Securities Trading Platforms

Can we use AI agents for stock market prediction? 😮 Recently, LLM-based agents have demonstrated remarkable advancements in handling multi-modal data, enabling them to execute complex, multi-step decision-making tasks. This research introduces a multi-modal multi-agent system designed specifically for financial trading tasks. The framework employs a team of specialized LLM-based agents, each adept at processing and interpreting various forms of financial data, such as textual news reports, candlestick charts, and trading signal charts. The framework comprises four primary components: the Summarize Module, the Technical Analyst Module, the Prediction Module, and the Reflection Module. The Summarize Module condenses large volumes of textual news data into concise summaries that highlight factual information influencing stock trading decisions. The Technical Analyst Agent leverages the visual reasoning capabilities of LLMs to analyze candlestick charts with technical indicators, providing interpretations for next-day trading strategies. The Reflection Module consists of two parts: one assesses the short-term and medium-term performance of previous trades, while the other plots past trading signals, generates charts, and offers insights into the effectiveness of trades. The Prediction Agent integrates information from these components to forecast trading actions, determine position size as a percentage of the portfolio, and provide a detailed explanation of the decision. Based on the Prediction Agent’s output, the Reward Agent executes trades and calculates performance metrics. These metrics are then used by the Reflection and Prediction Agents in the subsequent iterations. The detailed flow of our framework is illustrated in the Figure. Know more about the framework in this practical research paper: https://lnkd.in/gxvEUGAA Here is my simple video explaining how AI agents work: https://lnkd.in/d_V9DqbH This is my practical hands-on guide on building multi-agent AI system: https://lnkd.in/gdaA5s3Z

People at high-frequency trading firms can make $1M+/yr making computers trade markets really fast. But how? Well, normal internet fiber is never in a straight line and makes light go 1.5x slower, so instead HFTs use... microwaves! Thread on the cool tech behind HFTs. 🧵 Many HFTs are based in Chicago there and in NYC markets. Every millisecond can mean reducing the arbitrage that makes you money. The public internet with regular routing, unoptimized software, this takes 30-40ms. Microwaves at 6-30GHz can get this down to <10ms round trip! Why aren't microwaves used everywhere then ? Well: a) they're expensive (>$10M+) and need many repeaters to amplify signal b) they attenuate quickly in poor weather conditions c) low bandwidth Check out Aviat, Dragonwave-X, Siklu and SIAE that make these! What other kind of hardware do HFTs use? HFTs also use special Network Interface Cards (NICs) like those made by Mellanox and Solarflare. As data comes in, it skips the kernel with direct memory access (no copying). They also use custom FPGAs from Xilinx and more. What are all the software optimizations? Probably not all, but some of the key ones: Kernel Bypass ~100μs Remote direct memory access ~50μs NIC TCP Offload ~40μs FPGA Processing ~5μs Optimized Stack ~200μs Together, you can squeeze ~0.4ms. How can they afford this expensive stuff? Some of these companies make the most money / employee of all companies. That's why they're pretty silent about it. They include: — Jump Trading — Citadel — Hudson River Trading — DRW — Virtu Financial HFTs ask the question "how do you make computers do things as fast as possible" with no money constraints? That's engineering at its best. They created a not-so-cottage industry of specialized hardware suppliers just fort themselves. Making money at an HFT isn't always that easy though. If you want a fun HFT horror story, search for "Knightmare hft"

🇦🇺 AUSTRAC has identified "undisclosed nested platforms" as a risk to digital currency exchanges - but what are they? In its 2024 Money Laundering National Risk Assessment, AUSTRAC identified "the operation of undisclosed nested platforms" as a feature that can "increase anonymity for customers and decrease visibility for authorities." At TRM Labs, we call these parasite exchanges and they are a risk every crypto compliance team should be on the lookout for. Nesting - where a smaller exchange relies on the architecture of a larger exchange - is not always bad. Many service providers establish official partnerships with well known globally recognized exchanges. Under these partnerships, when a user initiates a trade on their platform, the user’s request is routed to the most suitable exchange partner based on factors such as price, liquidity, and network fees. Where the problem comes is when the nesting is undisclosed - that's when you have a parasite. 🔹 TRM research shows that a majority of high-risk exchanges – those with weak or non-existent KYC and AML requirements – operate as parasite exchanges 🔹 Almost two thirds are based in Russia and Iran, which means they are highly exposed to funds linked to sanctioned and other high-risk entities. 🔹 As with all parasites, the one that suffers most in the relationship is the host, which is usually a regulated exchange. Hosting parasite exchanges and thereby potentially violating the terms of service and facilitating illicit transactions – including with sanctioned entities or jurisdictions – carries enormous regulatory, compliance and reputational risk. 🛑 Digital currency exchanges can detect parasite exchanges through a combination of transaction monitoring, risk scoring, compliance checks, blockchain intelligence and information. For more on how, check out our full blog post on the topic here: https://lnkd.in/g_4g45Du

🔌⚡ Energy and financial trading have one thing in common: real-time data matters. In the energy sector, real-time streams from price feeds, weather systems, IoT meters, and market events are critical to making the right trading decisions at the right time. 📈 Apache Kafka and Apache Flink power scalable and reliable real-time platforms across industries. Whether you are optimizing energy flows 🌞🔋 or executing financial trades 🏦📊, data streaming is the foundation for faster, smarter actions. In this article, I explore: - Real-world energy trading platforms from Uniper, re.alto, and Powerledger - Architectures for integrating IoT, SCADA, and market feeds - Why event-driven, scalable systems are key for energy trading This is not just about energy! 🔥 The same real-time architectures apply across FinServ trading desks, where milliseconds matter. 🔗 Dive into the article here: https://lnkd.in/eHAdJEcg #DataStreaming #ApacheKafka #ApacheFlink #EnergyTrading #FinServ #Trading #IoT #SCADA #RealTimeData #Confluent #EventDriven #DataArchitecture

🚀 Why if Statements Can Kill Performance in Low-Latency C++ In high-frequency trading (HFT), every nanosecond counts. One of the most overlooked performance killers? Branch misprediction. Modern CPUs try to guess the outcome of your if statements. If the guess is wrong → you pay the penalty: pipeline flushes, wasted cycles, and latency spikes. 💡 Naive code (branching): ``` if (x > threshold) { sum += x; } ``` ⚡ Branchless alternative: sum += (x > threshold) * x; Here, (x > threshold) evaluates to 0 or 1, avoiding unpredictable branches. 🔑 Takeaway: • Branchless code = more stable latency • Works best when branch predictability is low • In HFT, stability often beats raw average speed 👉 Idiomatic, low-latency C++ means writing code that works with the CPU architecture, not against it. ❓What’s your experience with branchless programming? Do you use it proactively, or only after profiling? #Cplusplus #LowLatency #HighFrequencyTrading #PerformanceEngineering #HFT

Navigating the Evolving Landscape of Wealth Management: The Role of Robo Advisors I first got exposed to the world of WM during my time at AXA. A lot has changed in the sector and the future holds some disruptive opportunities. One interesting evolution over the past decade has been the emergence of #roboadvisory , often perceived as a 'Starter Kit' for aspiring investors, Robo services are starting to have an impact on how we approach WM (slowly but surely). 🌟 Robo Advisors: Democratizing Wealth Management Robo Advisors have emerged as the gateway for many entering the investment arena (check out - Betterment , Nutmeg or FinaMaze). They all offer an accessible, user-friendly platform, perfect for those taking their first steps into financial planning. This isn't just about investment; it's about education and empowerment. 🔍 The Limitations of a Digital-Only Approach However, the journey of wealth management often outgrows the confines of a digital-only service. As personal wealth expands, the financial landscape becomes more intricate, necessitating a level of customization and personal interaction beyond what current Robo Advisors can offer and/or what the customer is willing to trust a digital only platform with. The human element - trust, understanding, and bespoke advice - become more important. This arguement also holds true for those who might need WM advise but don’t entirely trust a digital platform for the same. 🔗 The Future is starting to take shape: Integration and Hybrid Models The future of wealth management is not about choosing between digital or traditional methods but blending them harmoniously. The integration of Robo Advisory services into the broader WM framework is already happening. We're envisioning a world where traditional wealth management firms adopt hybrid models, combining the efficiency of Robo Advisors with the depth and personalization of human financial advisors. 🌐 A Competitive Imperative: Concluding Views Incorporating Robo Advisory is now a viable option for traditional wealth management firms (and playbooks are emerging). In a world driven by technology and changing consumer expectations, staying relevant means embracing digital transformation. Robo Advisors will become one of the many tools used by these firms, ensuring they meet the evolving needs of their diverse client base. The role of Robo Advisors in wealth management is a fascinating evolution, marking a shift towards more inclusive, education-oriented, and flexible financial planning. 🔥 Join the Conversation What's your take on the integration of Robo Advisors in traditional wealth management? Are we ready for this hybrid future in the GCC? Share your thoughts below! #WealthManagement #RoboAdvisors #FinancialPlanning #DigitalTransformation #InvestmentTrends #Fintech #HybridModels #wealthtech

The Rise of AI in Financial Markets - Manus AI, the Disruptor For decades, financial markets have been driven by speed, precision, and access to the right information at the right time. In recent years, artificial intelligence (AI) has emerged as a powerful force, reshaping how traders and investors make decisions. Among the leading innovations in this space is Manus AI, a Chinese-developed platform designed to revolutionize stock and commodity market analysis. By integrating machine learning, natural language processing, and real-time predictive analytics, Manus AI is not just a tool but a complete transformation of financial decision-making. Manus AI: A New Force in Financial Analytics Founded in 2021, Manus AI was created by a team of finance and AI experts with a mission to make advanced market analysis accessible to all investors—not just large institutions. Unlike traditional models that rely on static indicators, Manus AI’s deep neural networks identify non-linear relationships in market data, adapting to unpredictable shifts like geopolitical tensions or supply chain disruptions. Its advanced capabilities allow users to anticipate market changes with greater accuracy, making it a game-changer in the world of trading and investment analysis. The Power Behind Manus AI What sets Manus AI apart is its ability to synthesize vast amounts of global data—from stock exchanges and futures markets to news, social media, and economic reports—providing real-time insights. Its predictive models not only forecast price movements but also explain the reasoning behind them, enhancing investor confidence. Additionally, the platform’s sentiment analysis tracks market psychology by analyzing news and public discourse, allowing traders to react before major price swings occur. With customizable dashboards and built-in risk management tools, Manus AI caters to both short-term traders and long-term investors, positioning itself as a comprehensive solution in financial analytics. Impact and Challenges of AI in Trading Manus AI is already making waves, reducing prediction errors by 30-40% in commodities like gold and agricultural futures while helping analysts cut down research time. During the 2023 banking crisis, the platform successfully flagged liquidity risks in regional banks weeks before credit rating agencies did. The Future of AI in Financial Decision-Making Looking ahead, Manus AI aims to integrate quantum computing for even faster processing and explore blockchain for secure financial analytics. As competition grows from global financial giants like Bloomberg and Kensho Technologies, its success will depend on continuous innovation and transparency. But one thing is certain: AI is no longer a futuristic concept in finance—it is already transforming the industry. Check the article "Manus AI: Revolutionizing Stock and Commodity Market Analysis with Advanced AI Capabilities".

0 Account Opening and 0 Account Maintenance charges by Groww and Upstox are just Marketing Gimmicks. Especially for Swing Traders who carry out the trade in large quantities. → ZERODHA CHARGES Account Opening: ₹200 AMC: ₹354 / year (including GST) Delivery Charges: ₹0 DP Charges: ₹16 per sell (including GST) → GROWW CHARGES Account Opening: ₹0 AMC: ₹0 Delivery Charges: ₹20 or 0.05% (whichever is lower) DP Charges: ₹16 per sell (including GST) → UPSTOX CHARGES Account Opening: ₹0 AMC: ₹0 Delivery Charges: ₹20 or 2.5% (whichever is lower) DP Charges: ₹22 per sell (including GST) → ANGEL ONE CHARGES Account Opening: ₹0 AMC: ₹283 / year (including GST) Delivery Charges: ₹0 DP Charges: ₹24 per sell (including GST) Let's Imagine: • 5 delivery transactions a month including both Buy & Sell. So, 60 in a year (12 * 5) • ₹20,000 each. So, ₹12 Lakh a year (20,000 * 5 * 12) • 30 transactions will be of Buy and 30 of sell of the same amount. No capital appreciation. Now let's see how much you will pay the broker in a year. → ZERODHA Account Opening & AMC: ₹554 DP Charges: ₹480 (16 * 30) 𝗧𝗢𝗧𝗔𝗟: ₹𝟭,𝟬𝟯𝟰 → ANGEL ONE Only AMC: ₹283 DP Charges: ₹720 (24 * 30) 𝗧𝗢𝗧𝗔𝗟: ₹𝟭,𝟬𝟬𝟯 → GROWW Delivery Charges: ₹600 (12 Lakh * 0.05%) DP Charges: ₹480 (16 * 30) 𝗧𝗢𝗧𝗔𝗟: ₹𝟭,𝟬𝟴𝟬 → UPSTOX Delivery Charges: ₹1200 (₹20 * 60) DP Charges: ₹660 (22 * 30) 𝗧𝗢𝗧𝗔𝗟: ₹𝟭,𝟴𝟲𝟬 NOTE:- 1. I have not considered other charges such as STT, Transaction Charges, SEBI charges, Stamp Charges. These are generally the same for every broker. 2. This is only made for Delivery trades and not for Intraday and FNO. P.S. Open for any suggestions or corrections. Follow Abhinav Goel for more.❤️ LinkedIn LinkedIn Guide to Creating #finance #stocks #education #marketing

Trading with Time Series Causal Discovery: An Empirical Study” by Ruijie Tang. - Business School, Imperial College London A study by Ruijie Tang (Imperial College London) applies cutting-edge causal discovery algorithms to financial time series — turning theoretical models into real-world trading strategies. Key Highlights: The paper tests how causal discovery algorithms (tsFCI, VarLiNGAM, TiMINo) can uncover cause-effect relationships between stock prices. Using these insights, it constructs daily long-short strategies — buy the predicted winners, sell the losers. Tested on real data from S&P 500, CSI300 (China), and even Nancy Pelosi’s portfolio! Findings: VarLiNGAM was the most effective: scalable, accurate, and delivered the highest returns — especially in large datasets like the S&P 500. Strategies built on causal structures clearly outperformed those relying only on each stock’s past prices. Optimal results emerged from short time lags (1–2 days) and selecting the top 1–6% of stocks based on predicted returns. Limitations: Algorithms like tsFCI and TiMINo struggled with scalability — unable to process large datasets within 24 hours. Performance dropped with small portfolios, where important causal signals might be missing. Why It Matters: This work shows that causal inference isn’t just academic — it can drive profitable and data-driven decision-makingin the markets. A major step forward in the convergence of AI and quantitative trading. For more detail, you can see the full paper attached to this post. #QuantFinance #CausalAI #AlgorithmicTrading #TimeSeries #VarLiNGAM #AIinFinance #FinancialEngineering

This concept is the reason you can track your Uber ride in real time, detect credit card fraud within milliseconds, and get instant stock price updates.  At the heart of these modern distributed systems is stream processing—a framework built to handle continuous flows of data and process it as it arrives.     Stream processing is a method for analyzing and acting on real-time data streams. Instead of waiting for data to be stored in batches, it processes data as soon as it’s generated making distributed systems faster, more adaptive, and responsive.  Think of it as running analytics on data in motion rather than data at rest.  ► How Does It Work?  Imagine you’re building a system to detect unusual traffic spikes for a ride-sharing app:  1. Ingest Data: Events like user logins, driver locations, and ride requests continuously flow in.   2. Process Events: Real-time rules (e.g., surge pricing triggers) analyze incoming data.   3. React: Notifications or updates are sent instantly—before the data ever lands in storage.  Example Tools:   - Kafka Streams for distributed data pipelines.   - Apache Flink for stateful computations like aggregations or pattern detection.   - Google Cloud Dataflow for real-time streaming analytics on the cloud.  ► Key Applications of Stream Processing  - Fraud Detection: Credit card transactions flagged in milliseconds based on suspicious patterns.   - IoT Monitoring: Sensor data processed continuously for alerts on machinery failures.   - Real-Time Recommendations: E-commerce suggestions based on live customer actions.   - Financial Analytics: Algorithmic trading decisions based on real-time market conditions.   - Log Monitoring: IT systems detecting anomalies and failures as logs stream in.  ► Stream vs. Batch Processing: Why Choose Stream?   - Batch Processing: Processes data in chunks—useful for reporting and historical analysis.   - Stream Processing: Processes data continuously—critical for real-time actions and time-sensitive decisions.  Example:   - Batch: Generating monthly sales reports.   - Stream: Detecting fraud within seconds during an online payment.  ► The Tradeoffs of Real-Time Processing   - Consistency vs. Availability: Real-time systems often prioritize availability and low latency over strict consistency (CAP theorem).  - State Management Challenges: Systems like Flink offer tools for stateful processing, ensuring accurate results despite failures or delays.  - Scaling Complexity: Distributed systems must handle varying loads without sacrificing speed, requiring robust partitioning strategies.  As systems become more interconnected and data-driven, you can no longer afford to wait for insights. Stream processing powers everything from self-driving cars to predictive maintenance turning raw data into action in milliseconds.  It’s all about making smarter decisions in real-time.