PYTH PYTH / NODE Crypto vs XMLNZ XMLNZ / NODE Crypto

Stats Comprehensive Analytics for the Selected Time Period

Detailed statistical analysis including performance metrics, risk indicators, technical analysis, and advanced ratios.

Settings

🤖 AI Analysis

Model:

Ask me anything about the statistics below. I can help explain metrics, identify patterns, or answer specific questions.

Asset PYTH / NODEXMLNZ / NODE

📈 Performance Metrics

Start Price 1.58105.47

End Price 2.48157.74

Price Change % +56.76%+49.56%

Period High 2.65157.74

Period Low 0.9974.41

Price Range % 166.1%112.0%

🏆 All-Time Records

All-Time High 2.65157.74

Days Since ATH 3 days0 days

Distance From ATH % -6.3%+0.0%

All-Time Low 0.9974.41

Distance From ATL % +149.3%+112.0%

New ATHs Hit 10 times14 times

📌 Easy-to-Understand Stats

Avg Daily Change % 7.32%6.49%

Biggest Jump (1 Day) % +1.04+21.43

Biggest Drop (1 Day) % -0.40-23.56

Days Above Avg % 54.8%42.9%

Extreme Moves days 2 (2.4%)4 (4.8%)

Stability Score % 0.0%92.3%

Trend Strength % 49.4%56.6%

Recent Momentum (10-day) % +4.09%+23.04%

📊 Statistical Measures

Average Price 1.90106.10

Median Price 1.99103.29

Price Std Deviation 0.4720.57

🚀 Returns & Growth

CAGR % +622.13%+487.15%

Annualized Return % +622.13%+487.15%

Total Return % +56.76%+49.56%

⚠️ Risk & Volatility

Daily Volatility % 14.54%8.18%

Annualized Volatility % 277.85%156.33%

Max Drawdown % -37.13%-31.98%

Sharpe Ratio 0.0920.101

Sortino Ratio 0.1540.098

Calmar Ratio 16.75715.234

Ulcer Index 15.2214.33

📅 Daily Performance

Win Rate % 50.0%56.6%

Positive Days 4147

Negative Days 4136

Best Day % +104.78%+20.53%

Worst Day % -21.11%-22.43%

Avg Gain (Up Days) % +9.57%+6.56%

Avg Loss (Down Days) % -6.85%-6.66%

Profit Factor 1.401.29

🔥 Streaks & Patterns

Longest Win Streak days 56

Longest Loss Streak days 33

💹 Trading Metrics

Omega Ratio 1.3971.286

Expectancy % +1.36%+0.83%

Kelly Criterion % 2.07%1.89%

📅 Weekly Performance

Best Week % +44.93%+28.53%

Worst Week % -17.13%-18.97%

Weekly Win Rate % 64.3%71.4%

📆 Monthly Performance

Best Month % +41.52%+48.46%

Worst Month % -15.92%-17.84%

Monthly Win Rate % 60.0%40.0%

🔧 Technical Indicators

RSI (14-period) 61.2176.13

Price vs 50-Day MA % +11.41%+34.91%

Performance Metrics: Shows the price at the start and end of the period, total change, and the highest/lowest prices reached during this time frame. | All-Time Records: All-time records show the highest and lowest prices ever reached during this period, how far the current price is from those extremes, and how long ago they occurred. | Easy-to-Understand Stats: Easy-to-understand metrics including typical daily price movements, biggest single-day gains/losses, how often price stayed above average, stability measures, and short-term momentum trends. | Returns & Growth: CAGR (Compound Annual Growth Rate) shows the annualized return rate if this growth continued consistently, while annualized and total returns show performance scaled to different time periods. | Risk & Volatility: Risk metrics show price volatility (daily and annualized), maximum drawdown (worst peak-to-trough decline), and various ratios (Sharpe, Sortino, Calmar, Treynor, Information) that measure risk-adjusted returns. | Daily Performance: Daily performance shows positive vs negative days, win rate, best and worst single days, average gains/losses on up/down days, gain/loss ratio, and profit factor (total gains divided by total losses). | Trading Metrics: Trading metrics include Omega ratio (probability-weighted gains vs losses), payoff ratio (avg win/avg loss), expectancy (expected return per trade), Kelly Criterion (optimal position sizing %), and price efficiency (trending vs choppy).

📊 Asset Correlations

Correlation coefficient ranges from -1 (perfectly inverse) to +1 (perfectly correlated).

PYTH (PYTH) vs XMLNZ (XMLNZ):	0.731 (Strong positive)

Correlation shows how closely asset prices move together: +1.0 means perfect positive correlation (move in sync), 0 means no relationship, -1.0 means perfect negative correlation (move opposite). Lower correlation can help with portfolio diversification.

Data sources

PYTH: Kraken
XMLNZ: Kraken