> For the complete documentation index, see [llms.txt](https://alpha-frame.gitbook.io/alpha-frame-docs/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://alpha-frame.gitbook.io/alpha-frame-docs/how-algoframe-works.md).

# ⚙️ How AlgoFrame Works

<figure><img src="/files/QBgNbEwkCcFvLvpMKQMD" alt=""><figcaption></figcaption></figure>

> **AlgoFrame is the engine. You’re the architect.**\
> Algola just adapts to the blueprint you give it.

Most trading bots are rigid. One idea, one script, one fragile edge.\
**AlgoFrame is different.** It’s an empty canvas for strategy automation—designed to adapt, observe, and strike with precision.

At its core, AlgoFrame lets you design **Agents**.\
An Agent isn’t a prediction. It’s a *decision-making framework* built from three simple modules:

1. **Scope** – *Where does Algola look?*
2. **Trigger** – *When does Algola act?*
3. **Execution** – *How does Algola exit?*

No code. No guesswork. Just structured intent.

To see how powerful this is, let’s break down a real strategy that users already rely on.

***

### The XP Grinder (Airdrop Farmer)

> **Mission:** Generate trading volume on `opinion.trade` for airdrops or leaderboard XP—**without directional risk**.

Many platforms reward *activity*, not accuracy. The challenge is creating that activity **without bleeding capital**.

***

### 🧩 Agent Configuration: XP Grinder

#### 1️⃣ Scope -> *Where Algola Looks*

We don’t care *what* the market is about.\
We care **where bonuses are active**.

| Parameter       | Value            |
| --------------- | ---------------- |
| Market Type     | Global Scan      |
| Category Filter | `Active Bonuses` |
| Market Status   | Open & Liquid    |

> Algola scans the entire opinion.trade ecosystem, but only pays attention to markets that reward volume.

***

#### 2️⃣ Trigger -> *When Algola Acts*

The goal is zero exposure. That means entering only when the spread is razor-thin.

| Condition           | Threshold                  |
| ------------------- | -------------------------- |
| Bid–Ask Spread      | `< 0.5%`                   |
| Available Liquidity | ≥ $100                     |
| Market State        | Stable (no halt / no lock) |

> If there’s friction, Algola waits. Chameleons don’t rush.

***

#### 3️⃣ Execution -> *How Algola Exits*

This is the stealth strike.

| Action        | Value                |
| ------------- | -------------------- |
| Entry Size    | $100                 |
| Exit Mode     | Immediate Round-Trip |
| Hold Time     | \~0 seconds          |
| Risk Exposure | Near-zero            |

**Flow:**

1. Buy $100 at market
2. Immediately sell $100 back
3. Collect volume credit
4. Repeat quietly

> The only cost is the microscopic spread.\
> The reward is pure XP.

***

### 🎯 Outcome

* ✔️ Generates volume for airdrops & leaderboards
* ✔️ No market prediction required
* ✔️ Minimal capital risk
* ✔️ Fully automated

Algola does the boring work—flawlessly.

***

### Remix It 🎛️

The **XP Grinder** is just one configuration.

Change the modules, and you change the personality:

* **Sports Market Sniper**\
  \&#xNAN;*Scope:* Sports categories\
  \&#xNAN;*Trigger:* Odds move after injury news\
  \&#xNAN;*Execution:* Take-profit ladder
* **Breaking News Trader**\
  \&#xNAN;*Scope:* Politics & World Events\
  \&#xNAN;*Trigger:* Sudden probability spike\
  \&#xNAN;*Execution:* Timed exit before resolution
* **Long-Tail Value Hunter**\
  \&#xNAN;*Scope:* Low-attention markets\
  \&#xNAN;*Trigger:* Mispriced probabilities\
  \&#xNAN;*Execution:* Hold to resolution

Same engine. Different instincts.

***

### Final Thought

AlgoFrame doesn’t tell you *what* to trade.\
It gives you the frame to express **how you think**.

Algola adapts.\
You design the hunt.


---

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