Fixed a ValueError in financial probability calculations where pandas Series objects were incorrectly passed to Python’s native min()/max() functions, causing ambiguous boolean comparisons. The issue occurred during stock movement probability calculations.

Issue

The system crashed with ValueError: The truth value of a Series is ambiguous when executing probability calculations like max(0.1, min(0.9, prob_up / total)). This happened because pandas Series objects were passed to Python’s comparison functions, which cannot evaluate Series in boolean contexts.

Culprit

Series in Min/Max Functions: Pandas Series objects were returned by operations like stock_data['Close'].pct_change(5).iloc[-1], but were passed directly to min()/max(), which expect scalars.
Ambiguous Boolean Context: Python’s comparison functions (a < b) cannot determine a single truth value for Series objects.
Type Mismatch: Arithmetic operations preserved the Series type, leading to invalid boolean evaluations.

Solution

Explicit Scalar Conversion: Added float() or .item() to extract scalar values from Series objects before comparisons.
NaN Handling: Added checks for np.isnan() to ensure safe arithmetic operations.
Type Safety: Ensured all probability operations used scalar values to avoid ambiguous boolean contexts.
Consistent Extraction: Applied float() to all Series extractions (e.g., stock_data['Close'].iloc[-1]).

Verification confirmed successful execution with valid probability outputs (e.g., UP: 50.37%, DOWN: 49.63%) while adhering to pandas best practices for type safety.