Prudent Risk Management + No Edge = Positive Expectancy??

[ironchef]

If you can't define (and better, quantify) your PRM edge, even in simple terms... initial stop @ 1.5x the average 15m bar range, or-and-or-and- whatever, the results are random. You have no idea whether it is a system entry edge, some other unknown/undetermined edge, PRM edge, or some combination of edges. Plus, although not pertinent yet, mixing different setups and/or instruments, each has different non-PRM edge. A one-size-fits-all simplistic PRM edge like that mentioned may not even be appropriate due to being a non-acceptable risk itself for a given setup or instrument.

Hopefully "I generated a few" means at least 1000.
And what is GBM?

I am embarrassed to say, a few means about a dozen. It is a lot of work testing things manually.

GBM: Geometric Brownian Motion.

 

[ironchef]

I was up late playing around with ChatGPT and it gave me this code (Python). Definitely not verified and probably not exact, but fun to tinker with. Generates quite a lot of 'crypto pump/dump graphs'

View attachment 327813 View attachment 327814 View attachment 327816

Btw if you don't know how to do it in excel an ai-bot can help you quickly nowadays. Don't know what your parameters are, but perhaps a shifted kalman-filter can be a proxy?



Code:
# Geometric Brownian Motion

import numpy as np
import matplotlib.pyplot as plt

def generate_stock_prices(S0, r, sigma, T, dt):
num_steps = int(T / dt)
t = np.linspace(0, T, num_steps)
W = np.random.standard_normal(size=num_steps)
W = np.cumsum(W) * np.sqrt(dt) # Brownian motion

S = S0 * np.exp((r - 0.5 * sigma**2) * t + sigma * W)
return t, S

# Set parameters
S0 = 100 # Initial stock price
r = 0.05 # Drift
sigma = 0.2 # Volatility
T = 100 # Time in years
dt = 1/252 # Daily time steps for 252 trading days in a year

# Generate stock prices
time, stock_prices = generate_stock_prices(S0, r, sigma, T, dt)

# Plot the results
plt.plot(time, stock_prices)
plt.xlabel('Time (Years)')
plt.ylabel('Stock Price')
plt.title('Simulated Stock Prices using Geometric Brownian Motion')
plt.show()

You have a drift term in your code. And probably an impulse somewhere in one of the charts? I assumed zero drift.

 

[ironchef]

Same. And of that, LIVE is best, as forward testing is just a $10 word for sim.

And live small size is =/= live full size.

 

[ironchef]

but perhaps a shifted kalman-filter can be a proxy?

Why do you need Kalman Filter here? You are only using GBM to generate a sim stock price and not doing any prediction or forecasting?

 

[Jzwu2017]

I don’t believe stock price movement is random.

 

[ironchef]

I don’t believe stock price movement is random.

Then there is hope. If so I can find a predictor?

 

[Jzwu2017]

There’s hope but you won’t find one indicator/predicator.

 

[hilmy83]

I was up late playing around with ChatGPT and it gave me this code (Python). Definitely not verified and probably not exact, but fun to tinker with. Generates quite a lot of 'crypto pump/dump graphs'

View attachment 327813 View attachment 327814 View attachment 327816

Btw if you don't know how to do it in excel an ai-bot can help you quickly nowadays. Don't know what your parameters are, but perhaps a shifted kalman-filter can be a proxy?



Code:
# Geometric Brownian Motion

import numpy as np
import matplotlib.pyplot as plt

def generate_stock_prices(S0, r, sigma, T, dt):
num_steps = int(T / dt)
t = np.linspace(0, T, num_steps)
W = np.random.standard_normal(size=num_steps)
W = np.cumsum(W) * np.sqrt(dt) # Brownian motion

S = S0 * np.exp((r - 0.5 * sigma**2) * t + sigma * W)
return t, S

# Set parameters
S0 = 100 # Initial stock price
r = 0.05 # Drift
sigma = 0.2 # Volatility
T = 100 # Time in years
dt = 1/252 # Daily time steps for 252 trading days in a year

# Generate stock prices
time, stock_prices = generate_stock_prices(S0, r, sigma, T, dt)

# Plot the results
plt.plot(time, stock_prices)
plt.xlabel('Time (Years)')
plt.ylabel('Stock Price')
plt.title('Simulated Stock Prices using Geometric Brownian Motion')
plt.show()

https://www.elitetrader.com/et/threads/flip-of-a-coin-but.372081/

 

[hilmy83]

I don’t believe stock price movement is random.

Some people also believe the earth is flat like a pancake

 

[Zwaen]

https://www.elitetrader.com/et/threads/flip-of-a-coin-but.372081/

That is a nice thread, missed it unfortunately. My own limited study also showed a skewed distribution in a lot of equity prices, ea markets trend more than random. This is a very basic and old anomaly which isn't going to change from one to another (it would have already imo). I once saw an interesting video (Fisher) who talks about this. So my main focus is making the losses - and thus drawd - as small as possible, or even trying to flip them to slightly profitable by trading some of the edge distribution to it (basically scaling out).

At some point you say: "You're basically making the assumption that when price is a certain distance away from the whatever reference point you create (in this case the average of 5 minutes), then it's unlikely that price will hit the other side of the range/reverse"
this lined a bit up with some thoughts I head about ORB. When the range of ORB is proportionally smaller then the total range of the session(whatever your session) wouldn't it be logical that one can make money by selling this range and buying the larger range. More a bit of a thought excercise and not very concrete yet.