SeaLiquidity
Performance and methodology

How we evaluate and present information.

What we use, how we calculate it, how we label it, and what it can't tell you. We do not publish performance statistics, win rates or returns unless they come from verifiable records over a clearly stated period.

Data sources

Where the numbers come from

DataSourceNotes
Crypto prices and candlesApex Omni public market dataThe platform's charts and scanners use closed candles only
Macro: S&P 500, Nasdaq 100, VIX, US 10-year, goldYahoo Finance; FRED where configuredEach value states its source and session
DXYComputed from ECB reference rates (ICE DXY formula)Daily reference fix
BTC dominanceConduit, or CoinGecko
NewsConduit and public feedsEach headline names its publisher
Economic calendarForexFactory (USD events)With forecast, previous and actual where published
Stablecoin supply and DeFi TVLDefiLlama
Website demonstrationsCoinbase public market dataCalculated in your browser; labelled on each demo
Data timestamps

Every value says when it was taken

Each live value is shown with its source and an "as of" time in Eastern Time. Market values carry their session (for example a live quote, or the last close). When a source fails, its last good value is kept and labelled with its time, or the value is shown as unavailable; it is never replaced with an estimate.

Indicator calculations

The formulas

Deans

Deans = (End − Start) ÷ Start × 10,000

Four signed measurements per candle (upper wick, body, lower wick, full range), each from its own starting price. Full definition.

Efficiency ratio and regimes

ER = |Ct − Ct−20| ÷ Σ20 |Ci − Ci−1|

Trending with the 20 / 50-period averages aligned and ER ≥ 0.30; range-bound below 0.20; transitioning otherwise; confirmed after 3 agreeing observations.

Realised volatility

σ = stdev(ln(Ci ÷ Ci−1), 20) × √(periods per year) × 100

Ranked against the last 180 observations; high at or above the 80th percentile, low at or below the 20th.

Correlation

r = cov(a, b) ÷ (σa σb), t = r √((n − 2) ÷ (1 − r²))

Daily log returns on common dates; 7, 30 and 90-day windows with minimum samples of 5, 15 and 40; treated as significant only when |t| ≥ 2.

Reward-to-risk and sizing

R:R = |Target − Entry| ÷ |Entry − Stop|, Size = Risk amount ÷ |Entry − Stop|

Statistical intervals

Win rates in the ICC research are shown with a 95% Wilson score interval and their sample size; results under 10 samples are flagged and not ranked.

Historical analysis

How sequences are recorded

Every ICC sequence the analyst detects gets one permanent Global ID. Each phase is measured once, from the closed candles available at that moment, and never overwritten; later changes are appended to its history. After an entry, the outcome is read from later closed candles: the target or the stop, whichever came first; a candle that touches both counts as the stop; maximum favourable and adverse excursion are recorded. Detected, completed and executed counts are kept apart, and trade results come only from closed trades the customer logged against that sequence.

Backtesting methodology

Historical tests, labelled as such

Backtests run on real exchange history. They report raw historical performance first, then what survived further testing. Simulations are labelled as simulations, and small samples are flagged. Backtesting tools cannot place orders.

Findings from historical data are checked on a later, held-out period before they are presented as holding up, and they are always described as observations, not rules.

Simulated versus actual

Four kinds of result, never mixed

Live results

Closed trades actually placed on an exchange and recorded.

Historical results

What happened to recorded setups in the market, measured from real candles after the fact.

Backtested results

Rules applied to past data. Useful for research; they did not happen as trades.

Simulated results

Hypothetical scenarios, such as the website's execution preview. Always labelled.

Limitations

What the models can't do

  • Past behaviour does not guarantee future behaviour. Regimes change and relationships between markets break.
  • Correlation shows co-movement, not cause.
  • Backtests can overfit and ignore costs such as fees, slippage and funding.
  • Small samples are unreliable, which is why they are flagged.
  • Data sources can be late, revised or unavailable; values are labelled with their time and source.
  • Technical concepts such as fair value gaps or structure breaks are definitions, applied consistently — not predictions.
  • Nothing on the website or the platform is financial advice.