smc

You can detect Smart Money Concepts by processing OHLCV pandas DataFrames through a signal engine that identifies Break of Structure (BOS), Change of Character (ChoCH), Fair Value Gaps (FVG), and order blocks programmatically. It outputs time-aligned directional signals (1 for long, -1 for short, 0 for stand aside).

Yes, SMC signal generation is designed for algorithmic trading, backtesting, and journaling workflows. The engine emits time-aligned directional signals based on configurable swing_length and close_break parameters, which can be integrated directly into a local strategy engine for trade entry filtering.

If your OHLCV data feed lacks tick volume, the signal engine utilizes a volume fallback mechanism. This ensures robust data processing and accurate structural event detection, allowing SMC signal generation to continue functioning without missing or noisy signals.

Order block detection and other SMC signals can be configured using the swing_length and close_break parameters within the signal engine. These settings adjust the sensitivity of structural event detection, allowing you to tailor the time-aligned 1, -1, and 0 outputs to your specific timeframe analysis.

Yes, the signal engine supports multi-instrument timeframe analysis by accepting a mapping of instrument symbols to OHLCV pandas DataFrames. This allows you to run SMC signal generation, including BOS, ChoCH, and FVG detection, across multiple assets simultaneously for comprehensive algorithmic trading.

The best way to automate order block detection is by applying a programmatic signal engine to OHLCV price series, which eliminates manual chart inspection and reduces missed or noisy signals. This approach integrates with the smartmoneyconcepts Python library to derive directional trading signals automatically.