Quant Trading Live Report

Bitcoin Mining Difficulty and Portfolio Allocation Insights

In this episode, Brian of QuantLabs.org discusses two important topics regarding Bitcoin. Firstly, he addresses the increasing difficulty levels for Bitcoin miners, tying it back to articles from newsbtc.com. He explains their implications and how such changes affected the value of Bitcoin in the past. The conversation gets technical, touching on important concepts such as the meaning of mining difficulty and its possible impact on the price of Bitcoin.

 

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The second article discussed is from JP Morgan, focusing on how Bitcoin has overtaken gold in investor portfolio allocation. This part of the conversation delves into why Bitcoin's allocation to investors is 3.7 times greater than that of gold, and what this could mean in the long term. Brian also takes time to question the assumptions made about Bitcoin's demand and how the constant volatility affects perception.

Throughout the episode, Brian also casts a critical eye on other crypto debates, such as the energy efficiency of Bitcoin mining and Ethereum's high gas fees. Potential futures for Bitcoin and crypto ETFs are explored too with some skepticism, as Brian questions the stability of these financial products and the viability of deriving them from Bitcoin. The episode concludes with a call to join the QuantLabs.org community for anyone keen on deepening their understanding of crypto trading.

 

Bitcoin Mining Difficulty and Portfolio Allocation Insights - QUANTLABS.NET

Diving Deep into the Dynamics of High-Frequency Trading

In this episode, Brian from quantlabs.net explores a thought-provoking question posted by Maeve R. Ritz on quant.stackexchange.com. The query focused on the quantitative mechanisms behind identifying flow-based alphas during incredibly short lookout periods and their significance in the context of ultra-high-frequency lookout periods up to 100 milliseconds. Brian attempts to demystify this complex issue, primarily associated with high-frequency trading.

 

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A response from LaHal that garnered Brian's attention pointed to a 2019 paper he authored with Curls, Albert, titled "Incorporating Signals into Optimal Trading". The research revolves around employing specific signals for optimal trading and provides a wealth of statistical data. By incorporating calculations grounded in the imbalance sigma, the paper piques the interests of quant high-frequency trading modelers.

As Brian navigates this mathematically dense paper, he sheds light on the dense collection of mathematical formulas and proofs. Although the level of mathematical proficiency required to comprehend the content is high, he finds the exercise profoundly enlightening. He invites listeners who can understand this level of mathematical intricacies to get in touch and share their insights.

Join Brian as he continues to unravel the complex world of high-frequency trading in future discussions. He extends an invitation to join the Quant Labs Discord community and sub stack for more insights into his trading and investment choices. For those seeking technical trading books, visit quantlabs.net/books for two handy e-books packed with trade secrets and free software to help you decode the market in unique ways.

 

quant.stackexchange.com/questions/78631/what-are-some-quantitative-approaches-to-figure-out-flow-based-alphas-on-extreme

 

link.springer.com/article/10.1007/s00780-019-00382-7

Deep Dive into Global Private Equity Performance

Welcome again to another enriching podcast episode, recorded on March 13th. Today, we will dissect an insightful article from alphaarchitect.com about private equity performance around the globe. A novel approach for us, we're curious to see how our listeners respond to this type of content. Feel free to engage and give us feedback in our Discord community, where the link can be found below.

 

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In the article, we first ponder on the questions. Are fund performances persistent across different investment geographies and fund investment styles? We then take a deep dive into analyzing how each region - Europe, Asia Pacific, rest of the world - and investment style such as Buyout Funds, Growth Strategy, and Venture Capital behave differently, and how these variations can impact the overall fund growth.

Further in-depth we look into how these differences in fund performance are strongly influenced by factors. Such as investment strategies, market dynamics, availability to capital, regulatory environment, managerial expertise and the exit environment within different regions. For instance, the North American private equity markets are more pulled towards technology and innovation-driven ventures. Europe typically invests in traditional industries and established companies, whereas the Asia Pacific markets lean more into growth opportunities in emerging markets.

We also take a hard-nose dive into the results and findings of the research and discuss how those findings can open up diversification benefits, risk management opportunities for growth, due diligence, and a keen eye for accessing local expertise.

To sum up, in this episode we aim to provoke some thought among our listeners, especially in our growing Discord community which just hit the 200 members mark. If you're intrigued by our content or looking for investment tips, remember to check out my SubStack link, and for a deeper understanding on the technical side, visit quantlabs.net/books to get some technical books to guide you in stock market analysis and selection.

We hope you find this episode illuminating and beneficial in broadening your understanding of private equity performances around the globe. So sit back, relax, and plunge into the universe of global private equity performance!

 

alphaarchitect.com/2024/03/international-private-equity/

Systematic Hedging of a Cryptocurrency Portfolio

Join Brian from QuantLabs 109 as he explores and breaks down the results of a comprehensive research article titled "Systematic Hedging of the Cryptocurrency Portfolio" from quantopedia.com. In this episode, delve into the intriguing world of cryptocurrencies and discover how a top-five cryptocurrency index portfolio was hedged. Expect the episode to uncover the research methodology, the selected cryptocurrencies, and the unique hedging strategy.

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Brian also unpacks the report's results, contrasting the returns yielded by the top five coins against Bitcoin. Watch as he raises critical questions about the usefulness of individual coins, citing examples such as Solana and Chainlink. Plus, learn how the research sets up two different periods to study volatility in spread and unpack complex mathematical methods used.

The episode climaxes with a sobering look at the risks involved in this high volatility investment world. Get the scoop on max drawdowns, sharp ratios, the compounded annual per annum of the top five and Bitcoin, and more. Listen as Brian critiques the results, outlining how professionals would view the high risk-to-reward ratio and contrasting it to simpler methods that yield better sharp ratios.

Finally, hear Brian's analysis of the research's conclusion, ranging from the researchers' hedging strategy and its impact on returns, volatility, and drawdowns, to the enduring danger of high drawdowns in the crypto world. Despite the drawn-back returns, Brian highlights the necessity of incorporating hedging strategies in crypto portfolios. Don't miss his final thoughts on the extreme volatility of cryptocurrencies, potential legal issues, and the trade-offs of hedging.

To further explore Brian's analysis and stay updated on his findings, join his Discord community and subscribe to his Substack page and email newsletter. Links are provided in the podcast description. Tune in for an enlightening look at systematic cryptocurrency portfolio hedging.

 

quantpedia.com/systematic-hedging-of-the-cryptocurrency-portfolio/

Gold, Bitcoin or Banks?

Welcome to our latest podcast episode, where host Brian delves into an increasingly poignant topic in today’s turbulent financial climate: should we invest in Gold, Bitcoin, or trust in Banks? Brian shares his insights drawn from an intriguing article from CryptoDaily.co.uk and reflects upon its key points.

 

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The article outlines a series of concerns and trends the financial world is currently facing, from a banking system that seems to favor the wealthy, to fears surrounding inflation and currency debasement. Particularly spotlighted is the massive influence of the Federal Reserve in the US and the alarming rate at which the US dollar is debasing in purchasing power. Brian points out the irony that the best performing currency out of the major ones is the Russian ruble—the one that has been sanctioned the most.

Diving further, Brian evaluates two major players in the investment landscape: gold and Bitcoin. He reports that people are starting to load up on gold and silver, driving prices skyward. Bitcoin, too, is experiencing a spike, and a halving is expected in the near future—a factor that could elongate the time to mine the remaining 1.5 million.

However, the podcast is not just about the pros of these assets. Reflecting on instances where gold was banned, Brian urges listeners to consider the potential risks and drawbacks of investing in these seemingly invincible assets. As countries lean towards storing gold, Brian questions whether the cost of storage and insurance make gold a truly worthwhile investment.

Finally, Brian leaves listeners with sobering facts about the high stakes of today's financial landscape, urging them to educate themselves about the true nature of their investments. For more detailed insights and discussions, listeners are directed to join the host's Discord community, and access trading textbooks via provided links.

 

https://cryptodaily.co.uk/2024/03/gold-or-bitcoin-certainly-not-banks-though

Are these the best practices for machine learning operations

Welcome to another exciting episode with Brian from quantlabs.net. Recorded on the 13th of March, noontime, this engaging and enlightening talk revolves around machine learning and the best practices in engineering with machine learning. Although Brian admits to not being an expert, he invites listeners, even those who may not find the subject generally useful, to engage with him as he explores this intriguing world.

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The podcast delves into an article originally discovered on Reddit, within the toting subreddit. Another piece of content that sparks discussion is an article from Medium.com penned by Luis Bermondes which gives an overview of ML Ops (Machine Learning Operations). Of particular interest is a diagram depicting the ML op stack and the direction it operates in.

Brian undertakes a comprehensive walkthrough of the ML Ops stack, pointing out key areas such as the Data Collection, Experimentation, Evaluation, and Maintenance. He additionally highlights the right-hand side of the diagram, ascending from Infrastructure layer, Component layer, Pipeline layer, to Run layer.

This episode invites listeners to join the conversation about machine learning and artificial intelligence by sharing their insights and comments through various platforms. Brian encourages feedback and insights via his discord community, email, his website, or social media. Everyone is urged to share their thoughts whether they consider themselves 'novices' or experts in the field, contributing to this fascinating exploration.

 

medium.com/machinevision/overview-of-mlops-a07053fc2a80

reddit.com/r/coding/comments/1bd4w76/what_are_best_practices_for_machine_learning/

Algo Trading Strategies Development , Automations, and Profitable Decisions in Python

In this insightful and practical episode of Brian's trading podcast, the host gives an informative deep dive into the powerful realm of Price Action Trading - a significant yet often overlooked aspect of successful trading. Brian unpacks various trading strategies and highlights how these tactics can be used according to market conditions. Strategies such as support and resistance, trend following, candlestick patterns, and breakout trading are all brought to light.

 

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Brian further explores the automation of these strategies using Python and the analysis capabilities of platforms such as Yahoo Finance and Alpha Vantage. He focuses particularly on the utilisation of TradingView for signal generation and Python for trade execution, discussing the flexibility and adaptability that these tools offer for trading in volatile markets. He also points out the necessity of reconfiguring Python codes and TradingView scripts to accommodate changing market trends.

The podcast delves into key aspects of an effective trading strategy, with Brian emphasizing the importance of clearly defined rules for trade entry and exit points. He makes a compelling connection between Price Action Analysis, wider market trends, and global economic conditions, highlighting the underscored importance of informed decision-making for traders.

Listeners will also gain invaluable insight into liquidity considerations when choosing trading assets, the benefits of trading with high-liquid assets, and the critical role of volume analysis in trading. By analyzing multiple Gold ETFs, Brian demonstrates how assets delivering top performance can be selected, and how tools such as TradingView can facilitate the process.

The episode concludes with a discussion on crucial risk management techniques such as position sizing, stop loss orders, trailing stops, and diversification. Brian shares common pitfalls novice traders fall into, emphasizing the importance of continuous learning, patience, emotional control, and adaptive strategies for successful trading. This podcast is a treasure trove of trading knowledge and insight, ideal for both beginners and experienced traders alike.

 

blog.quantinsti.com/price-action-trading

Methods to Improve Signal Quality for High Performance

Welcome to a new episode with Brian from Monolabs. This episode is focused on the analysis of trading algorithms, specifically how to evaluate and enhance the quality of their trading signals. This information-packed session is an invaluable resource for everyone keen on optimizing their trading strategies.

 

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Brian dives into a question posted on quant.stackexchange.com about how to deeply assess a trading signal. Discussing it from the perspective of a company's performance under different market conditions on various instruments. The takeaway is the development of a multi-layered approach to evaluation- measuring performance based on instruments, sectors, market volatility, and more.

The episode subsequently explores how to select well-performing sectors, employing strategies such as using forward guidance and assessing professional analyst recommendations. It unravels how to filter through stocks and ETFs to find those that best meet specific conditions. The focus is on identifying companies demonstrating strong forward guidance and low volatility.

Timing is highlighted as a key element throughout the analysis. Brian also highlights the importance of avoiding high volatile stocks and ETFs under volatile market conditions. Also noteworthy is the importance of high performing sectors tracking and using ETF holdings to measure stock performance.

Brian also provides enlightening insights on how to manage top-performing, low volatile, and well-timed stocks that could break out. Also highlighted is how to manage exposure to market losses caused by underlying challenging market conditions.

Moreover, the episode provides valuable strategies on stock analysis such as identifying strong guidance and recommendations. It also outlines how to design custom strategies for platforms like TradingView using Ichimoku.

This and much more information about trading algorithms and signal quality can be accessed in our community on Discord, available training books on Excel and Java, and through our sub-stack. All links are available in the description.

Don't miss out on these invaluable insights. Join us and let’s level up our trading game! We look forward to having you on board!

 

Methods to Improve Signal Quality for High Performance - QUANTLABS.NET

In-Depth Analysis of Risk Model Quant in London

Welcome to today's discussion where our host, Brian, is answering a burning question about financial careers: What's the typical salary and bonus structure for a Quantitative Risk (Quant Risk) Model Management Associate position at Goldman Sachs in London, UK?

In this podcast episode, we delve deep into an interesting question raised on Quantitative Finance at quant.stackexchange.com regarding salient financial compensation details at Goldman Sachs, a top global investment banking firm. This topic reveals invaluable information for anyone considering a career in quantitative risk model management.

 

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As Brian highlights, risk management is a crucial appointee in the financial services industry. While the role does not generate revenue and may not be as exciting as other positions, it plays a pivotal role in compliance and paperwork handling. As an entry-level quant risk manager, you can anticipate a base salary between £60,000 to £70,000 per year, with performance-based bonuses of an additional 10% to 20%.

We also find that compensation structures differ across various roles within the investment banking space. The 'front office quant' salary, for instance, can reach a hefty sum of around $120,000 to $150,000, with bonuses potentially tipping over 50% of the base figure. Comparatively, Salaries for quant roles in risk management, although lower than those of front-office positions, provide a comfortable life, especially when factoring in the UK's average salary.

Brian further insights the listeners toward the job specifics that can significantly impact salaries. At Goldman Sachs, for example, a front office quant typically supports direct trade, while a Strategist quant contributes to trading strategies. The unique Goldman Sachs model has influenced many other investment banks to eventually follow suit.

Going on to discuss roles across varying experience levels, Brian shares that junior quant roles tend to pull in around 50 PA (per annum), while senior roles can generate up to a staggering 300 PA. Moreover, bonuses lean more generously towards the senior end with potential to double the annual salary.

This episode offers a promising understanding of the salary range for quants in risk model management in Goldman Sachs' London office. Make sure to tune in and gain a scoop of career insights right from the financial world!

 

In-Depth Analysis of Risk Model Quant in London - QUANTLABS.NET

A Comprehensive Warning Guide on Overvaluing Bitcoin

Welcome to an in-depth exploration of the notional value of Bitcoin and Gold with Brian from quantlabs.net. In this episode, he analyzes and shares his understanding of notional value, a term widely used by derivative traders. Referencing articles from coindesk.com and Investopedia, Brian breaks down what notional value entails and its impact on trading strategies.

Brian then dives into the analysis from JP Morgan, talking about how the Bitcoin spot ETF is expected to grow to $60 billion in the next two to three years, shifting the market price vs the implied price. An important aspect to note is the heightened volatility of Bitcoin, nearly 3.7 times higher than Gold, hence affecting the metrics of comparison. Brian also talks about the future scenario of Bitcoin and how the net flows into spot Bitcoin ETFs could influence the market.

Furthermore, Brian shares his own investment experiences and the performances of various coins. He highlights Bitcoin as the preferred choice for investment due to its consistent performance. He also shares viable watchlist options and advises caution while dealing with smaller, lesser-known coins owing to their higher volatility.

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https://quantlabs.substack.com/

Lastly, Brian reminds listeners of the technical trading updates and market analyses available on his Discord server and provides a link to join. He also promises to share his current watch list and trading insights via his substack, while reflecting on the weak forward guidance observed for stocks in the recent quarter. Concluding his episode, Brian shares a link to an email list that provides technical and algo trading books.