idx-cli/docs/CONVENTIONS.md

Conventions

Design Philosophy

Schema-Driven Development

Define data types FIRST, then build logic around them. Types are the spec.

// ✅ Good: rich type with documented fields, integer precision
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct KseiHolding {
    /// Ownership percentage in basis points: 41.10% → 4110.
    pub percentage_bps: i64,
    /// Total shares held (absolute count).
    pub total_shares: i64,
}

// ❌ Bad: stringly-typed, float precision
pub struct Holding {
    pub percentage: f64,       // what unit? what precision?
    pub shares: String,        // why string?
}

Functional Approach

Parse/transform functions are pure: take input, return Result<T, IdxError>, no side effects.

// ✅ Good: pure function, testable in isolation
pub fn parse_id_number(s: &str) -> Result<i64, IdxError> { ... }
pub fn normalize_name(raw: &str) -> String { ... }
pub fn parse_quote_from_str(symbol: &str, raw: &str) -> Result<Quote, IdxError> { ... }

// ❌ Bad: function with side effects, hard to test
pub fn fetch_and_save_quote(symbol: &str) -> Result<(), IdxError> { ... }

Types Over Primitives

Use newtypes, enums, and rich structs. Avoid String where a domain type exists.

// ✅ Good
pub struct InvestorTypeCode(pub String);
pub enum Locality { Local, Foreign }
pub enum FlowSignal { Holder, Buyer, Seller, NewPosition, Exited }

// ❌ Bad
pub type InvestorType = String;
pub type Locality = String;

Naming

Files

• types.rs — domain data types for a module
• mod.rs — module declarations and re-exports
• client.rs — HTTP client code
• map.rs / parse.rs — response mapping / parsing functions
• raw_types.rs — raw API response shapes (before normalization)

Functions

• parse_* — deserialize raw data into domain types
• normalize_* — clean/transform data (names, numbers, dates)
• resolve_* — lookup/match entities
• query_* — read from database
• fetch_* — HTTP requests to external APIs
• render_* — output formatting (tables, JSON)
• handle — CLI command dispatch entry point

Types

• *Raw / *RawRow — pre-normalization data (strings from API/PDF)
• *Holding — ownership fact row
• *Metrics — computed analytics
• *Row — display-ready composite type
• *Args — clap command arguments

Patterns

Provider Trait Pattern

pub trait QuoteProvider {
    fn quote(&self, symbol: &str) -> Result<Quote, IdxError>;
}

// Factory function, not direct construction
pub fn default_provider(kind: ProviderKind) -> Box<dyn MarketDataProvider> { ... }

Parse Pipeline Pattern

// Raw API response → domain type, always via parse function
let raw: &str = &response_body;
let quote = yahoo::parse_quote_from_str("BBCA.JK", raw)?;

DB Function Pattern

// Take &Connection, caller manages lifetime. Use transactions for bulk.
pub fn insert_ksei_holdings(conn: &Connection, holdings: &[KseiHolding]) -> Result<usize, IdxError> { ... }
pub fn query_ticker_holdings(conn: &Connection, code: &str) -> Result<TickerOwnership, IdxError> { ... }

Error Propagation

// Map external errors to IdxError variants
let conn = Connection::open(path)
    .map_err(|e| IdxError::DatabaseError(e.to_string()))?;

Integer Precision

• Percentages: basis points (i64). 41.10% → 4110
• Shares: absolute count (i64). No floats.
• Prices: whole IDR (i64). Rounded from float at parse boundary.
• Money (USD): whole dollars (i64) for Bing data.

Testing

• Canonical verification: nix flake check -L locally and before pushing; CI uses the same gate. Do not repeat full Cargo build/lint/test runs after a successful flake check.
• Focused debugging: use nix develop --command cargo test <filter> when iterating on one failure.
• Unit tests: retain precise parser, normalization, calculation, and boundary regressions; avoid pinning incidental wording, enum counts, or internal wiring.
• Integration tests: prefer CLI workflows, real temporary/in-memory SQLite, and deterministic local provider fixtures for user-visible contracts and state transitions.
• Output contracts: parse JSON from stdout on success and stderr on failure; errors must leave stdout empty. Assert meaningful values, types, and stable error codes rather than substrings or diagnostic wording.
• Stateful workflows: compare cached data across offline/failure transitions, and verify failed snapshot updates preserve both database bytes and query results. Keep packaged-binary smoke representative; put exhaustive boundaries in integration tests.
• Fixtures: tests/fixtures/*.json — real API responses, sanitized
• No live API calls in CI — IDX_USE_MOCK_PROVIDER=1
• Test naming: test_<function>_<scenario> (e.g., test_parse_id_number_with_dots)
• Coverage over counts: replacing redundant unit assertions with stronger integration scenarios must retain meaningful error, boundary, and data-integrity coverage.

Git / VCS

• jj (Jujutsu) as local workflow, colocated with git
• Push via nix develop --command git push (for prek hooks)
• Branch naming: feat/<name>, fix/<name>
• Commit messages: conventional commits (feat:, fix:, refactor:, docs:)