Technical analysis reference
76 crypto trading indicators, explained as rules you can test.
Gimmer combines 76 public technical indicators with five current strategy engines, creating more than 80 signal-building tools. This reference explains what every indicator measures, how crypto traders commonly apply it, and where it can fail.
How Gimmer evaluates technical indicators
The Indicators engine transforms historical OHLCV data from the configured exchange, crypto pair, and candle interval. Gimmer evaluates the completed candle, combines the selected rules, applies any configured inversion, and passes the resulting decision to a separate execution and risk layer.
- Choose one market thesis. Decide whether the strategy is following trend, trading momentum, responding to volatility, or testing a range.
- Select independent evidence. Combining three moving averages does not provide three independent confirmations.
- Configure the exact window. A 14-period calculation means something different on 15-minute, four-hour, and daily crypto candles.
- Backtest complete market regimes. Include trends, ranges, liquidations, quiet periods, and realistic fees.
- Observe simulation ticks. Confirm each selected pair evaluates after every expected closed candle before considering live execution.
An indicator describes historical data. It does not predict a guaranteed outcome.
Thresholds, crosses, divergence, overbought readings, and volatility bands can all fail. Use position sizing and risk controls independently of the indicator signal.
Complete Gimmer indicator catalog
Search by acronym, full name, calculation, crypto trading use, or risk.
No indicator matches that filter. Try an acronym such as MACD or a concept such as trend, volume, momentum, or volatility.
24 indicators
Trend and moving averages
Tools for direction, trend strength, adaptive smoothing, and price relationships across a chosen candle interval.
ADX Average Directional Movement Index
ADX: Average Directional Movement Index for crypto trading
What it measures. ADX estimates trend strength from directional movement and true range. It describes intensity, not whether the market is moving up or down.
How crypto traders use it. Crypto traders can use ADX as a regime filter, allowing breakout or trend-following rules only after strength rises above a tested level.
Risk and limitation. A high reading can arrive after much of a move has occurred, while a falling reading does not by itself predict a reversal.
ADXR Average Directional Movement Rating
ADXR: Average Directional Movement Rating for crypto trading
What it measures. ADXR smooths ADX by comparing the current value with an earlier value, producing a slower view of persistent trend strength.
How crypto traders use it. It can reduce reactions to one-candle volatility spikes when a bot needs confirmation that a Bitcoin or altcoin trend is sustained.
Risk and limitation. Extra smoothing adds lag. Fast crypto reversals may develop before ADXR reflects the new regime.
AROON Aroon
AROON: Aroon for crypto trading
What it measures. Aroon Up and Aroon Down measure how recently a market printed its highest high and lowest low within the lookback window.
How crypto traders use it. The pair can help distinguish a fresh directional structure from a range and can confirm breakouts in continuously traded crypto markets.
Risk and limitation. Repeated local highs and lows in a choppy market can make the lines switch quickly and generate whipsaws.
AROONOSC Aroon Oscillator
AROONOSC: Aroon Oscillator for crypto trading
What it measures. The Aroon Oscillator subtracts Aroon Down from Aroon Up, compressing recency-based trend information into one positive or negative series.
How crypto traders use it. A strategy can test zero-line crosses or extreme readings as confirmation that recent highs dominate recent lows, or the reverse.
Risk and limitation. It measures timing of extremes rather than distance moved, so a small noisy breakout can resemble a meaningful trend change.
DEMA Double Exponential Moving Average
DEMA: Double Exponential Moving Average for crypto trading
What it measures. DEMA combines an EMA with an EMA of that EMA to reduce some of the delay found in a conventional moving average.
How crypto traders use it. It is useful when a crypto bot needs a more responsive trend baseline or faster price crossover than a similarly sized EMA.
Risk and limitation. Greater responsiveness also increases sensitivity to short-lived moves, spreads, and thin-market noise.
DI Directional Indicator
DI: Directional Indicator for crypto trading
What it measures. DI produces positive and negative directional lines from upward and downward movement relative to true range.
How crypto traders use it. Traders commonly compare +DI and -DI to express directional control, often pairing the result with ADX for strength confirmation.
Risk and limitation. Crosses can cluster during consolidation, and their usefulness changes materially with the candle interval and lookback.
DM Directional Movement
DM: Directional Movement for crypto trading
What it measures. Directional Movement separates positive and negative changes in candle extremes before they are normalized into related DMI indicators.
How crypto traders use it. A crypto strategy can compare the two series to inspect whether buyers or sellers are extending the recent trading range.
Risk and limitation. Large wicks, price gaps, and exchange-specific candles can dominate the calculation without creating a durable trend.
DX Directional Movement Index
DX: Directional Movement Index for crypto trading
What it measures. DX measures the separation between positive and negative directional indicators as a percentage of their combined magnitude.
How crypto traders use it. It can identify candles where directional imbalance becomes pronounced before a strategy applies additional entry or exit rules.
Risk and limitation. DX is volatile before ADX smoothing and can overreact to isolated range expansion.
EMA Exponential Moving Average
EMA: Exponential Moving Average for crypto trading
What it measures. EMA weights recent closes more heavily than older closes, creating a continuously updated estimate of the current price trend.
How crypto traders use it. Crypto bots often use price-to-EMA or fast-to-slow EMA crosses for trend filters, entries, exits, and dynamic support or resistance rules.
Risk and limitation. EMA remains a lagging transformation of price and can reverse repeatedly when the market has no clear direction.
HMA Hull Moving Average
HMA: Hull Moving Average for crypto trading
What it measures. HMA combines weighted moving averages to create a smooth line designed to react faster than many traditional averages.
How crypto traders use it. Its slope and price relationship can help a bot follow directional moves while limiting some of the visual lag of an SMA.
Risk and limitation. A smooth appearance is not predictive. Rapid slope changes in volatile altcoins can still produce frequent false transitions.
ICHIMOKU Ichimoku Cloud
ICHIMOKU: Ichimoku Cloud for crypto trading
What it measures. Ichimoku combines conversion, base, leading, and lagging relationships to describe trend, momentum, and projected support or resistance.
How crypto traders use it. A crypto strategy can require agreement between price, the cloud, and line direction before treating a move as a qualified trend.
Risk and limitation. Its components use shifted windows and can become ambiguous in ranges. Every rule must define exactly which lines and timestamps are compared.
KAMA Kaufman Adaptive Moving Average
KAMA: Kaufman Adaptive Moving Average for crypto trading
What it measures. KAMA changes its smoothing speed according to price efficiency, reacting faster in directional markets and slower during noise.
How crypto traders use it. It can provide an adaptive baseline for crypto assets that alternate between strong trends and irregular 24-hour consolidation.
Risk and limitation. The efficiency calculation depends on the selected window, and sudden crypto shocks can still leave the average behind price.
LINREG Linear Regression
LINREG: Linear Regression for crypto trading
What it measures. Linear Regression fits a straight line to recent prices and returns the estimated value at the end of the selected window.
How crypto traders use it. Bots can compare price with the fitted trend or use the regression value as a statistically defined moving baseline.
Risk and limitation. A straight-line model can be misleading during parabolic moves, structural breaks, and strongly curved recoveries.
LINREGSLOPE Linear Regression Slope
LINREGSLOPE: Linear Regression Slope for crypto trading
What it measures. Linear Regression Slope measures the rate and direction of the best-fit line across the lookback period.
How crypto traders use it. It can turn crypto trend direction into an explicit numerical rule, such as requiring a positive slope before a long entry.
Risk and limitation. Slope magnitude depends on asset price scale and timeframe, so thresholds should not be copied blindly between crypto pairs.
PSAR Parabolic Stop and Reverse
PSAR: Parabolic Stop and Reverse for crypto trading
What it measures. PSAR places an accelerating trailing series above or below price and changes sides when its reversal condition is reached.
How crypto traders use it. It can support directional filters or systematic trailing exits during sustained Bitcoin and altcoin trends.
Risk and limitation. Sideways markets can cause repeated reversals, and a displayed PSAR level does not guarantee an exchange fill at that price.
SMA Simple Moving Average
SMA: Simple Moving Average for crypto trading
What it measures. SMA calculates the arithmetic mean of prices across a fixed number of closed candles.
How crypto traders use it. It provides a transparent crypto-market baseline for trend direction, mean-reversion distance, or fast-and-slow moving-average systems.
Risk and limitation. Every observation has equal weight, which creates lag and can hide a sudden regime change until several new candles arrive.
TEMA Triple Exponential Moving Average
TEMA: Triple Exponential Moving Average for crypto trading
What it measures. TEMA combines single, double, and triple exponential averages to reduce lag while retaining a smoothed price series.
How crypto traders use it. A bot can use TEMA for responsive trend confirmation when a conventional EMA reacts too slowly for the tested interval.
Risk and limitation. Reduced lag is obtained through a more sensitive formula, which can amplify short-lived price movement.
TREMA Triple-Recursive Exponential Moving Average
TREMA: Triple-Recursive Exponential Moving Average for crypto trading
What it measures. Gimmer TREMA applies exponential smoothing three times and compares the resulting baseline with the latest close.
How crypto traders use it. It offers a heavily smoothed trend reference for crypto strategies that want to ignore smaller candle-to-candle fluctuations.
Risk and limitation. Triple recursive smoothing can respond late to abrupt reversals and should not be confused with the lag-corrected TEMA formula.
TRIMA Triangular Moving Average
TRIMA: Triangular Moving Average for crypto trading
What it measures. TRIMA applies greater effective weight to prices near the center of the window through double smoothing.
How crypto traders use it. It can serve as a stable trend or mean reference when a strategy values smoothness over immediate response.
Risk and limitation. The additional smoothing increases delay and can miss the early portion of fast crypto breakouts.
TSF Time Series Forecast
TSF: Time Series Forecast for crypto trading
What it measures. TSF projects the endpoint of a rolling linear regression, extending the recent line to estimate the current-period value.
How crypto traders use it. Traders can compare close with the forecast line or use its direction as a trend condition in systematic strategies.
Risk and limitation. It is a mathematical extrapolation, not a price prediction, and breaks down when the recent linear relationship changes.
VIDYA Variable Index Dynamic Average
VIDYA: Variable Index Dynamic Average for crypto trading
What it measures. VIDYA adjusts exponential smoothing with a volatility or momentum efficiency factor so the average can change speed.
How crypto traders use it. It can adapt a crypto trend baseline across quiet consolidation and faster directional movement without manually switching periods.
Risk and limitation. Adaptive behavior depends on its parameters and can still lag sudden liquidations, gaps, or exchange-specific shocks.
WILDLERS Wilder's Smoothing
WILDLERS: Wilder's Smoothing for crypto trading
What it measures. Wilder's Smoothing updates a prior average gradually and is the smoothing method behind several classic directional and volatility indicators.
How crypto traders use it. It can create a steady baseline or smooth a noisy input series before another crypto trading condition is evaluated.
Risk and limitation. The deliberately slow response can postpone entries and exits when market structure changes quickly.
WMA Weighted Moving Average
WMA: Weighted Moving Average for crypto trading
What it measures. WMA assigns linearly larger weights to more recent observations, making it more responsive than an equal-weight SMA.
How crypto traders use it. It can be used for price crosses, dual-average systems, or a faster trend filter on actively traded crypto pairs.
Risk and limitation. The calculation is price-weighted by time, not trading volume, and its responsiveness can increase whipsaws.
ZLEMA Zero-Lag Exponential Moving Average
ZLEMA: Zero-Lag Exponential Moving Average for crypto trading
What it measures. ZLEMA adjusts the input series for estimated lag before applying exponential smoothing.
How crypto traders use it. It can help a rule-based bot respond earlier to directional changes than a conventional EMA with the same period.
Risk and limitation. No moving average has zero real-world latency, and the adjustment can overreact to transient candles and bad ticks.
22 indicators
Momentum and oscillators
Measurements of acceleration, relative position, cycles, and the pace of change in crypto prices.
AO Awesome Oscillator
AO: Awesome Oscillator for crypto trading
What it measures. AO subtracts a longer simple average of median price from a shorter one to show changes in market momentum.
How crypto traders use it. Zero-line direction and momentum shifts can confirm whether a crypto move is accelerating with the selected candle interval.
Risk and limitation. It is derived from moving averages and can remain positive or negative long after the best entry has passed.
APO Absolute Price Oscillator
APO: Absolute Price Oscillator for crypto trading
What it measures. APO measures the absolute difference between fast and slow exponential moving averages.
How crypto traders use it. A bot can test zero-line crosses or widening separation as evidence that short-term crypto momentum differs from the longer trend.
Risk and limitation. Absolute values depend on token price scale, making the same threshold unsuitable for Bitcoin and a low-priced altcoin.
CCI Commodity Channel Index
CCI: Commodity Channel Index for crypto trading
What it measures. CCI compares typical price with its moving average and mean deviation to show how unusual the current price is.
How crypto traders use it. Crypto traders test CCI extremes, zero-line crosses, or trend continuation rules to identify momentum away from a recent norm.
Risk and limitation. Extreme readings can persist in strong trends and should not be treated as automatic reversal signals.
CMO Chande Momentum Oscillator
CMO: Chande Momentum Oscillator for crypto trading
What it measures. CMO compares the sum of recent gains with the sum of recent losses on a bounded positive-to-negative scale.
How crypto traders use it. It can express momentum direction, overextended conditions, or a neutral zone for Bitcoin and altcoin strategies.
Risk and limitation. Bounded thresholds do not identify exact tops or bottoms, especially in persistent directional markets.
DPO Detrended Price Oscillator
DPO: Detrended Price Oscillator for crypto trading
What it measures. DPO removes a shifted moving trend from price to emphasize shorter cycles and deviations around a local mean.
How crypto traders use it. It can help crypto traders study recurring swings or mean-reversion timing without allowing the broader trend to dominate the series.
Risk and limitation. The shift makes DPO unsuitable as a standalone real-time forecast, and crypto cycles rarely remain stable.
FISHER Fisher Transform
FISHER: Fisher Transform for crypto trading
What it measures. The Fisher Transform normalizes recent price position and applies a logarithmic transform that makes extremes more pronounced.
How crypto traders use it. Crosses and turning points can make momentum changes easier to encode as explicit conditions for a crypto bot.
Risk and limitation. Normalization can create sharp-looking signals from ordinary noise, and extreme values are not guaranteed reversals.
FOSC Forecast Oscillator
FOSC: Forecast Oscillator for crypto trading
What it measures. FOSC expresses the difference between current price and a rolling linear-regression forecast, usually as a percentage.
How crypto traders use it. It can identify when a crypto asset is trading above or below its recent linear tendency and support mean-reversion or continuation tests.
Risk and limitation. Results depend on a linear model and can become unstable around sharp breaks or low-priced assets.
MACD Moving Average Convergence Divergence
MACD: Moving Average Convergence Divergence for crypto trading
What it measures. MACD compares fast and slow exponential averages and adds a signal average to describe trend and momentum relationships.
How crypto traders use it. Line crosses, zero-line position, and signal confirmation are common rule inputs for systematic crypto entries and exits.
Risk and limitation. MACD is lagging and can generate repeated crosses in ranges. Fast settings also react strongly to volatile candle noise.
MACDH MACD Histogram
MACDH: MACD Histogram for crypto trading
What it measures. MACDH is the distance between the MACD line and its signal line, showing whether their momentum relationship is expanding or contracting.
How crypto traders use it. A bot can use zero crosses or changes in histogram direction to confirm acceleration and deceleration in a crypto trend.
Risk and limitation. Histogram contraction can occur during a pause rather than a reversal, and the underlying moving-average lag remains.
MOM Momentum
MOM: Momentum for crypto trading
What it measures. Momentum subtracts an earlier price from the current price to measure the absolute change across the lookback period.
How crypto traders use it. It offers a direct way to require positive or negative movement before a bot acts on another condition.
Risk and limitation. Absolute change is sensitive to asset price scale and can be distorted by one large candle.
MSW Mesa Sine Wave
MSW: Mesa Sine Wave for crypto trading
What it measures. MSW estimates cyclical phase and returns sine and lead-wave components intended to identify turning relationships.
How crypto traders use it. It can be explored for range-bound crypto markets where cyclical behavior appears more useful than a directional trend model.
Risk and limitation. Market cycles shift, disappear, and change length. Crosses should be validated across regimes and never assumed to repeat.
PPO Percentage Price Oscillator
PPO: Percentage Price Oscillator for crypto trading
What it measures. PPO expresses the difference between fast and slow exponential averages as a percentage of the slow average.
How crypto traders use it. Percentage normalization makes momentum more comparable across crypto assets with very different nominal prices.
Risk and limitation. Comparability does not remove lag, and thin or newly listed markets can still produce unstable readings.
QSTICK Qstick
QSTICK: Qstick for crypto trading
What it measures. Qstick smooths the difference between candle close and open to estimate whether bullish or bearish candle bodies dominate.
How crypto traders use it. It can confirm directional crypto-candle pressure without relying only on the latest green or red candle.
Risk and limitation. Wicks and gaps are not fully represented, and exchange candle construction can change the result.
ROC Rate of Change
ROC: Rate of Change for crypto trading
What it measures. ROC measures the percentage change between current price and price a fixed number of candles earlier.
How crypto traders use it. It can rank momentum, confirm breakouts, or require a minimum percentage move before a crypto strategy enters.
Risk and limitation. The comparison point rolls forward, so the value can change sharply even when current price barely moves.
ROCR Rate of Change Ratio
ROCR: Rate of Change Ratio for crypto trading
What it measures. ROCR divides current price by its earlier value, centering unchanged price around a ratio of one.
How crypto traders use it. A strategy can test values above or below one as a scale-independent expression of positive or negative crypto momentum.
Risk and limitation. It shares ROC sensitivity to the rolling comparison candle and does not distinguish a smooth move from a single jump.
RSI Relative Strength Index
RSI: Relative Strength Index for crypto trading
What it measures. RSI compares smoothed recent gains and losses on a zero-to-100 scale to describe momentum balance.
How crypto traders use it. Crypto traders test thresholds, centerline crosses, and regime-specific RSI behavior for momentum or mean-reversion strategies.
Risk and limitation. Overbought and oversold describe the calculation, not guaranteed tops or bottoms. Strong trends can remain extreme for long periods.
RVI Relative Vigor Index
RVI: Relative Vigor Index for crypto trading
What it measures. Gimmer RVI compares the smoothed close-minus-open relationship with the smoothed high-low range to estimate directional vigor.
How crypto traders use it. Positive and negative zones can confirm whether candle bodies support a bullish or bearish crypto thesis.
Risk and limitation. Small ranges and irregular candles can magnify the ratio, while the simplified calculation should be tested against the exact market feed.
STOCH Stochastic Oscillator
STOCH: Stochastic Oscillator for crypto trading
What it measures. Stochastic compares the close with the recent high-low range and smooths the result into %K and %D lines.
How crypto traders use it. Bots can test line crosses, threshold exits, or range-bound reversal setups on closed crypto candles.
Risk and limitation. Readings can remain extreme in a trend, and short lookbacks produce many signals during volatile trading.
STOCHRSI Stochastic RSI
STOCHRSI: Stochastic RSI for crypto trading
What it measures. Stochastic RSI measures the position of RSI within its own recent range, making it more sensitive than standard RSI.
How crypto traders use it. It can detect short-term momentum turns within a broader crypto trend when a strategy needs a fast oscillator.
Risk and limitation. The extra transformation increases noise and can produce frequent extreme readings and crosses.
TRIX TRIX
TRIX: TRIX for crypto trading
What it measures. TRIX is the rate of change of a triple-smoothed exponential average, filtering smaller fluctuations while retaining trend momentum.
How crypto traders use it. Zero-line direction and turns can confirm longer momentum cycles in noisy, continuously traded markets.
Risk and limitation. Triple smoothing adds delay, so short crypto reversals may be visible in price before TRIX responds.
ULTOSC Ultimate Oscillator
ULTOSC: Ultimate Oscillator for crypto trading
What it measures. The Ultimate Oscillator blends buying pressure across three lookback periods to reduce dependence on one cycle length.
How crypto traders use it. It can confirm broad momentum or divergence when a strategy needs agreement across short, medium, and longer windows.
Risk and limitation. Multiple windows do not eliminate false divergence, and default thresholds require validation on each crypto interval.
WILLR Williams %R
WILLR: Williams %R for crypto trading
What it measures. Williams %R places the latest close within the recent high-low range on a negative bounded scale.
How crypto traders use it. It can support crypto overextension, range-reversal, or momentum-continuation rules using explicit closed-candle thresholds.
Risk and limitation. An extreme reading can persist throughout a powerful trend and is not a standalone instruction to trade against it.
8 indicators
Volatility and market regime
Indicators that describe range expansion, dispersion, and whether a crypto market is trending or rotating sideways.
ATR Average True Range
ATR: Average True Range for crypto trading
What it measures. ATR smooths true range, including gaps between candles, to estimate the typical absolute movement over a recent window.
How crypto traders use it. Crypto strategies use ATR for volatility filters, adaptive stops, breakout distances, and position-sizing research.
Risk and limitation. ATR measures magnitude rather than direction and expands after volatility has already increased.
BB Bollinger Bands
BB: Bollinger Bands for crypto trading
What it measures. Bollinger Bands place standard-deviation envelopes around a moving average, connecting price location with recent dispersion.
How crypto traders use it. Band width, breakouts, and returns toward the center can support volatility-expansion or mean-reversion crypto systems.
Risk and limitation. A band touch is not inherently a reversal. Price can walk a band during a strong trend or gap beyond it.
CVI Chaikin Volatility
CVI: Chaikin Volatility for crypto trading
What it measures. CVI measures the rate of change in a smoothed high-low range, highlighting expansion or contraction in intraperiod volatility.
How crypto traders use it. It can confirm whether a crypto breakout is accompanied by expanding range or whether activity is compressing.
Risk and limitation. Range expansion can occur in either direction and may reflect one liquidation candle rather than a lasting regime.
MASS Mass Index
MASS: Mass Index for crypto trading
What it measures. Mass Index studies repeated expansion and contraction of the high-low range without assigning bullish or bearish direction.
How crypto traders use it. It can flag a potential regime change or reversal setup for a strategy that obtains direction from another condition.
Risk and limitation. A reversal bulge is a setup, not a timing signal, and crypto markets can continue trending after it appears.
NATR Normalized Average True Range
NATR: Normalized Average True Range for crypto trading
What it measures. NATR expresses Average True Range relative to price, producing a percentage-like volatility measure.
How crypto traders use it. Normalization helps compare volatility filters across Bitcoin, higher-priced tokens, and low-priced altcoins.
Risk and limitation. Very low prices and discontinuous markets can distort the ratio, and historical volatility does not cap future movement.
STDDEV Standard Deviation
STDDEV: Standard Deviation for crypto trading
What it measures. Standard Deviation measures dispersion of recent prices around their average over the configured window.
How crypto traders use it. It can define crypto volatility regimes, statistical bands, or filters that avoid entering when recent movement exceeds a tested level.
Risk and limitation. The calculation is backward-looking and assumes the selected sample is relevant to the next market regime.
VHF Vertical Horizontal Filter
VHF: Vertical Horizontal Filter for crypto trading
What it measures. VHF compares net price range with the sum of individual price changes to estimate trend efficiency versus sideways movement.
How crypto traders use it. A bot can use VHF to switch between trend-following and range-oriented conditions without predicting direction.
Risk and limitation. Regime thresholds vary by market and timeframe, and transitions are usually recognized after they begin.
VOLATILITY Annualized Historical Volatility
VOLATILITY: Annualized Historical Volatility for crypto trading
What it measures. This indicator annualizes the dispersion of recent returns to express the observed pace of price variation.
How crypto traders use it. It can compare current crypto-market risk with quieter periods or gate strategies that are unsuitable for extreme volatility.
Risk and limitation. Annualization is a convention, especially in 24/7 crypto markets, and realized volatility cannot predict the size of the next shock.
13 indicators
Volume and market participation
Price-and-volume tools for studying participation, accumulation, distribution, and the quality of a move.
AD Accumulation/Distribution Line
AD: Accumulation/Distribution Line for crypto trading
What it measures. AD weights volume by where the close sits inside each candle range and accumulates the result over time.
How crypto traders use it. It can test whether reported exchange volume confirms price direction or diverges from a Bitcoin or altcoin move.
Risk and limitation. Crypto volume is fragmented across venues, so one exchange feed may not represent the broader market.
ADOSC Accumulation/Distribution Oscillator
ADOSC: Accumulation/Distribution Oscillator for crypto trading
What it measures. ADOSC subtracts a slow exponential average of the AD line from a fast one to show changes in volume-backed money flow.
How crypto traders use it. Zero-line crosses can confirm whether crypto-market participation is strengthening or weakening around a price move.
Risk and limitation. The oscillator inherits exchange-volume limitations and can be distorted by candles with tiny high-low ranges.
EMV Ease of Movement
EMV: Ease of Movement for crypto trading
What it measures. EMV relates movement of the candle midpoint to volume and range, estimating how easily price advances or declines.
How crypto traders use it. It can distinguish a broad crypto price move on relatively light volume from a move requiring heavier participation.
Risk and limitation. Reported volume quality, token liquidity, and extreme candle ranges can materially change interpretation.
KVO Klinger Volume Oscillator
KVO: Klinger Volume Oscillator for crypto trading
What it measures. KVO combines price trend, range, and volume into fast and slow cumulative volume-force averages.
How crypto traders use it. Traders use it to test whether longer-term volume flow supports a crypto trend while remaining sensitive to shorter changes.
Risk and limitation. Its multi-part calculation can be noisy, and signal behavior differs substantially across exchanges and timeframes.
MARKETFI Market Facilitation Index
MARKETFI: Market Facilitation Index for crypto trading
What it measures. Market Facilitation Index divides candle range by volume to estimate how much price movement each unit of activity produced.
How crypto traders use it. It can help crypto traders compare efficient range expansion with high-volume congestion on the selected exchange.
Risk and limitation. Low or inconsistent volume can create extreme values, and efficiency alone provides no trade direction.
MFI Money Flow Index
MFI: Money Flow Index for crypto trading
What it measures. MFI combines typical price and volume into a bounded oscillator that compares positive and negative money flow.
How crypto traders use it. It can add crypto exchange-volume context to momentum thresholds, divergence research, and range-reversal rules.
Risk and limitation. It is often described as volume-weighted RSI, but exchange-specific volume and persistent trends can make extremes unreliable.
NVI Negative Volume Index
NVI: Negative Volume Index for crypto trading
What it measures. NVI updates a cumulative price index only when volume is lower than the previous candle.
How crypto traders use it. It can isolate crypto price behavior during quieter participation and compare it with the broader trend.
Risk and limitation. The original market assumptions behind NVI may not transfer cleanly to fragmented, always-open crypto venues.
OBV On-Balance Volume
OBV: On-Balance Volume for crypto trading
What it measures. OBV adds or subtracts the full candle volume according to whether close rose or fell from the prior close.
How crypto traders use it. Its direction and divergence can test whether volume participation confirms a crypto price trend.
Risk and limitation. One abnormal volume candle can shift the cumulative line for a long time, and venue volume is not market-wide volume.
PVI Positive Volume Index
PVI: Positive Volume Index for crypto trading
What it measures. PVI updates a cumulative price index only when volume is higher than on the previous candle.
How crypto traders use it. It can isolate crypto price behavior during expanding participation and provide context for breakout confirmation.
Risk and limitation. Higher volume can accompany both accumulation and distribution, so PVI needs directional and risk rules.
TPO Time Price Opportunity Profile
TPO: Time Price Opportunity Profile for crypto trading
What it measures. Gimmer TPO groups recent candle ranges into price levels, estimates time and volume at each level, and derives a value area and point of control.
How crypto traders use it. It can help a strategy distinguish acceptance inside a high-participation area from movement beyond the recent profile.
Risk and limitation. Volume is distributed approximately across each candle range, so the profile is not an order-book or tick-level volume profile.
VOSC Volume Oscillator
VOSC: Volume Oscillator for crypto trading
What it measures. VOSC compares fast and slow averages of volume to measure whether trading participation is expanding or contracting.
How crypto traders use it. It can require crypto volume acceleration before accepting a breakout or identify declining participation during a trend.
Risk and limitation. Exchange outages, wash trading, and asset migrations can change reported volume without confirming genuine demand.
VWMA Volume Weighted Moving Average
VWMA: Volume Weighted Moving Average for crypto trading
What it measures. VWMA weights each price by its candle volume, giving high-volume observations more influence than low-volume observations.
How crypto traders use it. Price-to-VWMA relationships can test whether current crypto price is supported by the most active recent candles.
Risk and limitation. Its quality depends on the exchange volume feed, and a high-volume anomaly can dominate the window.
WAD Williams Accumulation/Distribution
WAD: Williams Accumulation/Distribution for crypto trading
What it measures. WAD accumulates price movement relative to the prior close and current high-low range to estimate buying or selling pressure.
How crypto traders use it. A strategy can test line direction, zero relationships, or divergence as supporting evidence for a directional trade.
Risk and limitation. Despite its name, this formula does not use reported volume and must not be confused with the Accumulation/Distribution Line.
9 indicators
Price transforms and signal utilities
Derived price series and reusable crossing, lag, ratio, and decay functions for explicit strategy conditions.
CROSSANY Crossany
CROSSANY: Crossany for crypto trading
What it measures. Crossany detects when either of two input series crosses the other between consecutive observations.
How crypto traders use it. It turns a two-way crossing relationship, such as price and an average, into an explicit event a bot can evaluate.
Risk and limitation. It reports both crossing directions, so the surrounding strategy must define whether each direction means buy, sell, close, or no action.
CROSSOVER Crossover
CROSSOVER: Crossover for crypto trading
What it measures. Crossover identifies the specific event where the first input series moves from at or below the second to above it.
How crypto traders use it. It is useful for precise bullish crypto-market crossing rules involving price, moving averages, oscillators, or custom derived series.
Risk and limitation. A one-candle cross can reverse immediately, so closed-candle confirmation and realistic cost testing remain essential.
DECAY Linear Decay
DECAY: Linear Decay for crypto trading
What it measures. Linear Decay carries a prior peak forward while reducing it by a fixed linear amount until a new input exceeds it.
How crypto traders use it. It can build trailing thresholds or fading signal memory from another crypto price or indicator series.
Risk and limitation. The decay period is a modeling choice, not a market property, and an unsuitable rate can hold stale information too long.
DIV Vector Division
DIV: Vector Division for crypto trading
What it measures. DIV performs element-by-element division between two aligned series. Gimmer uses it to compare close with open.
How crypto traders use it. Ratios can normalize candle movement or create a reusable relative series for thresholds across differently priced tokens.
Risk and limitation. Zero or near-zero denominators are invalid, and a ratio needs economic meaning before it becomes a trading condition.
EDECAY Exponential Decay
EDECAY: Exponential Decay for crypto trading
What it measures. Exponential Decay carries information forward while reducing its influence by a multiplicative rate rather than a fixed amount.
How crypto traders use it. It can model crypto signal persistence or a trailing threshold that fades quickly at first and more slowly later.
Risk and limitation. The chosen decay rate controls behavior and can produce stale or overly sensitive conditions if it is not backtested.
LAG Lag
LAG: Lag for crypto trading
What it measures. Lag returns a prior value of an input series from a configurable number of candles earlier.
How crypto traders use it. It enables explicit crypto price comparisons such as current versus prior price without accidentally using future data.
Risk and limitation. Large lags discard recent context, while small lags can magnify ordinary candle noise.
MEDPRICE Median Price
MEDPRICE: Median Price for crypto trading
What it measures. Median Price averages each candle high and low to represent the midpoint of its complete trading range.
How crypto traders use it. It can replace close as the input to averages or oscillators when a strategy wants range-centered price data.
Risk and limitation. It ignores both volume and where the candle closed, which may remove information relevant to directional pressure.
TYPPRICE Typical Price
TYPPRICE: Typical Price for crypto trading
What it measures. Typical Price averages high, low, and close to create a single representative value for each candle.
How crypto traders use it. It is a common crypto-strategy input for channel, volume, and mean calculations that should reflect more than the closing print.
Risk and limitation. A simple average cannot describe intrabar path, liquidity, spread, or executable price.
WCPRICE Weighted Close Price
WCPRICE: Weighted Close Price for crypto trading
What it measures. Weighted Close averages high, low, and twice the close, giving the final candle price half of the total weight.
How crypto traders use it. It can provide a close-sensitive crypto price series for trend or momentum rules while retaining some range information.
Risk and limitation. The weighting is fixed and does not use trading volume, order-book depth, or actual fill prices.
How this reference was built
The catalog was reconciled with the indicator records and implementations in the current Gimmer codebase. Standard function names were checked against the Tulip Indicators function index; Gimmer-specific behavior such as TREMA, RVI, TPO, and signal inversion is described from the product implementation.
The two internal database records named test and script_indicator are not presented as public technical indicators. Custom Code and AI Decision are documented as strategy engines because their behavior is broader than a single indicator calculation.
Choose combinations that add information
| Strategy question | Useful family | Common mistake |
|---|---|---|
| Is a directional move established? | Trend and moving averages | Stacking several averages that repeat the same price evidence. |
| Is price movement accelerating? | Momentum and oscillators | Treating an extreme reading as a guaranteed reversal. |
| Has the risk regime changed? | Volatility and regime | Using a backward-looking measure as a maximum future move. |
| Does participation confirm price? | Volume and market participation | Assuming one exchange represents all crypto-market volume. |
| Did one series cross another? | Price transforms and utilities | Defining a cross without a direction, close condition, or cost model. |