[{"content":"← back to levels list\n","date":"27 June 2026","externalUrl":null,"permalink":"/en/tutorials/level1-basic/","section":"Tutorials","summary":"","title":"Level 1: basic consepts","type":"tutorials"},{"content":"← back to levels list\n","externalUrl":null,"permalink":"/en/tutorials/level2/","section":"Tutorials","summary":"","title":"Level 2","type":"tutorials"},{"content":"← back to levels list\n","externalUrl":null,"permalink":"/en/tutorials/level3/","section":"Tutorials","summary":"","title":"Level 3","type":"tutorials"},{"content":" Decision Support Systems # Decision Support Systems (DSS) are among the simplest yet most practical forms of algorithmic trading systems. Contrary to a common misconception, the objective of every trading algorithm is not to execute trades automatically. In many cases, the algorithm\u0026rsquo;s role is limited to continuously monitoring the market, identifying potential trading opportunities, and notifying the trader, while the final decision to enter or exit a position remains entirely under human control.\nIn this type of system, the algorithm continuously scans a predefined set of financial instruments according to the rules of a trading strategy. Whenever the predefined entry or exit conditions are satisfied, the system generates a trading signal and delivers it to the trader. Notifications may be presented in various forms, such as the output of a live Python application, desktop or mobile notifications, or messages sent through communication platforms. A typical trading signal includes information such as the financial instrument, trade direction (long or short), entry price, target price, stop-loss level, and the recommended position size.\nAn important characteristic of Decision Support Systems is that they never execute trades automatically. Once a signal is generated, the trader has sufficient time to review current market conditions and decide whether the proposed trade should actually be executed. As a result, DSS can be viewed as a bridge between fully manual trading and fully automated trading. The algorithm improves the speed and accuracy of market analysis, while the responsibility for the final trading decision remains with the trader.\nOne of the greatest advantages of this approach is improved time efficiency. In manual trading, market participants often spend long hours monitoring charts to avoid missing profitable trading opportunities. With a Decision Support System, this responsibility is delegated to the algorithm. Consequently, traders only need to pay attention when a valid trading setup has been detected, allowing them to make much more efficient use of their time. This not only increases productivity but also significantly reduces the likelihood of missing high-quality trading opportunities due to the inability to monitor the market continuously. This advantage is particularly valuable for trading strategies that generate relatively few signals but exhibit a high success rate.\nAnother important benefit of this approach is its flexibility compared to fully automated trading systems. During periods of abnormal market behavior—such as major economic announcements, unexpected news events, or episodes of extreme volatility—a fully automated trading system may continue executing trades without recognizing the broader market context, potentially resulting in unnecessary losses. Decision Support Systems eliminate this risk because they never submit orders directly to the market. Instead, they simply provide trading recommendations, allowing the trader to recognize unusual market conditions and decide not to execute the suggested trade.\nHowever, this approach is not without limitations. Since the final decision remains with the trader, many of the behavioral challenges associated with manual trading still exist. Psychological factors such as fear, greed, hesitation, abandoning a trading plan, or second-guessing a valid trading signal can still influence trading performance. In other words, while Decision Support Systems substantially reduce the operational burden of continuously monitoring the market, they cannot eliminate human behavioral biases.\nOverall, Decision Support Systems are well suited for traders who wish to benefit from continuous market monitoring and automated signal generation while maintaining complete control over trade execution. For many market participants, they represent the first practical step toward algorithmic trading and are widely used in financial markets where human supervision remains an essential part of the decision-making process.\nLarge Language Models # Recent advances in artificial intelligence, particularly the emergence of Large Language Models (LLMs), have significantly expanded the capabilities of Decision Support Systems. In addition to analyzing numerical market data, these models are capable of processing unstructured textual information such as financial news, corporate reports, regulatory announcements, and other market-related documents, enabling them to provide richer context for trading decisions. The integration of LLMs into algorithmic trading is an advanced topic and will be discussed in detail in later sections of this course.\nSummary # In essence, a Decision Support System acts as an intelligent market observer. It continuously monitors financial markets, detects potential trading opportunities, and alerts the trader whenever the predefined conditions of a trading strategy are satisfied. By automating market surveillance rather than trade execution, DSS improves the speed and consistency of strategy implementation, increases the number of opportunities that can be monitored simultaneously, and allows traders to spend significantly less time watching the market while still retaining full control over every trading decision.\n","date":"27 June 2026","externalUrl":null,"permalink":"/en/tutorials/level1-basic/3-difrent-algo-manula/","section":"Tutorials","summary":"","title":"1-3- What Are Decision Support Systems?","type":"tutorials"},{"content":"","date":"27 June 2026","externalUrl":null,"permalink":"/en/tags/algorithmic-trading/","section":"Tags","summary":"","title":"Algorithmic Trading","type":"tags"},{"content":"","date":"27 June 2026","externalUrl":null,"permalink":"/en/tags/artificial-intelligence/","section":"Tags","summary":"","title":"Artificial Intelligence","type":"tags"},{"content":"","date":"27 June 2026","externalUrl":null,"permalink":"/en/categories/basic-concepts/","section":"Categories","summary":"","title":"Basic Concepts","type":"categories"},{"content":"","date":"27 June 2026","externalUrl":null,"permalink":"/en/categories/","section":"Categories","summary":"","title":"Categories","type":"categories"},{"content":"","date":"27 June 2026","externalUrl":null,"permalink":"/en/tags/decision-support-systems/","section":"Tags","summary":"","title":"Decision Support Systems","type":"tags"},{"content":"","date":"27 June 2026","externalUrl":null,"permalink":"/en/tags/financial-markets/","section":"Tags","summary":"","title":"Financial Markets","type":"tags"},{"content":"","date":"27 June 2026","externalUrl":null,"permalink":"/en/tags/human-supervision/","section":"Tags","summary":"","title":"Human Supervision","type":"tags"},{"content":"","date":"27 June 2026","externalUrl":null,"permalink":"/en/tags/manual-trading/","section":"Tags","summary":"","title":"Manual Trading","type":"tags"},{"content":"","date":"27 June 2026","externalUrl":null,"permalink":"/en/tags/market-analysis/","section":"Tags","summary":"","title":"Market Analysis","type":"tags"},{"content":"","date":"27 June 2026","externalUrl":null,"permalink":"/en/tags/productivity/","section":"Tags","summary":"","title":"Productivity","type":"tags"},{"content":" What is Quantistan? Start First Level ","date":"27 June 2026","externalUrl":null,"permalink":"/en/","section":"quantistan","summary":"","title":"quantistan","type":"page"},{"content":"","date":"27 June 2026","externalUrl":null,"permalink":"/en/tags/","section":"Tags","summary":"","title":"Tags","type":"tags"},{"content":"","date":"27 June 2026","externalUrl":null,"permalink":"/en/tags/trading-opportunities/","section":"Tags","summary":"","title":"Trading Opportunities","type":"tags"},{"content":"","date":"27 June 2026","externalUrl":null,"permalink":"/en/tags/trading-strategy/","section":"Tags","summary":"","title":"Trading Strategy","type":"tags"},{"content":"","date":"27 June 2026","externalUrl":null,"permalink":"/en/tutorials/","section":"Tutorials","summary":"","title":"Tutorials","type":"tutorials"},{"content":"","date":"27 June 2026","externalUrl":null,"permalink":"/tags/%D8%A7%D8%B3%D8%AA%D8%B1%D8%A7%D8%AA%DA%98%DB%8C-%D9%85%D8%B9%D8%A7%D9%85%D9%84%D8%A7%D8%AA%DB%8C/","section":"Tags","summary":"","title":"استراتژی معاملاتی","type":"tags"},{"content":"","date":"27 June 2026","externalUrl":null,"permalink":"/tags/%D8%A8%D8%A7%D8%B2%D8%A7%D8%B1%D9%87%D8%A7%DB%8C-%D9%85%D8%A7%D9%84%DB%8C/","section":"Tags","summary":"","title":"بازارهای مالی","type":"tags"},{"content":"","date":"27 June 2026","externalUrl":null,"permalink":"/tags/%D8%A8%D9%87%D8%B1%D9%87%D9%88%D8%B1%DB%8C/","section":"Tags","summary":"","title":"بهره‌وری","type":"tags"},{"content":"","date":"27 June 2026","externalUrl":null,"permalink":"/tags/%D8%AA%D8%AD%D9%84%DB%8C%D9%84-%D8%A8%D8%A7%D8%B2%D8%A7%D8%B1/","section":"Tags","summary":"","title":"تحلیل بازار","type":"tags"},{"content":"","date":"27 June 2026","externalUrl":null,"permalink":"/tags/%D8%B3%DB%8C%D8%B3%D8%AA%D9%85%D9%87%D8%A7%DB%8C-%D9%BE%D8%B4%D8%AA%DB%8C%D8%A8%D8%A7%D9%86-%D8%AA%D8%B5%D9%85%DB%8C%D9%85%DA%AF%DB%8C%D8%B1%DB%8C/","section":"Tags","summary":"","title":"سیستم‌های پشتیبان تصمیم‌گیری","type":"tags"},{"content":"","date":"27 June 2026","externalUrl":null,"permalink":"/tags/%D9%81%D8%B1%D8%B5%D8%AA%D9%87%D8%A7%DB%8C-%D9%85%D8%B9%D8%A7%D9%85%D9%84%D8%A7%D8%AA%DB%8C/","section":"Tags","summary":"","title":"فرصت‌های معاملاتی","type":"tags"},{"content":"","date":"27 June 2026","externalUrl":null,"permalink":"/tags/%DA%A9%D9%86%D8%AA%D8%B1%D9%84-%D8%A7%D9%86%D8%B3%D8%A7%D9%86%DB%8C/","section":"Tags","summary":"","title":"کنترل انسانی","type":"tags"},{"content":"","date":"27 June 2026","externalUrl":null,"permalink":"/tags/%D9%85%D8%B9%D8%A7%D9%85%D9%84%D8%A7%D8%AA-%D8%A7%D9%84%DA%AF%D9%88%D8%B1%DB%8C%D8%AA%D9%85%DB%8C/","section":"Tags","summary":"","title":"معاملات الگوریتمی","type":"tags"},{"content":"","date":"27 June 2026","externalUrl":null,"permalink":"/tags/%D9%85%D8%B9%D8%A7%D9%85%D9%84%D8%A7%D8%AA-%D8%AF%D8%B3%D8%AA%DB%8C/","section":"Tags","summary":"","title":"معاملات دستی","type":"tags"},{"content":"","date":"27 June 2026","externalUrl":null,"permalink":"/categories/%D9%85%D9%81%D8%A7%D9%87%DB%8C%D9%85-%D9%BE%D8%A7%DB%8C%D9%87/","section":"Categories","summary":"","title":"مفاهیم پایه","type":"categories"},{"content":"","date":"27 June 2026","externalUrl":null,"permalink":"/tags/%D9%87%D9%88%D8%B4-%D9%85%D8%B5%D9%86%D9%88%D8%B9%DB%8C/","section":"Tags","summary":"","title":"هوش مصنوعی","type":"tags"},{"content":"","date":"27 June 2026","externalUrl":null,"permalink":"/tags/%D8%A7%D8%B3%D8%AA%D8%B1%D8%A7%D8%AA%DA%98%DB%8C%D9%87%D8%A7%DB%8C-%D9%85%D8%A8%D8%AA%D9%86%DB%8C-%D8%A8%D8%B1-%D8%AF%D8%A7%D8%AF%D9%87/","section":"Tags","summary":"","title":"استراتژی‌های مبتنی بر داده","type":"tags"},{"content":"","date":"27 June 2026","externalUrl":null,"permalink":"/tags/%D8%A7%D8%B3%D8%AA%D8%B1%D8%A7%D8%AA%DA%98%DB%8C%D9%87%D8%A7%DB%8C-%D9%85%D8%B9%D8%A7%D9%85%D9%84%D8%A7%D8%AA%DB%8C/","section":"Tags","summary":"","title":"استراتژی‌های معاملاتی","type":"tags"},{"content":"","date":"27 June 2026","externalUrl":null,"permalink":"/tags/%D8%AA%D8%B5%D9%85%DB%8C%D9%85%D8%A7%D8%AA-%D8%B3%D8%B1%D9%85%D8%A7%DB%8C%D9%87%DA%AF%D8%B0%D8%A7%D8%B1%DB%8C/","section":"Tags","summary":"","title":"تصمیمات سرمایه‌گذاری","type":"tags"},{"content":"","date":"27 June 2026","externalUrl":null,"permalink":"/tags/%D8%AA%D8%B5%D9%85%DB%8C%D9%85%DA%AF%DB%8C%D8%B1%DB%8C-%D9%85%D8%A8%D8%AA%D9%86%DB%8C-%D8%A8%D8%B1-%D8%B4%D9%88%D8%A7%D9%87%D8%AF/","section":"Tags","summary":"","title":"تصمیم‌گیری مبتنی بر شواهد","type":"tags"},{"content":"","date":"27 June 2026","externalUrl":null,"permalink":"/tags/%D8%B1%D9%88%D8%B4%D9%87%D8%A7%DB%8C-%D8%A2%D9%85%D8%A7%D8%B1%DB%8C/","section":"Tags","summary":"","title":"روش‌های آماری","type":"tags"},{"content":"","date":"27 June 2026","externalUrl":null,"permalink":"/tags/%D9%85%D8%AF%D9%84%D8%B3%D8%A7%D8%B2%DB%8C-%D9%85%D8%A7%D9%84%DB%8C/","section":"Tags","summary":"","title":"مدل‌سازی مالی","type":"tags"},{"content":"","date":"27 June 2026","externalUrl":null,"permalink":"/tags/%D9%85%D8%B9%D8%A7%D9%85%D9%84%D8%A7%D8%AA-%DA%A9%D9%85%DB%8C/","section":"Tags","summary":"","title":"معاملات کمی","type":"tags"},{"content":"","date":"27 June 2026","externalUrl":null,"permalink":"/tags/%DB%8C%D8%A7%D8%AF%DA%AF%DB%8C%D8%B1%DB%8C-%D9%85%D8%A7%D8%B4%DB%8C%D9%86/","section":"Tags","summary":"","title":"یادگیری ماشین","type":"tags"},{"content":" Quantitative Trading # Quantitative trading is an approach to developing trading strategies in which investment decisions are driven by data, mathematical models, statistical methods, and quantitative analysis. Rather than relying on intuition, emotions, or subjective judgment, quantitative trading seeks to identify trading opportunities by analyzing historical market data and applying objective analytical techniques.\nThe primary goal of quantitative trading is to develop strategies whose decision-making rules can be clearly defined, tested, and evaluated. The process typically begins with collecting and analyzing market data, followed by formulating trading hypotheses based on statistical methods or mathematical models. These hypotheses are then tested using historical data to evaluate their performance. Only strategies that demonstrate satisfactory results during this evaluation process are considered suitable for deployment in live markets.\nOne of the defining characteristics of quantitative trading is its emphasis on evidence-based decision-making. Every component of a trading strategy—from entry and exit rules to risk management and capital allocation—is designed and validated using measurable results and quantitative analysis. Consequently, disciplines such as statistics, probability, optimization, machine learning, and data analysis have become fundamental tools in modern quantitative trading.\nMachine Learning in Quantitative Trading # One of the most influential data-driven approaches in quantitative trading today is the application of machine learning. Owing to their ability to process large volumes of data and uncover complex trading patterns, machine learning algorithms have demonstrated promising results in numerous academic studies. In the following lessons, we will examine how these algorithms can be applied to the design and development of systematic trading strategies.\nSummary: # Quantitative trading focuses on the design, analysis, and evaluation of trading strategies using data, mathematical and statistical models, and machine learning techniques. The outcome of this process forms the decision-making core of an algorithmic trading system.\nrefrence # Chan EP. Quantitative Trading. John Wiley \u0026amp; Sons; 2021.\n","date":"27 June 2026","externalUrl":null,"permalink":"/en/tutorials/level1-basic/2-what-is-quant-trade/","section":"Tutorials","summary":"","title":"1-2- What is Quantitative Trading?","type":"tutorials"},{"content":"","date":"27 June 2026","externalUrl":null,"permalink":"/en/tags/data-driven-strategies/","section":"Tags","summary":"","title":"Data-Driven Strategies","type":"tags"},{"content":"","date":"27 June 2026","externalUrl":null,"permalink":"/en/tags/evidence-based-decision-making/","section":"Tags","summary":"","title":"Evidence-Based Decision-Making","type":"tags"},{"content":"","date":"27 June 2026","externalUrl":null,"permalink":"/en/tags/financial-modeling/","section":"Tags","summary":"","title":"Financial Modeling","type":"tags"},{"content":"","date":"27 June 2026","externalUrl":null,"permalink":"/en/tags/investment-decisions/","section":"Tags","summary":"","title":"Investment Decisions","type":"tags"},{"content":"","date":"27 June 2026","externalUrl":null,"permalink":"/en/tags/machine-learning/","section":"Tags","summary":"","title":"Machine Learning","type":"tags"},{"content":"","date":"27 June 2026","externalUrl":null,"permalink":"/en/tags/quantitative-trading/","section":"Tags","summary":"","title":"Quantitative Trading","type":"tags"},{"content":"","date":"27 June 2026","externalUrl":null,"permalink":"/en/tags/statistical-methods/","section":"Tags","summary":"","title":"Statistical Methods","type":"tags"},{"content":"","date":"27 June 2026","externalUrl":null,"permalink":"/en/tags/trading-strategies/","section":"Tags","summary":"","title":"Trading Strategies","type":"tags"},{"content":" Algorithmic trading # Algorithmic trading refers to the use of computer algorithms and software to execute trades in financial markets based on a predefined set of rules. In this approach, trading decisions and order execution are carried out automatically or semi-automatically, minimizing the need for human intervention throughout the trading process.\nContrary to a common misconception, algorithmic trading is not limited to the automated execution of buy and sell orders. A complete algorithmic trading system typically covers the entire trading workflow, including market data analysis, signal generation, risk management, position sizing, order execution, and performance evaluation.\nThe defining characteristic of algorithmic trading is its rule-based nature. Every trading decision is made according to clearly defined and programmable rules rather than subjective judgment. As a result, trading strategies can be tested and evaluated on historical market data before being deployed in live markets. Under identical market conditions, the same algorithm is expected to produce consistent decisions, helping reduce the impact of emotions and psychological biases on trading.\nQuantitative Trading # Alongside algorithmic trading, another closely related concept is quantitative trading. Although the two are strongly connected, they are not the same. Algorithmic trading primarily focuses on the automated implementation and execution of trading strategies, whereas quantitative trading covers a broader range of activities, including statistical modeling, data analysis, quantitative research, and strategy development. In practice, many quantitative trading strategies are implemented through algorithmic trading systems; however, not every algorithmic trading system relies on sophisticated quantitative models. The next lesson will examine the concept of quantitative trading in greater detail.\nsummary: # Algorithmic Trading focuses on the automated implementation and execution of trading strategies in financial markets. Quantitative Trading focuses on the research, design, analysis, and evaluation of data-driven trading strategies. In practice, many modern trading systems combine elements of both disciplines. Refrence # Chan EP. Algorithmic Trading: Winning Strategies and Their Rationale. Hoboken, N.J.: John Wiley Et Sons, Inc; 2013.\n","date":"27 June 2026","externalUrl":null,"permalink":"/en/tutorials/level1-basic/1-what-is-algo-trade/","section":"Tutorials","summary":"","title":"1-1- What is Algorithmic Trading?","type":"tutorials"},{"content":"","date":"27 June 2026","externalUrl":null,"permalink":"/en/tags/genetic-algorithm/","section":"Tags","summary":"","title":"Genetic Algorithm","type":"tags"},{"content":"","date":"27 June 2026","externalUrl":null,"permalink":"/en/categories/open-access/","section":"Categories","summary":"","title":"Open Access","type":"categories"},{"content":"","date":"27 June 2026","externalUrl":null,"permalink":"/en/tags/portfolio-management/","section":"Tags","summary":"","title":"Portfolio Management","type":"tags"},{"content":"","date":"27 June 2026","externalUrl":null,"permalink":"/en/categories/preprint/","section":"Categories","summary":"","title":"Preprint","type":"categories"},{"content":"","date":"27 June 2026","externalUrl":null,"permalink":"/en/publications/","section":"Publications","summary":"","title":"Publications","type":"publications"},{"content":"","date":"27 June 2026","externalUrl":null,"permalink":"/en/tags/risk-management/","section":"Tags","summary":"","title":"Risk Management","type":"tags"},{"content":"A Risk-Aware Rule-Based Portfolio Management Framework for the Tehran Stock Exchange Using Genetic Algorithm Optimization\nAuthor: Mohammad Mahdi Masoumian, K. N. Toosi University of Technology\nPublisher/Release date: SSRN | 2026-06-02\nDwonload PDF\nAbstract\nAlgorithmic portfolio management has attracted significant attention due to its potential to reduce emotional decision-making and improve risk-adjusted investment performance. However, many conventional approaches based on deep neural networks and reinforcement learning are not well suited to the Tehran Stock Exchange due to structural limitations such as low liquidity, price-fluctuation limits, buy and sell queues, and the market\u0026rsquo;s one-sided nature. In addition, the relatively small and noisy dataset of the Iranian market increases the risk of overfitting in complex machine learning models. To address these challenges, this study proposes a rule-based portfolio management framework optimized using a Genetic Algorithm (GA). The model uses technical indicators, smartmoney activity, cash-flow metrics, and buyer-seller power to generate trading signals. Initially, twelve trading rules were designed, and the GA selected the most effective rules based on Sharpe ratio optimization. The optimization process was performed on approximately 200 randomly selected stocks with more than 1,000 daily observations covering the 2022-2026 period. Furthermore, the framework incorporates a dynamic risk allocation mechanism that adjusts the portfolio composition among equities, gold exchange-traded funds, and fixed-income assets according to investor risk tolerance. Backtesting results indicate that the proposed framework outperformed the Tehran Stock Exchange index in most scenarios while achieving more stable risk-adjusted returns and lower drawdowns. The findings suggest that interpretable rule-based systems combined with evolutionary optimization and adaptive risk management can provide a practical and robust solution for portfolio management in structurally inefficient emerging markets such as the Tehran Stock Exchange.\nKeywords: Portfolio Management, Genetic Algorithm, Tehran Stock Exchange, Risk Management, Rule-Based Trading System\ncitation:\nMasoumian, Mohammad Mahdi, A Risk-Aware Rule-Based Portfolio Management Framework for the Tehran Stock Exchange Using Genetic Algorithm Optimization (June 02, 2026). Available at SSRN: https://ssrn.com/abstract=6885240\n","date":"27 June 2026","externalUrl":null,"permalink":"/en/publications/risk-aware-portfolio-management-tehran-stock-exchange/","section":"Publications","summary":"","title":"Risk-Aware Portfolio Management for Tehran Stock Exchange","type":"publications"},{"content":"","date":"27 June 2026","externalUrl":null,"permalink":"/en/categories/tehran-stock/","section":"Categories","summary":"","title":"Tehran Stock","type":"categories"},{"content":"","date":"27 June 2026","externalUrl":null,"permalink":"/en/tags/tehran-stock-exchange/","section":"Tags","summary":"","title":"Tehran Stock Exchange","type":"tags"},{"content":" * Online Academy for Algorithmic Trading \u0026 Quantitative Finance\n* R\u0026D in Trading Systems, Risk Management \u0026 Decision Support\n* A Hub for Research and Industry Collaboration in FinTech\nQuantistan is an independent project in the field of algorithmic trading and quantitative finance, created with the goal of developing, documenting, and sharing knowledge in this domain. It aims to bridge education, research, and practical development, providing a common platform where enthusiasts, researchers, and market professionals can learn, exchange knowledge, and collaborate.\nOne of Quantistan\u0026rsquo;s main goals is to provide free, accurate, and easy-to-understand educational content on algorithmic trading and quantitative finance. Many existing resources are either overly technical or focus on teaching tools without explaining the underlying scientific concepts. Quantistan seeks to explain both fundamental and practical topics in a simple yet scientifically accurate way, making the learning process more accessible to everyone.\nAlongside its educational activities, Quantistan also serves as a research and development project. Its work focuses on designing, developing, and evaluating trading algorithms, data-driven models, decision support systems, and other related tools. The outcomes of these activities are shared through scientific papers, research reports, and software projects, and collaboration with researchers, developers, and other professionals is always welcome.\nOver time, Quantistan also aims to become a place where people interested in this field can connect, exchange ideas, and collaborate on research and development projects.\nContent Structure # The content published on Quantistan is organized into three main sections.\nTutorials # The Tutorials section provides a structured collection of concepts, fundamentals, and specialized topics in algorithmic trading and quantitative finance. The content follows a logical learning path and is continuously expanded and updated, helping readers progress from basic concepts to more advanced subjects.\nResearch Documentation # The Research Documentation section contains scientific papers, research reports, and studies evaluating different algorithms, strategies, and research hypotheses. It serves as an internal research archive, documenting ongoing research activities and sharing their results with the scientific community and professionals in the field.\nProjects # The Projects section presents software projects and tools developed by the Quantistan team that are available for public release. Whenever possible, project resources such as source code, datasets, documentation, and supporting tools are also provided so that others can use them or build upon them.\nVision # Quantistan was founded on the belief that knowledge becomes more valuable when it is understandable, well documented, and freely accessible. For this reason, it brings together education, research, and practical development in algorithmic trading and quantitative finance, creating a bridge between learning, knowledge creation, and real-world applications in financial markets.\nIts mission is to freely share knowledge and, whenever possible, contribute to the creation of new knowledge in this field. This journey begins with educational content, continues through research and the development of trading algorithms, and extends to sharing practical experience, software tools, and research outcomes with the broader community.\nThe long-term goal of Quantistan is to become a trusted resource for learning, research, development, and collaboration in algorithmic trading and quantitative finance, while contributing, even in a small way, to the growth of this field and its community.\n","date":"26 June 2026","externalUrl":null,"permalink":"/en/ourteam/quantistan/","section":"Quantistan's Team Members","summary":"","title":"About Quantistan","type":"ourteam"},{"content":" Quantistan’s proprietary algorithmic trading engine Powered by machine learning \u0026 genetic algorithms Fully automated trade execution Count Heshmat is the proprietary algorithmic trading engine of the Quantistan team, designed to operate as a fully autonomous trading system in financial markets. The project was initiated from Mahdi Masoumian\u0026rsquo;s M.Sc. thesis and continues to be one of Quantistan’s core R\u0026amp;D initiatives.\nThe system\u0026rsquo;s core relies on machine learning models for price prediction, with outputs filtered through genetically optimized trading rules to generate final signals. All decision‑making and trade execution are performed entirely automatically.\nThe naming convention is a light‑hearted fictional characterization and bears no relation to any real or historical person.\nKey Features # Price forecasting using machine learning models Signal filtering via genetically optimized trading rules Fully automated trade execution Active trade management and position monitoring Designed for continuous improvement in live market conditions Current Project Status # Count Heshmat is currently under active development and operational evaluation. While early results from demo testing have been highly promising, a sufficient long‑term track record on a real account is not yet available for definitive performance assessment.\nConsequently, the system remains under continuous monitoring, analysis, and refinement until a statistically reliable performance history is achieved, enabling operational deployment at scale.\nDevelopment Roadmap # Upon completion of the evaluation phase and accumulation of adequate live trading history, the development roadmap includes:\nOperational deployment on real accounts Public dissemination of trading signals Copy Trading services Capital management infrastructure and investor onboarding Development of next‑generation algorithms based on live results Performance Documentation # Below are sample outputs from Count Heshmat’s demo testing. The screenshots include the algorithm execution environment, open trades, closed trades, and recorded results over various periods. They are published solely as technical documentation of the development process.\n","date":"26 June 2026","externalUrl":null,"permalink":"/en/ourteam/countheshmat/","section":"Quantistan's Team Members","summary":"","title":"Count Heshmat","type":"ourteam"},{"content":"","date":"26 June 2026","externalUrl":null,"permalink":"/en/tags/development/","section":"Tags","summary":"","title":"Development","type":"tags"},{"content":"","date":"26 June 2026","externalUrl":null,"permalink":"/en/tags/education/","section":"Tags","summary":"","title":"Education","type":"tags"},{"content":"","date":"26 June 2026","externalUrl":null,"permalink":"/en/tags/guide/","section":"Tags","summary":"","title":"Guide","type":"tags"},{"content":"","date":"26 June 2026","externalUrl":null,"permalink":"/en/tags/quantistan/","section":"Tags","summary":"","title":"Quantistan","type":"tags"},{"content":" Quantistan # An independent project in algorithmic trading and quantitative finance that bridges education, research, and hands‑on development.\nThis page outlines the mission, vision, and overall structure of Quantistan.\nTutorials # A structured collection of concepts, fundamentals, and specialised topics in algorithmic trading and quantitative finance.\nThe section provides a coherent learning path from basic to advanced material and is regularly expanded.\nResearch Publications # Findings from studies, scientific papers, research reports, and evaluations of various algorithms and hypotheses are published here.\nThis research archive is designed to document and share results with the broader scientific community.\nProjects # Software projects and tools developed by the Quantistan team, complete with source code, datasets, and documentation.\nYou can use ready‑made projects or follow their ongoing development.\nTeam Members # Meet the people behind Quantistan, their roles, and areas of expertise.\nThis section offers a snapshot of the team structure and each member’s responsibilities.\nCollaboration # If you are interested in working with the Quantistan team on research, technical, or industry projects,\nplease review this page and reach out through the channels provided.\n","date":"26 June 2026","externalUrl":null,"permalink":"/en/ourteam/help/","section":"Quantistan's Team Members","summary":"","title":"Quantistan site Guide","type":"ourteam"},{"content":" ◩ Quantistan (web) Role: Algorithmic Trading \u0026 Quantitative Finance Online Academy Rank: Realm ♚ Mohammad Mahdi Masoumian (Human) Role: Founder \u0026 Author Rank: King ♛ open position (Human) Role: Admin Rank: Queen ♝ Count Heshmat (Bot) Role: Trader Rank: Bishop ♜ Gemini (AI) Role: research assistant Rank: Rook ♞ DeepSeek (AI) Role: Programmer Rank: Knight ♟ NotebookLM (AI) Role: Content Manager Rank: Pawn ♟ ChatGPT (AI) Role: Translator \u0026 Editor Rank: Pawn ♟ Grok (AI) Role: Resource Searcher Rank: Pawn ","date":"26 June 2026","externalUrl":null,"permalink":"/en/ourteam/","section":"Quantistan's Team Members","summary":"","title":"Quantistan's Team Members","type":"ourteam"},{"content":"","date":"26 June 2026","externalUrl":null,"permalink":"/en/tags/quantitative-finance/","section":"Tags","summary":"","title":"Quantitative Finance","type":"tags"},{"content":"","date":"26 June 2026","externalUrl":null,"permalink":"/en/tags/research/","section":"Tags","summary":"","title":"Research","type":"tags"},{"content":"","date":"26 June 2026","externalUrl":null,"permalink":"/en/tags/sitemap/","section":"Tags","summary":"","title":"Sitemap","type":"tags"},{"content":"","date":"26 June 2026","externalUrl":null,"permalink":"/tags/%D8%A2%D9%85%D9%88%D8%B2%D8%B4/","section":"Tags","summary":"","title":"آموزش","type":"tags"},{"content":"","date":"26 June 2026","externalUrl":null,"permalink":"/tags/%D8%A7%D9%84%DA%AF%D9%88%D8%B1%DB%8C%D8%AA%D9%85-%DA%98%D9%86%D8%AA%DB%8C%DA%A9/","section":"Tags","summary":"","title":"الگوریتم ژنتیک","type":"tags"},{"content":"","date":"26 June 2026","externalUrl":null,"permalink":"/tags/%D8%A8%D9%88%D8%B1%D8%B3-%D8%A7%D9%88%D8%B1%D8%A7%D9%82-%D8%A8%D9%87%D8%A7%D8%AF%D8%A7%D8%B1-%D8%AA%D9%87%D8%B1%D8%A7%D9%86/","section":"Tags","summary":"","title":"بورس اوراق بهادار تهران","type":"tags"},{"content":"","date":"26 June 2026","externalUrl":null,"permalink":"/categories/%D8%A8%D9%88%D8%B1%D8%B3-%D8%AA%D9%87%D8%B1%D8%A7%D9%86/","section":"Categories","summary":"","title":"بورس تهران","type":"categories"},{"content":"","date":"26 June 2026","externalUrl":null,"permalink":"/tags/%D9%BE%DA%98%D9%88%D9%87%D8%B4/","section":"Tags","summary":"","title":"پژوهش","type":"tags"},{"content":"","date":"26 June 2026","externalUrl":null,"permalink":"/categories/%D9%BE%DB%8C%D8%B4-%DA%86%D8%A7%D9%BE/","section":"Categories","summary":"","title":"پیش چاپ","type":"categories"},{"content":"","date":"26 June 2026","externalUrl":null,"permalink":"/tags/%D8%AA%D9%88%D8%B3%D8%B9%D9%87/","section":"Tags","summary":"","title":"توسعه","type":"tags"},{"content":"","date":"26 June 2026","externalUrl":null,"permalink":"/categories/%D8%AF%D8%B3%D8%AA%D8%B1%D8%B3%DB%8C-%D8%A2%D8%B2%D8%A7%D8%AF/","section":"Categories","summary":"","title":"دسترسی آزاد","type":"categories"},{"content":"","date":"26 June 2026","externalUrl":null,"permalink":"/tags/%D8%B1%D8%A7%D9%87%D9%86%D9%85%D8%A7/","section":"Tags","summary":"","title":"راهنما","type":"tags"},{"content":"","date":"26 June 2026","externalUrl":null,"permalink":"/tags/%DA%A9%D9%88%D8%A7%D9%86%D8%AA%D8%B3%D8%AA%D8%A7%D9%86/","section":"Tags","summary":"","title":"کوانتستان","type":"tags"},{"content":"","date":"26 June 2026","externalUrl":null,"permalink":"/tags/%D9%85%D8%A7%D9%84%DB%8C-%DA%A9%D9%85%DB%8C/","section":"Tags","summary":"","title":"مالی کمی","type":"tags"},{"content":"","date":"26 June 2026","externalUrl":null,"permalink":"/tags/%D9%85%D8%AF%DB%8C%D8%B1%DB%8C%D8%AA-%D9%BE%D8%B1%D8%AA%D9%81%D9%88%DB%8C/","section":"Tags","summary":"","title":"مدیریت پرتفوی","type":"tags"},{"content":"","date":"26 June 2026","externalUrl":null,"permalink":"/tags/%D9%85%D8%AF%DB%8C%D8%B1%DB%8C%D8%AA-%D8%B1%DB%8C%D8%B3%DA%A9/","section":"Tags","summary":"","title":"مدیریت ریسک","type":"tags"},{"content":"","date":"26 June 2026","externalUrl":null,"permalink":"/tags/%D9%86%D9%82%D8%B4%D9%87-%D8%B3%D8%A7%DB%8C%D8%AA/","section":"Tags","summary":"","title":"نقشه سایت","type":"tags"},{"content":"Mohammad Mahdi Masoumian is available to undertake research and technical projects in areas related to data analysis, systems modeling, and the development of computational methods in financial markets. His work focuses on designing and implementing data‑driven systems for analysis, forecasting, and decision‑making within financial market environments. These activities can be defined in both research settings and applied projects, on‑site or remotely.\nAreas of collaboration: # Development of algorithmic trading systems and data-driven trading strategies Time series modeling and forecasting using statistical and machine learning methods Design of financial decision support systems Portfolio optimization, risk management, and capital allocation algorithms Simulation and performance evaluation of trading systems Additionally, completed research, scientific publications, and working papers are available in the publications section, while implemented systems and applied projects, including trading algorithms and decision support tools, can be found in the Projects section.\nContact # Email: mm.masoumian@gmail.com\nTelegram: @mahdi_masoumian\nLocation: Iran – Tehran\n","date":"23 June 2026","externalUrl":null,"permalink":"/en/ourteam/collaboration/","section":"Quantistan's Team Members","summary":"","title":"Collaboration","type":"ourteam"},{"content":" * Quantitative Finance, Algorithmic Trading, and Machine Learning Researcher\n* M.Sc. in Systems Modeling and Data Analytics\n* National Scientific Olympiad Award Recipient in Industrial Engineering\nAbout # Mohammad Mahdi Masoumian is a researcher in Quantitative Finance and Machine Learning with an academic background in Industrial Engineering, specializing in Systems Modeling and Data Analytics. His work focuses on the design and development of Algorithmic Trading systems, data-driven forecasting models for financial markets, and the application of artificial intelligence techniques to financial data analysis.\nHis research interests include the development of data-driven Decision Support Systems, trading algorithm design, and the evaluation of intelligent systems in dynamic financial environments. His master\u0026rsquo;s research was dedicated to the practical application of data-driven forecasting models in financial markets.\nResearch and Professional Interests # Algorithmic Trading and Trading System Development Machine Learning and Data-Driven Forecasting Models Financial Data Analytics and Time Series Modeling Optimization Algorithms and Portfolio Management Trading Decision Support Systems Simulation and Performance Evaluation of Trading Strategies Explore completed projects, including trading and decision-support algorithms, in the Projects section.\nExplore publications, research findings, and working papers in the publications section.\nEducation # M.Sc. in Industrial Engineering — Systems Modeling and Data Analytics # K. N. Toosi University of Technology | 2023–2025\nB.Sc. in Industrial Engineering — Systems Engineering # Tafresh University | 2019–2023\nAcademic Achievements # Ranked 10th Nationwide in the Iranian National Scientific Olympiad in Industrial Engineering | 2023 Outstanding Undergraduate Student | 2022–2023 Secretary of the Industrial Engineering Student Scientific Association | 2023 Teaching Experience # Teaching Assistant, Probability Theory and Project Control | 2022 Instructor of Python Programming, Tafresh University | 2026–Present Contact # Email: mm.masoumian@gmail.com\nTelegram: @mahdi_masoumian\nLocation: Iran – Tehran\n","date":"23 June 2026","externalUrl":null,"permalink":"/en/ourteam/author/","section":"Quantistan's Team Members","summary":"","title":"Mohammad Mahdi Masoumian","type":"ourteam"},{"content":"","externalUrl":null,"permalink":"/en/authors/","section":"Authors","summary":"","title":"Authors","type":"authors"},{"content":"","externalUrl":null,"permalink":"/en/projects/","section":"Projects","summary":"","title":"Projects","type":"projects"},{"content":"","externalUrl":null,"permalink":"/en/series/","section":"Series","summary":"","title":"Series","type":"series"}]