Channel Title: QuantPy Channel Description: This channel is all about learning quantitative finance with python. So many channels, books, people, and even universities out there only explain financial concepts, but don't show how to implement these concepts in a meaningful and practical way. Here I aim to implement financial concepts with Python, because I believe the best way to learn is to Build Something! My name is Jonathon Emerick and despite studying a Bachelor of Chemical Engineering and a Master in Financial Mathematics, I felt that I hadn't learned how to address real world problems or gained the skills required for success in the financial industry. On this channel I try to fill the gaps in my own knowledge, while helping others with concepts that I have only solidified after leaving university. This channel keeps me accountable, and I hope you can gain value or insight from these learnings. <video> A 20-Year Veteran Reveals the World of Options Market Making Description: EP 002 QuantPy Insights Podcast | Anonymous Guest Welcome back to the second episode of QuantPy Insights, where we dissect the complex world of quantitative finance and break it down for you! 📈 About This Episode: Today, we have a very special guest who has spent over two decades in the fast-paced realm of options market making. We dig deep into the intricacies of market making, from how firms make their money to the challenges and opportunities that new quants face in this dynamic industry. 🔑 Key Takeaways: - What is market making and how do firms profit from it? - The ins and outs of trading, risk management, and portfolio optimization. - Expert advice on how to differentiate yourself and make your mark in the market-making world. 🎯 Who Should Watch? If you're a university student intrigued by a career in quantitative finance, or a seasoned quant looking to diversify your skill set and advance your career, this episode is for you! ★ ★ QuantPy GitHub ★ ★ Collection of resources used on QuantPy YouTube channel. https://github.com/thequantpy ★ ★ Discord Community ★ ★ Join a small niche community of like-minded quants on discord. https://discord.com/invite/aY2Af4CxHP ★ ★ Support our Patreon Community ★ ★ Get access to Jupyter Notebooks that can run in the browser without downloading python. https://www.patreon.com/quantpy ★ ★ ThetaData API ★ ★ ThetaData's API provides both realtime and historical options data for end-of-day, and intraday trades and quotes. Use coupon 'QPY1' to receive 20% off on your first month. https://www.thetadata.net/ ★ ★ ONLINE TUTORIALS ★ ★ WEBSITE: https://quantpy.com.au ★ ★ CONTACT US ★ ★ EMAIL: pythonforquants@gmail.com Disclaimer: All ideas, opinions, recommendations and/or forecasts, expressed or implied in this content, are for informational and educational purposes only and should not be construed as financial product advice or an inducement or instruction to invest, trade, and/or speculate in the markets. Any action or refraining from action; investments, trades, and/or speculations made in light of the ideas, opinions, and/or forecasts, expressed or implied in this content, are committed at your own risk an consequence, financial or otherwise. As an affiliate of ThetaData, QuantPy Pty Ltd is compensated for any purchases made through the link provided in this description. </video> <video> Is your Sharpe Ratio is Lying to you? Use this instead Description: “Although skewness and kurtosis does not affect the point estimate of Sharpe ratio, it greatly impacts its confidence bands, and consequently its statistical significance” Bailey and López de Prado (2012). In the last video we explained the downfalls of relying on the Central Limit Theorem (CLT) and using the mean and standard deviation to calculate a point estimate of the Sharpe Ratio. In this video, we’ll delve into the limitations of the Sharpe Ratio, introduce you to the concept of Probabilistic Sharpe Ratio (PSR) developed by Bailey and López de Prado, and guide you through its Python implementation. So, if you’re eager to enhance your understanding of risk and performance metrics, you’re in the right place. Written Tutorial & Code Available on Medium: https://medium.com/@pythonforquants/is-your-sharpe-ratio-lying-to-you-meet-the-probabilistic-sharpe-ratio-d06077e423e8 ★ ★ Code Available on GitHub ★ ★ GitHub: https://github.com/TheQuantPy Specific Tutorial Link: https://github.com/TheQuantPy/youtube-tutorials/blob/51446e63304245542321985b6eec1bdd7c295442/2023/004%20Oct-Dec/2023-10-13%20Is%20your%20Sharpe%20Ratio%20is%20Lying%20to%20you_%20Use%20this%20instead.ipynb ★ ★ QuantPy GitHub ★ ★ Collection of resources used on QuantPy YouTube channel. https://github.com/thequantpy ★ ★ Discord Community ★ ★ Join a small niche community of like-minded quants on discord. https://discord.com/invite/aY2Af4CxHP ★ ★ Support our Patreon Community ★ ★ Get access to Jupyter Notebooks that can run in the browser without downloading python. https://www.patreon.com/quantpy ★ ★ ThetaData API ★ ★ ThetaData's API provides both realtime and historical options data for end-of-day, and intraday trades and quotes. Use coupon 'QPY1' to receive 20% off on your first month. https://www.thetadata.net/ ★ ★ Online Quant Tutorials ★ ★ WEBSITE: https://quantpy.com.au ★ ★ Contact Us ★ ★ EMAIL: pythonforquants@gmail.com Disclaimer: All ideas, opinions, recommendations and/or forecasts, expressed or implied in this content, are for informational and educational purposes only and should not be construed as financial product advice or an inducement or instruction to invest, trade, and/or speculate in the markets. Any action or refraining from action; investments, trades, and/or speculations made in light of the ideas, opinions, and/or forecasts, expressed or implied in this content, are committed at your own risk an consequence, financial or otherwise. As an affiliate of ThetaData, QuantPy Pty Ltd is compensated for any purchases made through the link provided in this description. </video> <video> Risk Neutral Pricing of Weather Derivatives Description: In this video, we finally use the risk-neutral pricing methodology for valuation of our temperature options in python. In this online tutorial series dedicated to weather derivatives we have estimated the parameters of our modified mean-reverting Ornstein-Uhlenbeck process which defines our Temperature dynamics, and have now implemented different models for our time varying volatility. Now we move on to calculating option prices using both Monte Carlo simulation method under the risk neutral probability measure and an alternative black scholes like approximation for winter seasons! Online written tutorial: https://quantpy.com.au/weather-derivatives/risk-neutral-pricing-of-weather-derivatives/ In this series we take a deep dive into a type of exotic financial products weather derivatives. Weather derivatives are financial instruments that can be used to reduce risk associated with adverse weather conditions like temperature, rainfall, frost, snow, and wind speeds. Historical Data, Weather Observations for Sydney, Australia – Observatory Hill: http://www.bom.gov.au/climate/data/stations/ ★ ★ Code Available on GitHub ★ ★ GitHub: https://github.com/TheQuantPy Specific Tutorial Link: https://github.com/TheQuantPy/youtube-tutorials/blob/8e64e19629cee840928b51baf4660e5c777e87e7/2022/004%20Oct-Dec/2022-10-17%20Risk%20Neutral%20Pricing%20of%20Weather%20Derivatives.ipynb ★ ★ QuantPy GitHub ★ ★ Collection of resources used on QuantPy YouTube channel. https://github.com/thequantpy ★ ★ Discord Community ★ ★ Join a small niche community of like-minded quants on discord. https://discord.com/invite/aY2Af4CxHP ★ ★ Support our Patreon Community ★ ★ Get access to Jupyter Notebooks that can run in the browser without downloading python. https://www.patreon.com/quantpy ★ ★ ThetaData API ★ ★ ThetaData's API provides both realtime and historical options data for end-of-day, and intraday trades and quotes. Use coupon 'QPY1' to receive 20% off on your first month. https://www.thetadata.net/ ★ ★ Online Quant Tutorials ★ ★ WEBSITE: https://quantpy.com.au ★ ★ Contact Us ★ ★ EMAIL: pythonforquants@gmail.com Disclaimer: All ideas, opinions, recommendations and/or forecasts, expressed or implied in this content, are for informational and educational purposes only and should not be construed as financial product advice or an inducement or instruction to invest, trade, and/or speculate in the markets. Any action or refraining from action; investments, trades, and/or speculations made in light of the ideas, opinions, and/or forecasts, expressed or implied in this content, are committed at your own risk an consequence, financial or otherwise. As an affiliate of ThetaData, QuantPy Pty Ltd is compensated for any purchases made through the link provided in this description. </video> <video> Can You Compare Intraday Volatility Surfaces? Description: In today's tutorial we investigate how you can use ThetaData's API to retreive historical options data for end-of-day, and intraday trades and quotes. We will create volatility surfaces use an interpolation method (B-Splines) to compare surfaces between the morning (10am) implied volalitity and afternoon (2pm) implied volatility surfaces. Check out ThetaData's API (includes free tier) https://www.thetadata.net/ Online written tutorial: https://quantpy.com.au/realworld-option-hedging/intraday-option-vol-surfaces/ ★ ★ Code Available on GitHub ★ ★ GitHub: https://github.com/TheQuantPy Specific Tutorial Link: https://github.com/TheQuantPy/youtube-tutorials/blob/8e64e19629cee840928b51baf4660e5c777e87e7/2022/003%20Jul-Sep/2022-09-08%20Can%20You%20Compare%20Intraday%20Volatility%20Surfaces_.ipynb ★ ★ QuantPy GitHub ★ ★ Collection of resources used on QuantPy YouTube channel. https://github.com/thequantpy ★ ★ Discord Community ★ ★ Join a small niche community of like-minded quants on discord. https://discord.com/invite/aY2Af4CxHP ★ ★ Support our Patreon Community ★ ★ Get access to Jupyter Notebooks that can run in the browser without downloading python. https://www.patreon.com/quantpy ★ ★ ThetaData API ★ ★ ThetaData's API provides both realtime and historical options data for end-of-day, and intraday trades and quotes. Use coupon 'QPY1' to receive 20% off on your first month. https://www.thetadata.net/ ★ ★ Online Quant Tutorials ★ ★ WEBSITE: https://quantpy.com.au ★ ★ Contact Us ★ ★ EMAIL: pythonforquants@gmail.com Disclaimer: All ideas, opinions, recommendations and/or forecasts, expressed or implied in this content, are for informational and educational purposes only and should not be construed as financial product advice or an inducement or instruction to invest, trade, and/or speculate in the markets. Any action or refraining from action; investments, trades, and/or speculations made in light of the ideas, opinions, and/or forecasts, expressed or implied in this content, are committed at your own risk an consequence, financial or otherwise. As an affiliate of ThetaData, QuantPy Pty Ltd is compensated for any purchases made through the link provided in this description. </video> <video> Stop making investment decisions using this metric! Description: The Pitfalls of Relying on the Central Limit Theorem in Portfolio Return Analysis. In the world of finance, both individuals and investing professionals alike strive for making sound investment decisions with consideration of risk. Usually this process involves understanding and analysis of portfolio returns. Central to this methodology is the Central Limit Theorem (CLT), a statistical concept widely employed to assess the behavior of financial data. However, beneath its apparent utility lies a series of assumptions that can mislead rather than illuminate. This article delves into why a critical examination of the CLT’s applicability in portfolio return analysis is imperative, shedding light on its limitations and the alternatives that offer a more accurate view of financial reality. Online Written Tutorial & Code Available on Medium: https://medium.com/@pythonforquants/you-need-to-stop-using-the-sharpe-ratio-7dfed7183e48 ★ ★ Code Available on GitHub ★ ★ GitHub: https://github.com/TheQuantPy Specific Tutorial Link: https://github.com/TheQuantPy/youtube-tutorials/blob/51446e63304245542321985b6eec1bdd7c295442/2023/003%20Jul-Sep/2023-09-29%20Stop%20making%20investment%20decisions%20using%20this%20metric!.ipynb ★ ★ QuantPy GitHub ★ ★ Collection of resources used on QuantPy YouTube channel. https://github.com/thequantpy ★ ★ Discord Community ★ ★ Join a small niche community of like-minded quants on discord. https://discord.com/invite/aY2Af4CxHP ★ ★ Support our Patreon Community ★ ★ Get access to Jupyter Notebooks that can run in the browser without downloading python. https://www.patreon.com/quantpy ★ ★ ThetaData API ★ ★ ThetaData's API provides both realtime and historical options data for end-of-day, and intraday trades and quotes. Use coupon 'QPY1' to receive 20% off on your first month. https://www.thetadata.net/ ★ ★ Online Quant Tutorials ★ ★ WEBSITE: https://quantpy.com.au ★ ★ Contact Us ★ ★ EMAIL: pythonforquants@gmail.com Disclaimer: All ideas, opinions, recommendations and/or forecasts, expressed or implied in this content, are for informational and educational purposes only and should not be construed as financial product advice or an inducement or instruction to invest, trade, and/or speculate in the markets. Any action or refraining from action; investments, trades, and/or speculations made in light of the ideas, opinions, and/or forecasts, expressed or implied in this content, are committed at your own risk an consequence, financial or otherwise. As an affiliate of ThetaData, QuantPy Pty Ltd is compensated for any purchases made through the link provided in this description. </video> <video> Monte Carlo Simulation of Temperature for Weather Derivative Pricing Description: In this online tutorial series dedicated to weather derivatives we have estimated the parameters of our modified mean-reverting Ornstein-Uhlenbeck process which defines our Temperature dynamics, and have now implemented different models for our time varying volatility. Now we move on to simulating temperature paths using Monte Carlo simulation method under the physical probability measure. Once we have completed these simulation functions we can move onto implementing the Monte Carlo method under the risk-neutral pricing methodology for valuation of our temperature options. Online written tutorial: https://quantpy.com.au/weather-derivatives/monte-carlo-simulation-of-temperature-for-weather-derivative-pricing/ In this series we take a deep dive into a type of exotic financial products weather derivatives. Weather derivatives are financial instruments that can be used to reduce risk associated with adverse weather conditions like temperature, rainfall, frost, snow, and wind speeds. Historical Data, Weather Observations for Sydney, Australia – Observatory Hill: http://www.bom.gov.au/climate/data/stations/ ★ ★ Code Available on GitHub ★ ★ GitHub: https://github.com/TheQuantPy Specific Tutorial Link: https://github.com/TheQuantPy/youtube-tutorials/blob/8e64e19629cee840928b51baf4660e5c777e87e7/2022/003%20Jul-Sep/2022-08-31%20Monte%20Carlo%20Simulation%20of%20Temperature%20for%20Weather%20Derivative%20Pricing.ipynb ★ ★ QuantPy GitHub ★ ★ Collection of resources used on QuantPy YouTube channel. https://github.com/thequantpy ★ ★ Discord Community ★ ★ Join a small niche community of like-minded quants on discord. https://discord.com/invite/aY2Af4CxHP ★ ★ Support our Patreon Community ★ ★ Get access to Jupyter Notebooks that can run in the browser without downloading python. https://www.patreon.com/quantpy ★ ★ ThetaData API ★ ★ ThetaData's API provides both realtime and historical options data for end-of-day, and intraday trades and quotes. Use coupon 'QPY1' to receive 20% off on your first month. https://www.thetadata.net/ ★ ★ Online Quant Tutorials ★ ★ WEBSITE: https://quantpy.com.au ★ ★ Contact Us ★ ★ EMAIL: pythonforquants@gmail.com Disclaimer: All ideas, opinions, recommendations and/or forecasts, expressed or implied in this content, are for informational and educational purposes only and should not be construed as financial product advice or an inducement or instruction to invest, trade, and/or speculate in the markets. Any action or refraining from action; investments, trades, and/or speculations made in light of the ideas, opinions, and/or forecasts, expressed or implied in this content, are committed at your own risk an consequence, financial or otherwise. As an affiliate of ThetaData, QuantPy Pty Ltd is compensated for any purchases made through the link provided in this description. </video> <video> Real-Time Streaming of Every Option Trade Description: After searching for a number of years for a good solution for all my financial data needs, I have finally found ThetaData's API which now provides Real-time Streaming of Options Tick Level Data! In this video we go through how to add all available tick level NBBO trades data to your ThetaData real-time stream. We then explore how you can add only specific contract streams you are interested in, and we take this one step further by adding this to a Pandas Dataframe cached in memory using dictionary's so you can access your data for real-time analysis. Online written tutorial: https://quantpy.com.au/options-data/real-time-streaming-of-options-trades-data/ ★ ★ Code Available on GitHub ★ ★ GitHub: https://github.com/TheQuantPy Specific Tutorial Link: https://github.com/TheQuantPy/youtube-tutorials/blob/8e64e19629cee840928b51baf4660e5c777e87e7/2023/001%20Jan-Mar/2023-02-03%20Real-Time%20Streaming%20of%20Every%20Option%20Trade.ipynb ★ ★ QuantPy GitHub ★ ★ Collection of resources used on QuantPy YouTube channel. https://github.com/thequantpy ★ ★ Discord Community ★ ★ Join a small niche community of like-minded quants on discord. https://discord.com/invite/aY2Af4CxHP ★ ★ Support our Patreon Community ★ ★ Get access to Jupyter Notebooks that can run in the browser without downloading python. https://www.patreon.com/quantpy ★ ★ ThetaData API ★ ★ ThetaData's API provides both realtime and historical options data for end-of-day, and intraday trades and quotes. Use coupon 'QPY1' to receive 20% off on your first month. https://www.thetadata.net/ ★ ★ Online Quant Tutorials ★ ★ WEBSITE: https://quantpy.com.au ★ ★ Contact Us ★ ★ EMAIL: pythonforquants@gmail.com Disclaimer: All ideas, opinions, recommendations and/or forecasts, expressed or implied in this content, are for informational and educational purposes only and should not be construed as financial product advice or an inducement or instruction to invest, trade, and/or speculate in the markets. Any action or refraining from action; investments, trades, and/or speculations made in light of the ideas, opinions, and/or forecasts, expressed or implied in this content, are committed at your own risk an consequence, financial or otherwise. As an affiliate of ThetaData, QuantPy Pty Ltd is compensated for any purchases made through the link provided in this description. </video> <video> You will need to be the kind of person who loves to solve problems · Octavio Baraldo Queijeiro Description: EP 001 QuantPy Insights Podcast | Octavio Baraldo Queijeiro Octavio began his journey in quantitative finance after completing a mathematically-oriented economics undergraduate program in Argentina. Intrigued by the rapidly evolving landscape and machine learning developments he witnessed in China, his curiosity steered him toward the field of quantitative finance. Armed with this newfound motivation, Octavio relocated to Spain to pursue a master's degree in quantitative finance. Post-graduation, he entered the consulting industry as a derivatives and market risk analyst. Since then, Octavio has advanced his career by working in various capacities within quantitative finance, initially in front office quantitative pricing and valuation in the consulting industry, and subsequently transitioning to the banking sector. He now focuses on credit and counterparty risk, specifically on refining and developing models and methodology, at Santander Bank. In this podcast, Octavio outlines his journey and shares valuable insights on the tools and skills that can help aspiring quantitative analysts or young professionals progress in the field of quant finance. Comment from Octavio after Interview: **Opinions are my own and not the views of my employer** Octavio's Book Recommendations: Math: - Introduction to probability, Bertsekas - Mathematical Statistics, Knight - Linear Algebra Done Right, Sheldon Axler - Calculus Vol I, II, Spivak Quant Finance: - Stochastic Calculus for Finance I and II: Shreve - Quantitative Finance, Willmot - Options, futures and other derivatives, C. Hull - Interest Rate Models, Brigo and Mercurio - XVA Credit Funding and Capital Valuation Adjustments, Green Timeline: 00:00 Background and Career Journey 06:18 Key Concepts in Quantitative Finance 13:22 Challenges of Working in Risk Department 16:51 Opportunities for the Banking Industry 21:45 The Importance of Problem Solving 26:05 Advice for Aspiring Quantitative Analysts 30:32 How to Progress in your Career as a Quant 36:30 Creating Long-lasting Value in your Quant Role 42:50 Closing Thoughts and Recommendations ★ ★ QuantPy GitHub ★ ★ Collection of resources used on QuantPy YouTube channel. https://github.com/thequantpy ★ ★ Discord Community ★ ★ Join a small niche community of like-minded quants on discord. https://discord.com/invite/aY2Af4CxHP ★ ★ Support our Patreon Community ★ ★ Get access to Jupyter Notebooks that can run in the browser without downloading python. https://www.patreon.com/quantpy ★ ★ ThetaData API ★ ★ ThetaData's API provides both realtime and historical options data for end-of-day, and intraday trades and quotes. Use coupon 'QPY1' to receive 20% off on your first month. https://www.thetadata.net/ ★ ★ Online Quant Tutorials ★ ★ WEBSITE: https://quantpy.com.au ★ ★ Contact Us ★ ★ EMAIL: pythonforquants@gmail.com Disclaimer: All ideas, opinions, recommendations and/or forecasts, expressed or implied in this content, are for informational and educational purposes only and should not be construed as financial product advice or an inducement or instruction to invest, trade, and/or speculate in the markets. Any action or refraining from action; investments, trades, and/or speculations made in light of the ideas, opinions, and/or forecasts, expressed or implied in this content, are committed at your own risk an consequence, financial or otherwise. As an affiliate of ThetaData, QuantPy Pty Ltd is compensated for any purchases made through the link provided in this description. </video> <video> Time Varying Volatility Models for Stochastic Finance | Weather Derivatives Description: Now that we have a defined the parameters of our modified mean-reverting Ornstein-Uhlenbeck process which defines our Temperature dynamics, in this tutorial we will now be looking to implement different models for our time varying volatility patterns. We have a number of options to model temperature volatility across seasons. - Piece-wise Constant Functions (volatility for each season) - Parametric Regression - Polynomial - Local and Nonparametric Regression - Splines - Fourier Series - Stochastic Differential Equations Online written tutorial: https://quantpy.com.au/weather-derivatives/temperature-volatility-models/ In this series we take a deep dive into a type of exotic financial products weather derivatives. Weather derivatives are financial instruments that can be used to reduce risk associated with adverse weather conditions like temperature, rainfall, frost, snow, and wind speeds. Historical Data, Weather Observations for Sydney, Australia – Observatory Hill: http://www.bom.gov.au/climate/data/stations/ ★ ★ Code Available on GitHub ★ ★ GitHub: https://github.com/TheQuantPy Specific Tutorial Link: https://github.com/TheQuantPy/youtube-tutorials/blob/8e64e19629cee840928b51baf4660e5c777e87e7/2022/003%20Jul-Sep/2022-08-22%20Time%20Varying%20Volatility%20Models%20for%20Stochastic%20Finance%20_%20Weather%20Derivatives.ipynb ★ ★ QuantPy GitHub ★ ★ Collection of resources used on QuantPy YouTube channel. https://github.com/thequantpy ★ ★ Discord Community ★ ★ Join a small niche community of like-minded quants on discord. https://discord.com/invite/aY2Af4CxHP ★ ★ Support our Patreon Community ★ ★ Get access to Jupyter Notebooks that can run in the browser without downloading python. https://www.patreon.com/quantpy ★ ★ ThetaData API ★ ★ ThetaData's API provides both realtime and historical options data for end-of-day, and intraday trades and quotes. Use coupon 'QPY1' to receive 20% off on your first month. https://www.thetadata.net/ ★ ★ Online Quant Tutorials ★ ★ WEBSITE: https://quantpy.com.au ★ ★ Contact Us ★ ★ EMAIL: pythonforquants@gmail.com Disclaimer: All ideas, opinions, recommendations and/or forecasts, expressed or implied in this content, are for informational and educational purposes only and should not be construed as financial product advice or an inducement or instruction to invest, trade, and/or speculate in the markets. Any action or refraining from action; investments, trades, and/or speculations made in light of the ideas, opinions, and/or forecasts, expressed or implied in this content, are committed at your own risk an consequence, financial or otherwise. As an affiliate of ThetaData, QuantPy Pty Ltd is compensated for any purchases made through the link provided in this description. </video> <video> Inferring the Aggressor using Options Data Description: We will be implementing the bulk volume classification algorithm to attempt to discern information from tick by tick trade data. We will be using ThetaData's API which provides both Historical and Real-time Streaming of Options Tick Level Data! We first explore what algorithms have been used previously to attempt to infer the aggressor (the trader who initiates the trade), which would classify every trade as either a buy or sell initiated trade. In todays world of high frequency and complex execution algorithms that can split orders up into multiple child order and distribute across exchanges, the two papers we discuss argue that these traditional classification algorithms are not so relevant. Therefor we implement the Bulk Volume Classification algorithm that looks at aggregated trades, and therefore captures market makers response to trade flow over trade periods. We have completed this analysis using only Historical trades data, however in the next video we will implement this algorithm with Real-time Streaming. Online written tutorial: https://quantpy.com.au/options-data/information-detection-from-trade-data/ ★ ★ Code Available on GitHub ★ ★ GitHub: https://github.com/TheQuantPy Specific Tutorial Link: https://github.com/TheQuantPy/youtube-tutorials/blob/8e64e19629cee840928b51baf4660e5c777e87e7/2023/002%20Apr-Jun/2023-06-13%20Inferring%20the%20Aggressor%20using%20Options%20Data.ipynb ★ ★ QuantPy GitHub ★ ★ Collection of resources used on QuantPy YouTube channel. https://github.com/thequantpy ★ ★ Discord Community ★ ★ Join a small niche community of like-minded quants on discord. https://discord.com/invite/aY2Af4CxHP ★ ★ Support our Patreon Community ★ ★ Get access to Jupyter Notebooks that can run in the browser without downloading python. https://www.patreon.com/quantpy ★ ★ ThetaData API ★ ★ ThetaData's API provides both realtime and historical options data for end-of-day, and intraday trades and quotes. Use coupon 'QPY1' to receive 20% off on your first month. https://www.thetadata.net/ ★ ★ Online Quant Tutorials ★ ★ WEBSITE: https://quantpy.com.au ★ ★ Contact Us ★ ★ EMAIL: pythonforquants@gmail.com Disclaimer: All ideas, opinions, recommendations and/or forecasts, expressed or implied in this content, are for informational and educational purposes only and should not be construed as financial product advice or an inducement or instruction to invest, trade, and/or speculate in the markets. Any action or refraining from action; investments, trades, and/or speculations made in light of the ideas, opinions, and/or forecasts, expressed or implied in this content, are committed at your own risk an consequence, financial or otherwise. As an affiliate of ThetaData, QuantPy Pty Ltd is compensated for any purchases made through the link provided in this description. </video> <video> Modifying the Ornstein-Uhlenbeck process | A practical application of stochastic calculus for Quants Description: Our goal today is to use our knowledge of stochastic calculus in a practical way to fit a mean-reverting stochastic process to real world data under the physical probability measure. We will attempting to model the variation of the difference between daily average temperature (DAT) and our deterministic seasonal temperature model which takes into consideration temperature trend and seasonality. The detrended and deseasonalized temperature series is mean-reverting and displays heteroskedasticity. In this tutorial we use Ito calculus and specifically the Ito doeblin formula to justify the modified mean-reverting Ornstein-Uhlenbeck process (sometimes called an OU process) dynamics we eventually use to model this detrended and deseasonalized temperature series. We will then go through the estimatation of the key parameters within this modified mean-reverting Ornstein-Uhlenbeck process, including the speed of mean reversion. Online written tutorial: https://quantpy.com.au/weather-derivatives/modelling-temperature-with-a-modified-ornstein-uhlenbeck-process/ In this series we take a deep dive into a type of exotic financial products weather derivatives. Weather derivatives are financial instruments that can be used to reduce risk associated with adverse weather conditions like temperature, rainfall, frost, snow, and wind speeds. Historical Data, Weather Observations for Sydney, Australia – Observatory Hill: http://www.bom.gov.au/climate/data/stations/ ★ ★ Code Available on GitHub ★ ★ GitHub: https://github.com/TheQuantPy Specific Tutorial Link: https://github.com/TheQuantPy/youtube-tutorials/blob/8e64e19629cee840928b51baf4660e5c777e87e7/2022/003%20Jul-Sep/2022-07-20%20Modifying%20the%20Ornstein-Uhlenbeck%20process%20_%20A%20practical%20application%20of%20stochastic%20calculus%20for%20Quants.ipynb ★ ★ QuantPy GitHub ★ ★ Collection of resources used on QuantPy YouTube channel. https://github.com/thequantpy ★ ★ Discord Community ★ ★ Join a small niche community of like-minded quants on discord. https://discord.com/invite/aY2Af4CxHP ★ ★ Support our Patreon Community ★ ★ Get access to Jupyter Notebooks that can run in the browser without downloading python. https://www.patreon.com/quantpy ★ ★ ThetaData API ★ ★ ThetaData's API provides both realtime and historical options data for end-of-day, and intraday trades and quotes. Use coupon 'QPY1' to receive 20% off on your first month. https://www.thetadata.net/ ★ ★ Online Quant Tutorials ★ ★ WEBSITE: https://quantpy.com.au ★ ★ Contact Us ★ ★ EMAIL: pythonforquants@gmail.com Disclaimer: All ideas, opinions, recommendations and/or forecasts, expressed or implied in this content, are for informational and educational purposes only and should not be construed as financial product advice or an inducement or instruction to invest, trade, and/or speculate in the markets. Any action or refraining from action; investments, trades, and/or speculations made in light of the ideas, opinions, and/or forecasts, expressed or implied in this content, are committed at your own risk an consequence, financial or otherwise. As an affiliate of ThetaData, QuantPy Pty Ltd is compensated for any purchases made through the link provided in this description. </video> <video> OpenAI & Python: The Ultimate Twitter Automation Guide Description: In this tutorial, we dive into automating your Twitter feed by leveraging OpenAI's Chat GPT, Python, and the Tweepy module. The bot is designed to generate tweets from a list of quantitative Python ideas using a specialized OpenAI prompt template. All of this is set up in a Jupyter Notebook environment for easy testing and modification. Tweets are then scheduled to be posted every 12 hours using Python's 'apscheduler' module. The entire operation runs as an OS service, allowing you to manage your Twitter bot directly from the command line of your remote server. We also share our roadmap for future enhancements, including a specialized QuantPy Main Twitter Feed dedicated to Quantitative Finance. Tweets will be automatically generated using OpenAI's Chat GPT and scheduled via a Raspberry Pi headless server. The tutorial also offers a glimpse into how you can access logs generated by the Twitter bot for performance analytics. Watch on to learn how to automate your social media activities effectively. GitHub Repo: https://github.com/TheQuantPy/quantpy-twitter-bot OpenAI Account & API: https://platform.openai.com/account/api-keys Twitter (X) Dev Account & API: https://developer.twitter.com ★ ★ QuantPy GitHub ★ ★ Collection of resources used on QuantPy YouTube channel. https://github.com/thequantpy ★ ★ Discord Community ★ ★ Join a small niche community of like-minded quants on discord. https://discord.com/invite/aY2Af4CxHP ★ ★ Support our Patreon Community ★ ★ Get access to Jupyter Notebooks that can run in the browser without downloading python. https://www.patreon.com/quantpy ★ ★ ThetaData API ★ ★ ThetaData's API provides both realtime and historical options data for end-of-day, and intraday trades and quotes. Use coupon 'QPY1' to receive 20% off on your first month. https://www.thetadata.net/ ★ ★ ONLINE TUTORIALS ★ ★ WEBSITE: https://quantpy.com.au ★ ★ CONTACT US ★ ★ EMAIL: pythonforquants@gmail.com Disclaimer: All ideas, opinions, recommendations and/or forecasts, expressed or implied in this content, are for informational and educational purposes only and should not be construed as financial product advice or an inducement or instruction to invest, trade, and/or speculate in the markets. Any action or refraining from action; investments, trades, and/or speculations made in light of the ideas, opinions, and/or forecasts, expressed or implied in this content, are committed at your own risk an consequence, financial or otherwise. As an affiliate of ThetaData, QuantPy Pty Ltd is compensated for any purchases made through the link provided in this description. </video> <video> Machine Learning in Finance Course Description: Master the most in-demand skill-set of the world's top financial institutions with one of the most practical, comprehensive and affordable courses in Financial Machine Learning. ★ ★ Machine Learning in Finance ★ ★ Release Date: EST 1200 01-Dec Learn more and register your interest here: https://quantpy.com.au/machine-learning-in-finance-course/ Why I started an online Course? _______________________________ ★ The QuantPy Story ★ If you don't know me, my is Jonathon Emerick and I started a YouTube channel called QuantPy. I noticed a lack of video style quantitative finance resources online, and decided to embark on the journey of creating online tutorials and documenting my journey towards "becoming a Quant". The goal was to follow my interests in quantitative finance and attempt to help others along the way. ★ Collaboration with Hariom Tatsat ★ I never intended to make an online course, however at the end of 2021 I was introduced to Hariom Tatsat a co-author of the book "Machine Learning and Data Science Blueprints for Finance". Hariom has extensive market knowledge and experience in applying machine learning to many financial applications, having done so throughout his career working at large Investment Banks. Leaning on his years of experience and knowledge, he proposed creating a course that delivered a very complete and practical approach to the applications of machine learning in finance. ★ My Contribution ★ Along with my ability to explain complex financial topics simply and effectively, I have a strong interest in the potential applications of Machine Learning and AI within both my own industry, the energy industry, and the financial industry into the future. Hence I was happy to enbark on this collaboration, both for solidifying concepts I was introduced to while studying Financial Mathematics at University, as well as learning from the extensive experience Hariom has gained throughout his career in applying machine learning to real world problems. ★ Our Goal ★ We developed this course with an emphasis on providing value first and foremost. Therefore, we hope to have not only delivered a complete and practical course on Machine Learning in Finance, but most importantly an affordable course, having designed this program keeping students in mind. We know you'll appreciate the amount of value and insight provided by Hariom's experience and access to 15+ case studies with over 10,000+ lines of code applying machine learning to real world financial problems. For more details, please read the Machine Learning in Finance section of this website. ★ ★ QuantPy GitHub ★ ★ Collection of resources used on QuantPy YouTube channel. https://github.com/thequantpy ★ ★ Discord Community ★ ★ Join a small niche community of like-minded quants on discord. https://discord.com/invite/aY2Af4CxHP ★ ★ Support our Patreon Community ★ ★ Get access to Jupyter Notebooks that can run in the browser without downloading python. https://www.patreon.com/quantpy ★ ★ ThetaData API ★ ★ ThetaData's API provides both realtime and historical options data for end-of-day, and intraday trades and quotes. Use coupon 'QPY1' to receive 20% off on your first month. https://www.thetadata.net/ ★ ★ Online Quant Tutorials ★ ★ WEBSITE: https://quantpy.com.au ★ ★ Contact Us ★ ★ EMAIL: pythonforquants@gmail.com Disclaimer: All ideas, opinions, recommendations and/or forecasts, expressed or implied in this content, are for informational and educational purposes only and should not be construed as financial product advice or an inducement or instruction to invest, trade, and/or speculate in the markets. Any action or refraining from action; investments, trades, and/or speculations made in light of the ideas, opinions, and/or forecasts, expressed or implied in this content, are committed at your own risk an consequence, financial or otherwise. As an affiliate of ThetaData, QuantPy Pty Ltd is compensated for any purchases made through the link provided in this description. </video> <video> Historical vs Implied Volatility with 10yrs Options Data! Description: In today's tutorial we investigate how you can use ThetaData's API to retreive 10 years of historical options data on Microsoft (MSFT) for comparing Implied Volatility to Historical Volatility. We also describe what the difference between historical volatility and implied volatility actually is. Realized volatility (rv) is the actual stock price variability due to randomness of the underlying Brownian motion or Wiener Process of the stock price. While the Implied volatility (iv) is how the market is pricing the option currently. To calculate implied volatility you use the market price of the option (as well as the contract terms) and a theoretical pricing model depending on the type of option being priced. But what is the difference? Since the market does not have perfect knowledge about the future these two numbers can and will be different. Therein, lies the risk management problem, or perhaps the business or trading opportunity? Online written tutorial: https://quantpy.com.au/implied-volatility/implied-volatility-vs-historical-volatility/ ★ ★ Code Available on GitHub ★ ★ GitHub: https://github.com/TheQuantPy Specific Tutorial Link: https://github.com/TheQuantPy/youtube-tutorials/blob/8e64e19629cee840928b51baf4660e5c777e87e7/2022/004%20Oct-Dec/2022-11-21%20Historical%20vs%20Implied%20Volatility%20with%2010yrs%20Options%20Data!.ipynb ★ ★ QuantPy GitHub ★ ★ Collection of resources used on QuantPy YouTube channel. https://github.com/thequantpy ★ ★ Discord Community ★ ★ Join a small niche community of like-minded quants on discord. https://discord.com/invite/aY2Af4CxHP ★ ★ Support our Patreon Community ★ ★ Get access to Jupyter Notebooks that can run in the browser without downloading python. https://www.patreon.com/quantpy ★ ★ ThetaData API ★ ★ ThetaData's API provides both realtime and historical options data for end-of-day, and intraday trades and quotes. Use coupon 'QPY1' to receive 20% off on your first month. https://www.thetadata.net/ ★ ★ Online Quant Tutorials ★ ★ WEBSITE: https://quantpy.com.au ★ ★ Contact Us ★ ★ EMAIL: pythonforquants@gmail.com Disclaimer: All ideas, opinions, recommendations and/or forecasts, expressed or implied in this content, are for informational and educational purposes only and should not be construed as financial product advice or an inducement or instruction to invest, trade, and/or speculate in the markets. Any action or refraining from action; investments, trades, and/or speculations made in light of the ideas, opinions, and/or forecasts, expressed or implied in this content, are committed at your own risk an consequence, financial or otherwise. As an affiliate of ThetaData, QuantPy Pty Ltd is compensated for any purchases made through the link provided in this description. </video> <video> From Black Holes to Black-Scholes Description: EP 003 QuantPy Insights Podcast | Davide Bufalini | The Journey from Academia to Quant Finance 📈 About This Episode: Today, we have a very special guest, Davide Bufalini, who has transitioned from academia having studied a PhD in theoretical physics to the world of quantitative finance. In this episode, we discuss some of the largest challenges, transferrable skills and recommendations for making the transition from university to the quant industry. 🔑 Key Takeaways (Guest Perspective): From solving one of the biggest challenges in theoretical physics to understanding market flows and behaviour, what I learned in my PhD applies to my job on a daily basis. To successfully transition to quant finance, it is crucial to have the right motivations, strong math fundamentals, have studied the right books, and have asked experienced practitioners about their opinions at the right time. 🎯 Who Should Watch? If you're a university student/researcher intrigued by a career in quantitative finance, or a seasoned quant looking to diversify your skill set and advance your career, this episode is for you! 🏛 Guest Background, Motivations, Insights and Resources (Guest Perspective) My PhD focused on one of the biggest challenges in theoretical physics: solving the black hole information paradox, first formulated by Stephen Hawking. I contributed to this issue within the framework of string theory, today's leading theory of quantum gravity. Despite the stimulating and interesting topic, the academic lifestyle was not something that I wanted to pursue because of many issues, unfortunately common to numerous researchers. While deciding to change career, I learned more about the fascinating world of quant finance, how I could continue to have fun with math, and apply my skills to new exciting challenges. 🎓 Useful skillset from PhD to Quant? From solving supergravity equations to the Black-Scholes’ PDE, from expectations values of operator products to expectations under martingale measures, the overlap between the fields is broader than what it seems at first glance. Problem-solving skills, research abilities, statistical physics, differential equations, Fourier transforms, and Lebesgue integrals: all of these concepts apply to my job, and help in understand research papers and books with relative ease. 🔋 What skillset do you use every day? Daily, I program in Python and use traditional and stochastic calculus to actively produce work. To read and understand research papers, knowledge of hypergeometric functions and complex analysis has been proven useful. Most importantly, my approach to solving problems is still very similar to that of the PhD, and the rigorous imprint and technical background is likewise crucial in a field such as quantitative finance. 📚 Recommended Books & Resources BASICS & OPTION PRICING 1. Wilmott - Paul Wilmott introduces Quantitative Finance 2. Baxter, Rennie - Financial Calculus 3. Bjork - Arbitrage Theory in Continuous Time 4. The two books by Steven Shreve (a classic!) I strongly recommend following the above order, and I recommend studying the Black-Scholes model and the Greeks, as a minimum requirement. Note that the list is non-exhaustive. 5. [Advanced, and only for physicists with a strong math background] Labordere - Analysis, Geometry, and Modeling in Finance: Advanced Methods in Option Pricing INTERVIEWS - Joshi, Denson, Downes - Quant Job Interview Questions And Answers - Crack - Heard on the Street: Quantitative Questions from Wall Street Job Interviews - Wilmott - Frequently Asked Questions in Quantitative Finance CODING SKILLS - Python for research and AI, machine learning, and deep learning. - C++ or other low latency language for front office roles - Big banks and institutions may have their proprietary programming language, so understanding the logic behind programming and algorithms is crucial. ★ ★ QuantPy GitHub ★ ★ Collection of resources used on QuantPy YouTube channel. https://github.com/thequantpy ★ ★ Discord Community ★ ★ Join a small niche community of like-minded quants on discord. https://discord.com/invite/aY2Af4CxHP ★ ★ CONTACT US ★ ★ EMAIL: pythonforquants@gmail.com Disclaimer: All ideas, opinions, recommendations and/or forecasts, expressed or implied in this content, are for informational and educational purposes only and should not be construed as financial product advice or an inducement or instruction to invest, trade, and/or speculate in the markets. Any action or refraining from action; investments, trades, and/or speculations made in light of the ideas, opinions, and/or forecasts, expressed or implied in this content, are committed at your own risk an consequence, financial or otherwise. As an affiliate of ThetaData, QuantPy Pty Ltd is compensated for any purchases made through the link provided in this description. </video>

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This channel is all about learning quantitative finance with python.

So many channels, books, people, and even universities out there only explain financial concepts, but don't show how to implement these concepts in a meaningful and practical way. Here I aim to implement financial concepts with Python, because I believe the best way to learn is to Build Something!

My name is Jonathon Emerick and despite studying a Bachelor of Chemical Engineering and a Master in Financial Mathematics, I felt that I hadn't learned how to address real world problems or gained the skills required for success in the financial industry.

On this channel I try to fill the gaps in my own knowledge, while helping others with concepts that I have only solidified after leaving university. This channel keeps me accountable, and I hope you can gain value or insight from these learnings.

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