PYTH PYTH / IP Crypto vs RENDER RENDER / 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 / IPRENDER / USD

📈 Performance Metrics

Start Price 0.0310.29

End Price 0.031.60

Price Change % +17.40%-84.43%

Period High 0.0410.29

Period Low 0.011.57

Price Range % 293.8%557.0%

🏆 All-Time Records

All-Time High 0.0410.29

Days Since ATH 190 days343 days

Distance From ATH % -27.0%-84.4%

All-Time Low 0.011.57

Distance From ATL % +187.6%+2.3%

New ATHs Hit 19 times0 times

📌 Easy-to-Understand Stats

Avg Daily Change % 4.84%4.21%

Biggest Jump (1 Day) % +0.02+0.89

Biggest Drop (1 Day) % -0.01-1.35

Days Above Avg % 59.8%33.1%

Extreme Moves days 7 (2.8%)13 (3.8%)

Stability Score % 0.0%0.0%

Trend Strength % 47.6%53.9%

Recent Momentum (10-day) % +1.22%-8.71%

📊 Statistical Measures

Average Price 0.034.26

Median Price 0.033.85

Price Std Deviation 0.011.71

🚀 Returns & Growth

CAGR % +26.40%-86.18%

Annualized Return % +26.40%-86.18%

Total Return % +17.40%-84.43%

⚠️ Risk & Volatility

Daily Volatility % 9.08%5.60%

Annualized Volatility % 173.42%106.97%

Max Drawdown % -74.60%-84.78%

Sharpe Ratio 0.045-0.068

Sortino Ratio 0.064-0.067

Calmar Ratio 0.354-1.017

Ulcer Index 37.2260.90

📅 Daily Performance

Win Rate % 47.8%45.9%

Positive Days 119157

Negative Days 130185

Best Day % +93.40%+25.51%

Worst Day % -33.73%-30.41%

Avg Gain (Up Days) % +5.83%+4.19%

Avg Loss (Down Days) % -4.54%-4.26%

Profit Factor 1.170.83

🔥 Streaks & Patterns

Longest Win Streak days 77

Longest Loss Streak days 117

💹 Trading Metrics

Omega Ratio 1.1750.834

Expectancy % +0.41%-0.38%

Kelly Criterion % 1.57%0.00%

📅 Weekly Performance

Best Week % +48.24%+27.55%

Worst Week % -28.17%-24.59%

Weekly Win Rate % 54.1%48.1%

📆 Monthly Performance

Best Month % +22.34%+20.99%

Worst Month % -45.51%-34.15%

Monthly Win Rate % 60.0%30.8%

🔧 Technical Indicators

RSI (14-period) 58.2743.97

Price vs 50-Day MA % +21.68%-31.88%

Price vs 200-Day MA % +18.64%-54.23%

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 RENDER (RENDER):	0.129 (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
RENDER: Kraken