Description
Zscore Momentum
Zscore Momentum is an intelligent automated trading system designed to recognize the current market phase and adapt its strategy in real time.
Through a combined analysis of Z-Score, ATR, ADX, Bollinger Bands, and Moving Averages, the bot classifies the market environment into three operational levels:
- High: when the market is in strong directional momentum, pushing decisively in one direction.
- Trend: when the market shows a clear and sustained directional movement.
- Range: when price moves sideways and has not yet decided on a direction.
Zscore Momentum automatically adapts its trading logic and risk/profit settings according to current market conditions.
At the core of the algorithm lies the Z-Score calculation, a statistical measure that evaluates how far the current price deviates from its mean.
This allows the bot to identify situations of volatility expansion or compression and anticipate potential acceleration or reversal points.
All trades are managed with dynamic stop loss and take profit levels calculated from the ATR, constantly adjusting risk according to current volatility.
In “High” mode, the system can automatically perform a 50% partial close once a predefined profit threshold is reached, locking in gains and reducing exposure.
Zscore Momentum is designed for the H1 and H4 timeframes and the EURUSD pair.
Customizable Parameters
🔹 High Trade Risk Management
- HighTakeProfitX: ATR multiplier for calculating take profit in “High” phases. Higher values set a wider target.
- HighStopLossX: ATR multiplier for calculating stop loss in “High” phases. Higher values allow greater tolerance to pullbacks.
- ClosePartialHigh: Enables (
Yes) or disables (No) the partial close function for “High” trades. - %OfLotsClosePartial: Percentage of trade volume to close when the partial close is triggered. Example:
50means half the position will be closed. - HighProfitForPartial: Defines the profit threshold (as a fraction of total TP) at which the partial close is executed.
Example: with a TP of 90 pips and a value of 3, the partial close occurs at 30 pips profit.
🔹 Trend Risk Management
- TrendStopLossX: ATR multiplier for calculating stop loss during “Trend” phases.
- TrendTakeProfitX: ATR multiplier for calculating take profit during “Trend” phases.
🔹 Range Risk Management
- RangeStopLossX: ATR multiplier for calculating stop loss during “Range” phases.
- RangeTakeProfitX: ATR multiplier for calculating take profit during “Range” phases.
🔹 Time Filter
- Start Hour Filter: Defines the hour at which the bot starts trading (24h format). Trades are opened only after this time.
- End Hour Filter: Defines the hour at which the bot stops opening new positions.
🔹 Lot Management
- Fixed Lots: Fixed trade volume for each operation.
Future versions will allow different lot sizes for High, Trend, and Range trades.
Get Started
Create your cBot now in an easy and intuitive way!
👉 Try AlgoBuilderX now — no registration required.
You can find the AlgoBuilderX project for free in our Discord channel
You’ll be able to import it into the AlgoBuilderX workspace, view it, edit it, or export it.
⚠️ Important Note
This cBot is a demonstration example created with AlgoBuilderX and is not optimized for live use.
It is designed to illustrate the functionality of AlgoBuilderX and serve as a starting point for creating custom strategies.
It is recommended that you carefully test and optimize any strategy before using it on a real trading account.
Summary
Key features include dynamic stop loss and take profit levels calculated from ATR to reflect current volatility, and an optional partial close function in High momentum phases to lock in profits by closing a configurable portion of the position. Users can customize risk parameters such as ATR multipliers for stop loss and take profit in each market phase, partial close settings, trading hours, and fixed lot sizes.
This cBot serves as a demonstration example created with AlgoBuilderX, illustrating adaptive strategy implementation and risk management techniques. It is intended as a starting point for developing custom strategies and requires thorough testing and optimization before live deployment.
