Implement ML-powered autonomous trading platform with ensemble models and real-time dashboard

[Copilot]

Implements an autonomous AI trading system with ensemble ML models, sophisticated risk management, and real-time monitoring dashboard.

Architecture

ML Trading Engine (trading_bot.py)

• Ensemble architecture: LSTM (temporal), Random Forest (MACD), XGBoost (trend) with weighted voting (0.4/0.3/0.3)
• 20+ technical indicators: SMA/EMA (5-200 period), RSI, MACD, Bollinger Bands, ATR, volume analysis
• Kelly Criterion position sizing with volatility adjustment
• Dynamic stop-loss (2%) and take-profit (5%) with portfolio risk limits

Data Pipeline (data_pipeline.py)

• Market data ingestion with quality validation
• Feature engineering and market regime detection (trending/ranging/volatile)
• OHLCV consistency checks and outlier detection

Dashboard (dashboard.py)

• Real-time performance tracking: Sharpe ratio, win rate, max drawdown, P&L
• Portfolio and trade history visualization
• HTML + console interfaces

Usage

from trading_bot import AITradingBot, generate_sample_data

bot = AITradingBot('config.json')
market_data = generate_sample_data('AAPL', days=252)
processed_data = bot.process_market_data(market_data)

for i in range(len(processed_data)):
    bot.execute_trading_logic(processed_data.iloc[i], 'AAPL')
    bot.update_portfolio_value({'AAPL': processed_data.iloc[i]['close']})

print(bot.get_performance_metrics())

Configuration

All parameters configurable via config.json:

• ML model weights and thresholds
• Risk limits (max position size, portfolio risk, win rate assumptions)
• Trading costs (commission, slippage)
• Technical indicator periods

Dashboard

Shows real-time metrics, portfolio positions, trade history, and system features. Generated via python dashboard.py.

Dependencies

• numpy>=1.21.0
• pandas>=1.3.0

Original prompt

State-of-the-art cutting-edge machine learning algorithms augmented intelligence autonomous AI Trading Bot Platform System and Dashboard

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