Seperti yang diminta oleh banyak dari Anda, kami sekarang bekerja keras untuk menyediakan contoh beberapa kode dan paket pembelajaran mesin kami.

TensorFlow, PyTorch, Keras, Numpy, Pandas dan banyak paket .NET lainnya untuk memulai di dalam cTrader.

Misi kami adalah membuat Pembelajaran Mesin di dalam cTrader menjadi lebih mudah untuk semua orang.

Selamat berburu!

*** Kode ini tidak melakukan perdagangan apapun (hanya mencetak data dll). Ini hanyalah contoh kode bagaimana Anda dapat mulai membuat model AI Anda sendiri menggunakan paket Pembelajaran Mesin kami.

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using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using cAlgo.API;
using cAlgo.API.Collections;
using cAlgo.API.Indicators;
using cAlgo.API.Internals;

using NumSharp;
using np = NumSharp.np;
using Shape = NumSharp.Shape;

using PandasNet;
using static PandasNet.PandasApi;

namespace cAlgo.Robots
{
    [Robot(TimeZone = TimeZones.UTC, AccessRights = AccessRights.None)]
    public class E7BBKGNumSharpSample : Robot
    {
        [Parameter("Versi 1.01", DefaultValue = "Versi 1.01")]
        public string Version { get; set; }

        [Parameter("Sumber")]
        public DataSeries Source { get; set; }

        [Parameter("Jumlah Bar Diperlukan", DefaultValue = 50, MinValue = 1, MaxValue = 10000, Step = 1)]
        public int BarsRequired { get; set; }

        [Parameter("Nama Metode", DefaultValue = MethodName.DataSplitPrints)]
        public MethodName Mode { get; set; }
        public enum MethodName
        {
            DataSplitPrints,
            PandasPrints,
            NDArrayPrints
        }
        
        protected override void OnStart()
        {
            // Inisialisasi indikator apapun
        }

        protected override void OnBar()
        {
            coba
            {
                jika (Mode == MethodName.DataSplitPrints)
                {
                    DataSplitPrints();
                }
                lain jika (Mode == MethodName.PandasPrints)
                {
                    PandasPrints();
                }
                lain jika (Mode == MethodName.NDArrayPrints)
                {
                    NDArrayPrints();
                }
            }
            tangkap (Exception ex)
            {
                Print($"Kesalahan: {ex.Message}");
                jika (ex.InnerException != null)
                {
                    Print($"Inner Exception: {ex.InnerException.Message}");
                    lempar;
                }
            }
        }

        private float[,] GetDataSet()
        {
            int startBar = Bars.ClosePrices.Count - BarsRequired;
            float[,] inputSignals = new float[BarsRequired, 5];

            for (int i = 0; i < BarsRequired; i++)
            {
                int barIndex = startBar + i;
                inputSignals[i, 0] = (float)Bars.OpenPrices[barIndex];
                inputSignals[i, 1] = (float)Bars.HighPrices[barIndex];
                inputSignals[i, 2] = (float)Bars.LowPrices[barIndex];
                inputSignals[i, 3] = (float)Bars.ClosePrices[barIndex];
                inputSignals[i, 4] = (float)Bars.TickVolumes[barIndex];
            }
            return inputSignals;
        }
        
        private float[,] GetTargetDataSet()
        {
            int startBar = Bars.ClosePrices.Count - BarsRequired;
            float[,] inputSignals = new float[BarsRequired, 5];

            for (int i = 0; i < BarsRequired; i++)
            {
                int barIndex = startBar + i;
                inputSignals[i, 0] = (float)Bars.OpenPrices[barIndex];
                inputSignals[i, 1] = (float)Bars.HighPrices[barIndex];
                inputSignals[i, 2] = (float)Bars.LowPrices[barIndex];
                inputSignals[i, 3] = (float)Bars.ClosePrices[barIndex];
                inputSignals[i, 4] = (float)Bars.TickVolumes[barIndex];
            }
            return inputSignals;
        }
        
        /// Cetakan Pembagian Data NumSharp
        public void DataSplitPrints()
        {
            // Mengubah bentuk data input agar sesuai dengan bentuk input yang diharapkan model
            //var inputShape = new Shape(-1, BarsRequired, 5);
            NDArray inputData = np.array<float>(GetDataSet());
            Print("Input NDarray: " + string.Join(", ", inputData));
            
            // Mengubah bentuk data target agar sesuai dengan bentuk target yang diharapkan model
            //var targetShape = new Shape(-1, 5);
            NDArray targetData = np.array<float>(GetTargetDataSet());
            Print("Target NDarray: " + string.Join(", ", targetData));
            
            // Membagi data menjadi set pelatihan dan pengujian
            int testSize = (int)(0.2 * inputData.shape[0]); // 20% untuk pengujian
            var (x_train, x_test) = (inputData[$":{inputData.shape[0] - testSize}"], inputData[$"{inputData.shape[0] - testSize}:"]);
            var (y_train, y_test) = (targetData[$":{targetData.shape[0] - testSize}"], targetData[$"{targetData.shape[0] - testSize}:"]);
            
            Print("Data X_train: " + string.Join(", ", x_train));
            Print("Data X_test: " + string.Join(", ", x_test));
            Print("Data Y_train: " + string.Join(", ", y_train));
            Print("Data Y_test: " + string.Join(", ", y_test));
        }
        
        /// Cetakan PandasNet
        public void PandasPrints()
        {
            // Mengonversi float[,] ke List<Series>
            var inputData = GetDataSet();
            var targetData = GetTargetDataSet();
            
            var inputSeriesList = new List<Series>();
            var targetSeriesList = new List<Series>();
            
            for (int col = 0; col < inputData.GetLength(1); col++)
            {
                List<float> columnData = new List<float>();
                for (int row = 0; row < inputData.GetLength(0); row++)
                {
                    columnData.Add(inputData[row, col]);
                }
                inputSeriesList.Add(new Series(columnData.ToArray()));
            }
            
            for (int col = 0; col < targetData.GetLength(1); col++)
            {
                List<float> columnData = new List<float>();
                for (int row = 0; row < targetData.GetLength(0); row++)
                {
                    columnData.Add(targetData[row, col]);
                }
                targetSeriesList.Add(new Series(columnData.ToArray()));
            }
            
            // Membuat DataFrame
            DataFrame inputDataFrame = new DataFrame(inputSeriesList);
            DataFrame targetDataFrame = new DataFrame(targetSeriesList);
            
            Print("DataFrame Input: " + inputDataFrame);
            Print("DataFrame Target: " + targetDataFrame);
            
            //Print("DataFrame Input: " + string.Join(", ", inputDataFrame));
            //Print("DataFrame Target: " + string.Join(", ", targetDataFrame));
        }
        
        /// Cetakan NDArrays NumSharp Sederhana
        public void NDArrayPrints()
        {
            jika (Bars.ClosePrices.Count < BarsRequired)
                kembali;

            coba
            {
                // Memanggil Data Input float[,] Anda
                float[,] inputData = GetDataSet();

                // Mengonversi ke NDArray dan mengubah bentuk menjadi (BarsRequired, 5)
                NDArray inputNDArray = np.array(inputData);   // NumSharp
                Print("Data NumSharp NDarray Input : " + string.Join(", ", inputNDArray));
                Print("Bentuk NumSharp NDarray Input: " + string.Join(", ", inputNDArray.shape));
                
                int expectedLength = BarsRequired * 5;
                Print($"Panjang NumSharp NDarray yang Diharapkan: {expectedLength}");
                Print($"Ukuran NumSharp NDarray Input: {inputNDArray.size}");

                jika (inputNDArray.size != expectedLength)
                {
                    Print($"Ketidaksesuaian Panjang: Panjang yang Diharapkan {expectedLength}, tetapi mendapatkan Ukuran {inputNDArray.size}");
                    kembali;
                }
            }
            tangkap (Exception ex)
            {
                Print("Exception: " + ex.Message);
                Print("StackTrace: " + ex.StackTrace);

                Exception innerException = ex.InnerException;
                while (innerException != null)
                {
                    Print("Inner Exception: " + innerException.Message);
                    Print("Inner Exception StackTrace: " + innerException.StackTrace);
                    innerException = innerException.InnerException;
                }
            }
        }
    }
}