Simon/lowkey_backtest
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Initial project setup with Python version 3.12, main script for backtesting trading strategies, and configuration files for project management. Added necessary dependencies and documentation structure.

Browse Source
This commit is contained in:
Simon Moisy 2025-08-05 12:08:59 +08:00
parent cac0629dcd
commit 56dca05a3e
17 changed files with 1871 additions and 0 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

61
.cursor/rules/always-global.mdc Normal file
View File

@ -0,0 +1,61 @@
---
description: Global development standards and AI interaction principles
globs:
alwaysApply: true
---
# Rule: Always Apply - Global Development Standards
## AI Interaction Principles
### Step-by-Step Development
- **NEVER** generate large blocks of code without explanation
- **ALWAYS** ask "provide your plan in a concise bullet list and wait for my confirmation before proceeding"
- Break complex tasks into smaller, manageable pieces (≤250 lines per file, ≤50 lines per function)
- Explain your reasoning step-by-step before writing code
- Wait for explicit approval before moving to the next sub-task
### Context Awareness
- **ALWAYS** reference existing code patterns and data structures before suggesting new approaches
- Ask about existing conventions before implementing new functionality
- Preserve established architectural decisions unless explicitly asked to change them
- Maintain consistency with existing naming conventions and code style
## Code Quality Standards
### File and Function Limits
- **Maximum file size**: 250 lines
- **Maximum function size**: 50 lines
- **Maximum complexity**: If a function does more than one main thing, break it down
- **Naming**: Use clear, descriptive names that explain purpose
### Documentation Requirements
- **Every public function** must have a docstring explaining purpose, parameters, and return value
- **Every class** must have a class-level docstring
- **Complex logic** must have inline comments explaining the "why", not just the "what"
- **API endpoints** must be documented with request/response examples
### Error Handling
- **ALWAYS** include proper error handling for external dependencies
- **NEVER** use bare except clauses
- Provide meaningful error messages that help with debugging
- Log errors appropriately for the application context
## Security and Best Practices
- **NEVER** hardcode credentials, API keys, or sensitive data
- **ALWAYS** validate user inputs
- Use parameterized queries for database operations
- Follow the principle of least privilege
- Implement proper authentication and authorization
## Testing Requirements
- **Every implementation** should have corresponding unit tests
- **Every API endpoint** should have integration tests
- Test files should be placed alongside the code they test
- Use descriptive test names that explain what is being tested
## Response Format
- Be concise and avoid unnecessary repetition
- Focus on actionable information
- Provide examples when explaining complex concepts
- Ask clarifying questions when requirements are ambiguous

237
.cursor/rules/architecture.mdc Normal file
View File

@ -0,0 +1,237 @@
---
description: Modular design principles and architecture guidelines for scalable development
globs:
alwaysApply: false
---
# Rule: Architecture and Modular Design
## Goal
Maintain a clean, modular architecture that scales effectively and prevents the complexity issues that arise in AI-assisted development.
## Core Architecture Principles
### 1. Modular Design
- **Single Responsibility**: Each module has one clear purpose
- **Loose Coupling**: Modules depend on interfaces, not implementations
- **High Cohesion**: Related functionality is grouped together
- **Clear Boundaries**: Module interfaces are well-defined and stable
### 2. Size Constraints
- **Files**: Maximum 250 lines per file
- **Functions**: Maximum 50 lines per function
- **Classes**: Maximum 300 lines per class
- **Modules**: Maximum 10 public functions/classes per module
### 3. Dependency Management
- **Layer Dependencies**: Higher layers depend on lower layers only
- **No Circular Dependencies**: Modules cannot depend on each other cyclically
- **Interface Segregation**: Depend on specific interfaces, not broad ones
- **Dependency Injection**: Pass dependencies rather than creating them internally
## Modular Architecture Patterns
### Layer Structure
```
src/
├── presentation/ # UI, API endpoints, CLI interfaces
├── application/ # Business logic, use cases, workflows
├── domain/ # Core business entities and rules
├── infrastructure/ # Database, external APIs, file systems
└── shared/ # Common utilities, constants, types
```
### Module Organization
```
module_name/
├── __init__.py # Public interface exports
├── core.py # Main module logic
├── types.py # Type definitions and interfaces
├── utils.py # Module-specific utilities
├── tests/ # Module tests
└── README.md # Module documentation
```
## Design Patterns for AI Development
### 1. Repository Pattern
Separate data access from business logic:
```python
# Domain interface
class UserRepository:
def get_by_id(self, user_id: str) -> User: ...
def save(self, user: User) -> None: ...
# Infrastructure implementation
class SqlUserRepository(UserRepository):
def get_by_id(self, user_id: str) -> User:
# Database-specific implementation
pass
```
### 2. Service Pattern
Encapsulate business logic in focused services:
```python
class UserService:
def __init__(self, user_repo: UserRepository):
self._user_repo = user_repo
def create_user(self, data: UserData) -> User:
# Validation and business logic
# Single responsibility: user creation
pass
```
### 3. Factory Pattern
Create complex objects with clear interfaces:
```python
class DatabaseFactory:
@staticmethod
def create_connection(config: DatabaseConfig) -> Connection:
# Handle different database types
# Encapsulate connection complexity
pass
```
## Architecture Decision Guidelines
### When to Create New Modules
Create a new module when:
- **Functionality** exceeds size constraints (250 lines)
- **Responsibility** is distinct from existing modules
- **Dependencies** would create circular references
- **Reusability** would benefit other parts of the system
- **Testing** requires isolated test environments
### When to Split Existing Modules
Split modules when:
- **File size** exceeds 250 lines
- **Multiple responsibilities** are evident
- **Testing** becomes difficult due to complexity
- **Dependencies** become too numerous
- **Change frequency** differs significantly between parts
### Module Interface Design
```python
# Good: Clear, focused interface
class PaymentProcessor:
def process_payment(self, amount: Money, method: PaymentMethod) -> PaymentResult:
"""Process a single payment transaction."""
pass
# Bad: Unfocused, kitchen-sink interface
class PaymentManager:
def process_payment(self, ...): pass
def validate_card(self, ...): pass
def send_receipt(self, ...): pass
def update_inventory(self, ...): pass # Wrong responsibility!
```
## Architecture Validation
### Architecture Review Checklist
- [ ] **Dependencies flow in one direction** (no cycles)
- [ ] **Layers are respected** (presentation doesn't call infrastructure directly)
- [ ] **Modules have single responsibility**
- [ ] **Interfaces are stable** and well-defined
- [ ] **Size constraints** are maintained
- [ ] **Testing** is straightforward for each module
### Red Flags
- **God Objects**: Classes/modules that do too many things
- **Circular Dependencies**: Modules that depend on each other
- **Deep Inheritance**: More than 3 levels of inheritance
- **Large Interfaces**: Interfaces with more than 7 methods
- **Tight Coupling**: Modules that know too much about each other's internals
## Refactoring Guidelines
### When to Refactor
- Module exceeds size constraints
- Code duplication across modules
- Difficult to test individual components
- New features require changing multiple unrelated modules
- Performance bottlenecks due to poor separation
### Refactoring Process
1. **Identify** the specific architectural problem
2. **Design** the target architecture
3. **Create tests** to verify current behavior
4. **Implement changes** incrementally
5. **Validate** that tests still pass
6. **Update documentation** to reflect changes
### Safe Refactoring Practices
- **One change at a time**: Don't mix refactoring with new features
- **Tests first**: Ensure comprehensive test coverage before refactoring
- **Incremental changes**: Small steps with verification at each stage
- **Backward compatibility**: Maintain existing interfaces during transition
- **Documentation updates**: Keep architecture documentation current
## Architecture Documentation
### Architecture Decision Records (ADRs)
Document significant decisions in `./docs/decisions/`:
```markdown
# ADR-003: Service Layer Architecture
## Status
Accepted
## Context
As the application grows, business logic is scattered across controllers and models.
## Decision
Implement a service layer to encapsulate business logic.
## Consequences
**Positive:**
- Clear separation of concerns
- Easier testing of business logic
- Better reusability across different interfaces
**Negative:**
- Additional abstraction layer
- More files to maintain
```
### Module Documentation Template
```markdown
# Module: [Name]
## Purpose
What this module does and why it exists.
## Dependencies
- **Imports from**: List of modules this depends on
- **Used by**: List of modules that depend on this one
- **External**: Third-party dependencies
## Public Interface
```python
# Key functions and classes exposed by this module
```
## Architecture Notes
- Design patterns used
- Important architectural decisions
- Known limitations or constraints
```
## Migration Strategies
### Legacy Code Integration
- **Strangler Fig Pattern**: Gradually replace old code with new modules
- **Adapter Pattern**: Create interfaces to integrate old and new code
- **Facade Pattern**: Simplify complex legacy interfaces
### Gradual Modernization
1. **Identify boundaries** in existing code
2. **Extract modules** one at a time
3. **Create interfaces** for each extracted module
4. **Test thoroughly** at each step
5. **Update documentation** continuously

123
.cursor/rules/code-review.mdc Normal file
View File

@ -0,0 +1,123 @@
---
description: AI-generated code review checklist and quality assurance guidelines
globs:
alwaysApply: false
---
# Rule: Code Review and Quality Assurance
## Goal
Establish systematic review processes for AI-generated code to maintain quality, security, and maintainability standards.
## AI Code Review Checklist
### Pre-Implementation Review
Before accepting any AI-generated code:
1. **Understand the Code**
- [ ] Can you explain what the code does in your own words?
- [ ] Do you understand each function and its purpose?
- [ ] Are there any "magic" values or unexplained logic?
- [ ] Does the code solve the actual problem stated?
2. **Architecture Alignment**
- [ ] Does the code follow established project patterns?
- [ ] Is it consistent with existing data structures?
- [ ] Does it integrate cleanly with existing components?
- [ ] Are new dependencies justified and necessary?
3. **Code Quality**
- [ ] Are functions smaller than 50 lines?
- [ ] Are files smaller than 250 lines?
- [ ] Are variable and function names descriptive?
- [ ] Is the code DRY (Don't Repeat Yourself)?
### Security Review
- [ ] **Input Validation**: All user inputs are validated and sanitized
- [ ] **Authentication**: Proper authentication checks are in place
- [ ] **Authorization**: Access controls are implemented correctly
- [ ] **Data Protection**: Sensitive data is handled securely
- [ ] **SQL Injection**: Database queries use parameterized statements
- [ ] **XSS Prevention**: Output is properly escaped
- [ ] **Error Handling**: Errors don't leak sensitive information
### Integration Review
- [ ] **Existing Functionality**: New code doesn't break existing features
- [ ] **Data Consistency**: Database changes maintain referential integrity
- [ ] **API Compatibility**: Changes don't break existing API contracts
- [ ] **Performance Impact**: New code doesn't introduce performance bottlenecks
- [ ] **Testing Coverage**: Appropriate tests are included
## Review Process
### Step 1: Initial Code Analysis
1. **Read through the entire generated code** before running it
2. **Identify patterns** that don't match existing codebase
3. **Check dependencies** - are new packages really needed?
4. **Verify logic flow** - does the algorithm make sense?
### Step 2: Security and Error Handling Review
1. **Trace data flow** from input to output
2. **Identify potential failure points** and verify error handling
3. **Check for security vulnerabilities** using the security checklist
4. **Verify proper logging** and monitoring implementation
### Step 3: Integration Testing
1. **Test with existing code** to ensure compatibility
2. **Run existing test suite** to verify no regressions
3. **Test edge cases** and error conditions
4. **Verify performance** under realistic conditions
## Common AI Code Issues to Watch For
### Overcomplication Patterns
- **Unnecessary abstractions**: AI creating complex patterns for simple tasks
- **Over-engineering**: Solutions that are more complex than needed
- **Redundant code**: AI recreating existing functionality
- **Inappropriate design patterns**: Using patterns that don't fit the use case
### Context Loss Indicators
- **Inconsistent naming**: Different conventions from existing code
- **Wrong data structures**: Using different patterns than established
- **Ignored existing functions**: Reimplementing existing functionality
- **Architectural misalignment**: Code that doesn't fit the overall design
### Technical Debt Indicators
- **Magic numbers**: Hardcoded values without explanation
- **Poor error messages**: Generic or unhelpful error handling
- **Missing documentation**: Code without adequate comments
- **Tight coupling**: Components that are too interdependent
## Quality Gates
### Mandatory Reviews
All AI-generated code must pass these gates before acceptance:
1. **Security Review**: No security vulnerabilities detected
2. **Integration Review**: Integrates cleanly with existing code
3. **Performance Review**: Meets performance requirements
4. **Maintainability Review**: Code can be easily modified by team members
5. **Documentation Review**: Adequate documentation is provided
### Acceptance Criteria
- [ ] Code is understandable by any team member
- [ ] Integration requires minimal changes to existing code
- [ ] Security review passes all checks
- [ ] Performance meets established benchmarks
- [ ] Documentation is complete and accurate
## Rejection Criteria
Reject AI-generated code if:
- Security vulnerabilities are present
- Code is too complex for the problem being solved
- Integration requires major refactoring of existing code
- Code duplicates existing functionality without justification
- Documentation is missing or inadequate
## Review Documentation
For each review, document:
- Issues found and how they were resolved
- Performance impact assessment
- Security concerns and mitigations
- Integration challenges and solutions
- Recommendations for future similar tasks

93
.cursor/rules/context-management.mdc Normal file
View File

@ -0,0 +1,93 @@
---
description: Context management for maintaining codebase awareness and preventing context drift
globs:
alwaysApply: false
---
# Rule: Context Management
## Goal
Maintain comprehensive project context to prevent context drift and ensure AI-generated code integrates seamlessly with existing codebase patterns and architecture.
## Context Documentation Requirements
### PRD.md file documentation
1. **Project Overview**
- Business objectives and goals
- Target users and use cases
- Key success metrics
### CONTEXT.md File Structure
Every project must maintain a `CONTEXT.md` file in the root directory with:
1. **Architecture Overview**
- High-level system architecture
- Key design patterns used
- Database schema overview
- API structure and conventions
2. **Technology Stack**
- Programming languages and versions
- Frameworks and libraries
- Database systems
- Development and deployment tools
3. **Coding Conventions**
- Naming conventions
- File organization patterns
- Code structure preferences
- Import/export patterns
4. **Current Implementation Status**
- Completed features
- Work in progress
- Known technical debt
- Planned improvements
## Context Maintenance Protocol
### Before Every Coding Session
1. **Review CONTEXT.md and PRD.md** to understand current project state
2. **Scan recent changes** in git history to understand latest patterns
3. **Identify existing patterns** for similar functionality before implementing new features
4. **Ask for clarification** if existing patterns are unclear or conflicting
### During Development
1. **Reference existing code** when explaining implementation approaches
2. **Maintain consistency** with established patterns and conventions
3. **Update CONTEXT.md** when making architectural decisions
4. **Document deviations** from established patterns with reasoning
### Context Preservation Strategies
- **Incremental development**: Build on existing patterns rather than creating new ones
- **Pattern consistency**: Use established data structures and function signatures
- **Integration awareness**: Consider how new code affects existing functionality
- **Dependency management**: Understand existing dependencies before adding new ones
## Context Prompting Best Practices
### Effective Context Sharing
- Include relevant sections of CONTEXT.md in prompts for complex tasks
- Reference specific existing files when asking for similar functionality
- Provide examples of existing patterns when requesting new implementations
- Share recent git commit messages to understand latest changes
### Context Window Optimization
- Prioritize most relevant context for current task
- Use @filename references to include specific files
- Break large contexts into focused, task-specific chunks
- Update context references as project evolves
## Red Flags - Context Loss Indicators
- AI suggests patterns that conflict with existing code
- New implementations ignore established conventions
- Proposed solutions don't integrate with existing architecture
- Code suggestions require significant refactoring of existing functionality
## Recovery Protocol
When context loss is detected:
1. **Stop development** and review CONTEXT.md
2. **Analyze existing codebase** for established patterns
3. **Update context documentation** with missing information
4. **Restart task** with proper context provided
5. **Test integration** with existing code before proceeding

67
.cursor/rules/create-prd.mdc Normal file
View File

@ -0,0 +1,67 @@
---
description: Creating PRD for a project or specific task/function
globs:
alwaysApply: false
---
---
description: Creating PRD for a project or specific task/function
globs:
alwaysApply: false
---
# Rule: Generating a Product Requirements Document (PRD)
## Goal
To guide an AI assistant in creating a detailed Product Requirements Document (PRD) in Markdown format, based on an initial user prompt. The PRD should be clear, actionable, and suitable for a junior developer to understand and implement the feature.
## Process
1. **Receive Initial Prompt:** The user provides a brief description or request for a new feature or functionality.
2. **Ask Clarifying Questions:** Before writing the PRD, the AI *must* ask clarifying questions to gather sufficient detail. The goal is to understand the "what" and "why" of the feature, not necessarily the "how" (which the developer will figure out).
3. **Generate PRD:** Based on the initial prompt and the user's answers to the clarifying questions, generate a PRD using the structure outlined below.
4. **Save PRD:** Save the generated document as `prd-[feature-name].md` inside the `/tasks` directory.
## Clarifying Questions (Examples)
The AI should adapt its questions based on the prompt, but here are some common areas to explore:
* **Problem/Goal:** "What problem does this feature solve for the user?" or "What is the main goal we want to achieve with this feature?"
* **Target User:** "Who is the primary user of this feature?"
* **Core Functionality:** "Can you describe the key actions a user should be able to perform with this feature?"
* **User Stories:** "Could you provide a few user stories? (e.g., As a [type of user], I want to [perform an action] so that [benefit].)"
* **Acceptance Criteria:** "How will we know when this feature is successfully implemented? What are the key success criteria?"
* **Scope/Boundaries:** "Are there any specific things this feature *should not* do (non-goals)?"
* **Data Requirements:** "What kind of data does this feature need to display or manipulate?"
* **Design/UI:** "Are there any existing design mockups or UI guidelines to follow?" or "Can you describe the desired look and feel?"
* **Edge Cases:** "Are there any potential edge cases or error conditions we should consider?"
## PRD Structure
The generated PRD should include the following sections:
1. **Introduction/Overview:** Briefly describe the feature and the problem it solves. State the goal.
2. **Goals:** List the specific, measurable objectives for this feature.
3. **User Stories:** Detail the user narratives describing feature usage and benefits.
4. **Functional Requirements:** List the specific functionalities the feature must have. Use clear, concise language (e.g., "The system must allow users to upload a profile picture."). Number these requirements.
5. **Non-Goals (Out of Scope):** Clearly state what this feature will *not* include to manage scope.
6. **Design Considerations (Optional):** Link to mockups, describe UI/UX requirements, or mention relevant components/styles if applicable.
7. **Technical Considerations (Optional):** Mention any known technical constraints, dependencies, or suggestions (e.g., "Should integrate with the existing Auth module").
8. **Success Metrics:** How will the success of this feature be measured? (e.g., "Increase user engagement by 10%", "Reduce support tickets related to X").
9. **Open Questions:** List any remaining questions or areas needing further clarification.
## Target Audience
Assume the primary reader of the PRD is a **junior developer**. Therefore, requirements should be explicit, unambiguous, and avoid jargon where possible. Provide enough detail for them to understand the feature's purpose and core logic.
## Output
* **Format:** Markdown (`.md`)
* **Location:** `/tasks/`
* **Filename:** `prd-[feature-name].md`
## Final instructions
1. Do NOT start implmenting the PRD
2. Make sure to ask the user clarifying questions
3. Take the user's answers to the clarifying questions and improve the PRD

244
.cursor/rules/documentation.mdc Normal file
View File

@ -0,0 +1,244 @@
---
description: Documentation standards for code, architecture, and development decisions
globs:
alwaysApply: false
---
# Rule: Documentation Standards
## Goal
Maintain comprehensive, up-to-date documentation that supports development, onboarding, and long-term maintenance of the codebase.
## Documentation Hierarchy
### 1. Project Level Documentation (in ./docs/)
- **README.md**: Project overview, setup instructions, basic usage
- **CONTEXT.md**: Current project state, architecture decisions, patterns
- **CHANGELOG.md**: Version history and significant changes
- **CONTRIBUTING.md**: Development guidelines and processes
- **API.md**: API endpoints, request/response formats, authentication
### 2. Module Level Documentation (in ./docs/modules/)
- **[module-name].md**: Purpose, public interfaces, usage examples
- **dependencies.md**: External dependencies and their purposes
- **architecture.md**: Module relationships and data flow
### 3. Code Level Documentation
- **Docstrings**: Function and class documentation
- **Inline comments**: Complex logic explanations
- **Type hints**: Clear parameter and return types
- **README files**: Directory-specific instructions
## Documentation Standards
### Code Documentation
```python
def process_user_data(user_id: str, data: dict) -> UserResult:
"""
Process and validate user data before storage.
Args:
user_id: Unique identifier for the user
data: Dictionary containing user information to process
Returns:
UserResult: Processed user data with validation status
Raises:
ValidationError: When user data fails validation
DatabaseError: When storage operation fails
Example:
>>> result = process_user_data("123", {"name": "John", "email": "john@example.com"})
>>> print(result.status)
'valid'
"""
```
### API Documentation Format
```markdown
### POST /api/users
Create a new user account.
**Request:**
```json
{
"name": "string (required)",
"email": "string (required, valid email)",
"age": "number (optional, min: 13)"
}
```
**Response (201):**
```json
{
"id": "uuid",
"name": "string",
"email": "string",
"created_at": "iso_datetime"
}
```
**Errors:**
- 400: Invalid input data
- 409: Email already exists
```
### Architecture Decision Records (ADRs)
Document significant architecture decisions in `./docs/decisions/`:
```markdown
# ADR-001: Database Choice - PostgreSQL
## Status
Accepted
## Context
We need to choose a database for storing user data and application state.
## Decision
We will use PostgreSQL as our primary database.
## Consequences
**Positive:**
- ACID compliance ensures data integrity
- Rich query capabilities with SQL
- Good performance for our expected load
**Negative:**
- More complex setup than simpler alternatives
- Requires SQL knowledge from team members
## Alternatives Considered
- MongoDB: Rejected due to consistency requirements
- SQLite: Rejected due to scalability needs
```
## Documentation Maintenance
### When to Update Documentation
#### Always Update:
- **API changes**: Any modification to public interfaces
- **Architecture changes**: New patterns, data structures, or workflows
- **Configuration changes**: Environment variables, deployment settings
- **Dependencies**: Adding, removing, or upgrading packages
- **Business logic changes**: Core functionality modifications
#### Update Weekly:
- **CONTEXT.md**: Current development status and priorities
- **Known issues**: Bug reports and workarounds
- **Performance notes**: Bottlenecks and optimization opportunities
#### Update per Release:
- **CHANGELOG.md**: User-facing changes and improvements
- **Version documentation**: Breaking changes and migration guides
- **Examples and tutorials**: Keep sample code current
### Documentation Quality Checklist
#### Completeness
- [ ] Purpose and scope clearly explained
- [ ] All public interfaces documented
- [ ] Examples provided for complex usage
- [ ] Error conditions and handling described
- [ ] Dependencies and requirements listed
#### Accuracy
- [ ] Code examples are tested and working
- [ ] Links point to correct locations
- [ ] Version numbers are current
- [ ] Screenshots reflect current UI
#### Clarity
- [ ] Written for the intended audience
- [ ] Technical jargon is explained
- [ ] Step-by-step instructions are clear
- [ ] Visual aids used where helpful
## Documentation Automation
### Auto-Generated Documentation
- **API docs**: Generate from code annotations
- **Type documentation**: Extract from type hints
- **Module dependencies**: Auto-update from imports
- **Test coverage**: Include coverage reports
### Documentation Testing
```python
# Test that code examples in documentation work
def test_documentation_examples():
"""Verify code examples in docs actually work."""
# Test examples from README.md
# Test API examples from docs/API.md
# Test configuration examples
```
## Documentation Templates
### New Module Documentation Template
```markdown
# Module: [Name]
## Purpose
Brief description of what this module does and why it exists.
## Public Interface
### Functions
- `function_name(params)`: Description and example
### Classes
- `ClassName`: Purpose and basic usage
## Usage Examples
```python
# Basic usage example
```
## Dependencies
- Internal: List of internal modules this depends on
- External: List of external packages required
## Testing
How to run tests for this module.
## Known Issues
Current limitations or bugs.
```
### API Endpoint Template
```markdown
### [METHOD] /api/endpoint
Brief description of what this endpoint does.
**Authentication:** Required/Optional
**Rate Limiting:** X requests per minute
**Request:**
- Headers required
- Body schema
- Query parameters
**Response:**
- Success response format
- Error response format
- Status codes
**Example:**
Working request/response example
```
## Review and Maintenance Process
### Documentation Review
- Include documentation updates in code reviews
- Verify examples still work with code changes
- Check for broken links and outdated information
- Ensure consistency with current implementation
### Regular Audits
- Monthly review of documentation accuracy
- Quarterly assessment of documentation completeness
- Annual review of documentation structure and organization

207
.cursor/rules/enhanced-task-list.mdc Normal file
View File

@ -0,0 +1,207 @@
---
description: Enhanced task list management with quality gates and iterative workflow integration
globs:
alwaysApply: false
---
# Rule: Enhanced Task List Management
## Goal
Manage task lists with integrated quality gates and iterative workflow to prevent context loss and ensure sustainable development.
## Task Implementation Protocol
### Pre-Implementation Check
Before starting any sub-task:
- [ ] **Context Review**: Have you reviewed CONTEXT.md and relevant documentation?
- [ ] **Pattern Identification**: Do you understand existing patterns to follow?
- [ ] **Integration Planning**: Do you know how this will integrate with existing code?
- [ ] **Size Validation**: Is this task small enough (≤50 lines, ≤250 lines per file)?
### Implementation Process
1. **One sub-task at a time**: Do **NOT** start the next sub‑task until you ask the user for permission and they say "yes" or "y"
2. **Step-by-step execution**:
- Plan the approach in bullet points
- Wait for approval
- Implement the specific sub-task
- Test the implementation
- Update documentation if needed
3. **Quality validation**: Run through the code review checklist before marking complete
### Completion Protocol
When you finish a **sub‑task**:
1. **Immediate marking**: Change `[ ]` to `[x]`
2. **Quality check**: Verify the implementation meets quality standards
3. **Integration test**: Ensure new code works with existing functionality
4. **Documentation update**: Update relevant files if needed
5. **Parent task check**: If **all** subtasks underneath a parent task are now `[x]`, also mark the **parent task** as completed
6. **Stop and wait**: Get user approval before proceeding to next sub-task
## Enhanced Task List Structure
### Task File Header
```markdown
# Task List: [Feature Name]
**Source PRD**: `prd-[feature-name].md`
**Status**: In Progress / Complete / Blocked
**Context Last Updated**: [Date]
**Architecture Review**: Required / Complete / N/A
## Quick Links
- [Context Documentation](./CONTEXT.md)
- [Architecture Guidelines](./docs/architecture.md)
- [Related Files](#relevant-files)
```
### Task Format with Quality Gates
```markdown
- [ ] 1.0 Parent Task Title
- **Quality Gate**: Architecture review required
- **Dependencies**: List any dependencies
- [ ] 1.1 [Sub-task description 1.1]
- **Size estimate**: [Small/Medium/Large]
- **Pattern reference**: [Reference to existing pattern]
- **Test requirements**: [Unit/Integration/Both]
- [ ] 1.2 [Sub-task description 1.2]
- **Integration points**: [List affected components]
- **Risk level**: [Low/Medium/High]
```
## Relevant Files Management
### Enhanced File Tracking
```markdown
## Relevant Files
### Implementation Files
- `path/to/file1.ts` - Brief description of purpose and role
- **Status**: Created / Modified / Needs Review
- **Last Modified**: [Date]
- **Review Status**: Pending / Approved / Needs Changes
### Test Files
- `path/to/file1.test.ts` - Unit tests for file1.ts
- **Coverage**: [Percentage or status]
- **Last Run**: [Date and result]
### Documentation Files
- `docs/module-name.md` - Module documentation
- **Status**: Up to date / Needs update / Missing
- **Last Updated**: [Date]
### Configuration Files
- `config/setting.json` - Configuration changes
- **Environment**: [Dev/Staging/Prod affected]
- **Backup**: [Location of backup]
```
## Task List Maintenance
### During Development
1. **Regular updates**: Update task status after each significant change
2. **File tracking**: Add new files as they are created or modified
3. **Dependency tracking**: Note when new dependencies between tasks emerge
4. **Risk assessment**: Flag tasks that become more complex than anticipated
### Quality Checkpoints
At 25%, 50%, 75%, and 100% completion:
- [ ] **Architecture alignment**: Code follows established patterns
- [ ] **Performance impact**: No significant performance degradation
- [ ] **Security review**: No security vulnerabilities introduced
- [ ] **Documentation current**: All changes are documented
### Weekly Review Process
1. **Completion assessment**: What percentage of tasks are actually complete?
2. **Quality assessment**: Are completed tasks meeting quality standards?
3. **Process assessment**: Is the iterative workflow being followed?
4. **Risk assessment**: Are there emerging risks or blockers?
## Task Status Indicators
### Status Levels
- `[ ]` **Not Started**: Task not yet begun
- `[~]` **In Progress**: Currently being worked on
- `[?]` **Blocked**: Waiting for dependencies or decisions
- `[!]` **Needs Review**: Implementation complete but needs quality review
- `[x]` **Complete**: Finished and quality approved
### Quality Indicators
- ✅ **Quality Approved**: Passed all quality gates
- ⚠️ **Quality Concerns**: Has issues but functional
- ❌ **Quality Failed**: Needs rework before approval
- 🔄 **Under Review**: Currently being reviewed
### Integration Status
- 🔗 **Integrated**: Successfully integrated with existing code
- 🔧 **Integration Issues**: Problems with existing code integration
- ⏳ **Integration Pending**: Ready for integration testing
## Emergency Procedures
### When Tasks Become Too Complex
If a sub-task grows beyond expected scope:
1. **Stop implementation** immediately
2. **Document current state** and what was discovered
3. **Break down** the task into smaller pieces
4. **Update task list** with new sub-tasks
5. **Get approval** for the new breakdown before proceeding
### When Context is Lost
If AI seems to lose track of project patterns:
1. **Pause development**
2. **Review CONTEXT.md** and recent changes
3. **Update context documentation** with current state
4. **Restart** with explicit pattern references
5. **Reduce task size** until context is re-established
### When Quality Gates Fail
If implementation doesn't meet quality standards:
1. **Mark task** with `[!]` status
2. **Document specific issues** found
3. **Create remediation tasks** if needed
4. **Don't proceed** until quality issues are resolved
## AI Instructions Integration
### Context Awareness Commands
```markdown
**Before starting any task, run these checks:**
1. @CONTEXT.md - Review current project state
2. @architecture.md - Understand design principles
3. @code-review.md - Know quality standards
4. Look at existing similar code for patterns
```
### Quality Validation Commands
```markdown
**After completing any sub-task:**
1. Run code review checklist
2. Test integration with existing code
3. Update documentation if needed
4. Mark task complete only after quality approval
```
### Workflow Commands
```markdown
**For each development session:**
1. Review incomplete tasks and their status
2. Identify next logical sub-task to work on
3. Check dependencies and blockers
4. Follow iterative workflow process
5. Update task list with progress and findings
```
## Success Metrics
### Daily Success Indicators
- Tasks are completed according to quality standards
- No sub-tasks are started without completing previous ones
- File tracking remains accurate and current
- Integration issues are caught early
### Weekly Success Indicators
- Overall task completion rate is sustainable
- Quality issues are decreasing over time
- Context loss incidents are rare
- Team confidence in codebase remains high

70
.cursor/rules/generate-tasks.mdc Normal file
View File

@ -0,0 +1,70 @@
---
description: Generate a task list or TODO for a user requirement or implementation.
globs:
alwaysApply: false
---
---
description:
globs:
alwaysApply: false
---
# Rule: Generating a Task List from a PRD
## Goal
To guide an AI assistant in creating a detailed, step-by-step task list in Markdown format based on an existing Product Requirements Document (PRD). The task list should guide a developer through implementation.
## Output
- **Format:** Markdown (`.md`)
- **Location:** `/tasks/`
- **Filename:** `tasks-[prd-file-name].md` (e.g., `tasks-prd-user-profile-editing.md`)
## Process
1. **Receive PRD Reference:** The user points the AI to a specific PRD file
2. **Analyze PRD:** The AI reads and analyzes the functional requirements, user stories, and other sections of the specified PRD.
3. **Phase 1: Generate Parent Tasks:** Based on the PRD analysis, create the file and generate the main, high-level tasks required to implement the feature. Use your judgement on how many high-level tasks to use. It's likely to be about 5. Present these tasks to the user in the specified format (without sub-tasks yet). Inform the user: "I have generated the high-level tasks based on the PRD. Ready to generate the sub-tasks? Respond with 'Go' to proceed."
4. **Wait for Confirmation:** Pause and wait for the user to respond with "Go".
5. **Phase 2: Generate Sub-Tasks:** Once the user confirms, break down each parent task into smaller, actionable sub-tasks necessary to complete the parent task. Ensure sub-tasks logically follow from the parent task and cover the implementation details implied by the PRD.
6. **Identify Relevant Files:** Based on the tasks and PRD, identify potential files that will need to be created or modified. List these under the `Relevant Files` section, including corresponding test files if applicable.
7. **Generate Final Output:** Combine the parent tasks, sub-tasks, relevant files, and notes into the final Markdown structure.
8. **Save Task List:** Save the generated document in the `/tasks/` directory with the filename `tasks-[prd-file-name].md`, where `[prd-file-name]` matches the base name of the input PRD file (e.g., if the input was `prd-user-profile-editing.md`, the output is `tasks-prd-user-profile-editing.md`).
## Output Format
The generated task list _must_ follow this structure:
```markdown
## Relevant Files
- `path/to/potential/file1.ts` - Brief description of why this file is relevant (e.g., Contains the main component for this feature).
- `path/to/file1.test.ts` - Unit tests for `file1.ts`.
- `path/to/another/file.tsx` - Brief description (e.g., API route handler for data submission).
- `path/to/another/file.test.tsx` - Unit tests for `another/file.tsx`.
- `lib/utils/helpers.ts` - Brief description (e.g., Utility functions needed for calculations).
- `lib/utils/helpers.test.ts` - Unit tests for `helpers.ts`.
### Notes
- Unit tests should typically be placed alongside the code files they are testing (e.g., `MyComponent.tsx` and `MyComponent.test.tsx` in the same directory).
- Use `npx jest [optional/path/to/test/file]` to run tests. Running without a path executes all tests found by the Jest configuration.
## Tasks
- [ ] 1.0 Parent Task Title
- [ ] 1.1 [Sub-task description 1.1]
- [ ] 1.2 [Sub-task description 1.2]
- [ ] 2.0 Parent Task Title
- [ ] 2.1 [Sub-task description 2.1]
- [ ] 3.0 Parent Task Title (may not require sub-tasks if purely structural or configuration)
```
## Interaction Model
The process explicitly requires a pause after generating parent tasks to get user confirmation ("Go") before proceeding to generate the detailed sub-tasks. This ensures the high-level plan aligns with user expectations before diving into details.
## Target Audience
Assume the primary reader of the task list is a **junior developer** who will implement the feature.

236
.cursor/rules/iterative-workflow.mdc Normal file
View File

@ -0,0 +1,236 @@
---
description: Iterative development workflow for AI-assisted coding
globs:
alwaysApply: false
---
# Rule: Iterative Development Workflow
## Goal
Establish a structured, iterative development process that prevents the chaos and complexity that can arise from uncontrolled AI-assisted development.
## Development Phases
### Phase 1: Planning and Design
**Before writing any code:**
1. **Understand the Requirement**
- Break down the task into specific, measurable objectives
- Identify existing code patterns that should be followed
- List dependencies and integration points
- Define acceptance criteria
2. **Design Review**
- Propose approach in bullet points
- Wait for explicit approval before proceeding
- Consider how the solution fits existing architecture
- Identify potential risks and mitigation strategies
### Phase 2: Incremental Implementation
**One small piece at a time:**
1. **Micro-Tasks** (≤ 50 lines each)
- Implement one function or small class at a time
- Test immediately after implementation
- Ensure integration with existing code
- Document decisions and patterns used
2. **Validation Checkpoints**
- After each micro-task, verify it works correctly
- Check that it follows established patterns
- Confirm it integrates cleanly with existing code
- Get approval before moving to next micro-task
### Phase 3: Integration and Testing
**Ensuring system coherence:**
1. **Integration Testing**
- Test new code with existing functionality
- Verify no regressions in existing features
- Check performance impact
- Validate error handling
2. **Documentation Update**
- Update relevant documentation
- Record any new patterns or decisions
- Update context files if architecture changed
## Iterative Prompting Strategy
### Step 1: Context Setting
```
Before implementing [feature], help me understand:
1. What existing patterns should I follow?
2. What existing functions/classes are relevant?
3. How should this integrate with [specific existing component]?
4. What are the potential architectural impacts?
```
### Step 2: Plan Creation
```
Based on the context, create a detailed plan for implementing [feature]:
1. Break it into micro-tasks (≤50 lines each)
2. Identify dependencies and order of implementation
3. Specify integration points with existing code
4. List potential risks and mitigation strategies
Wait for my approval before implementing.
```
### Step 3: Incremental Implementation
```
Implement only the first micro-task: [specific task]
- Use existing patterns from [reference file/function]
- Keep it under 50 lines
- Include error handling
- Add appropriate tests
- Explain your implementation choices
Stop after this task and wait for approval.
```
## Quality Gates
### Before Each Implementation
- [ ] **Purpose is clear**: Can explain what this piece does and why
- [ ] **Pattern is established**: Following existing code patterns
- [ ] **Size is manageable**: Implementation is small enough to understand completely
- [ ] **Integration is planned**: Know how it connects to existing code
### After Each Implementation
- [ ] **Code is understood**: Can explain every line of implemented code
- [ ] **Tests pass**: All existing and new tests are passing
- [ ] **Integration works**: New code works with existing functionality
- [ ] **Documentation updated**: Changes are reflected in relevant documentation
### Before Moving to Next Task
- [ ] **Current task complete**: All acceptance criteria met
- [ ] **No regressions**: Existing functionality still works
- [ ] **Clean state**: No temporary code or debugging artifacts
- [ ] **Approval received**: Explicit go-ahead for next task
- [ ] **Documentaion updated**: If relevant changes to module was made.
## Anti-Patterns to Avoid
### Large Block Implementation
**Don't:**
```
Implement the entire user management system with authentication,
CRUD operations, and email notifications.
```
**Do:**
```
First, implement just the User model with basic fields.
Stop there and let me review before continuing.
```
### Context Loss
**Don't:**
```
Create a new authentication system.
```
**Do:**
```
Looking at the existing auth patterns in auth.py, implement
password validation following the same structure as the
existing email validation function.
```
### Over-Engineering
**Don't:**
```
Build a flexible, extensible user management framework that
can handle any future requirements.
```
**Do:**
```
Implement user creation functionality that matches the existing
pattern in customer.py, focusing only on the current requirements.
```
## Progress Tracking
### Task Status Indicators
- 🔄 **In Planning**: Requirements gathering and design
- ⏳ **In Progress**: Currently implementing
- ✅ **Complete**: Implemented, tested, and integrated
- 🚫 **Blocked**: Waiting for decisions or dependencies
- 🔧 **Needs Refactor**: Working but needs improvement
### Weekly Review Process
1. **Progress Assessment**
- What was completed this week?
- What challenges were encountered?
- How well did the iterative process work?
2. **Process Adjustment**
- Were task sizes appropriate?
- Did context management work effectively?
- What improvements can be made?
3. **Architecture Review**
- Is the code remaining maintainable?
- Are patterns staying consistent?
- Is technical debt accumulating?
## Emergency Procedures
### When Things Go Wrong
If development becomes chaotic or problematic:
1. **Stop Development**
- Don't continue adding to the problem
- Take time to assess the situation
- Don't rush to "fix" with more AI-generated code
2. **Assess the Situation**
- What specific problems exist?
- How far has the code diverged from established patterns?
- What parts are still working correctly?
3. **Recovery Process**
- Roll back to last known good state
- Update context documentation with lessons learned
- Restart with smaller, more focused tasks
- Get explicit approval for each step of recovery
### Context Recovery
When AI seems to lose track of project patterns:
1. **Context Refresh**
- Review and update CONTEXT.md
- Include examples of current code patterns
- Clarify architectural decisions
2. **Pattern Re-establishment**
- Show AI examples of existing, working code
- Explicitly state patterns to follow
- Start with very small, pattern-matching tasks
3. **Gradual Re-engagement**
- Begin with simple, low-risk tasks
- Verify pattern adherence at each step
- Gradually increase task complexity as consistency returns
## Success Metrics
### Short-term (Daily)
- Code is understandable and well-integrated
- No major regressions introduced
- Development velocity feels sustainable
- Team confidence in codebase remains high
### Medium-term (Weekly)
- Technical debt is not accumulating
- New features integrate cleanly
- Development patterns remain consistent
- Documentation stays current
### Long-term (Monthly)
- Codebase remains maintainable as it grows
- New team members can understand and contribute
- AI assistance enhances rather than hinders development
- Architecture remains clean and purposeful

24
.cursor/rules/project.mdc Normal file
View File

@ -0,0 +1,24 @@
---
description:
globs:
alwaysApply: true
---
# Rule: Project specific rules
## Goal
Unify the project structure and interraction with tools and console
### System tools
- **ALWAYS** use UV for package management
- **ALWAYS** use windows PowerShell command for terminal
### Coding patterns
- **ALWYAS** check the arguments and methods before use to avoid errors with whron parameters or names
- If in doubt, check [CONTEXT.md](mdc:CONTEXT.md) file and [architecture.md](mdc:docs/architecture.md)
- **PREFER** ORM pattern for databases with SQLAclhemy.
- **DO NOT USE** emoji in code and comments
### Testing
- Use UV for test in format *uv run pytest [filename]*

237
.cursor/rules/refactoring.mdc Normal file
View File

@ -0,0 +1,237 @@
---
description: Code refactoring and technical debt management for AI-assisted development
globs:
alwaysApply: false
---
# Rule: Code Refactoring and Technical Debt Management
## Goal
Guide AI in systematic code refactoring to improve maintainability, reduce complexity, and prevent technical debt accumulation in AI-assisted development projects.
## When to Apply This Rule
- Code complexity has increased beyond manageable levels
- Duplicate code patterns are detected
- Performance issues are identified
- New features are difficult to integrate
- Code review reveals maintainability concerns
- Weekly technical debt assessment indicates refactoring needs
## Pre-Refactoring Assessment
Before starting any refactoring, the AI MUST:
1. **Context Analysis:**
- Review existing `CONTEXT.md` for architectural decisions
- Analyze current code patterns and conventions
- Identify all files that will be affected (search the codebase for use)
- Check for existing tests that verify current behavior
2. **Scope Definition:**
- Clearly define what will and will not be changed
- Identify the specific refactoring pattern to apply
- Estimate the blast radius of changes
- Plan rollback strategy if needed
3. **Documentation Review:**
- Check `./docs/` for relevant module documentation
- Review any existing architectural diagrams
- Identify dependencies and integration points
- Note any known constraints or limitations
## Refactoring Process
### Phase 1: Planning and Safety
1. **Create Refactoring Plan:**
- Document the current state and desired end state
- Break refactoring into small, atomic steps
- Identify tests that must pass throughout the process
- Plan verification steps for each change
2. **Establish Safety Net:**
- Ensure comprehensive test coverage exists
- If tests are missing, create them BEFORE refactoring
- Document current behavior that must be preserved
- Create backup of current implementation approach
3. **Get Approval:**
- Present the refactoring plan to the user
- Wait for explicit "Go" or "Proceed" confirmation
- Do NOT start refactoring without approval
### Phase 2: Incremental Implementation
4. **One Change at a Time:**
- Implement ONE refactoring step per iteration
- Run tests after each step to ensure nothing breaks
- Update documentation if interfaces change
- Mark progress in the refactoring plan
5. **Verification Protocol:**
- Run all relevant tests after each change
- Verify functionality works as expected
- Check performance hasn't degraded
- Ensure no new linting or type errors
6. **User Checkpoint:**
- After each significant step, pause for user review
- Present what was changed and current status
- Wait for approval before continuing
- Address any concerns before proceeding
### Phase 3: Completion and Documentation
7. **Final Verification:**
- Run full test suite to ensure nothing is broken
- Verify all original functionality is preserved
- Check that new code follows project conventions
- Confirm performance is maintained or improved
8. **Documentation Update:**
- Update `CONTEXT.md` with new patterns/decisions
- Update module documentation in `./docs/`
- Document any new conventions established
- Note lessons learned for future refactoring
## Common Refactoring Patterns
### Extract Method/Function
```
WHEN: Functions/methods exceed 50 lines or have multiple responsibilities
HOW:
1. Identify logical groupings within the function
2. Extract each group into a well-named helper function
3. Ensure each function has a single responsibility
4. Verify tests still pass
```
### Extract Module/Class
```
WHEN: Files exceed 250 lines or handle multiple concerns
HOW:
1. Identify cohesive functionality groups
2. Create new files for each group
3. Move related functions/classes together
4. Update imports and dependencies
5. Verify module boundaries are clean
```
### Eliminate Duplication
```
WHEN: Similar code appears in multiple places
HOW:
1. Identify the common pattern or functionality
2. Extract to a shared utility function or module
3. Update all usage sites to use the shared code
4. Ensure the abstraction is not over-engineered
```
### Improve Data Structures
```
WHEN: Complex nested objects or unclear data flow
HOW:
1. Define clear interfaces/types for data structures
2. Create transformation functions between different representations
3. Ensure data flow is unidirectional where possible
4. Add validation at boundaries
```
### Reduce Coupling
```
WHEN: Modules are tightly interconnected
HOW:
1. Identify dependencies between modules
2. Extract interfaces for external dependencies
3. Use dependency injection where appropriate
4. Ensure modules can be tested in isolation
```
## Quality Gates
Every refactoring must pass these gates:
### Technical Quality
- [ ] All existing tests pass
- [ ] No new linting errors introduced
- [ ] Code follows established project conventions
- [ ] No performance regression detected
- [ ] File sizes remain under 250 lines
- [ ] Function sizes remain under 50 lines
### Maintainability
- [ ] Code is more readable than before
- [ ] Duplicated code has been reduced
- [ ] Module responsibilities are clearer
- [ ] Dependencies are explicit and minimal
- [ ] Error handling is consistent
### Documentation
- [ ] Public interfaces are documented
- [ ] Complex logic has explanatory comments
- [ ] Architectural decisions are recorded
- [ ] Examples are provided where helpful
## AI Instructions for Refactoring
1. **Always ask for permission** before starting any refactoring work
2. **Start with tests** - ensure comprehensive coverage before changing code
3. **Work incrementally** - make small changes and verify each step
4. **Preserve behavior** - functionality must remain exactly the same
5. **Update documentation** - keep all docs current with changes
6. **Follow conventions** - maintain consistency with existing codebase
7. **Stop and ask** if any step fails or produces unexpected results
8. **Explain changes** - clearly communicate what was changed and why
## Anti-Patterns to Avoid
### Over-Engineering
- Don't create abstractions for code that isn't duplicated
- Avoid complex inheritance hierarchies
- Don't optimize prematurely
### Breaking Changes
- Never change public APIs without explicit approval
- Don't remove functionality, even if it seems unused
- Avoid changing behavior "while we're here"
### Scope Creep
- Stick to the defined refactoring scope
- Don't add new features during refactoring
- Resist the urge to "improve" unrelated code
## Success Metrics
Track these metrics to ensure refactoring effectiveness:
### Code Quality
- Reduced cyclomatic complexity
- Lower code duplication percentage
- Improved test coverage
- Fewer linting violations
### Developer Experience
- Faster time to understand code
- Easier integration of new features
- Reduced bug introduction rate
- Higher developer confidence in changes
### Maintainability
- Clearer module boundaries
- More predictable behavior
- Easier debugging and troubleshooting
- Better performance characteristics
## Output Files
When refactoring is complete, update:
- `refactoring-log-[date].md` - Document what was changed and why
- `CONTEXT.md` - Update with new patterns and decisions
- `./docs/` - Update relevant module documentation
- Task lists - Mark refactoring tasks as complete
## Final Verification
Before marking refactoring complete:
1. Run full test suite and verify all tests pass
2. Check that code follows all project conventions
3. Verify documentation is up to date
4. Confirm user is satisfied with the results
5. Record lessons learned for future refactoring efforts

44
.cursor/rules/task-list.mdc Normal file
View File

@ -0,0 +1,44 @@
---
description: TODO list task implementation
globs:
alwaysApply: false
---
---
description:
globs:
alwaysApply: false
---
# Task List Management
Guidelines for managing task lists in markdown files to track progress on completing a PRD
## Task Implementation
- **One sub-task at a time:** Do **NOT** start the next sub‑task until you ask the user for permission and they say “yes” or "y"
- **Completion protocol:**
1. When you finish a **sub‑task**, immediately mark it as completed by changing `[ ]` to `[x]`.
2. If **all** subtasks underneath a parent task are now `[x]`, also mark the **parent task** as completed.
- Stop after each sub‑task and wait for the user’s go‑ahead.
## Task List Maintenance
1. **Update the task list as you work:**
- Mark tasks and subtasks as completed (`[x]`) per the protocol above.
- Add new tasks as they emerge.
2. **Maintain the “Relevant Files” section:**
- List every file created or modified.
- Give each file a one‑line description of its purpose.
## AI Instructions
When working with task lists, the AI must:
1. Regularly update the task list file after finishing any significant work.
2. Follow the completion protocol:
- Mark each finished **sub‑task** `[x]`.
- Mark the **parent task** `[x]` once **all** its subtasks are `[x]`.
3. Add newly discovered tasks.
4. Keep “Relevant Files” accurate and up to date.
5. Before starting work, check which sub‑task is next.
6. After implementing a sub‑task, update the file and then pause for user approval.

1
.python-version Normal file
View File

@ -0,0 +1 @@
3.12

16
.vscode/launch.json vendored Normal file
View File

@ -0,0 +1,16 @@
{
// Use IntelliSense to learn about possible attributes.
// Hover to view descriptions of existing attributes.
// For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
"version": "0.2.0",
"configurations": [
{
"name": "Python Debugger: main.py",
"type": "debugpy",
"request": "launch",
"program": "main.py",
"console": "integratedTerminal"
}
]
}

1
OHLCVPredictor Symbolic link
View File

@ -0,0 +1 @@
../OHLCVPredictor

203
main.py Normal file
View File

@ -0,0 +1,203 @@
import pandas as pd
import numpy as np
from ta.volatility import AverageTrueRange
def load_data(since):
df = pd.read_csv('../data/btcusd_1-min_data.csv')
df['Timestamp'] = pd.to_datetime(df['Timestamp'], unit='s')
df = df[df['Timestamp'] >= pd.Timestamp(since)]
return df
def aggregate_data(df, timeframe):
df = df.set_index('Timestamp')
df = df.resample(timeframe).agg({
'Open': 'first',
'High': 'max',
'Low': 'min',
'Close': 'last',
'Volume': 'sum'
})
df = df.reset_index()
return df
def calculate_okx_taker_maker_fee(amount, is_maker=False):
fee_rate = 0.0008 if is_maker else 0.0010
return amount * fee_rate
def calculate_supertrend(df, period, multiplier):
"""
Calculate the Supertrend indicator for a given period and multiplier.
Optionally displays progress during calculation.
Args:
df (pd.DataFrame): DataFrame with 'High', 'Low', 'Close' columns.
period (int): ATR period.
multiplier (float): Multiplier for ATR.
progress_step (int): Step interval for progress display.
show_progress (bool): Whether to print progress updates.
Returns:
pd.Series: Supertrend values.
"""
high = df['High'].values
low = df['Low'].values
close = df['Close'].values
atr = AverageTrueRange(df['High'], df['Low'], df['Close'], window=period).average_true_range().values
hl2 = (high + low) / 2
upperband = hl2 + (multiplier * atr)
lowerband = hl2 - (multiplier * atr)
supertrend = np.full_like(close, np.nan)
in_uptrend = True
supertrend[0] = upperband[0]
total_steps = len(close) - 1
for i in range(1, len(close)):
if close[i] > upperband[i-1]:
in_uptrend = True
elif close[i] < lowerband[i-1]:
in_uptrend = False
# else, keep previous trend
if in_uptrend:
supertrend[i] = max(lowerband[i], supertrend[i-1] if not np.isnan(supertrend[i-1]) else lowerband[i])
else:
supertrend[i] = min(upperband[i], supertrend[i-1] if not np.isnan(supertrend[i-1]) else upperband[i])
return pd.Series(supertrend, index=df.index)
def add_supertrend_indicators(df):
"""
Adds Supertrend indicators to the dataframe for the specified (period, multiplier) pairs.
Args:
df (pd.DataFrame): DataFrame with columns 'High', 'Low', 'Close'.
Returns:
pd.DataFrame: DataFrame with new Supertrend columns added.
"""
supertrend_params = [(12, 3.0), (10, 1.0), (11, 2.0)]
for period, multiplier in supertrend_params:
try:
st_col = f'supertrend_{period}_{multiplier}'
df[st_col] = calculate_supertrend(df, period, multiplier)
except Exception as e:
print(f"Error calculating Supertrend {period}, {multiplier}: {e}")
df[f'supertrend_{period}_{multiplier}'] = np.nan
return df
def precompute_1min_slice_indices(df_aggregated, df_1min):
"""
Precompute start and end indices for each aggregated bar using searchsorted.
Returns a list of (start_idx, end_idx) tuples for fast iloc slicing.
"""
timestamps = df_aggregated['Timestamp'].values
one_min_timestamps = df_1min['Timestamp'].values
# Ensure both are sorted
sorted_1min = np.argsort(one_min_timestamps)
one_min_timestamps = one_min_timestamps[sorted_1min]
indices = []
prev_idx = 0
for i in range(1, len(timestamps)):
start, end = timestamps[i-1], timestamps[i]
# Find indices using searchsorted (right for start, right for end)
start_idx = np.searchsorted(one_min_timestamps, start, side='right')
end_idx = np.searchsorted(one_min_timestamps, end, side='right')
indices.append((start_idx, end_idx))
return indices, sorted_1min
def backtest(df_aggregated, df_1min, stop_loss_pct, progress_step=1000):
"""
Backtest trading strategy based on Supertrend indicators with trailing stop loss.
Buys when all three Supertrend columns are positive (>0),
sells when any is negative (<0), or when trailing stop loss is hit.
Args:
df_aggregated (pd.DataFrame): Aggregated OHLCV data with Supertrend columns.
df_1min (pd.DataFrame): 1-minute OHLCV data.
stop_loss_pct (float): Trailing stop loss percentage (e.g., 0.02 for 2%).
progress_step (int): Step interval for progress display.
"""
required_st_cols = ["supertrend_12_3.0", "supertrend_10_1.0", "supertrend_11_2.0"]
for col in required_st_cols:
if col not in df_aggregated.columns:
raise ValueError(f"Missing required Supertrend column: {col}")
# Precompute 1-min slice indices for each aggregated bar
slice_indices, sorted_1min = precompute_1min_slice_indices(df_aggregated, df_1min)
df_1min_sorted = df_1min.iloc[sorted_1min].reset_index(drop=True)
in_position = False
init_usd = 1000
usd = init_usd
coin = 0
highest_price = None
nb_stop_loss = 0
total_steps = len(df_aggregated) - 1
for i in range(1, len(df_aggregated)):
st_vals = [df_aggregated[col][i] for col in required_st_cols]
all_positive = all(val > 0 for val in st_vals)
any_negative = any(val < 0 for val in st_vals)
close_price = df_aggregated['Close'][i]
# Buy condition: all Supertrend values positive
if not in_position and all_positive:
in_position = True
coin = usd / close_price
usd = 0
highest_price = close_price
# If in position, update highest price and check stop loss on 1-min data
elif in_position:
# Update highest price if new high on aggregated bar
if close_price > highest_price:
highest_price = close_price
# Use precomputed indices for this bar
start_idx, end_idx = slice_indices[i-1]
df_1min_slice = df_1min_sorted.iloc[start_idx:end_idx]
stop_triggered = False
for _, row in df_1min_slice.iterrows():
# Update highest price if new high in 1-min bar
if row['Close'] > highest_price:
highest_price = row['Close']
# Trailing stop loss condition on 1-min close
if row['Close'] < highest_price * (1 - stop_loss_pct):
in_position = False
usd = coin * row['Close']
coin = 0
# print(f"Stop loss triggered at {row['Close']:.2f} on {row['Timestamp']}")
nb_stop_loss += 1
highest_price = None
stop_triggered = True
break
# If stop loss was triggered, skip further checks for this bar
if stop_triggered:
continue
# Sell condition: any Supertrend value negative (on aggregated bar close)
if any_negative:
in_position = False
usd = coin * close_price
coin = 0
highest_price = None
if i % progress_step == 0 or i == total_steps:
percent = (i / total_steps) * 100
print(f"Progress: {percent:.1f}% ({i}/{total_steps})")
print(f"Total profit: {usd - init_usd}")
print(f"Number of stop losses: {nb_stop_loss}")
if __name__ == "__main__":
df_1min = load_data('2020-01-01')
df_aggregated = aggregate_data(df_1min, '5min')
# Add Supertrend indicators
df_aggregated = add_supertrend_indicators(df_aggregated)
df_aggregated['log_return'] = np.log(df_aggregated['Close'] / df_aggregated['Close'].shift(1))
# Example: 2% trailing stop loss
backtest(df_aggregated, df_1min, stop_loss_pct=0.02)

7
pyproject.toml Normal file
View File

@ -0,0 +1,7 @@
[project]
name = "lowkey-backtest"
version = "0.1.0"
description = "Add your description here"
readme = "README.md"
requires-python = ">=3.12"
dependencies = []