7 Python Built-in Functions for Code Optimization and Performance Tips

When you're writing backtesters, working with tick data, or building real-time trading bots, code clarity and performance matter. Python offers powerful built-in functions that many developers overlook. In this article, you'll discover practical Python performance tips, tricks, and built-ins that can make your quant code faster, more readable, and easier to maintain.

1. functools.lru_cache: Free Speedup for Expensive Lookups

You don't always want to recalculate indicators or read from disk. This built-in decorator adds transparent memoization.

from functools import lru_cache

@lru_cache(maxsize=512)
def get_symbol_metadata(symbol):
    # Simulate an expensive lookup
    return fetch_metadata_from_db(symbol)

Use it to cache:

• Strategy param lookups
• Instrument metadata
• Expensive indicator values (if the input is hashable)

2. contextlib.suppress: Cleaner Exception Handling in Pipelines

During backtests or live runs, sometimes you just want to ignore known, non-critical errors like a missing file or a key that occasionally doesn't exist.

from contextlib import suppress

with suppress(KeyError):
    del strategy_state["position"]

Great for:

• Backtest cleanup
• Retry loops
• Suppressing annoying but expected edge cases

3. itertools.pairwise: Clean Adjacent Iteration

Useful when you need to compare adjacent ticks, deltas, or price bars.

from itertools import pairwise

for prev_bar, curr_bar in pairwise(bars):
    if curr_bar["high"] > prev_bar["high"]:
        print("New high")

You'll never write zip(bars, bars[1:]) again.

4. operator.itemgetter: Fast, Clean Sorts and Lookups

A cleaner, faster way to sort or find max PnL, drawdown, etc.

from operator import itemgetter

trades = [{"id": 1, "pnl": -20}, {"id": 2, "pnl": 75}]
best = max(trades, key=itemgetter("pnl"))

Why it's great:

• Works with dicts, lists, tuples
• More readable and faster than lambda for many use cases
• Handy for ranking strategies, trades, or asset performance

5. heapq: Efficient Priority Queues and Top-N Elements

Need to find the top N trades by PnL, or maintain a rolling best/worst list? heapq gives you fast, memory-efficient tools.

import heapq

top_trades = heapq.nlargest(5, all_trades, key=lambda t: t["pnl"])

You can also build a real-time priority queue:

heap = []
heapq.heappush(heap, (trade["timestamp"], trade))
next_trade = heapq.heappop(heap)

Perfect for:

• Order book simulation
• Event queue processing
• Ranking trade candidates

6. bisect: Fast Binary Search on Sorted Lists

If you're working with sorted timestamps, price levels, or indicators, bisect gives you fast binary search and insert:

import bisect

levels = [1000, 1020, 1050]
index = bisect.bisect_left(levels, 1030)

Use cases:

• Inserting into sorted order book levels
• Finding nearest key levels (e.g. VPOC, high/low)
• Efficient real-time strategy logic without linear scans

7. tempfile: Safe Temporary File and Directory Management

Especially handy in backtests, bot testing, or when handling market data transforms.

import tempfile

with tempfile.TemporaryDirectory() as tmpdir:
    file_path = f"{tmpdir}/output.csv"
    with open(file_path, "w") as f:
        f.write("timestamp,price\n")

Why it's underrated:

• Auto-cleans scratch data
• Useful in test environments or batch processing
• Avoids hardcoded file paths during experiments

Final Thoughts on Python Code Optimization and Built-in Tools

These tools aren't flashy, but they're the kind of things that make your codebase tighter, cleaner, and faster. When you're juggling 50M rows of tick data or running strategy evaluations across hundreds of symbols, little efficiencies like these add up.

If you found this useful, feel free to share. Happy coding !