# Backtest an agent

## Overview

Before running an agent live, it's recommended to **test its past performance** on historical data. This is called a **backtest**.

The goal is to understand how the agent would have reacted in different market conditions — bullish phases, bearish phases, periods of high volatility, drawdowns, etc.

{% hint style="info" %}
Backtests are relevant for **trading bots** and **investing agents** with deterministic rules. For an **AI trader**, backtesting is less representative since the AI exploits the present context (news, positioning) which can't be faithfully replayed in the past.
{% endhint %}

***

## Launching a backtest

From an agent's page, click **Create a backtest**. A dialog opens in three steps:

### 1. Parameters

#### Backtest period

Define a **start date** and **end date** for your backtest. Only this period will be used to simulate past operations and analyze the agent's performance.

**Presets** are available: last month, last 6 months, last year, last 5 years.

* We have **over 20 years of history** for most assets.
* If the chosen start date predates our price history, the backtest will automatically start at **the earliest date available** in our database.
* Our data is updated **daily** to integrate the latest price changes.

{% hint style="info" %}
Available history depth depends on your plan: 2 years (Free), 5 years (Core), 15 years (Plus), 20 years (Pro). See [Pricing](/misc/pricing.md).
{% endhint %}

#### Starting capital

Define a **starting capital** for your backtest.

* **For a trading bot**: starting capital is required, as it evolves dynamically based on gains and losses generated by each trade.
* **For an investing agent**: capital can be defined directly in the description (DCA, conditional purchases, etc.). If an initial amount must be invested in one go, it should also be specified in the description.

{% hint style="success" %}
While the starting capital doesn't affect the trades historically executed, choosing a realistic value helps you better project the evolution.
{% endhint %}

### 2. Target assets

For each asset referenced in the agent, choose the **exact symbol** to use for the backtest. This lets the agent run on the precise assets available at the targeted broker or exchange.

### 3. Target exchange

Choose the **exchange or broker** for the backtest. The fees and market specifications of that platform are then applied automatically.

{% hint style="success" %}
You can also choose the **Historical** option to use Obside's warehouse data, without depending on a specific exchange.
{% endhint %}

***

## Backtest results

Once the backtest is complete, you can access several tabs to analyze performance:

* **Metrics** — dozens of detailed indicators (return, drawdown, Sharpe ratio, win rate, etc.).
* **Performance** — capital evolution curve over the period.
* **Trades** — full list of executed operations, exportable.
* **Chart** — TradingView visualization of all entry and exit points, with the reason for each trade.

{% hint style="info" %}
The **Obside Score** estimates the quality of a backtest by combining performance, risk and statistical reliability. See [Obside Score](/misc/obside-score.md).
{% endhint %}

***

## Iterating on the agent

If the backtest results aren't satisfactory, you can **continue the conversation** in the chat linked to the agent to adjust its description, then run a new backtest with the same parameters or different ones.


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