Data Sources

This page shows what powers the DepthSignal platform: the market data we collect, how we process it, and how the combined intelligence surface stays grounded in visible inputs. You can inspect the exchange coverage, derived context, and compliance commitments without guessing where the numbers come from. The same feeds support chart context, pressure context, and organized technical-analysis context.

Exchange Coverage

We collect public market data from the following exchanges and normalize it into one shared trader platform.

Centralized Exchanges

Exchange	Data Type	Connection	Status
Binance	Spot	REST	Supported
Bitfinex	Spot	REST	Supported
Bitstamp	Spot	REST	Supported
Bybit	Spot	REST	Supported
Coinbase	Spot	REST	Supported
Gate.io	Spot	REST	Supported
Gemini	Spot	REST	Supported
HTX	Spot	REST	Supported
Kraken	Spot	REST	Supported
KuCoin	Spot	REST	Supported
MEXC	Spot	REST	Supported
OKX	Spot	REST	Supported
CoinMetro	Spot	REST	Supported
Cryptocom	Spot	REST	Supported

Futures Venues

Exchange	Data Type	Connection	Status

Decentralized Exchanges (CLOB)

Exchange	Data Type	Connection	Status

What We Collect

All data is collected exclusively via public, unauthenticated API endpoints. We do not use private endpoints, authenticated sessions, or any data that requires exchange account credentials.

Public orderbook snapshots

Public bid and ask depth around the current price. Shows visible supply and demand without redistributing raw books.

Public trade streams

Aggregated trade data used to compute Cumulative Volume Delta (CVD), taker buy/sell ratio, and positioning context.

Public liquidation feeds

Public liquidation data from supported venues, used to compute cascade stress and long/short imbalance.

Public funding rates and open interest

Derivatives data from Binance, Bybit, and OKX. Used to compute funding consensus, open interest changes, and long/short ratios.

Derived chart and context

Normalized venue price series support chart context, market-pressure summaries, and organized technical-analysis context.

Collection frequency

Data freshness varies by venue and transport. Fresh feeds are monitored continuously, and stale venue data is flagged before it is used in derived outputs.

How We Process It

Raw public data goes through proprietary computation to produce 49+ market-contextfeatures. The examples below show the kind of processing we apply.

Raw public orderbook, trade, and derivatives data→Computed market-context features

Raw inputs are transformed into readable market context, pressure summaries, and structured TA context. Internal design and computation details are not disclosed.

Normalized venue price series→Chart overlays and context

The platform derives chart structure, pressure context, and TA context from the same normalized market feed so visible price action stays aligned.

Zone Memory Model

Support and resistance zones are computed and time-decayed using a proprietary zone memory model so traders can read structure without sorting raw feed noise. Specific parameters and decay functions are not disclosed.

Cross-Exchange Analysis

By ingesting data from 14 supported venues simultaneously, we compute features that help traders compare pressure and structure across markets: cross-exchange flow relationships, depth divergence, and large-participant agreement.

5 Composite Intelligence Reads

Our highest-level outputs combine multiple analysis features into simple public-facing reads:

Directional Pressure: Composite market-pressure read derived from multiple orderbook measurements.
Liquidity Risk: Proprietary composite measuring current market liquidity conditions.
Large-Order Activity: Proprietary composite tracking large-participant positioning.
Flow Momentum: Proprietary composite tracking the acceleration of buying and selling activity.
Cross-Exchange Agreement: Proprietary composite measuring agreement across supported venues.

What We Sell

We turn raw market data into readable market context.

DepthSignal API responses contain scalar values: single computed numbers per feature, per exchange. For example, a directional-pressure read of 0.42 or a price-impact read of 0.0034.

Our API responses do not contain:

Raw bid/ask arrays or orderbook depth snapshots
Raw trade records or tick-level data
Raw liquidation event streams
Raw funding rate histories

Every value returned by our API is the result of proprietary computation applied to public data. The raw inputs are never exposed, forwarded, or redistributed in any form.

Example API response structure

{
  "symbol": "BTC",
  "features": {
    "binance": {
      "bid_ask_spread": 0.00012,
      "depth_imbalance": 0.15,
      "smart_money_score": 0.42,
      "price_impact": 0.0034,
      "spoof_score": 0.08
    }
  },
  "data_freshness": {
    "binance": { "age_seconds": 2, "is_stale": false }
  }
}

Note: All values are scalar (single numbers). No arrays of bids, asks, trades, or raw market data are present in any response.

Our Commitment to Exchanges

We respect exchange Terms of Service

Our data collection practices are designed to comply with the public API terms of each exchange we connect to. We monitor rate limits, adhere to usage policies, and adjust our collection patterns as terms are updated.

We do not redistribute raw data

No raw orderbook snapshots, trade records, or market data streams are stored for redistribution or made available through our API. All outputs are the result of meaningful proprietary transformation.

We transform data through proprietary computation

Our 49+ analysis features are the result of substantial engineering work and proprietary algorithms. The distance between raw input and computed output is intentional and irreversible.

Per-exchange compliance controls

Our architecture supports disabling data collection from any individual exchange instantly, without affecting the rest of the system. If an exchange contacts us with concerns, we can respond immediately.

If you represent an exchange and have questions about our data practices, please contact us at [email protected]. We aim to maintain a cooperative relationship with every venue we connect to and will work with you to address any concerns.

Data Freshness

Every API response includes a data_freshness object that reports the age of the underlying data for each exchange, in seconds.

"data_freshness": {
  "binance": { "age_seconds": 2, "is_stale": false },
  "bybit":   { "age_seconds": 3, "is_stale": false },
  "okx":     { "age_seconds": 4, "is_stale": false }
}

Typical freshness: Low-latency for streaming venues and short-interval polling for non-streaming venues.

Stale data flagging: Any exchange data older than 30 seconds is explicitly flagged with is_stale: true. This lets your application tell the difference between fresh reads and potentially outdated values.

Symbol Coverage

DepthSignal covers 40 major market symbols, including:

BTCETHSOLXRPBNBADADOGEAVAXDOTLINKUNIATOMFILAPTARBOPINJSUINEARFTMAAVEMKRLDORNDRTIASEIJUPWIFPEPESHIBLTCBCHETCTRXTONHBARVETALGOICPRUNE

Coverage varies by exchange and data type. Use the /v1/symbols endpoint for real-time symbol availability, including which data types (orderbook, liquidations, spot-futures, CVD, positioning) are available for each symbol.

Market-Context Analytics

Beyond standard analysis features, DepthSignal computes higher-order market-context analytics from public market data using proprietary statistical models. The public surface describes inputs, transformed context, and limitations; internal training and model structure are not disclosed. These outputs are informational only and are not trading signals, price predictions, or advice.

Proprietary Regime Classification (describing whether the market is trending, ranging, or volatile)

Our proprietary model classifies current market conditions into discrete regimes. Internal classification design and training details are not disclosed.

Statistical Drift Detection (noticing when market behaviour changes structurally)

Proprietary drift detection identifies structural changes in market microstructure in real time. Specific statistical methods are not disclosed.

Ensemble Consensus (combining internal components into one market-pressure view)

Multiple internal model components may contribute to a market-pressure consensus score. Model count, architecture, and aggregation methodology are proprietary.

Method Boundaries: Not Financial Advice

All market-context analytics produce purely computed statistical outputs. They are not instructions to trade, investment advice, or recommendations. Models are updated as part of internal maintenance, not as a public promise of prediction or performance.

This page reflects our current data practices as of June 2026. Our data collection and processing methods may evolve as we add exchanges, expand coverage, and refine our analytics. For questions about our data practices, contact [email protected].