Securities Trading Platforms

"Palak, what's your secret to consistent profits?? Students expect some magic indicator. Some hidden pattern. My answer disappoints them: I have a system. And I follow it. That's it. No magic. Here's what I learned the hard way: When I started trading, every day was different. Monday: Used RSI and moving averages Tuesday: Tried candlestick patterns Wednesday: Listened to a "guru" and followed his calls Thursday: Tried a new strategy I saw on YouTube Friday: Confused why nothing worked I was trading randomly, hoping for consistent results. Then I built a system. My current trading system: 1. Scan for stocks above 50 & 200 MA (trend filter) 2. Check RSI - avoid extremes (momentum filter) 3. Identify support/resistance zones (entry planning) 4. Wait for volume confirmation (conviction check) 5. Enter with 2% risk max (position sizing) 6. Set stop loss immediately (risk management) 7. Define target before entering (exit strategy) 𝐄𝐯𝐞𝐫𝐲. 𝐒𝐢𝐧𝐠𝐥𝐞. 𝐓𝐫𝐚𝐝𝐞. 𝐅𝐨𝐥𝐥𝐨𝐰𝐬. 𝐓𝐡𝐢𝐬. No gut feelings. No "this time is different." Some days I find no setups. So I don't trade. Some days I find 5. I still only take 2 (quality over quantity). 𝐓𝐡𝐞 𝐫𝐞𝐬𝐮𝐥𝐭 𝐚𝐟𝐭𝐞𝐫 𝟔 𝐲𝐞𝐚𝐫𝐬? Not 100% win rate. I still lose 30% of my trades. But I'm consistently profitable. Because my losses are small and controlled. My wins are planned and captured. 𝐒𝐲𝐬𝐭𝐞𝐦 𝐛𝐞𝐚𝐭𝐬 𝐬𝐩𝐨𝐧𝐭𝐚𝐧𝐞𝐢𝐭𝐲. 𝐄𝐯𝐞𝐫𝐲 𝐭𝐢𝐦𝐞. This is what I teach: Not just analysis. Not just patterns. 𝐇𝐨𝐰 𝐭𝐨 𝐛𝐮𝐢𝐥𝐝 𝐘𝐎𝐔𝐑 𝐬𝐲𝐬𝐭𝐞𝐦 𝐭𝐡𝐚𝐭 𝐰𝐨𝐫𝐤𝐬 𝐟𝐨𝐫 𝐘𝐎𝐔𝐑 𝐩𝐞𝐫𝐬𝐨𝐧𝐚𝐥𝐢𝐭𝐲. Because my system might not suit you. But the process of building one? That's universal. Do you have a trading system? Or are you still trading by feel? 👇 #TradingSystem #TradingStrategy #ConsistentTrading #StockMarket #TradingDiscipline #SystematicTrading #StockMarketIndia #FinanceWithPalak #TradingEducation #ingTrading

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.

Portfolio optimization, grounded in Modern Portfolio Theory (MPT), is the foundational process of selecting the optimal distribution of assets to achieve maximum financial return while minimizing investment risk. Traditional financial methods like mean-variance optimization (MVO), uniform constant rebalanced portfolios (UCRP), and standard factor-based investment strategies are still widely adopted for asset allocation. In the last decade or so, quantitative finance has shifted toward machine-/deep-learning (ML/DL) and reinforcement learning (RL) to automate trading decision-making. However, current portfolio optimization approaches still face critical challenges. Traditional methods rely too heavily on rigid, historical data assumptions and struggle to adapt to volatile environments. Meanwhile, pure RL models suffer from a narrow focus; they primarily optimize for technical features like price signals or model architectures, completely ignoring macro market conditions and established economic theories (such as factor-based insights), leading to unstable performance during regime shifts. To bridge this research gap mentioned above, the authors of [1] introduce the Dynamic Factor Portfolio Model (DFPM), a hybrid framework that embeds financial domain expertise directly into a Deep Reinforcement Learning (DRL) structure. The DFPM addresses current shortcomings by utilizing a dual-module system: • Dynamic Factor Module (DFM): It tracks and dynamically scores five macroeconomically significant fundamental factors; Size, Value, Beta, Investment, and Quality. • Price Score Module (PSM): It analyzes real-time individual asset price data and inter-asset correlations. By integrating macroeconomic trends via the DFM with stock-level patterns from the PSM, the RL agent gains a comprehensive perspective. This enables the DFPM model to execute highly adaptive, interpretative, and stable asset weight adjustments as market environments shift. The DFPM was benchmarked against prominent baselines, including traditional strategies (like MVO, UCRP and conventional factor models) and state-of-the-art RL methods (such as PPO, A2C, and DDPG) across rigorous testing on the Nasdaq 100 and Dow Jones datasets. The experimental results demonstrate that the DFPM consistently and significantly outperforms all benchmarked baselines. It achieves superior risk-adjusted returns, as evidenced by its higher Sharpe ratios and Fractional Accumulated Portfolio Value (fAPV). The DFPM proves to be better precisely because it utilizes 'dynamic factor-informed knowledge' to recognize broad market contexts. This ensures it captures upward momentum during bull markets while aggressively reducing drawdowns and mitigating capital loss during periods of high volatility. The link to the paper [1] is posted in the comments.

🏦 15 GitHub repos every AI Agent in Finance developer should bookmark The finance AI ecosystem is exploding. But finding the best resources costs you weeks. Here's the map. All open-source, all finance-specific. 1./ Financial LLMs - FinGPT — Open-source financial LLM (sentiment, forecasting, robo-advisory) - FinNLP — Democratizing internet-scale financial data 2./ Trading Agents - Quantitative Platform - TradingAgents — Multi-agent LLM trading framework (Bull/Bear researchers + risk managers) - AI Hedge Fund — Simulated hedge fund powered by LLM agents (Ben Graham, Cathie Wood…) - FinRL-Trading — Modular quantitative trading platform (Python + ML strategies ...) 3./ Multi-Agent Platforms - FinRobot — Full-stack AI agent platform for equity research & valuation - AgenticTrading — From algorithmic to agentic trading (NeurIPS 2025) - Open-Finance-Lab — Research-grade multi-agent orchestration 4./ Specialized Use Cases - FinRL-DeepSeek — LLM-infused risk-sensitive RL for trading - Qlib (Microsoft) — AI-oriented quant investment research platform 5./ Learning & Reference - FinAI Contest 2025 — Open challenges on FinRL + FinGPT agents - AI4Finance Foundation — The hub of open-source financial AI - 500 AI Agents Projects — Curated finance agent use cases   - Open-Finance-Lab — Research-grade FinLLM Leaderboard and multi-agent orchestration The Repos: FinGPT → https://lnkd.in/eHJCdshT FinNLP → https://lnkd.in/eBzDwtxV TradingAgents → https://lnkd.in/eVxfgiZi AI Hedge Fund → https://lnkd.in/edsq8YcP FinRL → https://lnkd.in/eFptF9ve FinRobot → https://lnkd.in/ek3qYZUy AgenticTrading → https://lnkd.in/ewHnpJPp FinRL-DeepSeek → https://lnkd.in/ezrbnKhk Qlib (Microsoft) → https://lnkd.in/envcWYmu FinAI Contest 2025 → https://lnkd.in/eRi5G6fP AI4Finance Foundation → https://lnkd.in/eVegp4fZ 500 AI Agents Projects (Finance section) → https://lnkd.in/egtup-rV Open-Finance-Lab → https://lnkd.in/ewzRNQCK Others: FinRL → https://lnkd.in/ehr9WafR FinLLM-Leaderboard → https://lnkd.in/esWXp-Dt ❓ Which one are you already using? Drop it in the comments 👇 💾 Bookmark this. The ecosystem won't wait for you. ♻️ Repost if your team is still building finance workflows without agents.

I’ve been trading for ~30 years. First half: fully discretionary, living inside futures microstructure. It worked—until algos started exploiting the same patterns and reacting in microseconds. Edge decay was real. So ~10 years ago I switched to systematic. Now I run many uncorrelated strategies in parallel without babysitting screens all day. I wouldn’t go back. My biggest unlock: reusability of know‐how. When I finish a new system, I plug it into a ready workflow in minutes. It monitors itself; I move my brain to the next big thing. Here’s the playbook I wish I had from day one: - Framework (design once → reuse forever) - Data → clean, feature, label. - Hypothesis → simple, testable edges (breakouts, momentum, mean reversion). - Validation → IS/OOS, realistic costs/slippage. - Risk → position sizing, max heat, portfolio exposure caps. - Deploy → automated orders, fail‐safes. - Monitor → health dashboards, kill‐switch rules, mobile app. - Iterate → new systems slot into the same pipeline. Principles that compound: - Many small, independent edges > one “genius” setup. - Process beats prediction. - Correlation control is alpha. - Shipping beats perfecting. Discretionary taught me markets. Systems gave me scale. #SystematicTrading

Still searching for the “perfect” timing signal? Too often traders chase the Holy Grail strategy. With high probability, this ends up producing unstable and fragile systems that fail quickly in live markets. A more robust approach is to build simple, uncorrelated (by design) models, each one capturing a distinct statistical edge. Then, if capital allows, diversify not only across edges but also within each edge (by varying time frames and signals) and across instruments. For example: 🔹 Want intraday trend exposure? Don’t just pick one breakout system. Combine multiple ORBs with other volatility breakout systems... ideally across several tickers. 🔹 Then layer in orthogonal edges, such as mean reversion or short-volatility. The real magic happens thanks to low correlation across models. In the example attached, combining 3 intraday volatility breakout systems with 2 daily mean-reversion approaches on S&P500 futures delivered: ▶️ Correlation (Trend vs Mean Reversion): -0.04 ▶️ Sharpe Trend: 1.30 ▶️ Sharpe Mean Reversion: 0.98 ▶️ Combined Sharpe: 1.73 ▶️ CAGR (2007–2025): 31% ▶️ CAGR (2019–2025): 62% 👉 Don’t aim for the “best” single strategy. Target the essence of the edge — and let an ensemble of models work for you. ❓ Based on your experience, which edges do you consider the most orthogonal — both from a principled and an empirical perspective? If you have any questions, feel free to DM me or send an email to carlo@concretumgroup.com

HFT Market-Making Systems I spent the first 13 years of my career designing algorithmic trading systems for large HFTs in options, futures, and ETFs (intl/US equities, commodities, fixed income, volatility). Here’s a summary of each component, note the role AI/ML actually plays. Market data. A market-making system requires realtime orderbook deltas and trades, not just for the symbol that’s being quoted but also for every related instrument used in computing the theoretical fair value of the instrument. Every exchange has a different protocol specification and format, from JSON over websocket (slowest) to FIX (common) to fixed-width binary (fastest). The lowest latency systems will ingest market data on a different thread or process to avoid interfering with the core trading logic of the system. Fair Value. Successful market making depends on a precise understanding of the theoretic fair value of an instrument, which is the indifference point between buying and selling the instrument modulo fees and other costs. The complexity spans from a several-parameter model based on one or two instruments to highly multivariate calculations across a basket of different symbols. This component is usually the firm’s most guarded intellectual property. Order placement. Accurately predicting the price of an instrument is only half of the core logic of a trading system; deciding where and how to place orders is equally determinative of P&L. Maintaining balanced two-sided quotes, optimizing for queue position, minimizing traffic to comply with order rate limits, canceling to avoid adverse selection, and order sizing are all intricately determined by the strategy designer. Exchange connectivity. Similar to the market data component, all trading systems must send orders and cancels using each exchange’s unique messaging protocol. JSON, FIX, and binary formats are all used, with the last most common among traditional exchanges where latency and throughput are optimized. The connectivity layer can be embedded within the order placement process or segregated as its own gateway process. Offline training. Training is not part of realtime transformation of ticks to trades, but it is one of most important differentiators between trading firms. All parameters that define fair value calculation and order placement are set via offline compute tasks that are often guided by machine learning. A significant amount of the GPUs that large HFTs buy or rent are directed to model discovery and parameter optimization. A handful of HFTs already use specialized GPUs for the linear algebra behind valuation, but the practice remains rare, confined to strategies where predictive power matters more than shaving the last nanoseconds off latency. As purpose-built inference silicon such as Google’s TPU and OpenAI’s Jalapeño becomes widely available, more HFTs will fold online ML and inference into their systems’ trading loops.

Researchers studied 1,710 futures pair portfolios across equities, bonds, currencies, and commodities from Jan 1985 through Sep 2023. They found dynamic trading methods boost returns and reveal hidden interactions between asset classes. These strategies improve diversification and risk control. Results depend on data limits and need real-world tests before finance teams adopt them. This study shows that targeting top “base pairs” can triple average annual returns at fixed leverage. Key findings: 📈 Performance Boost: Focusing on the top 5% of base pairs lifts the “All” portfolio from 3.4% to 10.4% annualized returns at fixed leverage. 🔄 Diversification Edge: Cross-asset interactions across equities, bonds, currencies, and commodities reveal shifting risk-return dynamics and enhanced diversification. 🔍 Predictive Drivers: Cross-asset effects account for up to 55% of performance heterogeneity; signal-mean imbalances and correlations further shape pair returns. ⚙️ Strategy Revival: Underperforming momentum approaches convert into winners when high-θ pairs are selected each month. ✅Practitioner tips: Use monthly θ (risk-adjusted return strength) scoring to rank base pairs, prune the bottom 95%, and allocate equally to the top pairs. Rebalance each month, monitor cross-asset signals, and standardize leverage, start with a 5% selectivity threshold to boost returns and diversify risk. 🎓🏛✍️ Authors & affiliations: Christian Goulding, Auburn University Harbert College of Business Business, Auburn University Campbell Harvey, Duke University, National Bureau of Economic Research 👉 Read the full study on SSRN:5193565 ✅ If you are interested in keeping up with new papers and research in Quant Finance/AI/LLMs, Sign-Up to our Monthly Quant Finance and AI/LLM Research Newsletter, link in the comments. #Finance #Trading #Investing #PortfolioOptimization #RiskManagement #Diversification #QuantitativeFinance #FuturesTrading #AssetAllocation #InvestmentResearch #MarketAnalysis #DataDriven #TradingStrategies #FinancialMarkets #QuantTrading #AlternativeInvestments #FinancialModeling #SmartInvesting #FinancialInnovation #InstitutionalInvesting

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

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.