This application leverages real-time data and AI to predict stock and commodity prices, providing automated trading recommendations and displaying prices in multiple currencies for informed global trading.

To develop an advaned trading application, that provides a real-time, accurate market data-anaysis of stocks and commoditites.

My primary motive is to build a FinTech (Financial Technology) Software, which must few inlcudes the components such as:

HFT (High Frequency Trading) Platform - This system is designed to execute orders at extremely high speed and uncertain volume, by several stock broking firms around the world. Which aims to execute all of the functionality within milliseconds.

Algorithmic Trading - Dignifies to analyse the market by using complex algorithms, identify oppurunities and execute the trade automatically.

Real-Time Data Feed Providers - This services delivers the minimal latency, and plays an essential role for HFT and Algortithms where timely information is paramount

Real-Time Currency Conversion Tools - As this software used in different parts of the globe at the same time, it need to display the same or most accurate converted stock prices in different currencies. This needed to be integrated with the real-time currency exchange rate data provider.

Key Features:

Low Latency - The algorithm is designed to consequence the least delay in the data processing and the order of execution.

High Throughput - It's designed to handle a larger volume of trades and data efficiency.

Predictive Modeling - This code is incorporates with machine learning algorithms such as Time series analysis, and Deep Learning, in order to forecast future price movements.

Risk Management - Essential to mitigate potential losses.

Key Considerations :

Regulatory Compliance - HFt and Active Trading are subject to strict regulations.

Data Security : This code is written just as project and doesn't comply to any of the security standards, and have a definite risk of data breaching. It is never advised to compile this code as a source code to any of the console.

Algorithms Used :

Time Series Analysis - Through Autoregressive Integrated Moving Average (ARIMA), we can models the relationship between a variable and it's past values. And by Expoential Smoothing, we can assign expotential decreasing weights to past observations.

Regression Models :

Linear Regression - It estabilish a linear relationship between the target variable (Price) and predictor variable (e.g. Past prices, volume, price settlements)

Support Vector Regression (SVR) - To find the best hyperplane to fit the data while maximizing the margin, which can be effective for the non-linear relationship

Recurrent Neural Networks (RNNs) - Used for time-series data as they capture long-term dependencies.

Ensemble Methods :

Random Forest - Combines multiples decision trees to reduce overfitting and improve the predictive accuracy.

Gradient Boosting Machine (GBM) - Builds an ensemble of weak learners (Decision trees in stage-wise stage)