Overview
This prompt aims to guide the development of a customizable MQL5 trading bot for effective trading strategies. Programmers and traders will benefit from the detailed specifications and structured implementation steps.
Prompt Overview
Purpose: This trading bot aims to automate trading decisions based on EMA crossover signals for optimized performance.
Audience: Designed for traders seeking to enhance their strategies with automated, configurable trading solutions in MQL5.
Distinctive Feature: The bot features dynamic trade management, including ATR-based risk controls and loss recovery mechanisms.
Outcome: Users can expect improved trading efficiency and potential profitability through automated execution and risk management.
Quick Specs
- Media: Text
- Use case: Generation
- Industry: Consulting (Management, Strategy), Fintech & Digital Banking, Machine Learning & Data Science
- Techniques: Function Calling / Tool Use, Role/Persona Prompting, Structured Output
- Models: Claude 3.5 Sonnet, Gemini 2.0 Flash, GPT-4o, Llama 3.1 70B
- Estimated time: 5-10 minutes
- Skill level: Beginner
Variables to Fill
No inputs required — just copy and use the prompt.
Example Variables Block
No example values needed for this prompt.
The Prompt
Develop an aggressive MQL5 trading bot compatible with any symbol, featuring a configurable minimum lot size and utilizing the crossover of the 5-period EMA and 500-period EMA as trend signals.
**Details:**
– Monitor the 5 EMA and 500 EMA on the current chart symbol and time frame for crossover signals.
– When the 5 EMA crosses above the 500 EMA, initiate a buy entry batch as follows:
– Immediately open one trade.
– If, after one candle, the price moves against the trade, add one more trade.
– Repeat adding trades up to a maximum of 8 trades in this initial batch.
– If trades move favorably, add one trade per candle, up to a maximum of 10 trades in total.
– The batch number of trades, minimum lot size, and trade limits must be customizable.
– Implement a smart algorithm to manage losing trades by aiming to close them at a profit or minimal loss.
– Use average true range (ATR) based volatility to set dynamic take profit (TP), stop loss (SL), and trailing stops.
– If a trade is losing and a new EMA crossover occurs (new signal), double the lot size of new trades to recover losses faster; close trades once profitable.
– Ensure the bot can recover losses immediately if trends change rapidly.
– The bot must respect all maximum trade limits and manage edge cases such as rapid signal changes.
**Steps to Implement:**
1. Compute and monitor 5 EMA and 500 EMA crossover signals.
2. On crossover up, open initial buy trade batch with immediate trade.
3. Detect price movement against the batch after one candle; add trades accordingly, max 8 initially.
4. When trades move favorably, add trades per candle up to max 10.
5. Dynamically calculate TP, SL, and trailing stop using ATR values.
6. Manage losing trades by doubling new trade lots on new crossovers to recover losses.
7. Handle rapid signal changes, enforce max trades, and maintain robust error handling.
**Output Format:**
Provide the complete MQL5 source code implementing this strategy with comprehensive comments explaining each major block of the algorithm.
– The code must be efficient, robust, and suitable for live trading on any symbol with adjustable minimum lot size and maximum trades.
**Notes:**
– Prioritize maximizing recovery from losses while dynamically controlling risk.
– Make all key parameters configurable.
– Carefully handle all edge cases, including rapid EMA crossovers and maintenance of trade limits.
**Formatting Guidance:**
– Use clear and consistent indentation for code readability.
– Include comments for each major function and logic block to enhance maintainability.
Screenshot Examples
How to Use This Prompt
- Copy the prompt provided above.
- Paste the prompt into your preferred coding environment.
- Adjust any parameters as needed for your trading strategy.
- Implement the MQL5 code based on the outlined steps.
- Test the bot in a demo environment before live trading.
- Monitor performance and make adjustments as necessary.
Tips for Best Results
- Understand EMA Crossovers: Familiarize yourself with how the 5-period and 500-period EMAs interact to identify trend changes.
- Configurable Parameters: Ensure the bot allows users to set minimum lot size and maximum trades for flexibility in different market conditions.
- Dynamic Risk Management: Implement ATR-based calculations for setting take profit and stop loss to adapt to market volatility.
- Trade Recovery Strategy: Design a mechanism to double lot sizes on new signals to quickly recover from losing trades while respecting trade limits.
FAQ
- What is the main function of the MQL5 trading bot?
The bot trades based on the crossover of the 5-period and 500-period EMAs. - How does the bot handle losing trades?
It doubles the lot size on new crossovers to recover losses faster. - What is the maximum number of trades the bot can open?
The bot can open a maximum of 10 trades in total. - How does the bot determine take profit and stop loss?
It uses average true range (ATR) to dynamically set TP and SL levels.
Compliance and Best Practices
- Best Practice: Review AI output for accuracy and relevance before use.
- Privacy: Avoid sharing personal, financial, or confidential data in prompts.
- Platform Policy: Your use of AI tools must comply with their terms and your local laws.
Revision History
- Version 1.0 (February 2026): Initial release.
