Futures Trading Systems

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

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

"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

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.