* feat(core): add 3 trade-flow microstructure indicators
SignedVolume (per-trade size signed by aggressor), CumulativeVolumeDelta
(running signed-volume total), and TradeImbalance (rolling buy/sell volume
imbalance over a trade window). All consume the Trade type, with full unit
coverage. Extends the Microstructure family.
* feat(bindings): expose trade-flow microstructure indicators
Python, Node and WASM bindings for SignedVolume, CumulativeVolumeDelta and
TradeImbalance. Each takes a trade via update(price, size, is_buy); Python and
Node expose a batch over three parallel arrays, WASM exposes per-trade update.
Regenerates node index.d.ts/.js.
* test(bindings,fuzz,bench): cover trade-flow microstructure indicators
Python and Node: reference values, streaming-vs-batch, lifecycle/repr and input
validation (zero window, negative size, non-positive price, mismatched batch
lengths). New indicator_update_trade fuzz target. Synthetic trade-tape benches
(signed_volume cheapest, trade_imbalance windowed/expensive).
* docs: add trade-flow indicators + bump counter to 227
README Microstructure family row gains signed volume / CVD / trade imbalance and
the counter goes 224 -> 227; CHANGELOG records the trade-flow indicators.
* feat(core): add microstructure input types (OrderBook, Trade, TradeQuote)
New non-OHLCV value types for the order-book / trade-flow indicator family:
Level, OrderBook (sorted, uncrossed depth snapshot), Side, Trade (with
aggressor side), and TradeQuote (trade paired with prevailing mid). Each has a
validating constructor plus a new_unchecked hot-path constructor, with full
unit coverage. Adds InvalidOrderBook / InvalidTrade error variants.
* feat(core): add 5 order-book microstructure indicators
OrderBookImbalanceTop1/TopN/Full (signed depth imbalance), Microprice
(size-weighted fair value), and QuotedSpread (top-of-book spread in bps). All
consume the OrderBook snapshot type, emit f64, are stateless and ready after
the first snapshot, with full unit coverage. Registers a new Microstructure
family in the taxonomy.
* feat(bindings): expose order-book microstructure indicators
Python, Node, and WASM bindings for OrderBookImbalanceTop1/TopN/Full,
Microprice and QuotedSpread. Each takes a depth snapshot via four equal-length
(bid_px, bid_sz, ask_px, ask_sz) arrays. Python and Node expose a batch over a
list of snapshots; WASM exposes per-snapshot update (the streaming model that
fits a browser book feed). Regenerates node index.d.ts/.js and registers the
new InvalidOrderBook/InvalidTrade arms in the Python error mapping.
* test(bindings,fuzz): cover order-book microstructure indicators
Python: smoke, reference values, streaming-vs-batch, lifecycle/repr and input
validation (mismatched lengths, crossed book, misordered levels, zero levels)
for all five order-book indicators. Node: reference values, streaming-vs-batch,
and rejection cases. Adds an indicator_update_orderbook fuzz target driving
every order-book indicator over arbitrary (incl. degenerate) snapshots.
* bench(microstructure): synthetic order-book benchmarks
Add a bench_orderbook_input harness and synthesise a five-level book around
each candle close (no order-book dataset ships with the repo). Benches the
cheapest (top-of-book imbalance) and most-expensive (full-depth imbalance) plus
microprice, matching the curated cheapest/expensive-per-family approach.
* docs: add Microstructure family + bump indicator counter to 224
README gains the Microstructure family row (order-book imbalance, microprice,
quoted spread) and the indicator counter goes 219 -> 224 across seventeen
families; CHANGELOG records the new order-book indicators and value types.
* feat(core): add signed dragonfly/gravestone encoding to Doji
Doji gains an opt-in `.signed()` mode that classifies a detected Doji by the
position of its body within the bar range: dragonfly (long lower shadow) emits
+1.0 (bullish), gravestone (long upper shadow) emits -1.0 (bearish), and a
long-legged/standard Doji emits 0.0. The default detection-flag behaviour
(+1.0/0.0) is unchanged, so existing callers are unaffected.
The other 14 candlestick patterns already emit the uniform +1 bull / -1 bear /
0 none convention; document that explicitly with a "Signed +-1 encoding"
section on each so the whole family is a consistent drop-in ML feature.
* feat(bindings): expose Doji signed mode in python, node, wasm
Hand-write the Doji binding in all three language bindings (instead of the
shared candle-pattern macro) so it accepts an opt-in `signed` flag and exposes
an `is_signed`/`isSigned` accessor:
- Python: `Doji(signed=False)` keyword argument
- Node: `new Doji(signed?)` optional constructor argument (index.d.ts/.js
regenerated via napi build)
- WASM: `new Doji(signed?)` optional constructor argument
The default construction is unchanged, so existing callers keep the
direction-less +1/0 detection flag.
* test(bindings,fuzz): cover Doji signed dragonfly/gravestone encoding
- python: dragonfly(+1)/gravestone(-1)/neutral(0) and default-flag cases in
test_known_values
- node: equivalent signed/default assertions in indicators.test.js
- fuzz: drive a signed Doji alongside the default in indicator_update_candle
* docs: document signed candlestick convention and Doji signed mode
README gains a candlestick sign-convention note; CHANGELOG records the new
opt-in Doji signed dragonfly/gravestone encoding under [Unreleased].
* feat(core): add PairwiseBeta cross-asset indicator
Rolling OLS slope of one asset's log-returns on another's. Unlike Beta,
which regresses the raw inputs it is fed, PairwiseBeta differences
consecutive prices into log-returns internally -- the conventional way to
measure cross-asset beta, where a beta on price levels would be dominated
by the shared trend.
Two-series Indicator<Input = (f64, f64)>, exposed in Rust, Python, Node
and WASM, with unit/known-value/streaming tests and a pair fuzz target.
* feat(core): add PairSpreadZScore cross-asset indicator
Standardised log-spread ln(a) - beta*ln(b) of a pair, where beta is a
rolling-OLS hedge ratio and the spread is z-scored over its own look-back.
The canonical mean-reversion / statistical-arbitrage entry signal, with
independent beta_period and z_period windows.
Two-series Indicator<Input = (f64, f64)>, exposed in Rust, Python, Node
and WASM, with sign/known-value/streaming tests and a pair fuzz target.
* feat(core): add LeadLagCrossCorrelation cross-asset indicator
Reports the integer offset k in [-max_lag, max_lag] that maximises
|corr(a[t], b[t+k])|, answering which of two assets leads the other and by
how many bars. A positive lag means a leads b. Fully causal: a's window is
held centred while b's window slides across the buffered history, so every
lag is evaluated only against data already seen.
Struct output { lag, correlation }, exposed in Rust, Python, Node and WASM
with lead-detection/streaming tests and a pair fuzz driver.
* feat(core): add Cointegration (Engle-Granger + ADF) indicator
Rolling pairs-trading screen: an OLS hedge ratio of a on b, the spread
(residual) a - (alpha + beta*b), and an augmented Dickey-Fuller t-statistic
on the spread with configurable lags. A strongly negative statistic flags a
mean-reverting, tradeable spread. Includes a small Gaussian-elimination
solver for the augmented regression.
Struct output { hedge_ratio, spread, adf_stat }, exposed in Rust, Python,
Node and WASM with stationarity/hedge-ratio/streaming tests and a pair fuzz
driver.
* feat(core): add RelativeStrengthAB cross-asset indicator
Comparative relative strength of two assets: the ratio line a/b together
with its moving average and its RSI, the classic asset-vs-asset /
asset-vs-index rotation screen. Composes the existing Sma and Rsi over the
ratio; a zero denominator or non-finite price is skipped.
Struct output { ratio, ratio_ma, ratio_rsi }, exposed in Rust, Python, Node
and WASM with flat/rising-ratio/streaming tests and a pair fuzz driver.
* test(cointegration): cover ADF guard branches
The ADF helper's short-series and degrees-of-freedom guards and the
zero-dispersion (perfect AR) path are unreachable through the public
Cointegration API (period >= 2*adf_lags + 4), so exercise them with direct
unit tests on adf_no_constant. The second linear solve cannot be singular
once the coefficient solve on the same matrix has succeeded, so it now uses
expect() instead of a dead error branch.
* build(node): track the generated index.d.ts (P5.1)
index.js was committed but index.d.ts was gitignored, an inconsistency that
also contradicts CONTRIBUTING ('regenerate both .d.ts/.js when a binding API
changes'). Track index.d.ts too so the repo carries the TypeScript types as a
matched pair with index.js. Generated by napi build; ~214 indicator classes.
* fix(node): reject invalid periods instead of clamping them (P5.4)
The Node scalar-indicator macro clamped period 0 to 1 (via clamp_period + must)
and the multi-parameter constructors did the same, silently swallowing the
core's PeriodZero validation. The core rejects period 0 (Error::PeriodZero),
and the Python and WASM bindings already propagate it — Node was the outlier,
masking caller mistakes. Make the macro constructor fallible and let every
constructor propagate the core error via map_err, removing clamp_period/must.
Update the smoke test (period 0 now throws, matching core/Python/WASM).
* docs(changelog): note the Node period-validation change (P5.4)
Behavior change per CONTRIBUTING: Node constructors now reject invalid periods
instead of clamping. Add an [Unreleased] Changed entry.
* chore: drop accidentally committed scratch log
kingchenc