E7 BBKG NumSharp Sample
"E7 BBKG NumSharp Sample" โลโก้
03/09/2025
80
Desktop, Mobile, Web
"E7 BBKG NumSharp Sample" ภาพที่อัปโหลด

As requested by many of you, we are now working hard to provide examples of some of our machine learning code and packages.

TensorFlow, PyTorch, Keras, Numpy, Pandas and many more .NET packages to get going inside of cTrader.

Our mission is to make Machine Learning inside cTrader easier for everyone.

Happy hunting!

*** This code does not trade anything (it only prints out data etc). It is simply sample code of how you can start creating your own AI models using our Machine Learning packages.

.......................................................

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()
        {
            // Initialize any indicators
        }

        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($"Error: {ex.Message}");
                if (ex.InnerException != null)
                {
                    Print($"Inner Exception: {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 Data Split Prints
        public void DataSplitPrints()
        {
            // Reshape input data to match the model's expected input shape
            //var inputShape = new Shape(-1, BarsRequired, 5);
            NDArray inputData = np.array<float>(GetDataSet());
            Print("Input NDarray: " + string.Join(", ", inputData));
            
            // Reshape target data to match the target shape expected by the model
            //var targetShape = new Shape(-1, 5);
            NDArray targetData = np.array<float>(GetTargetDataSet());
            Print("Target NDarray: " + string.Join(", ", targetData));
            
            // Split data into training and test sets
            int testSize = (int)(0.2 * inputData.shape[0]); // 20% for testing
            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 data: " + string.Join(", ", x_train));
            Print("X_test data: " + string.Join(", ", x_test));
            Print("Y_train data: " + string.Join(", ", y_train));
            Print("Y_test data: " + string.Join(", ", y_test));
        }
        
        /// PandasNet Prints
        public void PandasPrints()
        {
            // Convert float[,] to 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()));
            }
            
            // Create DataFrames
            DataFrame inputDataFrame = new DataFrame(inputSeriesList);
            DataFrame targetDataFrame = new DataFrame(targetSeriesList);
            
            Print("Input DataFrame: " + inputDataFrame);
            Print("Target DataFrame: " + targetDataFrame);
            
            //Print("Input DataFrame: " + string.Join(", ", inputDataFrame));
            //Print("Target DataFrame: " + string.Join(", ", targetDataFrame));
        }
        
        /// Simple NumSharp NDArrays Prints
        public void NDArrayPrints()
        {
            if (Bars.ClosePrices.Count < BarsRequired)
                return;

            try
            {
                // Calling your Input Data float[,]
                float[,] inputData = GetDataSet();

                // Convert to NDArray and reshape to (BarsRequired, 5)
                NDArray inputNDArray = np.array(inputData);   // NumSharp
                Print("Input NumSharp NDarray Data : " + string.Join(", ", inputNDArray));
                Print("Input NumSharp NDarray Shape: " + string.Join(", ", inputNDArray.shape));
                
                int expectedLength = BarsRequired * 5;
                Print($"Expected NumSharp NDarray Length: {expectedLength}");
                Print($"Input NumSharp NDarray Size: {inputNDArray.size}");

                if (inputNDArray.size != expectedLength)
                {
                    Print($"Length MisMatch: Expected Length {expectedLength}, but got Size {inputNDArray.size}");
                    return;
                }
            }
            catch (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;
                }
            }
        }
    }
}

0.0
รีวิว: 0
รีวิวจากลูกค้า
ยังไม่มีรีวิวสำหรับผลิตภัณฑ์นี้ หากเคยลองแล้ว ขอเชิญมาเป็นคนแรกที่บอกคนอื่น!
เพิ่มเติมจากผู้เขียนคนนี้
เรตติ้งสูง
ฟรี
E7 Polynomial Regression Channel
Polynomial Regression Channel which also reflects the volatility of the underlying asset.
E7 BBKG Indicator
E7 BBKG indicator with 80% plus accuracy used to show both, possible reversal and trend.
เรตติ้งสูง
ฟรี
E7 Volume Profile
E7 Volume Profile, more modern look and feel.
เรตติ้งสูง
ฟรี
E7 Harmonic Structures Basic
E7 Harmonic Structures Basic.
E7 BlackScholes Model
Option pricing using the BlackScholes model and the Math.Numerics packages
E7 Indicators Free Overlays
Bollinger Band Cloud, Heiken Ashi, Trend Follower and Parabolic SAR.
E7 Indicators Free Studies
ADXR, KDJ, SineWave, Bollinger Band Volatility and AEOscillator.
นอกจากนี้คุณยังอาจชอบ
cBot
Grid
NAS100
NZDUSD
+13
GragonGridCbotDemo
Demo for bot https://ctrader.com/products/1655
cBot
Signal
High Profit CADJPY
Bot is based on Price Action strategy to open & manage orders. It is effective capital management and high profitability
cBot
Martingale
Ichimoku Kinko Hyo Martingale (Made with AlgoBuilderX)
This cBot uses Ichimoku, martingale, and time filters. Buy/sell signals based on Tenkan Sen and Kijun Sen crosses.
cBot
NAS100
NZDUSD
Martingale
+26
One Click SL TP Setter Smart Trade Management
OneClick SL/TP Setter — Smart Trade Management Made Simple
dave rsi
RSI Scalping cBot scalper for volatile indexes.
cBot
Indices
Stocks
Forex
+1
Risk And Reward Management
The cTrader Risk & Reward management tool can easily help you to set the risk vs reward values
cBot
Indices
RSI
Forex
+2
US 2000+MACD+ADR+ADMIR+RSI+MANY MORE-TF4M
N.B.: Results with an initial invested capital of 100 euros.
cBot
Commodities
MACD
SparrowBot-FreeTrial-V2
This bot can automatically generate buy and sell orders based on three specific Fixed bugs
cBot
Forex
Signal
Supertrend
+1
[Fx4U] GBPUSD - Price Action
Bot is based on Price Action strategy to open & manage orders. It is effective capital management and high profitability
ยอดนิยม
$ 19
cBot
Scalping
FRAMEWORK
ICT Framework cBot – Advanced Trading Automation for Any Pair & Timeframe
DAX INTRADAY EXPIRY 30.08.25
DAX BOT FOR INTRADAY TRADING
ยอดนิยม
$ 49
/
$98
cBot
XAUUSD
Commodities
Signal
+2
PTFX Supertrend Gold
Dominate gold markets with Supertrend Gold – Backtested with +962% ROI!
ยอดนิยม
$ 39
/
$59
FTMO_1
XAU/USD SWING BOT
cBot
RSI
XAUUSD
Martingale
+9
RedAndBlack
RedAndBlack: buy on bullish close, sell on bearish close. Simple trend-following trading strategy.
cBot
NAS100
NZDUSD
RSI
+12
EMA RSI Risk cBot
A robust trend-following cBot
cBot
NAS100
NZDUSD
Martingale
+26
ElliottWaveBot
N.B.: Results with an initial invested capital of 100 euros.
cBot
Grid
RSI
XAUUSD
+4
MatrixGridPlus
Professional Multi-Strategy Grid Trading Bot
cBot
NZDUSD
XAUUSD
Breakout
+10
ICT Silver Bullet Strategy cBot
💎 ICT Silver Bullet Strategy cBot — liquidity sweep & breakout algorithm with risk control.