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@@ -55,21 +55,18 @@ def calculate_phi(prices, k, S0):
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return phi
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-def calculate_integral_phi(prices, k, S0, delta_T):
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+def calculate_integral_phi(prices, k, S0, time_points):
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"""
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- 计算 ∫ φ(k, ξ) dξ 的值
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+ 计算 ∫ φ(k, ξ) dξ 的值，越大说明波动率也越大（价格波动）。
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:param prices: 时间序列的价格数据
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:param k: 参数 k
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:param S0: 初始价格
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- :param delta_T: 积分的上限
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+ :param time_points: 时间点数组
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:return: ∫ φ(k, ξ) dξ 的值
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"""
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# 计算每个时间点的 φ(k, ξ) 值
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phi_values = [calculate_phi(prices[:i + 1], k, S0) for i in range(len(prices))]
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- # 创建时间点数组
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- time_points = np.linspace(0, delta_T, len(phi_values))
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-
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# 使用梯形法计算积分
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integral_phi = trapz(phi_values, time_points)
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@@ -83,10 +80,12 @@ def process_depth_data(order_book_snapshots):
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if S0 < 0:
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S0 = S_values[0]
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- delta_T = len(S_values) - 1 # 假设每个时间点间隔为1
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+ # 提取时间戳并计算时间间隔
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+ timestamps = [snapshot['timestamp'] for snapshot in order_book_snapshots]
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+ time_points = [(timestamp - timestamps[0]).total_seconds() for timestamp in timestamps]
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# 计算 ∫ φ(k, ξ) dξ
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- integral_phi_value = calculate_integral_phi(S_values, k_initial, S0, delta_T)
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+ integral_phi_value = calculate_integral_phi(S_values, k_initial, S0, time_points)
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# 计算 log(∫ φ(k, ξ) dξ)
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log_integral_phi_value = np.log(integral_phi_value)
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