Glossary

IBKR + Python glossary

A shared vocabulary for Interactive Brokers, TWS, Gateway, market data, order handling, backtesting, portfolio metrics, and Python workflows.

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Reset IBKR TWS Gateway Market data Orders Backtesting Portfolio metrics Python

IBKR

Interactive Brokers account, contract, routing, and API concepts.

7 terms

TWS

Trader Workstation terms for desktop API development and manual review.

5 terms

Gateway

IB Gateway terms for lighter, longer-running API sessions.

3 terms

Market data

Live, delayed, historical, and permissioned data terms.

8 terms

Orders

Order types, identifiers, controls, and safety vocabulary.

8 terms

Backtesting

Research validation terms and common sources of overstatement.

7 terms

Portfolio metrics

Return, risk, exposure, and performance measurement terms.

8 terms

Python

Python environment, data, async, and reproducibility terms.

8 terms

Results

8 matching terms

Python

asyncio event loop

Python's runtime mechanism for scheduling and running asynchronous tasks, callbacks, and coroutines.

Event-loop conflicts are common when combining notebooks, async broker libraries, and application frameworks.

Related: coroutine, ib_async, Jupyter notebook

Python

coroutine

An async Python function result that must be awaited or scheduled before it actually runs.

Warnings about an unawaited coroutine usually mean async code was constructed but never executed.

Related: asyncio event loop, await, task

Python

DataFrame

A two-dimensional labeled table, most commonly from pandas, used for market data, positions, account values, and research results.

Explicit column names, dtypes, index meaning, and time zones make DataFrames easier to review.

Related: pandas, Series, CSV

Python

package pinning

Recording exact or constrained dependency versions so code can be reproduced later with fewer unexpected package changes.

Pinning is especially helpful for pandas, broker API wrappers, and notebook examples.

Related: virtual environment, requirements.txt, lockfile

Python

Series

A one-dimensional labeled pandas object often used for a single price, return, signal, or metric time series.

Series index alignment can change calculations; inspect indexes before joining data.

Related: DataFrame, time series, pandas

Python

timezone-aware datetime

A Python datetime value that carries time zone information, reducing ambiguity when aligning market sessions and historical bars.

Always record whether timestamps are UTC, exchange-local, broker-local, or naive.

Related: historical bars, bar size, time series

Python

type hints

Python annotations that describe expected value types for functions, variables, and data structures.

Type hints improve reviewability for trading and data pipelines where implicit shapes are risky.

Related: mypy, dataclass, pydantic

Pythonvenv

virtual environment

An isolated Python environment that keeps project dependencies separate from the system Python and other projects.

Use one environment per project to make IBKR and data-analysis examples reproducible.

Related: package pinning, requirements.txt, dependency drift

Educational boundary

This content is for educational and technical research purposes only. It is not financial advice, investment advice, trading advice, tax advice, or legal advice. Backtests and examples may contain errors or omissions. Past performance does not guarantee future results. Always test code in a safe environment before using it with real accounts or live trading systems.

Interactive Brokers, IBKR, Trader Workstation, and IB Gateway are trademarks or registered trademarks of their respective owners. InQuantWeTrust is independent and is not affiliated with, endorsed by, or sponsored by Interactive Brokers.