PYTH PYTH / COQ Crypto vs VIRTUAL VIRTUAL / COQ Crypto

Stats Comprehensive Analytics for the Selected Time Period

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

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🤖 AI Analysis

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Asset PYTH / COQVIRTUAL / COQ

📈 Performance Metrics

Start Price 207,565.933,244,271.51

End Price 380,933.664,586,240.79

Price Change % +83.52%+41.36%

Period High 448,149.595,374,560.38

Period Low 146,060.061,776,699.03

Price Range % 206.8%202.5%

🏆 All-Time Records

All-Time High 448,149.595,374,560.38

Days Since ATH 67 days24 days

Distance From ATH % -15.0%-14.7%

All-Time Low 146,060.061,776,699.03

Distance From ATL % +160.8%+158.1%

New ATHs Hit 4 times3 times

📌 Easy-to-Understand Stats

Avg Daily Change % 4.26%5.10%

Biggest Jump (1 Day) % +221,168.27+2,981,285.21

Biggest Drop (1 Day) % -83,311.19-710,761.93

Days Above Avg % 53.3%30.0%

Extreme Moves days 3 (2.5%)2 (1.7%)

Stability Score % 100.0%100.0%

Trend Strength % 53.8%51.3%

Recent Momentum (10-day) % +8.09%+2.24%

📊 Statistical Measures

Average Price 280,335.842,848,417.30

Median Price 296,505.542,395,358.36

Price Std Deviation 77,933.961,042,191.01

🚀 Returns & Growth

CAGR % +543.88%+189.15%

Annualized Return % +543.88%+189.15%

Total Return % +83.52%+41.36%

⚠️ Risk & Volatility

Daily Volatility % 10.38%12.79%

Annualized Volatility % 198.39%244.28%

Max Drawdown % -45.61%-47.34%

Sharpe Ratio 0.0880.066

Sortino Ratio 0.1490.126

Calmar Ratio 11.9243.995

Ulcer Index 20.6729.28

📅 Daily Performance

Win Rate % 53.8%51.3%

Positive Days 6461

Negative Days 5558

Best Day % +97.44%+124.57%

Worst Day % -25.47%-27.13%

Avg Gain (Up Days) % +4.95%+5.95%

Avg Loss (Down Days) % -3.79%-4.54%

Profit Factor 1.521.38

🔥 Streaks & Patterns

Longest Win Streak days 54

Longest Loss Streak days 47

💹 Trading Metrics

Omega Ratio 1.5191.379

Expectancy % +0.91%+0.84%

Kelly Criterion % 4.84%3.10%

📅 Weekly Performance

Best Week % +88.01%+13.23%

Worst Week % -13.48%-22.08%

Weekly Win Rate % 57.9%42.1%

📆 Monthly Performance

Best Month % +126.01%+24.61%

Worst Month % -17.27%-38.91%

Monthly Win Rate % 60.0%40.0%

🔧 Technical Indicators

RSI (14-period) 58.2845.93

Price vs 50-Day MA % +14.08%+31.13%

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 VIRTUAL (VIRTUAL):	0.536 (Moderate 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
VIRTUAL: Kraken