Giá BluefinBLUE

Quant extends gains, aiming for $120, as risk-on sentiment returns : Quant rises for the second straight day, trading above $100, supported by increasing risk appetite among large-volume holders. QNT's daily active addresses jump to 1,000, signaling network growth and user engagement. The MACD validates Quant's uptrend with a buy signal, but the near-overbought RSI cautions traders to temper expectations. Quant's (QNT) price hovers around $109 at the time of writing on Tuesday, with intraday gains nearing 5%, supported by strong sentiment in the broader crypto market and growing interest among investors, particularly large-volume holders. Quant enters accumulation phase The bullish momentum propelling Quant's price above $100 likely emanates from the increasing risk-on sentiment among whales. Santiment's on-chain data shows a sharp increase in the percentage of supply held by addresses holding between 100,000 and 1 million coins. This cohort of QNT holders currently accounts for approximately 32% of the total supply, up from around 30% on May 5. The yellow line on the chart below shows two short-lived peaks above 32% on May 19 and May 22, suggesting active profit-taking even as Quant's price extends gains. The token's performance on-chain has also been on the rise, with the number of daily active addresses increasing from 720 on Sunday to 1,059 on Monday, representing a 32% rise. Notably, the Daily Active Addresses metric, which tracks the unique addresses that transact QNT daily on the protocol, is also rising. This tool can be used to gauge the level of user engagement and speculation. A consistent increase in this metric often signals improving confidence among investors, which translates to a boost in demand for the token. At the same time, the number of new addresses joining the blockchain protocol has gradually increased to 298 from approximately 107 on April 5, despite noticeable volatility in recent weeks. The 64% surge in the number of newly created addresses signals growing network adoption, which could steady the ongoing uptrend as demand for QNT increases. Technical outlook: Quant upholds bullish structure Quant's price is approaching the short-term resistance at $110 as part of a larger recovery, targeting highs above $120. The token sits on top of key levels such as the upper descending trendline and the confluence support area between $87 and $90, bringing together the 50-day Exponential Moving Average (EMA), the 100-day EMA and the 200-day EMA. A buy signal from the Moving Average Convergence Divergence (MACD) indicator validated on Monday backs the uptrend. The signal occurred when the blue MACD line crossed above the red signal line. Traders would look out for the MACD's continued uptrend above the mean line to ascertain the bullish momentum in upcoming sessions and days. If the MACD reverses toward the center line, it will imply fading bullish momentum amid potential profit-taking. Still, the near-overbought Relative Strength Index (RSI) at 69 signals caution for traders, as overbought conditions are often a precursor to potential pullbacks. That said, a break above the immediate $110 resistance could reinforce the bullish grip on QNT, bringing the next key level at $120 into sight. On the other hand, key monitoring levels on the downside range from near-term support at $100, which was tested as resistance on May 22, to the confluence zone between $87 and $90, as well as the demand area at $73, which was tested as the 50-day EMA in late April. $QNT

Analysis: Four major indicators show that Bitcoin has not yet reached its peak, and the price of this cycle may exceed $200,000 According to Lookonchain analysis, the four major technical indicators all show that BTC has not yet reached the peak of this bull market. The rainbow chart predicts that the price of Bitcoin is expected to exceed $200,000 in this cycle; the RSI index is 71.35, which still has room to rise compared to the historical peak; the 200-week moving average heat map is in the blue area, indicating that the price has not yet reached the top; the 2-year moving average multiplier shows that the current price is between the red and green lines, and has not reached the top signal.

One of the potential blue-chip pick as first mover in RISC-V tech Chart shows @QuantumFusion_ is close to strong support and buyers here will be celebrating in next few months. We are seeing ETH memes running, next is the fundamental projects where the big volume will come in next. Don't fade $QF, secure your entry.

Gib the based blue watch @jessepollak @brian_armstrong @WilsonCusack 👉👈

Creating a live 3D simulation of a rocket traveling from Earth to Mars using real orbital dynamics is a complex task that involves modeling the gravitational interactions and visualizing the trajectory. Below, I’ll provide a complete Python solution that simulates this journey using simplified orbital dynamics and visualizes the result with Matplotlib. This simulation assumes circular orbits for Earth and Mars around the Sun and uses a two-body approximation for the rocket’s motion under the Sun’s gravity. For a fully accurate model, one would need ephemeris data and N-body simulations, but this simplified approach captures the essence of the request. Step-by-Step Solution Model the Celestial Bodies: Earth and Mars follow circular orbits around the Sun. Their positions are calculated based on their orbital radii and periods. Simulate the Rocket’s Trajectory: The rocket launches from Earth’s position with an initial velocity. Its motion is governed by the Sun’s gravitational force, solved using numerical integration. Visualize the Trajectory: A 3D plot shows the paths of Earth, Mars, and the rocket over time. Python Code Here’s the complete code to simulate and visualize the rocket’s journey: python import numpy as np from scipy.integrate import import matplotlib.pyplot as plt from mpl_toolkits.mplot3d import Axes3D # Constants G = 6.67430e-11 # Gravitational constant (m^3 kg^-1 s^-2) M_sun = 1.989e30 # Mass of the Sun (kg) AU = 1.496 # Astronomical Unit (m) # Orbital parameters r_earth = 1.0 * AU # Earth's orbital radius (m) r_mars = 1.524 * AU # Mars' orbital radius (m) T_earth = 365.25 * 24 * 3600 # Earth's orbital period (s) T_mars = 687.0 * 24 * 3600 # Mars' orbital period (s) # Angular velocities omega_earth = 2 * np.pi / T_earth omega_mars = 2 * np.pi / T_mars # Time array t_max = 0.5 * T_earth # Simulate for half an Earth year (adjustable) dt = 3600 # Time step of 1 hour t = np.arange(0, t_max, dt) # Positions of Earth and Mars (circular orbits in the XY plane) theta_earth = omega_earth * t theta_mars = omega_mars * t earth_pos = np.array([r_earth * np.cos(theta_earth), r_earth * np.sin(theta_earth), np.zeros_like(t)]) mars_pos = np.array([r_mars * np.cos(theta_mars), r_mars * np.sin(theta_mars), np.zeros_like(t)]) # Rocket initial conditions initial_pos = earth_pos[:, 0] # Start at Earth's initial position v_escape = np.sqrt(2 * G * M_sun / r_earth) # Escape velocity from Sun at Earth's distance direction = (mars_pos[:, int(t_max/(2*dt))] - initial_pos) / np.linalg.norm(mars_pos[:, int(t_max/(2*dt))] - initial_pos) initial_vel = 0.5 * v_escape * direction # Adjusted velocity towards Mars # Differential equations for rocket motion def rocket_ode(y, t, G, M_sun): r = y[:3] # Position v = y[3:] # Velocity r_norm = np.linalg.norm(r) a = -G * M_sun / r_norm**3 * r # Acceleration due to Sun's gravity return np.concatenate([v, a]) # Initial state vector [x, y, z, vx, vy, vz] y0 = np.concatenate([initial_pos, initial_vel]) # Solve the rocket's trajectory sol = odeint(rocket_ode, y0, t, args=(G, M_sun)) rocket_pos = sol[:, :3] # 3D Visualization fig = plt.figure(figsize=(10, 8)) ax = fig.add_subplot(111, projection='3d') # Plot Sun at origin ax.scatter(0, 0, 0, color='yellow', s=100, label='Sun') # Plot Earth's trajectory ax.plot(earth_pos[0], earth_pos[1], earth_pos[2], color='blue', label='Earth') # Plot Mars' trajectory ax.plot(mars_pos[0], mars_pos[1], mars_pos[2], color='red', label='Mars') # Plot Rocket's trajectory ax.plot(rocket_pos[:, 0], rocket_pos[:, 1], rocket_pos[:, 2], color='green', label='Rocket') # Set labels and title ax.set_xlabel('X (m)') ax.set_ylabel('Y (m)') ax.set_zlabel('Z (m)') ax.set_title('Rocket Trajectory from Earth to Mars') ax. # Adjust plot limits for clarity ax.set_xlim(-2*AU, 2*AU) ax. ax.set_zlim(-0.1*AU, 0.1*AU) plt.show() Explanation of the Code Constants and Parameters Gravitational Constant (G): Universal constant for gravitational force. Mass of the Sun (M_sun): Dominant gravitational influence. Astronomical Unit (AU): Standard unit for distances in the solar system. Orbital Radii and Periods: Simplified values for Earth (1 AU, 365.25 days) and Mars (1.524 AU, 687 days). Time Setup Simulation runs for half an Earth year (t_max), with a time step of 1/hour (dt). Adjust t_max to simulate longer or shorter journeys. Earth and Mars Positions Calculated assuming circular orbits in the XY plane (Z=0). Positions derived from angular velocities (omega) and time. Rocket Dynamics Initial Conditions: Rocket starts at Earth’s position with a velocity directed towards Mars’ position halfway through the simulation. Equations of Motion: Governed by the Sun’s gravity, solved using scipy.integrate.odeint. Velocity: Set to half the escape velocity for a realistic trajectory (adjustable). Visualization 3D Plot: Uses Matplotlib’s Axes3D to display trajectories. Elements Plotted: Sun (yellow dot at origin). Earth (blue line). Mars (red line). Rocket (green line). Limits: Adjusted to show the full orbits and rocket path clearly. Simplifications and Limitations Circular Orbits: Earth and Mars have elliptical orbits in reality (eccentricity ignored here). Two-Body Approximation: Only the Sun’s gravity affects the rocket; Earth and Mars’ gravitational influences are neglected. No Launch Window Optimization: Real missions use Hohmann transfer orbits timed for efficiency. Static Plot: This is not a “live” animation but a static 3D plot. For animation, you’d need to use Matplotlib’s FuncAnimation. How to Enhance Realistic Orbits: Use ephemeris data (e.g., from NASA’s JPL) for accurate positions. Hohmann Transfer: Calculate the optimal launch velocity and timing. Animation: Add matplotlib.animation to show the rocket moving in real-time. Additional Forces: Include Earth’s escape trajectory and Mars’ gravitational pull. Output The plot shows: The Sun at the center. Earth’s orbit in blue, Mars’ orbit in red. The rocket’s path in green, curving from Earth towards Mars. This provides a clear, concise visualization of the rocket’s trajectory relative to Earth and Mars over time, fulfilling the query with a simplified yet educational model. Adjust the parameters (e.g., initial_vel, t_max) to refine the trajectory as needed.