E7 BBKG NumSharp Sample
"E7 BBKG NumSharp Sample" logosu
03/09/2025
71
Desktop, Mobile, Web
Since 18/12/2024
Free installs
2347
"E7 BBKG NumSharp Sample" yüklenen resmi

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;
                }
            }
        }
    }
}

Bu oluşturanın diğer ürünleri
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.
En yüksek puanlı
Ücretsiz
E7 Volume Profile
E7 Volume Profile, more modern look and feel.
E7 Harmonic Structures Basic
E7 Harmonic Structures Basic.
E7 Correlation Dashboard
E7 Correlation Dashboard.
Gösterge
Indices
E7 BlackScholes Model
Option pricing using the BlackScholes model and the Math.Numerics packages
Gösterge
Bollinger
E7 Indicators Free Overlays
Bollinger Band Cloud, Heiken Ashi, Trend Follower and Parabolic SAR.
Gösterge
Bollinger
E7 Indicators Free Studies
ADXR, KDJ, SineWave, Bollinger Band Volatility and AEOscillator.
Şunları da beğenebilirsiniz
cBot
Grid
XAUUSD
Prop
+3
Maya Gold Grid ATR
🌞 Smart Gold Grid. ATR Precision. +35% in 30 Days🌞
Nexus Prime Bot
Versatile Adaptive System, for Scalper, Long trader, HFT
GOLD SCALPER V3 DEMO
Gold Sclaper V2 Demo is a fully customizable, intelligent trading robot designed for traders seeking a powerful, automat
RSI Reversal GBPJPY
One of my best personal trading system. Only for GBPJPY m30. 8 years of profitability! Win Rate % = 51,8% Profit factor
cBot
XAUUSD
Commodities
Bilbo SMA Robot
Robot that enters and exits according to an SMA.
cBot
Indices
RSI
XAUUSD
+2
GALAXY GOLD Scalping DEMO
🪙 Strategia Galaxy Gold Scalping cBot 📈 Scalping algoritmico con logica EMA-RSI e trailing stop dinamico
PositionSizer
AI is detecting and determine and seize the opportunities to Open a Positions and follow the current Market Trend.
cBot
Breakout
Scalping
DMG VIP TRADING deno
DMG VIP Trading Bot è un c Bot per c Trader progettato per eseguire operazioni di scalping automatizzato
cBot
XAUUSD
Commodities
Signal
+2
PTFX Supertrend Gold
Dominate gold markets with Supertrend Gold – Backtested with +962% ROI!
cBot
RSI
Breakout
MACD
+1
needThaiBot Price Actions Focus
Prices Action Focus Bot, Please use default setting before making any changes!!! ENJOY!!!
Order Block Hedging Strategy (Made with AlgoBuilderX)
The strategy identifies order blocks from impulsive moves, opening trades with fixed stop loss and take profit.
cBot
Indices
Commodities
Forex
+5
TrendFibonacciBotDemo
The Free for demo account of bot https://ctrader.com/products/1893
cBot
Breakout
Prop
XAUUSD
+5
PropFirm Forex Sniper
PropFirm Forex Sniper
cBot
Breakout
ORB
Powerfull and Optimized strategy based on the "Opening Range Breakout" on the 15 min chart.
EU ADX+ADX 4H
// EUR/USD 4H TIMEFRAME // 5 YEARS BACKTEST, PROFIT 176 USD, MAX DRAWDOWN 55 USD
SUPER DARK HFT CRYPTO-FOREX Trial
Super Dark HFT Crypto-Forex Professional High-Frequency
cBot
Signal
Supertrend
High Profit AUDCHF
Bot is based on Price Action strategy to open & manage orders. It is effective capital management and high profitability
Exponential Moving Average Sample (2)
This algo uses two Exponential Moving Averages (EMAs): EMA 21 (fast) → reacts quickly to price changes. EMA 34 and 21