PYTH PYTH / SIS 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

Ask me anything about the statistics below. I can help explain metrics, identify patterns, or answer specific questions.
Asset PYTH / SISRENDER / USD
📈 Performance Metrics
Start Price 4.014.21
End Price 1.902.00
Price Change % -52.54%-52.56%
Period High 4.5610.49
Period Low 1.422.00
Price Range % 221.2%425.1%
🏆 All-Time Records
All-Time High 4.5610.49
Days Since ATH 340 days311 days
Distance From ATH % -58.3%-81.0%
All-Time Low 1.422.00
Distance From ATL % +33.8%+0.0%
New ATHs Hit 3 times15 times
📌 Easy-to-Understand Stats
Avg Daily Change % 4.63%4.24%
Biggest Jump (1 Day) % +1.54+1.42
Biggest Drop (1 Day) % -0.71-1.35
Days Above Avg % 44.8%28.8%
Extreme Moves days 9 (2.6%)15 (4.4%)
Stability Score % 0.0%0.0%
Trend Strength % 51.3%51.3%
Recent Momentum (10-day) % -9.33%-15.90%
📊 Statistical Measures
Average Price 2.574.78
Median Price 2.434.04
Price Std Deviation 0.671.80
🚀 Returns & Growth
CAGR % -54.76%-54.78%
Annualized Return % -54.76%-54.78%
Total Return % -52.54%-52.56%
⚠️ Risk & Volatility
Daily Volatility % 7.56%5.61%
Annualized Volatility % 144.37%107.17%
Max Drawdown % -68.86%-80.96%
Sharpe Ratio 0.004-0.010
Sortino Ratio 0.005-0.010
Calmar Ratio -0.795-0.677
Ulcer Index 46.1356.17
📅 Daily Performance
Win Rate % 48.7%48.7%
Positive Days 167167
Negative Days 176176
Best Day % +88.45%+23.05%
Worst Day % -19.58%-30.41%
Avg Gain (Up Days) % +4.80%+4.26%
Avg Loss (Down Days) % -4.50%-4.16%
Profit Factor 1.010.97
🔥 Streaks & Patterns
Longest Win Streak days 97
Longest Loss Streak days 77
💹 Trading Metrics
Omega Ratio 1.0120.973
Expectancy % +0.03%-0.06%
Kelly Criterion % 0.13%0.00%
📅 Weekly Performance
Best Week % +47.45%+34.12%
Worst Week % -21.19%-24.59%
Weekly Win Rate % 50.0%53.8%
📆 Monthly Performance
Best Month % +52.70%+111.11%
Worst Month % -38.78%-29.02%
Monthly Win Rate % 30.8%30.8%
🔧 Technical Indicators
RSI (14-period) 49.5417.64
Price vs 50-Day MA % -13.04%-42.88%
Price vs 200-Day MA % -12.30%-47.53%
💰 Volume Analysis
Avg Volume 26,317,609280,694
Total Volume 9,053,257,64796,558,789

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): 0.621 (Moderate positive)

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