Detect trend continuation, reversal, and fair value gap signals using smartmoneyconcepts.
npx skills add https://github.com/NigarumOvum/AutoTrading --skill smc-nigarumovum
Please help me install this Agent Skill. Skill: smc Source: https://github.com/NigarumOvum/AutoTrading/tree/main/Vibe-Trading/agent/src/skills/smc Command: npx skills add https://github.com/NigarumOvum/AutoTrading --skill smc-nigarumovum
The smc skill unit solves the problem of analyzing institutional trading behavior by providing signals for trend continuation, reversal, and target pricing.
Activate the smc skill to receive trading signals for a specific asset pair.
High-intent search queries and answers about installing and using this skill.
To detect Smart Money Concepts signals for institutional trading analysis, you can use this signal engine to identify trend continuation, reversal, and fair value gap signals for specified assets. It utilizes the smartmoneyconcepts library to process data and generate actionable trading outputs.
Break of Structure (BOS) and Change of Character (ChoCH) are technical analysis patterns used to confirm trend continuation or reversal. This signal engine detects these structural market shifts to help identify institutional trading behavior and trend direction.
To calculate Fair Value Gap (FVG) and identify order blocks, the signal engine analyzes institutional order concentration zones. It targets price refill levels for FVG and highlights liquidity zones where stop-hunts are likely to occur.
Yes, you can use pandas and numpy to perform technical analysis on specified asset pairs. The signal engine relies on these dependencies alongside the smartmoneyconcepts library to process asset data and deliver institutional trading signals.
Yes, this signal engine supports fair value gap signal generation for trend reversals. It implements a comprehensive signal detection mechanism that identifies both trend continuation and reversal patterns based on institutional capital behavior.
When using smartmoneyconcepts for technical analysis, limitations include its strict dependency on specific data inputs and structural market patterns. It requires accurate asset pair data to effectively detect order blocks and fair value gaps without generating false signals.
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