PYTH PYTH / IMX Crypto vs XLM XLM / 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

Model:

Ask me anything about the statistics below. I can help explain metrics, identify patterns, or answer specific questions.

Asset PYTH / IMXXLM / USD

📈 Performance Metrics

Start Price 0.320.25

End Price 0.230.25

Price Change % -29.51%-0.53%

Period High 0.420.56

Period Low 0.190.22

Price Range % 125.6%152.9%

🏆 All-Time Records

All-Time High 0.420.56

Days Since ATH 61 days330 days

Distance From ATH % -45.9%-55.9%

All-Time Low 0.190.22

Distance From ATL % +22.1%+11.5%

New ATHs Hit 5 times6 times

📌 Easy-to-Understand Stats

Avg Daily Change % 2.97%3.75%

Biggest Jump (1 Day) % +0.19+0.18

Biggest Drop (1 Day) % -0.05-0.09

Days Above Avg % 45.9%48.0%

Extreme Moves days 7 (2.0%)10 (2.9%)

Stability Score % 0.0%0.0%

Trend Strength % 53.9%50.4%

Recent Momentum (10-day) % +3.97%-10.41%

📊 Statistical Measures

Average Price 0.250.34

Median Price 0.250.33

Price Std Deviation 0.030.08

🚀 Returns & Growth

CAGR % -31.08%-0.56%

Annualized Return % -31.08%-0.56%

Total Return % -29.51%-0.53%

⚠️ Risk & Volatility

Daily Volatility % 6.22%5.94%

Annualized Volatility % 118.87%113.40%

Max Drawdown % -55.68%-60.45%

Sharpe Ratio 0.0080.027

Sortino Ratio 0.0130.034

Calmar Ratio -0.558-0.009

Ulcer Index 30.9540.81

📅 Daily Performance

Win Rate % 45.9%49.4%

Positive Days 157168

Negative Days 185172

Best Day % +90.01%+53.59%

Worst Day % -13.75%-21.91%

Avg Gain (Up Days) % +3.26%+3.88%

Avg Loss (Down Days) % -2.67%-3.47%

Profit Factor 1.031.09

🔥 Streaks & Patterns

Longest Win Streak days 66

Longest Loss Streak days 106

💹 Trading Metrics

Omega Ratio 1.0341.092

Expectancy % +0.05%+0.16%

Kelly Criterion % 0.56%1.20%

📅 Weekly Performance

Best Week % +69.93%+116.61%

Worst Week % -22.76%-16.02%

Weekly Win Rate % 50.0%48.1%

📆 Monthly Performance

Best Month % +61.10%+112.53%

Worst Month % -34.18%-40.30%

Monthly Win Rate % 46.2%23.1%

🔧 Technical Indicators

RSI (14-period) 55.8637.35

Price vs 50-Day MA % -0.09%-28.16%

Price vs 200-Day MA % -5.32%-25.10%

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 XLM (XLM):	-0.147 (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
XLM: Coinbase