PythonRuntimeRuntimeError: This event loop is already running
Notebook or async code is already running an event loop
Typical symptoms
- RuntimeError about an event loop
- Async examples fail in Jupyter
- Synchronous helper code works in a script but not a notebook
Likely causes
- Jupyter already owns the event loop
- The code mixes blocking and async APIs
- A helper starts a nested loop
Checks
- Identify whether the code is running in Jupyter, IPython, or a plain script
- Use one async style consistently
- Avoid starting nested event loops
Resolution path
- Use notebook-compatible async patterns or the package helper intended for notebooks
- Keep script examples and notebook examples separate in documentation
Related terms: asyncio, Jupyter, IPython, await, nest_asyncio, ib_insync
PythonSetupModuleNotFoundError
Python cannot import ib_insync or ib_async
Typical symptoms
- No module named ib_insync
- No module named ib_async
- The import works in one terminal but not another
Likely causes
- The package is installed in a different environment
- The notebook kernel does not match the terminal interpreter
- The virtual environment is not active
Checks
- Print sys.executable in the failing process
- Run python -m pip show for the package in that same interpreter
- Confirm the notebook kernel path
Resolution path
- Install dependencies with the interpreter that runs the code
- Use a project virtual environment and document activation steps
Related terms: import, pip, venv, kernel, environment, ib_async
PythonDataTypeError: can't compare offset-naive and offset-aware datetimes
Python datetime values mix timezone-aware and naive timestamps
Typical symptoms
- Datetime comparison fails
- pandas filters fail after joining data sources
- Backtest windows shift unexpectedly
Likely causes
- Some timestamps include timezone information and others do not
- IBKR bars, CSV files, and local timestamps were normalized differently
Checks
- Inspect timezone metadata before merging
- Record exchange timezone and bar-close convention
- Verify daylight-saving transitions in sample data
Resolution path
- Normalize timestamps deliberately at ingestion
- Keep a documented convention such as UTC internally and display time zones only at the edge
Related terms: datetime, timezone, UTC, pandas, tz_localize, tz_convert
PythonData
Request succeeds but the resulting DataFrame is empty
Typical symptoms
- No exception is raised
- The DataFrame has zero rows
- Downstream calculations produce NaN or empty output
Likely causes
- The request returned no bars or positions
- The code filtered all rows
- The contract, date range, or permissions do not match available data
Checks
- Inspect the raw API response before building the DataFrame
- Log row counts after every filter
- Test a known liquid symbol and a short time range
Resolution path
- Handle empty data as a first-class branch
- Fail fast with a clear diagnostic before calculations or order-related logic
Related terms: pandas, DataFrame, empty, NaN, filter, historical data
Educational boundary
This lookup is for technical troubleshooting and reproducibility. It is not financial, investment, trading, tax, or legal advice. Prefer paper trading and read-only checks while diagnosing live connectivity or data issues.
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