PYTH PYTH / Q Crypto vs PYTH PYTH / 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 / QPYTH / USD
📈 Performance Metrics
Start Price 17.520.35
End Price 6.580.16
Price Change % -62.46%-53.80%
Period High 18.220.53
Period Low 3.460.09
Price Range % 426.0%518.7%
🏆 All-Time Records
All-Time High 18.220.53
Days Since ATH 34 days310 days
Distance From ATH % -63.9%-69.3%
All-Time Low 3.460.09
Distance From ATL % +89.9%+89.7%
New ATHs Hit 1 times13 times
📌 Easy-to-Understand Stats
Avg Daily Change % 13.50%4.35%
Biggest Jump (1 Day) % +3.58+0.11
Biggest Drop (1 Day) % -8.12-0.09
Days Above Avg % 28.9%30.1%
Extreme Moves days 2 (5.4%)5 (1.5%)
Stability Score % 0.0%0.0%
Trend Strength % 70.3%49.3%
Recent Momentum (10-day) % -9.67%+4.42%
📊 Statistical Measures
Average Price 7.960.21
Median Price 5.960.16
Price Std Deviation 4.270.12
🚀 Returns & Growth
CAGR % -99.99%-56.25%
Annualized Return % -99.99%-56.25%
Total Return % -62.46%-53.80%
⚠️ Risk & Volatility
Daily Volatility % 21.38%7.75%
Annualized Volatility % 408.43%147.98%
Max Drawdown % -80.99%-83.84%
Sharpe Ratio -0.0290.002
Sortino Ratio -0.0440.003
Calmar Ratio -1.235-0.671
Ulcer Index 60.9363.41
📅 Daily Performance
Win Rate % 29.7%50.7%
Positive Days 11173
Negative Days 26168
Best Day % +95.01%+99.34%
Worst Day % -48.36%-18.09%
Avg Gain (Up Days) % +20.68%+4.48%
Avg Loss (Down Days) % -9.64%-4.57%
Profit Factor 0.911.01
🔥 Streaks & Patterns
Longest Win Streak days 27
Longest Loss Streak days 66
💹 Trading Metrics
Omega Ratio 0.9071.008
Expectancy % -0.63%+0.02%
Kelly Criterion % 0.00%0.09%
📅 Weekly Performance
Best Week % +77.60%+65.86%
Worst Week % -15.29%-27.08%
Weekly Win Rate % 50.0%52.9%
📆 Monthly Performance
Best Month % +31.86%+65.32%
Worst Month % -73.89%-31.62%
Monthly Win Rate % 50.0%41.7%
🔧 Technical Indicators
RSI (14-period) 62.4369.99
Price vs 50-Day MA % N/A+2.15%
Price vs 200-Day MA % N/A+18.68%
💰 Volume Analysis
Avg Volume 210,824,0891,922,641
Total Volume 8,011,315,374657,543,282

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 PYTH (PYTH): 0.217 (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
PYTH: Kraken