Are you a passionate MQL4 programmer looking for a challenging and rewarding project? Solve the Code and Prove Yourself
We're seeking exceptional programmers to join our elite team and work on a groundbreaking project with the potential for significant financial rewards. However, to ensure we're working with the best of the best, we'll need you to prove your worth. Dedicated individuals to join a long-term project that has the potential for significant financial gain. This is a volunteer-based project, but the rewards could be substantial.
The Challenge:
If you believe you have the skills and determination to join our elite programming team, please leave a comment below requesting the coding puzzle and your email address. Once you've solved it and submitted your solution, we'll review it and let you know if you've made the cut.
Remember: This is a competitive challenge, and only the most talented programmers will be selected. Good luck!
Read Neural EUR Bot main project.
EA Functionality Breakdown
1. Data Preprocessing and Feature Engineering**
Indicator Calculation: The EA calculates the Bruce Price Predictor (BPP), Stochastic Oscillator (STO), and Moving Average (MA) indicators from historical price data.
Data Preparation: The `input_data` function (which is currently missing implementation) is likely responsible for preparing the calculated indicator values into a suitable format for the LightGBM model. This might involve normalization, scaling, or other preprocessing techniques.
2. AI Model Prediction
LightGBM Model: The EA uses a pre-trained LightGBM model to predict future price movements.
Input Data: The `input_data` function would provide the prepared indicator values as input to the model.
Prediction: The model would generate a prediction based on the input data.
3. Trading Signal Generation
Strategy-Based Signal: The `signal` function generates a trading signal based on the calculated indicator values and predefined rules.
AI-Enhanced Signal: The EA combines the strategy-based signal with the prediction from the LightGBM model. If both signals agree (e.g., both indicate a buy or sell), a trade is considered.
4. Order Execution
Order Placement: If a trade is deemed suitable, the EA opens a buy or sell order on the EUR/USD currency pair.
Order Parameters: The `OpenBuy` and `OpenSell` functions set the order parameters such as lot size, stop loss, take profit, and slippage.
5. Risk Management
Lot Size Calculation: The `CalculateLotSize` function determines the appropriate lot size based on the user's risk tolerance and account balance.
Margin Check: The `IsTradeAllowed` function ensures that the account has sufficient margin to open a new position.
6. AI Labeling (Optional)
Data Logging: The code includes references to an AI labeling system, suggesting that the EA might be logging trade data for further training or analysis of the AI model.
Key Points:
The EA combines a technical trading strategy with AI-generated predictions to enhance decision-making.
The LightGBM model likely plays a crucial role in providing additional insights or confirming the strategy-based signals.
The effectiveness of the EA would depend on the quality of the pre-trained LightGBM model, the accuracy of the indicator-based strategy, and the effectiveness of the risk management measures.
Missing Information:
The specific implementation of the `input_data` function and the LightGBM model's training data are not provided.
The exact rules and parameters used in the `signal` function are not fully specified.
Additional Considerations:
The EA might benefit from incorporating additional technical indicators or machine learning algorithms.
Regular backtesting and optimization would be necessary to fine-tune the parameters and ensure the EA's profitability.
Risk management should be a top priority to protect the trading account from significant losses.
We're seeking exceptional programmers to join our elite team and work on a groundbreaking project with the potential for significant financial rewards. However, to ensure we're working with the best of the best, we'll need you to prove your worth. Dedicated individuals to join a long-term project that has the potential for significant financial gain. This is a volunteer-based project, but the rewards could be substantial.
The Challenge:
- Coding Puzzle MQL4: We'll provide you with a challenging coding puzzle.
- Time Limit: You'll have a limited amount of time to solve the puzzle.
- Success is Key: Only those who successfully solve the puzzle will be invited to join our team.
- We need individuals with strong programming skills and a passion for creating innovative solutions.
- Problem solvers: The project will require creative thinking and a willingness to tackle complex challenges.
- Team players: We're looking for individuals who can collaborate effectively with others and contribute to a positive team environment.
- Opportunity for profit: The project has the potential for significant financial rewards.
- Gain valuable experience: You'll have the chance to work on a challenging and rewarding project.
- Be part of something special: You'll be contributing to a project that could make a real difference.
- High-Impact Project: You'll be working on a project that could change the industry.
- Financial Rewards: The project has the potential for substantial financial gains.
- Elite Team: You'll be surrounded by talented and dedicated programmers.
If you believe you have the skills and determination to join our elite programming team, please leave a comment below requesting the coding puzzle and your email address. Once you've solved it and submitted your solution, we'll review it and let you know if you've made the cut.
Remember: This is a competitive challenge, and only the most talented programmers will be selected. Good luck!
Read Neural EUR Bot main project.
EA Functionality Breakdown
1. Data Preprocessing and Feature Engineering**
Indicator Calculation: The EA calculates the Bruce Price Predictor (BPP), Stochastic Oscillator (STO), and Moving Average (MA) indicators from historical price data.
Data Preparation: The `input_data` function (which is currently missing implementation) is likely responsible for preparing the calculated indicator values into a suitable format for the LightGBM model. This might involve normalization, scaling, or other preprocessing techniques.
2. AI Model Prediction
LightGBM Model: The EA uses a pre-trained LightGBM model to predict future price movements.
Input Data: The `input_data` function would provide the prepared indicator values as input to the model.
Prediction: The model would generate a prediction based on the input data.
3. Trading Signal Generation
Strategy-Based Signal: The `signal` function generates a trading signal based on the calculated indicator values and predefined rules.
AI-Enhanced Signal: The EA combines the strategy-based signal with the prediction from the LightGBM model. If both signals agree (e.g., both indicate a buy or sell), a trade is considered.
4. Order Execution
Order Placement: If a trade is deemed suitable, the EA opens a buy or sell order on the EUR/USD currency pair.
Order Parameters: The `OpenBuy` and `OpenSell` functions set the order parameters such as lot size, stop loss, take profit, and slippage.
5. Risk Management
Lot Size Calculation: The `CalculateLotSize` function determines the appropriate lot size based on the user's risk tolerance and account balance.
Margin Check: The `IsTradeAllowed` function ensures that the account has sufficient margin to open a new position.
6. AI Labeling (Optional)
Data Logging: The code includes references to an AI labeling system, suggesting that the EA might be logging trade data for further training or analysis of the AI model.
Key Points:
The EA combines a technical trading strategy with AI-generated predictions to enhance decision-making.
The LightGBM model likely plays a crucial role in providing additional insights or confirming the strategy-based signals.
The effectiveness of the EA would depend on the quality of the pre-trained LightGBM model, the accuracy of the indicator-based strategy, and the effectiveness of the risk management measures.
Missing Information:
The specific implementation of the `input_data` function and the LightGBM model's training data are not provided.
The exact rules and parameters used in the `signal` function are not fully specified.
Additional Considerations:
The EA might benefit from incorporating additional technical indicators or machine learning algorithms.
Regular backtesting and optimization would be necessary to fine-tune the parameters and ensure the EA's profitability.
Risk management should be a top priority to protect the trading account from significant losses.
