PYTH PYTH / COQ Crypto vs WEN WEN / 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 / COQWEN / USD
📈 Performance Metrics
Start Price 207,565.930.00
End Price 310,857.330.00
Price Change % +49.76%-77.82%
Period High 448,149.590.00
Period Low 146,060.060.00
Price Range % 206.8%1,091.8%
🏆 All-Time Records
All-Time High 448,149.590.00
Days Since ATH 46 days332 days
Distance From ATH % -30.6%-89.5%
All-Time Low 146,060.060.00
Distance From ATL % +112.8%+25.7%
New ATHs Hit 4 times7 times
📌 Easy-to-Understand Stats
Avg Daily Change % 4.82%6.59%
Biggest Jump (1 Day) % +221,168.27+0.00
Biggest Drop (1 Day) % -83,311.190.00
Days Above Avg % 46.5%29.9%
Extreme Moves days 2 (2.0%)15 (4.4%)
Stability Score % 100.0%0.0%
Trend Strength % 52.0%54.2%
Recent Momentum (10-day) % +7.67%-4.55%
📊 Statistical Measures
Average Price 261,544.640.00
Median Price 222,458.360.00
Price Std Deviation 72,675.420.00
🚀 Returns & Growth
CAGR % +350.08%-79.86%
Annualized Return % +350.08%-79.86%
Total Return % +49.76%-77.82%
⚠️ Risk & Volatility
Daily Volatility % 11.37%8.84%
Annualized Volatility % 217.24%168.87%
Max Drawdown % -45.61%-91.61%
Sharpe Ratio 0.078-0.007
Sortino Ratio 0.136-0.009
Calmar Ratio 7.675-0.872
Ulcer Index 21.1776.81
📅 Daily Performance
Win Rate % 52.0%45.3%
Positive Days 51154
Negative Days 47186
Best Day % +97.44%+63.91%
Worst Day % -25.47%-31.18%
Avg Gain (Up Days) % +5.51%+6.92%
Avg Loss (Down Days) % -4.12%-5.85%
Profit Factor 1.450.98
🔥 Streaks & Patterns
Longest Win Streak days 56
Longest Loss Streak days 47
💹 Trading Metrics
Omega Ratio 1.4490.979
Expectancy % +0.89%-0.07%
Kelly Criterion % 3.91%0.00%
📅 Weekly Performance
Best Week % +88.01%+64.88%
Worst Week % -13.48%-38.56%
Weekly Win Rate % 62.5%42.3%
📆 Monthly Performance
Best Month % +126.01%+69.39%
Worst Month % -17.27%-49.97%
Monthly Win Rate % 40.0%38.5%
🔧 Technical Indicators
RSI (14-period) 57.9735.88
Price vs 50-Day MA % -4.11%-33.11%
Price vs 200-Day MA % N/A-35.85%

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 WEN (WEN): -0.276 (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
WEN: Kraken