smc

ICT Smart Money Concepts generate trading signals by detecting Break of Structure and Change of Character in OHLCV time series data to infer trend direction. The engine processes per-instrument DataFrames to output long, short, or neutral signals for market entries.

Yes, you can backtest ICT order blocks and FVGs on both crypto and traditional markets. The engine processes standard OHLCV time series data, supporting historical evaluation and real-time live signaling across multiple asset classes.

Yes, you need the smartmoneyconcepts Python library alongside pandas and numpy to detect ChoCH and BOS. These dependencies are required to execute the signal engine and process OHLCV time series data for structure-based market analysis.

The best way to filter trading signals using Fair Value Gaps is to use an automated signal engine that applies FVG detection to validate and prune initial BOS and ChoCH structures. This approach confirms market inefficiencies before generating trade entries.

Yes, the signal engine supports live signaling and signal auditing for real-time decision making. It continuously evaluates OHLCV time series data to generate structure-based signals while maintaining records for historical audit and performance review.