Market Condition Monitoring

Understanding market conditions is essential for profitable trading. Nova Agents continuously analyze and interpret a wide array of market metrics to help you stay informed and ahead of the curve.

• Volume Spikes: Detect sudden increases in trading volume to identify opportunities or potential risks.

• Market Sentiment: Monitor social media chatter, news, and on-chain activity to gauge the community’s mood around specific tokens or trends.

• Liquidity Insights: Ensure you have clear data on market liquidity before committing to trades.

With Nova Agents, you’ll always have a finger on the pulse of the market, enabling you to adapt quickly to changing conditions.

use std::collections::HashMap;

// Represents token metrics
#[derive(Debug)]
struct TokenMetrics {
    name: String,
    trading_volume: f64,
    sentiment_score: f64,  // Positive: >0, Negative: <0
    liquidity: f64,        // Liquidity in the market
}

// Tracker for market conditions
struct MarketTracker {
    tokens: HashMap<String, TokenMetrics>, // Token name to metrics
}

impl MarketTracker {
    // Initialize the tracker
    fn new() -> Self {
        MarketTracker {
            tokens: HashMap::new(),
        }
    }

    // Update token metrics
    fn update_token_metrics(&mut self, name: String, volume: f64, sentiment: f64, liquidity: f64) {
        self.tokens.insert(
            name.clone(),
            TokenMetrics {
                name,
                trading_volume: volume,
                sentiment_score: sentiment,
                liquidity,
            },
        );
    }

    // Detect volume spikes
    fn detect_volume_spikes(&self) {
        for (_, token) in &self.tokens {
            if token.trading_volume > 10_000.0 {
                println!(
                    "Volume Spike Detected for {}:\n  Trading Volume: {:.2}",
                    token.name, token.trading_volume
                );
            }
        }
    }

    // Analyze market sentiment
    fn analyze_sentiment(&self) {
        for (_, token) in &self.tokens {
            let sentiment = if token.sentiment_score > 0.5 {
                "Positive"
            } else if token.sentiment_score < -0.5 {
                "Negative"
            } else {
                "Neutral"
            };

            println!(
                "Market Sentiment for {}:\n  Sentiment Score: {:.2} ({})",
                token.name, token.sentiment_score, sentiment
            );
        }
    }

    // Assess liquidity
    fn assess_liquidity(&self) {
        for (_, token) in &self.tokens {
            if token.liquidity < 5000.0 {
                println!(
                    "Low Liquidity Warning for {}:\n  Liquidity: {:.2}",
                    token.name, token.liquidity
                );
            }
        }
    }

    // Perform a comprehensive market analysis
    fn analyze_market(&self) {
        println!("Market Analysis:");
        self.detect_volume_spikes();
        self.analyze_sentiment();
        self.assess_liquidity();
    }
}

fn main() {
    let mut tracker = MarketTracker::new();

    // Simulate market metrics for tokens
    tracker.update_token_metrics("TokenA".to_string(), 15_000.0, 0.8, 10_000.0); // Volume Spike, Positive Sentiment
    tracker.update_token_metrics("TokenB".to_string(), 3000.0, -0.6, 4000.0);    // Low Liquidity, Negative Sentiment
    tracker.update_token_metrics("TokenC".to_string(), 8000.0, 0.2, 7000.0);     // Neutral Sentiment

    // Perform market analysis
    tracker.analyze_market();
}