PYTH PYTH / FORTH Crypto vs A A / FORTH 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 / FORTHA / FORTH

📈 Performance Metrics

Start Price 0.100.22

End Price 0.040.10

Price Change % -58.01%-53.15%

Period High 0.100.22

Period Low 0.040.10

Price Range % 157.6%116.2%

🏆 All-Time Records

All-Time High 0.100.22

Days Since ATH 343 days117 days

Distance From ATH % -58.0%-53.2%

All-Time Low 0.040.10

Distance From ATL % +8.2%+1.3%

New ATHs Hit 0 times0 times

📌 Easy-to-Understand Stats

Avg Daily Change % 4.02%2.17%

Biggest Jump (1 Day) % +0.04+0.02

Biggest Drop (1 Day) % -0.02-0.03

Days Above Avg % 48.0%55.9%

Extreme Moves days 9 (2.6%)7 (6.0%)

Stability Score % 0.0%0.0%

Trend Strength % 54.2%57.3%

Recent Momentum (10-day) % -3.01%-6.26%

📊 Statistical Measures

Average Price 0.050.16

Median Price 0.050.17

Price Std Deviation 0.010.03

🚀 Returns & Growth

CAGR % -60.28%-90.61%

Annualized Return % -60.28%-90.61%

Total Return % -58.01%-53.15%

⚠️ Risk & Volatility

Daily Volatility % 7.99%3.43%

Annualized Volatility % 152.56%65.45%

Max Drawdown % -61.18%-53.75%

Sharpe Ratio 0.002-0.171

Sortino Ratio 0.003-0.152

Calmar Ratio -0.985-1.686

Ulcer Index 43.9031.83

📅 Daily Performance

Win Rate % 45.6%42.2%

Positive Days 15649

Negative Days 18667

Best Day % +101.56%+10.90%

Worst Day % -35.54%-18.51%

Avg Gain (Up Days) % +4.46%+1.87%

Avg Loss (Down Days) % -3.71%-2.39%

Profit Factor 1.010.57

🔥 Streaks & Patterns

Longest Win Streak days 54

Longest Loss Streak days 810

💹 Trading Metrics

Omega Ratio 1.0090.573

Expectancy % +0.02%-0.59%

Kelly Criterion % 0.11%0.00%

📅 Weekly Performance

Best Week % +67.10%+7.18%

Worst Week % -33.96%-13.42%

Weekly Win Rate % 46.2%36.8%

📆 Monthly Performance

Best Month % +45.57%+-3.15%

Worst Month % -25.15%-14.30%

Monthly Win Rate % 23.1%0.0%

🔧 Technical Indicators

RSI (14-period) 40.4734.17

Price vs 50-Day MA % -15.99%-17.90%

Price vs 200-Day MA % -19.54%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 A (A):	0.284 (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
A: Kraken