Forestfox predictive analysis dashboard displayed on a trading workstation

Precision in Volatility

Forestfox applies predictive modelling to live liquidity and volume data, translating market noise into defined, risk-aware entry points. The output is not a signal to chase — it is a structured dollar-cost averaging schedule built around where the market is actually accumulating.

Explore the Engine See the methodology

Composite Signal Inputs

Liquidity depth

Volume profile skew

Volatility band width

Order flow imbalance

Methodology

How the model finds its entries

Forestfox does not generate signals from price action alone. It reads the order book for where liquidity is clustering and cross-references that against historical volume profiles, so each entry is placed where resting orders already suggest a floor or ceiling.

The purpose is not to predict direction with certainty — no model can do that responsibly. It is to reduce the cost of being wrong by timing accumulation around points the market has already shown it respects.

Data ingestion & normalisation

Order book snapshots, trade prints, and volume data are pulled from connected venues and normalised into a single time series.
Liquidity cluster mapping

The model identifies price zones with disproportionate resting liquidity, which historically act as support or resistance.
Volume profile analysis

Volume-at-price data is layered on top to confirm whether a liquidity zone has genuine participation behind it.
DCA schedule optimisation

Entry tranches are sized and spaced around the confirmed zones, reducing average cost exposure to any single price point.

Systemic Intelligence

Three pillars behind every decision the system makes

Each capability is designed to remove a specific point of friction between raw market data and a disciplined trading decision.

Analytics

Real-time analytics

Latency-optimised data ingestion keeps order book and trade data within milliseconds of the live market, across all connected venues.

You react to liquidity shifts before they solidify into a visible trend on a standard chart.

Risk

Predictive risk modelling

Volatility bands are recalculated continuously against recent realised movement, rather than relying on static, backward-looking indicators.

Position sizing adjusts automatically as conditions change, instead of leaving exposure fixed through a regime shift.

Execution

Automated execution

Confirmed entry tranches are routed directly through your connected exchange API, with no manual order placement required.

Your DCA schedule executes on schedule, even outside your own working hours.

Interface

A command centre for strategy, not a wall of charts

The dashboard is built on the assumption that complexity should be handled by the model, not displayed to the user. Liquidity clusters, volume profiles, and volatility bands are processed in the background; what reaches the screen is a short list of current and upcoming DCA tranches.

One view per asset, showing the active entry schedule and its rationale
Plain-language notes on why a tranche was delayed or resized
A single risk indicator per position, rather than a dozen overlapping metrics

Transparency

Questions we expect from a technical audience

These are the questions most often raised by quantitative traders evaluating an automated system. We would rather answer them directly than leave them unaddressed.

Where does the underlying market data come from?

Order book and trade data are sourced directly from the exchange APIs you connect, rather than from a single aggregated third-party feed. This keeps latency low and avoids introducing a data provider as an additional point of failure.

How is the predictive model trained and validated?

The model is trained on historical order book and volume data across multiple market regimes, including periods of low liquidity and high volatility. It is re-validated on a rolling basis against recent data to check for model drift, and entry logic is adjusted when performance against out-of-sample data degrades.

What happens if the model's assumptions stop holding?

No predictive model performs equally well in every regime. Volatility bands widen automatically when realised movement departs from recent norms, which reduces tranche size and slows the DCA schedule until conditions stabilise.

How is exchange access and account security handled?

Execution relies on API keys with trade-only permissions; Forestfox does not request withdrawal access. Keys are encrypted at rest, and you retain full control to revoke access from your exchange account at any time.

Why dollar-cost averaging rather than a single entry?

A single entry point assumes the model has identified the exact bottom or top, which is not a claim we make. Spreading entries across confirmed liquidity zones reduces the cost of being early or late to any one tranche.

Strategic entry, automated consistency

If disciplined, data-led execution fits how you already think about risk, connecting your exchange account takes a few minutes and gives you full visibility over every scheduled tranche before it runs.

Connect to the API