Python script for jade's quant case

volatility.py

import math

data = list()
f = open("data")

for line in f:
    data.append(float(line))

T = len(data) - 1

u = list()

for i in range(1, T + 1):
    u.append(math.log(data[i] / data[i - 1]))

mean = sum(u) / len(u)
square = sum(math.pow(ret - mean, 2) for ret in u)
s = math.sqrt(1.0 / (T - 1) * square)
volatility = s * math.sqrt(252)

print volatility