Glossary
Learning Center
Plain-English explanations of the concepts, metrics, and market structure ideas used across MPM Markets.
MPM Concepts
The core ideas behind the Market Probability Model — what an MPM Zone is, and how MPM measures whether price reaches a zone and how it reacts once there. Everything else in the Learning Center builds on these concepts, which describe measured historical behaviour rather than predictions.
- Market ProbabilityMarket Probability measures how price has historically behaved around statistically derived MPM Zones. Learn how these historical frequencies are measured, calibrated, and interpreted—and why they represent evidence, not predictions or trading signals.
- MPM ZoneUnderstanding the statistically derived price areas used throughout the MPM research framework.
- Reach ProbabilityReach Probability measures how frequently price has historically arrived at a published MPM Zone during the trading session. It answers the question "How often did price get there?" — not what happened after arrival.
- Reaction ProbabilityUnderstanding how often a published MPM Zone historically held after price reached it.
Market Structure
The frameworks traders use to make sense of price — trends, ranges, the areas where price repeatedly reacts, and the conditions those behaviours occur in. These pages explain each concept in plain terms, then show how MPM measures the parts of market structure that can be measured, rather than read by eye.
- Market Structure in TradingMarket structure is the framework traders use to make sense of price — the highs, lows, and areas that give an otherwise continuous stream of prices its shape. This page explains what market structure is, the pieces it's built from, the mistakes people make reading it, and how MPM turns that structure into something it can measure rather than eyeball.
- Mean ReversionMean reversion is the tendency for price to move back toward a typical level after stretching unusually far from it. This page explains what it means, when it has and hasn't held historically, and how MPM measures it.
- Momentum in TradingMomentum is the tendency for price that has been moving strongly in one direction to keep moving that way, rather than turning back. This page explains what momentum is, how it differs from mean reversion, the mistakes people make trading it, and how MPM measures behaviour around its zones without trying to predict whether a move will continue.
- Range in TradingA range is a market going sideways — price bouncing between a floor and a ceiling without making sustained progress in either direction. This page explains what a range is, how it differs from a trend, the mistakes people make trading ranges, and how MPM measures behaviour at the boundaries of a range without predicting when it will break.
- Support and Resistance in TradingSupport and resistance are price areas where the market has reacted again and again in the past — support below, where falling prices have tended to slow or stop, and resistance above, where rising prices have tended to stall. This page explains what they are, why they form, the mistakes people make with them, and how MPM measures what actually happens around them.
- Trend in TradingA trend is a market that keeps moving in one direction — a staircase of higher highs and higher lows on the way up, or lower highs and lower lows on the way down. This page explains what a trend is, how to tell one is intact versus over, the mistakes people make trading trends, and how MPM measures behaviour around its zones without trying to call the trend itself.
- Volatility Regime in TradingA volatility regime is the market's current "temperature" — whether price is moving in small, contained steps (a calm regime) or large, fast swings (a turbulent one). This page explains what volatility and volatility regimes are, why the distinction matters, the mistakes people make around it, and how MPM treats the regime as context for measuring behaviour around its zones rather than as something it predicts.
Trading Statistics
The numbers used to judge whether a trading strategy actually works — and whether its results can be trusted. No single metric tells the whole story: profitability, risk, and the quality of the evidence behind a figure all matter together. These pages explain the core measures and, just as importantly, how each one can mislead when read alone.
- Expectancy in TradingExpectancy — the trading world's name for expected value (EV) — is the average amount a strategy wins or loses per trade, over many trades. It's the single number that answers the question win rate can't: does this strategy actually make money? This page explains what expectancy is, how it combines win rate and win/loss size into one figure, the mistakes people make with it, and how MPM treats it as a core part of honest evaluation.
- How to Evaluate a Trading StrategyNo single number tells you whether a trading strategy works. A high win rate can hide losses; a great average can hide a ruinous drawdown; any figure can be a fluke if it rests on too few trades. Evaluating a strategy honestly means asking a sequence of questions — about the evidence, the profitability, the risk, and the survivability — and letting the answers work together. This page walks through that sequence and links to the detailed explanation of each measure.
- Maximum Drawdown in TradingMaximum drawdown is the largest drop from a peak to a low point that a strategy or account has suffered — the deepest hole it fell into before recovering. It answers a question the profitability metrics don't: not 'does this make money?' but 'how painful was the worst stretch?' This page explains what maximum drawdown is, why it matters as much as profit, the mistakes people make with it, and how MPM treats risk as an inseparable part of honest evaluation.
- Profit Factor in TradingProfit factor is a single number that compares everything a strategy won against everything it lost. A profit factor above 1 means the strategy made money over the tested period; below 1 means it lost. This page explains what profit factor is, how it relates to win rate and expectancy, the mistakes people make with it, and how MPM treats it as one part of a complete evaluation rather than a headline.
- Reward-to-Risk Ratio in TradingThe reward-to-risk ratio compares how much a trade aims to make against how much it risks to lose. A ratio of 2 means the potential reward is twice the potential loss. Together with win rate, it's one of the two numbers that decide whether a strategy makes money. This page explains what the ratio is, how it trades off against win rate, the mistakes people make with it, and how MPM treats it as one input to a complete evaluation.
- Risk of Ruin in TradingRisk of ruin is the probability that a series of losses wipes out an account — or drops it below the point where it can keep trading — before a strategy's edge has a chance to play out. It's the question that sits beneath every other metric: not 'does this make money on average?' but 'could a bad run end the game first?' This page explains what risk of ruin is, why even a profitable strategy can carry it, the mistakes people make, and how MPM treats survival as a precondition for everything else.
- Sample Size in TradingSample size is simply how many trades — or how many observations — a statistic is based on. It's the least glamorous number in trading and arguably the most important, because every other metric is only as trustworthy as the sample behind it. This page explains what sample size is, why a small sample can make almost any result look good, the mistakes people make with it, and why sample size sits at the centre of how MPM decides whether a number counts as evidence.
- Win Rate in TradingWin rate is the percentage of trades that end in a profit. It's one of the most quoted numbers in trading — and one of the most misunderstood. This page explains what win rate is, why a high win rate does not mean a profitable strategy, the mistakes people make with it, and why MPM treats win rate as only one small piece of a larger picture rather than a headline figure.
Statistics
The statistical building blocks HIE (the Historical Intelligence Engine) uses to turn plain-English market questions into precise, disclosed, testable definitions — z-scores, percentiles, sample-size discipline, and the assumptions behind them.
- Mean (Average)The mean is the plain average — add up a set of numbers and divide by how many there are. It's the single most familiar statistic in the world, and in markets it's the backbone of the "moving average": the average price over the last N bars, recalculated as each new bar arrives. This page explains what the mean is, why "compared to its own average" is such a useful question, how HIE computes it, and the mistakes people make reading it.
- MedianThe median is the middle value — line your numbers up from smallest to largest, and the median is the one in the middle. It answers "what's normal?" better than the average often can, because it isn't thrown off by a few unusually big or small values. It just reports the middle of the pack. This page explains what the median is, why it's often more honest than the mean, how HIE computes it, and the mistakes people make reading it.
- PercentileIs today's trading volume high? A percentile answers that in plain terms: it tells you what share of recent readings were lower than today's. If volume is in the 95th percentile, today was higher than 95% of recent sessions — genuinely elevated. If it's in the 40th percentile, it was fairly ordinary. This page explains what a percentile is, why it's often more honest than an average, how HIE computes it, and the mistakes people make reading it.
- Rolling Maximum & MinimumThe rolling maximum is the highest value over a recent stretch of bars; the rolling minimum is the lowest. "Volume at its 250-bar maximum" simply means today's volume is the highest it's been in the last 250 bars. They're the most direct way to ask "is this the most extreme reading lately?" — and in HIE there's one honest detail about price highs and lows that's worth understanding. This page explains what rolling max and min are, how HIE computes them, and where a subtle routing choice keeps one concept to one computation.
- Standard DeviationStandard deviation measures how spread out a set of numbers is — how far, on average, readings tend to sit from the middle. When it's small, the values stay close together (a calm, steady market). When it's large, they're all over the place (a volatile, jumpy market). It's one of the most widely used measures of "how spread out" anything is — and it's the building block a z-score is made from. This page explains what standard deviation is, why it matters, how HIE computes it, and the mistakes people make reading it.
- Z-ScoreToday's move is bigger than usual — but how much bigger? A z-score answers exactly that, by measuring a value against its own recent history and expressing the gap in standard-deviation units. A z-score of 0 means "exactly average"; +2 means "two standard deviations above average — an unusually high reading"; −2 means "unusually low." It's one of the cleanest ways to turn a raw number into a statement about how rare it is. This page explains what a z-score is, why it's useful, how HIE computes it, and the mistakes people make reading it.
Methodology
How MPM decides whether a number counts as evidence. These pages cover the research disciplines — out-of-sample testing, holdouts, and the honest-refusal posture — that separate a real finding from a curve-fit coincidence.
- Consistency ScoreBefore you trust any statistic, there's a question worth asking that almost no tool asks: should this number exist at all? MPM's Consistency Score is the badge on every HIE report that answers it. It doesn't grade the pattern you asked about — it grades the ground you're standing on: whether statistics measured on this market and timeframe have a track record of holding up on data they were never measured on. This page explains what the badge means, why it exists, and the important two-layer distinction behind it.
- Out-of-Sample TestingIt is the single most important idea in all of trading research, and one of the easiest to explain badly. In one sentence: in-sample is the data you used to find a pattern; out-of-sample is data the pattern never saw — and only the second one can tell you whether you found something real. This page explains why that distinction decides whether a statistic is worth anything, and exactly how every HIE report puts it to work.
- Reading an HIE Report: Different Ways to Use the Data, Charts, and StatisticsA guide to the different ways the data inside an HIE report — the path statistics, the charts, the out-of-sample check — can be read and used. This is not a strategy, not a signal, and not advice. It explores how the numbers in a report can be interpreted, so you can make more of your own information. Every decision remains entirely yours.
Indicators & Measurements
- Average Directional Index (ADX)ADX measures trend strength on a 0–100 scale, independent of direction. High ADX = strong trend (either way); low ADX = ranging. MPM discloses the exact ADX threshold behind "strong trend" and reports it as context, never as a signal.
- Average True Range (ATR)ATR measures how much a market typically moves per bar. It's a ruler for volatility, not a signal — and MPM uses it as a distance unit so 'stretched' or 'far from a level' means the same thing across markets.
- Bollinger BandsBollinger Bands = moving average ± N standard deviations. They describe relative position within recent volatility. A band touch is not a reversal signal. MPM maps band position to a disclosed value and reports history around it.
- Relative Strength Index (RSI)RSI is a momentum oscillator (0–100). "Overbought" and "oversold" describe momentum, not reversals — markets can sit at those extremes for a long time. MPM discloses the exact threshold and reports historical behaviour, never a signal.