Calculating the Probability of a Stock Reaching a Given Price in a Specified Time Window in Excel
In a previous video, I showed how to calculate the probability a stock will touch a given value in a specified amount of time. We did this in Python using Monte Carlo techniques. A viewer asked if I could show how to do the same type of calculation in Excel. While Excel isn’t the ideal tool for this, it can be done, and I’ll show how it can be done.
A really simple video this time. We use Numpy/Scipy and Pandas to calculate some simple statistics on the daily returns of the S&P 500 index. Specifically, we calculate the mean, standard deviation, skewness, and kurtosis. I also talk about generating a histogram and modifying the plot to represent a density.
Github: https://github.com/kpmooney/numerical_methods_youtube/tree/master/market_analysis
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https://www.youtube.com/watch?v=oWQfgFZ1oHw
I look at using Newton’s method to solve for the implied volatility of an option. This is done using the Black-Scholes model and a simple Python script.
My mouth and brain were apparently totally out of sync when discussing the numbers in the slide showing the spreadsheet results for the roots of a parabola. This is one of those situations where you should pay attention to what I am thinking rather than what I am saying. In any case, the numbers in the spreadsheet are correct.
Github: https://github.com/kpmooney/numerical_methods_youtube
Original Blog Post: https://kevinpmooney.blogspot.com/2017/07/calculating-implied-volatility-from.html
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https://www.youtube.com/watch?v=Jpy3iCsijIU
This will be the first in a series of videos whereby we work through freshman/sophomore physics problems using Python and the Numpy/Scipy packages. The purpose of these videos is get get comfortable using numerical methods and computation resources to solve problems. Because these problems can be solved by hand, it provides a nice way to double check our work. In some cases I will embellish the problems to make them more suitable for use with a computer.
The first video here is on motion if one dimension.We use trapz to perform numerical integration and use a spline to interpolate between data points.
Github: https://github.com/kpmooney/numerical_methods_youtube/tree/master/physics/2_11
Donate: http://paypal.me/kpmooney
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https://www.youtube.com/watch?v=IG69WHp_8kU
This is part 3 of our gradient descent series. We look at a linear regression and a logistic regression. We extend what we did in the previous two videos in multi-dimensional systems.
Part 1: https://youtu.be/trvgzYjUr-Y
Part 2: https://youtu.be/J1ghebX8XGY
Github: https://github.com/kpmooney/numerical_methods_youtube/tree/master/gradient_descent
Donate: paypal.me/kpmooney
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https://www.youtube.com/watch?v=Twxe59IjHDk
As a prelude/supplement to videos on mean-reverting trading, I want to briefly discuss the topic of pairs trades. So this video will be a bit different in that I won’t be doing any computational mathematics, and only using the computer to do some plots. This video will simply define pairs trades and briefly talk about why one might want to do one.
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https://www.youtube.com/watch?v=vHzlZECzyPE
In this video we will return to boundary value problems and solve the same equation we used for the shooting method video, this time via a finite difference method. We will transform the problem from a differential equation into a coupled system of algebraic equations. In this case, since the system is linear, we can make use of our basic linear algebra to get the solution. In the non-linear case, we could run the resulting system through a nonlinear solver like fsolve to get an answer.
Shooting Method: https://youtu.be/3hf2v39HJQE
Github: https://github.com/kpmooney/numerical_methods_youtube/blob/master/bvp/BVP%20Via%20Finite%20Difference.ipynb
Tip Jar: https://paypal.me/kpmooney
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https://www.youtube.com/watch?v=qrS1L1VfP-k
When managing a portfolio with many option positions, it is useful to recast the individual deltas so as to compare each position to a common underlying, say SPY. This is referred to as beta-weighting your deltas. Some trading platforms will do this for you, but still many more do not. In this video, I will show how to do this calculation yourself.
Calculation a stock’s beta coefficient: https://youtu.be/jmKfDvk4k6g
Github: https://github.com/kpmooney/numerical_methods_youtube/tree/master/beta_wieghted_delta
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https://www.youtube.com/watch?v=PNnOCKHJh8U
A viewer asked if I could do a video on calculating the implied volatility of options that trade in India. The process is identical to that of US options. The math involved is explained in my first video on the topic (https://youtu.be/Jpy3iCsijIU). Please consult that if the concepts are unfamiliar.
Github: https://github.com/kpmooney/numerical_methods_youtube/blob/master/root_finding/implied_volatility/IV%20of%20Indian%20Stocks.ipynb
Tipjar: https://paypal.me/kpmooney
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https://www.youtube.com/watch?v=UTJ9v40NB5o
We review our Monte Carlo code for simulating a large number of stick runs. This is a detailed breakdown on how the matrices involved are built.
Part 1: https://youtu.be/v_S7cOL5ZWU
Github: https://github.com/kpmooney/numerical_methods_youtube/blob/master/prob_50/More%20on%20Linear%20Systems.ipynb
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https://www.youtube.com/watch?v=AC_4gjSYzu0