💥 Gate Square Event: #PostToWinPORTALS# 💥
Post original content on Gate Square related to PORTALS, the Alpha Trading Competition, the Airdrop Campaign, or Launchpool, and get a chance to share 1,300 PORTALS rewards!
📅 Event Period: Sept 18, 2025, 18:00 – Sept 25, 2025, 24:00 (UTC+8)
📌 Related Campaigns:
Alpha Trading Competition: Join for a chance to win rewards
👉 https://www.gate.com/announcements/article/47181
Airdrop Campaign: Claim your PORTALS airdrop
👉 https://www.gate.com/announcements/article/47168
Launchpool: Stake GT to earn PORTALS
👉 https://www.gate.com/announcements/articl
Monte Carlo Simulation Applied to Bitcoin Price: Predicted BTC Price Six Months Ahead! Here are the results
Cryptocurrency analytics firm MarktQuant has released the results of a Monte Carlo simulation that predicts the price of Bitcoin over the next six months. Based on thousands of simulated price paths, the forecast offers a range of possible outcomes that illuminate both potential gains and risks.
According to the Results, Bitcoin Could Fall Below $51K with a 5% Probability
According to MarktQuant's statement, a starting Bitcoin price of $82,655.52 was used in the simulation. The results show that the average final price is $258,445.24
According to MarktQuant's statement, a starting Bitcoin price of $82,655.52 was used in the simulation. The results show that the average final price is $258,445.24, indicating a significant potential increase. However, the range of results is wide, with the 5th percentile result predicting a possible decline to $51,430.23, while the 95th percentile result indicates that Bitcoin has reached as high as $712,118.81.
This means that in 5% of the scenarios simulated, Bitcoin's price could be at or below this value. This represents a worst-case scenario within the model.
On the other hand, the 95th percentile means that in 95% of the scenarios simulated, Bitcoin's price is at or below this value. This represents the best-case scenario within the model.
Monte Carlo simulation is a statistical method used to model possible future price movements by running numerous random scenarios based on historical volatility and other market factors. It helps analysts predict the probability of different price outcomes instead of making predictions from a single point.