多くの方からのご要望により、現在、当社の機械学習コードやパッケージのいくつかの例を提供するために懸命に取り組んでいます。

TensorFlow、PyTorch、Keras、Numpy、Pandas、そして多くの.NETパッケージをcTrader内で利用開始できます。

私たちの使命は、cTrader内での機械学習を誰にとってもより簡単にすることです。

ハッピー・ハンティング！

*** このコードは何も取引しません（データを出力するだけです）。これは、当社の機械学習パッケージを使用して独自のAIモデルを作成し始める方法のサンプルコードに過ぎません。

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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("Version 1.01", DefaultValue = "Version 1.01")]
        public string Version { get; set; }

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

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

        [Parameter("Method Name", DefaultValue = MethodName.DataSplitPrints)]
        public MethodName Mode { get; set; }
        public enum MethodName
        {
            DataSplitPrints,
            PandasPrints,
            NDArrayPrints
        }
        
        protected override void OnStart()
        {
            // インジケーターを初期化します
        }

        protected override void OnBar()
        {
            try
            {
                if (Mode == MethodName.DataSplitPrints)
                {
                    DataSplitPrints();
                }
                else if (Mode == MethodName.PandasPrints)
                {
                    PandasPrints();
                }
                else if (Mode == MethodName.NDArrayPrints)
                {
                    NDArrayPrints();
                }
            }
            catch (Exception ex)
            {
                Print($"エラー: {ex.Message}");
                if (ex.InnerException != null)
                {
                    Print($"内部例外: {ex.InnerException.Message}");
                    throw;
                }
            }
        }

        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;
        }
        
        /// NumSharp データ分割プリント
        public void DataSplitPrints()
        {
            // モデルの期待する入力形状に合わせて入力データをリシェイプ
            //var inputShape = new Shape(-1, BarsRequired, 5);
            NDArray inputData = np.array<float>(GetDataSet());
            Print("入力NDarray: " + string.Join(", ", inputData));
            
            // モデルが期待するターゲット形状に合わせてターゲットデータをリシェイプ
            //var targetShape = new Shape(-1, 5);
            NDArray targetData = np.array<float>(GetTargetDataSet());
            Print("ターゲットNDarray: " + string.Join(", ", targetData));
            
            // データをトレーニングセットとテストセットに分割
            int testSize = (int)(0.2 * inputData.shape[0]); // テスト用に20%
            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("X_train データ: " + string.Join(", ", x_train));
            Print("X_test データ: " + string.Join(", ", x_test));
            Print("Y_train データ: " + string.Join(", ", y_train));
            Print("Y_test データ: " + string.Join(", ", y_test));
        }
        
        /// PandasNet プリント
        public void PandasPrints()
        {
            // float[,] を List<Series> に変換
            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()));
            }
            // DataFrameを作成
            DataFrame inputDataFrame = new DataFrame(inputSeriesList);
            DataFrame targetDataFrame = new DataFrame(targetSeriesList);
            
            Print("入力DataFrame: " + inputDataFrame);
            Print("ターゲットDataFrame: " + targetDataFrame);
            
            //Print("Input DataFrame: " + string.Join(", ", inputDataFrame));
            //Print("Target DataFrame: " + string.Join(", ", targetDataFrame));
        }
        
        /// シンプルなNumSharp NDArraysプリント
        public void NDArrayPrints()
        {
            if (Bars.ClosePrices.Count < BarsRequired)
                return;

            try
            {
                // 入力データ float[,] を呼び出す
                float[,] inputData = GetDataSet();

                // NDArrayに変換し (BarsRequired, 5) にリシェイプ
                NDArray inputNDArray = np.array(inputData);   // NumSharp
                Print("入力NumSharp NDarrayデータ : " + string.Join(", ", inputNDArray));
                Print("入力NumSharp NDarray形状: " + string.Join(", ", inputNDArray.shape));
                
                int expectedLength = BarsRequired * 5;
                Print($"期待されるNumSharp NDarrayの長さ: {expectedLength}");
                Print($"入力NumSharp NDarrayのサイズ: {inputNDArray.size}");

                if (inputNDArray.size != expectedLength)
                {
                    Print($"長さ不一致: 期待長さ {expectedLength}、しかしサイズは {inputNDArray.size} でした");
                    return;
                }
            }
            catch (Exception ex)
            {
                Print("例外: " + ex.Message);
                Print("スタックトレース: " + ex.StackTrace);

                Exception innerException = ex.InnerException;
                while (innerException != null)
                {
                    Print("内部例外: " + innerException.Message);
                    Print("内部例外スタックトレース: " + innerException.StackTrace);
                    innerException = innerException.InnerException;
                }
            }
        }
    }
}