Hybrid Sentiment-Momentum (HSM) Model

Here are the functional and logical issues found in the main.py:

1. The technical indicators (RSI, MACD, ADX, ATR) are initialized inside the Update() method, not during algorithm/alpha model construction or in a securities-changed handler. This approach delays indicator warmup and may cause missed signals at startup, especially if Update() is only called after live data is received.

2. ATR is created, checked for readiness, but not actually used in the trading signal logic. If it is intended only for risk management, this is fine; otherwise, this is incomplete logic.

3. The AlphaModel never emits a “flat” (neutral/exit) or “down” Insight, so once a position is entered the only way it can be exited is through risk management (e.g., trailing stop). This can lead to unwanted long exposure if only entry signals are generated.

4. The model only emits a signal once per day (uses lastInsightTime.date()). This prevents duplicate signals per day, but means it will ignore all changes within the same day, including signals to potentially close the position intraday.

5. Indicator instantiation is done only for the initial TSLA symbol and lacks any OnSecuritiesChanged support for multi-symbol, dynamic universes. As written, the logic does not extend cleanly to multi-security support and will fail to initialize for additional securities if the universe changes.

These are the primary potential issues to consider for robustness and correct behavior in QuantConnect’s algorithm framework.

Sources: Leveraged ETFs With Systematic Risk Management, Short Term Reversal, Key Concepts - QuantConnect.com, Opening Range Breakout for Stocks in Play, Ichimoku Clouds In The Energy Sector