nyxs_owl 0.5.0

A comprehensive Rust library for trading, forecasting, and financial analysis
Documentation 
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use chrono::{Datelike, Duration, TimeZone, Timelike, Utc};
use forecast_trade::data::TimeSeriesData;
use forecast_trade::models::oxidiviner::ExponentialSmoothingAdapter;
use forecast_trade::models::ForecastModel;

fn main() -> Result<(), Box<dyn std::error::Error>> {
    println!("Forecast Trade: Basic Forecasting Example");
    println!("=========================================\n");

    // Create sample data
    println!("Creating sample data...");
    let daily_data = create_sample_daily_data();
    let minute_data = create_sample_minute_data();

    println!(
        "Sample data created: {} daily points, {} minute points\n",
        daily_data.len(),
        minute_data.len()
    );

    // Create models with different parameters for daily and minute data
    println!("Training models...");

    // Daily model - longer smoothing (less reactive)
    let daily_model = ExponentialSmoothingAdapter::new(0.2)?;

    // Minute model - shorter smoothing (more reactive)
    let minute_model = ExponentialSmoothingAdapter::new(0.4)?;

    println!("Models created successfully\n");

    // Generate forecasts
    println!("Generating forecasts...");
    let daily_forecast = daily_model.forecast(&daily_data, 5)?;
    println!("Daily forecast (5 days): {:?}", daily_forecast.values);

    // For minute data, forecast 30 minutes ahead
    let minute_forecast = minute_model.forecast(&minute_data, 30)?;
    println!("Minute forecast (30 minutes): {:?}", minute_forecast.values);

    println!("\nForecasting complete!");

    // Calculate forecast validation metrics if available
    if let Some(error_metrics) = &daily_forecast.error_metrics {
        println!("\nDaily forecast metrics:");
        println!("  MAE: {:.4}", error_metrics.mae);
        println!("  MSE: {:.4}", error_metrics.mse);
        println!("  RMSE: {:.4}", error_metrics.rmse);
        println!("  MAPE: {:.4}%", error_metrics.mape * 100.0);
    }

    if let Some(error_metrics) = &minute_forecast.error_metrics {
        println!("\nMinute forecast metrics:");
        println!("  MAE: {:.4}", error_metrics.mae);
        println!("  MSE: {:.4}", error_metrics.mse);
        println!("  RMSE: {:.4}", error_metrics.rmse);
        println!("  MAPE: {:.4}%", error_metrics.mape * 100.0);
    }

    println!("\nSummary:");
    println!("1. Different alpha values are used for daily vs. minute data");
    println!("2. Daily data uses alpha=0.2 for smoother forecasts");
    println!("3. Minute data uses alpha=0.4 for more responsive forecasts");
    println!("4. Forecast metrics help evaluate model performance");

    Ok(())
}

/// Create sample daily data with a trend and some seasonality
fn create_sample_daily_data() -> TimeSeriesData {
    let mut dates = Vec::with_capacity(100);
    let mut prices = Vec::with_capacity(100);

    let start_date = Utc.with_ymd_and_hms(2023, 1, 1, 0, 0, 0).unwrap();

    // Create 100 days of data with a trend and some seasonality
    let mut price = 100.0;
    let trend = 0.05; // 0.05 points per day upward trend

    for i in 0..100 {
        let current_date = start_date + Duration::days(i);
        dates.push(current_date);

        // Add some weekly seasonality
        let day_of_week = current_date.weekday().num_days_from_monday() as f64;
        let seasonality = (day_of_week * std::f64::consts::PI / 7.0).sin() * 2.0;

        // Add some noise
        let noise = (i as f64 * 0.1).sin() * 1.0;

        price = price + trend + seasonality + noise;
        prices.push(price);
    }

    TimeSeriesData::new(dates, prices).unwrap()
}

/// Create sample minute data with higher volatility
fn create_sample_minute_data() -> TimeSeriesData {
    let mut dates = Vec::with_capacity(500);
    let mut prices = Vec::with_capacity(500);

    let start_date = Utc.with_ymd_and_hms(2023, 1, 1, 9, 0, 0).unwrap();

    // Create 500 minutes of data (about 8 hours)
    let mut price = 100.0;
    let trend = 0.002; // Smaller trend per minute

    for i in 0..500 {
        let current_date = start_date + Duration::minutes(i);
        dates.push(current_date);

        // Add some intraday pattern (U-shape)
        let minute_of_day = current_date.hour() * 60 + current_date.minute();
        let normalized_time = minute_of_day as f64 / (24.0 * 60.0);
        let intraday = ((normalized_time - 0.5) * 2.0).powi(2) * 1.0;

        // Add higher frequency noise
        let noise = (i as f64 * 0.5).sin() * 0.2 + (i as f64 * 0.3).cos() * 0.3;

        price = price + trend + intraday + noise;
        prices.push(price);
    }

    TimeSeriesData::new(dates, prices).unwrap()
}