Raven Quantis analyzes order book depth, volatility, and sentiment data in real time to calibrate each DCA execution, reducing slippage and removing manual guesswork from recurring entries.
The chart above represents the volatility corridor of a sample asset over 90 days, with entry markers placed where the model identified statistically favorable conditions rather than fixed intervals.
Classic DCA executes on a calendar, not on conditions. That discipline protects against emotional decisions, but it also buys indiscriminately into short-term volatility spikes that a basic data model can flag in advance.
Instead of buying every Monday at 09:00, the engine evaluates a rolling volatility band and short-term order flow imbalance, then selects an execution window within a defined tolerance of your configured interval.
Allocation per execution is bounded by a maximum deviation from your baseline amount. The model cannot overextend into a single entry; it can only shift timing and sizing within limits you set in advance.
Once a window and size are determined, orders are placed through your connected exchange account via API. No dashboard refresh or manual click is required for a scheduled cycle to complete.
The model does not rely on a single indicator. Each cycle combines several data layers, weighted according to the asset's recent behavior, before an entry score is produced.
Bid/ask imbalance across the top levels, sampled continuously to detect short-term liquidity shifts.
Rolling standard deviation across multiple timeframes, used to size the acceptable entry band.
Traded volume distribution by price level, identifying zones of prior accumulation or rejection.
Aggregated directional bias from public market commentary, weighted as a secondary, not primary, input.
Where applicable, perpetual funding rates and spot-futures basis are tracked for crowding signals.
Past drawdown patterns for the asset class, used to calibrate how aggressively size can shift.
For traders running short-cycle DCA strategies, the dashboard surfaces each execution window alongside the order book conditions that triggered it, allowing a manual override to be logged if a position already exists elsewhere.
For investors contributing on a monthly basis, the system compresses the execution window around the scheduled date rather than abandoning the calendar entirely, keeping contribution discipline intact while reducing exposure to single-day spikes.
Raven Quantis does not forecast price direction. The engine's task is narrower: identify, within a short window, when conditions are less costly for a scheduled entry, and execute within the limits you configure.
Every parameter — deviation caps, data weighting, execution windows — remains visible and adjustable. Nothing runs as a closed box; the scoring logic behind each decision is available in your account log.
Start with a read-only analysis of your current allocation plan before connecting any execution permissions. No trading access is required to review the model's entry scoring.
API connections use scoped, revocable permissions. Withdrawal rights are never requested.