Plain-language explanations of every term used on this site and in our signals — what it means and why it's useful to know. Many terms link to a full lesson in the Academy.
The words used on this site and in our signals. The groups below are the wider vocabulary of the Academy.
One or more buys on a coin, sold together at a gain. A cycle starts with the first buy, may add lower buys as the price falls, and completes when the whole lot is sold above its average cost.
Why it matters: It is the unit Stepgrid's track record counts: cycles completed, cycles in progress. A rung (one buy) is not a result; the cycle is.
The set of buy prices in one signal, each lower than the last. One buy is a rung; the whole set is the ladder.
Why it matters: The lower rungs only fill if the price falls, so a ladder needs money kept in reserve for them — that reserve is what buys the dip.
Dollar-cost averaging: buying in fixed steps as price falls, at pre-planned levels (a 'grid' or 'ladder') decided before price is known, then selling the whole stack at one target.
Why it matters: It converts volatility itself into the source of profit, with no prediction of direction required — at the cost of capital sitting tied up during drawdowns.
Our signal is a message: the coin, the buy prices of the ladder and the sale target. Cornix reads it and places the orders.
Why it matters: Everything in it is a price known at the moment it is posted, so a signal can be checked afterwards against what actually happened — that is what makes the track record possible.
A widely used bot that reads signals from Telegram channels and automatically executes them on your own exchange account through API keys you control.
Why it matters: It trades for you around the clock, on your own exchange account. The access it needs should always be trade-only, never withdrawal-enabled.
Credentials an exchange issues so a program — a trading bot, for instance — can act on an account without a password, placing and managing orders on your behalf.
Why it matters: Exchanges let a key's permissions be scoped narrowly. A trade-only key can place and close orders; a key with withdrawal permission enabled can move funds off the exchange entirely. Those are very different risk levels, and a key connected to any signal bot should never have withdrawals enabled.
Growth applied to a base that already includes prior gains or losses — each period multiplies the account rather than adding to it. An account experiences the product of its returns over time, not their average.
Why it matters: One very bad period permanently cancels multiplication that came before it: a single ×0 in the chain zeroes everything after it, no matter how strong the earlier periods were. It's the arithmetic reason surviving is treated as a precondition for growing, not a separate goal.
Free lesson: Survive first: compounding needs a survivor
The decline from an account's equity peak to the lowest point that follows, usually expressed in percent. Because percentage losses and gains aren't symmetric, the gain needed to recover is always larger than the loss: required gain = drawdown ÷ (1 − drawdown).
Why it matters: A 10% loss needs +11% back; a 50% loss needs +100%; a 90% loss needs +900%. Drawdown, not any single bad trade, is what actually forces people to abandon strategies — position sizing exists to keep it on the shallow part of that curve.
Free lesson: The arithmetic of losses
Running a trading strategy's rules against historical price data to see how it would have performed, before any live signal is published from it.
Why it matters: A backtest can show a rule was consistent with the past; it cannot prove the future will resemble it. Costs, slippage and look-ahead bias — accidentally using information that wasn't actually available at the time — are the most common ways a backtest overstates what a live version would do.
What you're actually holding, and the numbers that describe who else is holding it.
Spot means buying the actual coin — it can sit on the exchange or move to your own wallet, nothing about it expires, and the worst case is the price going to zero. A future is a contract that tracks a coin's price without you owning the coin; traditional futures carry an expiry date, which is the mechanism that forces the contract price and the real price back together. A perpetual future ("perp") is a future with the expiry deleted — it can be held forever, and a recurring payment called funding does the job the expiry date used to do.
Why it matters: Leverage, shorting and liquidation exist only on the contract side of that list. Spot is slow and survivable by construction — no leverage, no liquidation price — which is why it suits someone still learning.
Common misconception: "Futures" doesn't mean a riskier coin. It's the same coin wrapped in a different contract, with a different set of rules for how far and how fast you can lose.
Free lesson: Spot, futures, perpetuals: what you're actually buying
Long means betting a price rises: buy now, aim to sell higher later. Short means betting it falls — on futures, you can sell borrowed exposure first and buy it back cheaper, profiting from the drop.
Why it matters: Shorting makes the down half of the market tradeable. Without it, the only way to make money is a move up.
A payment exchanged every 8 hours between long and short holders of a perpetual future, sized as a small percentage of position value. It exists because perpetuals never expire, so nothing else forces their price back to the real (spot) price: when the perp trades above spot, longs pay shorts; when it trades below, shorts pay longs.
Why it matters: The size of the rate is a real-money read on crowding. The more extreme it is, the more one side is paying to stay in the trade — and the more lopsided that side of the market has become.
Common misconception: Positive funding is not a sell signal by itself. It's normal, sometimes for months, in a genuinely rising market — a crowding gauge, most informative at its extremes, and even extremes resolve more than one way.
Free lesson: Funding: the price of a crowded trade
The total number of futures contracts currently open on a market. Every contract pairs one long with one short, so OI counts live pairs of opposing bets: it rises when a new buyer meets a new seller, and falls when an existing long and an existing short both close.
Why it matters: Price alone tells you what happened; price next to OI tells you who did it. A rally on rising OI is new money opening longs; the same rally on falling OI is shorts buying their way out (short covering) — different fuel, a different tendency afterwards.
Free lesson: Open interest: reading who's in the pool
The share of futures accounts on an exchange currently positioned long versus short, usually shown as a ratio like 1.8:1.
Why it matters: A crowding gauge, same family as funding: when almost everyone is already long, there is little fresh buying left to push price further, and cascades trigger more easily.
Common misconception: A lopsided ratio is not a standalone reversal trigger — it reads best alongside funding and open interest, not on its own.
A chain reaction where leveraged positions are force-closed one after another. A liquidation is not a warning — it is an automatic market order placed by the exchange at whatever price is available, and that forced trade pushes price further, reaching the next tier of leveraged positions in turn.
Why it matters: The moves it produces overshoot because the selling (or buying) is forced, not chosen: nobody in the chain is weighing value, and order books are thinnest exactly when the flow is heaviest. The same mechanic running upward against shorts is a short squeeze.
Free lesson: Liquidation cascades: how crypto really moves
Historical — CME moved to 24/7 crypto trading on 2026-05-29, so these no longer form. CME is a regulated US futures exchange. Until that date its crypto futures closed over the weekend while crypto itself carried on trading, so Monday's CME chart opened away from Friday's close — that distance was the gap. The unfilled ones from before the cutover are still real price levels, but the phenomenon itself is finished.
Why it matters: Enough traders watch these levels that they are worth knowing about. But the widely-quoted fill rates trace back to articles that do not contain the numbers they are credited with, so measure rather than repeat: on our own history most gaps were eventually revisited — and price revisits ANY level that distance away roughly half the time anyway, purely from ordinary volatility. The gap is a level of interest, not a magnet.
Common misconception: 'Usually fills' is not 'guaranteed to fill soon', and it is not an edge. Some gaps stay open for months, and a fill rate quoted without its baseline tells you almost nothing.
Free lesson: Weekend drift: what happens when the market reopens
A 0-100 crypto sentiment index (alternative.me) combining volatility, volume, social activity and dominance. 0 = extreme fear, 100 = extreme greed.
Why it matters: Useful mainly at the extremes, as contrarian context rather than a trade trigger on its own.
Bitcoin's share of the total crypto market capitalization.
Why it matters: Rising dominance usually means money concentrating in the biggest, most liquid asset; falling dominance typically accompanies broader altcoin strength.
A basket trade that goes long a group of relatively stronger assets and short a group of relatively weaker ones at the same time, sized so a broad market move up or down mostly cancels out between the two sides.
Why it matters: Ordinary spreading across several coins isn't automatically diversified — in a crypto sell-off, correlations rise toward one, and several separate long positions can behave like a single leveraged bet on 'crypto up.' Pairing longs with shorts is one of the few structures that actually reduces that shared exposure.
Free lesson: Five alt longs is one bet
How borrowed exposure works, and the two very different ways an exchange can decide what it's allowed to take from you.
Trading with borrowed exposure: at 10X, $1,000 of your own money (the margin) controls a $10,000 position. Gains and losses are computed on the full $10,000, not the $1,000 posted — profit and loss are both multiplied relative to the stake.
Why it matters: Leverage is the single biggest lever on how fast a losing streak can end an account. The higher it goes, the smaller the adverse move needed to wipe the margin — roughly 100 ÷ leverage, in percent.
Common misconception: Higher leverage is not a 'more confident' or 'better' version of a trade. It changes only how much a given price move costs you — never whether the trade is right.
Free lesson: Leverage, liquidation, and an ordinary Tuesday
A margin mode where each position has its own walled-off collateral. When a position's losses eat that collateral, only that position is force-closed (liquidated) — the rest of the account is untouched.
Why it matters: It caps the damage of any single bad trade at that trade's own margin. The alternative, cross margin, lets a losing position draw on the entire futures wallet — including the margin behind healthy, unrelated positions — until nothing is left.
Common misconception: Cross margin isn't the 'safer' choice because liquidations happen less often under it. That's the trap: it converts many small, survivable failures into one rare, total one.
Free lesson: Margin: isolated vs cross
The minimum collateral a leveraged position must keep to stay open. Losses eat the margin down toward this floor; once they cross it, the exchange liquidates the position — slightly before the collateral technically reaches zero.
Why it matters: It's why 'distance to liquidation ≈ 100 ÷ leverage' is a rough upper bound, not exact: maintenance margin shortens the real distance a little further.
A futures account mode that allows one long AND one short position on the same trading pair at the same time, tracked as separate slots instead of netting into one.
Why it matters: It lets a short-term short coexist with a longer-term long on the same coin instead of the two cancelling out. Only a second position in the same direction is blocked, because it would simply merge with the first.
Deciding the loss before the trade exists, and treating the whole account as the unit that matters — not one position at a time.
A price set in advance at which a losing position closes automatically, capping the loss at a known amount before the trade is ever placed.
Why it matters: The stop is the trade's maximum planned loss, and it's the number a position-sizing calculation needs (risk fraction ÷ stop distance = size). A signal without a stop isn't a plan — it's a hope.
Free lesson: Fixed-fractional sizing: the 1-2% idea
A price set in advance at which a winning position closes automatically, usually sized as a multiple of the stop distance — which is what fixes the trade's reward-to-risk ratio before entry.
Why it matters: Deciding the exit before entry removes the least reliable decision-maker in trading: you, mid-trade, watching the number move.
How much of an account is committed to a single trade. Fixed-fractional sizing sets it by fixing the dollar risk first — a small percentage of the account, commonly 1-2% — then computing size backward from the stop distance: position size = (account × risk fraction) ÷ stop distance.
Why it matters: The size setting, more than entry quality, decides whether a losing streak is survivable. The same run of losses that a 1%-risk account shrugs off can put a 10%-risk account down by more than half — identical trades, identical luck, only the size differs.
Free lesson: Fixed-fractional sizing: the 1-2% idea
The sum of what every open position would lose if all of its stops were hit today, expressed as a percentage of the account. Per-trade sizing caps each position's own risk; nothing caps the total unless heat gets its own limit.
Why it matters: Five careful 2%-risk positions add up to a 10% day before correlation even enters the picture. An account experiences totals, even when every position was individually sized as 'small.'
Free lesson: Portfolio heat: your real position is the sum
How much two assets' prices move together. In calm markets, crypto coins can look loosely correlated — different news, different communities. Under stress, correlations tend to move toward one: when Bitcoin drops hard, most coins drop with it, usually harder.
Why it matters: A handful of altcoin longs isn't automatically diversified. On the day diversification would have paid off, it typically disappears, and the group behaves like one large leveraged bet on 'crypto up' rather than several independent decisions.
Free lesson: Five alt longs is one bet
Cutting position size — temporarily and deliberately — in response to a preset trigger: a drawdown threshold, a run of consecutive losses, a new or untested system, or a market it wasn't built for.
Why it matters: It's the one risk defense that costs nothing, needs no forecast, and is reversible the moment conditions change. Halving the risk fraction roughly doubles how long a losing streak can run before a drawdown budget is spent.
Free lesson: Sizing down: the only free defense
What a chart can and can't tell you, and the vocabulary for describing it precisely instead of by feel.
A measure of how far an asset typically moves per bar (candle), averaged over recent history — one ATR is roughly 'a normal move' for that asset on that timeframe.
Why it matters: Saying a move is '3 ATR' works across coins with completely different price scales; it turns 'big move' from a subjective impression into a number that's comparable across markets.
An average of recent closing prices that weights the newest prices more heavily than older ones, drawn as a smoothed line on a chart.
Why it matters: Price sitting above a rising EMA is the simplest widely used definition of an uptrend — a building block other trend reads are constructed from, not a signal by itself.
The period each candle (bar) on a chart represents — 15 minutes, 1 hour, 4 hours, a day, a week. The same coin, at the same moment, can show a downtrend on one timeframe and an uptrend on another without either chart being wrong.
Why it matters: Conflicting reads across timeframes are the normal state of a market, not an error to resolve — a pullback inside a larger uptrend genuinely is a downtrend on a smaller one. Picking a home timeframe and treating the rest as context (never switching mid-trade to make a losing position look better) is the practical discipline.
Free lesson: Timeframes: one market, many stories
The two broad states a market spends its time in. In a trend, price moves directionally and pullbacks stay shallow; in a range, price oscillates around an anchor and pushes toward the edges tend to stall and rotate back.
Why it matters: Every strategy is effectively built for one of the two — trend tools need price to keep going, range tools need it to come back — and running one in the other's market is a common, purely structural way to lose, independent of execution quality.
Free lesson: Trend or range: the first question
A price area where buying or selling interest tends to cluster — a prior swing low, a round number, an old high many people bought near. It's a band, not a single price, because the decisions that create it don't all land on one tick.
Why it matters: Treating a zone as an exact line manufactures false precision: a stop parked exactly at the line sits at the point of maximum ambiguity, right where ordinary overshoot lives. A zone marks where a reaction would be informative — not a promise that one is coming.
Common misconception: A tested level that gets 'broken' by a hair and then reverses isn't a failed level — it's a line drawn where a zone should have been.
Free lesson: Support and resistance are zones, not lines
Reading price charts and derived indicators to structure a trading decision. It does not predict the future — most classic chart folklore, tested honestly against real data, does little or nothing, and some of it runs backwards from its popular version.
Why it matters: What it reliably does: gives a non-arbitrary place for a stop (which makes sizing computable at all), makes the same chart read the same way every time (so results are reviewable), and gives shared landmarks that enough participants watch to make reactions near them at least explicable.
Free lesson: What technical analysis can and cannot do
How to actually judge a method — and the arithmetic and psychology of staying in the game long enough to find out.
How many trades (or months) a result is based on. Ten trades from a genuinely working method can still show a losing stretch, and a losing method can produce a great-looking ten-trade run — purely from ordinary variance, with no change in the underlying edge.
Why it matters: Verdicts about whether a method works need hundreds of trades, not tens. A screenshot, a hot week, or a single bad month proves close to nothing about the underlying edge by itself.
Free lesson: Ten trades tell you nothing
A run of consecutive losing trades, or a losing month. Every real trading method produces them — a working, unchanged system with a genuine edge still finishes a meaningful share of its own short stretches in the red, by variance alone.
Why it matters: A losing streak by itself is not evidence a method has stopped working — but it is exactly the moment the strongest urge to override or abandon the rules shows up, and overriding at that point replaces a system with a track record for one that has none.
Free lesson: Losing months, and following a system you didn't invent
The average result a trading method produces per trade, combining how often it wins with how much it wins and loses by: (win rate × average win) − (loss rate × average loss), measured in R (see R-multiple). It's the actual scorecard for a method — win rate alone is not.
Why it matters: A system that wins 4 trades in 10 with large winners can out-earn one that wins 7 in 10 with small ones. Frequency of being right is a different question from profitability, and the two can point in opposite directions.
Free lesson: Expectancy: why win rate isn't the score
A trade's result expressed as a multiple of what was risked on it, rather than in dollars or percent. If a trade's stop-loss defines $100 of risk, a trade that hits its stop is −1R; a trade that makes $200 before exit is +2R.
Why it matters: Measuring in R strips out account size and position size, so two trades — or two entire systems — can be compared honestly regardless of how much capital or leverage sat behind either one.
Free lesson: Expectancy: why win rate isn't the score
The share of closed trades that were profitable, out of all closed trades.
Why it matters: Alone it says almost nothing — a 90% win rate with rare, oversized losers can still lose money overall. It's one half of expectancy; read it together with average win and average loss size, never by itself.
Free lesson: Expectancy: why win rate isn't the score
The well-documented tendency to weigh a loss roughly twice as heavily, emotionally, as a same-sized gain — an ordinary feature of human decision-making, not a personal weakness or a sign of an unsuitable temperament.
Why it matters: It's a large part of why a first real trading loss feels like proof of failure even when it's a normal, expected outcome from a working method — a system that wins 4 trades in 10 loses on its very first trade more often than not.
Free lesson: The first loss: why most people quit here
A written record of each trade — what was decided, why, and with what size — logged before the outcome is known. A minimum useful version covers seven fields: date, setup, entry reason, size and risk, exit and its reason, result in R, and the lesson.
Why it matters: It's the tool that separates decision quality from outcome quality. A good decision can lose money and a bad one can get paid; without a written record, that distinction disappears into memory, which quietly rewrites itself to fit how the trade ended.
Free lesson: The journal is the edge you control