XOM and CVX are cointegrated energy majors — when they diverge it's temporary and mean reverts. That divergence is your edge. tho in recent year there have been divergence in both asset
Novice
Blue line (Underfit) — too simple, misses the pattern. Like using a 200-day moving average on a fast-moving market. Misses most signals.Teal line (Good fit) — captures the real trend without chasing n
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Novice
reading research paper on Stock price prediction using geometric Brownian motion trying to make model
Novice
i was working on this inflation prediction model it takes past historical data of CPI and PPI from early 2000 and try to predict next CPI pretty cool model still working on it to make accurate
Novice
The final draft of the US-Iran agreement has been reached with the mediation of Pakistan, which is expected to be announced within the next few hours - Iran IRNA Citing Al Arabiya
Novice
this is the strategy iam backtesting from few weeks this report generated by quantconnect
Novice
THIS IS UPCOMING MONTHLY SEASONALITY RETURNS WHICH ARE POSITIVE FOR BTC AS OF MAY WE ARE AT 5.44% AVERAGE IS 9.87%
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Novice
went long 295 after earning closed at 340 looking to build a dollar cost average position. reasons Visa delivered its strongest revenue growth since 2022 in Q2 FY2026, beating on every metric. The bu
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Novice
WHAT YOU GUYS THINK ABOUT CEREBRAS AI CHIP MAKING COMPANIES IPO THIS WEEK
Novice
code for geometric Brownian motion
Code
29 lines
import numpy as np
import matplotlib.pyplot as plt
def simulate_geometric_random_walk(S0, T=2, N=1000, mu=0.1, sigma=0.05):
dt = T / N
t = np.linspace(0, T, N)
W = np.random.standard_normal(size=N)
# N(0,dt) = sqrt(dt)
W = np.cumsum(W) * np.sqrt(dt)
X = (mu - 0.5 * sigma**2) * t + sigma * W
S = S0 * np.exp(X)
print(W)
return t, S
plt.figure(figsize=(12, 5))
t, S = simulate_geometric_random_walk(100)
plt.plot(t, S, color='steelblue', lw=1.2)
plt.title(f'Geometric Brownian Motion μ=0.1 σ=0.05')
plt.xlabel('Time (years)')
plt.ylabel('Price')
plt.grid(True, alpha=0.3)
plt.show()
if __name__ == "__main__":
simulate_geometric_random_walk(10)
Novice
working on a linear regression trading model right in backtest face some overfitting bias are coming ahead trying to solve it after that test on paper trading then real account right result not bad
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@montacarlos939
reposted
Novice
made GLD conditional vs unconditional parameter optimization model from quantitive trading book by ernest chan
Novice
BACKTESTED NEW STRATEGY ON QUANTCONNECT
on btc and some fx pairs
on btc and some fx pairs
Novice
went long btc last week as seasonality for bitcoin has been strong during month of may through 10 year historical data ran monte carlo sim for probablity fundamentals are strong and new fed chair has
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