CoinBoltX guide · Smart Money Bot
How Smart Money Bot uses SMC
See how CBX Smart Money Bot evaluates fair value gaps, BOS, CHoCH, equilibrium, volume, and higher-timeframe context.
How Smart Money Bot uses SMCEvidence before an entry
Smart Money Bot combines measured candle patterns with AI review. It evaluates fair value gaps (FVGs), breaks of structure (BOS), changes of character (CHoCH), premium and discount zones, equilibrium, candle volume, and higher-timeframe context.
A marked FVG is not automatically an order. The bot also checks confirmation time, current price, room to the target, available funds, existing exposure, and its execution rules. A signal that formed before the run started or has expired is not replayed as a fresh trade.
Three entry patterns
Outer-zone entries can use the first qualifying confirmed FVG after a zone touch and bounce. A qualifying rejection candle is an alternative. Later aligned gaps can support holding an existing position without adding another entry.
Equilibrium is a separate setup: a qualifying FVG and matching recent CHoCH through or rejecting the midpoint can support a short below equilibrium toward discount, or a long above it toward premium. The pattern still needs enough target room and a valid AI and execution review.
How a position is managed
A confirmed rejection at equilibrium can close the full position. A successful break can retain it toward the opposite-zone target. A fresh qualifying opposite FVG after entry can close a reversing position.
Protective stops and targets remain active. Trailing protection requires favorable movement before it can arm and uses the bot’s risk rules. AI reviews add context; they do not remove hard protective limits or guarantee an exit price.
Choose the timeframe before Start
The available run timeframes are 5m, 15m, 30m, 1H, and 4H. Select the market, timeframe, collateral, leverage, and hedge setting before starting. Viewing another chart is not a request to change the saved settings of an existing run.
The bot can use verified closed-trade outcomes as decision context. That is outcome memory, not continuous retraining of model weights. AI can be wrong, chart patterns can fail, and automated trading can lose money. No win rate or return is promised.
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