New Course launched on Agentic AI for Trading! Agentic AI is AI that can plan, use tools, and work like a team. From hypothesis to backtest, end to end. Start for free now: quantra.quantinsti.com/cours…

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Agentic AI for Trading QuantInsti just launched Agentic AI for Trading — a course designed for quants who want AI that can plan, use tools, and collaborate like a team. From hypothesis generation to backtesting, automate the repetitive parts of strategy development and focus on what matters: alpha. 🎯 Special Offer for Quantpedia readers: Use code QUANTPEDIA to get 7% off. If you're serious about systematic trading and AI-driven research workflows, this is worth a look. quantra.quantinsti.com/cours…

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📈 Quant Trading Success: Watch the Overview of Alpha Generation in Italian Stocks! 🇮🇹 We are excited to share the overview of our EPAT project: Cointegrated Pairs Trading on the FTSE MIB by our EPAT Mangiucca Andrea Mangiucca. This statistical arbitrage strategy successfully exploited potential market inefficiencies in the Italian stock market, generating significant alpha against the benchmark index. Key Findings (2017–2023 Backtest): • Total Return: The strategy delivered an overall return of 67.2%, significantly outperforming the Italian Stock Index return of 43.1%. • Risk-Adjusted Performance: Achieved an excellent Sharpe Ratio of 2.05 (full sample) and annual volatility of only 4.00%. • Out-of-Sample Robustness: The results improved during the out-of-sample period, yielding an even higher Sharpe Ratio of 2.32 and reducing the Max Drawdown to -2.33%. The Strategy: This Statistical Arbitrage Trading method is a contrarian strategy that profits from the mean-reverting behavior of stock pairs. 1. Selection: Andrea analysed the 40 largest Italian companies in the FTSE MIB. Tradable pairs were identified using OLS regression and confirmed for cointegration (stationarity) using the ADF Test at a 95% confidence level. All selected pairs also had a correlation > 0.8. 2. Execution: Entries were triggered when the Z-score of the spread reached ±2.25 standard deviations. Trades were closed when the spread reverted to zero (take profit at 0 value of std). Resilience & Future Improvements: The strategy proved resilient, maintaining performance even through periods of high volatility, such as the COVID-19 pandemic, benefiting from the empirically observed increase in stock correlation during crises. The analysis affirms the theoretical effectiveness of the strategy. Future work will focus on incorporating transaction and slippage costs and testing multiple entry thresholds (e.g., ±1.80 and ±2.25) for potential optimization. Project by Andrea Mangiucca, Trading Analyst and EPAT Certificate of Excellence recipient. Watch the full video overview below for a deeper dive into the methodology and performance graphs! Read the full project here: quantinsti.com/articles/stat… #QuantTrading #PairsTrading #StatisticalArbitrage #FTSEMIB #AlgorithmicTrading #Finance #EPAT #QuantitativeFinance

Stop guessing and start using the market's secret calendar! 🗓️ Seasonality Trading is a fascinating phenomenon that explores predictable patterns and trends that occur in financial markets at specific times during the year. It’s not just industry jargon; it's a trading tool used to gain an edge in the dynamic world of finance. Why Seasonality Matters: • It helps identify recurring trends in stock prices. • It combines historical data analysis with well-defined trading rules, offering a simple, repeatable strategy edge. • It promotes data-driven decision-making by encouraging traders to rely on historical facts and historical patterns rather than emotions. We’ve compiled the core concepts and actionable strategies you need: ➡️ Swipe to uncover the full blueprint! You'll see examples like the famous "Santa Claus Rally", the historical tendency for stocks to experience a positive upswing during the last five trading days of December and the first two trading days of January. We also break down essential calendar effects like the "Sell in May and Go Away" strategy and how to implement these strategies using Python. Remember, while seasonality offers a valuable tool for decision-making, it should always be used in conjunction with other forms of analysis, as it does not account for unexpected external factors. Ready to learn more about Seasonal Opportunities? 📚 Read the full guide: If you wish to learn more, check out the comprehensive blog: "Seasonality Trading: A Beginners Guide": blog.quantinsti.com/seasonal… 🎓 Enroll in the course: Explore our course on Event driven strategies where you can create and backtest eight seasonal strategies to capitalize on anomalies in equities, treasury, and volatility markets. You can also take advantage of the course's Preview feature for free. quantra.quantinsti.com/cours… #SeasonalityTrading #TradingStrategy #FinanceTips #StockMarket #AlgoTrading #DataDrivenDecisions #Quantra #TradingEdge

Stop guessing and start using the market's secret calendar! 🗓️ Seasonality Trading is a fascinating phenomenon that explores predictable patterns and trends that occur in financial markets at specific times during the year. It’s not just industry jargon; it's a trading tool used to gain an edge in the dynamic world of finance. Why Seasonality Matters: • It helps identify recurring trends in stock prices. • It combines historical data analysis with well-defined trading rules, offering a simple, repeatable strategy edge. • It promotes data-driven decision-making by encouraging traders to rely on historical facts and historical patterns rather than emotions. We’ve compiled the core concepts and actionable strategies you need! You'll see examples like the famous "Santa Claus Rally", the historical tendency for stocks to experience a positive upswing during the last five trading days of December and the first two trading days of January. We also break down essential calendar effects like the "Sell in May and Go Away" strategy and how to implement these strategies using Python. Remember, while seasonality offers a valuable tool for decision-making, it should always be used in conjunction with other forms of analysis, as it does not account for unexpected external factors. Ready to learn more about Seasonal Opportunities? 📚 Read the full guide: If you wish to learn more, check out the comprehensive blog: "Seasonality Trading: A Beginners Guide": blog.quantinsti.com/seasonal… 🎓 Enroll in the course: Explore our course on Event driven strategies where you can create and backtest eight seasonal strategies to capitalize on anomalies in equities, treasury, and volatility markets. You can also take advantage of the course's Preview feature for free. quantra.quantinsti.com/cours… #SeasonalityTrading #TradingStrategy #FinanceTips #StockMarket #AlgoTrading #DataDrivenDecisions #Quantra #TradingEdge

Zerodha x QuantInsti: Algo Trading with Kite Connect in Python This 11-hour course, offered free by Quantra by QuantInsti, provides a complete walkthrough across 22 sections and over 200 units. It guides you from the initial introduction to successfully running codes on your local machine. We've included five detailed videos, each of which is a complete unit from the full course. For full access to the complete course, including notebooks and quizzes, please visit: quantra.quantinsti.com/cours… Part 4. Designing Algorithmic Strategies For traders who ask: •How do I move from a trade idea to a coded strategy on Kite Connect •How do I work with WebSocket callbacks and rolling windows of data •How do I generate clean entry and exit signals This session demonstrates: •Subscribing to live ticks and extracting LTP from the WebSocket stream inside the tick handler •Maintaining a rolling window of prices in memory to compute short and long moving averages •Detecting moving average crossovers and converting them into long or exit signals •Connecting the signal layer to order placement functions so that a complete end to end bot is created •Practical tips such as starting with small quantity, logging every decision, and separating config from code About this free course on Quantra by QuantInsti Automate trading with Zerodha’s Kite Connect API using Python. In this hands on course, you will build, test, and deploy a complete algorithmic trading system, from fetching live and historical market data and placing advanced orders to streaming data with WebSockets. Execute strategies, manage risk, and handle special order types like GTT and Iceberg. With practical notebooks, quizzes, and real world examples, gain the skills to create scalable, rule based trading systems like a pro. Free course quantra.quantinsti.com/cours… Authors Zerodha @zerodha was founded on August 15, 2010, to break down the barriers Indian traders and investors face in cost, support, and technology. The name combines Zero with Rodha, the Sanskrit word for barrier. Today, Zerodha is India’s largest stockbroker, with over 1.6 crore clients contributing over 15 percent of all retail trading volumes. Zerodha’s developer friendly APIs and robust technology infrastructure have made algorithmic trading accessible to retail traders across India. The Kite Connect API powers thousands of trading algorithms, enabling traders to automate strategies and execute orders programmatically at scale. QuantInsti @QuantInsti is the world's leading algorithmic and quantitative trading research and training institute with registered users in 190 plus countries and territories. An initiative by founders of iRage, one of India’s top HFT firms, QuantInsti has been helping its users grow in this domain through its learning and financial applications based ecosystem for more than 10 years. #TradingStrategies #AlgoTrading #Python #KiteConnect

🚨 Happening Now! 🚨 Join Dr. Ernest Chan LIVE at the GenAI & Automated Trading Summit 🎯 Learn how Generative AI is reshaping algorithmic trading! 📍 Don’t miss out – you can still join! us06web.zoom.us/webinar/regi…

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