GSWIFT GSWIFT / MOG Crypto vs PYTH PYTH / MOG 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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Asset GSWIFT / MOGPYTH / MOG

📈 Performance Metrics

Start Price 25,158.77233,279.54

End Price 4,485.58285,213.29

Price Change % -82.17%+22.26%

Period High 69,065.98418,913.04

Period Low 3,836.3562,758.98

Price Range % 1,700.3%567.5%

🏆 All-Time Records

All-Time High 69,065.98418,913.04

Days Since ATH 316 days197 days

Distance From ATH % -93.5%-31.9%

All-Time Low 3,836.3562,758.98

Distance From ATL % +16.9%+354.5%

New ATHs Hit 8 times18 times

📌 Easy-to-Understand Stats

Avg Daily Change % 7.38%5.02%

Biggest Jump (1 Day) % +12,244.94+123,936.39

Biggest Drop (1 Day) % -20,501.02-64,854.22

Days Above Avg % 48.8%42.7%

Extreme Moves days 16 (4.7%)10 (2.9%)

Stability Score % 100.0%100.0%

Trend Strength % 53.4%53.6%

Recent Momentum (10-day) % -14.70%+36.22%

📊 Statistical Measures

Average Price 21,381.39193,800.00

Median Price 19,858.03178,647.50

Price Std Deviation 14,612.6480,978.75

🚀 Returns & Growth

CAGR % -84.38%+23.85%

Annualized Return % -84.38%+23.85%

Total Return % -82.17%+22.26%

⚠️ Risk & Volatility

Daily Volatility % 9.10%8.65%

Annualized Volatility % 173.87%165.24%

Max Drawdown % -94.45%-85.02%

Sharpe Ratio -0.0100.043

Sortino Ratio -0.0110.053

Calmar Ratio -0.8930.281

Ulcer Index 70.6549.86

📅 Daily Performance

Win Rate % 46.6%53.6%

Positive Days 158184

Negative Days 181159

Best Day % +35.16%+103.46%

Worst Day % -29.68%-25.51%

Avg Gain (Up Days) % +7.18%+5.08%

Avg Loss (Down Days) % -6.44%-5.07%

Profit Factor 0.971.16

🔥 Streaks & Patterns

Longest Win Streak days 610

Longest Loss Streak days 76

💹 Trading Metrics

Omega Ratio 0.9731.160

Expectancy % -0.09%+0.38%

Kelly Criterion % 0.00%1.46%

📅 Weekly Performance

Best Week % +75.51%+90.32%

Worst Week % -43.32%-41.67%

Weekly Win Rate % 49.0%63.5%

📆 Monthly Performance

Best Month % +84.81%+146.78%

Worst Month % -47.98%-47.36%

Monthly Win Rate % 30.8%46.2%

🔧 Technical Indicators

RSI (14-period) 44.4074.77

Price vs 50-Day MA % -32.47%+35.69%

Price vs 200-Day MA % -61.35%+71.10%

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).

GSWIFT (GSWIFT) vs PYTH (PYTH):	0.576 (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

GSWIFT: Bybit
PYTH: Kraken