Python金融大数据分析——第15章 估值框架 笔记

    import datetime as dt
    
    dates = [dt.datetime(2018, 1, 1), dt.datetime(2018, 7, 1), dt.datetime(2019, 1, 1)]
    
    (dates[1] - dates[0]).days / 365
    # 0.4958904109589041
    
    (dates[2] - dates[1]).days / 365
    # 0.5041095890410959
    
    fractions = [0.0, 0.5, 1.0]
    # dates 和 fraction 定义（大致）等价
    
    import numpy as np
    
    
    # 获取年分数
    def get_year_deltas(date_list, day_count=365.):
    """
    Return vector of floats with day deltas in years.
    Initial value normalized to zero.
    :param date_list: list or array
    collection of datetime objects
    :param day_count: float
    number of days for a year
    :return:
    delta_list:array
    year fractions
    """
    start = date_list[0]
    delta_list = [(date - start).days / day_count for date in date_list]
    return np.array(delta_list)
    
    
    import datetime as dt
    
    dates = [dt.datetime(2018, 1, 1), dt.datetime(2018, 7, 1), dt.datetime(2019, 1, 1)]
    get_year_deltas(dates)
    # array([0.        , 0.49589041, 1.        ])
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    

    class constant_short_rate(object):
    """
    Class for constant short rate discounting
    """
    
    def __init__(self, name, short_rate):
        """
    
        :param name:string
         name of the object
        :param short_rate:float(positive)
         constant rate for discounting
        """
        self.name = name
        self.short_rate = short_rate
        if short_rate < 0:
            raise ValueError('Short rate negative.')
    
    def get_discount_factors(self, date_list, dtobjects=True):
        """
        get discount factors given a list/array of datetime objects or year fractions
        """
        if dtobjects is True:
            dlist = get_year_deltas(date_list)
        else:
            dlist = np.array(date_list)
        dflist = np.exp(self.short_rate * np.sort(-dlist))
        return np.array((date_list, dflist)).T
    
    
    import datetime as dt
    
    dates = [dt.datetime(2018, 1, 1), dt.datetime(2018, 7, 1), dt.datetime(2019, 1, 1)]
    csr = constant_short_rate('csr', 0.05)
    csr.get_discount_factors(dates)
    # array([[datetime.datetime(2018, 1, 1, 0, 0), 0.951229424500714],
    #        [datetime.datetime(2018, 7, 1, 0, 0), 0.9755103387657228],
    #        [datetime.datetime(2019, 1, 1, 0, 0), 1.0]], dtype=object)
    
    deltas = get_year_deltas(dates)
    csr.get_discount_factors(deltas, dtobjects=False)
    # array([[0.        , 0.95122942],
    #        [0.49589041, 0.97551034],
    #        [1.        , 1.        ]])
    
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    

    # 建立由常量、 列表和曲线组成的市场环境模型的类
    class market_environment(object):
    def __init__(self, name, pricing_date):
        self.name = name
        self.pricing_date = pricing_date
        self.constants = {}
        self.lists = {}
        self.curves = {}
    
    def add_constant(self, key, constant):
        self.constants[key] = constant
    
    def get_constant(self, key):
        return self.constants[key]
    
    def add_list(self, key, list_object):
        self.lists[key] = list_object
    
    def get_list(self, key):
        return self.lists[key]
    
    def add_curve(self, key, curve):
        self.curves[key] = curve
    
    def get_curve(self, key):
        return self.curves[key]
    
    def add_environment(self, env):
        for key in env.constants:
            self.constants[key] = env.constants[key]
        for key in env.lists:
            self.lists[key] = env.lists[key]
        for key in env.curves:
            self.curves[key] = env.curves[key]
    
    
    import datetime as dt
    
    dates = [dt.datetime(2018, 1, 1), dt.datetime(2018, 7, 1), dt.datetime(2019, 1, 1)]
    csr = constant_short_rate('csr', 0.05)
    me_1 = market_environment('me_1', dt.datetime(2018, 1, 1))
    me_1.add_list('symbols', ['AAPL', 'MSFT', 'FB'])
    me_1.get_list('symbols')
    # ['AAPL', 'MSFT', 'FB']
    
    me_2 = market_environment('me_2', dt.datetime(2018, 1, 1))
    me_2.add_constant('volatility', 0.2)
    me_2.add_curve('short_rate', csr)
    me_2.get_curve('short_rate')
    # <__main__.constant_short_rate at 0x1a226781cc0>
    
    me_1.add_environment(me_2)
    me_1.get_curve('short_rate')
    # <__main__.constant_short_rate at 0x1a226781cc0>
    
    me_1.constants
    # {'volatility': 0.2}
    
    me_1.lists
    # {'symbols': ['AAPL', 'MSFT', 'FB']}
    
    me_1.curves
    # {'short_rate': <__main__.constant_short_rate at 0x1a226781cc0>}
    
    me_1.get_curve('short_rate').short_rate
    # 0.05