# Slippage & Market Impact Models

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Operator and jurisdiction: BASIS is operated by BASIS DIGITAL INFRASTRUCTURE LTD, a Seychelles IBC (LEI: [254900IX2F2KCWNSSS64](https://lei.bloomberg.com/leis/view/254900IX2F2KCWNSSS64)).

Research Partner: Base58 Labs contributes execution research, systems modeling, and risk design.
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Slippage is not random noise. It is a measurable function of market depth, volatility, time, and routing quality.

BASIS models slippage to improve execution precision and structural alpha capture across fragmented liquidity. The BHLE stack is designed for sub-50μs decision latency, 100K+ OPS, and proprietary routing under deterministic, math-constrained state machine risk controls.

***

## 1) Slippage components

{% tabs %}
{% tab title="Instant impact" %}
Immediate price movement caused by consuming visible liquidity at the top of the book.
{% endtab %}

{% tab title="Transient impact" %}
Price drift during the execution window while the order is still being completed.
{% endtab %}

{% tab title="Adverse selection" %}
Loss from interacting with liquidity just before new information is reflected in the market.
{% endtab %}

{% tab title="Routing friction" %}
Execution loss introduced by venue choice, queue position, latency, and hedge completion quality.
{% endtab %}
{% endtabs %}

A reliable model estimates conservative bounds for each component, then applies routing and stop conditions before execution.

***

## 2) Depth-based slippage estimation

A first-order depth model starts with two quantities:

* Order size: Q
* Available cumulative depth to a target price level: D

As Q / D increases, expected slippage generally increases.

### Intuition

| Condition              | Expected effect                                     |
| ---------------------- | --------------------------------------------------- |
| Small Q relative to D  | Lower immediate impact                              |
| Large Q relative to D  | Higher immediate impact                             |
| Fast depth refill      | Lower realized slippage than static depth suggests  |
| Thin or unstable books | Higher realized slippage than static depth suggests |

{% hint style="warning" %}
Depth alone is not sufficient. Order books are dynamic, and visible liquidity can disappear under stress. BASIS therefore combines book depth, refill behavior, volatility, venue quality, and latency-sensitive routing signals.
{% endhint %}

***

## 3) Nonlinear impact intuition

Many markets exhibit sublinear impact growth with size. A common empirical heuristic is:

$$
\text{Impact} \propto \sigma \cdot \sqrt{\frac{Q}{V}}
$$

| Variable | Meaning           |
| -------- | ----------------- |
| $\sigma$ | Market volatility |
| $Q$      | Order size        |
| $V$      | Traded volume     |

This captures a practical idea:

* higher volatility tends to increase impact
* larger orders tend to increase impact
* deeper volume tends to reduce impact

{% hint style="info" %}
This square-root form is a useful empirical guide, not a universal law. Production execution systems should calibrate it by asset, venue, and volatility regime.
{% endhint %}

***

## 4) Conservative execution bound

BASIS does not rely on a single impact number. It composes a conservative bound from multiple microstructure terms.

```
conservative_slippage_bound
= instant_impact(depth, spread, queue)
+ transient_impact(volatility, execution_time)
+ adverse_selection(toxicity, information_speed)
+ routing_friction(latency, venue_quality, hedge_path)
```

### Practical interpretation

| Term              | What it protects against                            |
| ----------------- | --------------------------------------------------- |
| Instant impact    | Sweeping shallow displayed liquidity                |
| Transient impact  | Price movement during completion                    |
| Adverse selection | Being late to new information                       |
| Routing friction  | Suboptimal pathing, venue mismatch, or latency loss |

This structure supports deterministic pre-trade checks and post-trade attribution.

***

## 5) Why order slicing helps, and when it hurts

Order slicing can reduce instantaneous impact by trading in smaller pieces. It also increases exposure time, which can raise adverse selection risk.

{% stepper %}
{% step %}

#### When slicing helps

Use smaller clips when the opportunity window is wide, liquidity is stable, and hedge completion remains safe.
{% endstep %}

{% step %}

#### When slicing hurts

Avoid over-slicing when the price gap is fleeting, volatility is elevated, or book toxicity is rising.
{% endstep %}

{% step %}

#### What BASIS does

Routing logic evaluates whether to execute in one shot, split across venues, or pause entirely if state machine constraints indicate poor execution precision.
{% endstep %}
{% endstepper %}

### Decision guide

| Market state                   | Preferred behavior                      |
| ------------------------------ | --------------------------------------- |
| Stable liquidity, low toxicity | Controlled slicing may improve outcomes |
| Fast market, short-lived edge  | Faster completion may dominate          |
| Weak hedge path                | Reduce size or do not execute           |
| Venue instability detected     | Re-route or stop                        |

***

## 6) Post-trade slippage analytics 📊

A production-grade system maintains realized slippage distributions across multiple dimensions.

| Dimension    | Examples                           |
| ------------ | ---------------------------------- |
| Percentiles  | p50, p90, p99                      |
| Venue        | CEX, DEX, internal route class     |
| Asset        | BTC, ETH, SOL, PAXG                |
| Regime       | low vol, mid vol, stress           |
| Order shape  | single-shot, sliced, multi-venue   |
| Latency band | local, cross-venue, degraded state |

These analytics inform:

* venue scoring
* order sizing constraints
* route eligibility
* stop conditions
* model recalibration

{% hint style="success" %}
The goal is not to eliminate slippage entirely. The goal is to keep realized execution inside deterministic bounds often enough to preserve structural alpha after costs.
{% endhint %}

***

## 7) BASIS execution design principles

| Principle               | BASIS approach                                   |
| ----------------------- | ------------------------------------------------ |
| Deterministic execution | Pre-trade checks and bounded routing logic       |
| Latency discipline      | BHLE architecture with sub-50μs decision latency |
| Throughput              | 100K+ OPS routing and state handling             |
| Risk controls           | Math constraints and state machine enforcement   |
| Research loop           | Continuous calibration with the Research Partner |

This is why slippage modeling is treated as core infrastructure, not a cosmetic metric.

***

## 8) Key takeaways 🎯

* Slippage is a structured microstructure problem
* Static depth is necessary, but not sufficient
* Nonlinear impact matters at larger size
* Slicing can improve or worsen outcomes depending on regime
* Deterministic routing and post-trade attribution are essential for execution precision

***

Next: read Execution Precision & On-chain Routing.


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