PYTH PYTH / PYTH Crypto vs BROCCOLI714 BROCCOLI714 / 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 / PYTHBROCCOLI714 / USD

📈 Performance Metrics

Start Price 1.000.07

End Price 1.000.01

Price Change % +0.00%-80.11%

Period High 1.000.07

Period Low 1.000.01

Price Range % 0.0%420.1%

🏆 All-Time Records

All-Time High 1.000.07

Days Since ATH 343 days234 days

Distance From ATH % +0.0%-80.1%

All-Time Low 1.000.01

Distance From ATL % +0.0%+3.5%

New ATHs Hit 0 times0 times

📌 Easy-to-Understand Stats

Avg Daily Change % 0.00%6.41%

Biggest Jump (1 Day) % +0.00+0.01

Biggest Drop (1 Day) % 0.00-0.03

Days Above Avg % 0.0%40.4%

Extreme Moves days 0 (0.0%)13 (5.6%)

Stability Score % 100.0%0.0%

Trend Strength % 0.0%52.1%

Recent Momentum (10-day) % +0.00%-5.72%

📊 Statistical Measures

Average Price 1.000.03

Median Price 1.000.03

Price Std Deviation 0.000.01

🚀 Returns & Growth

CAGR % +0.00%-91.94%

Annualized Return % +0.00%-91.94%

Total Return % +0.00%-80.11%

⚠️ Risk & Volatility

Daily Volatility % 0.00%9.30%

Annualized Volatility % 0.00%177.77%

Max Drawdown % -0.00%-80.77%

Sharpe Ratio 0.000-0.023

Sortino Ratio 0.000-0.023

Calmar Ratio 0.000-1.138

Ulcer Index 0.0058.28

📅 Daily Performance

Win Rate % 0.0%47.2%

Positive Days 0109

Negative Days 0122

Best Day % +0.00%+43.92%

Worst Day % 0.00%-57.23%

Avg Gain (Up Days) % +0.00%+6.19%

Avg Loss (Down Days) % -0.00%-5.94%

Profit Factor 0.000.93

🔥 Streaks & Patterns

Longest Win Streak days 07

Longest Loss Streak days 07

💹 Trading Metrics

Omega Ratio 0.0000.931

Expectancy % +0.00%-0.22%

Kelly Criterion % 0.00%0.00%

📅 Weekly Performance

Best Week % +0.00%+43.13%

Worst Week % 0.00%-40.93%

Weekly Win Rate % 0.0%44.4%

📆 Monthly Performance

Best Month % +0.00%+69.43%

Worst Month % 0.00%-54.04%

Monthly Win Rate % 0.0%50.0%

🔧 Technical Indicators

RSI (14-period) 100.0043.83

Price vs 50-Day MA % +0.00%-32.61%

Price vs 200-Day MA % +0.00%-53.90%

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 BROCCOLI714 (BROCCOLI714):	0.000 (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
BROCCOLI714: Binance