Tutorials

These tutorials will help you get started with quantammsim.

• Getting Started
  • Your First Simulation
  • Understanding the Results
  • Basic Usage
  • Advanced Configuration
  • Next Steps
• Pool Architecture
  • Balancer Pools
  • CowAMM Pools
  • Gyroscope Pools
  • QuantAMM Pools
• Temporal Function Market Makers: Introduction to Dynamic AMMs
  • Core Concepts
  • Update Rules
• QuantAMM: Pools as Portfolios
  • Constrained vs unconstrained parameters
  • Pre-implemented Strategies
• Implementing a Custom QuantAMM Strategy
  • Weight Calculation Paths
  • Vectorized Implementation
  • Scan-Based Implementation
  • Implementing Both Paths
  • Creating a Custom Rule
  • Using Custom Rules
  • Design Considerations
• Balancer Pools
  • Key Features
  • Multi-Token Pools
  • Arbitrage Modeling
  • Dynamic Parameters
  • Arbitrage Configuration
• CoW Pools
  • Key Features
  • Arbitrage Modeling
  • Dynamic Parameters
  • Parameter Configuration
  • Arbitrage Configuration
• Gyroscope Pools
  • Key Features
  • Weight Targeting
  • Dynamic Parameters
  • Arbitrage Configuration
• Implementing a new AMM
  • Core Implementation Requirements
• Training Pipeline
  • Overview
  • Basic Training Setup
  • Optimizer Configuration
  • Batch Training
  • Initial Parameters
  • Training Objectives
  • Advanced: Hessian-Based Training
  • Backpropagation Module
  • Straight-Through Estimators
  • Monitoring Training
  • Saving and Loading
  • Example: Complete Training Script
  • See Also
• Training Pools
  • Why Train Pools?
  • Basic Training Setup
  • Choosing an Optimization Approach
  • Getting Started
  • Data Handling
  • Gradient Descent Training
  • Gradient-Free Optimization
  • Parameter & Run Configuration
  • Understanding Bout Offset
  • Advanced Features
  • Performance Considerations
  • See Also
• Walk-Forward Analysis
  • Why Walk-Forward Analysis?
  • Basic Walk-Forward Evaluation
  • Interpreting Results
  • Rademacher Complexity
  • Comparing Training Approaches
  • Walk-Forward with Regularisation
  • Generating Cycles Manually
  • Decision Framework
  • See Also
• Ensemble Training
  • Why Ensemble Averaging?
  • Basic Ensemble Setup
  • Initialisation Methods
  • Multi-Hook Chaining
  • Ensemble + Walk-Forward Validation
  • Parameter Shapes
  • Best Practices
  • See Also
• Hyperparameter Tuning
  • The Three-Level Hierarchy
  • Basic Hyperparameter Tuning
  • Multi-Period SGD
  • Multi-Objective Tuning
  • Custom Search Spaces
  • Production Workflow
  • Analysing Results
  • See Also