kingchenc
3ab2d6ec2d
## Summary Adds the **Seasonality & Session** family — the first family that reads the wall-clock fields of `Candle::timestamp`. A new private `calendar` module decomposes an epoch-millisecond instant (shifted by a per-indicator `utc_offset_minutes`) into civil fields via Howard Hinnant's branch-light `civil_from_days` algorithm. Session / day / month rollovers are detected automatically, so callers never have to invoke `reset()` at a boundary. Indicator counter **339 → 351**; family count **20 → 21**. ## Indicators | Shape | Indicators | |-------|-----------| | Scalar (`f64`) | `SessionVwap`, `AverageDailyRange`, `OvernightGap`, `TurnOfMonth`, `SeasonalZScore` | | Struct | `SessionHighLow`, `SessionRange` (Asia/EU/US), `OvernightIntradayReturn` | | Profile (`Vec<f64>`) | `TimeOfDayReturnProfile`, `DayOfWeekProfile`, `IntradayVolatilityProfile`, `VolumeByTimeProfile` | ## Bindings The input is the **full** candle (`open, high, low, close, volume, timestamp`), not the `high/low/close` slice the value-indicator helper assumes, so the Python / Node / WASM bindings are custom full-candle implementations: - **Python** — `update((o,h,l,c,v,ts))`; `batch(open, high, low, close, volume, timestamp)` → `PyArray1` (scalar) / `PyArray2` (struct & profile), warmup rows `NaN`. - **Node** — `update(open, high, low, close, volume, timestamp)`; `batch(...)` → flat `Vec<f64>`; struct outputs as `#[napi(object)]` values. - **WASM** — `update` only (multi-input precedent); profiles as `Float64Array`, structs as camelCase objects, `timestamp` as `BigInt`. ## Verification - `wickra-core`: full per-branch unit tests, **100%** coverage target; 2852 lib tests + 334 doctests green. - `cargo clippy --workspace --all-targets --all-features -- -D warnings`: clean. - Node: 428 tests (dedicated `seasonality.test.js` streaming-vs-batch). - Python: full suite + dedicated `test_seasonality.py` streaming-vs-batch. - Counter check: mod-count == counted lib block == 351.
Wickra — Node.js
Streaming-first technical indicators for Node.js. npm install wickra —
prebuilt native binary, no system dependencies.
Wickra is a multi-language technical-analysis library with a Rust core and bindings for Python, Node.js, and WebAssembly. Every indicator is an O(1) streaming state machine, so live trading bots and historical backtests share the exact same implementation. This package is the Node.js binding (napi-rs); it exposes 200+ streaming-first indicators across sixteen families.
Install
npm install wickra
The native addon ships as a prebuilt binary per platform (Linux, macOS, Windows — x64 and arm64), selected automatically through optional dependencies. There is nothing to compile.
Quick start
const wickra = require('wickra');
// Batch: run an indicator over a whole array.
const prices = Array.from({ length: 1000 }, (_, i) => 100 + i * 0.1);
const values = new wickra.RSI(14).batch(prices); // null during warmup
// Streaming: the same indicator, fed tick by tick in O(1).
const rsi = new wickra.RSI(14);
for (const price of liveFeed) {
const value = rsi.update(price); // no recomputation over history
if (value !== null && value > 70) {
console.log('overbought');
}
}
batch(prices) and feeding the same prices through update() produce
identical values — the equivalence is enforced by the test suite.
Documentation
The full indicator catalogue, guides, quickstarts, and API reference live in the main repository and documentation site:
- Repository & full indicator list: https://github.com/wickra-lib/wickra
- Docs (quickstarts, cookbook, TA-Lib migration): https://docs.wickra.org
- Runnable examples:
examples/node/
Wickra ships four bindings — Python, Node.js, WebAssembly, and Rust — that all
expose the same indicators from the shared, unsafe-forbidden Rust core.
Disclaimer
Wickra is an indicator toolkit, not a trading system. The values it computes are deterministic transforms of the input data — they are not financial advice and do not predict the market. Any use in a live trading context is at your own risk. The library is provided as is, without warranty of any kind.
License
Licensed under either of Apache-2.0 or MIT at your option.