How to Read Market Structure: HH, HL, LH and LL
Learn how connected swing highs and lows describe trend, transition and invalidation—and why confirmation matters more than prediction.
Read complete guide →Trading.Red Learning Hub
Technical analysis explained without false certainty
Each guide starts with what the tool measures, then explains confirmation, invalidation and failure modes. The goal is to help you question a chart—not merely admire it.
Support, Resistance and Floor Pivot Points
Why support and resistance are areas rather than promises, and how deterministic pivot formulas differ from AI-generated levels.
Read complete guide →Breakouts, Retests and Failed Breakouts
Learn which evidence separates a developing breakout from a confirmed event and why failed moves deserve their own label.
Read complete guide →RSI, MACD, EMA and VWAP Without Indicator Overload
Use indicators as independent evidence instead of counting several versions of the same price movement as separate confirmation.
Read complete guide →Multi-Timeframe Analysis and Risk–Reward
A framework for separating horizons, calculating reward relative to risk and avoiding false precision.
Read complete guide →Volume and ATR: Participation Versus Price Noise
Learn what volume and ATR can confirm, what they cannot predict, and why comparisons must use compatible sessions and timeframes.
Read complete guide →Gaps, Premarket and Extended-Hours Price Context
Understand why a valid overnight price may still be a poor execution reference and how session-aware charts avoid distorted candles.
Read complete guide →Trend, Range and Transition: Reading Market Regimes
Why a model should change its expectations when the market changes character—and why neutral is sometimes the most useful output.
Read complete guide →Market-Data Quality, Splits and the Latest Candle
A chart is only as reliable as its inputs. Learn the checks that prevent one bad candle or split from corrupting every downstream level.
Read complete guide →Backtesting, Calibration and Honest Model Scores
Understand why a model score is not automatically a probability and how saved publications make performance claims testable.
Read complete guide →Our standard for educational content
We distinguish observed facts from model interpretation. We state when data is incomplete, avoid presenting heuristic scores as calibrated probabilities and keep promotional claims out of methodology.
Guides are reviewed when the corresponding product logic changes. Material corrections are made in place; the editorial policy explains how methodology, external sources and automated tools are handled.