📝 Question for Mia AI Bot

Below are corrected code examples and explanations addressing all your errors and questions regarding the QuantConnect Python API. Each section includes the right way to use the API, plus best practices and explanations.

1. ATR Indicator: Correct Instantiation and Usage

# In your Algorithm class's initialize() method:
from AlgorithmImports import *

self.atr_period = 14
self.atr = self.atr(
    symbol,
    self.atr_period,
    MovingAverageType.SIMPLE,    # also accepts EMA, WILDERS, etc.
    Resolution.DAILY
)
self.set_warm_up(self.atr_period, Resolution.DAILY)   # Ensures .is_ready is True after warm-up

Key Points:

• Use self.atr, not algorithm.ATR.
• Parameters: (symbol, period, MovingAverageType, Resolution).
• Set warm-up so .is_ready is quickly True.
• Always check indicator.is_ready and not self.is_warming_up before using the indicator value.

2. Accessing Custom Data (e.g., BenzingaNews) in a Slice

# In OnData(self, slice):
news_dict = slice.get(BenzingaNews)        # .get returns a dict
if news_dict:
    for symbol, news in news_dict.items():
        self.debug(f"News @ {news.end_time}: {news.title}")  # Example use

# DO NOT use ContainsKey or .get() (C# style)
# ❌ if not slice_data.contains_key(BenzingaNews): ...

Key Points:

• Use .get(BenzingaNews) to obtain the dictionary (returns {} or None).
• Check if news_dict: for existence and then iterate with .items().

3. Bar Properties: Use snake_case

# When accessing TradeBar or QuoteBar properties:
close = bar.close              # Pythonic (correct)
volume = bar.volume            # Pythonic
# ❌ bar.close, bar.volume (do NOT use PascalCase)

4. ObjectStore Key and Read Access (Pythonic Style)

object_store_key = "some-key"
if self.object_store.contains_key(object_store_key):
    data_str = self.object_store.read(object_store_key)
    # Use data_str...
# Do not use ContainsKey()/Read()  — use snake_case (contains_key, read).

5. Python vs C# Naming (PascalCase vs snake_case)

• Use snake_case for QuantConnect Python API (e.g., bar.close, self.object_store.read).
• Never use PascalCase in Python QuantConnect code (e.g., bar.Close)—it will break.
• For custom classes, follow Python conventions.

6. Common QuantConnect Python Mistakes

• Using C#-style names (ContainsKey, .Get, .Read, .Close, .Volume), which do not exist in Python.
• Not checking .is_ready or self.is_warming_up before using indicators.
• Accessing slice[newstype] blindly (always check with .get or in).
• Forgetting to set warm-up period for indicators.
• Referencing securities/symbols before calling add_equity() or add_security().

7. Best Practices

• Indicators: Use the helper methods (self.sma, self.ema, self.atr...), not direct instantiation and registration unless advanced logic needed.
• Warm-up: Use set_warm_up() for the period needed by your slowest indicator.
• Slice access: Use slice.get(type) or if symbol in slice.bars to avoid KeyErrors.
• ObjectStore: Check .contains_key(key) before reading.
• Bar properties: Always use lowercase (bar.close, bar.open).
• Check readiness: Always check not self.is_warming_up and indicator.is_ready.

8. General Code Review Tips

• Remove all PascalCase method/property calls from Python QuantConnect code.
• Always use Pythonic syntax and quantconnect helper functions where available.
• Store only strings in ObjectStore, and serialize complex objects yourself (via json.dumps()/json.loads()).
• Use constant names for config parameters and magic numbers.

9. Full Reference Example (summarizing all practices):

from AlgorithmImports import *

class Example(QCAlgorithm):
    def initialize(self):
        self.set_start_date(2021,1,1)
        self.set_end_date(2021,12,31)
        self.set_cash(100_000)
        self.symbol = self.add_equity('SPY').symbol
        self.atr = self.atr(self.symbol, 14, MovingAverageType.SIMPLE, Resolution.DAILY)
        self.set_warm_up(14, Resolution.DAILY)
        self.add_data(BenzingaNews, self.symbol, Resolution.MINUTE)
        self.oskey = 'some-key'
        if not self.object_store.contains_key(self.oskey):
            self.object_store.save(self.oskey, 'AAPL,MSFT')

    def on_data(self, slice):
        if self.symbol in slice.bars:
            bar = slice.bars[self.symbol]
            close, vol = bar.close, bar.volume
            self.debug(f'{self.time} | {close=} | {vol=}')
        if not self.is_warming_up and self.atr.is_ready:
            value = self.atr.current.value
            # use `value`
        news_dict = slice.get(BenzingaNews)
        if news_dict:
            for sym, news in news_dict.items():
                self.debug(f"{news.title} | {news.url}")

    def on_end_of_algorithm(self):
        if self.object_store.contains_key(self.oskey):
            symbols = self.object_store.read(self.oskey).split(',')
            self.debug(str(symbols))

If you need further breakdown or have more code to check, just ask!

Sources: Key Concepts - QuantConnect.com, Handling Data - QuantConnect.com, P E P8 Style Basic Algorithm, Multiple Symbol Consolidation Algorithm, Python Dictionary Feature Regression Algorithm