PROMPT: 99% Accurate Web-Based Trading Signal Bot (🔥 Full Stack + AI + Strategy)

🧠 For developers or AI assistants to build a real-time, full-featured, intelligent Quotex signal system — NO SCREENSHOTS, only live data, and maximum trading intelligence.  

🎯 PURPOSE

Build a real-time, cloud-based trading signal bot with:

✅ Live data scraping from Quotex  

✅ Automatic & manual signal generation  

✅ 99% accuracy using layered technical, AI, and statistical engines  

✅ Telegram alerts  

✅ Professional dashboard & admin panel  

✅ No screenshots or dummy data  

✅ Fully optimized for scalability, accuracy, and speed  

🔗 SYSTEM MODULES

1. 📡 LIVE DATA SCRAPING (REAL-TIME)

Use headless automation to extract chart data directly from Quotex (JS-powered):
Tool Description
Puppeteer (Node.js) or Playwright Headless browser control
Cheerio / JS DOM Parse HTML structure for candles
Anti-Bot Bypass Random UA, delay injection, stealth plugins

✅ Pull:

Current candle OHLC  

Time & asset name  

Asset status (active/locked)  

2. 📈 STRATEGY ENGINE: ALL INDICATORS

Combine technical, AI, statistical, and pattern-based strategies.  

📊 BASIC TECHNICAL INDICATORS (MUST INCLUDE)
Indicator Usage
RSI (Relative Strength Index) Overbought/oversold levels
MACD Momentum + trend direction
EMA & SMA (5/10/20/50/200) Moving average crossovers
Bollinger Bands Volatility breakout or squeeze
Stochastic Oscillator Trend exhaustion
ATR (Average True Range) Volatility check
CCI (Commodity Channel Index) Trend reversal spots
ADX Trend strength confirmation
Parabolic SAR Trailing trend direction
Ichimoku Cloud Full market structure view
🧠 AI & MACHINE LEARNING (OPTIONAL BUT POWERFUL)
Tool Purpose
TensorFlow / PyTorch Predict next candle direction (AI-based)
LSTM / GRU Neural Nets Sequence forecasting for candle patterns
Scikit-learn Signal classification & accuracy filtering
Reinforcement Learning Learn optimal signal strategies over time

Train with historical candle data from Quotex or simulation.  

🧠 ADVANCED STRATEGIES
Strategy Description
Supply & Demand Detect zones for reversal signals
Smart Money Concepts (SMC) Institutional candle patterns
Breakout & Retest Zones Price action logic
Fibonacci Retracement Key level bounces
Multi-timeframe Confluence M1 + M5 + M15 agree = strong signal
Martingale Engine Recovery after loss (optional toggle)
Confirmation Stacking Use 3+ indicators to validate 1 signal
3. ⚙️ SIGNAL GENERATION ENGINE

Each signal must include:

Direction (CALL / PUT)  

Asset (e.g., EUR/USD)  

Timeframe (default 1M)  

Confidence (Low/Medium/High)  

Strategy ID (e.g., RSI-MACD combo)  

Timestamp  

Add:

Auto mode  

Manual override (admin only)  

Backtest support (for strategy testing)  

4. 🔔 TELEGRAM BOT MODULE

Integrate Telegram Bot to send:

🔔 SIGNAL ALERT
Pair: EUR/USD
Action: 📈 CALL
Confidence: 🔵 HIGH
Time: 12:02 UTC
Strategy: RSI + MACD

Support:

API token + Chat ID  

Button for "Next Signal", "Manual Signal", "Disable Auto"  

5. 🌐 MODERN WEB DASHBOARD (RESPONSIVE)
Page Features
Dashboard Live signals feed, sorting/filter
Admin Panel Signal logs, strategy toggles, manual send
Manual Trade Trigger signal from interface
Accuracy Report Win/loss %, graph/chart logs
Settings Choose indicators, update Telegram keys, etc.

Tech:

React.js / Next.js  

Tailwind CSS / ShadCN UI  

Framer Motion (animations)  

Chart.js / Recharts (graphs)  

6. 🧰 BACKEND SYSTEM
Tool Purpose
Node.js + Express / FastAPI API server for signals
MongoDB / Firebase / Supabase Save signals, logs, user data
Redis / Queue (optional) Handle fast async processing
WebSockets Live signal push to frontend
Cron / Task Scheduler Retry missed signals, stats cleanup
7. ☁️ DEPLOYMENT
Platform Usage
Railway / Render / Vercel Host backend + frontend
Docker Containerize scraper + bot
UptimeRobot / Cron Ensure bot stays online 24/7
GitHub Actions Auto deploy on updates
🔒 OPTIONAL FEATURES

✅ Admin Auth + JWT  

✅ Signal result tracking (auto-check win/loss)  

✅ Signal blacklist (avoid bad setups)  

✅ Multilingual support  

✅ Multi-asset multi-tab scraping  

📦 OUTPUT FORMAT EXAMPLE

{
"asset": "BTC/USD",
"timestamp": "2025-05-29T12:05:00Z",
"open": 29250.12,
"close": 29268.55,
"signal": "PUT",
"confidence": "High",
"strategy": "RSI + Bollinger + MACD",
"accuracy": "Backtested 94.2%",
"mode": "Auto"
}

✅ WHAT TO DELIVER

✅ Quotex scraper module (live candlestick JSON)  

✅ Signal engine (99% accuracy target)  

✅ Telegram bot system  

✅ Web dashboard (admin + live signals)  

✅ Full setup guide (domain + deployment)      You are a senior AI developer and full-stack engineer. I want to build a 99% accurate, real-time, web-based binary options trading signal bot that analyzes OTC market pairs (1-minute timeframe) and provides CALL/PUT alerts using high-performance technical indicators, smart rule-based logic, and later, AI/ML.  

✅ Project Goals

Signal Alerts Only – no auto-trading  

Real-time (1-minute candles) – must update every 60s  

Highly accurate signals (aiming for 99%)  

Professional web dashboard (React + Tailwind)  

Live + OTC market pair support  

Early-stage simplicity → scalable ML system  

Transparent decision logic (no black-box-only AI)  

🧱 Technologies & Architecture
💻 Frontend (Web UI)

React + Tailwind CSS  

Pair Selector Dropdown  

Signal Card: direction, confidence, strategy reason  

Countdown Timer (live signal update every 60s)  

Signal History Table  

Mobile responsive  

⚙️ Backend

FastAPI (Python)  

Endpoint: GET /analyze?pair=...  

SQLite logging (early stage), Supabase/MongoDB later  

Logic pipeline:  

    Candle data (synthetic or scraped)  

    Indicator calculation  

    Rule-based signal generation  

    Confidence scoring + strategy explanation  

📊 Indicators to Use (early and advanced)

RSI  

MACD  

EMA Crosses (e.g., 5/13)  

Bollinger Bands  

Stochastic Oscillator  

ATR  

Volume Spike Detection  

Candlestick Patterns (Doji, Engulfing, Pin Bar)  

🤖 ML Models to Add Later

LSTM (sequential candle forecasting)  

Random Forest / Decision Tree  

Gradient Boost (XGBoost)  

Ensemble AI (multiple strategies)  

📩 Alerts

Telegram Bot integration using python-telegram-bot  

Push signals to a private or public channel (e.g., @IBS_TradeNest)  

🔐 Auth & Admin

Static login (early): username/password  

Upgrade to Firebase/Auth later  

Admin-only strategy dashboard  

✅ Supported Pairs

Forex OTC: EUR/USD, GBP/USD, AUD/USD, USD/JPY, USD/CHF, USD/CAD, EUR/GBP, EUR/JPY, GBP/JPY, EUR/CHF
Commodities OTC: Gold (XAU/USD), Silver, Brent Crude
Crypto OTC: BTC/USD, ETH/USD, LTC/USD, XRP/USD

Add support for Live markets too (optional toggle)  

📂 Final Folder Structure

quotex-signal-bot/
├── backend/
│ ├── app/
│ │ ├── main.py # FastAPI app + routes
│ │ ├── scraper.py # Data fetcher (synthetic or from Quotex)
│ │ ├── indicators.py # RSI, MACD, EMA, etc.
│ │ ├── signal_engine.py # Rule-based signal logic
│ │ ├── logger.py # Logs signals into SQLite
│ │ ├── telegram_bot.py # Telegram integration (optional)
│ │ └── models.py # Pydantic & DB models
│ └── requirements.txt
│
├── frontend/
│ ├── public/
│ ├── src/
│ │ ├── components/
│ │ │ ├── PairSelector.jsx
│ │ │ ├── SignalCard.jsx
│ │ │ └── SignalHistory.jsx
│ │ ├── App.jsx
│ │ └── main.jsx
│ ├── tailwind.config.js
│ └── package.json
│
├── .env
├── README.md
└── docker-compose.yml (optional)

✅ Features Planned by Phase
Phase Features Included
Phase 1 UI + signal engine + history logging + static login
Phase 2 Telegram alerts + signal logs + backtesting API
Phase 3 ML-powered decision model + Supabase integration
Phase 4 Strategy testing dashboard + performance optimizer
Phase 5 Export signals to Excel/PDF + AI chatbot assistant (optional)
✅ Final Output Example

{
"pair": "EUR/USD OTC",
"signal": "CALL",
"confidence": 96,
"reasons": [
"RSI oversold (<30)",
"MACD crossover",
"EMA 5 > EMA 13"
],
"timestamp": "2025-05-31 14:59:00"
}

✅ This prompt includes:

All logic  

All indicators  

Full tech stack  

Development phases  

Real-world use cases