> ## Documentation Index
> Fetch the complete documentation index at: https://kalpi.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# Build Your Own Basket

> Create dynamic, rule-based portfolios from scratch using custom filters and ranking metrics.

A screener helps you find stocks, but the [Kalpi Portfolio Builder](https://kalpi.ai/research/simple-strategy) lets you craft a complete, dynamic investment strategy. Watch our quick 4-minute walkthrough to see how to define your universe, set filters, rank assets, and deploy a live strategy.

[Build Your Own Rule-Based Portfolio in Minutes](https://www.youtube.com/embed/56RTYFyW8Yo?si=YourUniqueCode\&rel=0)

## The Builder Workflow

Building your own custom basket allows you to create highly tailored systematic strategies without writing a single line of code.

Our no-code [**Portfolio Builder**](https://kalpi.ai/research/simple-strategy) operates on a strict, sequential four-step process to ensure your strategy is logically sound before backtesting.

**1. Universe**

Define the boundary of your strategy. This determines the pool of assets your logic will scan. You can select pre-defined universes (like **Nifty 50** or **Nifty 500**) or create custom thematic pools.

**2. Filtration**

Apply technical or fundamental filters to narrow down your universe. This is where you eliminate stocks that do not meet your baseline criteria (e.g., filtering out stocks below their 200-Day Moving Average or with poor liquidity).

**3. Ranking & Scoring**

Once filtered, assign scores to the remaining assets based on your chosen factors (like *Momentum*, *Value*, or *Quality*). The system will rank the assets from highest to lowest based on your specific logic.

**4. Weightage**

Determine how capital is allocated among your ranked assets. Choose from **Equal Weight**, **Market Cap Weight**, or assign custom dynamic weights based on volatility or conviction scores.

> [!NOTE]
>
> **Save and Backtest:** Once all four steps are complete, save your configuration. You can then immediately run a backtest to simulate historical performance and view your strategy's *Win Rate*, *Max Drawdown*, and *Sharpe Ratio* before taking it live.

> [card] [View Template Baskets](https://kalpi.ai/docs/basket-builder/template-baskets.md)
>
> Want a faster route? Explore our expert-built strategy templates.

> [card] [Analyze Your Backtest](https://kalpi.ai/docs/metrics/win-rate)
>
> Learn how to read your strategy's performance metrics.


This documentation is built and hosted on [Mintlify](https://mintlify.com), a developer documentation platform.