I want to make a expert advisor based on super trend

Hi, I'm interested in ordering an MT4 trading bot. Before we begin, could you please send me the technical specifications and requirements you'll need? Specifically, I'd like to know: - The trading strategy the bot will use. - The currency pairs or instruments it will trade. - The timeframes it supports. - Risk management features (lot sizing, stop loss, take profit, trailing stop, maximum drawdown). - Whether it

Hello Traders and Investors, I am a professional algorithmic trading developer specialized in building high-quality Expert Advisors (EAs), Indicators, Scripts, and Trade Management Tools for MetaTrader 4 and MetaTrader 5. With extensive experience in financial markets and trading automation, I can transform your trading ideas into reliable and efficient solutions with clean, optimized, and well-structured code. My

Hello everybody, I'm looking for an experienced MQL4/MQL5 developer to optimize a High-Frequency Trading (HFT) Expert Advisor for both MT4 and MT5. The EA performs consistently and profitably on demo accounts, but when it is run on Raw and Standard live accounts under what appear to be the same trading conditions, it begins generating losses. I do not have the original source code (.mq4/.mq5); I only have the

I have a High-Frequency Trading (HFT) Expert Advisor for both MT4 and MT5 designed primarily for US30 (Dow Jones Index) . The EA performs consistently and profitably on demo accounts, but when I run it on an IC Markets Raw or Standard live account, it starts generating losses under what appear to be the same trading conditions. At this time, I cannot provide the source code (.mq4/.mq5). I can only provide the

1. Use two EMAs: 20 and 50. If EMA 20 is above EMA 50 → uptrend (look for buys) If EMA 20 is below EMA 50 → downtrend (look for sells) 2. Wait for a pullback into the area between the two EMAs. - For buys: price must touch or move between EMA 20 and EMA 50 during the last few candles. - For stils: same idea, but in a downtrend. 3. Entry signal: Buy: a bullish engulfing candle in an uptrend after the pullback

Ниже представлено готовое, технически выверенное Техническое задание (ТЗ) . Вы можете полностью скопировать этот текст и разместить его на бирже фриланса (например, MQL5.com в разделе «Фриланс» или на Smart-Lab). Данное ТЗ написано на профессиональном языке, понятном разработчикам торговых систем для терминала QUIK (на языке Lua) . ТЕХНИЧЕСКОЕ ЗАДАНИЕ (ТЗ) Разработка мультивалютного торгового робота для терминала

Start ↓ Detect Trend (H4) ↓ Confirm Structure (H1) ↓ Wait for Pullback ↓ Check Indicators ↓ Calculate Confidence Score ↓ Score ≥ 80? ├── No → Wait └── Yes ↓ Calculate Lot Size ↓ Place Order ↓ Set Stop Loss ↓ Set Take Profit ↓ Manage Trade ↓ Move to Break-even ↓ Trail Stop ↓ Close Trade. IF Price > EMA200 (H4) AND EMA50 > EMA200 (H4) AND ADX > 25 AND RSI between 55 and 70 AND MACD Main > Signal AND Bullish engulfing

HIGH-FREQUENCY M5/M15 CONCURRENT ENTRY SNIPER import time class HighFrequencySniper: def __init__(self): self.target_profit = 25.00 # Targeted Delta Move self.max_execution_time = 3600 # 1 Hour Sandbox (Seconds) self.lot_allocation = "CALIBRATED_TO_RISK" def execute_hft_scan(self, current_price, m5_rsi, m15_order_block): print(f"[SCANNING] Current Kernel Metric: ${current_price:.2f

Act as a professional Quantitative Developer and Risk Manager. I want to build a systematic trading strategy rulebook that prioritizes capital preservation and statistical edge over raw performance. Please generate a structured trading strategy using the following framework: 1. ASSET CLASS & TIMEFRAME: - Asset: [e.g., Apple (AAPL), Bitcoin (BTC), or EUR/USD] - Timeframe: [e.g., 5-minute, 1-hour, Daily] 2. CORE

Part 1: Project setup Input settings (risk, stop loss, take profit, EMA periods) Indicator initialization Trade management framework Part 2: Trading logic EMA crossover detection Buy/Sell entry rules One-trade-per-symbol check Part 3: Risk management Automatic lot size calculation Stop-loss and take-profit placement Trade execution and error handling Part 4: Final touches On-screen information Optimization