173 live indicators across 25 categories. Each signal feeds into the Blackperp decision engine in real time.
Detects genuine vs spoofed liquidity in crypto order books by analyzing bid/ask placement patterns, cancellation rates, and fill ratios across exchanges.
Identifies institutional-grade order flow through size clustering, timing patterns, and execution algorithm detection in perpetual futures markets.
Measures when trending moves are losing internal support from volume, momentum, and order flow convergence in crypto perpetual futures.
Quantifies the probability that current market orders are informed (toxic) vs uninformed flow, using microstructure analysis of perpetual futures.
Detects when overcrowded leveraged positioning is beginning to unwind, measuring exit pressure and cascade probability across perpetual futures.
Combines price momentum with order flow and open interest data to determine whether momentum has genuine conviction or is fading.
Identifies forced selling capitulation being absorbed by limit order walls, signaling potential reversal points in perpetual futures.
Detects divergence between market narrative (sentiment, social data) and actual on-chain and order flow data in crypto markets.
Measures the rate and direction of price change across multiple timeframes. Detects acceleration, deceleration, and momentum divergences in crypto perpetual futures.
Measures price distance from Volume Weighted Average Price across multiple timeframes, identifying mean-reversion and trend-following zones.
Detects statistically unusual volume patterns that deviate from rolling baselines, flagging potential breakouts or reversals in perpetual futures.
Analyzes candlestick body/wick ratios, rejection patterns, and structural transitions across timeframes in crypto perpetual futures.
Identifies and ranks key price levels based on historical rejection frequency, volume concentration, and order book depth analysis.
Quantifies the strength and sustainability of price trends using ADX-derived metrics and multi-timeframe confirmation for perpetual futures.
Identifies consolidation ranges, measures range width relative to volatility, and estimates breakout probability for crypto perpetual futures.
Measures directional agreement across 1m, 5m, 15m, 1h, and 4h timeframes, identifying high-conviction trend alignment in perpetual futures.
Tracks key session-based price levels including daily open, previous highs/lows, and pivot points for crypto perpetual futures trading.
Provides rolling statistical measures including returns distribution, z-scores, and percentile rankings for price action analysis.
Tracks aggressive buy volume (taker buys) in real time across all tracked perpetual futures markets, measuring bullish conviction.
Tracks aggressive sell volume (taker sells) in real time across all tracked perpetual futures markets, measuring bearish pressure.
Measures the net difference between aggressive buying and selling volume per time interval in crypto perpetual futures.
Calculates the ratio of buy volume to sell volume, identifying dominance shifts and flow asymmetry in perpetual futures.
Tracks the rate of contract transfers between participants, measuring market activity intensity and participation changes.
Measures cumulative buildup of volume delta over rolling windows, identifying sustained buying or selling pressure in perpetual futures.
Identifies hidden large orders being executed in smaller chunks through fill pattern analysis in crypto perpetual futures order books.
Detects clusters of large trades in time and price, identifying institutional activity zones in crypto perpetual futures markets.
Identifies extreme volume spikes that often precede reversals or major trend continuation moves in perpetual futures.
Volume-Synchronized Probability of Informed Trading — estimates toxicity of order flow and informed trading probability in real time.
Measures the asymmetry between buy and sell market orders, identifying directional pressure in crypto perpetual futures.
Composite measure of adverse selection risk from market microstructure signals, quantifying informed vs uninformed order flow.
Real-time ratio of taker buy volume to taker sell volume across perpetual futures, measuring net aggression direction.
Tracks the number of market orders per interval, measuring urgency and aggression levels in crypto perpetual futures.
Monitors average market order size to distinguish retail from institutional execution patterns in perpetual futures.
Tracks passive limit order placement frequency, measuring resting liquidity provision in crypto perpetual futures order books.
Monitors average limit order size to identify institutional resting orders and support/resistance conviction levels.
Detects and quantifies algorithmic trading activity through order timing, size patterns, and execution consistency in perpetual futures.
Measures the ratio of unique participants to total volume, distinguishing broad market engagement from concentrated activity.
Quantifies how aggressively takers are hitting bids or lifting offers relative to available liquidity in perpetual futures.
Measures the rate and density of trade executions per unit time, identifying acceleration and deceleration in market activity.
Estimates market impact per unit of order flow, measuring how much prices move for each dollar of net buying or selling pressure.
Tracks bid-ask spread behavior including widening, narrowing, and asymmetric spread shifts in crypto perpetual futures.
Tracks aggregate positioning of top traders by position size, identifying smart money conviction and directional bias.
Monitors the number of top trader accounts on each side of the market, measuring smart money consensus on direction.
Tracks changes in top trader account positioning from anchored reference points, measuring positioning velocity and shifts.
Measures position size changes among top traders from anchored reference points, identifying accumulation and distribution phases.
Aggregates account-level positioning data across all exchange participants, measuring broad market sentiment in perpetual futures.
Tracks changes in global account positioning from anchored baseline measurements, identifying sentiment shifts over time.
Measures the speed at which top trader positions are changing, identifying rapid conviction shifts in crypto perpetual futures.
Detects when top traders are positioning opposite to retail participants, a historically predictive contrarian signal.
Long/short ratio by position size among top exchange traders, measuring smart money directional lean.
Long/short ratio by account count among top exchange traders, measuring breadth of smart money consensus.
Aggregate net positioning across all market participants measured by proprietary data feeds for crypto perpetual futures.
Rate of change in net long/short positioning, identifying acceleration in positioning shifts and sentiment turning points.
Comprehensive long/short ratio across all exchange accounts for the target symbol, measuring global market positioning.
Measures the divergence between large account (whale) and small account (retail) positioning in perpetual futures markets.
Tracks whale vs retail positioning divergence from anchored reference points, identifying when divergence is building or resolving.
Isolates genuine retail trader positioning by filtering out institutional and algorithmic accounts from the long/short ratio.
Measures the concentration of open interest in large accounts, indicating the degree of whale control over market direction.
Quantifies the gap between trader positioning and price action, identifying sentiment extremes and potential reversals.
Identifies whether whales are in accumulation (building positions) or distribution (unwinding positions) phase in perpetual futures.
Real-time bid and ask depth at the top of the order book, providing immediate price level analysis for perpetual futures.
Ratio of total bid depth to ask depth, measuring directional order book imbalance in crypto perpetual futures.
Net change in bid vs ask depth over rolling intervals, tracking order book momentum and shifting support/resistance.
Rate of change in the bid/ask ratio, detecting acceleration in order book imbalance shifts in perpetual futures.
Total visible depth across both bid and ask sides, measuring overall market thickness and liquidity conditions.
Tracks whether bid-side depth is increasing or decreasing over time, signaling support building or withdrawal.
Tracks whether ask-side depth is increasing or decreasing over time, signaling resistance building or withdrawal.
Measures imbalance in order queue positioning, identifying priority advantage in order execution for perpetual futures.
Measures how quickly order book depth replenishes after large market orders, indicating genuine support and resistance.
Aggregated bid/ask data from multiple exchanges for a comprehensive cross-venue order book view of perpetual futures.
Cross-exchange bid/ask ratio combining depth data from multiple major perpetual futures venues.
Total visible depth across all monitored exchanges for the target symbol, measuring system-wide liquidity.
Net change in global bid vs ask depth across all exchanges over rolling intervals, tracking cross-venue order book shifts.
Rate of change in the global bid/ask ratio across multiple exchanges, detecting coordinated order book shifts.
Tracks cross-exchange bid depth changes, identifying coordinated support building or withdrawal across venues.
Tracks cross-exchange ask depth changes, identifying coordinated resistance building or withdrawal across venues.
Comprehensive order book imbalance metric combining depth, flow, and resilience measures for perpetual futures.
Maps key price levels where leveraged positions face liquidation, identifying magnetic price targets in perpetual futures.
Real-time tracking of forced position closures across all monitored perpetual futures exchanges, measuring cascade pressure.
Density-mapped visualization of estimated liquidation levels across the price range for crypto perpetual futures.
Running total of liquidation volume at each price level, identifying high-impact liquidation zones in perpetual futures.
Aggregated liquidation flow across all exchanges and symbols, measuring system-wide forced selling pressure.
Estimates the probability and potential magnitude of liquidation cascades at current price levels in perpetual futures.
Measures the gravitational pull of large liquidation clusters on price, identifying likely magnetic price targets.
Simulates potential cascade scenarios based on current leverage distribution and price movement in perpetual futures.
Live feed of liquidation events as they occur, tracking forced position closures in real time across perpetual futures.
Current perpetual futures funding rate, measuring the cost of holding long vs short positions and market-wide leverage bias.
Classifies the current funding rate environment into contango, backwardation, or neutral regimes for perpetual futures.
Identifies arbitrage opportunities between perpetual funding rates and spot/futures basis spreads across crypto exchanges.
Forecasts upcoming funding rate direction and magnitude based on current market positioning and order flow data.
Tracks the premium or discount of perpetual futures price relative to spot across exchanges, measuring leverage demand.
Total outstanding contract value across perpetual futures markets for each tracked symbol, measuring market participation.
Rate of change in open interest, identifying periods of position building or unwinding in crypto perpetual futures.
Detects divergence between open interest direction and price direction, a historically reliable reversal signal in perpetual futures.
Open interest-weighted CVD that adjusts volume delta for the current level of market participation in perpetual futures.
Measures the acceleration and velocity of open interest changes, identifying conviction shifts in crypto perpetual futures.
Tracks the premium/discount index across derivative venues, measuring market-wide leverage bias and directional positioning.
Bitcoin implied and realized volatility index, measuring current market turbulence and expected price range for perpetual futures.
High-low-open-close volatility estimator that captures intraday price range more efficiently than close-to-close methods.
Volatility of volatility — measures how rapidly volatility itself is changing, identifying regime transitions in perpetual futures.
Classifies current volatility environment into low, normal, high, or extreme regimes for position sizing in perpetual futures.
Generalized Autoregressive Conditional Heteroskedasticity model forecasting next-period volatility for crypto perpetual futures.
Statistical bands based on z-score of volatility-adjusted returns, identifying extreme deviations from normal behavior.
Ranks current metric values against their historical distribution, contextualizing how extreme current readings are.
Measures return distribution asymmetry and tail thickness, identifying directional bias and crash risk in perpetual futures.
Estimates whether price is trending (H>0.5), mean-reverting (H<0.5), or random walk (H≈0.5) in crypto perpetual futures.
Measures the strength and speed of mean-reverting behavior, identifying rubber-band snap-back opportunities in perpetual futures.
Quantifies how persistent momentum is across different time horizons, distinguishing sustainable trends from noise.
Traditional market fear and greed index adapted for crypto perpetual futures context, measuring emotional extremes.
Crypto-specific fear and greed index combining volatility, volume, social media, and dominance data from proprietary sentiment feeds.
Data-driven fear measurement using liquidation intensity, funding extremes, and volatility spikes in perpetual futures.
Composite measure of market euphoria combining funding rate extremes, leverage levels, and retail volume surges.
Multi-source sentiment aggregation combining social media, funding rates, positioning, and flow data for perpetual futures.
Tracks stablecoin supply changes on exchanges, measuring potential buying power and capital inflows/outflows.
Measures retail trader attention through search trends, social media activity, and small-order frequency in crypto markets.
Tracks net cryptocurrency flows into and out of exchanges, measuring accumulation vs distribution behavior on-chain.
Monitors Bitcoin mempool congestion and fee pressure, identifying on-chain demand surges and network stress.
Total cryptocurrency held on exchange wallets, with declining reserves signaling accumulation and reduced selling pressure.
Tracks miner selling behavior and reserve changes, identifying miner capitulation or accumulation phases.
Net Unrealized Profit/Loss ratio positioning the current market within the historical Bitcoin profit cycle.
Market Value to Realized Value ratio, measuring whether crypto is overvalued or undervalued relative to cost basis.
Tracks Wrapped BTC minting and burning on Ethereum, measuring cross-chain capital movement and DeFi demand.
Bitcoin market cap dominance percentage, indicating capital rotation between BTC and altcoins in crypto markets.
Global M2 money supply tracking, measuring macro liquidity conditions that drive risk asset and crypto prices.
US Dollar Index strength measurement, inversely correlated with crypto prices and risk asset performance.
Measures real-time correlation between crypto and traditional macro indicators, identifying coupling/decoupling phases.
Classifies the current macro environment into risk-on, risk-off, or transitional regimes for crypto market context.
Binary risk appetite indicator combining equity, bond, and crypto signals to determine the current risk environment.
CBOE Volatility Index tracking, measuring equity market fear and its cross-asset impact on crypto perpetual futures.
S&P 500 momentum tracking and its leading/lagging relationship with crypto perpetual futures markets.
NASDAQ composite momentum, particularly relevant for crypto correlation during tech-driven market moves.
Tracks momentum and flow in crypto-related equities (MSTR, COIN, MARA) as leading indicators for crypto prices.
Treasury yield movements and their impact on risk asset positioning and crypto capital flows.
Gold-to-Bitcoin price ratio tracking, measuring relative safe-haven preference between traditional and digital gold.
Composite score combining all traditional finance signals into a single risk appetite reading for crypto context.
Implied volatility skew between OTM puts and calls, measuring directional fear premium in crypto options markets.
Options max pain price calculation, identifying the price level where most options expire worthless for crypto.
Implied volatility across different expiration dates, identifying expected volatility timeline for crypto options.
Total Value Locked flow momentum across DeFi protocols, measuring capital allocation trends and protocol preference.
Rate of change in DeFi lending and staking yields, indicating capital flow direction between DeFi and CeFi.
Spread between DeFi and centralized exchange rates, identifying arbitrage pressure and capital preference shifts.
Order flow and positioning data from decentralized perpetual exchanges, capturing DeFi futures activity and sentiment.
Compares liquidity depth across centralized and decentralized venues, identifying fragmentation and venue preference.
Detects funding rate divergence across exchanges, identifying venue-specific positioning extremes in perpetual futures.
Compares open interest distribution across exchanges, measuring capital concentration shifts in perpetual futures.
Calculates the funding rate spread between exchanges, identifying cross-venue arbitrage opportunities and positioning asymmetry.
Measures whether positioning on different exchanges agrees or diverges, indicating market consensus on direction.
Tracks capital movement between exchanges based on OI and volume shifts, identifying flow direction and venue preference.
Volume-weighted aggregate funding rate across all monitored exchanges for each symbol, measuring true market-wide cost.
Compares volume distribution across exchanges, identifying venue dominance and activity shifts in perpetual futures.
Tracks Bitcoin and Ethereum ETF inflow/outflow momentum, measuring institutional demand and capital allocation.
Monitors corporate Bitcoin treasury changes and institutional accumulation patterns, tracking smart money at scale.
Real-time correlation matrix between all tracked symbols, identifying contagion risk and divergence opportunities.
Measures relative performance between symbols, identifying leaders and laggards within the crypto perpetual futures complex.
Detects when historical correlations between symbols break down, often preceding major moves or regime changes.
Classifies current market conditions into trending, ranging, volatile, or quiet regimes using multiple indicators.
Identifies the current correlation regime between crypto and traditional markets, measuring coupling and decoupling.
Detects regime transitions in volatility structure, identifying shifts between calm and turbulent market periods.
Value at Risk percentile calculation, estimating maximum expected loss at given confidence levels for perpetual futures.
Probability estimation of maximum drawdown magnitude based on current volatility and leverage conditions.
Measures the probability and expected magnitude of extreme price moves beyond normal distribution in perpetual futures.
Optimal position sizing based on Kelly criterion, using historical win rate and reward-risk ratios for perpetual futures.
Measures aggregate leverage levels across the market, identifying overleveraged conditions and cascade vulnerability.
Estimates expected execution slippage based on current order book depth and trade size in crypto perpetual futures.
Historical hourly performance patterns, identifying statistically significant time-of-day biases in crypto perpetual futures.
Tracks the 8-hour funding payment cycle and its predictable impact on price action and positioning in perpetual futures.
Historical day-of-week performance analysis, identifying recurring weekly patterns in crypto perpetual futures.
Identifies high-probability breakout entry conditions using volume, momentum, and order book confirmation signals.
Identifies optimal mean-reversion entry timing using statistical bands and order flow exhaustion in perpetual futures.
Measures how many of the 173 signals agree on direction, serving as a meta-confidence indicator for the decision engine.
Tracks the rate of change in overall signal agreement, identifying conviction buildups and breakdowns across the engine.
Detects when signal categories disagree on direction (e.g., flow bullish but sentiment bearish), flagging conflicted conditions.
Weighted ensemble of all signal confidences, producing an overall system confidence score for the decision engine.