How would you challenge this algo?

[SimpleMeLike]

I did answer that already, 10 posts ago. I am just replying out of politeness but I am not going to repeat myself. Read the whole thread slowly and you will see why I use a small data set. It is not a big deal anyway.

Hello Drawdown Addict,

You need more than 50 back test trades my friend.

 

[Drawdown Addict]

Let's post something useful in between all of this garbage.

This is the response from ChatGPT for a Geometric Brownian Motion simulator in Python. It is pretty neat.

import numpy as np
import matplotlib.pyplot as plt

# Parameters
mu = 0.1        # Drift (average return)
sigma = 0.2     # Volatility (standard deviation of return)
S0 = 100        # Initial stock price
T = 1           # Time period in years
N = 252         # Number of trading days in a year (assuming 252)
dt = T / N      # Time interval
num_simulations = 5

# Simulate GBM
np.random.seed(42)
for _ in range(num_simulations):
    t = np.linspace(0, T, N+1)
    W = np.random.standard_normal(size=N+1)  # Brownian motion increments
    W = np.cumsum(W) * np.sqrt(dt)  # Brownian motion

    # Geometric Brownian Motion equation
    S = S0 * np.exp((mu - 0.5 * sigma**2) * t + sigma * W)
  
    # Plot
    plt.plot(t, S, lw=2, alpha=0.6)

plt.xlabel('Time')
plt.ylabel('Stock Price')
plt.title('Geometric Brownian Motion Simulation')
plt.grid(True)
plt.show()

 

[SimpleMeLike]

Let's post something useful in between all of this garbage.

This is the response from ChatGPT for a Geometric Brownian Motion simulator in Python. It is pretty neat.

import numpy as np
import matplotlib.pyplot as plt

# Parameters
mu = 0.1        # Drift (average return)
sigma = 0.2     # Volatility (standard deviation of return)
S0 = 100        # Initial stock price
T = 1           # Time period in years
N = 252         # Number of trading days in a year (assuming 252)
dt = T / N      # Time interval
num_simulations = 5

# Simulate GBM
np.random.seed(42)
for _ in range(num_simulations):
    t = np.linspace(0, T, N+1)
    W = np.random.standard_normal(size=N+1)  # Brownian motion increments
    W = np.cumsum(W) * np.sqrt(dt)  # Brownian motion

    # Geometric Brownian Motion equation
    S = S0 * np.exp((mu - 0.5 * sigma**2) * t + sigma * W)

    # Plot
    plt.plot(t, S, lw=2, alpha=0.6)

plt.xlabel('Time')
plt.ylabel('Stock Price')
plt.title('Geometric Brownian Motion Simulation')
plt.grid(True)
plt.show()

Hello Drawdown Addict,

Click back test and Post the performance report only.

 

[virtusa]

I developped my system on a few months of data (intraday trading). I tested it, so let it run without changing anything, on about 1,000 trades. This represented at that time about 2 years of data. It was clear that in any market circumstances the system always was profitable. Never a single losing week. So that test confirmed that the system did adapt itself very well to any type of market behavior. I never believed , and still don't believe, in computer nerds that write blackboxes, let it do a lot of number crunching and then watch what the result is. These people in general never really studied the markets. I meet several of them.

To have a dynamical system you should have a profound knowledge of the market behavior as you have to tell your computer what to do and why. ChatGPT is a product of the laziness of people.
A computer only knows what people feed him. And as most of these computer nerds have no clue about the market they give worthless feed which results in worthless results.