---
title: "Optimization"
description: "Parameter optimization is the process of finding the optimal algorithm parameters to maximize or minimize an objective function. Just be wary of overfitting."
canonical: https://www.quantconnect.com/docs/v2/cloud-platform/optimization
markdown: https://www.quantconnect.com/docs/v2/cloud-platform/optimization.md
language: python
source: https://github.com/QuantConnect/Documentation/tree/master/01%20Cloud%20Platform/11%20Optimization
modified: 2026-10-22
index: https://www.quantconnect.com/llms.txt
---

# Optimization

[Documentation](https://www.quantconnect.com/docs/v2.md) > [Cloud Platform](https://www.quantconnect.com/docs/v2/cloud-platform.md) > Optimization

> Code examples are shown in Python. Append `?lang=csharp` to this URL for the C# version of this page.

Parameter optimization is the process of finding the optimal algorithm parameters to maximize or minimize an objective function. For instance, you can optimize your indicator parameters to maximize the [Sharpe ratio](https://www.quantconnect.com/docs/v2/writing-algorithms/key-concepts/glossary.md#30-Sharpe-ratio) that your algorithm achieves over a backtest. Optimization can help you adjust your strategy to achieve better backtesting performance, but be wary of overfitting. If you select parameter values that model the past too closely, your algorithm may not be robust enough to perform well using out-of-sample data.

- [Getting Started](https://www.quantconnect.com/docs/v2/cloud-platform/optimization/getting-started.md): Learn the basics
- [Parameters](https://www.quantconnect.com/docs/v2/cloud-platform/optimization/parameters.md): Variables being optimized
- [Objectives](https://www.quantconnect.com/docs/v2/cloud-platform/optimization/objectives.md): Target metric of performance
- [Strategies](https://www.quantconnect.com/docs/v2/cloud-platform/optimization/strategies.md): How parameters are adjusted
- [Deployment](https://www.quantconnect.com/docs/v2/cloud-platform/optimization/deployment.md): Run optimization jobs
- [Results](https://www.quantconnect.com/docs/v2/cloud-platform/optimization/results.md): Your performance dashboard

### See Also

- [Running Optimizations](https://www.quantconnect.com/docs/v2/cloud-platform/optimization/deployment.md)
- [Reviewing Results](https://www.quantconnect.com/docs/v2/cloud-platform/optimization/results.md)

## Pages in This Section

- [Getting Started](https://www.quantconnect.com/docs/v2/cloud-platform/optimization/getting-started.md): Parameter optimization is the process of finding the optimal algorithm parameters to maximize or minimize an objective function. Just be wary of overfitting.
- [Parameters](https://www.quantconnect.com/docs/v2/cloud-platform/optimization/parameters.md): Parameters are project variables that your algorithm uses to define the value of internal variables like indicator arguments or the length of lookback windows.
- [Objectives](https://www.quantconnect.com/docs/v2/cloud-platform/optimization/objectives.md): An optimization objective is the performance metric that's used to compare the backtest performance of different parameter values.
- [Strategies](https://www.quantconnect.com/docs/v2/cloud-platform/optimization/strategies.md): Optimization strategies control how the optimizer adjusts parameters for each new backtest that's run in the optimization job.
- [Deployment](https://www.quantconnect.com/docs/v2/cloud-platform/optimization/deployment.md): Deploy optimization jobs for your trading algorithms to optimize your algorithm parameters for the objective that you specify.
- [Results](https://www.quantconnect.com/docs/v2/cloud-platform/optimization/results.md): The optimization results page shows your algorithm's live trading performance with the various parameter values. Review the page to investigate improvements.