Diagnoses slow QuantConnect Python (.py) and C# (.cs) backtests using the Performance Chart first and Python cProfile only when the chart does not pinpoint the hot function. Trigger phrases: "slow backtest", "high CPU", "algorithm slow", "CPU usage", "RAM usage", "memory usage", "performance chart", "profiling", "bottleneck", "debug performance", "taking too long", "optimize algorithm".
Diagnoses slow QuantConnect Python (.py) and C# (.cs) backtests using the Performance Chart first and Python cProfile only when the chart does not pinpoint the hot function. Trigger phrases: "slow backtest", "high CPU", "algorithm slow", "CPU usage", "RAM usage", "memory usage", "performance chart", "profiling", "bottleneck", "debug performance", "taking too long", "optimize algorithm".
Diagnoses slow QuantConnect Python (.py) and C# (.cs) backtests using the Performance Chart first and Python cProfile only when the chart does not pinpoint the hot function. Trigger phrases: "slow backtest", "high CPU", "algorithm slow", "CPU usage", "RAM usage", "memory usage", "performance chart", "profiling", "bottleneck", "debug performance", "taking too long", "optimize algorithm".
Diagnoses QuantConnect Python (.py) and C# (.cs) algorithm failures: runtime exceptions and zero-trade backtests. Invoked by other agents the moment an algorithm throws a runtime error or completes a backtest with 0 orders. Walks an ordered checklist to the root cause without hiding it. Trigger phrases: "runtime error", "stack trace", "0 orders", "no trades", "flat equity curve", "unknown property", "AttributeError", "KeyNotFoundException", "wasn't found in the DataDictionary", "insufficient buying power", "indicator not ready", "debug the algorithm".
Diagnoses QuantConnect Python (.py) and C# (.cs) algorithm failures: runtime exceptions and zero-trade backtests. Invoked by other agents the moment an algorithm throws a runtime error or completes a backtest with 0 orders. Walks an ordered checklist to the root cause without hiding it. Trigger phrases: "runtime error", "stack trace", "0 orders", "no trades", "flat equity curve", "unknown property", "AttributeError", "KeyNotFoundException", "wasn't found in the DataDictionary", "insufficient buying power", "indicator not ready", "debug the algorithm".
Diagnoses QuantConnect Python (.py) and C# (.cs) algorithm failures: runtime exceptions and zero-trade backtests. Invoked by other agents the moment an algorithm throws a runtime error or completes a backtest with 0 orders. Walks an ordered checklist to the root cause without hiding it. Trigger phrases: "runtime error", "stack trace", "0 orders", "no trades", "flat equity curve", "unknown property", "AttributeError", "KeyNotFoundException", "wasn't found in the DataDictionary", "insufficient buying power", "indicator not ready", "debug the algorithm".
Use when subscribing to a QuantConnect/LEAN alternative-data class via `AddData<AltClass>(symbol)` and reading the result from `slice` in `OnData`. Triggers — "is this dataset a list or single point per bar", "why does iterating slice[dataset_symbol] fail", "why does .property error on a Quiver/RegAlytics/EODHDEconomicEvents value", missing-attribute errors after `slice[_datasetSymbol]`. Skip when — the dataset is a universe (use alternative-data-universes), Morningstar fundamentals, ETF constituents, or the price feed comes through `AddEquity` / `AddOption` instead of `AddData`.
Use when subscribing to a QuantConnect/LEAN alternative-data class via `add_data(<AltClass>, symbol)` and reading the result from `slice` in `on_data`. Triggers — "is this dataset a list or single point per bar", "why does iterating slice[dataset_symbol] fail", "why does .property error on a Quiver/RegAlytics/EODHDEconomicEvents value", missing-attribute errors after `slice[dataset_symbol]`. Skip when — the dataset is a universe (use alternative-data-universes), Morningstar fundamentals, ETF constituents, or the price feed comes through `add_equity` / `add_option` instead of `add_data`.