用Python徒手撸一个股票回测框架搭建【推荐】

def initialize(self):
  """在回测开始前的初始化"""
  pass
 def before_on_tick(self, tick):
  pass
 def after_on_tick(self, tick):
  pass
 def before_trade(self, order):
  """在交易之前会调用此函数
  可以在此放置资金管理及风险管理的代码
  如果返回True就允许交易，否则放弃交易
  """
  return True
 def on_order_ok(self, order):
  """当订单执行成功后调用"""
  pass
 def on_order_timeout(self, order):
  """当订单超时后调用"""
  pass
 def finish(self):
  """在回测结束后调用"""
  pass
 @abstractmethod
 def on_tick(self, bar):
  """
  回测实例必须实现的方法，并编写自己的交易逻辑
  """
  pass

def buy(self, code, price, shares, ttl=-1):
  """
  限价提交买入订单
  ---------
  Parameters:
   code:str
    股票代码
   price:float or None
    最高可买入的价格, 如果为None则按市价买入
   shares:int
    买入股票数量
   ttl:int
    订单允许存在的最大时间，默认为-1，永不超时
  ---------
  return:
   dict
    {
    "type": 订单类型, "buy",
    "code": 股票代码,
    "date": 提交日期,
    "ttl": 存活时间, 当ttl等于0时则超时，往后不会在执行
    "shares": 目标股份数量,
    "price": 目标价格,
    "deal_lst": 交易成功的历史数据，如
     [{"price": 成交价格,
      "date": 成交时间,
      "commission": 交易手续费,
      "shares": 成交份额
     }]
    ""
   }
  """
  if price is None:
   stock_info = self.ctx.tick_data[code]
   price = stock_info[self.deal_price]
  order = {
   "type": "buy",
   "code": code,
   "date": self.ctx.now,
   "ttl": ttl,
   "shares": shares,
   "price": price,
   "deal_lst": []
  }
  self.submit(order)
  return order
 def sell(self, code, price, shares, ttl=-1):
  """
  限价提交卖出订单
  ---------
  Parameters:
   code:str
    股票代码
   price:float or None
    最低可卖出的价格, 如果为None则按市价卖出
   shares:int
    卖出股票数量
   ttl:int
    订单允许存在的最大时间，默认为-1，永不超时
  ---------
  return:
   dict
    {
    "type": 订单类型, "sell",
    "code": 股票代码,
    "date": 提交日期,
    "ttl": 存活时间, 当ttl等于0时则超时，往后不会在执行
    "shares": 目标股份数量,
    "price": 目标价格,
    "deal_lst": 交易成功的历史数据，如
     [{"open_price": 开仓价格,
      "close_price": 成交价格,
      "close_date": 成交时间,
      "open_date": 持仓时间,
      "commission": 交易手续费,
      "shares": 成交份额,
      "profit": 交易收益}]
    ""
   }
  """
  if code not in self.position:
   return
  if price is None:
   stock_info = self.ctx.tick_data[code]
   price = stock_info[self.deal_price]
  order = {
   "type": "sell",
   "code": code,
   "date": self.ctx.now,
   "ttl": ttl,
   "shares": shares,
   "price": price,
   "deal_lst": []
  }
  self.submit(order)
  return order

class Scheduler(object):
 """

 ctx.get_hist
 """
 def __init__(self):
  """"""
  self.ctx = Context()
  self._pre_hook_lst = []
  self._post_hook_lst = []
  self._runner_lst = []
 def run(self):
  # runner指存在可调用的initialize, finish, run(tick)的对象
  runner_lst = list(chain(self._pre_hook_lst, self._runner_lst, self._post_hook_lst))
  # 循环开始前为broker, backtest, hook等实例绑定ctx对象及调用其initialize方法
  for runner in runner_lst:
   runner.ctx = self.ctx
   runner.initialize()
  # 创建交易日历
  if "trade_cal" not in self.ctx:
   df = list(self.ctx.feed.values())[0]
   self.ctx["trade_cal"] = df.index
  # 通过遍历交易日历的时间依次调用runner
  # 首先调用所有pre-hook的run方法
  # 然后调用broker,backtest的run方法
  # 最后调用post-hook的run方法
  for tick in self.ctx.trade_cal:
   self.ctx.set_currnet_time(tick)
   for runner in runner_lst:
    runner.run(tick)
  # 循环结束后调用所有runner对象的finish方法
  for runner in runner_lst:
   runner.finish()

class Context(UserDict):
 def __getattr__(self, key):
  # 让调用这可以通过索引或者属性引用皆可
  return self[key]
 def set_currnet_time(self, tick):
  self["now"] = tick
  tick_data = {}
  # 获取当前所有有报价的股票报价
  for code, hist in self["feed"].items():
   df = hist[hist.index == tick]
   if len(df) == 1:
    tick_data[code] = df.iloc[-1]
  self["tick_data"] = tick_data
 def get_hist(self, code=None):
  """如果不指定code, 获取截至到当前时间的所有股票的历史数据"""
  if code is None:
   hist = {}
   for code, hist in self["feed"].items():
    hist[code] = hist[hist.index <= self.now]
  elif code in self.feed:
   return {code: self.feed[code]}
  return hist
class Scheduler(object):
 """

  ctx.feed: {code1: pd.DataFrame, code2: pd.DataFrame}对象
  ctx.now: 循环所处时间
  ctx.tick_data: 循环所处时间的所有有报价的股票报价
  ctx.trade_cal: 交易日历
  ctx.broker: Broker对象
  ctx.bt/ctx.backtest: Backtest对象

  ctx.get_hist
 """
 def __init__(self):
  """"""
  self.ctx = Context()
  self._pre_hook_lst = []
  self._post_hook_lst = []
  self._runner_lst = []
 def add_feed(self, feed):
  self.ctx["feed"] = feed
 def add_hook(self, hook, typ="post"):
  if typ == "post" and hook not in self._post_hook_lst:
   self._post_hook_lst.append(hook)
  elif typ == "pre" and hook not in self._pre_hook_lst:
   self._pre_hook_lst.append(hook)
 def add_broker(self, broker):
  self.ctx["broker"] = broker
 def add_backtest(self, backtest):
  self.ctx["backtest"] = backtest
  # 简写
  self.ctx["bt"] = backtest
 def add_runner(self, runner):
  if runner in self._runner_lst:
   return
  self._runner_lst.append(runner)

class Stat(Base):
  def __init__(self):
    self._date_hist = []
    self._cash_hist = []
    self._stk_val_hist = []
    self._ast_val_hist = []
    self._returns_hist = []

  def run(self, tick):
    self._date_hist.append(tick)
    self._cash_hist.append(self.ctx.broker.cash)
    self._stk_val_hist.append(self.ctx.broker.stock_value)
    self._ast_val_hist.append(self.ctx.broker.assets_value)

  @property
  def data(self):
    df = pd.DataFrame({"cash": self._cash_hist,
              "stock_value": self._stk_val_hist,
              "assets_value": self._ast_val_hist}, index=self._date_hist)
    df.index.name = "date"
    return df

def get_dropdown(self):
    high_val = -1
    low_val = None
    high_index = 0
    low_index = 0
    dropdown_lst = []
    dropdown_index_lst = []
    for idx, val in enumerate(self._ast_val_hist):
      if val >= high_val:
        if high_val == low_val or high_index >= low_index:
          high_val = low_val = val
          high_index = low_index = idx
          continue
        dropdown = (high_val - low_val) / high_val
        dropdown_lst.append(dropdown)
        dropdown_index_lst.append((high_index, low_index))
        high_val = low_val = val
        high_index = low_index = idx
      if low_val is None:
        low_val = val
        low_index = idx
      if val < low_val:
        low_val = val
        low_index = idx
    if low_index > high_index:
      dropdown = (high_val - low_val) / high_val
      dropdown_lst.append(dropdown)
      dropdown_index_lst.append((high_index, low_index))
    return dropdown_lst, dropdown_index_lst
  @property
  def max_dropdown(self):
    """最大回车率"""
    dropdown_lst, dropdown_index_lst = self.get_dropdown()
    if len(dropdown_lst) > 0:
      return max(dropdown_lst)
    else:
      return 0
  @property
  def annual_return(self):
    """
    年化收益率
    y = (v/c)^(D/T) - 1
    v: 最终价值
    c: 初始价值
    D: 有效投资时间(365)

    """
    D = 365
    c = self._ast_val_hist[0]
    v = self._ast_val_hist[-1]
    days = (self._date_hist[-1] - self._date_hist[0]).days
    ret = (v / c) ** (D / days) - 1
    return ret

import json
from backtest import BackTest
from reporter import Plotter
class MyBackTest(BackTest):
  def initialize(self):
    self.info("initialize")
  def finish(self):
    self.info("finish")
  def on_tick(self, tick):
    tick_data = self.ctx["tick_data"]
    for code, hist in tick_data.items():
      if hist["ma10"] > 1.05 * hist["ma20"]:
        self.ctx.broker.buy(code, hist.close, 500, ttl=5)
      if hist["ma10"] < hist["ma20"] and code in self.ctx.broker.position:
        self.ctx.broker.sell(code, hist.close, 200, ttl=1)
if __name__ == '__main__':
  from utils import load_hist
  feed = {}
  for code, hist in load_hist("000002.SZ"):
    # hist = hist.iloc[:100]
    hist["ma10"] = hist.close.rolling(10).mean()
    hist["ma20"] = hist.close.rolling(20).mean()
    feed[code] = hist
  mytest = MyBackTest(feed)
  mytest.start()
  order_lst = mytest.ctx.broker.order_hist_lst
  with open("report/order_hist.json", "w") as wf:
    json.dump(order_lst, wf, indent=4, default=str)
  stats = mytest.stat
  stats.data.to_csv("report/stat.csv")
  print("策略收益： {:.3f}%".format(stats.total_returns * 100))
  print("最大回彻率: {:.3f}% ".format(stats.max_dropdown * 100))
  print("年化收益: {:.3f}% ".format(stats.annual_return * 100))
  print("夏普比率: {:.3f} ".format(stats.sharpe))
  plotter = Plotter(feed, stats, order_lst)
  plotter.report("report/report.png")