1137 lines
37 KiB
Markdown
1137 lines
37 KiB
Markdown
# CTA策略模块
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## 模块构成
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CTA策略模块主要由7部分构成，如下图：
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- base：定义了CTA模块中用到的一些基础设置，如引擎类型（回测/实盘）、回测模式（K线/Tick）、本地停止单的定义以及停止单状态（等待中/已撤销/已触发）。
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- template：定义了CTA策略模板（包含信号生成和委托管理）、CTA信号（仅负责信号生成）、目标仓位算法（仅负责委托管理，适用于拆分巨型委托，降低冲击成本）。
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- strategies: 官方提供的cta策略示例，包含从最基础的双均线策略，到通道突破类型的布林带策略，到跨时间周期策略，再到把信号生成和委托管理独立开来的多信号策略。
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- backesting：包含回测引擎和参数优化。其中回测引擎定义了数据载入、委托撮合机制、计算与统计相关盈利指标、结果绘图等函数。
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- converter：定义了针对上期所品种平今/平昨模式的委托转换模块；对于其他品种用户也可以通过可选参数lock切换至锁仓模式。
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- engine：定义了CTA策略实盘引擎，其中包括：RQData客户端初始化和数据载入、策略的初始化和启动、推送Tick订阅行情到策略中、挂撤单操作、策略的停止和移除等。
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- ui：基于PyQt5的GUI图形应用。
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## 历史数据
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### 回测历史数据
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回测所需要的历史数据可通过运行getdata.py文件进行下载。该文件处于根目录下tests\backtesting文件夹内。
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下载历史数据的原理是调用RQData的get_price()函数把数据下载到内存里面；再通过generate_bar_from_row()函数，以固定格式把数据从内存载入到硬盘数据库中。
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下面介绍具体流程：
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- 填写RQData的账号密码，初始化RQData
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```
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import rqdatac as rq
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USERNAME = ""
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PASSWORD = ""
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FIELDS = ["open", "high", "low", "close", "volume"]
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rq.init(USERNAME, PASSWORD, ("rqdatad-pro.ricequant.com", 16011))
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```
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- 定义数据插入格式。需要插入的数据包括：合约代码、交易所、K线周期、开盘价、最高价、最低价、收盘价、成交量、数据库名称、vt_symbol（注意：K线周期可以是"1m"、"1h"、"d"、"w"。to_pydatetime()用于时间转换成datetime格式）
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```
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def generate_bar_from_row(row, symbol, exchange):
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""""""
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bar = DbBarData()
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bar.symbol = symbol
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bar.exchange = exchange
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bar.interval = "1m"
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bar.open_price = row["open"]
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bar.high_price = row["high"]
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bar.low_price = row["low"]
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bar.close_price = row["close"]
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bar.volume = row["volume"]
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bar.datetime = row.name.to_pydatetime()
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bar.gateway_name = "DB"
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bar.vt_symbol = f"{symbol}.{exchange}"
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return bar
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```
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- 定义数据下载函数。主要调用RQData的get_price()获取指定合约或合约列表的历史数据（包含起止日期，日线或分钟线）。目前仅支持中国市场的股票、期货、ETF和上金所现货的行情数据，如黄金、铂金和白银产品。（注意：起始日期默认是2013-01-04，结束日期默认是2014-01-04）
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```
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def download_minute_bar(vt_symbol):
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"""下载某一合约的分钟线数据"""
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print(f"开始下载合约数据{vt_symbol}")
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symbol, exchange = vt_symbol.split(".")
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start = time()
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df = rq.get_price(symbol, start_date="2018-01-01", end_date="2019-01-01", frequency="1m", fields=FIELDS)
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with DB.atomic():
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for ix, row in df.iterrows():
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print(row.name)
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bar = generate_bar_from_row(row, symbol, exchange)
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DbBarData.replace(bar.__data__).execute()
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end = time()
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cost = (end - start) * 1000
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print(
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"合约%s的分钟K线数据下载完成%s - %s，耗时%s毫秒"
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% (symbol, df.index[0], df.index[-1], cost)
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)
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```
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### 实盘历史数据
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在实盘中，RQData通过实时载入数据进行策略的初始化。该功能主要在CTA实盘引擎engine.py内实现。
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下面介绍具体流程：
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- 配置json文件：在用户目录下.vntrader文件夹找到vt_setting.json，输入RQData的账号和密码，如图。
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- 初始化RQData客户端：从vt_setting.json中读取RQData的账户、密码到rq_client.init()函数进行初始化
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```
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def init_rqdata(self):
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"""
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Init RQData client.
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"""
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username = SETTINGS["rqdata.username"]
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password = SETTINGS["rqdata.password"]
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if not username or not password:
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return
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import rqdatac
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self.rq_client = rqdatac
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self.rq_client.init(username, password,
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('rqdatad-pro.ricequant.com', 16011))
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```
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- RQData载入实盘数据：输入vt_symbol后，首先会转换成符合RQData格式的rq_symbol，通过get_price()函数下载数据，并且插入到数据库中。
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```
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def query_bar_from_rq(
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self, vt_symbol: str, interval: Interval, start: datetime, end: datetime
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):
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"""
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Query bar data from RQData.
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"""
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symbol, exchange_str = vt_symbol.split(".")
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rq_symbol = to_rq_symbol(vt_symbol)
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if rq_symbol not in self.rq_symbols:
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return None
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end += timedelta(1) # For querying night trading period data
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df = self.rq_client.get_price(
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rq_symbol,
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frequency=interval.value,
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fields=["open", "high", "low", "close", "volume"],
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start_date=start,
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end_date=end
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)
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data = []
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for ix, row in df.iterrows():
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bar = BarData(
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symbol=symbol,
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exchange=Exchange(exchange_str),
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interval=interval,
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datetime=row.name.to_pydatetime(),
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open_price=row["open"],
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high_price=row["high"],
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low_price=row["low"],
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close_price=row["close"],
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volume=row["volume"],
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gateway_name="RQ"
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)
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data.append(bar)
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```
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## 策略开发
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CTA策略模板提供完整的信号生成和委托管理功能，用户可以基于该模板自行开发策略。新策略可以放在根目录下vnpy\app\cta_strategy\strategies文件夹内，也可以放在用户运行的文件内（VN Station模式）。注意：策略文件命名是以下划线模式，如boll_channel_strategy.py；而策略类命名采用的是驼峰式，如BollChannelStrategy。
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下面通过BollChannelStrategy策略示例，来展示策略开发的具体步骤：
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### 参数设置
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定义策略参数并且初始化策略变量。策略参数为策略类的公有属性，用户可以通过创建新的实例来调用或者改变策略参数。
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如针对rb1905品种，用户可以创建基于BollChannelStrategy的策略示例，如RB_BollChannelStrategy，boll_window可以由18改成30。
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创建策略实例的方法有效地实现了一个策略跑多个品种，并且其策略参数可以通过品种的特征进行调整。
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```
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boll_window = 18
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boll_dev = 3.4
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cci_window = 10
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atr_window = 30
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sl_multiplier = 5.2
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fixed_size = 1
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boll_up = 0
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boll_down = 0
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cci_value = 0
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atr_value = 0
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intra_trade_high = 0
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intra_trade_low = 0
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long_stop = 0
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short_stop = 0
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```
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### 类的初始化
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初始化分3步：
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- 通过super( )的方法继承CTA策略模板，在__init__( )函数传入CTA引擎、策略名称、vt_symbol、参数设置。
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- 调用K线生成模块:通过时间切片来把Tick数据合成1分钟K线数据，然后更大的时间周期数据，如15分钟K线。
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- 调用K线时间序列管理模块：基于K线数据，如1分钟、15分钟，来生成相应的技术指标。
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```
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def __init__(self, cta_engine, strategy_name, vt_symbol, setting):
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""""""
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super(BollChannelStrategy, self).__init__(
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cta_engine, strategy_name, vt_symbol, setting
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)
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self.bg = BarGenerator(self.on_bar, 15, self.on_15min_bar)
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self.am = ArrayManager()
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```
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### 策略的初始化、启动、停止
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通过“CTA策略”组件的相关功能按钮实现。
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注意：函数load_bar(10)，代表策略初始化需要载入10个交易日的历史数据。该历史数据可以是Tick数据，也可以是K线数据。
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```
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def on_init(self):
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"""
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Callback when strategy is inited.
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"""
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self.write_log("策略初始化")
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self.load_bar(10)
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def on_start(self):
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"""
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Callback when strategy is started.
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"""
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self.write_log("策略启动")
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def on_stop(self):
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"""
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Callback when strategy is stopped.
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"""
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self.write_log("策略停止")
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```
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### Tick数据回报
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策略订阅某品种合约行情，交易所会推送Tick数据到该策略上。
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由于BollChannelStrategy是基于15分钟K线来生成交易信号的，故收到Tick数据后，需要用到K线生成模块里面的update_tick函数，通过时间切片的方法，聚合成1分钟K线数据，并且推送到on_bar函数。
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```
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def on_tick(self, tick: TickData):
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"""
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Callback of new tick data update.
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"""
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self.bg.update_tick(tick)
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```
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### K线数据回报
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收到推送过来的1分钟K线数据后，通过K线生成模块里面的update_bar函数，以分钟切片的方法，合成15分钟K线数据，并且推送到on_15min_bar函数。
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```
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def on_bar(self, bar: BarData):
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"""
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Callback of new bar data update.
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"""
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self.bg.update_bar(bar)
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```
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### 15分钟K线数据回报
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负责CTA信号的生成，由3部分组成：
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- 清空未成交委托：为了防止之前下的单子在上一个15分钟没有成交，但是下一个15分钟可能已经调整了价格，就用cancel_all()方法立刻撤销之前未成交的所有委托，保证策略在当前这15分钟开始时的整个状态是清晰和唯一的。
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- 调用K线时间序列管理模块：基于最新的15分钟K线数据来计算相应计算指标，如布林带通道上下轨、CCI指标、ATR指标
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- 信号计算：通过持仓的判断以及结合CCI指标、布林带通道、ATR指标在通道突破点挂出停止单委托（buy/sell)，同时设置离场点(short/cover)。
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注意：CTA策略具有低胜率和高盈亏比的特定：在难以提升胜率的情况下，研究提高策略盈亏比有利于策略盈利水平的上升。
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```
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def on_15min_bar(self, bar: BarData):
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""""""
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self.cancel_all()
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am = self.am
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am.update_bar(bar)
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if not am.inited:
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return
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self.boll_up, self.boll_down = am.boll(self.boll_window, self.boll_dev)
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self.cci_value = am.cci(self.cci_window)
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self.atr_value = am.atr(self.atr_window)
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if self.pos == 0:
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self.intra_trade_high = bar.high_price
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self.intra_trade_low = bar.low_price
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if self.cci_value > 0:
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self.buy(self.boll_up, self.fixed_size, True)
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elif self.cci_value < 0:
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self.short(self.boll_down, self.fixed_size, True)
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elif self.pos > 0:
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self.intra_trade_high = max(self.intra_trade_high, bar.high_price)
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self.intra_trade_low = bar.low_price
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self.long_stop = self.intra_trade_high - self.atr_value * self.sl_multiplier
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self.sell(self.long_stop, abs(self.pos), True)
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elif self.pos < 0:
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self.intra_trade_high = bar.high_price
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self.intra_trade_low = min(self.intra_trade_low, bar.low_price)
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self.short_stop = self.intra_trade_low + self.atr_value * self.sl_multiplier
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self.cover(self.short_stop, abs(self.pos), True)
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self.put_event()
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```
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### 委托回报、成交回报、停止单回报
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在策略中可以直接pass，其具体逻辑应用交给回测/实盘引擎负责。
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```
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def on_order(self, order: OrderData):
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"""
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Callback of new order data update.
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"""
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pass
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def on_trade(self, trade: TradeData):
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"""
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Callback of new trade data update.
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"""
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self.put_event()
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def on_stop_order(self, stop_order: StopOrder):
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"""
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Callback of stop order update.
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"""
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pass
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```
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## 回测研究
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backtesting.py定义了回测引擎，下面主要介绍相关功能函数，以及回测引擎应用示例：
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### 加载策略
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把CTA策略逻辑，对应合约品种，以及参数设置（可在策略文件外修改）载入到回测引擎中。
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```
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def add_strategy(self, strategy_class: type, setting: dict):
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""""""
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self.strategy_class = strategy_class
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self.strategy = strategy_class(
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self, strategy_class.__name__, self.vt_symbol, setting
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)
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```
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### 载入历史数据
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负责载入对应品种的历史数据，大概有4个步骤：
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- 根据数据类型不同，分成K线模式和Tick模式；
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- 通过select().where()方法，有条件地从数据库中选取数据，其筛选标准包括：vt_symbol、 回测开始日期、回测结束日期、K线周期（K线模式下）；
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- order_by(DbBarData.datetime)表示需要按照时间顺序载入数据；
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- 载入数据是以迭代方式进行的，数据最终存入self.history_data。
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```
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def load_data(self):
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""""""
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self.output("开始加载历史数据")
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if self.mode == BacktestingMode.BAR:
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s = (
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DbBarData.select()
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.where(
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(DbBarData.vt_symbol == self.vt_symbol)
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& (DbBarData.interval == self.interval)
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& (DbBarData.datetime >= self.start)
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& (DbBarData.datetime <= self.end)
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)
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.order_by(DbBarData.datetime)
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)
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self.history_data = [db_bar.to_bar() for db_bar in s]
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else:
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s = (
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DbTickData.select()
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.where(
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(DbTickData.vt_symbol == self.vt_symbol)
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& (DbTickData.datetime >= self.start)
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& (DbTickData.datetime <= self.end)
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)
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.order_by(DbTickData.datetime)
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)
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self.history_data = [db_tick.to_tick() for db_tick in s]
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self.output(f"历史数据加载完成，数据量：{len(self.history_data)}")
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```
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### 撮合成交
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载入CTA策略以及相关历史数据后，策略会根据最新的数据来计算相关指标。若符合条件会生成交易信号，发出具体委托（buy/sell/short/cover），并且在下一根K线成交。
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根据委托类型的不同，回测引擎提供2种撮合成交机制来尽量模仿真实交易环节：
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- 限价单撮合成交：（以买入方向为例）先确定是否发生成交，成交标准为委托价>= 下一根K线的最低价；然后确定成交价格，成交价格为委托价与下一根K线开盘价的最小值。
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|
||
- 停止单撮合成交：（以买入方向为例）先确定是否发生成交，成交标准为委托价<= 下一根K线的最高价；然后确定成交价格，成交价格为委托价与下一根K线开盘价的最大值。
|
||
|
||
|
||
|
||
下面展示在引擎中限价单撮合成交的流程：
|
||
- 确定会撮合成交的价格；
|
||
- 遍历限价单字典中的所有限价单，推送委托进入未成交队列的更新状态；
|
||
- 判断成交状态，若出现成交，推送成交数据和委托数据；
|
||
- 从字典中删除已成交的限价单。
|
||
|
||
```
|
||
def cross_limit_order(self):
|
||
"""
|
||
Cross limit order with last bar/tick data.
|
||
"""
|
||
if self.mode == BacktestingMode.BAR:
|
||
long_cross_price = self.bar.low_price
|
||
short_cross_price = self.bar.high_price
|
||
long_best_price = self.bar.open_price
|
||
short_best_price = self.bar.open_price
|
||
else:
|
||
long_cross_price = self.tick.ask_price_1
|
||
short_cross_price = self.tick.bid_price_1
|
||
long_best_price = long_cross_price
|
||
short_best_price = short_cross_price
|
||
|
||
for order in list(self.active_limit_orders.values()):
|
||
# Push order update with status "not traded" (pending)
|
||
if order.status == Status.SUBMITTING:
|
||
order.status = Status.NOTTRADED
|
||
self.strategy.on_order(order)
|
||
|
||
# Check whether limit orders can be filled.
|
||
long_cross = (
|
||
order.direction == Direction.LONG
|
||
and order.price >= long_cross_price
|
||
and long_cross_price > 0
|
||
)
|
||
|
||
short_cross = (
|
||
order.direction == Direction.SHORT
|
||
and order.price <= short_cross_price
|
||
and short_cross_price > 0
|
||
)
|
||
|
||
if not long_cross and not short_cross:
|
||
continue
|
||
|
||
# Push order udpate with status "all traded" (filled).
|
||
order.traded = order.volume
|
||
order.status = Status.ALLTRADED
|
||
self.strategy.on_order(order)
|
||
|
||
self.active_limit_orders.pop(order.vt_orderid)
|
||
|
||
# Push trade update
|
||
self.trade_count += 1
|
||
|
||
if long_cross:
|
||
trade_price = min(order.price, long_best_price)
|
||
pos_change = order.volume
|
||
else:
|
||
trade_price = max(order.price, short_best_price)
|
||
pos_change = -order.volume
|
||
|
||
trade = TradeData(
|
||
symbol=order.symbol,
|
||
exchange=order.exchange,
|
||
orderid=order.orderid,
|
||
tradeid=str(self.trade_count),
|
||
direction=order.direction,
|
||
offset=order.offset,
|
||
price=trade_price,
|
||
volume=order.volume,
|
||
time=self.datetime.strftime("%H:%M:%S"),
|
||
gateway_name=self.gateway_name,
|
||
)
|
||
trade.datetime = self.datetime
|
||
|
||
self.strategy.pos += pos_change
|
||
self.strategy.on_trade(trade)
|
||
|
||
self.trades[trade.vt_tradeid] = trade
|
||
```
|
||
|
||
|
||
|
||
### 计算策略盈亏情况
|
||
|
||
基于收盘价、当日持仓量、合约规模、滑点、手续费率等计算总盈亏与净盈亏，并且其计算结果以DataFrame格式输出，完成基于逐日盯市盈亏统计。
|
||
|
||
下面展示盈亏情况的计算过程
|
||
|
||
- 浮动盈亏 = 持仓量 \*（当日收盘价 - 昨日收盘价）\* 合约规模
|
||
- 实际盈亏 = 持仓变化量 \* （当时收盘价 - 开仓成交价）\* 合约规模
|
||
- 总盈亏 = 浮动盈亏 + 实际盈亏
|
||
- 净盈亏 = 总盈亏 - 总手续费 - 总滑点
|
||
|
||
```
|
||
def calculate_pnl(
|
||
self,
|
||
pre_close: float,
|
||
start_pos: float,
|
||
size: int,
|
||
rate: float,
|
||
slippage: float,
|
||
):
|
||
""""""
|
||
self.pre_close = pre_close
|
||
|
||
# Holding pnl is the pnl from holding position at day start
|
||
self.start_pos = start_pos
|
||
self.end_pos = start_pos
|
||
self.holding_pnl = self.start_pos * \
|
||
(self.close_price - self.pre_close) * size
|
||
|
||
# Trading pnl is the pnl from new trade during the day
|
||
self.trade_count = len(self.trades)
|
||
|
||
for trade in self.trades:
|
||
if trade.direction == Direction.LONG:
|
||
pos_change = trade.volume
|
||
else:
|
||
pos_change = -trade.volume
|
||
|
||
turnover = trade.price * trade.volume * size
|
||
|
||
self.trading_pnl += pos_change * \
|
||
(self.close_price - trade.price) * size
|
||
self.end_pos += pos_change
|
||
self.turnover += turnover
|
||
self.commission += turnover * rate
|
||
self.slippage += trade.volume * size * slippage
|
||
|
||
# Net pnl takes account of commission and slippage cost
|
||
self.total_pnl = self.trading_pnl + self.holding_pnl
|
||
self.net_pnl = self.total_pnl - self.commission - self.slippage
|
||
```
|
||
|
||
|
||
|
||
|
||
### 计算策略统计指标
|
||
calculate_statistics函数是基于逐日盯市盈亏情况（DateFrame格式）来计算衍生指标，如最大回撤、年化收益、盈亏比、夏普比率等。
|
||
|
||
```
|
||
df["balance"] = df["net_pnl"].cumsum() + self.capital
|
||
df["return"] = np.log(df["balance"] / df["balance"].shift(1)).fillna(0)
|
||
df["highlevel"] = (
|
||
df["balance"].rolling(
|
||
min_periods=1, window=len(df), center=False).max()
|
||
)
|
||
df["drawdown"] = df["balance"] - df["highlevel"]
|
||
df["ddpercent"] = df["drawdown"] / df["highlevel"] * 100
|
||
|
||
# Calculate statistics value
|
||
start_date = df.index[0]
|
||
end_date = df.index[-1]
|
||
|
||
total_days = len(df)
|
||
profit_days = len(df[df["net_pnl"] > 0])
|
||
loss_days = len(df[df["net_pnl"] < 0])
|
||
|
||
end_balance = df["balance"].iloc[-1]
|
||
max_drawdown = df["drawdown"].min()
|
||
max_ddpercent = df["ddpercent"].min()
|
||
|
||
total_net_pnl = df["net_pnl"].sum()
|
||
daily_net_pnl = total_net_pnl / total_days
|
||
|
||
total_commission = df["commission"].sum()
|
||
daily_commission = total_commission / total_days
|
||
|
||
total_slippage = df["slippage"].sum()
|
||
daily_slippage = total_slippage / total_days
|
||
|
||
total_turnover = df["turnover"].sum()
|
||
daily_turnover = total_turnover / total_days
|
||
|
||
total_trade_count = df["trade_count"].sum()
|
||
daily_trade_count = total_trade_count / total_days
|
||
|
||
total_return = (end_balance / self.capital - 1) * 100
|
||
annual_return = total_return / total_days * 240
|
||
daily_return = df["return"].mean() * 100
|
||
return_std = df["return"].std() * 100
|
||
|
||
if return_std:
|
||
sharpe_ratio = daily_return / return_std * np.sqrt(240)
|
||
else:
|
||
sharpe_ratio = 0
|
||
```
|
||
|
||
|
||
### 统计指标绘图
|
||
通过matplotlib绘制4幅图：
|
||
- 资金曲线图
|
||
- 资金回撤图
|
||
- 每日盈亏图
|
||
- 每日盈亏分布图
|
||
|
||
```
|
||
def show_chart(self, df: DataFrame = None):
|
||
""""""
|
||
if not df:
|
||
df = self.daily_df
|
||
|
||
if df is None:
|
||
return
|
||
|
||
plt.figure(figsize=(10, 16))
|
||
|
||
balance_plot = plt.subplot(4, 1, 1)
|
||
balance_plot.set_title("Balance")
|
||
df["balance"].plot(legend=True)
|
||
|
||
drawdown_plot = plt.subplot(4, 1, 2)
|
||
drawdown_plot.set_title("Drawdown")
|
||
drawdown_plot.fill_between(range(len(df)), df["drawdown"].values)
|
||
|
||
pnl_plot = plt.subplot(4, 1, 3)
|
||
pnl_plot.set_title("Daily Pnl")
|
||
df["net_pnl"].plot(kind="bar", legend=False, grid=False, xticks=[])
|
||
|
||
distribution_plot = plt.subplot(4, 1, 4)
|
||
distribution_plot.set_title("Daily Pnl Distribution")
|
||
df["net_pnl"].hist(bins=50)
|
||
|
||
plt.show()
|
||
```
|
||
|
||
|
||
|
||
### 单策略回测示例
|
||
|
||
- 导入回测引擎和CTA策略
|
||
- 设置回测相关参数，如：品种、K线周期、回测开始和结束日期、手续费、滑点、合约规模、起始资金
|
||
- 载入策略和数据到引擎中，运行回测。
|
||
- 计算基于逐日统计盈利情况，计算统计指标，统计指标绘图。
|
||
|
||
|
||
```
|
||
from vnpy.app.cta_strategy.backtesting import BacktestingEngine
|
||
from vnpy.app.cta_strategy.strategies.boll_channel_strategy import (
|
||
BollChannelStrategy,
|
||
)
|
||
from datetime import datetime
|
||
|
||
engine = BacktestingEngine()
|
||
engine.set_parameters(
|
||
vt_symbol="IF88.CFFEX",
|
||
interval="1m",
|
||
start=datetime(2018, 1, 1),
|
||
end=datetime(2019, 1, 1),
|
||
rate=3.0/10000,
|
||
slippage=0.2,
|
||
size=300,
|
||
pricetick=0.2,
|
||
capital=1_000_000,
|
||
)
|
||
|
||
engine.add_strategy(AtrRsiStrategy, {})
|
||
engine.load_data()
|
||
engine.run_backtesting()
|
||
df = engine.calculate_result()
|
||
engine.calculate_statistics()
|
||
engine.show_chart()
|
||
```
|
||
|
||
|
||
|
||
### 投资组合回测示例
|
||
|
||
投资组合回测是基于单策略回测的，其关键是每个策略都对应着各自的BacktestingEngine对象，下面介绍具体流程：
|
||
|
||
- 创建回测函数run_backtesting()，这样每添加一个策略就创建其BacktestingEngine对象。
|
||
```
|
||
from vnpy.app.cta_strategy.backtesting import BacktestingEngine, OptimizationSetting
|
||
from vnpy.app.cta_strategy.strategies.atr_rsi_strategy import AtrRsiStrategy
|
||
from vnpy.app.cta_strategy.strategies.boll_channel_strategy import BollChannelStrategy
|
||
from datetime import datetime
|
||
|
||
def run_backtesting(strategy_class, setting, vt_symbol, interval, start, end, rate, slippage, size, pricetick, capital):
|
||
engine = BacktestingEngine()
|
||
engine.set_parameters(
|
||
vt_symbol=vt_symbol,
|
||
interval=interval,
|
||
start=start,
|
||
end=end,
|
||
rate=rate,
|
||
slippage=slippage,
|
||
size=size,
|
||
pricetick=pricetick,
|
||
capital=capital
|
||
)
|
||
engine.add_strategy(strategy_class, setting)
|
||
engine.load_data()
|
||
engine.run_backtesting()
|
||
df = engine.calculate_result()
|
||
return df
|
||
```
|
||
|
||
|
||
|
||
- 分别进行单策略回测，得到各自的DataFrame，(该DataFrame包含交易时间、今仓、昨仓、手续费、滑点、当日净盈亏、累计净盈亏等基本信息，但是不包括最大回撤，夏普比率等统计信息),然后把DataFrame相加并且去除空值后即得到投资组合的DataFrame。
|
||
|
||
```
|
||
df1 = run_backtesting(
|
||
strategy_class=AtrRsiStrategy,
|
||
setting={},
|
||
vt_symbol="IF88.CFFEX",
|
||
interval="1m",
|
||
start=datetime(2019, 1, 1),
|
||
end=datetime(2019, 4, 30),
|
||
rate=0.3/10000,
|
||
slippage=0.2,
|
||
size=300,
|
||
pricetick=0.2,
|
||
capital=1_000_000,
|
||
)
|
||
|
||
df2 = run_backtesting(
|
||
strategy_class=BollChannelStrategy,
|
||
setting={'fixed_size': 16},
|
||
vt_symbol="RB88.SHFE",
|
||
interval="1m",
|
||
start=datetime(2019, 1, 1),
|
||
end=datetime(2019, 4, 30),
|
||
rate=1/10000,
|
||
slippage=1,
|
||
size=10,
|
||
pricetick=1,
|
||
capital=1_000_000,
|
||
)
|
||
|
||
dfp = df1 + df2
|
||
dfp =dfp.dropna()
|
||
```
|
||
|
||
|
||
|
||
|
||
- 创建show_portafolio()函数，同样也是创建新的BacktestingEngine对象，对传入的DataFrame计算如夏普比率等统计指标，并且画图。故该函数不仅能显示单策略回测效果，也能展示投资组合回测效果。
|
||
```
|
||
def show_portafolio(df):
|
||
engine = BacktestingEngine()
|
||
engine.calculate_statistics(df)
|
||
engine.show_chart(df)
|
||
|
||
show_portafolio(dfp)
|
||
```
|
||
|
||
|
||
|
||
## 参数优化
|
||
参数优化模块主要由3部分构成：
|
||
|
||
### 参数设置
|
||
|
||
- 设置参数优化区间：如boll_window设置起始值为18，终止值为24，步进为2，这样就得到了[18, 20, 22, 24] 这4个待优化的参数了。
|
||
- 设置优化目标字段：如夏普比率、盈亏比、总收益率等。
|
||
- 随机生成参数对组合：使用迭代工具产生参数对组合，然后把参数对组合打包到一个个字典组成的列表中
|
||
|
||
```
|
||
class OptimizationSetting:
|
||
"""
|
||
Setting for runnning optimization.
|
||
"""
|
||
|
||
def __init__(self):
|
||
""""""
|
||
self.params = {}
|
||
self.target_name = ""
|
||
|
||
def add_parameter(
|
||
self, name: str, start: float, end: float = None, step: float = None
|
||
):
|
||
""""""
|
||
if not end and not step:
|
||
self.params[name] = [start]
|
||
return
|
||
|
||
if start >= end:
|
||
print("参数优化起始点必须小于终止点")
|
||
return
|
||
|
||
if step <= 0:
|
||
print("参数优化步进必须大于0")
|
||
return
|
||
|
||
value = start
|
||
value_list = []
|
||
|
||
while value <= end:
|
||
value_list.append(value)
|
||
value += step
|
||
|
||
self.params[name] = value_list
|
||
|
||
def set_target(self, target_name: str):
|
||
""""""
|
||
self.target_name = target_name
|
||
|
||
def generate_setting(self):
|
||
""""""
|
||
keys = self.params.keys()
|
||
values = self.params.values()
|
||
products = list(product(*values))
|
||
|
||
settings = []
|
||
for p in products:
|
||
setting = dict(zip(keys, p))
|
||
settings.append(setting)
|
||
|
||
return settings
|
||
```
|
||
|
||
|
||
|
||
### 参数对组合回测
|
||
|
||
多进程优化时，每个进程都会运行optimize函数，输出参数对组合以及目标优化字段的结果。其步骤如下：
|
||
- 调用回测引擎
|
||
- 输入回测相关设置
|
||
- 输入参数对组合到策略中
|
||
- 运行回测
|
||
- 返回回测结果，包括：参数对组合、目标优化字段数值、策略统计指标
|
||
|
||
```
|
||
def optimize(
|
||
target_name: str,
|
||
strategy_class: CtaTemplate,
|
||
setting: dict,
|
||
vt_symbol: str,
|
||
interval: Interval,
|
||
start: datetime,
|
||
rate: float,
|
||
slippage: float,
|
||
size: float,
|
||
pricetick: float,
|
||
capital: int,
|
||
end: datetime,
|
||
mode: BacktestingMode,
|
||
):
|
||
"""
|
||
Function for running in multiprocessing.pool
|
||
"""
|
||
engine = BacktestingEngine()
|
||
engine.set_parameters(
|
||
vt_symbol=vt_symbol,
|
||
interval=interval,
|
||
start=start,
|
||
rate=rate,
|
||
slippage=slippage,
|
||
size=size,
|
||
pricetick=pricetick,
|
||
capital=capital,
|
||
end=end,
|
||
mode=mode
|
||
)
|
||
|
||
engine.add_strategy(strategy_class, setting)
|
||
engine.load_data()
|
||
engine.run_backtesting()
|
||
engine.calculate_result()
|
||
statistics = engine.calculate_statistics()
|
||
|
||
target_value = statistics[target_name]
|
||
return (str(setting), target_value, statistics)
|
||
```
|
||
|
||
|
||
|
||
### 多进程优化
|
||
|
||
- 根据CPU的核数来创建进程：若CPU为4核，则创建4个进程
|
||
- 在每个进程都调用apply_async( )的方法运行参数对组合回测，其回测结果添加到results中 （apply_async是异步非阻塞的，即不用等待当前进程执行完毕，随时根据系统调度来进行进程切换。）
|
||
- pool.close()与pool.join()用于进程跑完任务后，去关闭进程。
|
||
- 对results的内容通过目标优化字段标准进行排序，输出结果。
|
||
|
||
```
|
||
pool = multiprocessing.Pool(multiprocessing.cpu_count())
|
||
|
||
results = []
|
||
for setting in settings:
|
||
result = (pool.apply_async(optimize, (
|
||
target_name,
|
||
self.strategy_class,
|
||
setting,
|
||
self.vt_symbol,
|
||
self.interval,
|
||
self.start,
|
||
self.rate,
|
||
self.slippage,
|
||
self.size,
|
||
self.pricetick,
|
||
self.capital,
|
||
self.end,
|
||
self.mode
|
||
)))
|
||
results.append(result)
|
||
|
||
pool.close()
|
||
pool.join()
|
||
|
||
# Sort results and output
|
||
result_values = [result.get() for result in results]
|
||
result_values.sort(reverse=True, key=lambda result: result[1])
|
||
|
||
for value in result_values:
|
||
msg = f"参数：{value[0]}, 目标：{value[1]}"
|
||
self.output(msg)
|
||
|
||
return result_values
|
||
```
|
||
|
||
|
||
|
||
## 实盘运行
|
||
在实盘环境，用户可以基于编写好的CTA策略来创建新的实例，一键初始化、启动、停止策略。
|
||
|
||
|
||
### 创建策略实例
|
||
用户可以基于编写好的CTA策略来创建新的实例，策略实例的好处在于同一个策略可以同时去运行多个品种合约，并且每个实例的参数可以是不同的。
|
||
在创建实例的时候需要填写如图的实例名称、合约品种、参数设置。注意：实例名称不能重名；合约名称是vt_symbol的格式，如IF1905.CFFEX。
|
||
|
||
|
||
|
||
创建策略流程如下：
|
||
- 检查策略实例重名
|
||
- 添加策略配置信息(strategy_name, vt_symbol, setting)到strategies字典上
|
||
- 添加该策略要订阅行情的合约信息到symbol_strategy_map字典中；
|
||
- 把策略配置信息保存到json文件内；
|
||
- 在图形化界面更新状态信息。
|
||
|
||
```
|
||
def add_strategy(
|
||
self, class_name: str, strategy_name: str, vt_symbol: str, setting: dict
|
||
):
|
||
"""
|
||
Add a new strategy.
|
||
"""
|
||
if strategy_name in self.strategies:
|
||
self.write_log(f"创建策略失败，存在重名{strategy_name}")
|
||
return
|
||
|
||
strategy_class = self.classes[class_name]
|
||
|
||
strategy = strategy_class(self, strategy_name, vt_symbol, setting)
|
||
self.strategies[strategy_name] = strategy
|
||
|
||
# Add vt_symbol to strategy map.
|
||
strategies = self.symbol_strategy_map[vt_symbol]
|
||
strategies.append(strategy)
|
||
|
||
# Update to setting file.
|
||
self.update_strategy_setting(strategy_name, setting)
|
||
|
||
self.put_strategy_event(strategy)
|
||
```
|
||
|
||
|
||
|
||
### 初始化策略
|
||
- 调用策略类的on_init()回调函数,并且载入历史数据；
|
||
- 恢复上次退出之前的策略状态；
|
||
- 调用接口的subcribe()函数订阅指定行情信息；
|
||
- 策略初始化状态变成True，并且更新到日志上。
|
||
|
||
```
|
||
def _init_strategy(self):
|
||
"""
|
||
Init strategies in queue.
|
||
"""
|
||
while not self.init_queue.empty():
|
||
strategy_name = self.init_queue.get()
|
||
strategy = self.strategies[strategy_name]
|
||
|
||
if strategy.inited:
|
||
self.write_log(f"{strategy_name}已经完成初始化，禁止重复操作")
|
||
continue
|
||
|
||
self.write_log(f"{strategy_name}开始执行初始化")
|
||
|
||
# Call on_init function of strategy
|
||
self.call_strategy_func(strategy, strategy.on_init)
|
||
|
||
# Restore strategy data(variables)
|
||
data = self.strategy_data.get(strategy_name, None)
|
||
if data:
|
||
for name in strategy.variables:
|
||
value = data.get(name, None)
|
||
if value:
|
||
setattr(strategy, name, value)
|
||
|
||
# Subscribe market data
|
||
contract = self.main_engine.get_contract(strategy.vt_symbol)
|
||
if contract:
|
||
req = SubscribeRequest(
|
||
symbol=contract.symbol, exchange=contract.exchange)
|
||
self.main_engine.subscribe(req, contract.gateway_name)
|
||
else:
|
||
self.write_log(f"行情订阅失败，找不到合约{strategy.vt_symbol}", strategy)
|
||
|
||
# Put event to update init completed status.
|
||
strategy.inited = True
|
||
self.put_strategy_event(strategy)
|
||
self.write_log(f"{strategy_name}初始化完成")
|
||
|
||
self.init_thread = None
|
||
```
|
||
|
||
|
||
|
||
### 启动策略
|
||
- 检查策略初始化状态；
|
||
- 检查策略启动状态，避免重复启动；
|
||
- 调用策略类的on_start()函数启动策略；
|
||
- 策略启动状态变成True，并且更新到图形化界面上。
|
||
|
||
```
|
||
def start_strategy(self, strategy_name: str):
|
||
"""
|
||
Start a strategy.
|
||
"""
|
||
strategy = self.strategies[strategy_name]
|
||
if not strategy.inited:
|
||
self.write_log(f"策略{strategy.strategy_name}启动失败，请先初始化")
|
||
return
|
||
|
||
if strategy.trading:
|
||
self.write_log(f"{strategy_name}已经启动，请勿重复操作")
|
||
return
|
||
|
||
self.call_strategy_func(strategy, strategy.on_start)
|
||
strategy.trading = True
|
||
|
||
self.put_strategy_event(strategy)
|
||
```
|
||
|
||
|
||
|
||
### 停止策略
|
||
- 检查策略启动状态；
|
||
- 调用策略类的on_stop()函数停止策略；
|
||
- 更新策略启动状态为False；
|
||
- 对所有为成交的委托（市价单/限价单/本地停止单）进行撤单操作；
|
||
- 在图形化界面更新策略状态。
|
||
|
||
```
|
||
def stop_strategy(self, strategy_name: str):
|
||
"""
|
||
Stop a strategy.
|
||
"""
|
||
strategy = self.strategies[strategy_name]
|
||
if not strategy.trading:
|
||
return
|
||
|
||
# Call on_stop function of the strategy
|
||
self.call_strategy_func(strategy, strategy.on_stop)
|
||
|
||
# Change trading status of strategy to False
|
||
strategy.trading = False
|
||
|
||
# Cancel all orders of the strategy
|
||
self.cancel_all(strategy)
|
||
|
||
# Update GUI
|
||
self.put_strategy_event(strategy)
|
||
```
|
||
|
||
|
||
|
||
### 编辑策略
|
||
- 重新配置策略参数字典setting；
|
||
- 更新参数字典到策略中；
|
||
- 在图像化界面更新策略状态。
|
||
|
||
```
|
||
def edit_strategy(self, strategy_name: str, setting: dict):
|
||
"""
|
||
Edit parameters of a strategy.
|
||
"""
|
||
strategy = self.strategies[strategy_name]
|
||
strategy.update_setting(setting)
|
||
|
||
self.update_strategy_setting(strategy_name, setting)
|
||
self.put_strategy_event(strategy)
|
||
```
|
||
|
||
|
||
|
||
### 移除策略
|
||
- 检查策略状态，只有停止策略后从可以移除策略；
|
||
- 从json文件移除策略配置信息(strategy_name, vt_symbol, setting)；
|
||
- 从symbol_strategy_map字典中移除该策略订阅的合约信息；
|
||
- 从strategy_orderid_map字典移除活动委托记录；
|
||
- 从strategies字典移除该策略的相关配置信息。
|
||
|
||
```
|
||
def remove_strategy(self, strategy_name: str):
|
||
"""
|
||
Remove a strategy.
|
||
"""
|
||
strategy = self.strategies[strategy_name]
|
||
if strategy.trading:
|
||
self.write_log(f"策略{strategy.strategy_name}移除失败，请先停止")
|
||
return
|
||
|
||
# Remove setting
|
||
self.remove_strategy_setting(strategy_name)
|
||
|
||
# Remove from symbol strategy map
|
||
strategies = self.symbol_strategy_map[strategy.vt_symbol]
|
||
strategies.remove(strategy)
|
||
|
||
# Remove from active orderid map
|
||
if strategy_name in self.strategy_orderid_map:
|
||
vt_orderids = self.strategy_orderid_map.pop(strategy_name)
|
||
|
||
# Remove vt_orderid strategy map
|
||
for vt_orderid in vt_orderids:
|
||
if vt_orderid in self.orderid_strategy_map:
|
||
self.orderid_strategy_map.pop(vt_orderid)
|
||
|
||
# Remove from strategies
|
||
self.strategies.pop(strategy_name)
|
||
|
||
return True
|
||
```
|