I've had hit/miss luck installing the stock launcher on FreeBSD 14. One time it worked. One time it didn't. Most times it'll run, sometimes it won't.
XIVLauncher works out of the box, EZ-PZ
HOW:
pkg install wine-devel mesa-dri wine-proton
wineboot -u
WINE=/uslocal/wine-proton/bin/wine winetricks dxvk
WINE=/uslocal/wine-proton/bin/wine winetricks vkd3d
Download XIVLauncher
WINE_SIMULATE_WRITECOPY=1 /uslocal/wine-proton/bin/wine Setup.exe
WINE_SIMULATE_WRITECOPY=1 /uslocal/wine-proton/bin/wine64 /path/to/launcher
You will also need to install DOTNET48 with winecfg
The installer will bitch that you are running it as admin - who cares? If you care, you can appease it by appending
WINE_SIMULATE_WRITECOPY=1 /uslocal/wine-proton/bin/wine64 trustlevel:0x20000 /path/to/launcher
(you must be on PKG latest not quarterly). It will not work if you install wine8/9. It will only works in wine-devel which is wine9.8. Why? Why Oh ... IDK....it just does
(yes this is really what I do at 03:00 in the morning)
https://preview.redd.it/2zsu51mk4i5d1.png?width=2560&format=png&auto=webp&s=697ae0fbeb09e403534aa3c181adc616e8a029fb

Trading halts from DFV's stream have been meming hard. But are they really what we think they are? This post will get quick and dirty and try to answer that question with a rough estimation using video frames as a replacement for the raw exchange data.
Before we begin, one rule that we all must try to understand is the Limit Up-Limit Down (LULD) rule. More about that can be read here:
https://nasdaqtrader.com/content/MarketRegulation/LULD_FAQ.pdf
Simplified TLDR - Not counting the latter end of power hour, we halt when the price of our beloved stock moves 5% away from the average of all trades over the last 5 minutes.
https://preview.redd.it/a3c2ank9kh5d1.jpg?width=1200&format=pjpg&auto=webp&s=278eec4fdbff8311e6bab6354d0b14b606d33ec5
When trying to do an estimation like this, one's first instinct may be to eyeball the prices on the screen and maybe write down some numbers for calculations. But.. I can't even be trusted with a box of crayons, so how about letting those machines do that work for us.
Like my previous post, the recommended easy way to code along would be using a hosted notebook like Jupyter Lab.

Step 1 - Data Extraction

If have about 800 free MB, 3 hours of computer processing time, and a local environment set up with the necessary libraries (Jupyter lab won't work here), follow along with this step. It's pretty cool the kind of things that can be done with open source applications! If it sounds like too much work, I have uploaded a CSV of the raw extracted data that can get you up to speed to start directly on Step 2.
To do this step you will need to have installed ffmpeg, pytesseract, and OpenCV. You will also need to have the full quality stream (720p 60fps) ripped from YouTube. I'd love to shout out how to do that from the rooftops here, but as a precaution for the sake of our lovely subreddit, I'm going to zip my lips and just say "figure that part out."
Once you have the video, we will use ffmpeg to extract cropped pngs of every single frame. I've already chosen an ideal cropping that minimizes the confusion introduced from text that we are not interested in.
First the Linux command for making a folder called "png" that the frames will go into

mkdir png 

Then the ffmpeg command that extracts 182,881 (yea 50 minutes is a LOT of frames) 80 x 30 images around the price ticker area of the video.

ffmpeg -i "Roaring Kitty Live Stream - June 7, 2024-U1prSyyIco0.mp4" -vf "crop=80:30:160:240" png/dfv_%06d.png 

The codeblocks will use Python. You can the rest of Step 1 in a notebook (but pytesseract and OpenCV would need to be installed).
Import the necessary libraries

import os import cv2 import pandas as pd import pytesseract 

Loop through every still in the png folder using OCR to extract the text to a list. Warning: this step will likely take several hours.

files = sorted(os.listdir("png")) results = [] for file in files: path = os.path.join("png", file) img = cv2.imread(path) text = pytesseract.image_to_string(img) results.append(text) 

Saves a csv of the raw extracted text

raw = pd.Series(results) raw.to_csv("price_extraction_raw.csv", index=False) 

Step 2 - Data Cleaning

If your continuing from Step 1, you'll probably already have a local environment setup that you feel comfortable working in. If not, just upload the CSV of the raw data from the earlier download link to a hosted notebook and you'll be good to go.
First inside the notebook, run this cell to import the libraries and the CSV with the raw frame data.

import numpy as np import pandas as pd # Loads the csv raw = pd.read_csv("price_extraction_raw.csv").squeeze() # Strips out unintended newline characters. raw=raw.str.replace(r"\n", "", regex=True) 

Since we ran the optical recognition over all video frames, there will be some junk in the data. Don't worry though, the structure of the prices will make it very easy to clean up.

# Shows the rows with detected text. raw.dropna() 

https://preview.redd.it/nxzdtp8cwh5d1.png?width=289&format=png&auto=webp&s=d11ea0c8b0395d335fe6c4514d8153773e88865c
This small codeblock will take care of the false positives.

# Eliminate any characters that are not numbers or decimals. cleaned = raw.str.replace(r"[^\d\.]", "", regex=True).str.strip().replace("", None) # Clear any rows that have less than 5 characters (two digits, a period, and two decimal places). cleaned = np.where(cleaned.str.len() < 5, None, cleaned) 

Since we used the entire video, the index accurately references the current frame number. To make it easier to navigate, we can add additional columns containing the minute, second, and frame number (that starts over every 60 frames).

# Converts the single column Series into a multi-column DataFrame. cleaned = pd.DataFrame(cleaned, columns=["price"]) # Creates the time columns cleaned["m"] = cleaned.index//3600 # 60 frames * 60 seconds per minute cleaned["s"] = (cleaned.index // 60) % 60 cleaned["f"] = (cleaned.index % 3600) % 60 

At this point, we are almost done cleaning, but on some frames, the optical recognition accidentally detected a fake decimal at the end.

cleaned[cleaned["price"].str.len() > 5] 

https://preview.redd.it/80mjac9zwh5d1.png?width=210&format=png&auto=webp&s=826fdddec734e183fe8724bb6e67980231ebb6ea
If we check those with the video, we can see that they are indeed valid (image is cropped here, but holds true for all), so it is safe to remove the last character here.

# Removes trailing characters when there are more than 5 of them. cleaned["price"] = np.where(cleaned["price"].str.len() > 5, cleaned["price"].str[:5], cleaned["price"]) # Changes the datatype to allow calculations to be made. cleaned["price"] = cleaned["price"].astype(float) 

It will also be handy to have each frame indicate if the price reflects that of a trading halt.

# A list of the start and end of every trading halt in video (by price change). halts = [(10802, 19851), # Initial video halt (26933, 45977), # 2nd halt (61488, 80414), # 3rd halt (81325, 100411), # 4th halt (100778, 119680), # 5th halt (136992, 119680), # 6th halt (166473, 178210), # 7th halt ] # Uses the halt frames, to indicate halts in the dataset. cleaned["halted"] = np.where(cleaned["price"].isna(), None, False) # Assumes no unknown values for (start, end) in halts: cleaned["halted"] = np.where((cleaned.index >= start) & (cleaned.index < end), True, cleaned["halted"]) 

A quick preview showing the frames with indicated halts.

cleaned[cleaned["halted"] == True] 

https://preview.redd.it/3usz3cnlyh5d1.png?width=255&format=png&auto=webp&s=5fa94f1e313029b7e568d7c8f4b0cc620a1dc17d

Step 3 - Calculating the bands

At this point, we've done enough to run some basic calculations across all of the frames. The following function will automatically do them for any given specified frame number.

def assess_halt(df, index): # The frame that is exactly 5 minutes before the frame examined. frame_offset = index - (5 * 60 * 60) # Since there will be no volume during a halt, we want to exclude # remove values where a halt is indicated. prices = df["price"].copy() prices = np.where(df["halted"] == True, np.nan, prices) # The price at the requested frame. halt_price = df["price"][index] # the frame right before (to rule out the halt suppressing the actual amount) price_before_halt = df["price"][index-1] # The average of all extractable prices in the five minute window. average = np.nanmean(prices[frame_offset:index]) # If there is insufficient at the specified frame, this ends calculations early. if np.isnan(average) or np.isnan(price_before_halt): return halt_price, price_before_halt, None, None, None, None, None # The count can help gauge robustness of the estimated average. count = np.count_nonzero(~np.isnan(prices[frame_offset:index])) seconds = count / 60 # The estimated bands are calculated by adding and subrtracting 5% from the average. band_low = average - .05 * average band_high = average + .05 * average # Logic to test whether the halt price or the price just before the halt is estimated to be beyond the 5% bands. outside = ((halt_price < band_low) or (halt_price > band_high)) or ((price_before_halt < band_low) or (price_before_halt > band_high)) return halt_price, price_before_halt, average, seconds, band_low, band_high, outside 

Using the list of halts earlier, we can conveniently loop through and make some rough estimations.

rows = [] for halt in halts: row = assess_halt(cleaned, halt[0]) rows.append(row) assessment = pd.DataFrame(rows, columns=["halt_price", "price_before_halt", "price_average", "seconds_of_data", "band_low", "band_high", "outside_bands"]) assessment 

https://preview.redd.it/hznds6dc7i5d1.png?width=721&format=png&auto=webp&s=9ccd4eb9358c78c77599cf30934f22e633a733e0

Thoughts

What is shown here is highly interesting! To see every recorded stop "inside the band" indicates that an overly zealous circuit breaker (or maybe even strategically priced trades to create halts) is not entirely outside the realm of possibility. But it should be noted that these estimations are by no means definitive. Most importantly this method does not account for fluctuations in trading volume. To do it right, we would need access to the raw trading data which as far as I know is unavailable.
I hope this can serve as a good starting point for anyone who is able to take this further.
Edited: just now to fix bug in final outside band logic.

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The Chicago Tribune wrote that “ALEXANDER SCOURBY has the Greatest Voice ever recorded” Now you can HEAR that voice read the King James Bible like no other, Available now free to view on the Scourby YouBible Channel
It is also free to share the Scourby YouBible Channel Video Links. However please note all audio/video uploads or downloads of the Bible by Alexander Scourby to any network, YouTube channel, Website or app, DVD/CD or Tape etc is now Prohibited by Copyright Law.

Bethel Church & Music update https://youtu.be/6MOs-lXbQIY?feature=shared
The Book of 1 Timothy Read by Alexander Scourby is available in Full @ https://youtu.be/nudhaZ-dhao?feature=shared
For an Audio Bible Playlist from Scourby YouBible Channel it is available @ https://www.youtube.com/playlist?list=PLKDRpYU2fA10kTQcQzpZDwulHlvJe79wD
If you wish to help with the broadcasting fund Please Read through our Donation Fund Information Available @ https://bit.ly/APSGospelDonationFundInformation
Donate to the Broadcasting Fund @ paypal.com/donate/?hosted_button_id=P9XUG4JZ9UFWW
The Chicago Tribune wrote that “ALEXANDER SCOURBY has the Greatest Voice ever recorded” Now you can HEAR that voice read the King James Bible like no other, Available now free to view on the Scourby YouBible Channel
It is also free to share the Scourby YouBible Channel Video Links. However please note all audio/video uploads or downloads of the Bible by Alexander Scourby to any network, YouTube channel, Website or app, DVD/CD or Tape etc is now Prohibited by Copyright Law.