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QuantPy
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Приєднався 23 чер 2017
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. Try searching on the channel page, or via quantpy.com.au/
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. Try searching on the channel page, or via quantpy.com.au/
From Black Holes to Black-Scholes
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 UA-cam channel. github.com/thequantpy
★ ★ Discord Community ★ ★
Join a small niche community of like-minded quants on discord. 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.
📈 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 UA-cam channel. github.com/thequantpy
★ ★ Discord Community ★ ★
Join a small niche community of like-minded quants on discord. 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.
Переглядів: 9 634
Відео
OpenAI & Python: The Ultimate Twitter Automation Guide
Переглядів 6 тис.6 місяців тому
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 usin...
A 20-Year Veteran Reveals the World of Options Market Making
Переглядів 30 тис.7 місяців тому
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 ...
Is your Sharpe Ratio is Lying to you? Use this instead
Переглядів 5 тис.7 місяців тому
“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...
You will need to be the kind of person who loves to solve problems · Octavio Baraldo Queijeiro
Переглядів 10 тис.7 місяців тому
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 moti...
Stop making investment decisions using this metric!
Переглядів 7 тис.7 місяців тому
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 ...
Inferring the Aggressor using Options Data
Переглядів 11 тис.11 місяців тому
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 ...
Real-Time Streaming of Every Option Trade
Переглядів 16 тис.Рік тому
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,...
Machine Learning in Finance Course
Переглядів 16 тис.Рік тому
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: quantpy.com.au/machine-learning-in-finance-course/ Why I started an online Course? ★ The QuantPy Story ★ If y...
Historical vs Implied Volatility with 10yrs Options Data!
Переглядів 14 тис.Рік тому
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 B...
Risk Neutral Pricing of Weather Derivatives
Переглядів 6 тис.Рік тому
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 m...
Can You Compare Intraday Volatility Surfaces?
Переглядів 12 тис.Рік тому
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) www.theta...
Monte Carlo Simulation of Temperature for Weather Derivative Pricing
Переглядів 7 тис.Рік тому
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...
Time Varying Volatility Models for Stochastic Finance | Weather Derivatives
Переглядів 8 тис.Рік тому
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) - Parame...
Modifying the Ornstein-Uhlenbeck process | A practical application of stochastic calculus for Quants
Переглядів 16 тис.Рік тому
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....
Detrending and deseasonalizing data with fourier series
Переглядів 18 тис.Рік тому
Detrending and deseasonalizing data with fourier series
Statistical Analysis of Temperature Data | Time Series Analysis in Python | Weather Derivatives
Переглядів 14 тис.Рік тому
Statistical Analysis of Temperature Data | Time Series Analysis in Python | Weather Derivatives
Introduction to Temperature Derivatives | Weather Derivatives
Переглядів 10 тис.Рік тому
Introduction to Temperature Derivatives | Weather Derivatives
Does Index Fund Investing Still Work in 2023?
Переглядів 8 тис.Рік тому
Does Index Fund Investing Still Work in 2023?
The Magic Formula for Trading Options Risk Free
Переглядів 19 тис.Рік тому
The Magic Formula for Trading Options Risk Free
You Need to Learn Importance Sampling NOW | Deep Out of the Money Options
Переглядів 7 тис.Рік тому
You Need to Learn Importance Sampling NOW | Deep Out of the Money Options
You've been using the Wrong Random Numbers! - Monte Carlo Simulations
Переглядів 9 тис.2 роки тому
You've been using the Wrong Random Numbers! - Monte Carlo Simulations
Lookback Call Options with Stochastic Volatility
Переглядів 3,5 тис.2 роки тому
Lookback Call Options with Stochastic Volatility
Pricing Asian Options in the Australian Electricity Market
Переглядів 6 тис.2 роки тому
Pricing Asian Options in the Australian Electricity Market
Monte Carlo Pricing of a European Barrier Option
Переглядів 7 тис.2 роки тому
Monte Carlo Pricing of a European Barrier Option
Monte Carlo Simulation with Multiple Factors | European spread options with stochastic volatility
Переглядів 6 тис.2 роки тому
Monte Carlo Simulation with Multiple Factors | European spread options with stochastic volatility
Stochastic Volatility Models used in Quantitative Finance
Переглядів 24 тис.2 роки тому
Stochastic Volatility Models used in Quantitative Finance
Heston Model Calibration in the "Real" World with Python - S&P500 Index Options
Переглядів 23 тис.2 роки тому
Heston Model Calibration in the "Real" World with Python - S&P500 Index Options
Simulating the Heston Model with Python | Stochastic Volatility Modelling
Переглядів 23 тис.2 роки тому
Simulating the Heston Model with Python | Stochastic Volatility Modelling
Trading stock volatility with the Ornstein-Uhlenbeck process
Переглядів 23 тис.2 роки тому
Trading stock volatility with the Ornstein-Uhlenbeck process
portfolio_sims meaning portfolio of simulations? Tip: Use the typing module to explain types and use descriptive variables to make sense
Nice! but te music is too loud.
lame
The only ones who are really winning: Market makers and brokerage firms. The others are at the mercy of luck, with inconsistent wins and losses.
error: module is not found for pandas_datareader
does someone have the link to 2rd part of video?
Hi PyQuant! May I know is it possible to simulate stock prices using the same technique? In this video you assume the returns follows multivariate normal distribution and then do the Monte Carlo simulation for returns. I am wondering if I can do the same thing for stock prices by assuming certain distribution such as GBM?
It would be interesting to see how time value is determined.
Newbes may become frustrated by this video ... but ... hes telling truth ... Investors gain success only by developping strategies beyond well known infos and models. Total transparency causes no chance for mechanical/software-driven success. First you have to learn all about common tools and strategies ... then you have to select and check ... then make a decision about your own strategy ... then check the markets for chances ... Dont forget - the closer you stay at mechanical workflows, automated ones the more compettitors you get ... and the more you develop your own system the more risk you take until reality fits to it.
Another clown. Stopped watching after hearing "I will show you my 3 strategies that are guaranteed to make money". What a joke 😂😂
This is more relevant to ML strategies. Good video anyway
Doom is coming for the world get ready worst than corona
As an Algorithmic Trading Quant, which is the most exclusive and cryptic type of Quant, I can objectivelly and without any bias that this role is meant only for men who have an eidetic memory, prophetic vision, omniscient sagacity and a coruscant brilliancy to easily master fields which are by nature both recondite and often out of reach for the common genius. Basically, a quant is a person who has a profitable trading algorithm with a track record which has stood the test of time. Also, if you're a lower class of Quant, you're basically a data scientist or wanker. If you think physics and maths is hard, remind yourself that those guys try to be quants and fail so hard they start to teach physics to PhD.
I want discussion on topics not mentioned in books like people on other side of trade and orderflow analysis
Background music that last for two minutes is very noisy
Johnatan how is it possible to transfer cryptocurrency to your account for HFT traiding under your control?
The video is cool and useful but the code don't work, the move of price of the option is not coherent with the strike one
What is with the stupid background music?
I don't get it. Why is the covariance matrix used? This means the stocks are not independent from each other. They are the same industry but apart from that they should not be tied together. What am I missing? Does this portray the sentiment of the market as a whole?
Ok, after taking a closer look at the scatter plot STO vs. BHP I agree. They are tied together.
The Black and Scholes equation is wrong: The Black and Scholes (risk-neutral) premium is the first moment of the option expiry for an asset that has all risk and no market return (the risk-neutral measure), that which has been debased of market return (by holding portfolio returns fixed flat at r). This idiotic asset (the risk-neutral measure) is stochastically dominated by bonds in that bonds have the same return (r) but without the risk whilst it is stochastically dominated by stocks since stocks earn market return for the equivalent amount of risk: bonds have LOWER RISK for the SAME RETURN as the debased market asset (the risk-neutral measure) whilst stocks have HIGHER RETURN for the SAME RISK as the debased market asset (the risk-neutral measure) Either way, the 'risk-neutral measure' is totally idiotic and stochastically dominated by all non-redundant asset classes. It is not deep and it is not abstract. All it is is the market asset without return (which is then used to price the derivative and so is wrong and inaccurate). If a trader wants an option, then he must not take an offsetting position that nullifies the option position. There is nothing risk-neutral about that. An option premium must have a mean mu in the drift term, otherwise it is wrong... wrong for derivatives and wrong for efficient and non-communist finance. nb: I had to say 'no risk' when I sat several of the courses in undergraduate (almost two decades ago). It was clear as day to me then that it was inaccurate (and proved by me definitively now more than one decade ago).
THanks, I learned a lot
For those struggling to get the Yahoo data: instead of using "from pandas_datareader import data as pdr", use "import yfinance as yf" then, the function should be "StockData = yf.download(stocks, start, end)['Adj Close']"
Excellent video, Congrats!!...btw, I could not find the website you refered to at the end of the video. I wonder if you could share the URL or link with us. Thx in advance!
im on mac and im not really sure how to get jupyter lab working i have pandas and pandas-datareader downloaded in python but it is not working
The problem is knowing what questions to ask. Making answers without understanding the questions is garbage in garbage out. The questions come from life experience that can't be learned from books solely
You never gave the retail trader an understanding of where his advantage may lie or how he could increase his chances of making consistent profit. You never explained what Alpha looks like as an example so that your audience understands what Alpha is ?
Thank you very much! Very good explained!
'NoneType' object has no attribute 'group' getting this error
fantastic video!! Quick question - what does it mean to say "variance accumulates at rate one per unit time"? Thanks!
Wrong.
On a serious note 1. I would recommend the book on Probability Theory by Atanasious Papoulis. 2. Then the book Introduction to Econophysics Next what I would like to point is that everything you read on Finance will always use Gaussian distributions as the basic principle. However if you look at real market data you will see that real world is not “Normal”. But the first book that I recommended will give you the tools needed to navigate this abnormal market world.
You mention the next video, but there's no link :(
Wow very good video, POV I didn't think of. So far from trading, I see that there a strategies that can help get that edge, but the best strategies are the ones that keep you in the trade longer while managing low risk. So for me (and this can change after writing this) that would be Supply and Demand, why? because that is literally what moves markets! Thank you for the video
if I pull out one year of historical prices data of a particular stock, how can I estimate the mu and sigma in this case?
Thank you so much for this educational video. Don't know much about Phyton if not at all😂🙏, but DCA is the answer for me to be disciplined about money
can someone give me the link for the asx options calculator ? thanks
Can I ask a serious question? How often are you actually correct with this model? Meaning, how close does the model actually come to accurately measuring realized volatility?
Short selling is you sell when the price is high to buy it low
awesome tutorial! Learnt a lot of new stuff ,thanks a lot mate!
bro, buy a 5$ microphone you are trashing your product yourself, it makes an ear cancer to hear it
Direct and quick!!! Excellent!
very well explained tutorial , thanks a lot @QuantPy !!
✨
Making $$$ by trading is not easy. Most people are simply "undercapitalized". Shortage of $$$. Shortage of equipment and understanding
Greats vids mate. Very informative. I work at Lacima with Les and Chris in Sydney and fyi Les' surname is pronounced as "Clue - low". Low as in not so high ;-) Keep up the great content 🙂
Hi I'm not understand about what you mention, bj can combine to b and uj can combine to u, can you explain more further in detail, thank you!
When I study time series data, I always doubt that how come they use only date and value of stock to predict the future outcome while they can just observe the social economic and political policy such as inflation rate, unemployed rate,..
the github specific tutorial link goes to the lookback option code ?
Are there any short term certifications available that could qualify me as a Quantitative Analyst? I hold a B.A. in Business Administration and am not inclined towards pursuing a Master's degree. By short term I mean 1 year or less. My dream job is to work for a firm that does HFT(high-frequency trading). Any advice or suggestion to point me in the right direction is appreciated. Thank you.
amazing, im definitely going to follow along and build my own the slow way this week to see how it all works line by line. been trying to learn some of this stuff to do my own forecasting for probability of profit / probability in the money / probability of touch... i get that data from my broker using Thinkorswim currently, but i'd like to have my own in-house probability forecasting. hoping that breaking down this model will help me build my own in the future. thx for your contribution, you're helping a lot of ppl! cheers from kuwait