Hello,
I would like to ask if there is a python library that is somethink like simulink,
My goal is to design a discrete PI controller cascading IIR filters. In simulink would be very easily done but I dont have matlab in home, thus I am searching somethink open source.
thank you in advance

I've read a lot of complaints about the oversharpening on images from the latest iPhones and other leading smartphones. In this post I will share my technique for reducing the oversharpening in overly sharp RAW files using Photoshop. This technique will also work on JPEG image files but the quality, of course, will be lower.
First, I use a 12 Pro Max and generally shoot in RAW. And yes, I hate the oversharpened RAW images and agree that we should be able to turn off whatever post processing is being applied when we shoot in RAW. But the purpose of this post is to share my fix, not to vent...
The technique involves a round trip into the LAB color mode to apply a small blur on the Lightness channel.
HOW TO REDUCE OVERSHARPENING in PHOTOSHOP (v. 25.7.0)

1. Open your raw file in Photoshop.
2. Duplicate the Background layer into a New file. I usually label my new file " LAB [*File name*] "
3. Duplicate the Background layer in the new file.
4. Convert the file to LAB color mode: Edit >> Convert to Profile>> LAB mode
5. You will now have one Background layer again but in LAB. Duplicate that layer.
6. Go to Channels and select the Lightness channel.
7. Set the Zoom to 100% and apply a Gaussian Blur to the Lightness Channel. For most images, a blur amount of 0.4 - 0.7 is enough to reduce the sharpening. Use the Preview for the blur filter to check the effect on particularly oversharpened areas before committing the blur to the image.
8. This is an optional filter. Create a layer mask for the layer. Use Image >> Apply Image >> Lightness or paste the lightness layer into the mask. Invert the mask and use Find Edges on the mask. Feather the mask. This filter ensures that the Gaussian blur is only applied to the edges in the image. I find this step is unnecessary for most images but useful in some situations where I want to preserve non-edge area fidelity e.g. architectural photos.
9. Duplicate the new layer back to your original RGB file as a new layer. Toggle the layer on and off to see if you're happy with the reduction in sharpening.

You can record these steps as an Action to speed up the process.
Why do I use this approach? It seems tedious, but working in LAB mode on the Lightness channel ensures that the color data in the image is untouched by the blurring. You can of course just apply the Gaussian Blur to the RGB file but you will be losing some fidelity. If that fidelity is not important to you, go ahead and apply the Gaussian blur to combined channels of your RAW RGB file without all the messing about in LAB.
While this LAB process is the technique I prefer to use, I am sure there are many other ways to deal with this issue. I hope people can share their preferred technique. I'd love to read how other people reduce the sharpening in Affinity or other software, for example.

Hello! Long time lurker, first time poster.
I’m an aerospace engineer and I have been flying model rockets since I was in middle school. I have the necessary mechanical and electrical knowledge to build an actively stabilized rocket and flight computer, however the control theory part of it is where I am lacking.
In high school, I did quite a bit of programming for my robotics team, and I developed several automated programs that used sensor feedback to follow a specified trajectory. This gave me experience in using PID and kalman filters to develop a control system, which will be vital for an active stabilization system.
The part I am struggling on the most is with figuring out how the “kinematics” involved with stabilizing the rocket are calculated given the sensor readings. I would like to make a fin controlled rocket since that’s more in my wheelhouse than a TVC rocket. The problem I’m running into is how to create a control algorithm that can stabilize roll, pitch, and yaw. Controlling roll by itself is trivial, and controlling pitch and yaw together is also pretty simple. I just can’t wrap my head around a control scheme that uses the fins to control all three.
Another area I am lacking in is how to model all this. I can pull up matlab and start writing down all the equations necessary to look at the basic aerodynamics, forces, and moments involved during a flight and then write a program to calculate it, but is this the right approach? Will I get good control system gains using a method like this?
So my main question in all this is where can I find resources that can help me answer these questions? I’ve searched online, watched videos and read research papers, but all I can get is high level overviews of how these systems work. These were helpful in defining what I’ll need to develop, but now I am looking for how to develop these systems specifically.

Yet another course planning question :) Posted some of my background in the admissions thread, but I'll add more context here to hopefully get more quality comments. See TLDR below
Work:
3 YOE as a data scientist. Skills and tools at work: Python, SQL, AWS, Sagemaker for MLOps, some AutoML tools, Tableau, git, etc.
Education:
B.S. in Stats. Some relevant courses/subjects - OOP C++, Java. Data Structures C++ looong time ago. Most things you'd expect from a top stats program: Bayes, machine learning, time series, etc. Upper div linear algebra and numerical analysis too for math minor. Mostly in R but research projects in Python and a few classes using python. One big data class we used PySpark.
M.S. in Applied Math. Not as satisfying of a program as my undergrad but still pretty good. Classes mostly in/around numerical analysis/numerical linear algebra. One computer vision-like class that covers about the first 2 modules of the GTech one from what I can see online and a little bit of Kalman filter . One statistical computing class. Coding projects in Matlab (eeewwww), when possible I would do python.
Summary:
My math/stats background is pretty solid. My goal is to continue to improve my skills as a data scientist in machine learning but also round out and improve my traditional cs skills on the computing systems side in case I want to switch to more MLE type roles and just bc I find it interesting. The company I work at is pretty invested in my development so I get a lot of good experience with different tools and techniques on projects. Wanted to learn about a lot of these topics anyways so this gives me some structure and a degree. Fortunately, I already have degrees and good career to start so I'm not grinding for As anymore but just pure learning.
Computing Systems Specialization:
For sure - GIOS, AOS, HPCA, IHPC, DC
Likely one of - SDCC, CN, GPU (seems pretty new)
Less likely options - SDP, Compiler, ESO
ML Specialization:
For sure - ML, DL, RL
Less likely - CV, ML4T
Both:
For sure - GA
Likely - Network Science
TLDR:
Not concerned about difficulty of course. I want to take courses where I learn a lot. I like both theory and projects but prefer more projects. Aside from obvious prereqs, lmk if there are any good natural orderings. Forgive the grammar. Thanks!
Edit: Not too much activity. Tentatively I'm thinking if I do 10 classes then
ML, DL, RL, NS, GIOS, AOS, HPCA, IHPC, GA, DC
If I decide to do one or two more then GPU or SDCC

I hate two things in my life - getting up early in the morning and doing Matlab assignments. I was struggling with one particular task the other day, trying to understand image processing in Matlab for this assignment. We were tasked with analyzing a medical image (specifically an X-ray) and:

• Applying a noise reduction filter (instructor suggested a median filter)
• Segmenting the image to isolate the region of interest (likely the bones)
• Calculating some basic properties of the segmented region (e.g., area, perimeter)

I had no idea how to implement the median filter and image segmentation. As for segmentation, I'm clueless about which functions or techniques might be best suited for isolating the bones in the X-ray image.
So, I decided to get Matlab assignment help to save time as I could spend an eternity trying to understand the logic behind this task. I used a service my roommate recommended - Codinghomeworkhelp.org and wanted to share my experience with you.
I liked the option of choosing a Matlab expert because I picked the one my roommate recommended to be on the safe side. I uploaded the details of my assignment and made a payment.
I want to highlight the fact that experts place bids, and I can choose the one with the lowest price if I want to pay less. One more thing I liked about the payment process is that the expert I hire does not get the money right away. First, I need to get my assignment, review and approve it. I can give you guys one more tip: if you do not know which expert to choose and do not have a roommate who can give you recommendations, look through the profiles of writers to learn about their experience. It is a fast and convenient way of making an informed decision.
What was the result of getting Matlab homework help? I can say it was positive for me, as I learned that a common approach for X-rays is thresholding which involves converting the grayscale image to a binary image where pixels above a certain threshold are considered bone and pixels below are considered background.
I used the option of direct communication with my expert to ask questions. Also, there is a possibility of adding detailed comments from the expert to your assignment to understand the logic behind every step. Will I use this service again when I can't do my Matlab homework by myself? I guess I will. The prices are quite affordable, and the experts know what they are doing.
Have you ever used an online service to cope with Matlab assignments?
And if you have, did you like the results?

Hi guys, currently I am in my last year of uni with mechanical engineering background, first of all, I didn’t study very hard in my first few years, so that makes my GPA looking bad like 3~3.3, depending on the final grades. However, in this last year I’ve picked up my interest in control theory so as my final yr report I am doing a project specialising in control theory, mainly implementing LQpole placement /w state feedback, MPC and kalman filter to control motors using Arduino boards(self taught all of these in 2 months, spending everyday in the LAB). Ive also gained a lot of experiences using C++, matlab and Simulink(no python experiences).
Given the complexity of control engineering and typical expectations for higher GPAs in this field, I find my torn between pursuing a traditional mechanical engineering path(seems a bit boring) or specializing further in control systems. In an essence, I don’t know how competitive I am for this career.
Lastly, I am interested in doing a PhD, but I doubt the professor gonna take me in, haha.
A big thank you for everyone who read this. Any replies/experiences/advises will be very very much appreciated. :D

The Arnaud Legoux Moving Average (ALMA) is a technical indicator used in financial markets to smooth price data and identify trends more accurately. Developed by trader and analyst Arnaud Legoux, ALMA aims to reduce lag and increase responsiveness compared to traditional moving averages.
Key features of the Arnaud Legoux Moving Average (ALMA) include:

1. Unique Formula: ALMA uses a proprietary formula that incorporates Gaussian distributions to calculate the moving average. This formula assigns different weights to recent price data points based on their distance from the current price. As a result, ALMA gives more weight to recent price movements while still considering historical data. 2. Adaptive Nature: ALMA is adaptive, meaning it adjusts dynamically to changes in market conditions. It can adapt its smoothing parameters based on market volatility, allowing it to respond more quickly to price changes during periods of high volatility and provide smoother signals during stable market conditions. 3. Reduced Lag: Compared to traditional moving averages, ALMA aims to reduce lag by providing more timely signals of trend changes. Its adaptive nature and unique formula help to filter out noise and provide a smoother representation of price trends. 4. Customizable Parameters: ALMA allows traders to customize its parameters, such as the look-back period and the smoothing factor, to suit their trading preferences and the characteristics of the financial instrument being analyzed. Adjusting these parameters can fine-tune the sensitivity and responsiveness of the indicator. 5. Versatility: ALMA can be used in various trading strategies, including trend following, trend reversal, and momentum trading. Traders often use ALMA in conjunction with other technical indicators, chart patterns, and trading signals to confirm trends and identify entry and exit points. 6. Interpretation: In practice, traders typically interpret ALMA signals similarly to other moving averages. Bullish signals occur when the price crosses above the ALMA line, indicating a potential uptrend, while bearish signals occur when the price crosses below the ALMA line, suggesting a possible downtrend. 

Overall, the Arnaud Legoux Moving Average (ALMA) is a versatile technical indicator that aims to provide smoother and more responsive trend signals compared to traditional moving averages. Its adaptive nature and unique formula make it a valuable tool for traders and analysts seeking to identify trends and make informed trading decisions in financial markets.

fir2 - the documentation says that the backend implementation uses an ifft on the required frequency response and a hamming window to smooth out the ripples in both the passband and the stopband. when i try to do the same with the following code, what i get is a much worse filter than the one I get with fir2
h = ifft(Hd, 'symmetric'); hamm = hamming(N); h = conv(h,hamm)/(N/2+1);
firls - does MATLAB actually calculate the least squares filter and make it converge to the Wiener filter?

I'm working with the Kalman-Filter and writing the state-space representation matrices by hand is a quite tiring task.
¿Does anyone know about a code to automatize this task (in matlab, python or R)? It looks difficult to code, since I'm not expert in programming, so I would like too see first if there's something in the web. I have search in github but I haven't found anything.
Thank you in advance :)

Good morning,
I've been working in this function to implement Kalman Filter in R (its purpose is to be used on optimization). However, it is slow, so I would like to code it on a more efficient way:

ofn <- function(th) { # OFN Summary of this function goes here: # performs the Kalman filter operation for a state-space model. # calculates the likelihood of the model. # iterates over all obs predicting and updating the factor (beta) and P. # calculates likelihood at each step and returns the negative sum. # used for maximum likelihood estimation to find the parameter values. # Given th, we obtain matrices R, Q, H, F matrices_list <- matrices(th) R <- matrices_list$Rs Q <- matrices_list$Qs H <- matrices_list$Hs F <- matrices_list$Fs beta00 <- c(rep(0, 10)) # Matrix (10x1) P00 <- diag(10) # Identity Matrix (10x10) like <- numeric(captst) # Vector (T-1x1) # KALMAN FILTER it <- 1 # Kalman filter iterations while (it <= captst) { # While iteration number is smaller than or equal to number of observations beta10 <- F %*% beta00 # Prediction equations P10 <- F %*% P00 %*% t(F) + Q # beta is the hidden variable n10 <- yv[it,] - H %*% beta10 # Error forecast F10 <- H %*% P10 %*% t(H) + R # Likelihood calculation like[it] <- -0.5 * (log(2 * pi * det(F10)) + (t(n10) %*% solve(F10) %*% n10)) # Likelihood function (gaussian) K <- P10 %*% t(H) %*% solve(F10) # Kalman gain beta11 <- beta10 + K %*% n10 # Updating equations filter[it,] <- t(beta11) P11 <- P10 - K %*% H %*% P10 beta00 <- beta11 P00 <- P11 it <- it + 1 # Iterating } fun <- -(sum(like)) # Sum of the likelihood return(fun) } 

But when I optimize it, it takes a lot of time to converge:

# Define the options for the optimization function options <- list(maxit = 100) # The 'optim' function in R is similar to 'fminunc' in MATLAB result <- optim(par = startval, fn = function(x) ofn(x), method = "BFGS", control = options, hessian = TRUE) 

I have tried to vectorize it, but the problem stills:

ofn_v <- function(th) { # OFN Summary of this function goes here: # performs the Kalman filter operation for a state-space model. # calculates the likelihood of the model. # iterates over all obs predicting and updating the factor (beta) and P. # calculates likelihood at each step and returns the negative sum. # used for maximum likelihood estimation to find the parameter values. # Obtain matrices R, Q, H, F from th matrices_list <- matrices(th) R <- matrices_list$Rs Q <- matrices_list$Qs H <- matrices_list$Hs F <- matrices_list$Fs # Initial states beta00 <- c(rep(0, 10)) # Matrix (10x1) P00 <- diag(10) # Identity Matrix (10x10) like <- numeric(captst) # Vector (T-1x1) # Define a function to perform operations for each time step kalman_step <- function(it) { beta10 <- F %*% beta00 # Prediction equations P10 <- F %*% P00 %*% t(F) + Q # Prediction of P n10 <- yv[it,] - H %*% beta10 # Error forecast F10 <- H %*% P10 %*% t(H) + R # Forecast error variance # Likelihood calculation like[it] <<- -0.5 * (log(2 * pi * det(F10)) + (t(n10) %*% solve(F10) %*% n10)) # Likelihood function (gaussian) K <- P10 %*% t(H) %*% solve(F10) # Kalman gain beta11 <- beta10 + K %*% n10 # State update filter[it,] <<- t(beta11) P11 <- P10 - K %*% H %*% P10 # Covariance update # Update states for next iteration list(beta11, P11) } # Run the Kalman filter over all time steps results <- lapply(seq_len(captst), kalman_step) # Extract the final states from the last iteration final_states <- results[[length(results)]] beta00 <- final_states[[1]] P00 <- final_states[[2]] # Return negative sum of likelihood -sum(like) } 

Here is the rest of the code:
matrices function:

matrices <- function(z) { # Assuming 'n' and 'vfq' are already defined # Procedure to obtain Kalman matrices Rs <- matrix(0, n, n) # Empty matrix (n x n) h2 <- rbind( c(1, 0, 0, 0, 0, 0, 0, 0), # Manually defined Matrix c(0, 0, 1, 0, 0, 0, 0, 0), c(0, 0, 0, 0, 1, 0, 0, 0), c(0, 0, 0, 0, 0, 0, 1, 0) ) Hs <- cbind(z[1:n], matrix(0, n, 1), h2) # Importing data from vector z to matrix Hs z0 <- z[(n+1):(n+2)] z1 <- z[(n+3):(n+4)] z2 <- z[(n+5):(n+6)] z3 <- z[(n+7):(n+8)] z4 <- z[(n+9):(n+10)] f1 <- c(t(z0), rep(0, 8)) # Manually creating rows of matrix F f2 <- c(1, 0, rep(0, 8)) f3 <- c(rep(0, 2), t(z1), rep(0, 6)) f4 <- c(rep(0, 2), 1, rep(0, 7)) f5 <- c(rep(0, 4), t(z2), rep(0, 4)) f6 <- c(rep(0, 4), 1, rep(0, 5)) f7 <- c(rep(0, 6), t(z3), rep(0, 2)) f8 <- c(rep(0, 6), 1, rep(0, 3)) f9 <- c(rep(0, 8), t(z4)) f10 <- c(rep(0, 8), 1,0) Fs <- rbind(f1, f2, f3, f4, f5, f6, f7, f8, f9, f10) # Concatenating matrix Fs z2 <- kronecker(z[(n+11):(n+14)]^2, c(1, 0)) # Vector Multiplication Qs <- diag(c(vfq, 0, z2)) # Manually creating matrix Qs return(list(Rs = Rs, Qs = Qs, Hs = Hs, Fs = Fs)) } 

The initial part of the code:

#### Stock Watson Kalman Filter #### # R 4.3.3. # UTF-8 # 11/04/24 # Clear environment and console rm(list=ls()) cat("\014") # Imports # libraries library(readxl) library(stats) library(tidyverse) # functions source('matrices.R') source('ofn.R') source('kfilter.R') # Data Input yv <- read_excel("rawdata2.xls", range = "S87:V854", col_names = FALSE) # Importing xlsx file (cell B2 to Cell E633) T <- nrow(yv) # Number of Observations n <- ncol(yv) # Number of variables # Renaming the variable yv <- scale(yv) # Standarize to a N(0,1) vfq <- 1 # Normalized variance B <- c(0.9, 0.8, 0.7, 0.6) # Vector (n x 1) phif <- rep(0.3, 2) # Vector 0.3(2x1) phiy <- rep(0.3, n*2) # Vector 0.3(2n x 1) v <- apply(yv, 2, sd) # Standard Deviation of yv startval <- c(B, phif, phiy, v) # Vector Concatenation nth <- length(startval) # nth is number of parameters to be estimated captst <- T filter <- matrix(0, nrow=captst, ncol=10) # Filter inferences #### Maximizing the likelihood function #### # Define the options for the optimization function options <- list(maxit = 100) # The 'optim' function in R is similar to 'fminunc' in MATLAB result <- optim(par = startval, fn = function(x) ofn(x), method = "BFGS", control = options, hessian = TRUE) 

Thank you in advance for your time :)

The second in the trilogy of projects from this March, and my 50th overall from the last two years of video projects.
The edit of this film from 2018 is quite straightforward again, luckily – it’s mostly putting deleted scenes back in, though there are two versions of this film up for grabs, for reasons I’ll get into later.
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The first change is another extended opener sequence like the first films. This time it’s Hank Pym and Janet Van Dyne on mission together in Argentina (Buenos Aires) back in 1987, acting as the first ‘Ant-Man and The Wasp’ dynamic duo together. This was an original opening sequence the studio changed, and luckily I was able to find it online and in decent quality and only have to make a few changes to it, like colour grading and adding a mirror filter to one shot that’s clearly the wrong way round as you can see by a “Stop” sign appearing backwards. It’s good to put this sequence back in, because it revolves around Elihas Starr’s lab trying to harness the Quantum Realm’s powers after stealing Hank Pym and Bill Foster’s work, before things go badly wrong.
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The second change, again, is replacing the “Present Day” annotation with the one where Scott Lang is playing with Cassie in the house while he’s under confinement – this takes place in April 2018 in the story, so that’s what the new PNG overlay states.
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The third change is at nearly 42 minutes in, and is where the film splits into two versions. In one of them, the detour into Cassie’s school to retrieve the older Ant-Man suit is left in – in the other, it’s removed. It’s certainly an amusing sequence, but it’s definitely very sidequest-y in an otherwise very straightforward part in the film. Without it, Scott and Co. see Bill Foster and get some info about the past and answers about the present – and then they go right into applying those things on the old Ant-Man suit Scott says he has, and was safe, anyway – so it’s all just more straightforward.
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At fourth change is at 51 minutes in with no school or 55 minutes in with the school sequence. Another deleted scene is put in where Sonny Burch and his men are narrowing down where the lab is and starts asking questions about who Scott is in the CCTV footage they watch of the lab shrinking. There’s no clear place in the film where the deleted scene goes before the 52 minute mark in the theatrical release where Burch shows up at Luis’ office with the “truth serum”, but it feels appropriate where I put it for two reasons.
Firstly, it leads into the office scene a few minutes later much better than him showing up there out of the blue, and the place I put it goes between Scott and Co. escaping Ghost and Foster and then suddenly being in the enlarged lab in the forest. It’s a very abrupt cut in the theatrical release between the two scenes that doesn’t really carry well, so hopefully this works better. It’s also a little better for Burch’s character to see him in this scene – we get some information why he’s after the lab in the first place: tech, money, personal relevance etc – so there’s actual development and motivation there, not just having the character as someone who shows up randomly, wants the lab for unknown reasons and keeps talking about his restaurant when nobody really asks.
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Another deleted scene plays at 1hr 38mins in (1hr 34mins with the school sequence removed) – after Hank and Janey finally reunite in the Quantum Realm and she tells him about what it’s really like down there. This deleted scene ties into “Quantumania” very well, as Janet explains that the place is bigger than they imagined, to the point of *entire worlds and civilisations* being down there*. Also that she knows how to communicate with the quantum creatures down there, makes sense as she seems to have domesticated two of them in her time at her little spherical homestead in the past.
Also, the footage from the scene right after that, where Hank asks how Janet how she stopped the Quantum Realm messing with his head, is the same footage from the start of this deleted scene. So, to avoid repetition, I used the cut of the two of them getting into the Quantum Vehicle, but greatly slowed, to essentially paper over the cracks as best I could with the footage that was there. It’s not perfect, but hey ho
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At the very end of the film, the new title card I made is overlaid some of the footage of the film – since the theatrical title card left no room for me to overlay and then replace it, like with others. At the 1:55:15/1:51:30 mark, depending on which version you watch, I used the footage of Hank emerging from the Quantum Vehicle, because the arch-like structure in the background is reasonably similar to the theatrical ‘toy-like’ environment in that shot. Building on that, I reversed the footage to match the motion at the end of the theatrical cut, added a Gaussian Blur filter to really take the photorealistic edge off, so it's not so jarring against the end credits in the waxworky toy style. Lastly, I made the custom title card follow the same motion as in the film: starting distant, enlarging on the screen and then continuing to enlarge very slowly until the scene cuts – with some 3D text effects this time to match the theatrical again.
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Finally, the post-credit scene involving the drumming ant has been changed, because it feels like unnecessary levity in a moment that should probably remain dire and serious for obvious reasons. Now, you see the empty Lang house with the constant blare of the emergency TV broadcast, but instead of seeing and hearing the giant ant on the electric drumkit, it cuts to aerial shots of San Francisco and keeps the blaring noise the entire time, clearly implying the entire city is in the same boat – that is to say: *at least* just that one city.
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To answer “Quantumania” questions that might present themselves next; the plans for that film recut are still very much up in the air and still just plans at the moment. I know what I want to do with the film ideally, and what ideas I want to put in motion with what I would change and why, but there is the big project I’ve been doing slowly for the last month that I mentioned previously. Ideally, I would knuckle down and just bash that out and finally get it done before moving onto another project in a big way.
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Footnote: I am merely a self-taught video editor and VFX artist here, so some areas may be visibly *mostly* good, as I have to be realistic with the footage in front of me and what I can do with it. If you do have any particular notes and feedback, feel free to give me your thoughts, but please be constructive and don't be an ass about it.

Hi /investing, roughly 10 years ago there was a great discussion about how we select our stocks. I discussed my method - Trending Value - and that led to me sharing my results with many of you via a program I had written. Well, that program broke and remained dormant for years... until now.
So what is Trending Value? It's a method published by James O'Shaughnessey in his "What Works on Wall Street" book. During decades of backtesting, it generated over 21% annual returns with equal standard deviation to the total market. It generated a lot of discussion and lead to this follow-up thread. In my mind, this method resonates the truest... far truer than those who advocate watching the check-out lines at your grocery store to see which products are being purchased. The method, summarizing is the following:
When you run a screen for stocks with good P/E or P/B, what do you look for? You find a stock with a P/E multiple of 12. Is this good? Is this bad? Neither and both, without a frame of reference. O'Shaughnessey's method rates 6 key financial metrics for every stock against the whole market to come up with a cumulative VALUE BASED score. Those metrics are: P/E, P/B, P/FCF, P/S, EV/EBITDA, and Shareholder Yield, which is dividend yield + stock buyback yield (i.e. equity returned to the shareholder in some way).
All 6 metrics are ranked 0-100, where 100 is the "best" ratio in the stock universe, and 0 is the worst. The top 10% of stocks will typically have scores in the 420+ range. They are undervalued relative to the rest of the market.
However, just because a stock is undervalued DOES NOT mean that it's a good buy. It could be facing legal action, failing drug trials, having a CEO that just went to jail, or other event that can't be reflected in these numbers. That's where momentum comes into play.
We take the top decile of stocks and reorder them by 6-month price momentum. Invest equally in the top 25, hold for a year, liquidate, repeat. The companies you buy are undervalued and the market is rallying behind them. Back testing, while not a prediction of future results, yielded a 21.2% average ROI with this method.
Of course, there's no screener that does this, nor is there a way to even view "Shareholder Yield" in one location.
When I started this 10 years back, I used MatLab to solve this problem. Yes, MatLab. Wrong tool for the job. Thankfully, ChatGPT has turned even amateur hobbyist coders into useful contributors, and a python version has been born.
I'll post the results of today's run below. Feel free to ask for a specific stock and I'll post the results of that ticker. Keep in mind, it is filtered to Market Cap > $200M and the name of the method is Trending VALUE - your growth stocks (NVDA, TSLA, etc) are going to be viewed very poorly.
Edit: You'll occasionally see a value like "10000" for P/E or another field. That means the value was either missing or negative. This is to artificially just kill that field for that ticker.