AI Powered Stock Forecasting in .NET MAUI Cartesian Chart
10 Jul 202610 minutes to read
This guide demonstrates how to build an AI-powered .NET MAUI Cartesian Chart Candle series that forecasts stock prices for the next 45 days, helping traders make informed decisions using Syncfusion controls and Azure OpenAI.
NOTE
Prerequisite: Ensure that the required NuGet package is installed, the necessary namespaces are imported, and the SfCartesianChart control is properly configured in your application. For detailed setup and configuration instructions, refer to the Getting Started guide.
Integrating Azure OpenAI for Stock Forecasting
Azure OpenAI can analyze historical stock data and predict future trends. The model identifies patterns and generates financial (Open, High, Low, Close) values for upcoming days.
1. Configure Azure OpenAI Service
Ensure you have access to Azure OpenAI and a deployed model in the Azure portal. Set up the service endpoint and API key. You can find the Azure.AI.OpenAI NuGet package from the NuGet Gallery.
internal class AzureOpenAIService
{
internal const string Endpoint = "YOUR_END_POINT_NAME";
internal const string DeploymentName = "DEPLOYMENT_NAME";
internal const string ImageDeploymentName = "IMAGE_DEPOLYMENT_NAME";
internal const string Key = "API_KEY";
public AzureOpenAIService()
{
}
}To set up a connection to the Azure OpenAI service, create an OpenAIClient instance when needed:
//At the time of required.
var client = new OpenAIClient(new Uri(endpoint), new AzureKeyCredential(key));2. Generate Prompts and Retrieve AI Predictions
Prepare a structured prompt with historical data for the AI model, this helps to get more accurately predicted values.
//AI service.
internal string GeneratePrompt(List<DataModel> historicalData)
{
var prompt = "Predicted OHLC values for the next 45 time step(s) for the following data:\n";
foreach (var data in historicalData)
{
prompt += $"{data.Date:yyyy-MM-dd}: {data.High}, {data.Low}, {data.Open}, {data.Close}\n";
}
prompt += "and the prediction output data should be in the yyyy-MM-dd:High:Low:Open:Close, no other explanation required\n";
return prompt;
}Request predictions from Azure OpenAI using the ‘GetChatCompletionsAsync’:
//AI service.
//code omitted for brevity
public Task<ObservableCollection<DataModel>> GetAnswerFromGPT(string userPrompt, ViewModel viewModel, int index)
{
try
{
if (IsCredentialValid && Client != null)
{
ChatHistory = string.Empty;
// Add the system message and user message to the options
ChatHistory = ChatHistory + userPrompt;
var response = await Client.CompleteAsync(ChatHistory);
//Helps to convert the response to respective data model
return this.ConvertToCompaniesModelCollection(response.ToString());
}
}
catch (Exception)
{
return viewModel.GenerateDataSource();
}
return viewModel.GenerateDataSource();
}3. Implement the Syncfusion .NET MAUI Cartesian Chart to display forecasted data.
The Syncfusion .NET MAUI Cartesian Chart Candle series allows you to display financial data. Define the data Model that holds the financial data (High, Low, Open, Close) and ViewModel that holds the collection of data for binding.
//Model
//code omitted for brevity
public class DataModel
{
public double High { get; set; }
public double Low { get; set; }
public DateTime Date { get; set; }
public double Open { get; set; }
public double Close { get; set; }
}
// ViewModel
public class ViewModel : INotifyPropertyChanged
{
private ObservableCollection<DataModel> stockData;
public ObservableCollection<DataModel> StockData {
get { return stockData; }
set
{
stockData = value;
OnPropertyChanged(nameof(StockData));
}
}
// Helps to store and display the Forecast data in view from AI response.
public ObservableCollection<CompaniesModel> ForeCastData { get; set; }
public const string msftStockSource = "6/28/2024\t453.07\t455.38\t446.41\t446.95\t28,362,270\r\n6/27/2024\t452.18\t456.17\t451.77\t452.85\t14,806,320\r\n6/26/20..."; // Complete data from CSV file downloaded.
public event PropertyChangedEventHandler? PropertyChanged;
public ViewModel()
{
stockData = GenerateCollection(msftStockSource);
}
private ObservableCollection<DataModel> GenerateCollection(string dataSource)
{
string[] alphapetRows = dataSource.Split(new[] { "\r\n", "\n" }, StringSplitOptions.RemoveEmptyEntries);
ObservableCollection<DataModel> alphabetCollection = new ObservableCollection<DataModel>();
foreach (string row in alphapetRows)
{
string[] columns = row.Split('\t');
DataModel item = new DataModel(GetDateTime(columns[0]), GetDouble(columns[2]), GetDouble(columns[3]), GetDouble(columns[1]), GetDouble(columns[4]));
alphabetCollection.Add(item);
}
return alphabetCollection;
}
}4. Display Data Using Syncfusion Cartesian Chart
Bind your ViewModel to the chart and display both historical and forecasted data:
<!-- code omitted for brevity -->
<chart:SfCartesianChart x:Name="chart" >
<chart:SfCartesianChart.XAxes>
<chart:DateTimeAxis x:Name="dateTime" LabelExtent="30" IntervalType="Months" ShowMajorGridLines="False" >
</chart:SfCartesianChart.XAxes>
<chart:SfCartesianChart.YAxes>
<chart:NumericalAxis EdgeLabelsVisibilityMode="AlwaysVisible" EdgeLabelsDrawingMode="Center">
</chart:NumericalAxis>
</chart:SfCartesianChart.YAxes>
<chart:CandleSeries x:Name="candleSeries" ItemsSource="{Binding StockData}"
XBindingPath="Date"
Open="Open"
High="High"
Low="Low"
Close="Close" />
<chart:CandleSeries x:Name="forecastCandle"
ItemsSource="{Binding ForeCastData}"
XBindingPath="Date"
Open="Open"
High="High"
Low="Low"
Close="Close" />
</chart:SfCartesianChart>5. Trigger AI Forecasting
Invoke the AI service when the user clicks a button:
private async void AIButtonClicked(object sender, EventArgs e)
{
AzureOpenAIService service = new AzureOpenAIService();
var data = viewModel.StockData;
var last10Items = data.Skip(Math.Max(0, data.Count - 10)).Take(10).ToList();
//Build a prompt for AI.
var prompt = service.GeneratePrompt(last10Items);
var value = await service.GetAnswerFromGPT(prompt, viewModel, selected_Index);
//Update forecast value to chart.
await viewModel.AddRangeWithDelayAsync(value, 300);
}After receiving the AI-predicted stock trends, the chart updates to display both historical and forecasted data, providing a visual representation of potential market movements.
The following image demonstrates the output of the above AI powered stock forecasting .NET MAUI Cartesian Chart.
For more information, please visit the GitHub Sample