FinnewsHunter: Multi-agent financial intelligence platform powered by AgenticX. Real-time news analysis, sentiment fusion, and alpha factor mining.

Sentiment Analysis of news on stock prices

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.

Sentiment Analysis on Financial Stocks. Sources of information are Twitter, News, Yahoo Finance Statistics.

Built a sentiment analysis model to predict the sentiment of a Financial News article. A comparative study of different optimizers used for training was done.

FinTechForge is an open-source modular platform designed to provide smart, AI-powered financial tools and insights. From sentiment analysis of financial news to secure authentication and dashboards, this project empowers developers, analysts, and students to build and extend finance-focused applications.

An End-to-End Machine Learning Pipeline that combines financial news sentiment analysis with historical stock price features to predict short-term market movements.

Predicting the price movement of stocks using past prices and sentiment analysis scores from financial News.

The application is a cloud service that provides the functionality of performing sentiment analysis on stock market and financial data. The application can be hosted on Google App Engine and makes use of many of the GAE services like Search Service, MemCache, DataStore etc. Given the name of a company, data from various sources like Twitter, Facebook Graph, Google News, Google Finance etc is aggregated. For each source, different models have been pretrained using some prior data. Using different models provided us with a chance to utilize different Machine Learning methodologies based on the type of data from each source. The various techniques that we have built and tested on are :Naive Bayes, Multinomial and Bernoulli text representations, KNN.

Open-source suite for Indian financial market sentiment analysis, sector mapping, and news impact analytics

Identify the software project, create business case, arrive at a problem statement. REQUIREMENT: Window XP, Internet, MS Office, etc. Problem Description: - 1. Introduction of AI and Machine Learning: - Artificial Intelligence applies machine learning, deep learning and other techniques to solve actual problems. Artificial intelligence (AI) brings the genuine human-to-machine interaction. Simply, Machine Learning is the algorithm that give computers the ability to learn from data and then make decisions and predictions, AI refers to idea where machines can execute tasks smartly. It is a faster process in learning the risk factors, and profitable opportunities. They have a feature of learning from their mistakes and experiences. When Machine learning is combined with Artificial Intelligence, it can be a large field to gather an immense amount of information and then rectify the errors and learn from further experiences, developing in a smarter, faster and accuracy handling technique. The main difference between Machine Learning and Artificial Intelligence is , If it is written in python then it is probably machine learning, If it is written in power point then it is artificial intelligence. As there are many existing projects that are implemented using AI and Machine Learning , And one of the project i.e., Bitcoin Price Prediction :- Bitcoin (₿ ) (founder - Satoshi Nakamoto , Ledger start: 3 January 2009 ) is a digital currency, a type of electronic money. It is decentralized advanced cash without a national bank or single chairman that can be sent from client to client on the shared Bitcoin arrange without middle people's requirement. Machine learning models can likely give us the insight we need to learn about the future of Cryptocurrency. It will not tell us the future but it might tell us the general trend and direction to expect the prices to move. These machine learning models predict the future of Bitcoin by coding them out in Python. Machine learning and AI-assisted trading have attracted growing interest for the past few years. this approach is to test the hypothesis that the inefficiency of the cryptocurrency market can be exploited to generate abnormal profits. the application of machine learning algorithms to the cryptocurrency market has been limited so far to the analysis of Bitcoin prices, using random forests , Bayesian neural network , long short-term memory neural network , and other algorithms. 2. Applications/Scope of AI and Machine Learning :- a) Sentiment Analysis :- It is the classification of subjective opinions or emotions (positive, negative, and neutral) within text data using natural language processing. b) It is Characterized as a use of computerized reasoning where accessible data is utilized through calculations to process or help the handling of factual information. BITCOIN PRICE PREDICTION USING AI AND MACHINE LEARNING: - The main aim of this is to find the actual Bitcoin price in US dollars can be predicted. The chance to make a model equipped for anticipating digital currencies fundamentally Bitcoin. # It works the prediction by taking the coinMarkup cap. # CoinMarketCap provides with historical data for Bitcoin price changes, keep a record of all the transactions by recording the amount of coins in circulation and the volume of coins traded in the last 24-hours. # Quandl is used to filter the dataset by using the MAT Lab properties. 3. Problem statement: - Some AI and Machine Learning problem statements are: - a) Data Privacy and Security: Once a company has dug up the data, privacy and security is eye-catching aspect that needs to be taken care of. b) Data Scarcity: The data is a very important aspect of AI, and labeled data is used to train machines to learn and make predictions. c) Data acquisition: In the process of machine learning, a large amount of data is used in the process of training and learning. d) High error susceptibility: In the process of artificial intelligence and machine learning, the high amount of data is used. Some problem statements of Bitcoin Price Prediction using AI and Machine Learning: - a) Experimental Phase Risk: It is less experimental than other counterparts. In addition, relative to traditional assets, its level can be assessed as high because this asset is not intended for conservative investors. b) Technology Risks: There is a technological risk to other cryptocurrencies in the form of the potential appearance of a more advanced cryptocurrency. Investors may simply not notice the moment when their virtual assets lose their real value. c) Price Variability: The variability of the value of cryptocurrency are the large volumes of exchange trading, the integration of Bitcoin with various companies, legislative initiatives of regulatory bodies and many other, sometimes disregarded phenomena. d) Consumer Protection: The property of the irreversibility of transactions in itself has little effect on the risks of investing in Bitcoin as an asset. e) Price Fluctuation Prediction: Since many investors care more about whether the sudden rise or fall is worth following. Bitcoin price often fluctuates by more than 10% (or even more than 30%) at some times. f) Lacks Government Regulation: Regulators in traditional financial markets are basically missing in the field of cryptocurrencies. For instance, fake news frequently affects the decisions of individual investors. g) It is difficult to use large interval data (e.g., day-level, and month-level data) . h) The change time of mining difficulties is much longer. Moreover, do not consider the news information since it is hard to determine the authenticity of a news or predict the occurrence of emergencies.

本项目旨在利用BERT模型对金融新闻标题进行情感分析，识别新闻标题中的情感倾向（积极或消极）。通过构建一个基于BERT的情感分析系统，我们能够自动化和批量化地处理大量的金融新闻标题，从而大幅提高分析效率和准确性。这不仅有助于及时捕捉市场情绪变化，还能为投资决策提供数据支持，降低人工分析的成本和误差。

AI-powered algorithmic trading system that combines FinBERT sentiment analysis, spaCy NER, and TimeGPT forecasting to generate BUY/SELL/HOLD signals from real-time financial news.

An application for detecting sentiment in financial news

We apply from rule-based approach to BERT for a sentiment analysis task on financial texts.

Analyze financial news sentiment and its correlation with stock market movements. Use NLP, sentiment analysis, and financial analytics to uncover insights for enhanced financial forecasting and innovative investment strategies.

Source code for "Chinese Financial News Annotation For Target-based Sentiment Analysis"

Sentiment Analysis On Financial News Headlines With BERT & FinBERT

I have fineTuned FinBert Model on 4.9k Financial News Headlines, Got 81-82% ACC and it perfrom well in Financial Stock News Sentiment Analysis

NewsFeel: A Sentiment Analysis Tool for Financial News

We introduce a two-phase fine-tuning 012 strategy: first pre-training on the Financial PhraseBank dataset, then adapting to the Twitter Financial News Sentiment dataset.

In this project, we use two sets of data to draw insights on how media sentiment can be an indicator for the financial sector. For the financial data, we plan to use daily return of the market index <font color='green'>(^GSPC)</font>, which is a good indicator for market fluctuation; for media sentiment, we use summarized information of news pieces from top 10 most popular press because of their stronger influence in shaping people's perception of events that are happening in the world.** **Both sets of data are real-time, which means the source files are of the moment and need to be loaded each time analysis is performed. The sentiment analysis library returns a <font color='green'>polarity</font> score (-1.0 to 1.0) and a <font color='green'>polarity</font> score (0.0 to 1.0) on the news stories. Using quantified sentiment analysis, we juxtapose the two time series of data and observe if they present any correlation and search for potential causality. For example, we may test the hypothesis that when polarity among the daily news posts is higher (a.k.a., positive), the financial market that same day is more likely to rise. The rest of the notebook is a step-by-step instruction.

Stock prediction via sentiment analysis of financial news. Winner of VITHack 2020 in FinTech Domain

A machine learning-driven platform that integrates sentiment analysis and advanced predictive modeling to forecast stock market trends. It leverages TensorFlow, financial news analysis, and technical indicators within a Django web interface to offer insightful stock price predictions.

In this project, I generated investing insights by applying sentiment analysis on financial news headlines from Finviz.

A Financial News Sentiment Analysis and Ranking System using Logistic Regression, MLflow, and DVC. Analyzes financial news, determines sentiment (positive/negative), and ranks news based on investment impact.

Developed an end-to-end stock price prediction model by integrating LLM-based sentiment analysis of financial news with time series forecasting, leveraging Python, TensorFlow, and Hugging Face Transformers; achieved enhanced prediction accuracy by incorporating sentiment data.

FinBERT is a pre-trained NLP model to analyze the sentiment of the financial text. It is built by further training the BERT language model in the finance domain, using a large financial corpus and thereby fine-tuning it for financial sentiment classification. For the details, please see FinBERT: Financial Sentiment Analysis with Pre-trained Language Models. The project was deployed on the flask. Providing an indicator to buy, hold or sell stock based on the sentiment predicted for the following news headline/article.

Fine-Tuning Microsoft Phi-2 for financial news sentiment analysis improved accuracy from 0.349 to 0.872 and reduced training loss by 38%. Dataset: 4846 entries with "Neutral," "Positive," and "Negative" labels.

Multi-agent trading backtest framework (Rigid): Sentiment, Fundamental, and Valuation agents feed a Reasoning Agent that makes trading decisions; a Portfolio Manager handles allocation. Uses manually curated news data and historical prices to evaluate LLM performance in financial analysis.