XRP #BRICS #DeDollarization #CryptoNews #BinanceSquare #XRPL #Ripple #Web3 #WriteToEarnWCT

India & the UAE just made history by settling a crude oil deal without using the US dollar — they chose #XRP instead. 🛢💸

Payment was made in local currencies, but the transaction ran through the XRP Ledger System (XRPL) — and users were even rewarded with CryptoTradingFund (CTF) tokens as cashback! 💰🚀

🌍 The BRICS nations, now including the UAE after its 2024 expansion, are going full throttle on de-dollarization.

Why? Simple: economic independence. The dominance of the USD is being questioned due to geopolitical tensions & sanctions.

💥 Ripple is serious about building the future — it’s partnered with the Dubai International Financial Centre (DIFC) to empower developers via the DIFC Innovation Hub, the largest innovation community in the region with 1,000+ startups, labs, VCs, regulators, and educators.

💸 And Ripple’s putting serious funds behind it — committing 1 billion $XRP to grow new use cases on the XRPL.

📈 XRP: 2.2814 (+1.51%)

🧠 How far with de-dollarization?

The USD has ruled global finance for decades — but that’s changing.

Nations like Russia & Iran have faced sanctions, prompting others to diversify.

🇷🇺 Putin called de-dollarization “irreversible.”

🇧🇷 Lula da Silva questions why the dollar is even needed in trade.

The BRICS bloc is pushing to use local currencies, and even talking about launching a shared BRICS currency.

Sure, that’s easier said than done — economic gaps between BRICS nations are wide — but the momentum is real. ⚡

💬 Is the dollar’s dominance ending? Or just evolving?

👇 Drop your thoughts & let's talk
#XRP #BRICS #DeDollarization #CryptoNews #BinanceSquare #XRPL #Ripple #Web3 #WriteToEarnWCT
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
# Simulate XRP price data over the past 30 days
np.random.seed(42)
dates = [datetime.today() - timedelta(days=i) for i in range(29, -1, -1)]
prices = np.cumsum(np.random.normal(0.05, 0.15, size=30)) + 1.8 # Starting price ~1.8
# Create a DataFrame
df = pd.DataFrame({'Date': dates, 'XRP_Price': prices})
# Plotting
plt.figure(figsize=(10, 5))
plt.plot(df['Date'], df['XRP_Price'], color='purple', linewidth=2.5, marker='o')
plt.title('XRP Price Movement (Last 30 Days)', fontsize=14, fontweight='bold')
plt.xlabel('Date')
plt.ylabel('Price in USD')
plt.grid(True, linestyle='--', alpha=0.5)
plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%b %d'))
plt.xticks(rotation=45)
plt.tight_layout()
# Save the chart
chart_path = '/mnt/data/xrp_price_chart.png'
plt.savefig(chart_path)
plt.close()
chart_path