Automate swing trading using deep reinforcement learning. The deep deterministic policy gradient-based neural network model trains to choose an action to sell, buy, or hold the stocks to maximize the gain in asset value. The paper also acknowledges the need for a system that predicts the trend in stock value to work along with the reinforcement learning algorithm. We implement a sentiment analysis model using a recurrent convolutional neural network to predict the stock trend from the financial news. The objective of this paper is not to build a better trading bot, but to prove that reinforcement learning is capable of learning the tricks of stock trading.

An trading system basing on adaboost algorithm using weak classifiers assembled to predict.

This system collects various existing illegal transaction data, performs community analysis through complex networks based on the original transaction data, traces two- and three-layer networks, and models cryptocurrency transaction relationships using tracing algorithms, traceability algorithms, and traceability layer analysis. Based on the modeling, feature extraction, analysis of temporal features and network features are performed on the data to build a dataset for predicting illegal trading behavior using machine learning models. #TODO The system combines front and back-end, and the results can be visualized in the form of pictures for monitoring and viewing the analysis of current cryptocurrency trading behavior on the Web side.

The aim of the problem is to develop a forecasting model to predict a stock's short-term price movement. The use of such prediction models is widely prevalent in algorithmic trading. Algorithmic trading, sometimes referred to as high-frequency trading in specific circumstances, is the use of automated systems to identify true(money making) signals among massive amounts of data that capture the underlying stock dynamics. These models can be leveraged to develop profitable trading strategies(akin to hedge funds) to help investors/traders achieve better returns. Contestants are expected and encouraged to think of empirical models/heuristics in order to better predict the price evolution of the hypothetical stock

The financial market is a dynamic and composite system where people can buy and sell currencies, stocks, equities and derivatives over virtual platforms supported by brokers. Stock markets are affected by many factors causing the uncertainty and high volatility in the market. Although humans can take orders and submit them to the market, automated trading systems (ATS) that are operated by the implementation of computer programs can perform better and with higher momentum in submitting orders than any human. Since most of the dealings in the markets are done by automated systems, it has now been well established that training the past data can help us in finding patterns in the movement of the markets which can be used to predict the future prices. If implemented successfully with a higher accuracy than existing systems, it could turn into a financial support system with minimal amount of risk. We will be using a Random Forest Classification algorithm as the dataset that we train is completely discrete and we will be using several indicators to calculate the data on which the training will be performed.

The usage of Machine Learning Algorithms for analysing financial fundamental values to examine the performance over time to predict high return on publicly traded companies (Stocks). The performance will be measured on the return after taxes and inflation and compared with the Index. This thesis will examine discriminative machine learning methods such a Support Vector Machine (SVM), Linear Regression and Neural Networks to find winner stocks for future investments. Most professional investors combine the human expertise with computational techniques to filter different stock exchanges to separate winners- from losers stocks and make final investment judgements. This thesis shows how to create such a filter system and find the winner stocks for professional investors or retail investors by comparing different metrices and increase the future performance.

A "Risk-First" Algorithmic Trading System that combines Deep Learning (TCNs & Transformers) with Quantitative Logic (Z-Score Regime Detection) to predict S&P 500 trends.

Trading System with Genetic Programming for Feature Engineering, Multilayer Perceptron Neural Network, Logistic Regression with Elastic Net Regularization and Support Vector Machines with L1 Regularization for Predictive Models and Genetic Algorithms for Hyperparameter Optimization.

Binary-bot is a collection of algorithmic trading bots for IQ Option platform, using machine learning to predict market movements with ~60% accuracy. The system combines RandomForest and DecisionTree models with technical analysis to trade currency pairs including EURUSD, USDJPY, and EURJPY.

Enhance trading strategies with Alpha Capital's Predictive Trading System. Leverage machine learning for accurate stock price predictions, optimizing profits, and automating decision-making. Gain a competitive edge in dynamic markets. 📈💼 #AlgorithmicTrading #MachineLearning

AI-Driven Predictive Modelling and Algorithmic Trading System for Stock Market Forecasting

An end-to-end Algorithmic Trading system utilizing Random Forest to predict market movements. Features automated data ingestion (yfinance), technical indicator engineering, Hyperparameter tuning (Optuna), experiment tracking (MLflow), and a dynamic RTL-supported Streamlit dashboard.

Algorithmic trading system that predicts market moves and executes trades automatically using historical data and live signals. Developed in Python with ML prediction logic, finance analytics, and modular API integration, it includes visualized performance dashboards and optional Docker deployment.

Storm‑aware UAV path planning system that combines A* search, a Genetic Algorithm, and an LSTM storm predictor to route a drone through a 2D grid with obstacles and a moving storm, trading off distance, storm risk, and energy for safer paths.

This project explores the use of AI in financial markets by developing a system for stock trading optimization. It combines machine learning with search algorithms to predict stock returns and select the best trades. Key technologies include Python, scikit-learn, and the Alpha Vantage API for data retrieval. Also includes comprehensive backtesting.

**TITLE:** Autonomous Algorithmic Trading System with Predictive Analytics**DESCRIPTION:** This system will develop an AI-driven trading platform capable of autonomously identifying market trends, executing trades, and adapting strategies based on real-time data. It integrates predictive analytics to forecast price movements and optimizes risk man

A beginner-friendly algorithmic trading system that uses machine learning (Random Forest) to predict stock price movements and simulate trading strategies.

An algorithmic trading system that predicts where price of stock/crypto currency goes written in Python

Develop an AI system capable of autonomously researching, developing, and implementing high-frequency trading algorithms, leveraging real-time market data and predictive analytics.

A self-evolving AI system designed for predictive trading in financial markets. It utilizes genetic algorithms to evolve and optimize trading strategies based on real-time data, continuously learning and adapting to market dynamics.

A next-generation algorithmic trading system integrating quantum computing principles with classical machine learning for frontier financial modeling. This platform combines quantum-inspired algorithms with traditional quant infrastructure for superior predictive capabilities.

Developed an intelligent trading system that leverages machine learning algorithms to analyze market data, identify patterns, and execute trades automatically in real time. Enhanced trading efficiency and decision-making by integrating predictive analytics for optimal investment strategies.

A Python prototype for an end-to-end algorithmic trading system that backtests a rule-based (RSI + Moving Average) strategy, generates predictive ML analytics, and automates reporting to Google Sheets and Telegram.

**TITLE:** Autonomous Cross-Domain Quantum-Driven Trading Ecosystem (ACQDTE)**DESCRIPTION:** This ecosystem integrates quantum-inspired algorithms with reinforcement learning and neuroevolution to create a fully autonomous trading system. It enables real-time market adaptation, predictive analytics, and self-healing mechanisms for optimal performa

**TITLE: Real-Time Adaptive Trading System (RTAS)****DESCRIPTION:** The RTAS is designed to revolutionize trading by leveraging real-time data processing and adaptive machine learning algorithms. It integrates neural networks for predicting market trends and employs reinforcement learning to dynamically adjust trading strategies based on performan

Built a data-driven algorithmic trading system that analyzes historical market data, applies technical indicators, and uses machine learning models to predict price movements. The project also simulates and evaluates a rule-based trading strategy through backtesting to generate actionable market insights.

**TITLE:** Autonomous Trading Ecosystem Reinforcement **DESCRIPTION:** We aim to develop an autonomous AI-driven trading ecosystem that employs advanced machine learning algorithms for real-time market analysis, predictive modeling, and automated strategy execution. This system will continuously evolve by learning from market dynamics and histori

**TITLE:** Autonomous Liquidity-Predictive Trading Network (ALPTN)**DESCRIPTION:** The ALPTN is a self-evolving AI system designed to predict market liquidity and optimize trading strategies in real-time. It uses advanced machine learning algorithms to analyze historical and real-time data, adapt to changing market conditions, and execute trades a

An AI-powered algorithmic trading system for the Nifty50 index, using historical and real-time data to predict buy and sell signals. It leverages technical indicators and machine learning to improve trading accuracy and adaptability, aiming for better returns and risk management in volatile markets.

Experimental algorithmic trading bot leveraging machine learning and data analysis to predict market movements. Built with Python, the system processes historical price data, extracts technical indicators, and trains ML models to generate trading signals. Features backtesting capabilities, real-time data processing, and performance analytics.