2020-09-01 15:00:00',
backtest_adjust=ADJUST_PREV,
backtest_initial_cash=1000000,
backtest_commission_ratio=0.0001,
backtest_slippage_ratio=0.0001)
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