using KLHZ.Trader.Core.Contracts.Messaging.Dtos.Intarfaces;
using KLHZ.Trader.Core.Contracts.Messaging.Interfaces;
using MathNet.Filtering.Kalman;
using MathNet.Numerics.LinearAlgebra;
using Microsoft.Extensions.Hosting;
using System.Threading.Channels;

namespace KLHZ.Trader.Core.Math.Declisions.Dtos.Services
{
    public class KalmanPredictor : IHostedService
    {
        private readonly double r = 1000.0;  // Measurement covariance
        private readonly PriceHistoryCacheUnit _cache = new PriceHistoryCacheUnit("");
        private readonly Channel<INewPrice> _messages = Channel.CreateUnbounded<INewPrice>();
        private readonly IDataBus _dataBus;

        private DiscreteKalmanFilter? _dkf;

        public KalmanPredictor(IDataBus bus)
        {
            _dataBus = bus;
            bus.AddChannel(nameof(KalmanPredictor), _messages);
            _ = ProcessMessages();
        }

        public Task StartAsync(CancellationToken cancellationToken)
        {
            return Task.CompletedTask;
        }

        public Task StopAsync(CancellationToken cancellationToken)
        {
            return Task.CompletedTask;
        }

        private async Task Init()
        {

        }

        private async Task ProcessCalman(INewPrice message)
        {
            var H = Matrix<double>.Build.Dense(2, 2, new[] { 1d, 0d, 0d, 1d }); // Measurement matrix
            var length = _cache.Length;

            if (length > 2 && _dkf != null)
            {
                var data = await _cache.GetData();

                var dt = (data.timestamps[data.prices.Length - 1] - data.timestamps[data.prices.Length - 2]).TotalSeconds;
                if (dt < 0.0000001 || dt > 1800) return;
                var F = Matrix<double>.Build.Dense(2, 2, new[] { 1d, 0d, dt, 1 });   // State transition matrix
                var R = Matrix<double>.Build.Dense(2, 2, new[] { r, r / dt, r / dt, 2 * r / (dt * dt) });
                _dkf.Predict(F);

                var state = _dkf.State;
                var value = state[0, 0] + dt * state[1, 0];
                if (!double.IsNaN(value))
                {
                    await _dataBus.BroadcastProcessedPrice(new ProcessedPrice()
                    {
                        Figi = message.Figi,
                        Processor = nameof(KalmanPredictor) + "_predict",
                        Ticker = message.Ticker,
                        IsHistoricalData = message.IsHistoricalData,
                        Time = message.Time,
                        Value = (decimal)value,
                    });
                }


                var dprice = data.prices[data.prices.Length - 1] - data.prices[data.prices.Length - 2];
                var k = dprice / dt;
                var b = data.prices[data.prices.Length - 2];
                var z = Matrix<double>.Build.Dense(2, 1, new[] { b, k });

                _dkf.Update(z, H, R);

                var state2 = _dkf.State;
                var value2 = state2[0, 0] + dt * state2[1, 0];
                if (!double.IsNaN(value))
                {
                    await _dataBus.BroadcastProcessedPrice(new ProcessedPrice()
                    {
                        Figi = message.Figi,
                        Processor = nameof(KalmanPredictor) + "_state",
                        Ticker = message.Ticker,
                        IsHistoricalData = message.IsHistoricalData,
                        Time = message.Time,
                        Value = (decimal)value,
                    });
                }
            }
            else if (length >= 2)
            {
                var data = await _cache.GetData();
                var dprice = data.prices[data.prices.Length - 1] - data.prices[data.prices.Length - 2];
                var dt = (data.timestamps[data.prices.Length - 1] - data.timestamps[data.prices.Length - 2]).TotalSeconds;
                if (dt < 0.0000001) return;
                var k = dprice / dt;
                var b = data.prices[data.prices.Length - 2];


                Matrix<double> x0 = Matrix<double>.Build.Dense(2, 1, new[] { b, k });
                Matrix<double> P0 = Matrix<double>.Build.Dense(2, 2, new[] { r, r / dt, r / dt, 2 * r / (dt * dt) });

                _dkf = new DiscreteKalmanFilter(x0, P0);
            }
        }

        private async Task ProcessMovAv(INewPrice message, int window)
        {
            var data = await _cache.GetData();
            if (data.prices.Length < window) return;
            var sum = 0f;
            var count = 0;
            for (int i = 1; (i <= window); i++)
            {
                sum += data.prices[data.prices.Length - i];
                count++;
            }

            await _dataBus.BroadcastProcessedPrice(new ProcessedPrice()
            {
                Figi = message.Figi,
                Processor = nameof(KalmanPredictor) + "_mov_av_window" + window.ToString(),
                Ticker = message.Ticker,
                IsHistoricalData = message.IsHistoricalData,
                Time = message.Time,
                Value = (decimal)(sum / count),
            });
        }

        private async Task ProcessTimeWindow(INewPrice message, int window)
        {
            var data = await _cache.GetData();
            var sum = data.prices[data.prices.Length - 1];
            var count = 1;
            var startTime = data.timestamps[data.prices.Length - 1];
            for (int i = 2; (i < data.prices.Length && (startTime - data.timestamps[data.prices.Length - i]) < TimeSpan.FromSeconds(window)); i++)
            {
                sum += data.prices[data.prices.Length - i];
                count++;
            }

            await _dataBus.BroadcastProcessedPrice(new ProcessedPrice()
            {
                Figi = message.Figi,
                Processor = nameof(KalmanPredictor) + "_timeWindow" + window.ToString(),
                Ticker = message.Ticker,
                IsHistoricalData = message.IsHistoricalData,
                Time = message.Time,
                Value = (decimal)(sum / count),
            });

            var diffValue = System.Math.Abs((decimal)(sum / count) - message.Value);
            await _dataBus.BroadcastProcessedPrice(new ProcessedPrice()
            {
                Figi = message.Figi,
                Processor = nameof(KalmanPredictor) + "_diff" + window.ToString(),
                Ticker = message.Ticker,
                IsHistoricalData = message.IsHistoricalData,
                Time = message.Time,
                Value = diffValue > 3 ? diffValue : 0,
            });
        }

        private async Task ProcessMessages()
        {


            while (await _messages.Reader.WaitToReadAsync())
            {
                var message = await _messages.Reader.ReadAsync();
                await _cache.AddData(message);
                try
                {
                    //await ProcessCalman(message);
                    await ProcessMovAv(message, 3);
                    await ProcessTimeWindow(message, 5);
                    await ProcessTimeWindow(message, 15);
                    await ProcessTimeWindow(message, 120);
                }
                catch (Exception ex)
                {

                }
            }
        }
    }
}