接触到了不少Python相关的开源项目，也接触到了不少回测框架，感觉这些框架都比较难懂，加上自己用pandas做回测，效率有点低，要建立一套自己的回测框架。
读了不少Python回测框架作者创建框架的思路与理念，觉得使用事件驱动型框架比较好，另外，我要创建的这个框架将会模仿文华财经或者TB进行创建。正好最近读master Python for finnace 这本书，第九章有讲怎么创建一个回测框架。
在本文中，将这篇章节的大体思路翻译成汉语和代码，分享给大家。
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翻译的部分内容
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事件驱动回测系统的概念：
在真实的交易环境中，一般要包含以下模块：数据，订单匹配模块，订单管理，账户，更新仓位；
""" Store a single unit of data """ class TickData: def __init__(self, symbol, timestamp, last_price=0, total_volume=0): self.symbol = symbol self.timestamp = timestamp self.open_price = 0 self.last_price = last_price self.total_volume = total_volume
class MarketData: def __init__(self): self.__recent_ticks__ = dict() def add_last_price(self, time, symbol, price, volume): tick_data = TickData(symbol, time, price, volume) self.__recent_ticks__[symbol] = tick_data def add_open_price(self, time, symbol, price): tick_data = self.get_existing_tick_data(symbol, time) tick_data.open_price = price def get_existing_tick_data(self, symbol, time): if not symbol in self.__recent_ticks__: tick_data = TickData(symbol, time) self.__recent_ticks__[symbol] = tick_data return self.__recent_ticks__[symbol] def get_last_price(self, symbol): return self.__recent_ticks__[symbol].last_price def get_open_price(self, symbol): return self.__recent_ticks__[symbol].open_price def get_timestamp(self, symbol): return self.__recent_ticks__[symbol].timestamp
import pandas.io.data as web """ Download prices from an external data source """ class MarketDataSource: def __init__(self): self.event_tick = None self.ticker, self.source = None, None self.start, self.end = None, None self.md = MarketData() def start_market_simulation(self): data = web.DataReader(self.ticker, self.source, self.start, self.end) for time, row in data.iterrows(): self.md.add_last_price(time, self.ticker, row["Close"], row["Volume"]) self.md.add_open_price(time, self.ticker, row["Open"]) if not self.event_tick is None: self.event_tick(self.md)
class Order: def __init__(self, timestamp, symbol, qty, is_buy,is_market_order, price=0): self.timestamp = timestamp self.symbol = symbol self.qty = qty self.price = price self.is_buy = is_buy self.is_market_order = is_market_order self.is_filled = False self.filled_price = 0 self.filled_time = None self.filled_qty = 0
class Position: def __init__(self): self.symbol = None self.buys, self.sells, self.net = 0, 0, 0 self.realized_pnl = 0 self.unrealized_pnl = 0 self.position_value = 0 def event_fill(self, timestamp, is_buy, qty, price): if is_buy: self.buys += qty else: self.sells += qty self.net = self.buys - self.sells changed_value = qty * price * (-1 if is_buy else 1) self.position_value += changed_value if self.net == 0: self.realized_pnl = self.position_value
def update_unrealized_pnl(self, price): if self.net == 0: self.unrealized_pnl = 0 else: self.unrealized_pnl = price * self.net + \ self.position_value return self.unrealized_pnl
""" Base strategy for implementation """ class Strategy: def __init__(self): self.event_sendorder = None def event_tick(self, market_data): pass def event_order(self, order): pass def event_position(self, positions): pass def send_market_order(self, symbol, qty, is_buy, timestamp): if not self.event_sendorder is None: order = Order(timestamp, symbol, qty, is_buy, True) self.event_sendorder(order)
