Unlock Stock Insights: Analyzing Apple Prices with NumPy in Python

This article demonstrates how to use NumPy to read a CSV file containing Apple stock data and perform basic statistical analysis such as mean, weighted average, min/max, variance, daily returns, volatility, and weekday aggregation.

Reading CSV Data

Use np.loadtxt to load the closing price and volume columns from AAPL.csv.

import numpy as np
c, v = np.loadtxt('AAPL.csv', delimiter=',', usecols=(1,2), unpack=True)
print(c)
print(v)

Simple Statistics

Compute arithmetic mean, weighted average (VWAP), maximum, minimum, range, and variance.

mean_c = np.mean(c)
print(mean_c)

vwap = np.average(c, weights=v)
print(vwap)

max_price = np.max(c)
min_price = np.min(c)
range_price = np.ptp(c)
print(max_price, min_price, range_price)

var_c = np.var(c)
print(var_c)

Daily Returns and Volatility

Calculate daily returns, their standard deviation, and annualized volatility using logarithmic returns.

returns = -np.diff(c) / c[1:]
print(returns)

std_ret = np.std(returns)
print(std_ret)

logreturns = -np.diff(np.log(c))
volatility = np.std(logreturns) / np.mean(logreturns)
annual_volatility = volatility / np.sqrt(1/252)
print(volatility)
print(annual_volatility)

Date Handling and Weekday Analysis

Convert byte‑encoded date strings to datetime objects, extract weekdays, and compute average closing price for each weekday.

import datetime
def datestr2num(bytedate):
    return datetime.datetime.strptime(bytedate.decode('utf-8'), '%Y/%m/%d').date().weekday()

dates, c = np.loadtxt('AAPL.csv', delimiter=',', usecols=(0,1), converters={0: datestr2num}, unpack=True)

averages = np.zeros(5)
for i in range(5):
    idx = np.where(dates == i)
    prices = np.take(c, idx)
    averages[i] = np.mean(prices)
    print(f"Day {i} prices:{prices}, avg={averages[i]}")

top = np.max(averages)
top_day = np.argmax(averages)
bot = np.min(averages)
bot_day = np.argmin(averages)
print(f"highest:{top}, top day is {top_day}")
print(f"lowest:{bot}, bottom day is {bot_day}")

Additional NumPy Utilities

Demonstrate np.clip for value limiting and np.compress for conditional filtering.

a = np.arange(5)
print(a.clip(1,3))   # [1 1 2 3 3]

print(a.compress(a > 2))   # [3 4]

These examples provide a quick overview of essential NumPy functions for financial data analysis.