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[Add] GA_Pre_Final.ipynb
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docs/contribution.md
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# 贡献代码
---
@ -71,4 +70,5 @@ autopep8 --in-place --recursive .
[GithubVnpy]:https://github.com/vnpy/vnpy
[GithubDocForSync]:https://help.github.com/articles/syncing-a-fork/
[CreateIssue]:https://github.com/vnpy/vnpy/issues/new
[CreatePR]:https://github.com/vnpy/vnpy/compare?expand=1
[CreatePR]:https://github.com/vnpy/vnpy/compare?expand=1

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# CTA回测模块
CTA回测模块是基于PyQt5和pyqtgraph的图形化回测工具。启动VN Trader后，在菜单栏中点击“功能-> CTA回测”即可进入该图形化回测界面，如下图。CTA回测模块主要实现3个功能：历史行情数据的下载、策略回测、参数优化。
![](https://vnpy-community.oss-cn-shanghai.aliyuncs.com/forum_experience/yazhang/cta_backtester/cta_backtester.png)
 
## 1.下载数据
数据下载功能是基于RQData的get_price()函数实现的。
```
get_price(order_book_ids, start_date='2013-01-04', end_date='2014-01-04', frequency='1d', fields=None, adjust_type='pre', skip_suspended =False, market='cn')
```
在使用前要保证RQData初始化完毕，然后填写以下4个字段信息：
- 本地代码：格式为合约品种+交易所，如IF88.CFFEX、rb88.SHFE；然后在底层通过RqdataClient的to_rq_symbol()函数转换成符合RQData格式，对应RQData中get_price()函数的order_book_ids字段。
- K线周期：可以填1m、60m、1d，对应get_price()函数的frequency字段。
- 开始日期：格式为yy/mm/dd，如2017/4/21，对应get_price()函数的start_date字段。（点击窗口右侧箭头按钮可改变日期大小）
- 结束日期：格式为yy/mm/dd，如2019/4/22，对应get_price()函数的end_date字段。（点击窗口右侧箭头按钮可改变日期大小）
填写完字段信息后，点击下方“下载数据”按钮启动下载程序，下载成功如图所示。
![](https://vnpy-community.oss-cn-shanghai.aliyuncs.com/forum_experience/yazhang/cta_backtester/data_loader.png)
 
## 2.加载启动
## 3.策略回测
### 3.1统计数据
### 3.2图表分析
## 4.参数优化

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# 交易接口
## 如何连接
### 加载需要用的接口
### VN Trader界面操作
### 修改json配置文件
### 可交易的合约查看
## 接口分类
做一个表
## 接口详解
### CTP(ctp)
#### 如何加载
#### 相关字段
#### 获取账号
#### 其他特点
### 宽睿柜台(oes)
#### 如何加载
#### 相关字段
#### 获取账号
#### 其他特点
### 盈透证券(ib)
### 老虎证券(tiger)
### OKEX
### 火币

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docs/install.md
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注意事项：第4步会提示用户是否把VNConda注册成默认Python环境：若用户存在其他Python环境，则都不要勾选；反之，两个都勾选掉。
![enter image description here](https://vnpy-community.oss-cn-shanghai.aliyuncs.com/forum_experience/yazhang/install.bat/install_VNConda.png "enter image title here")
![](https://vnpy-community.oss-cn-shanghai.aliyuncs.com/forum_experience/yazhang/install.bat/install_VNConda.png "enter image title here")
 
@ -27,13 +27,13 @@
输入账号密码或者微信扫码登陆VNConda。（社区账号通过微信扫码可得）
![enter image description here](https://vnpy-community.oss-cn-shanghai.aliyuncs.com/forum_experience/yazhang/install.bat/login_VNConda.png "enter image title here")
![](https://vnpy-community.oss-cn-shanghai.aliyuncs.com/forum_experience/yazhang/install.bat/login_VNConda.png "enter image title here")
 
#### 4.使用VNStation
登录后会进入到VN Station的主界面。
![enter image description here](https://vnpy-community.oss-cn-shanghai.aliyuncs.com/forum_experience/yazhang/install.bat/login_VNConda_2.png "enter image title here")
![](https://vnpy-community.oss-cn-shanghai.aliyuncs.com/forum_experience/yazhang/install.bat/login_VNConda_2.png "enter image title here")
窗口下方有5个选项：
- VN Trade Lite：直接运行VN Trader (只有CTP接口)
@ -42,6 +42,17 @@
- 提问求助：提出相关问题，管理员会每天定时回复
- 后台更新：一键更新VN Station
 
#### 5.更新VNStation
更新VNStation除了“一键更新”外，也可以卸载老版本，安装新版本。
重新启动后，有几率会遇到下面的问题：“无法定位序数4540于动态链接库 \VNConda\Lib\site-packages\PyQt5\Qt\bin\ssleay.dll上。”类似的窗口弹出了几个，无法登录VN Station。
原因是操作系统中安装了其他的SSL组件，同时还影响了相关的环境变量，导致PyQt载入ssl模块失败。
解决方法是将 \VNConda\Lib\site-packages\PyQt5\Qt\bin 目录的两个动态库 libeay32.dll和 ssleay32.dll拷贝到 \VNConda\ 下。
 
 
@ -94,13 +105,13 @@ $ bash Miniconda3-latest-Linux-x86_64.sh
当询问是否把Miniconda设置为Python 默认环境时，把默认的"no"改成“yes”。原因是Ubuntu 18.04已有自带的Python 3.6与Python 2.7。
![enter image description here](https://vnpy-community.oss-cn-shanghai.aliyuncs.com/forum_experience/yazhang/install.bat/install_Miniconda_ubuntu.png "enter image title here")
![](https://vnpy-community.oss-cn-shanghai.aliyuncs.com/forum_experience/yazhang/install.bat/install_Miniconda_ubuntu.png "enter image title here")
重启Ubuntu后，打开终端直接输入"python" 然后按“Enter”键: 若出现如下图，则表示成功把Miniconda设置为Python默认环境。
![enter image description here](https://vnpy-community.oss-cn-shanghai.aliyuncs.com/forum_experience/yazhang/install.bat/Conda_Python_version.png "enter image title here")
![](https://vnpy-community.oss-cn-shanghai.aliyuncs.com/forum_experience/yazhang/install.bat/Conda_Python_version.png "enter image title here")
 

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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import random\n",
"import multiprocessing\n",
"import numpy as np\n",
"from deap import creator, base, tools, algorithms\n",
"from backtesting import BacktestingEngine,OptimizationSetting\n",
"from boll_channel_strategy import BollChannelStrategy\n",
"from atr_rsi_strategy import AtrRsiStrategy\n",
"from datetime import datetime\n",
"import multiprocessing #多进程\n",
"from scoop import futures #多进程\n",
"from functools import lru_cache"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"数据总体： 13824\n"
]
}
],
"source": [
"setting = OptimizationSetting()\n",
"#setting.add_parameter('atr_length', 10, 50, 2)\n",
"#setting.add_parameter('atr_ma_length', 10, 50, 2)\n",
"#setting.add_parameter('rsi_length', 4, 50, 2)\n",
"#setting.add_parameter('rsi_entry', 4, 30, 1)\n",
"setting.add_parameter('boll_window', 4, 50, 2)\n",
"#setting.add_parameter('boll_dev', 4, 50, 2)\n",
"setting.add_parameter('cci_window', 4, 50, 2)\n",
"setting.add_parameter('atr_window', 4, 50, 2)\n",
"\n",
"\n",
"local_setting = setting.generate_setting()\n",
"total_sample = len(local_setting)\n",
"print(\"数据总体：\",total_sample)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"dict_keys(['boll_window', 'cci_window', 'atr_window'])"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"setting_names = random.choice(local_setting).keys()\n",
"setting_names"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"def parameter_generate():\n",
" setting_param = list(random.choice(local_setting).values())\n",
" return setting_param"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[22, 28, 22]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"parameter_generate()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'boll_window': 24, 'cci_window': 14, 'atr_window': 28}"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"setting=dict(zip(setting_names,parameter_generate()))\n",
"setting"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"def object_func(strategy_avg):\n",
" \"\"\"\"\"\"\n",
" return run_backtesting(tuple(strategy_avg))\n",
" #return run_backtesting(strategy_avg)\n",
" \n",
"\n",
"@lru_cache(maxsize=1000000)\n",
"def run_backtesting(strategy_avg):\n",
" # 创建回测引擎对象\n",
" engine = BacktestingEngine()\n",
" engine.set_parameters(\n",
" vt_symbol=\"IF88.CFFEX\",\n",
" interval=\"1m\",\n",
" start=datetime(2016, 1, 1),\n",
" end=datetime(2019, 1,1),\n",
" rate=0.3/10000,\n",
" slippage=0.2,\n",
" size=300,\n",
" pricetick=0.2,\n",
" capital=1_000_000,\n",
" )\n",
" \n",
" setting=dict(zip(setting_names,strategy_avg))\n",
" \n",
"\n",
" #加载策略 \n",
" #engine.initStrategy(TurtleTradingStrategy, setting)\n",
" engine.add_strategy(BollChannelStrategy, setting)\n",
" engine.load_data()\n",
" engine.run_backtesting()\n",
" engine.calculate_result()\n",
" result = engine.calculate_statistics(output=False)\n",
"\n",
" return_drawdown_ratio = round(result['return_drawdown_ratio'],2) #收益回撤比\n",
" sharpe_ratio= round(result['sharpe_ratio'],2) #夏普比率\n",
" return return_drawdown_ratio , sharpe_ratio"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(-0.51, -0.28)"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"object_func(parameter_generate())"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"target_names = [\"return_drawdown_ratio\" , \"sharpe_ratio\"]\n",
"def show_result(hof):\n",
" for i in range(len(hof)):\n",
" solution = hof[i] \n",
" parameter=dict(zip(setting_names,solution))\n",
" result=dict(zip(target_names,list(object_func(solution))))\n",
" print({**parameter, **result})"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"from time import time\n",
"#设置优化方向：最大化收益回撤比，最大化夏普比率\n",
"creator.create(\"FitnessMax\", base.Fitness, weights=(1.0, 1.0)) # 1.0 求最大值；-1.0 求最小值\n",
"creator.create(\"Individual\", list, fitness=creator.FitnessMax)\n",
"\n",
"def optimize(population=None):\n",
" \"\"\"\"\"\" \n",
" start = time() \n",
" toolbox = base.Toolbox() \n",
"\n",
" # 初始化 \n",
" toolbox.register(\"individual\", tools.initIterate, creator.Individual,parameter_generate) \n",
" toolbox.register(\"population\", tools.initRepeat, list, toolbox.individual) \n",
" toolbox.register(\"mate\", tools.cxTwoPoint) \n",
" toolbox.register(\"mutate\", tools.mutUniformInt,low = 4,up = 40,indpb=1) \n",
" toolbox.register(\"evaluate\", object_func) \n",
" toolbox.register(\"select\", tools.selNSGA2) \n",
" #pool = multiprocessing.Pool()\n",
" #toolbox.register(\"map\", pool.map)\n",
" #toolbox.register(\"map\", futures.map)\n",
" \n",
" \n",
" #遗传算法参数设置\n",
" MU = 80 #设置每一代选择的个体数\n",
" LAMBDA = 100 #设置每一代产生的子女数\n",
" POP=100\n",
" CXPB, MUTPB, NGEN = 0.95, 0.05,30 #分别为种群内部个体的交叉概率、变异概率、产生种群代数\n",
" \n",
" if population==None:\n",
" LAMBDA = POP = int(pow(total_sample, 1/2.7))\n",
" MU = int(0.8*POP) \n",
" \n",
" pop = toolbox.population(POP) #设置族群里面的个体数量\n",
" hof = tools.ParetoFront() #解的集合：帕累托前沿(非占优最优集)\n",
"\n",
" stats = tools.Statistics(lambda ind: ind.fitness.values)\n",
" np.set_printoptions(suppress=True) #对numpy默认输出的科学计数法转换\n",
" stats.register(\"mean\", np.mean, axis=0) #统计目标优化函数结果的平均值\n",
" stats.register(\"std\", np.std, axis=0) #统计目标优化函数结果的标准差\n",
" stats.register(\"min\", np.min, axis=0) #统计目标优化函数结果的最小值\n",
" stats.register(\"max\", np.max, axis=0) #统计目标优化函数结果的最大值\n",
" print(\"开始运行遗传算法，每代族群总数：%s, 优良品种筛选个数：%s，迭代次数：%s，交叉概率：%s，突变概率：%s\" %(POP,MU,NGEN,CXPB,MUTPB))\n",
" \n",
"\n",
" #运行算法\n",
" algorithms.eaMuPlusLambda(pop, toolbox, MU, LAMBDA, CXPB, MUTPB, NGEN, stats,\n",
" halloffame=hof) #esMuPlusLambda是一种基于(μ+λ)选择策略的多目标优化分段遗传算法\n",
"\n",
" end = time()\n",
" cost = int((end - start))\n",
"\n",
" print(\"遗传算法优化完成，耗时%s秒\"% (cost))\n",
" print(\"输出帕累托前沿解集：\")\n",
" show_result(hof)\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"开始运行遗传算法，每代族群总数：34, 优良品种筛选个数：27，迭代次数：30，交叉概率：0.95，突变概率：0.05\n",
"gen\tnevals\tmean \tstd \tmin \tmax \n",
"0 \t34 \t[0.08852941 0.00352941]\t[0.5373362 0.29107188]\t[-0.7 -0.63]\t[1.51 0.5 ]\n",
"1 \t34 \t[0.60148148 0.27518519]\t[0.31013383 0.08573734]\t[0.32 0.18] \t[1.51 0.5 ]\n",
"2 \t34 \t[0.79333333 0.33851852]\t[0.27758215 0.06742369]\t[0.47 0.25] \t[1.54 0.5 ]\n",
"3 \t34 \t[1.00888889 0.39777778]\t[0.3147525 0.06214281]\t[0.7 0.33] \t[1.54 0.5 ]\n",
"4 \t34 \t[1.41074074 0.47444444]\t[0.22881217 0.04661373]\t[0.96 0.36] \t[1.92 0.57]\n",
"5 \t34 \t[1.59666667 0.51222222]\t[0.14714568 0.0255797 ]\t[1.51 0.49] \t[1.92 0.57]\n",
"6 \t34 \t[1.66259259 0.52185185]\t[0.16585564 0.02981884]\t[1.52 0.49] \t[1.92 0.57]\n",
"7 \t34 \t[1.8737037 0.55666667]\t[0.07713135 0.01763834]\t[1.75 0.53] \t[1.95 0.57]\n",
"8 \t34 \t[1.93666667 0.57 ]\t[0.01490712 0. ]\t[1.92 0.57] \t[1.95 0.57]\n",
"9 \t34 \t[1.95 0.57] \t[0. 0.] \t[1.95 0.57] \t[1.95 0.57]\n",
"10 \t34 \t[1.95 0.57] \t[0. 0.] \t[1.95 0.57] \t[1.95 0.57]\n",
"11 \t34 \t[1.95 0.57] \t[0. 0.] \t[1.95 0.57] \t[1.95 0.57]\n",
"12 \t34 \t[1.95 0.57] \t[0. 0.] \t[1.95 0.57] \t[1.95 0.57]\n",
"13 \t34 \t[1.95 0.57] \t[0. 0.] \t[1.95 0.57] \t[1.95 0.57]\n",
"14 \t34 \t[1.95 0.57] \t[0. 0.] \t[1.95 0.57] \t[1.95 0.57]\n",
"15 \t34 \t[1.95 0.57] \t[0. 0.] \t[1.95 0.57] \t[1.95 0.57]\n",
"16 \t34 \t[1.95 0.57] \t[0. 0.] \t[1.95 0.57] \t[1.95 0.57]\n",
"17 \t34 \t[1.95 0.57] \t[0. 0.] \t[1.95 0.57] \t[1.95 0.57]\n",
"18 \t34 \t[1.95 0.57] \t[0. 0.] \t[1.95 0.57] \t[1.95 0.57]\n",
"19 \t34 \t[1.95 0.57] \t[0. 0.] \t[1.95 0.57] \t[1.95 0.57]\n",
"20 \t34 \t[1.95 0.57] \t[0. 0.] \t[1.95 0.57] \t[1.95 0.57]\n",
"21 \t34 \t[1.95 0.57] \t[0. 0.] \t[1.95 0.57] \t[1.95 0.57]\n",
"22 \t34 \t[1.95 0.57] \t[0. 0.] \t[1.95 0.57] \t[1.95 0.57]\n",
"23 \t34 \t[1.95 0.57] \t[0. 0.] \t[1.95 0.57] \t[1.95 0.57]\n",
"24 \t34 \t[1.95 0.57] \t[0. 0.] \t[1.95 0.57] \t[1.95 0.57]\n",
"25 \t34 \t[1.95 0.57] \t[0. 0.] \t[1.95 0.57] \t[1.95 0.57]\n",
"26 \t34 \t[1.95 0.57] \t[0. 0.] \t[1.95 0.57] \t[1.95 0.57]\n",
"27 \t34 \t[1.95 0.57] \t[0. 0.] \t[1.95 0.57] \t[1.95 0.57]\n",
"28 \t34 \t[1.95 0.57] \t[0. 0.] \t[1.95 0.57] \t[1.95 0.57]\n",
"29 \t34 \t[1.95 0.57] \t[0. 0.] \t[1.95 0.57] \t[1.95 0.57]\n",
"30 \t34 \t[1.95 0.57] \t[0. 0.] \t[1.95 0.57] \t[1.95 0.57]\n",
"遗传算法优化完成，耗时309秒\n",
"输出帕累托前沿解集：\n",
"{'boll_window': 48, 'cci_window': 40, 'atr_window': 22, 'return_drawdown_ratio': 1.95, 'sharpe_ratio': 0.57}\n",
"{'boll_window': 48, 'cci_window': 50, 'atr_window': 22, 'return_drawdown_ratio': 1.95, 'sharpe_ratio': 0.57}\n"
]
}
],
"source": [
"optimize()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
" MU = 80 #设置每一代选择的个体数\n",
" POP = 100 #设置每一代产生的子女数\n",
" CXPB, MUTPB, NGEN = 0.95, 0.05,20 \n",
" print(\"开始运行遗传算法，每代族群总数：%s, 优良品种筛选个数：%s，迭代次数：%s，交叉概率：%s，突变概率：%s\" %(POP,MU,NGEN,CXPB,MUTPB))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.1"
},
"toc": {
"base_numbering": 1,
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