PYTH PYTH / API3 Crypto vs DMAIL DMAIL / USD Crypto

Stats Comprehensive Analytics for the Selected Time Period

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

Settings

🤖 AI Analysis

Ask me anything about the statistics below. I can help explain metrics, identify patterns, or answer specific questions.
Asset PYTH / API3DMAIL / USD
📈 Performance Metrics
Start Price 0.240.27
End Price 0.160.03
Price Change % -32.41%-90.28%
Period High 0.290.37
Period Low 0.070.03
Price Range % 290.0%1,332.9%
🏆 All-Time Records
All-Time High 0.290.37
Days Since ATH 231 days312 days
Distance From ATH % -44.8%-92.9%
All-Time Low 0.070.03
Distance From ATL % +115.3%+1.2%
New ATHs Hit 8 times6 times
📌 Easy-to-Understand Stats
Avg Daily Change % 3.18%4.79%
Biggest Jump (1 Day) % +0.10+0.06
Biggest Drop (1 Day) % -0.07-0.04
Days Above Avg % 45.6%32.5%
Extreme Moves days 7 (2.0%)22 (6.4%)
Stability Score % 0.0%0.0%
Trend Strength % 51.6%55.5%
Recent Momentum (10-day) % -4.61%-14.48%
📊 Statistical Measures
Average Price 0.190.12
Median Price 0.190.10
Price Std Deviation 0.040.08
🚀 Returns & Growth
CAGR % -34.09%-91.57%
Annualized Return % -34.09%-91.57%
Total Return % -32.41%-90.28%
⚠️ Risk & Volatility
Daily Volatility % 7.50%7.08%
Annualized Volatility % 143.25%135.28%
Max Drawdown % -74.36%-93.02%
Sharpe Ratio 0.017-0.061
Sortino Ratio 0.022-0.067
Calmar Ratio -0.458-0.984
Ulcer Index 34.9169.44
📅 Daily Performance
Win Rate % 48.4%43.8%
Positive Days 166149
Negative Days 177191
Best Day % +100.01%+40.97%
Worst Day % -40.21%-25.64%
Avg Gain (Up Days) % +3.65%+4.86%
Avg Loss (Down Days) % -3.18%-4.56%
Profit Factor 1.080.83
🔥 Streaks & Patterns
Longest Win Streak days 88
Longest Loss Streak days 106
💹 Trading Metrics
Omega Ratio 1.0760.830
Expectancy % +0.12%-0.44%
Kelly Criterion % 1.07%0.00%
📅 Weekly Performance
Best Week % +90.77%+50.64%
Worst Week % -34.62%-29.45%
Weekly Win Rate % 50.9%39.6%
📆 Monthly Performance
Best Month % +32.24%+65.93%
Worst Month % -54.84%-50.21%
Monthly Win Rate % 53.8%30.8%
🔧 Technical Indicators
RSI (14-period) 33.8614.10
Price vs 50-Day MA % -5.91%-18.43%
Price vs 200-Day MA % -4.05%-64.19%
💰 Volume Analysis
Avg Volume 1,949,3363,274,722
Total Volume 670,571,4131,129,779,235

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 DMAIL (DMAIL): 0.525 (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
DMAIL: Bybit