Securities Trading Platforms

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".

You can now run AI agents that trade prediction markets for you. Polymarket just open-sourced a full agent framework for automated trading, with 1.6k GitHub stars. It lets code decide, fetch data, reason, and place real trades. 𝗧𝗵𝗲 𝘀𝘆𝘀𝘁𝗲𝗺 𝘄𝗶𝗿𝗲𝘀 𝗔𝗜 𝗱𝗶𝗿𝗲𝗰𝘁𝗹𝘆 𝗶𝗻𝘁𝗼 𝗺𝗮𝗿𝗸𝗲𝘁𝘀 It connects agents straight to prediction markets through official APIs. No scraping. No manual execution loops. Agents can: - Read live market data - Pull news and external signals - Generate decisions with LLMs - Sign and submit trades programmatically Everything runs locally, remotely, or in Docker. 𝗗𝗮𝘁𝗮 𝗮𝗻𝗱 𝗿𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴 𝗮𝗿𝗲 𝗺𝗼𝗱𝘂𝗹𝗮𝗿 The architecture splits data, memory, and actions. • RAG support for news and historical signals • Pluggable vector stores for context • Typed models for markets, orders, events You control inputs, prompts, and execution paths. 𝗜𝘁 𝘀𝗼𝗹𝘃𝗲𝘀 𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻 This removes glue code between models and money. You write logic once, then let it trade continuously.

Ever noticed your exchange charging slightly more fees than expected after a crypto swap? You might have encountered what’s known as a cascading fee. Here’s how it works: You swap BTC for ETH. - The exchange charges a 0.1 ETH trading fee (paid in ETH). - But then it also charges another 0.05 ETH to cover the cost of paying that first fee - In total, you’ve paid 0.15 ETH — even though the platform originally showed just 0.1 ETH in fees. That extra 0.05 ETH is the cascading part — essentially, a fee on a fee. For investors doing their own portfolio tracking or tax reconciliation, cascading fees can be frustrating: They often show up as separate withdrawals or unlabeled transfers. Crypto tax software may misread them as extra trades or a separate fee. Without careful review, your realized gains, cost basis, and transaction counts can all be off. Even if each amount is small, it adds up quickly if you have many trades with cascading fees. Even experienced investors sometimes overlook these small discrepancies, until they get hit when filing their taxes. You may wonder, how cascading fees are treated for tax purposes? Both the original fee and any cascading fee are generally considered transaction costs, not income or capital events. Depending on the flow of the trade: - If the fee is taken from what you sold, it reduces your proceeds. - If it’s paid in what you bought, it increases your cost basis. Accurately classifying them ensures you’re not overstating gains or missing deductible expenses. Takeaway: Cascading fees are a small but important reminder that crypto accounting still requires human judgment. Software can only see what the blockchain shows, not what the transaction means. 📌 Tip: Next time when you review your exchange data, check whether any “extra” withdrawals might actually be cascading fees. They can add up over time and quietly distort your tax picture. Question: have you come across cascading fees in your own trading or tax reports? How did you spot them? Feel free to share in the comment below. 🙏 A special shoutout to Nick Waytula for bringing the cascading fees issue to my attention 😊 #Crypto #CryptoInvesting #CryptoTax #Blockchain #DigitalAssets #DeFi #TaxTips

I built an Algorithmic Trading Engine (at 18) and here is how you can too: After spending time on the theoretical foundations of calculus and probability, I decided to apply that knowledge directly: I built a functional algorithmic trading engine from scratch. It's a backtesting engine. Its purpose is to simulate how a trading strategy would have performed using historical market data. This is the essential first step for anyone looking to develop automated trading systems. Here's a breakdown of the core components and why they matter for any aspiring quant: 1️⃣ Market Data Loader: You need reliable data. My MarketDataLoader (in C++) reads historical stock prices from a CSV, transforming raw numbers into structured, usable data. Robust parsing is crucial here to handle real-world data imperfections. 2️⃣ Strategy Engine: This is the brain. My Strategy class implements a Simple Moving Average (SMA) Crossover. It generates buy or sell signals based on short-term versus long-term price trends. A key improvement was making the strategy aware of current cash and share holdings, preventing unexecutable trades. 3️⃣ Portfolio Manager: This class, Portfolio, acts as the virtual ledger. It manages cash, shares, executes simulated orders, and tracks overall equity. This provides a clear picture of performance. 4️⃣ Order System: A straightforward Order structure defines the trade instructions for the portfolio. My initial backtest showed a profit of over $5,000 on a $100,000 starting capital. This confirms the fundamental logic is sound. Building this engine reinforced the power of combining theoretical knowledge with practical application. Seeing these concepts come to life in code is incredibly rewarding. Feel free to read the full article here: https://lnkd.in/eVxa-q8s For a detailed look at the architecture and code, you can explore the full codebase here: https://lnkd.in/e2y_wDDh A shoutout to those supporting me on the journey and the ones I learn the most from: ➡️ C++ MasterClass with Herik Lima and Fabio Galuppo ➡️ Paul Bilokon, PhD for the push through Quant Insider and Tribhuvan Bisen ➡️ Ripul Dutt for his insight and professional help ➡️ Dimitri Bianco, FRM for his incredibly insightful Youtube videos ➡️ Prateek Yadav, FRM, CQF and Mehul Mehta for the awesome community they have built ➡️Quant Finance Institute (QFI) for their highly helpful posts #QuantitativeFinance #AlgorithmicTrading #C++ #Backtesting #Programming #FinTech #LearningJourney

I used to view VWAP as the standard for "safe" execution. The data proved me wrong. ⏹️ We often assume that smoothing execution over time hides our intent. But what I’ve learned from forensic analysis (and what Hendershott, Jones, and Menkveld confirmed in their 2011 study on Algorithmic Trading and Information) is that "smoothing" often just creates a "rhythm". The research shows that simple time-sliced algorithms (#TWAP #VWAP) create statistically detectable "heartbeats" in the order flow within just 3-5 executions. If your execution logic lacks randomization against order book pressure, you are broadcasting your trade!!! 🫨 Predatory algorithms detect these heartbeats in milliseconds, front-running the remaining 90% of your parent order. I’ve seen this specific pattern erode P&L on otherwise profitable desks. Next time you deploy your VWAP/TWAP algo, make sure to audit your execution logic against these types of predatory detections. #electronictrading #tradingstrategy #hft #algotrading

Stumbled upon an LLM-based Trading System that Genuinely Impressed me. It feels less like a script and more like a Real-Investment Team. This Multi-Agent Architecture masterfully blends AI reasoning, market signals, and built-in risk governance. It does not just automate trades. It models how humans make decisions under pressure. 𝐇𝐞𝐫𝐞 𝐢𝐬 𝐡𝐨𝐰 𝐢𝐭 𝐰𝐨𝐫𝐤𝐬: 𝟏. 𝐌𝐮𝐥𝐭𝐢-𝐬𝐨𝐮𝐫𝐜𝐞 𝐢𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 It pulls from everywhere: markets, news, earnings, Twitter, fundamentals. This is not just technical analysis it’s sentiment, narratives, and macro signals. 𝟐. 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡𝐞𝐫 𝐚𝐠𝐞𝐧𝐭𝐬 𝐟𝐨𝐫𝐦 𝐨𝐩𝐢𝐧𝐢𝐨𝐧𝐬 Each agent reads a different feed, builds a thesis, and takes a stance bullish or bearish. Then they debate. This isn’t a static pipeline it is a live reasoning loop. 𝟑. 𝐓𝐫𝐚𝐝𝐞𝐫 𝐚𝐠𝐞𝐧𝐭𝐬 𝐞𝐯𝐚𝐥𝐮𝐚𝐭𝐞 𝐭𝐡𝐞 𝐝𝐢𝐬𝐜𝐮𝐬𝐬𝐢𝐨𝐧 They take that input and propose actual trades with justification. You don’t just get the “𝐰𝐡𝐚𝐭.” You get the “𝐰𝐡𝐲.” 𝟒. 𝐑𝐢𝐬𝐤 𝐦𝐨𝐝𝐞𝐥𝐬 𝐬𝐡𝐚𝐩𝐞 𝐭𝐡𝐞 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐲 A separate agent (or team) evaluates trades against risk profiles aggressive, neutral, conservative. This is where governance enters the loop. 𝟓. 𝐌𝐚𝐧𝐚𝐠𝐞𝐫 𝐚𝐠𝐞𝐧𝐭 𝐦𝐚𝐤𝐞𝐬 𝐭𝐡𝐞 𝐟𝐢𝐧𝐚𝐥 𝐜𝐚𝐥𝐥 It weighs conviction, risk alignment, and timing and either executes or walks away. What is powerful here is not just the use of agents. It is how modular and human-like the process feels. - Research - Discussion - Proposal - Risk review - Execution Each part is Transparent. Tunable. Swappable. The future of AI is not just about faster automation. It is about designing systems that think and reason in steps just like we do. Here is the open repo if you want to dive deeper: https://lnkd.in/e2-RBEaK Where else do you think this kind of agentic architecture could apply? Let’s explore.

**The Bloomberg Terminal: A Key Resource in Investment Banking?** The Bloomberg Terminal serves as an indispensable resource for professionals in investment banking, asset management, and trading, facilitating advanced financial analysis, extensive market data access, and real-time news updates. **1. Market Data & Real-Time Analytics** - Access comprehensive real-time datasets across equities, fixed income, foreign exchange, and commodities. - Analyze historical financial data alongside macroeconomic indicators to inform strategic decisions. - Monitor global market movements and key economic indicators in real time. 🔹 **Key Commands:** - **WEI** – Overview of World Equity Indices - **WB** – Comprehensive World Bonds Overview - **FXC** – Foreign Exchange Rates Insights **2. News & Research** - Stay updated with breaking news from Bloomberg News and other reputable sources, ensuring you have the latest market insights at your fingertips. - Conduct deep-dive analyses of industry reports and scrutinize company filings for informed decision-making. - Set customized alerts for significant market developments to maintain a competitive edge. 🔹 **Key Commands:** - **TOP** – Access to Top News Headlines - **BI** – Bloomberg Intelligence (in-depth research) - **BETA** – Beta Analysis for assessing stock volatility and risk profiles **3. Financial Analysis & Valuation** - Execute comparative analyses of companies utilizing detailed financial statements and key ratios. - Implement valuation methodologies, including DCF (Discounted Cash Flow), among others, to derive intrinsic values. - Track high-stakes M&A activity, IPOs, and credit ratings with precision. 🔹 **Key Commands:** - **FA** – Company Financial Analysis Tool - **RV** – Relative Valuation for Peer Comparisons - **MA** – Comprehensive Mergers & Acquisitions Database **4. Fixed Income, Derivatives & Portfolio Management** - Analyze bond pricing, construct yield curves, and assess credit risk using advanced analytical tools. - Utilize sophisticated pricing models for options, swaps, and other derivatives. - Manage and monitor portfolios with a suite of risk management tools, aiding in performance analysis. 🔹 **Key Commands:** - **YCRV** – Yield Curve Analysis Functionality - **SRCH** – Bond Search Tool for fixed income analysis - **PORT** – Portfolio Management Dashboard for tracking and optimizing investment strategies Feel free to add any additional insights or information.

📈 Building an AI Stock Advisor Inside N8N I have been experimenting with real-time market analysis, and the result is a complete AI stock advisor built entirely in N8N. It pulls live data, scans market news, merges everything into one dataset, and then feeds it into an AI agent that delivers a clear buy, hold, or sell assessment sent via Telegram. Here's how the automation works: 📊 Live Market Data: The workflow collects 1-minute, 15-minute, and 1-hour price data from TwelveData. Each feed captures a different layer of market behaviour, which gives the AI a more reliable picture of short-term momentum. 📰 News Scan: A separate request checks the latest headlines for the selected stock. This helps the AI understand sentiment shifts that do not show up in pricing alone. 🔗 Data Fusion: All inputs are merged and aggregated so the AI agent receives one complete packet of structured market information instead of scattered pieces. 🤖 AI Reasoning: The AI agent reviews the combined dataset and produces a balanced analysis. It evaluates trends, risks, volatility, and sentiment before giving a buy, hold, or sell call based on the current conditions. 📨 Instant Delivery: The final assessment is sent directly to the user through Telegram, turning the workflow into a simple real-time stock advisor you can check anytime. Here's why this matters: ✔ Brings market data, news, and reasoning into one automated system ✔ Helps traders and investors make quicker, more informed decisions ✔ Shows how powerful N8N becomes when combined with AI agents ✔ A strong example of using automation to interpret complex data If you want the N8N template, comment "STOCKS" and I will send it over. #n8n #ai #automation #stocks #finances #trading #growth

Your F&O trades are about to get expensive from April 1st! As you already know, STT on futures is set to move up from 0.02% to 0.05%, while options premium will increase from 0.10% to 0.15% and exercise of options will rise from 0.125% to 0.15% But many of my clients and colleagues are asking how do I see the second & third order consequences of this increase in trading costs? But before we get into that, let’s understand the intent The first and simplest reason highlighted was to curb excessive speculation in derivatives markets, as F&O trading volumes in India are now more than 500 times the country’s GDP Also, SEBI’s data already shows that 9 out of 10 retail traders in F&O continue to incur losses, which makes this more of a behavioural intervention rather than a revenue-driven one Because, in a country like India, it is crucial to protect the uninformed one! __ So, how will it affect the market participants? For active and high-frequency traders, this increase in transaction cost will compound significantly and strategies operating on thin margins will have direct pressure on profits And the second order impact may be seen is on retail participation, as this could push many to rethink impulsive trading behaviour and shift towards more structured and risk aware approaches But from a personal observation, F&O volumes have continued to grow despite earlier increases in STT announced in the 2024 Budget Even after the 2024 hike, where STT on futures selling moved from 0.0125% to 0.02% and options premium from 0.0625% to 0.1%, trading activity did not meaningfully slow down In my view, this move is less about restricting access and more about introducing discipline in a segment that has seen very rapid and speculative growth Because the real issue was never access to derivatives, it was how they were being used #Futures #Options

𝗔𝗜 𝗦𝘁𝗼𝗰𝗸 𝗣𝗶𝗰𝗸𝘀 𝗕𝗲𝗮𝘁 𝗕𝗲𝗻𝗰𝗵𝗺𝗮𝗿𝗸 𝗶𝗻 𝗟𝗶𝘃𝗲 𝗠𝗮𝗿𝗸𝗲𝘁 𝗧𝗲𝘀𝘁: 𝗦𝘁𝘂𝗱𝘆 AI autonomously searched the web, scored all Russell 1000 stocks, and constructed a daily portfolio. Many AI + investing research papers suffer from the same problem: the models were trained on historical internet data that often contains the outcomes they are asked to predict. Ask a model today what happened to a stock in 2022, and it may already know. This look-ahead bias makes me skeptical of many papers with “big” conclusions. But since the models have become ubiquitous, researchers can test their theories in real time. Two Peking University researchers, Zefeng Chen and Darcy Pu, did just that. They ran a live, nine-month experiment asking a frontier AI model to pick stocks every night across the Russell 1000. 𝗛𝗲𝗿𝗲’𝘀 𝘄𝗵𝗮𝘁 𝘁𝗵𝗲𝘆 𝗱𝗶𝗱: Every night from April 2025 through January 2026, they queried a leading U.S. frontier AI model via its web interface with live search enabled, with no pre-selected news or filings fed to the model. The model autonomously searched the web, synthesized what it found, and returned a score (−5 to +5) for each Russell 1000 stock. Signals were generated after the 4pm close and before the next open. Portfolios were entered at the opening auction and exited the following open. They ranked about 1,000 stocks by the model’s daily score, built a portfolio of the top 20 weighted by market value, and tested its performance using standard factor models. 𝗛𝗲𝗿𝗲’𝘀 𝘄𝗵𝗮𝘁 𝘁𝗵𝗲𝘆 𝗳𝗼𝘂𝗻𝗱: The Top-20 portfolio accumulated approximately 50% returns over nine months versus ~26% for the Russell 1000, a gap that widened throughout the sample. The signal works mainly on the upside. The top-ranked stocks outperform, while the lowest-ranked stocks don’t reliably underperform. AI is better at finding winners than losers. Waiting a day to trade wipes out most of the profit, cutting daily alpha from 18.5 basis points to 1.6. Within a month, it’s gone. I asked author Darcy Pu for his main takeaway: “Fully agentic frontier AI is smart enough to beat a highly adversarial market test -- turning live, unstructured web information into implementable daily stock-selection alpha.” To hear more from Pu, check out my full writeup on AI Street. Link below Paper: Autonomous Market Intelligence: Agentic AI Nowcasting Predicts Stock Returns