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domain library on pond-ts: trading calendars, session-aligned OHLCV bars, and 60+ oracle-verified technical studies (SMA, EMA, RSI, MACD, Bollinger, ATR, VWAP, …)","maintainers":[{"name":"pjm17971","email":"peter71@gmail.com"}],"readme":"# @pond-ts/financial\n\n[![npm](https://img.shields.io/npm/v/@pond-ts/financial?label=%40pond-ts%2Ffinancial)](https://www.npmjs.com/package/@pond-ts/financial)\n[![CI](https://github.com/pond-ts/pond/actions/workflows/ci.yml/badge.svg)](https://github.com/pond-ts/pond/actions/workflows/ci.yml)\n[![docs](https://img.shields.io/badge/docs-pond--ts.org-1f6feb)](https://pond-ts.org/docs/financial/)\n\n**Technical studies and a trading calendar on [pond-ts](https://www.npmjs.com/package/pond-ts).**\n\nTwenty oracle-verified studies (moving averages, bands, RSI, MACD, ATR,\nstochastics, Donchian, OBV, VWAP, …) that append columns to a bar\n`TimeSeries`, plus a `TradingCalendar` that knows when the market is open so\nrolling windows, bucketing and chart axes stop at the close. Pure\ncomputation: browser + Node, no data fetching, no rendering, no React (the\nchart side lives in `@pond-ts/charts`).\n\n```sh\nnpm install @pond-ts/financial pond-ts\n```\n\n`pond-ts` is a peer dependency; the pond packages release together, so keep\ntheir ranges in step.\n\n## Quick start\n\nA study takes a series and options and returns the series with more columns\non it. Import the fluent entry once and chain them:\n\n```ts\nimport '@pond-ts/financial/fluent';\n\nconst study = bars // a TimeSeries with open/high/low/close/volume columns\n  .bollinger({ period: 20 })\n  .ema({ period: 10 })\n  .rsi({ period: 14 })\n  .macd({ fastPeriod: 12, slowPeriod: 26, signalPeriod: 9 })\n  .vwap({ period: 20 });\n// + bbUpper/bbMiddle/bbLower, ema, rsi, macdLine/macdSignal/macdHist, vwap\n```\n\nEvery study is also a plain function, for code that would rather not augment\n`TimeSeries`:\n\n```ts\nimport { bollinger, ema } from '@pond-ts/financial';\n\nconst same = ema(bollinger(bars, { period: 20 }), { period: 10 });\n```\n\nEach study reads a column (default `'close'`, or the named `high` / `low` /\n`close` / `volume` inputs for the multi-input ones) so it runs over any\nnumeric column, including another study's output. Periods are bar counts.\nWarm-up rows are `undefined` and the row count is preserved, so the result\nlines up on the source's time axis.\n\nA trading calendar turns \"5-minute bars\" into session-aligned bars — no\nweekend or holiday buckets, no bar spanning the close:\n\n```ts\nimport { TradingCalendar } from '@pond-ts/financial';\n\nconst cal = TradingCalendar.fromRules(\n  { timeZone: 'America/New_York', open: '09:30', close: '16:00' },\n  { from: '2026-01-05', to: '2026-02-13' },\n);\n\nconst fiveMin = ticks.aggregate(cal.barSequence('5m'), {\n  close: { from: 'price', using: 'last' },\n});\ncal.isOpen(instant); // inside a session and not inside a break\n```\n\n## What's in the box\n\n- **Studies** — verified bar-for-bar against a pandas oracle before they\n  ship, the named indicators against TA-Lib as well:\n  - _averages and bands_: `sma`, `ema`, `bollinger`, `envelope`, `donchian`,\n    `vwap`\n  - _oscillators_: `rsi`, `macd`, `stochastic`, `williamsR`, `momentum`,\n    `percentChange`, `zScore`, `obv`\n  - _volatility and range_: `atr`, `historicalVolatility`, `rollingStdev`,\n    `rollingMin`, `rollingMax`, `rollingPercentile`\n- **`@pond-ts/financial/fluent`** — mounts every study as a `TimeSeries`\n  method (opt-in by import; ESM only).\n- **`TradingCalendar`** — `fromRules` (hours, `weekmask`, `holidays`,\n  `breaks`, `earlyCloses`, resolved DST-correctly in the exchange's time zone)\n  or `fromSessions` (an explicit list); `sessions()`, `sessionOn()`,\n  `isTradingDay()`, `isOpen()`, `sessionSequence()` / `barSequence()` for\n  bucketing, and `tagSessions()` to key a `partitionBy` so stateful ops never\n  bridge a close.\n- **Discontinuity providers** — `weekendSkip()`, `segmentDiscontinuity()`,\n  `identityDiscontinuity()` and `calendar.discontinuities()`: the d3fc-style\n  five-method surface a trading-time axis consumes. `@pond-ts/charts` reads it\n  structurally, so there is no package coupling.\n- **`@pond-ts/financial/parallel`** — Node-only: `.withWorkers()` runs the\n  rolling studies across worker threads for large partitioned series.\n\nBar-for-bar vendor parity is a non-goal: where a study deliberately departs\nfrom TA-Lib (a flat window is `undefined`, not `0`; a gap in a running sum\npropagates rather than being skipped) the docstring says so and a test pins\nit.\n\n## Documentation\n\nGuides, live examples and the full API live at **<https://pond-ts.org>** — the\n[package page](https://pond-ts.org/docs/financial/) and the\n[financial charts hub](https://pond-ts.org/docs/charts/financial).\nSource and issues: [github.com/pond-ts/pond](https://github.com/pond-ts/pond).\n\n## License\n\nMIT\n","readmeFilename":"README.md"}