PYTH PYTH / FORTH Crypto vs MATH MATH / USD 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 / FORTHMATH / USD

📈 Performance Metrics

Start Price 0.140.28

End Price 0.050.06

Price Change % -64.85%-78.85%

Period High 0.140.39

Period Low 0.040.05

Price Range % 274.2%613.0%

🏆 All-Time Records

All-Time High 0.140.39

Days Since ATH 342 days319 days

Distance From ATH % -65.6%-84.8%

All-Time Low 0.040.05

Distance From ATL % +28.6%+8.6%

New ATHs Hit 1 times10 times

📌 Easy-to-Understand Stats

Avg Daily Change % 4.12%3.30%

Biggest Jump (1 Day) % +0.04+0.04

Biggest Drop (1 Day) % -0.02-0.04

Days Above Avg % 38.4%33.7%

Extreme Moves days 9 (2.6%)10 (2.9%)

Stability Score % 0.0%0.0%

Trend Strength % 55.1%54.5%

Recent Momentum (10-day) % -12.87%-20.96%

📊 Statistical Measures

Average Price 0.060.15

Median Price 0.060.13

Price Std Deviation 0.020.07

🚀 Returns & Growth

CAGR % -67.13%-80.86%

Annualized Return % -67.13%-80.86%

Total Return % -64.85%-78.85%

⚠️ Risk & Volatility

Daily Volatility % 8.06%4.78%

Annualized Volatility % 153.97%91.41%

Max Drawdown % -73.28%-85.97%

Sharpe Ratio -0.003-0.071

Sortino Ratio -0.005-0.076

Calmar Ratio -0.916-0.941

Ulcer Index 58.1062.96

📅 Daily Performance

Win Rate % 44.9%45.0%

Positive Days 154153

Negative Days 189187

Best Day % +101.56%+35.84%

Worst Day % -35.54%-25.52%

Avg Gain (Up Days) % +4.62%+3.33%

Avg Loss (Down Days) % -3.81%-3.35%

Profit Factor 0.990.81

🔥 Streaks & Patterns

Longest Win Streak days 59

Longest Loss Streak days 88

💹 Trading Metrics

Omega Ratio 0.9870.814

Expectancy % -0.03%-0.34%

Kelly Criterion % 0.00%0.00%

📅 Weekly Performance

Best Week % +67.10%+24.61%

Worst Week % -33.96%-19.41%

Weekly Win Rate % 46.2%44.2%

📆 Monthly Performance

Best Month % +45.57%+15.63%

Worst Month % -42.52%-24.24%

Monthly Win Rate % 23.1%30.8%

🔧 Technical Indicators

RSI (14-period) 34.2131.36

Price vs 50-Day MA % -16.02%-28.49%

Price vs 200-Day MA % -6.66%-43.55%

💰 Volume Analysis

Avg Volume 669,7782,375,062

Total Volume 230,403,508817,021,177

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 MATH (MATH):	0.774 (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
MATH: Coinbase