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Trading Bot Specification Trend-Based USD Profits

MQL5 Эксперты Forex Отладка робота/индикатора

Техническое задание

1. Objective

A fully automated trading bot that:
• Trades USD-quoted assets (forex pairs like EUR/USD, GBP/USD, crypto markets with USD trading pairs, and USD-denominated stocks/ETFs).
• Uses real-time data to detect trends and place entries/exits based on multiple strategies.
• Maximizes profit while maintaining robust risk controls and drawdown limits.

2. High-Level Architecture

Components

Component Purpose
Data Ingestion Engine Collects real-time price data (ticks & candles)
Signal Generation Engine Applies multiple strategy algorithms
Risk & Money Management Module Size positions & control risk
Execution Engine Sends orders to broker/API
Portfolio Manager Tracks P/L, open positions, equity curve
Database/Logger Stores trade history, signals, performance logs
Monitoring UI / Alerts Real-time dashboard & notifications

 Supported APIs
• Market Data: Binance, Coinbase Pro, OANDA, Alpaca, Interactive Brokers, etc.
• Order Execution: Same API provider with trading permissions.
• Websocket + REST combination for low latency.

3. *Data Input Requirements*

Market Data Type Interval
Forex OHLC + tick 1m, 5m, 15m, 1h
Crypto OHLC + tick 1m, 5m, 15m, 1h
Stocks/ETFs OHLC 1m, 5m, 15m

Data should include:
• Bid/Ask prices
• Volume
• Time
• Spread

4. *Strategy Framework*

Each strategy should generate a signal with:
• ENTRY_SIGNAL
• EXIT_SIGNAL
• CONFIDENCE_SCORE (0–1)
• RISK_SCORE (0–1)
• Aggregated into a final decision

Strategies run in parallel — final trade decision is weighted consensus.

 5. *Core Trading Strategies*

*Trend Following (Primary)*
• Based on EMA/SMA crossovers
• Example: EMA(8) crosses above EMA(21) → LONG
• EMA(8) crosses below EMA(21) → SHORT
• Confirmation via MACD direction and slope
• Longer trend filter: SMA(50) and SMA(200)

📌 Works well in trending markets

 *Momentum Breakouts*
• Detect strong breakouts above resistance / below support
• Filters:
• RS (Relative Strength) over short timeframe
• Volume spike filtering
• Entry:
• Price closes above resistance + volume > average

📌 Best for volatile markets

*Relative Strength Index (RSI) Strategy*
• RSI 14 period
• Buy when RSI < 30 and rising
• Sell when RSI > 70 and falling
• Optional exit:
• RSI crosses back through 50

📌 Helps avoid overbought/oversold traps

*Bollinger Band Reversion*
• Entry when price touches lower band and oversold
• Exit when price reverts to mid-band or upper
• Only valid if trend is neutral

📌 Works in sideways markets

*Breakout from Consolidation*
• Detect consolidation via low ATR & narrow range
• Place either buy or sell stop
• Use ATR for dynamic stop loss

📌 Captures explosive moves

6. *Risk & Money Management*

Position Sizing
• Fixed Fractional Risk: e.g., 1–2% of capital per trade
• Maximum exposure across all positions: 10–15% of account balance

Stop Loss
• ATR-based dynamic stops
• Stop = Entry ± (1.5 × ATR)
• Or chart-based support/resistance level

Take Profit
• Risk:Reward target 1:2 minimum
• Dynamic trailing stop

Daily Loss Limit
• Stop trading for the day if drawdown > 3% of capital

Correlation Filtering
• Avoid similar signals on strongly correlated pairs to reduce concentration risk

*7. Execution & Order Types*
• Limit orders when possible
• Market orders for fast-moving breakouts
• OCO orders for combined stop & take profit
• Slippage tolerance control

*8. Backtesting & Optimization*
• Historical data backtest with walk-forward validation
• Evaluate key metrics:
• CAGR (Annualized Return)
• Max Drawdown
• Win Rate
• Profit Factor
• Sharpe Ratio

Optimization
• Grid search on key parameters:
• EMA lengths
• RSI thresholds
• ATR multipliers
• Ensure out-of-sample stability

9. *Real-Time Monitoring & Alerts*

Alerts for:
• Filled orders
• Stop loss triggered
• Take profit hit
• Drawdown limit reached
• System errors

Delivery:
• SMS/Email/Telegram/Discord

10. *Tech Stack Options*

Languages
• Python (Pandas, NumPy, TA-Lib)
• Node.js (if low latency needed)
• Optional C++/Rust for core execution layer (high throughput)

Databases
• SQLite for local storage
• TimescaleDB/PostgreSQL for cloud scale

Hosting
• Cloud (AWS/GCP/Azure)
• On-prem VPS

11. *Reporting & Logging*

Daily/Weekly/Monthly reports:
• Equity curve graphs
• Drawdown chart
• Win/loss by strategy
• Pair/Asset performance breakdown

Store:
• Trade logs
• Strategy signal logs
• Performance snapshots

12. *Strategy Fusion Logic*

Final decision uses signal weighting model:

Score = Σ (Strategy_i_signal * strategy_weight_i * Confidence_i)

Only trigger trade if:
• Score > Entry threshold (buy)
• Score < Exit threshold (sell)

Weights can be dynamic and adjusted via performance.

*Bonus: AI/ML Enhancements*

Optional modules:
• Reinforcement learning for adaptive entry timing
• Clustering to detect regime shifts (Trend → Range)
• Sentiment analysis feed for crypto/stock markets

*Example Execution Flow*
1. Fetch latest market data
2. Run all strategies → produce signals
3. Fuse signals → produce final trade decision
4. Compute position size & risk parameters
5. Execute orders via broker API
6. Monitor positions & update trailing stops
7. Log trades + send alerts.

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