KDE Analysis

KDE Probability Analysis

Kernel Density Estimation & Backtesting on Close prices

Ticker:

Last Close: -

Number years of data:

Holding Trading Days:

Target Price ($):

Current Price: - Required Return: -

Kernel Density Estimation (KDE) Method: This tool uses advanced statistical modeling to estimate the probability distribution of future price movements based on historical data. Unlike Monte Carlo simulations, KDE provides a smooth, continuous probability density function that captures the underlying distribution of returns.

How it works: KDE creates a smooth probability distribution from historical returns. This distribution is used to calculate the likelihood of future price movements and backtest predictions against historical data.

Disclaimer: Past performance is not indicative of future results. Math-trading.com is not a financial advisor. This tool is for educational purposes only and should not be considered as financial advice or an inducement to trade.

KDE Return Distribution

Probability of Exceeding Target Price

0.0%

Probability Below: 0.0%

Chance that the price reaches at least $0.00

Backtest Realized Frequency

0.0%

Difference: 0.0%

Actual frequency over 0 years

Backtest Results with Brier Score

Probability to backtest

0.0%

Realized Frequency

0.0%

In-sample Brier Score

0.0000

Out-of-sample Brier Score

0.0000

Backtest Period

Detailed Metrics

Probability Prediction:

Backtest Results:

Model Reliability:

Brier Score Interpretation:

0.0: Perfect prediction

0.25: Random prediction

1.0: Worst prediction

Lower scores indicate better predictive accuracy

Prediction Statistics:

Analysis Parameters: