The Standard Library Tour

Now that you can import, the whole standard library opens up - hundreds of modules, free with Python. You don't need to know them all; you need to know which drawers exist so you can reach for the right one. This chapter is a quick guided tour of the drawers a trader opens most: dates, randomness for simulation, and the JSON that market data speaks. We'll keep each one brief - the deepest ones get their own chapters soon.

datetime: working with the calendar

Markets run on a calendar, and the datetime module handles dates, times and the arithmetic between them:

from datetime import date, timedelta

# A fixed date keeps this example's output stable (use date.today() for "now").
today = date(2026, 6, 25)

print("Date      :", today)
print("Day name  :", today.strftime("%A"))      # the weekday, spelled out
print("In 7 days :", today + timedelta(days=7))  # date maths with timedelta
print("Year      :", today.year)

Date      : 2026-06-25
Day name  : Thursday
In 7 days : 2026-07-02
Year      : 2026

strftime formats a date into readable text (here "%A" gives the weekday name), and timedelta lets you do date maths - "what's the date 7 trading-ish days from now?" We used a fixed date so the output stays stable; in real code date.today() gives you the live date. There's much more here - parsing, time zones, market sessions - and Chapter 23 gives it the full treatment.

random: simulation and chance

The random module generates random numbers - invaluable for simulating markets when you want to see how a strategy behaves across many possible futures:

import random

random.seed(42)        # seeding makes the "random" results reproducible

# Simulate 5 days of a price taking a random walk of +/- up to 1% a day.
price = 100.0
for day in range(1, 6):
    move = random.uniform(-1, 1)         # a random percentage move
    price = price * (1 + move / 100)
    print(f"Day {day}: move {move:+.2f}%  ->  price {price:.2f}")

Day 1: move +0.28%  ->  price 100.28
Day 2: move -0.95%  ->  price 99.33
Day 3: move -0.45%  ->  price 98.88
Day 4: move -0.55%  ->  price 98.33
Day 5: move +0.47%  ->  price 98.80

random.uniform(-1, 1) draws a random percentage move, and we compound it to walk a price forward. The crucial line is random.seed(42): it makes the randomness reproducible, so this simulation prints the same path every time you run it - and so will yours.

json: the language of market APIs

When a trading or data API sends you a quote, it almost always arrives as JSON text. The json module translates between that text and Python dictionaries:

import json

quote = {"symbol": "RELIANCE", "ltp": 1313.60, "change": 13.60}

# dumps: a Python dictionary -> a JSON text string (how APIs send data).
text = json.dumps(quote)
print("As JSON text:", text)

# loads: JSON text -> back into a Python dictionary you can use.
back = json.loads(text)
print("Back to dict:", back["ltp"])

As JSON text: {"symbol": "RELIANCE", "ltp": 1313.6, "change": 13.6}
Back to dict: 1313.6

json.dumps turns a dictionary into JSON text (to send or save); json.loads turns JSON text back into a dictionary (to read what you received). Notice the result is exactly the dictionaries you mastered in Chapter 11 - which is why JSON will feel familiar. Chapter 22 goes deeper, including nested data and reading from files.

And many more drawers

Those are three favourites, but the toolbox is deep. A few more worth knowing exist:

Try it yourself

• Use datetime to print the weekday name for date(2026, 1, 1). What day does 2026 begin on?
• Change the seed in the random walk to random.seed(7) and run it again - a different path, but reproducible. Remove the seed line entirely and run twice: now it differs each time.
• Take the dictionary {"a": 1, "b": [2, 3]}, run it through json.dumps, and notice how the list survives the trip.

Recap

• The standard library gives you hundreds of ready modules; know which drawers exist and reach for them.
• datetime handles dates, formatting (strftime) and date maths (timedelta).
• random generates numbers for simulation; random.seed() makes results reproducible.
• json converts between JSON text and Python dictionaries (dumps out, loads in).
• Many more drawers exist - math, statistics, csv, os - so look things up rather than memorising.

Everything so far has come free with Python. But the most powerful tools for a trader - NumPy, pandas, yfinance - aren't in the standard library; you have to install them. In the next chapter we'll do exactly that with pip, and learn to keep projects tidy with virtual environments.