PYTH PYTH / MOG Crypto vs CGPT CGPT / 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 PYTH / MOGCGPT / MOG

📈 Performance Metrics

Start Price 135,983.3559,755.46

End Price 241,221.37131,471.20

Price Change % +77.39%+120.02%

Period High 418,913.04238,605.90

Period Low 62,758.9855,030.18

Price Range % 567.5%333.6%

🏆 All-Time Records

All-Time High 418,913.04238,605.90

Days Since ATH 223 days250 days

Distance From ATH % -42.4%-44.9%

All-Time Low 62,758.9855,030.18

Distance From ATL % +284.4%+138.9%

New ATHs Hit 29 times15 times

📌 Easy-to-Understand Stats

Avg Daily Change % 4.70%4.97%

Biggest Jump (1 Day) % +123,936.39+84,068.67

Biggest Drop (1 Day) % -64,854.22-32,250.64

Days Above Avg % 46.8%38.7%

Extreme Moves days 7 (2.0%)11 (3.2%)

Stability Score % 100.0%100.0%

Trend Strength % 54.8%53.9%

Recent Momentum (10-day) % -6.53%-4.89%

📊 Statistical Measures

Average Price 198,898.94128,999.60

Median Price 190,153.11119,329.19

Price Std Deviation 82,126.0046,097.52

🚀 Returns & Growth

CAGR % +84.03%+131.43%

Annualized Return % +84.03%+131.43%

Total Return % +77.39%+120.02%

⚠️ Risk & Volatility

Daily Volatility % 8.28%7.83%

Annualized Volatility % 158.11%149.59%

Max Drawdown % -85.02%-76.69%

Sharpe Ratio 0.0540.065

Sortino Ratio 0.0700.078

Calmar Ratio 0.9881.714

Ulcer Index 49.4345.28

📅 Daily Performance

Win Rate % 55.0%53.9%

Positive Days 188185

Negative Days 154158

Best Day % +103.46%+76.82%

Worst Day % -18.14%-23.40%

Avg Gain (Up Days) % +4.77%+5.07%

Avg Loss (Down Days) % -4.82%-4.84%

Profit Factor 1.211.23

🔥 Streaks & Patterns

Longest Win Streak days 108

Longest Loss Streak days 67

💹 Trading Metrics

Omega Ratio 1.2081.226

Expectancy % +0.45%+0.51%

Kelly Criterion % 1.96%2.06%

📅 Weekly Performance

Best Week % +90.32%+110.31%

Worst Week % -36.88%-40.79%

Weekly Win Rate % 63.5%53.8%

📆 Monthly Performance

Best Month % +146.78%+91.46%

Worst Month % -47.36%-41.70%

Monthly Win Rate % 53.8%69.2%

🔧 Technical Indicators

RSI (14-period) 43.8747.92

Price vs 50-Day MA % -0.25%+9.00%

Price vs 200-Day MA % +50.51%+25.02%

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 CGPT (CGPT):	0.821 (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
CGPT: Bybit