指定

Strategy Overview
The trading robot will implement an AI-powered trend-following strategy that confirms trades on the 4-hour time frame and executes entries on the 15-minute time frame.

Key Components
1. Trend Confirmation (4-hour time frame):
    - Use a machine learning algorithm to analyze market trends and confirm trade directions.
    - Integrate with technical indicators (e.g., MA, RSI, Bollinger Bands) for trend validation.
2. Entry Signals (15-minute time frame):
    - Use a combination of technical indicators and AI-driven analysis to generate entry signals.
    - Enter long positions when the AI model predicts an upward trend.
    - Enter short positions when the AI model predicts a downward trend.
3. Stop Loss (SL) and Take Profit (TP) based on ETR (Expected Trading Range):
    - Calculate the ETR based on historical price movements and AI-driven analysis.
    - Set SL and TP levels according to the ETR.
4. Grid Strategy:
    - Implement a dynamic grid system that adjusts to market conditions.
    - Use AI to optimize grid size, spacing, and order placement.
5. Martingale Strategy:
    - Implement a dynamic martingale system that adjusts position sizes based on AI-driven risk assessment.
    - Use AI to optimize martingale multiplier and risk management.

AI-Powered Dashboard
1. Design Inspiration: Reference the Forex Gold Investor EA dashboard design.
2. Features:
    - Real-time market analysis and trend predictions.
    - Trade signal generation and execution.
    - Dynamic grid and martingale system management.
    - Risk management and position sizing.
    - Performance metrics and analytics.

Technical Requirements
1. MQL5 programming: Develop the trading robot using MQL5.
2. Machine Learning Integration: Integrate a machine learning library (e.g., TensorFlow, PyTorch) or use MQL5's built-in AI capabilities.
3. Dashboard Design: Create a user-friendly and interactive dashboard with real-time data visualization.

Deliverables
1. MQL5 code: Provide the complete MQL5 code for the trading robot.
2. AI model: Deliver the trained AI model and any necessary libraries or frameworks.
3. Dashboard: Provide a fully functional dashboard with real-time market analysis and trading capabilities.
4. Documentation: Document the strategy's logic, parameters, and risk management features.

Development Considerations
1. Back testing: Perform thorough back testing to ensure the strategy's effectiveness.
2. Risk Management: Implement robust risk management features to protect against market volatility.
3. Scalability: Ensure the dashboard and trading robot can handle high volumes of data and trades

応答済み

1
開発者 1
評価
(19)
プロジェクト
22
23%
仲裁
5
40% / 60%
期限切れ
2
9%
仕事中
2
開発者 2
評価
(15)
プロジェクト
34
24%
仲裁
4
0% / 50%
期限切れ
2
6%
仕事中
3
開発者 3
評価
(7)
プロジェクト
6
33%
仲裁
7
0% / 71%
期限切れ
0
類似した注文
Jonah 30+ USD
//+------------------------------------------------------------------+ //| RSI + Bollinger Bands EA (MT5) | //| Built for Jonah | //+------------------------------------------------------------------+ #property strict #property version "1.00" //================ INPUTS ================= input string SymbolName = "XAUUSD"; input double LotSize = 0.01; input int
Hello, I’m reaching out because I’m interested in hiring you to develop a custom trading bot for me. The bot should trade only XAUUSD (Gold) and be designed for long-term account growth using my own trading account size. Here are the core requirements: - Account size: $300 - Asset: XAUUSD only - Risk management: Strict and properly controlled - Risk-to-reward ratio: Clearly defined and consistently applied -
I am looking for an experienced MQL5 developer to convert a complex TradingView Pine Script (will provide the script from tradingview) into a fully automated MT5 Expert Advisor -bot. The TradingView script includes: Market Structure (BOS, CHoCH, Swing BOS) Strong / Weak High & Low Equilibrium (Premium / Discount zones) Volumetric Order Blocks Fair Value Gaps (FVG / VI / OG) Accumulation & Distribution zones Equal
Specifications – Development of an MQL5 Expert Advisor (Reverse Engineering) Project context: I have access to a real trading history consisting of more than 500 trades executed over a period of approximately 3 years. These trades have been exported into a CSV file containing all available information, including date, time, symbol, order type, entry price, and exit price. Important: I do not have access to the
1.Sinyal Perdagangan : Sinyal beli: garis MACD utama memotong garis sinyal ke atas (macd_current>signal_current && macd_previous<signal_previous). Sinyal jual: garis MACD utama memotong garis sinyal ke bawah (macd_current<signal_current && macd_previous>signal_previous). Gambar di bawah menunjukkan kasus beli dan jual. 2. Posisi ditutup pada sinyal yang berlawanan: Posisi beli ditutup pada sinyal jual, dan posisi
Project Description I am looking to collaborate with an experienced MQL5 / algorithmic trading developer who also has hands-on experience with Large Language Models (LLMs) and AI-driven systems. This is a long-term partnership opportunity , not a one-off paid freelance job. I bring 9 years of practical Elliott Wave trading experience , applied in live market conditions. The objective is to translate Elliott Wave
Hello. I am finding an experienced python developer who can implement my trading strategies into robots. I like trend-following swing trading strategies and am going to automate my idea. More details can be discussed by chatting. If you have similar working experience it can be a plus. Thanks
hello great developer Looking for an experienced Web3 / crypto bot developer to build a copy-trading bot for Polymarket . The bot should track selected traders or wallets in real time and automatically replicate trades with minimal delay. Experience with Polymarket, blockchain APIs, and low-latency trading bots is required. Open to custom features and long-term collaboration. Platform: Polymarket (Web3 / API-based)
This strategy is built around the idea that price seeks liquidity, and that retail traders often get trapped around key highs and lows. Instead of entering trades before price hits liquidity, this playbook waits for the market to run stops (take liquidity) and then trade the reversal after the trap is formed. The concept is simple: buy below lows, sell above highs, but only when those lows or highs have respected
* Use Fibonacci retracement (with adjusted values) to scale entry points. * Timeframe may differ depending on the projected target; but the Fibonacci conditions remain the same * date range into consideration as well * Applicable to indices, crypto and metals. * Activate entries on the second half of my fib *Usually takes the whole week to unfold (5 - 7 days) * Timeframes to consider 5m/15m, H1/H2 The attached images

プロジェクト情報

予算
40+ USD