fcb221ec03
Adds 19 streaming indicators so an external trading-bot feature extractor can replace its hand-built features with native, batch/streaming-equivalent ones. Each is a real gap (verified against the existing catalogue), production-only, with full Python/Node/WASM bindings, fuzz drivers, and tests. Five commits, one per family group; counter 377 -> 396. ## What's added **Price Statistics (6)** — `LogReturn`, `RealizedVolatility` (raw quadratic variation, the un-annualised counterpart to `HistoricalVolatility`), `RollingQuantile`, `RollingIqr`, `RollingPercentileRank`, `SpreadAr1Coefficient` (pairwise AR(1) rho of the spread; complements `OuHalfLife`). **Price Action (4)** — `CloseVsOpen`, `BodySizePct`, `WickRatio`, `HighLowRange` (stateless per-bar OHLC transforms). **Regime / Trend / Jump labels (3)** — `TrendLabel` (sign of the rolling OLS slope), `JumpIndicator` (return outliers vs trailing volatility, measured as deviation from the trailing mean so steady drift is not flagged), `RegimeLabel` (volatility-quantile regime split). **Risk / Performance (2)** — `WinRate`, `Expectancy` (R-multiple). **Microstructure (4)** — `OrderFlowImbalance` (Cont-Kukanov-Stoikov OFI), `Vpin`, `AmihudIlliquidity`, `RollMeasure`. These reuse the existing `OrderBook` / `Trade` inputs (no new input type). ## Intentionally NOT added (already present, would be duplicates) - **Population skew / kurtosis** — `skewness.rs` / `kurtosis.rs` are already population moments (divisor n). - **Hurst R/S** — `hurst_exponent.rs` already uses rescaled-range (R/S) analysis. - **Queue Imbalance** — exactly `OrderBookImbalanceTop1` ((bidSize - askSize) / (bidSize + askSize)). ## Verification `cargo test -p wickra-core` (lib 3187 + doc 354), `cargo clippy --workspace --all-targets --all-features -D warnings` clean, node `npm run build && npm test` (471), python `pytest` (784). Counter consistent across `mod.rs`, lib block, README, and docs/README at 396.
54 lines
2.0 KiB
Rust
54 lines
2.0 KiB
Rust
#![no_main]
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//! Fuzz order-book `Indicator<Input = OrderBook>` implementations with
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//! arbitrary depth snapshots.
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//!
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//! Each iteration consumes a byte stream, interprets it as a sequence of
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//! `f64` values (8 bytes each), packs consecutive values into `(price, size)`
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//! levels, and groups levels into order-book snapshots. Books are built with
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//! `OrderBook::new_unchecked` so the fuzzer can explore degenerate shapes
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//! (empty sides, crossed books, non-finite prices, negative sizes) that the
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//! validating constructor would reject — the indicators must never panic on
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//! any of them, streaming or batched.
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use libfuzzer_sys::fuzz_target;
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use wickra_core::{BatchExt, DepthSlope, Indicator, Level, Microprice, OrderBook, OrderBookImbalanceFull, OrderBookImbalanceTop1, OrderBookImbalanceTopN, OrderFlowImbalance, QuotedSpread};
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#[inline(never)]
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fn drive<I>(make: impl Fn() -> I, books: &[OrderBook])
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where
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I: Indicator<Input = OrderBook, Output = f64> + BatchExt,
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{
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let mut streaming = make();
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for book in books {
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let _ = streaming.update(book.clone());
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}
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let _ = make().batch(books);
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}
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fuzz_target!(|data: &[u8]| {
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let floats: Vec<f64> = data
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.chunks_exact(8)
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.map(|c| f64::from_le_bytes(c.try_into().expect("8 bytes")))
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.collect();
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let levels: Vec<Level> = floats
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.chunks_exact(2)
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.map(|c| Level::new_unchecked(c[0], c[1]))
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.collect();
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// Group levels into snapshots of up to four levels (split into bids / asks).
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let books: Vec<OrderBook> = levels
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.chunks(4)
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.map(|chunk| {
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let half = chunk.len() / 2;
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OrderBook::new_unchecked(chunk[..half].to_vec(), chunk[half..].to_vec())
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})
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.collect();
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drive(OrderBookImbalanceTop1::new, &books);
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drive(|| OrderBookImbalanceTopN::new(3).unwrap(), &books);
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drive(OrderBookImbalanceFull::new, &books);
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drive(Microprice::new, &books);
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drive(QuotedSpread::new, &books);
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drive(DepthSlope::new, &books);
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drive(|| OrderFlowImbalance::new(20).unwrap(), &books);
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});
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