E7 BBKG NumSharp Sample
Logo "E7 BBKG NumSharp Sample"
03/09/2025
80
Desktop, Mobile, Web
Ảnh "E7 BBKG NumSharp Sample" được tải lên

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
Đánh giá: 0
Đánh giá của khách hàng
Sản phẩm này chưa có đánh giá nào. Bạn đã dùng thử chưa? Hãy là người đầu tiên chia sẻ với mọi người!
Sản phẩm khác của tác giả này
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.
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.
Chỉ báo
Indices
E7 BlackScholes Model
Option pricing using the BlackScholes model and the Math.Numerics packages
Chỉ báo
Bollinger
E7 Indicators Free Overlays
Bollinger Band Cloud, Heiken Ashi, Trend Follower and Parabolic SAR.
Chỉ báo
Bollinger
E7 Indicators Free Studies
ADXR, KDJ, SineWave, Bollinger Band Volatility and AEOscillator.
Bạn cũng có thể thích
Engulfing Strategy with Fixed Risk (Made with AlgoBuilderX)
Auto-detects bullish/bearish engulfing patterns with fixed take profit and stop loss for powerful, simple trading.
cBot
Indices
NZDUSD
Prop
+4
PROPFIRM BOT FREE TEST 10.02.25
PROPFIRM BOT free Backtest until 10.02.2025
cBot
RSI
Breakout
XAUUSD
+13
SmartBar SMA Trader with ATR & Break-Even Protection
This robot is perfect for traders who want a hands-free, rule-based trading strategy with advanced risk management.
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.
TeleNotifier
TelegramBot: Real-time trade updates, ad URLs, and insights on Telegram. Runs 24/7 with an affordable VPS. Limited-time
Trading_stop Plus DEMO
📄 Trading_stop Plus DEMO 🧪 DEMO VERSION – LIMITED TO 48 HOURS This is the demo version of Trading_stop Plus. It works
cBot
Grid
XAUUSD
Prop
+3
Maya Gold Grid ATR - TEST VERSION
🌞 Smart Gold Grid. ATR Precision. + 1 800 000% ROI in 6 years backtest 🌞
cBot
RSI
ATR
Forex
+2
NEXUS PRIME RSI
Intelligent system, Available any instrument. Profit 12. Drawdown 1%
cBot
RSI
MACD
AI
needThaiBot The Ultimate Trading Bot
Auto increase lot on how many percent profits you made, please try default setting before making any changes
cBot
RSI
XAUUSD
Commodities
+4
Gold Sclaper V2_noSourceCode
"GoldScalperBot V2 – First release, V3 coming soon."
cBot
NAS100
NZDUSD
RSI
+12
EMA RSI Risk cBot
A robust trend-following cBot
cBot
NAS100
NZDUSD
Martingale
+26
Prop Ready Bot_v.2.0
The Prop-Ready Bot The Definitive Automaton for Challenges 🛡️ V2.0
cBot
Grid
NAS100
NZDUSD
+8
AlgoCorner Gradient Orders
This tool will help you spread your trades very quickly with a few clicks
cBot
Forex
Signal
MACD
SwingStratH6
Smart Trend cBot - Swing6h
cBot
RSI
MACD
AI
needThai Bot The Unreliable
Please Try and ENJOY !!! Try default setting before making any changes Please!!
cBot
Grid
EURUSD
DistEMATrendBot1.1
A trend following cBot using moving averages, ADX, and least squares regression to identify and extrapolate a trend.
cBot
Grid
Prop
XAUUSD
+3
Maya Gold Grid ATR
🌞 Smart Gold Grid. ATR Precision. +35% in 30 Days🌞
cBot
XAUUSD
Commodities
Signal
+2
PTFX Supertrend Gold
Dominate gold markets with Supertrend Gold – Backtested with +962% ROI!