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. Code: 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()
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.