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Showing 18 articles in #statistics

#statistics

Standard Deviation, Volatility, and Beta Calculations

Calculate standard deviation, annualized volatility, and Beta correctly for trading, with the common errors and the right windows for each measure.

#statistics#quantitative

Return Distributions: Normal, Lognormal, and Fat Tails

Distinguish normal, lognormal, and fat-tailed return distributions, test which fits your market, and adjust risk sizing for the tail behavior that breaks models.

#statistics#quantitative

Monte Carlo Simulation Implementation for Trading

Implement Monte Carlo simulation for trading in Python using bootstrap and parametric methods, with code patterns and the pitfalls that invalidate results.

#statistics#quantitative

Hypothesis Testing and Sample Size for Trading

Apply hypothesis testing and power analysis to trading research, determine minimum trade counts, and control type I and II errors when evaluating edges.

#statistics#quantitative

Expectancy and System Evaluation Metrics

Evaluate trading systems beyond expectancy with MAR, Calmar, Sortino, and profit factor, learning threshold values and which metrics to combine for decisions.

#statistics#quantitative

Correlation and Cointegration in Pairs Trading

Distinguish correlation from cointegration for pairs trading, run the Engle-Granger test, estimate half-life, and build a mean-reverting spread with entry rules.

#statistics#quantitative

Bayesian Updating for Trading Expectations

Apply Bayesian updating to revise trading edge estimates as new trades arrive, with a conjugate beta model and concrete shrinkage rules for live trading.

#statistics#quantitative

Time Series Basics: Autocorrelation and Stationarity

Price is a time series, and most trading models assume things about it that often aren't true. Learn autocorrelation and stationarity before you trust any indicator.

#statistics#quantitative

Standard Deviation: The Root of Bollinger Bands

Bollinger Bands look like magic, but they're built on one number: standard deviation. Learn the math and how volatility bands really behave.

#statistics#quantitative

Skewness, Kurtosis, and Fat Tail Risk

Skewness and kurtosis measure the shape of returns beyond mean and variance. Learn to read them to spot markets where the bell curve is a dangerous lie.

#statistics#quantitative

Linear Regression and Trend Quantification

Linear regression turns a chart full of noise into a single line that quantifies trend. Learn the math, the slope, and R-squared, and how traders use them.

#statistics#quantitative

Probability Basics: Odds, Expectancy, and Frequency

Trading is decision-making under uncertainty. Learn the probability concepts — odds, expected value, and frequency — that turn guessing into edge.

#statistics#quantitative