Juan Pablo DeSilva

This account is operated by Juan Pablo DeSilva, Chief Data Scientist @quantgate. Posts reflect personal views based on publicly available information. Nothing here is investment advice. I may hold and trade securities mentioned. For official company communications, rely on press releases and SEC filings.

Absolutely, @ChinezeToc4! 🚀 At @QuantGateSystems, we believe that the true edge lies in mastering the microstructure of markets. Our proprietary signals transform complex order-book dynamics into actionable insights, enabling traders to anticipate market moves with precision. 📈 As the trading landscape becomes more sophisticated, having a deeper understanding of liquidity, sentiment, and intent is crucial. Our goal is to empower institutions with technology that bridges the gap between data and execution, ensuring decisions are both timely and informed. #QuantitativeTrading #MarketIntelligence

🚀 Exciting times for the world of quantitative technology! At @QuantGateSystems, we're all about breaking barriers too—by transforming order-book data into actionable insights for professional traders. 📊 As markets evolve, our signal technology is designed to offer a clear edge, turning complex data into consistent decision-making. Imagine the power of real-time, intent-aware analytics fueling both traditional and tokenized markets. 🔍 Curious about how we're simplifying the path from signal to execution? Check out our latest updates! #QuantitativeTrading #MarketIntelligence #QGSI

The future of quantitative technology is here. 🚀 We are excited to spotlight @fundtir—a first-of-its-kind tokenized investment fund designed to democratize access to institutional-grade trading strategies. No more barriers. Just transparent, blockchain-backed growth. 🧵

$QGSI's scientific foundation? The order book. It's the most information-rich, real-time record of market intent. Our product strategy focuses on translating complex, high-frequency phenomena into stable, interpretable outputs. Because seeing data sooner isn't enough if it's not usable. #DataScience #MarketInsights

The future of institutional finance is here! With our new automated trading system, we aim to offer stronger risk controls and more repeatable decision-making to our clients. We're on the brink of a major breakthrough. Stay tuned for more updates! $QGSI #FinTech #Investing

In a world where markets move at machine speed, the edge belongs to those who can convert information into action. At QuantGate, we're engineering a future where consistent, repeatable decision-making in trading becomes the norm. Exciting times ahead! $QGSI #TradingTech #Finance

We believe that our new autonomous trading system will revolutionize the way our research is delivered to institutional users. This technology aims to reduce integration burden for market professionals, and create a clearer framework for evaluation and licensing. $QGSI #Trading #Technology

If a trading signal does not survive transaction costs, should it be discarded? In quantitative research, signals are often evaluated almost exclusively on their standalone net performance. If a signal fails after commissions and slippage, the conclusion is usually immediate: it has no economic value. In the second edition of QuanTips, we revisit this assumption. We introduce a class of ultra-short-term predictors — what we refer to as fast alphas. We document that these signals can display compelling gross-of-fee performance. Yet once realistic transaction costs are introduced, their standalone profitability disappears. At that point, most research would end. Instead, we ask a different question: Is tradability the only valid criterion for economic relevance? In this research piece, we introduce a distinction between: 🔹 Monetizable alpha: signals that remain profitable after costs when traded directly 🔹 Informational alpha: signals that may not be independently tradable, yet still contain economically meaningful information We then show empirically, using 5-minute SPY data from 2007 to 2026, how a fast-decaying mean-reversion signal can enhance the performance of a simple intraday trend trading strategy when used as an execution overlay. The baseline trend model delivers a double-digit CAGR with a solid Sharpe ratio on a standalone basis. When conditioned on the fast alpha signal, the strategy improves both CAGR (by roughly 200 bps) and Sharpe ratio while remaining robust net of costs. The signal itself does not generate standalone profits. But it improves how profits are generated. The results suggest that evaluating signals purely on standalone net performance may understate their true contribution within a multi-horizon framework. 📄 QuanTips #2 is now available. 🔗You can find the link to the full research piece in the comments below. As always, if you have any feedback or questions, feel free to DM. If you find the framework useful, feel free to share it with others interested in systematic trading.

Our focus at QuantGate Systems is clear: increased automation, stronger risk controls, and more repeatability in decision-making. Our autonomous algorithmic trading system is being designed to deliver just that. We're closer than ever to achieving our mission. $QGSI

Our dedicated team at QuantGate has been working tirelessly to bring you a unified system that can process real-time market data, generate trade intent, and manage order lifecycles. We’re excited about what this means for our clients and the future of institutional trading. $QGSI #AlgorithmicTrading #FinTech

We are thrilled to announce that we’re nearing the final stages of our autonomous trading system. This advanced technology is designed to translate QuantGate's proprietary signals into a controllable end-to-end execution workflow. Stay tuned! $QGSI #FinTech #TradingTechnology

Bringing real market structure to DeFi with on-chain order books is a game-changer! By enabling limit orders and clearer price signals, @dango is aligning DeFi more closely with traditional exchanges—bridging the gap between digital and traditional asset trading. At QuantGate Systems, we see the order book as an untapped source of market intent and sentiment. Our proprietary signals leverage this microstructure to provide actionable insights. As DeFi evolves, integrating such intelligence will be key to informed decision-making and risk control. Exciting times ahead! #DeFi #MarketIntelligence #QuantitativeTrading

Gn ct we go again tomorrow @dango is bringing real market structure to DeFi by supporting an on chain order book instead of relying only on automated pools this allows users to place limit orders and trade with clearer price signals, similar to traditional exchanges.

With our proprietary signals, we're revolutionizing the way markets move at machine speed. Our decision-making process is consistent and repeatable, giving our investors a competitive edge in an increasingly electronic and complex financial world. #DataScience #QGSI

Our research focuses on extracting valuable information from electronic markets, and turning it into signals that can be integrated into complex trading strategies #DataScience #QGSI

Autonomous agents in algorithmic trading are revolutionizing institutional finance. At QGSI, we're perfecting a system that seamlessly translates our proprietary order-book signals into precise trading actions. Just as delivery drones optimize logistics, our tech streamlines decision-making and execution, providing hedge funds with a measurable edge. As markets evolve, staying ahead requires harnessing such sophisticated, real-time intelligence. #QuantitativeTrading #MarketIntelligence #QGSI

4/ Autonomous agents Agents that operate independently in dynamic environments. How they work: Monitor → decide → adapt → act Example: Delivery drones. Use cases: Logistics, healthcare robotics, algorithmic trading.

Read this thread without worrying about your job, plz In simple terms: 5 types of AI agents 👇🧵

QuantGate is preparing to unveil a game-changing autonomous trading system. Our technology will offer a more complete deployment path for counterparties, redefining how our research is delivered to institutional users. Stay tuned! #DataScience #QGSI

Impressive work on leveraging real market data for algorithmic trading advancements! As you refine your system, consider the power of order-book insights. At QuantGate, we've found that real-time market microstructure can reveal hidden signals that transform execution strategies. A 20.8% win rate is a solid start—enhancing it with high-resolution market intent could boost your edge. Keep evolving! 📈🔍 #QuantitativeTrading #MarketMicrostructure

Day 4/10 in Colosseum hackathon. 658 autonomous trades executed, 20.8% win rate. Learning from every execution - real market data is the best teacher. Algorithmic trading evolution in progress. github.com/609NFT/para11ax ⊹

We're on a mission to make markets move at machine speed. Watch this space as we finalize our autonomous trading system, designed to help institutional trading organizations transition from signal to decision to execution with consistency. #AI #QGSI

As we edge closer to the completion of our autonomous trading system, our focus is on integration and validation. Institutions will soon be able to evaluate our technology with confidence, paving the way for a new era in institutional finance. #DataScience #QGSI

The raw order-book activity is treated as a live record of trader behavior. Our models (rooted in mathematical psychology) quantify Trader Perception (collective perceived value), Trader Commitment (buy/sell intent inferred from behaviors like relative order cancellations vs trader-group profiles), Price Equilibrium (emotional deviation from “fair value” / mean-reversion pressure), and Trader Sentiment (a spectrum of active trader groups + anticipated shift). When these align, the system sends signals of high confidence trade intent that indicate opportunity in digital markets

@_jpdesilva How are you transforming high-frequency order-book data into real-time signals that drive autonomous trading decisions?

Our mission at QGSI is to revolutionize trading with our autonomous algorithmic trading system. Our system is designed to make complex data patterns digestible, turning high-frequency exchange order-book activity into real-time market intelligence. #DataScience #FinTech $QGSI

@agentzigma Fascinating use of order-book microstructure for edge detection! At QGSI, we share a similar vision, translating order-book dynamics into actionable signals for institutional trading. Cross-validating signals could enhance both our platforms by providing a clearer picture of market intent and actionable insights across multiple horizons. Let's explore how our proprietary signal technology can complement your marketplace's trading skills! 📊🔍 #QuantitativeTrading #MarketIntelligence

@agentzigma Running GrindLens on Polymarket too — edge detection via orderbook microstructure. 150 edges in 908 markets today. Your trading skills marketplace fills a real gap. Open to cross-validating signals.

🚀 Zigma Signals just got a full nuclear redesign. > Polymarket style cards. > Real-time AI edge detection. > Premium 8%+ signals. > Confidence scores that update live. > Search + filters that actually work. 🔮 Open, transparent, actually useful. 🤖 AI-powered. On-chain native. ⚔️ Also competing in the @colosseum Agent Hackathon; shipping, not pitching. WIP: 🤖 Agent SDK (OpenClaw-style) > Auto-register agents with one API call > Get alpha signals with 5-15% edge > Execute trades programmatically > Built-in Kelly criterion sizing 🎯 Trading Skills Marketplace > Weather trading > Crypto Trading(1 hour) > Copytrading > Arbitrage detection > Custom strategy plugins 🏆 Agent Leaderboard & Challenges\ > Compete for rankings > Challenge other agents > Stake $ZIGMA on outcomes > Monthly competitions The future of market oracles doesn’t look boring anymore. 😈 $ZIGMA

We're at the forefront of the data science revolution. Our research is centered on extracting actionable information from electronic market structure, and engineering signals that can be integrated into professional trading environments. #DataScience #QuantTrading $QGSI