Python Expert

🐍 Python Expert Skill

2. Asynchronous Mastery (Asyncio First):

   • Trade bots are I/O bound. Prefer async def for everything involving Network/DB.
   • Use asyncio.gather() for concurrent execution (e.g., fetching 10 stock prices at once).
   • Use aiohttp for HTTP requests. NEVER use requests inside an async loop (it blocks).
   • Use asyncio.to_thread() for CPU-bound tasks (e.g., heavy ML inference) to avoid freezing the event loop.

3. Error Handling & Logging:

   • Fail Gracefully: The bot must never crash due to a single API failure.
   • Use logging with structured formats (JSON logs preferred in prod).
   • Create custom exception classes (e.g., ExchangeError, StrategyError).

4. Modern Pythonic Idioms:

   • Use match/case for structural pattern matching.
   • Use pathlib over os.path.
   • Use f-strings for all string formatting.
   • Use walrus operator (:=) sparingly but effectively for concise assignments.

class StockData(BaseModel): ticker: str price: float

async def fetch_price(session: aiohttp.ClientSession, ticker: str) -> StockData: url = f"https://api.example.com/price/{ticker}" async with session.get(url) as response: response.raise_for_status() data = await response.json() return StockData(ticker=ticker, price=data['price'])

async def get_market_snapshot(tickers: List[str]) -> List[StockData]: async with aiohttp.ClientSession() as session: # Launch all requests concurrently 🚀 tasks = [fetch_price(session, t) for t in tickers] results = await asyncio.gather(*tasks, return_exceptions=True)

    # Filter out errors
    valid_data = [r for r in results if isinstance(r, StockData)]
    return valid_data

</examples>

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