PYTH PYTH / MOG Crypto vs XUSD XUSD / MOG Crypto

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

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Asset PYTH / MOGXUSD / MOG
📈 Performance Metrics
Start Price 138,405.872,061,237.11
End Price 238,249.653,486,401.67
Price Change % +72.14%+69.14%
Period High 418,913.043,745,039.31
Period Low 62,758.98524,311.57
Price Range % 567.5%614.3%
🏆 All-Time Records
All-Time High 418,913.043,745,039.31
Days Since ATH 222 days11 days
Distance From ATH % -43.1%-6.9%
All-Time Low 62,758.98524,311.57
Distance From ATL % +279.6%+564.9%
New ATHs Hit 29 times10 times
📌 Easy-to-Understand Stats
Avg Daily Change % 4.70%6.81%
Biggest Jump (1 Day) % +123,936.39+749,412.79
Biggest Drop (1 Day) % -64,854.22-719,468.60
Days Above Avg % 46.5%35.3%
Extreme Moves days 7 (2.0%)12 (5.0%)
Stability Score % 100.0%100.0%
Trend Strength % 54.5%52.1%
Recent Momentum (10-day) % -6.31%+1.64%
📊 Statistical Measures
Average Price 198,591.421,527,440.69
Median Price 189,181.061,145,427.46
Price Std Deviation 82,154.19841,447.98
🚀 Returns & Growth
CAGR % +78.24%+122.40%
Annualized Return % +78.24%+122.40%
Total Return % +72.14%+69.14%
⚠️ Risk & Volatility
Daily Volatility % 8.28%8.86%
Annualized Volatility % 158.12%169.26%
Max Drawdown % -85.02%-85.53%
Sharpe Ratio 0.0530.069
Sortino Ratio 0.0690.072
Calmar Ratio 0.9201.431
Ulcer Index 49.3861.50
📅 Daily Performance
Win Rate % 54.5%52.3%
Positive Days 187125
Negative Days 156114
Best Day % +103.46%+42.04%
Worst Day % -18.14%-22.73%
Avg Gain (Up Days) % +4.79%+6.85%
Avg Loss (Down Days) % -4.77%-6.23%
Profit Factor 1.201.21
🔥 Streaks & Patterns
Longest Win Streak days 106
Longest Loss Streak days 67
💹 Trading Metrics
Omega Ratio 1.2031.205
Expectancy % +0.44%+0.61%
Kelly Criterion % 1.93%1.43%
📅 Weekly Performance
Best Week % +90.32%+43.85%
Worst Week % -36.88%-49.36%
Weekly Win Rate % 63.5%58.3%
📆 Monthly Performance
Best Month % +146.78%+78.75%
Worst Month % -47.36%-32.58%
Monthly Win Rate % 53.8%40.0%
🔧 Technical Indicators
RSI (14-period) 40.1147.59
Price vs 50-Day MA % -1.34%+36.59%
Price vs 200-Day MA % +48.61%+152.05%
💰 Volume Analysis
Avg Volume 2,397,451,166,42612,060,135,296,066
Total Volume 824,723,201,250,3872,906,492,606,351,825

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 XUSD (XUSD): 0.811 (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
XUSD: Binance
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