add-indicator

name: add-indicator description: Add a new technical analysis indicator to the project. Use when you need to implement a new indicator (e.g., SMA, EMA, RSI) following the project's streaming data patterns, Go generics, and channel-based API.

Add Indicator

This skill provides the workflow and standards for adding new technical analysis indicators to the indicator project.

Workflow

1. Identify Category and Location

Indicators are organized by their technical analysis category. Determine where the new indicator belongs:

trend/: Trend-following indicators (SMA, EMA, MACD, etc.)
momentum/: Momentum indicators (RSI, Stochastic, etc.)
volatility/: Volatility indicators (Bollinger Bands, ATR, etc.)
volume/: Volume-based indicators (OBV, Chaikin Money Flow, etc.)
valuation/: Asset valuation (FV, NPV, etc.)

2. Implementation Standards

Streaming Data Patterns

Indicators MUST operate on unlimited data streams using Go routines and channels.
Use <-chan T for inputs and <-chan T (or multiple channels) for outputs.
Indicators may have an "idle" or "warm-up" period where they don't produce output until their internal window is filled.

Generics and Types

All indicators must use Go generics with the helper.Number constraint.
Define a struct for the indicator and a New<Indicator> factory function.
Implement a Compute method: func (i *Indicator[T]) Compute(c <-chan T) <-chan T.
Implement an IdlePeriod method: func (i *Indicator[T]) IdlePeriod() int to return the number of elements it skips.

Example Implementation (inspired by `trend/macd.go`)

type MyIndicator[T helper.Number] struct {
    Period int
}

func NewMyIndicator[T helper.Number](period int) *MyIndicator[T] {
    return &MyIndicator[T]{Period: period}
}

func (m *MyIndicator[T]) Compute(c <-chan T) <-chan T {
    // Implementation using helper functions and Go routines
}

func (m *MyIndicator[T]) IdlePeriod() int {
    return m.Period - 1
}

3. Preventing Deadlocks

Streaming indicators are prone to deadlocks if not synchronized correctly.
Use helper.Skip (from helper/skip.go) to synchronize output streams or handle warm-up periods.
For repository-level synchronization, refer to asset/sync.go.

4. Testing Requirements

CSV Test Data: Every indicator MUST have a test data file in a testdata/ subdirectory (e.g., trend/testdata/my_indicator.csv).
Data Volume: Use a large dataset, typically 252 rows (representing a trading year), for consistency.
Deadlock Detection: When running tests for the first time, ALWAYS apply a timeout:
```
go test -v ./trend -run TestMyIndicator -timeout 30s
```
Verification: Use helper.CheckEquals and helper.RoundDigits for floating-point comparisons.

5. Documentation and Integration

Lint and Auto-Docs: After implementation, run the task command. This will execute lint checks and update the module-level README.md files automatically.
Main README: Manually update the root README.md to add the new indicator to the appropriate list under "Indicators Provided".

Reference Examples

Indicator Code: See trend/macd.go for a complex multi-stream implementation.
Test Data: See trend/testdata/macd.csv for the expected CSV format.
Helper Utilities: Explore the helper/ directory for common operations like Duplicate, Pipe, Skip, etc.