Stock Indicators for .NET is a C# NuGet package that transforms raw equity, commodity, forex, or cryptocurrency financial market price quotes into technical indicators and trading insights. You'll need this essential data in the investment tools that you're building for algorithmic trading, technical analysis, machine learning, or visual charting.

Financial markets analysis framework for programmers

Financial market technical analysis & indicators in Julia

AlphaSuite is an open-source quantitative analysis platform that gives you the power to build, test, and deploy professional-grade trading strategies. It's designed for traders and analysts who want to move beyond simple backtests and develop a genuine, data-driven edge in the financial markets.

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FinTwit-Bot is a Discord bot designed to track and analyze financial markets by pulling data from platforms like Twitter, Reddit, and Binance. It features customizable tools for sentiment analysis, market trends, and portfolio tracking to help traders stay informed and make data-driven decisions.

Real time scrapping of stock data in order to get the most recent available information. Cleaning, structuring and parsing of relevant data to provide an interactive and informative dashboard. Dashboard features include: Price history (chart) Basic information (name, sector, industry, market cap...) Insider and institutional ownership information Share return over the years (absolute growth and cagr) Business summary Earnings & growth analysis (revenue, gross profit. EBITDA, net income, free cash flow...) Profitability analysis (roa, roe, roic, gross profit margin, net income margin...) Financial health analysis (debt to equity, interest coverage, quick ratio, current ratio...) Valuation analysis (p/e, p/s, p/b, p/fcf, peg) + Discounted Cash Flow Model

Real-Time Financial Market Data Processing and Prediction application

AI-powered financial analysis agent for Indian stock markets using AngelOne SmartAPI + Claude

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An ANN is a model based on a collection of connected units or nodes called "artificial neurons", which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit information, a "signal", from one artificial neuron to another. An artificial neuron that receives a signal can process it and then signal additional artificial neurons connected to it. In common ANN implementations, the signal at a connection between artificial neurons is a real number, and the output of each artificial neuron is computed by some non-linear function of the sum of its inputs. The connections between artificial neurons are called "edges". Artificial neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Artificial neurons may have a threshold such that the signal is only sent if the aggregate signal crosses that threshold. Typically, artificial neurons are aggregated into layers. Different layers may perform different kinds of transformations on their inputs. Signals travel from the first layer (the input layer) to the last layer (the output layer), possibly after traversing the layers multiple times. The original goal of the ANN approach was to solve problems in the same way that a human brain would. However, over time, attention moved to performing specific tasks, leading to deviations from biology. Artificial neural networks have been used on a variety of tasks, including computer vision, speech recognition, machine translation, social network filtering, playing board and video games and medical diagnosis. Deep learning consists of multiple hidden layers in an artificial neural network. This approach tries to model the way the human brain processes light and sound into vision and hearing. Some successful applications of deep learning are computer vision and speech recognition.[68] Decision trees Main article: Decision tree learning Decision tree learning uses a decision tree as a predictive model to go from observations about an item (represented in the branches) to conclusions about the item's target value (represented in the leaves). It is one of the predictive modeling approaches used in statistics, data mining, and machine learning. Tree models where the target variable can take a discrete set of values are called classification trees; in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. Decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. In decision analysis, a decision tree can be used to visually and explicitly represent decisions and decision making. In data mining, a decision tree describes data, but the resulting classification tree can be an input for decision making. Support vector machines Main article: Support vector machines Support vector machines (SVMs), also known as support vector networks, are a set of related supervised learning methods used for classification and regression. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that predicts whether a new example falls into one category or the other.[69] An SVM training algorithm is a non-probabilistic, binary, linear classifier, although methods such as Platt scaling exist to use SVM in a probabilistic classification setting. In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called the kernel trick, implicitly mapping their inputs into high-dimensional feature spaces. Illustration of linear regression on a data set. Regression analysis Main article: Regression analysis Regression analysis encompasses a large variety of statistical methods to estimate the relationship between input variables and their associated features. Its most common form is linear regression, where a single line is drawn to best fit the given data according to a mathematical criterion such as ordinary least squares. The latter is often extended by regularization (mathematics) methods to mitigate overfitting and bias, as in ridge regression. When dealing with non-linear problems, go-to models include polynomial regression (for example, used for trendline fitting in Microsoft Excel[70]), logistic regression (often used in statistical classification) or even kernel regression, which introduces non-linearity by taking advantage of the kernel trick to implicitly map input variables to higher-dimensional space. Bayesian networks Main article: Bayesian network A simple Bayesian network. Rain influences whether the sprinkler is activated, and both rain and the sprinkler influence whether the grass is wet. A Bayesian network, belief network, or directed acyclic graphical model is a probabilistic graphical model that represents a set of random variables and their conditional independence with a directed acyclic graph (DAG). For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases. Efficient algorithms exist that perform inference and learning. Bayesian networks that model sequences of variables, like speech signals or protein sequences, are called dynamic Bayesian networks. Generalizations of Bayesian networks that can represent and solve decision problems under uncertainty are called influence diagrams. Genetic algorithms Main article: Genetic algorithm A genetic algorithm (GA) is a search algorithm and heuristic technique that mimics the process of natural selection, using methods such as mutation and crossover to generate new genotypes in the hope of finding good solutions to a given problem. In machine learning, genetic algorithms were used in the 1980s and 1990s.[71][72] Conversely, machine learning techniques have been used to improve the performance of genetic and evolutionary algorithms.[73] Training models Usually, machine learning models require a lot of data in order for them to perform well. Usually, when training a machine learning model, one needs to collect a large, representative sample of data from a training set. Data from the training set can be as varied as a corpus of text, a collection of images, and data collected from individual users of a service. Overfitting is something to watch out for when training a machine learning model. Federated learning Main article: Federated learning Federated learning is an adapted form of distributed artificial intelligence to training machine learning models that decentralizes the training process, allowing for users' privacy to be maintained by not needing to send their data to a centralized server. This also increases efficiency by decentralizing the training process to many devices. For example, Gboard uses federated machine learning to train search query prediction models on users' mobile phones without having to send individual searches back to Google.[74] Applications There are many applications for machine learning, including: Agriculture Anatomy Adaptive websites Affective computing Banking Bioinformatics Brain–machine interfaces Cheminformatics Citizen science Computer networks Computer vision Credit-card fraud detection Data quality DNA sequence classification Economics Financial market analysis[75] General game playing Handwriting recognition Information retrieval Insurance Internet fraud detection Linguistics Machine learning control Machine perception Machine translation Marketing Medical diagnosis Natural language processing Natural language understanding Online advertising Optimization Recommender systems Robot locomotion Search engines Sentiment analysis Sequence mining Software engineering Speech recognition Structural health monitoring Syntactic pattern recognition Telecommunication Theorem proving Time series forecasting User behavior analytics In 2006, the media-services provider Netflix held the first "Netflix Prize" competition to find a program to better predict user preferences and improve the accuracy of its existing Cinematch movie recommendation algorithm by at least 10%. A joint team made up of researchers from AT&T Labs-Research in collaboration with the teams Big Chaos and Pragmatic Theory built an ensemble model to win the Grand Prize in 2009 for $1 million.[76] Shortly after the prize was awarded, Netflix realized that viewers' ratings were not the best indicators of their viewing patterns ("everything is a recommendation") and they changed their recommendation engine accordingly.[77] In 2010 The Wall Street Journal wrote about the firm Rebellion Research and their use of machine learning to predict the financial crisis.[78] In 2012, co-founder of Sun Microsystems, Vinod Khosla, predicted that 80% of medical doctors' jobs would be lost in the next two decades to automated machine learning medical diagnostic software.[79] In 2014, it was reported that a machine learning algorithm had been applied in the field of art history to study fine art paintings and that it may have revealed previously unrecognized influences among artists.[80] In 2019 Springer Nature published the first research book created using machine learning.[81] Limitations Although machine learning has been transformative in some fields, machine-learning programs often fail to deliver expected results.[82][83][84] Reasons for this are numerous: lack of (suitable) data, lack of access to the data, data bias, privacy problems, badly chosen tasks and algorithms, wrong tools and people, lack of resources, and evaluation problems.[85] In 2018, a self-driving car from Uber failed to detect a pedestrian, who was killed after a collision.[86] Attempts to use machine learning in healthcare with the IBM Watson system failed to deliver even after years of time and billions of dollars invested.[87][88] Bias Main article: Algorithmic bias Machine learning approaches in particular can suffer from different data biases. A machine learning system trained on current customers only may not be able to predict the needs of new customer groups that are not represented in the training data. When trained on man-made data, machine learning is likely to pick up the same constitutional and unconscious biases already present in society.[89] Language models learned from data have been shown to contain human-like biases.[90][91] Machine learning systems used for criminal risk assessment have been found to be biased against black people.[92][93] In 2015, Google photos would often tag black people as gorillas,[94] and in 2018 this still was not well resolved, but Google reportedly was still using the workaround to remove all gorillas from the training data, and thus was not able to recognize real gorillas at all.[95] Similar issues with recognizing non-white people have been found in many other systems.[96] In 2016, Microsoft tested a chatbot that learned from Twitter, and it quickly picked up racist and sexist language.[97] Because of such challenges, the effective use of machine learning may take longer to be adopted in other domains.[98] Concern for fairness in machine learning, that is, reducing bias in machine learning and propelling its use for human good is increasingly expressed by artificial intelligence scientists, including Fei-Fei Li, who reminds engineers that "There’s nothing artificial about AI...It’s inspired by people, it’s created by people, and—most importantly—it impacts people. It is a powerful tool we are only just beginning to understand, and that is a profound responsibility.”[99] Model assessments Classification of machine learning models can be validated by accuracy estimation techniques like the holdout method, which splits the data in a training and test set (conventionally 2/3 training set and 1/3 test set designation) and evaluates the performance of the training model on the test set. In comparison, the K-fold-cross-validation method randomly partitions the data into K subsets and then K experiments are performed each respectively considering 1 subset for evaluation and the remaining K-1 subsets for training the model. In addition to the holdout and cross-validation methods, bootstrap, which samples n instances with replacement from the dataset, can be used to assess model accuracy.[100] In addition to overall accuracy, investigators frequently report sensitivity and specificity meaning True Positive Rate (TPR) and True Negative Rate (TNR) respectively. Similarly, investigators sometimes report the false positive rate (FPR) as well as the false negative rate (FNR). However, these rates are ratios that fail to reveal their numerators and denominators. The total operating characteristic (TOC) is an effective method to express a model's diagnostic ability. TOC shows the numerators and denominators of the previously mentioned rates, thus TOC provides more information than the commonly used receiver operating characteristic (ROC) and ROC's associated area under the curve (AUC).[101] Ethics Machine learning poses a host of ethical questions. Systems which are trained on datasets collected with biases may exhibit these biases upon use (algorithmic bias), thus digitizing cultural prejudices.[102] For example, using job hiring data from a firm with racist hiring policies may lead to a machine learning system duplicating the bias by scoring job applicants against similarity to previous successful applicants.[103][104] Responsible collection of data and documentation of algorithmic rules used by a system thus is a critical part of machine learning. Because human languages contain biases, machines trained on language corpora will necessarily also learn these biases.[105][106] Other forms of ethical challenges, not related to personal biases, are more seen in health care. There are concerns among health care professionals that these systems might not be designed in the public's interest but as income-generating machines. This is especially true in the United States where there is a long-standing ethical dilemma of improving health care, but also increasing profits. For example, the algorithms could be designed to provide patients with unnecessary tests or medication in which the algorithm's proprietary owners hold stakes. There is huge potential for machine learning in health care to provide professionals a great tool to diagnose, medicate, and even plan recovery paths for patients, but this will not happen until the personal biases mentioned previously, and these "greed" biases are addressed.[107] Hardware Since the 2010s, advances in both machine learning algorithms and computer hardware have led to more efficient methods for training deep neural networks (a particular narrow subdomain of machine learning) that contain many layers of non-linear hidden units.[108] By 2019, graphic processing units (GPUs), often with AI-specific enhancements, had displaced CPUs as the dominant method of training large-scale commercial cloud AI.[109] OpenAI estimated the hardware compute used in the largest deep learning projects from AlexNet (2012) to AlphaZero (2017), and found a 300,000-fold increase in the amount of compute required, with a doubling-time trendline of 3.4 months.[110][111] Software Software suites containing a variety of machine learning algorithms include the following: Free and open-source so

Network Analysis for Financial Markets

Technical Indicators implemented in Python only using Numpy-Pandas as Magic - Very Very Fast! Very tiny! Stock Market Financial Technical Analysis Python library . Quant Trading automation or cryptocoin exchange

Volatile: your day-to-day trading companion.

This repository provides tools and workflows for stock analysis using large language models (LLMs). It combines financial data processing with advanced natural language understanding to deliver insights, trends, and predictions in the stock market.

AI multi-agent system for comprehensive Bitcoin (BTC) analysis, combining financial news, market performance, and AI-driven price predictions for investment recommendations.

Form Trading Bot is an advanced algorithmic trading system designed to automate and optimize your trading strategies across multiple financial markets. Leveraging cutting-edge AI and quantitative analysis, it executes trades with precision and speed unmatched by manual trading.

HyperTrader Bot is an automated trading system designed to execute strategies across financial markets with speed and precision. It features real-time data analysis, customizable trading logic, and risk management tools, helping traders optimize performance and minimize manual effort.

Financial Agentic AI redefines financial analysis by integrating cutting-edge AI with real-time web search capabilities. This multi-agent system brings together financial insights and web intelligence to deliver precise, actionable, and up-to-date stock market information, all tailored to your needs.

Winter 2020 Course description: Econometric and statistical techniques commonly used in quantitative finance. Use of estimation application software in exercises to estimate volatility, correlations, stability, regressions, and statistical inference using financial time series. Topic 1: Time series properties of stock market returns and prices  Class intro: Forecasting and Finance  The random walk hypothesis  Stationarity  Time-varying volatility and General Least Squares  Robust standard errors and OLS Topic 2: Time-dependence and predictability  ARMA models  The likelihood function, exact and conditional likelihood estimation  Predictive regressions, autocorrelation robust standard errors  The Campbell-Shiller decomposition  Present value restrictions  Multivariate analysis: Vector Autoregression (VAR) models, the Kalman Filter Topic 3: Heteroscedasticity  Time-varying volatility in the data  Realized Variance  ARCH and GARCH models, application to Value-at-Risk Topic 4: Time series properties of the cross-section of stock returns  Single- and multifactor models  Economic factors: Models and data exploration  Statistical factors: Principal Components Analysis  Fama-MacBeth regressions and characteristics-based factors

Comparative Analysis of Techniques for Forecasting Time Series in Financial Markets

AI-powered stock analysis kit combining market data, financials, and valuation insights for single and multi-stock analysis.

If you are professionals, retailers or even organisms trading on a financial market, you know that data given on online platforms is not precise enough. Banks and huge financial institutions use powerful computers to transact a large number of orders at very fast speed. Thus, thousands of buying and selling orders are launched within a fraction of a second. So why can’t you see it? High Frequency Data analyser, is an innovative program helping you to analyze high frequency data in order to increase your understanding of the market. High Frequency Data takes trading to the next level, transforming your order book into a interactive visual map. You can now observe visual trading patterns and create new trading strategies. You can chose the parameters you are interested in. ( Volume/askBid). You can observe executed orders and analyse the impact of these executions on the market. You can pause and save the graph whenever you want in order to study more deeply a situation. It plays real time information and offers you many indicators to get an accurate vision of the market.(market indicator) You can visualize the available shares for each value with a very deep order book. (avancer dans le order book). You can also export your data into Excel and draw your most significant graphs. For a deeper and precise order book analysis, download High Frequency Data analyser now!

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RealTime StockStream is a streamlined, simulation system for processing live stock market data. It uses Apache Kafka for data input, Apache Spark for data handling, and Apache Cassandra for data storage, making it a powerful yet easy-to-use tool for financial data analysis

This repository contains the source code and content for the website algotradinglib.com, focused on algorithmic trading and financial market analysis.

This repository features data science projects focused on financial data analysis and forecasting. The projects apply machine learning algorithms to analyze stock market data, predict trends, and optimize investment strategies.

OxiDiviner: A production-ready, open-source Rust library for time series analysis and forecasting, especially for financial markets. Features a wide array of models including ARIMA, GARCH, ETS, Kalman Filters, Markov Regime-Switching, and more. Offers multiple API layers for all expertise levels.

Decentralized Finance (DeFi) Development Services & Solutions Eliminate the role of a middleman by availing decentralized finance (DEFI) development services & solutions. Get access to the major financial services through a blockchain network and experience the benefits of automation, a higher level of security, anonymity, interoperability, and transparency. Our wide range of services include Market-Making Consulting We take immense efforts in establishing financial markets that understand the customers’ proprietary algorithms. We aim at improving the access of liquidity to investors and democratize the whole system. We render customized features according to the customer’s expected return on investment. Decentralized Crypto Banking We ensure a frictionless user experience by facilitating the direct transfer of value between the involved parties supported by decentralization. Our ready-to-launch white-label mobile payment apps render a variety of services such as wallet integration, value holding, and detailed transactional analysis. Defi Lottery System Development We provide a no-loss lottery system that benefits our participants completely. We take steps to eliminate the custodianship of the pooled capital. We permit investing your capital in other related dapps and distribute the rewards in form of a major share of the interest earned to a winner randomly selected by the smart contracts. We assure the regular flow of returns. Derivatives Over Defi Platform We ensure seamless access to derivatives and maximize your earning potential by many notches. by establishing robust dapps, we enable traders to hedge their portfolio of investments and minimize risks by directly engaging with their peers through a democratic platform. We are experts in derivatives market-making and Dapp platform development. 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They are created with the solidity programming language, highly encrypted, and automates the tasks to be executed based on certain pre-set terms and conditions. Defi Dapp Development Defi Dapp development plays a critical role to avoid the risk of a central point of failure. They are highly secure when compared to centralized applications due to the absence of a central authority. Defi Tokens Development Defi tokens development has played a critical role in boosting the growth of decentralized applications. Their value is currently higher than bitcoin. it has a huge trading volume and has garnered a lot of attention from the mainstream crowd in recent times. Defi Dex Development Like Uniswap Uniswap is one of the leading defi projects being undertaken. 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Fcore Is a Framework for Financial Markets Analysis (In progress).