PYTH PYTH / FLOW Crypto vs ZIG ZIG / 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 / FLOWZIG / USD

📈 Performance Metrics

Start Price 0.580.10

End Price 0.330.07

Price Change % -42.33%-32.00%

Period High 0.600.13

Period Low 0.280.07

Price Range % 118.4%92.6%

🏆 All-Time Records

All-Time High 0.600.13

Days Since ATH 342 days25 days

Distance From ATH % -44.8%-47.5%

All-Time Low 0.280.07

Distance From ATL % +20.6%+1.2%

New ATHs Hit 1 times5 times

📌 Easy-to-Understand Stats

Avg Daily Change % 2.63%3.68%

Biggest Jump (1 Day) % +0.23+0.02

Biggest Drop (1 Day) % -0.06-0.03

Days Above Avg % 48.5%54.5%

Extreme Moves days 7 (2.0%)4 (7.4%)

Stability Score % 0.0%0.0%

Trend Strength % 55.7%59.3%

Recent Momentum (10-day) % -10.35%-11.61%

📊 Statistical Measures

Average Price 0.390.09

Median Price 0.390.09

Price Std Deviation 0.070.01

🚀 Returns & Growth

CAGR % -44.33%-92.63%

Annualized Return % -44.33%-92.63%

Total Return % -42.33%-32.00%

⚠️ Risk & Volatility

Daily Volatility % 5.38%5.78%

Annualized Volatility % 102.75%110.34%

Max Drawdown % -54.22%-48.08%

Sharpe Ratio -0.008-0.093

Sortino Ratio -0.014-0.097

Calmar Ratio -0.818-1.926

Ulcer Index 37.1524.79

📅 Daily Performance

Win Rate % 44.2%39.6%

Positive Days 15121

Negative Days 19132

Best Day % +76.49%+14.93%

Worst Day % -11.05%-24.63%

Avg Gain (Up Days) % +2.91%+3.96%

Avg Loss (Down Days) % -2.38%-3.51%

Profit Factor 0.970.74

🔥 Streaks & Patterns

Longest Win Streak days 64

Longest Loss Streak days 78

💹 Trading Metrics

Omega Ratio 0.9660.741

Expectancy % -0.04%-0.55%

Kelly Criterion % 0.00%0.00%

📅 Weekly Performance

Best Week % +46.08%+16.63%

Worst Week % -21.06%-19.61%

Weekly Win Rate % 44.2%50.0%

📆 Monthly Performance

Best Month % +40.22%+11.76%

Worst Month % -18.52%-21.94%

Monthly Win Rate % 38.5%66.7%

🔧 Technical Indicators

RSI (14-period) 29.5135.86

Price vs 50-Day MA % -16.73%-27.66%

Price vs 200-Day MA % -6.03%N/A

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 ZIG (ZIG):	0.794 (Strong 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
ZIG: Kraken