Technical Architecture Documentation

interface Order {
    id: string;
    type: 'market' | 'limit' | 'stop';
    side: 'buy' | 'sell';
    size: number;
    price?: number;
    leverage: number;
    timestamp: number;
}

class OrderBook {
    private bids: Order[] = [];
    private asks: Order[] = [];
    
    public matchOrder(order: Order): Trade[] {
        const trades: Trade[] = [];
        const oppositeBook = order.side === 'buy' ? this.asks : this.bids;
        
        while (order.size > 0 && oppositeBook.length > 0) {
            const match = oppositeBook[0];
            if (this.canMatch(order, match)) {
                const trade = this.executeTrade(order, match);
                trades.push(trade);
            }
        }
        return trades;
    }
}

const ws = new WebSocket('wss://api.marbleland.io/ws');

ws.onopen = () => {
    // Subscribe to price feeds
    ws.send(JSON.stringify({
        type: 'subscribe',
        channels: ['prices', 'trades', 'orderbook'],
        markets: ['BTC-USD', 'ETH-USD']
    }));
};

ws.onmessage = (event) => {
    const data = JSON.parse(event.data);
    // Handle real-time updates
    console.log('Price update:', data);
};

import tensorflow as tf
from sklearn.ensemble import RandomForestClassifier
import numpy as np

class PricePredictionModel:
    def __init__(self):
        self.model = self._build_lstm_model()
        
    def _build_lstm_model(self):
        model = tf.keras.Sequential([
            tf.keras.layers.LSTM(128, return_sequences=True),
            tf.keras.layers.LSTM(64),
            tf.keras.layers.Dense(32, activation='relu'),
            tf.keras.layers.Dense(1)
        ])
        return model
    
    def predict(self, market_data):
        # Preprocess and predict
        features = self.extract_features(market_data)
        prediction = self.model.predict(features)
        return prediction

class TradingModelEnsemble:
    def __init__(self):
        self.price_predictor = LSTMPriceModel()
        self.risk_assessor = RandomForestRisk()
        self.sentiment_analyzer = BERTSentiment()
        
    def predict_market_direction(self, features):
        # Ensemble prediction combining multiple models
        price_pred = self.price_predictor.predict(features['price_data'])
        risk_score = self.risk_assessor.assess(features['market_data'])
        sentiment = self.sentiment_analyzer.analyze(features['news_data'])
        
        return self._ensemble_prediction(price_pred, risk_score, sentiment)