PYTH PYTH / DMAIL Crypto vs GSWIFT GSWIFT / 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 / DMAILGSWIFT / USD
📈 Performance Metrics
Start Price 1.200.05
End Price 4.800.00
Price Change % +298.85%-95.50%
Period High 8.610.16
Period Low 0.630.00
Price Range % 1,257.4%7,514.5%
🏆 All-Time Records
All-Time High 8.610.16
Days Since ATH 46 days314 days
Distance From ATH % -44.3%-98.7%
All-Time Low 0.630.00
Distance From ATL % +656.4%+0.0%
New ATHs Hit 24 times11 times
📌 Easy-to-Understand Stats
Avg Daily Change % 6.28%6.77%
Biggest Jump (1 Day) % +4.85+0.04
Biggest Drop (1 Day) % -1.31-0.02
Days Above Avg % 22.1%30.7%
Extreme Moves days 8 (2.3%)16 (4.7%)
Stability Score % 0.0%0.0%
Trend Strength % 56.3%58.7%
Recent Momentum (10-day) % +9.58%-25.09%
📊 Statistical Measures
Average Price 2.090.03
Median Price 1.660.01
Price Std Deviation 1.270.03
🚀 Returns & Growth
CAGR % +335.86%-96.27%
Annualized Return % +335.86%-96.27%
Total Return % +298.85%-95.50%
⚠️ Risk & Volatility
Daily Volatility % 10.31%8.28%
Annualized Volatility % 196.96%158.14%
Max Drawdown % -73.50%-98.69%
Sharpe Ratio 0.081-0.068
Sortino Ratio 0.104-0.080
Calmar Ratio 4.569-0.976
Ulcer Index 32.8382.47
📅 Daily Performance
Win Rate % 56.3%41.3%
Positive Days 193142
Negative Days 150202
Best Day % +129.10%+43.94%
Worst Day % -33.36%-42.04%
Avg Gain (Up Days) % +6.12%+5.70%
Avg Loss (Down Days) % -5.96%-4.97%
Profit Factor 1.320.81
🔥 Streaks & Patterns
Longest Win Streak days 95
Longest Loss Streak days 611
💹 Trading Metrics
Omega Ratio 1.3210.807
Expectancy % +0.84%-0.56%
Kelly Criterion % 2.29%0.00%
📅 Weekly Performance
Best Week % +64.64%+70.81%
Worst Week % -40.25%-33.97%
Weekly Win Rate % 57.7%46.2%
📆 Monthly Performance
Best Month % +208.20%+156.21%
Worst Month % -52.94%-57.63%
Monthly Win Rate % 61.5%15.4%
🔧 Technical Indicators
RSI (14-period) 59.7413.60
Price vs 50-Day MA % -2.84%-60.73%
Price vs 200-Day MA % +97.93%-76.29%
💰 Volume Analysis
Avg Volume 34,702,9249,181,104
Total Volume 11,937,805,8773,167,480,871

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 GSWIFT (GSWIFT): -0.246 (Weak)

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
GSWIFT: Bybit