Amir Memar, Ph.D.

We've added the Cboe One Canada Summary Feed, a real-time Canadian equities market data product from Cboe Global Markets. This latest addition to dxFeed’s comprehensive suite of market data solutions further strengthens its commitment to delivering unparalleled insights and analytics to traders and investors. The total number of traded securities on Cboe Canada is more than 8000. “We are thrilled to introduce the Cboe One Canada Summary Feed, further expanding our offerings to meet the evolving needs of traders,” said Oleg Solodukhin, CEO of dxFeed. “With this latest addition, dxFeed continues its mission to provide reliable, high-quality market data solutions that empower traders to navigate the markets with confidence.” Adam Inzirillo, Executive Vice President, Head of Data and Access Solutions at Cboe Global Markets, stated: “Cboe is pleased to collaborate with dxFeed and provide our extensive Canadian equities market feed to its clients. We are always striving to find new ways to provide innovative market data solutions, and the launch of the Cboe One Canada Summary Feed on dxFeed underscores our commitment to offering traders timely and comprehensive market data.” Cboe Global Markets #cboe #dxFeed #marketdata #data #finance

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Evolution amid diverse change Inside the latest issue - https://lnkd.in/eDZak4zG Participants discuss the current securities lending landscape in the region, the technology evolution born from the move to T+1, and the Bank of Canada’s introduction of a settlement fail fee on bond and bill trades.

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How can you mitigate financial crime risk? Join CIBC Capital Markets' Loren Schwartz, Senior Director, Capital Markets Regulatory Technology, and other experts, for a discussion on how institutions can implement robust risk management frameworks that incorporate the latest artificial intelligence (AI) techniques and advanced analytics to detect and mitigate financial crime, hosted by A-Team Insight, from A-Team Group. Register here: https://lnkd.in/gv-qFwfb #FinancialCrime #DueDiligence #Screening

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North Pole Arbitrage is a privately owned quantitative trading firm based in Quebec, Canada. Over the years, we have forged strong business relationships with major financial institutions. Together with our strategists, we employ advanced algorithms to execute multiple sophisticated short-term trading strategies that are best characterized as data driven, quantitative and market neutral. Technology and data drive our trading business. We rely on extensive sources of data, machine learning, cloud supercomputing and proprietary analytics software. We spot patterns with an entire army of simulations on multiple markets. We offer Equity Traders, Analysts, and Portfolio Managers to become North Pole Strategist and access a turnkey solution to reach success and real financial freedom. The infrastructure of the company is built for handling funding, automation, executions, and reporting. This allows strategists to spend more time on the most important and scalable building blocks for success in quantitative trading: research and development of profitable strategies. At North Pole Arbitrage we compare our portfolio of strategies to a well maintained and well diversified real estate portfolio. We consider ourselves successful at harnessing true passive income from sophisticated quantitative trading.

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Join us on July 9 for an exclusive webinar with our Head of Data and Platform, Tiago Fernandes, where he will delve into the latest trends and performance insights of Structured Products in Canada. Gain a comprehensive understanding of the market, including current trends, performance analysis, and future outlook. The session will conclude with a Q&A, offering you the opportunity to have your questions answered by an industry expert. Don't miss out on this informative event, register via the link in the comments! #StructuredProducts #Webinar #CanadaMarket

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How to become Global Alpha Researcher at TrexQuant (a quant hedge fund) Here's a quick overview of the program and selection process along with resources needed for prep About TrexQuant : Trexquant is a quantitative investment firm that focuses on building data-driven investment strategies with AUM exceeding $5 billion. They use complex algorithms and a massive amount of data to develop "alphas" - basically profitable trading signals. They build what are called "market-neutral" portfolios, meaning they aim to make money regardless of the overall market direction. About GAR : A GAR (Global Alpha Researcher) is essentially a quantitative researcher who develops alphas. These alphas are designed to be profitable trading signals that can perform well in various market conditions. Once created, researchers can test and refine their ideas using cutting-edge simulation tools. If successful, these alphas can be implemented for live trading in the global markets. Eligibility - If you have completed or are currently doing an undergraduate degree in a STEM field, can spare ~20 hrs per week, and are comfortable with programming in Python, you are eligible to apply for the program. Selection Process : First there's a Resume Screening round followed by subsequent rounds. Round 1 : HR + Technical interview You have to solve one DSA problem in Python (generally leetcode medium level). Focus on DP problems for this round. Round 2 : Online Take home assignment - Hangman Challenge You are given 1 week to do this challenge. You can use any statistical or Machine learning based technique to do this challenge. If your submission exceeds the "accuracy threshold", you move ahead for further rounds. Round 3 : Discussion on Hangman Challenge + Technical You will be asked to explain your approach, why you used a particular technique over the others and deep questions to see and validate your submission. Round 4 : Technical round with CEO You could be asked questions related to alpha generation, time series, Machine learning or any standard statistics question. Once you clear all the rounds, you are offered the role of GAR. Successful GARs can also join as full time researchers or summer interns post completion of the program. Compensation : GARs are compensated in three ways - Each active GAR is paid a monthly stipend - GARs can earn a percentage of the profits made by their strategies - They are awarded weekly prizes for exemplary research and novel contributions Benefits of the Program : - Gain hands-on experience in real-world quantitative research and alpha development - Learn from and be mentored by senior researchers at Trexquant - Develop a strong foundation for a career in quantitative finance How to Apply : Check out this link -https://lnkd.in/gsPfTp_A Resources for Prep : Do below courses by MIT OpenCourseware along with Leetcode - Linear Algebra - Introduction to Probability and Statistics - Stochastic Calculus

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If you’ve been here — manual model monitoring for quant trading systems that doesn’t detect issues early enough Or here — deafening noise from alerts that makes it difficult to actually identify root causes and resolve issues efficiently, this one's for you. In our upcoming webinar 𝐌𝐨𝐝𝐞𝐥 𝐏𝐞𝐫𝐟𝐨𝐫𝐦𝐚𝐧𝐜𝐞 𝐌𝐨𝐧𝐢𝐭𝐨𝐫𝐢𝐧𝐠 𝐟𝐨𝐫 𝐐𝐮𝐚𝐧𝐭 𝐓𝐫𝐚𝐝𝐢𝐧𝐠 𝐒𝐲𝐬𝐭𝐞𝐦𝐬 you’ll hear: + How to get granular insights and systematically reduce noise to minimize false positives and alert fatigue. + Best practices for tailoring monitoring approaches to your specific needs, ensuring that critical issues are detected without overwhelming your team. + Real-world examples of how data science teams have successfully navigated this conundrum to improve model performance and operational efficiency. 🗓️ Date: October 10th, 2024 🕒 Time: 11am ET | 8am PT 👉 Learn more and register: https://lnkd.in/eUNiD8Mh #QuantTrading #DataScience #ModelMonitoring #QuantModels #MLOps #Webinar #AlgorithmicTrading #AlgoTrading #Mona

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Helen Bartholomew’s latest article on Risk.net discusses how the #CORRA rate has been above the Bank of Canada’s target and the impact of increased volume due to T+1 and hedge fund activity. Andrew Munn, head of CanDeal’s Benchmark Administration Services, discusses how some of the variations are due to “changes of supply and demand of securities and cash.” Andrew also discusses the fine-tuning of the rate by eliminating trade outliers, and how expanding beyond two decimals would lead to more transparency and “help increase activity in futures by creating a more interesting arbitrage on overnight versus the one-month future.”  https://lnkd.in/e2vaqCp6

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The Canadian Web3 Council recently provided feedback to the Office of the Superintendent of Financial Institutions Canada (OSFI) on public disclosures of crypto-asset exposures. Our submission highlights key challenges in the current framework and offers actionable recommendations to enhance transparency and operational risk management for crypto assets. Our Recommendations: 1. Improved transparency in financial disclosure. 2. A working group to develop a risk management framework for crypto assets. 3. Public-private initiatives to enhance financial literacy on digital assets. 🔗 Read our blog post and full submission: https://lnkd.in/eZCQPnTp

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Welcome to QuantFin where we focus on the used of cutting hedge machine learning methods ( and Reinforcement learning in particular) to tackle financial market problems , and more specifically Option trading/Market Making and risk management. In today's post, we talk about The paper "𝐌𝐚𝐫𝐤𝐞𝐭 𝐌𝐚𝐤𝐢𝐧𝐠 𝐨𝐟 𝐎𝐩𝐭𝐢𝐨𝐧𝐬 𝐯𝐢𝐚 𝐑𝐞𝐢𝐧𝐟𝐨𝐫𝐜𝐞𝐦𝐞𝐧𝐭 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠" . This paper introduces two cutting-edge reinforcement learning algorithms (Policy improvement Algorithm and Actor-critic Algorithm) aimed at solving the options market-making problem. Here are the key insights: 📊 𝐑𝐞𝐢𝐧𝐟𝐨𝐫𝐜𝐞𝐦𝐞𝐧𝐭 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐅𝐨𝐫𝐦𝐮𝐥𝐚𝐭𝐢𝐨𝐧: The authors model the market making problem as a Markov Decision Process (MDP). The state comprises inventory, traded options, and market conditions, while the action represents the bid and ask quotes. The reward is derived from trade profits and losses. 💡 𝐕𝐚𝐥𝐮𝐞 𝐅𝐮𝐧𝐜𝐭𝐢𝐨𝐧 𝐀𝐩𝐩𝐫𝐨𝐱𝐢𝐦𝐚𝐭𝐢𝐨𝐧: To solve the MDP, neural networks approximate the value function V(t,q), predicting expected future rewards based on the current state (time t and inventory q). 🔄 𝐏𝐨𝐥𝐢𝐜𝐲 𝐈𝐦𝐩𝐫𝐨𝐯𝐞𝐦𝐞𝐧𝐭 𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦: This algorithm leverages the policy improvement theorem and involves: Estimating the value function V^π using temporal difference learning. Updating the policy π' by optimizing the Bellman equation's right-hand side with the estimated V^π. A neural network is used for value function approximation. 🤖 𝐀𝐜𝐭𝐨𝐫-𝐂𝐫𝐢𝐭𝐢𝐜 𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦: This method uses two neural networks: Critic: Estimates the value function V(t,q). Actor: Represents the policy π(a|t,q), outputting bid-ask quotes based on the state. The actor and critic networks are trained simultaneously 𝐭𝐡𝐫𝐨𝐮𝐠𝐡 𝐩𝐨𝐥𝐢𝐜𝐲 𝐠𝐫𝐚𝐝𝐢𝐞𝐧𝐭𝐬 𝐚𝐧𝐝 𝐭𝐞𝐦𝐩𝐨𝐫𝐚𝐥 𝐝𝐢𝐟𝐟𝐞𝐫𝐞𝐧𝐜𝐞 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠. 🌟 𝐑𝐞𝐚𝐥𝐢𝐬𝐭𝐢𝐜 𝐀𝐬𝐬𝐮𝐦𝐩𝐭𝐢𝐨𝐧𝐬: Acknowledging simplifying assumptions like constant market order arrival intensity, the authors suggest that future work should incorporate more realistic factors to enhance the practical application of these algorithms. The link to the article : https://lnkd.in/eGbU5A5x #FinTech #ReinforcementLearning #MarketMaking #AI #MachineLearning #Innovation #Finance #Trading #OptionsTrading #ArtificialIntelligence

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Breaking down Maths behind Continuous Time Markov Chain (CTMC) and Implementation in Trading Models, with Illustration " A THREAD " https://lnkd.in/gj6ATdJg Follow Us on X, for more interesting Thread https://lnkd.in/ggsyM4sr

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Quantpedia in October 2024 – An enhanced Basic Overview report – new calculations and reporting capabilities – 11 new Quantpedia Premium strategies have been added to our database – 9 new related research papers have been included in existing Premium strategies during the last month – Additionally, we have produced 7 new backtests written in QuantConnect code – 5 new blog posts that you may find interesting have been published on our Quantpedia blog in the previous month https://lnkd.in/dXmvigrV

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Algorithmic trading, like our ICP Premium™, uses advanced mathematical models to execute trades at speeds and frequencies unattainable by humans. For small-cap Canadian companies, this can dramatically improve liquidity, enhancing stock attractiveness and stability. High liquidity ensures smoother transactions and reduces price volatility, making it a key factor for investor confidence. Our ICP Premium™ provides continuous buy and sell orders, narrowing the bid-ask spread and stabilizing prices. Learn how ICP Premium™ can revolutionize your trading experience and enhance market dynamics. Discover the benefits of ICP Premium™ and transform your trading strategy today. Visit https://lnkd.in/g3ka3-Fi #AlgorithmicTrading #Liquidity #SmallCapStocks #TradingStrategies #StockMarket #Investing #ICPsecurities

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TD Asset Management Inc.: TD Target Maturity Bond ETFs | Airing on BNN Bloomberg   TD Target Maturity Bond ETFs offer a unique investment opportunity, blending the stable income and maturity payouts of individual bonds with the added advantages of liquidity, diversification, and professional management. As a premier financial solution from one of Canada’s leading asset managers, these ETFs are designed to act like bonds while providing the flexibility and trading ease of ETFs, making them an attractive option for investors looking to optimize their fixed-income strategies.   Learn More: https://lnkd.in/gPZV9ZYx   #TDAssetManagement #BondETFs #ETFs #ExchangeTradedFunds #Diversification #Fixedincome #AssetManagement 

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