Thème

Statistiques

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Volatility in plain words
Linear vs log scale: when the y-axis misleads
Returns: simple, log and percentage points
Standard deviation: volatility as a number
Probability for analysts: base rates first
Inflation reports decoded: CPI and PCE
Distributions and fat tails, in pictures
Expectancy: the average outcome of a rule
Risk-reward and win rate, together
Reading a backtest report: metrics that matter
Y-axis playground: linear vs log
When averages disagree: arithmetic vs geometric
Jobs reports decoded
GDP and PMIs: two growth snapshots
Correlation without the confusion
Random walks can draw convincing patterns
Compounding and the arithmetic of losses
Nominal vs real: adjusting for inflation
Sample size: how many trades is enough?
R-multiples: outcomes in units of risk
Trend, noise or bounce? Guess the process
Reading a Monte Carlo fan
Market regimes: trending, ranging, calm, wild
Benchmarks: choosing the right yardstick
Three indicators agreeing can be one opinion
Volatility measures compared: ATR, stdev, HV, chop
Z-scores and percentile ranks
Bayesian thinking for analysts
Sharpe, Sortino and Calmar, side by side
Pandas time-series essentials for analysts
Build a non-redundant indicator panel
Crypto and macro: rates, liquidity and correlation
Regression basics: lines through noisy data
Autocorrelation: does today echo yesterday?
Quant formulas cheat sheet
Diversification and correlation
The yield curve: shapes and what they describe
Risk-on, risk-off: reading cross-asset moods
Statistical significance without the jargon
Stationarity: prices vs returns
Momentum vs mean reversion: the statistical view
Volatility clustering and calm-storm regimes
Sample size calculator for strategy metrics
Calendar and seasonal rules
Risk of ruin, illustrated
Machine learning for markets: what it can and can't do
Do stocks and bonds always move opposite?
Confidence intervals for strategy metrics
A small p-value is not a strategy
Combining signals without an echo chamber
The Kelly criterion: elegant math, fragile inputs
Risk-of-ruin simulator
Ranking screens: relative strength and percentiles
Features and labels: framing an ML question
The golden ratio in markets: history vs evidence
Bootstrapping: resampling your own results
Baselines: the model to beat is simple
Benchmarks and null strategies: beat something simple
Multiple testing: why many discoveries fail
The deflated Sharpe ratio, made intuitive
Many strategies, one dataset: selection bias
Efficient and adaptive markets: the debate
Normalising indicators: z-scores and percent ranks
Business cycles are labelled with hindsight
Trend quality: slope, efficiency and persistence
Linear regression tools: slope, channel and fit
Principal components: hidden common drivers
State-space models and the Kalman filter
Factor models: explaining returns with exposures
Value at Risk and expected shortfall
Reading published pattern statistics critically
Event study: measuring reactions to a release
Cointegration is not correlation
Volatility models (GARCH), conceptually
Risk contributions and weighting schemes
Cycles and seasonality: patterns in the calendar
Correlation explorer: relationships across regimes
Monte Carlo simulation: what could have happened
Probability of backtest overfitting (PBO)
Precision, recall and calibration for market models
Feature importance is not causation

Statistiques

Débutant

A pattern's match score is not a probability