any way to tell algo trading from price chart?

M

mute9003

Im watching this and its literaly creating micro traps without time and sales moving or any orders going through.
Is this what algo is doing?
creating flat line to make traders nervous and then creating a fast move down to trigger a selloff and then buy back again?

Where can i learn more about how algos are programmed and strategies they use? Or how they work in general?
 

Attachments

  • 20240911_084801.jpg
    20240911_084801.jpg
    2.8 MB · Views: 82
Code: 
import numpy as np
sigma=.2
r=.043
atmK=32.
d=45
N=252
alpha = lambda N: N/2*sigma * np.log(N*r) * (( r**2* 1/r ) - (r**atmK * r**-(atmK-1))) * np.sqrt(d/360.)
 
2rosy said:
Code: 
import numpy as np
sigma=.2
r=.043
atmK=32.
d=45
N=252
alpha = lambda N: N/2*sigma * np.log(N*r) * (( r**2* 1/r ) - (r**atmK * r**-(atmK-1))) * np.sqrt(d/360.)
More...

Here's a corrected and simplified version:


import numpy as np

# Define the constants
sigma = 0.2
r = 0.043
atmK = 32.0
d = 45
N = 252

# Define the alpha function
alpha = lambda N: (N / 2) * sigma * np.log(N * r) * r * np.sqrt(d / 360.)

# Example usage
result = alpha(N)
print(result)
 
2rosy said:
your simplified version doesnt return the same result. too dependent on chatGPT
More...

been real good for me is all i know. this is it's comments about your code

  • Lambda Function Structure: The function alpha should return a valid numerical result, but the expression inside contains several questionable elements:
    • N/2*sigma should be fine.
    • np.log(N*r) might lead to problems if N*r <= 0, so ensure N*r > 0.
    • Expressions like r**2*1/r are overly complex and reduce to just r, so simplifying them is beneficial.
  • Simplification:
    • r**2 * (1/r) is equivalent to r.
    • r**atmK * r**-(atmK-1) simplifies to r.
 
MarkBrown said:
been real good for me is all i know. this is it's comments about your code

  • Lambda Function Structure: The function alpha should return a valid numerical result, but the expression inside contains several questionable elements:
    • N/2*sigma should be fine.
    • np.log(N*r) might lead to problems if N*r <= 0, so ensure N*r > 0.
    • Expressions like r**2*1/r are overly complex and reduce to just r, so simplifying them is beneficial.
  • Simplification:
    • r**2 * (1/r) is equivalent to r.
    • r**atmK * r**-(atmK-1) simplifies to r.
More...
The simplification part is what makes my alpha lambda always return 0 no matter what values are set.
Code: 
(( r**2* 1/r ) - (r**atmK * r**-(atmK-1))) = 0
 
You must log in or register to reply here.
Back
Top