wickra-radar 0.1.3

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See liquidation cascades before they happen — open-interest, funding, order-book and liquidation signals scored across every perp in parallel.

▶ Live demos: the backtester compiled to WebAssembly, an equity curve building bar by bar — backtest-live.wickra.org; one StrategySpec side by side in Python, Rust, JS and Go — playground.wickra.org; all 514 indicators of the core over a real Binance feed — live.wickra.org. Zero backend, all of them.

Part of the Wickra ecosystem: the same data-driven core and ten-language binding surface also power wickra-exchange, wickra-backtest, wickra-terminal and 20 more — see the full list.

Wickra Radar is one data-driven core, wickra-radar-core: a serde RadarSpec is folded over a perp universe — open interest, funding, order-book and liquidation events — into a RadarReport of severity-scored RadarAlerts. Each symbol runs a handful of O(1) streaming signals; the per-signal scores are aggregated with weights into a single severity in [0, 1]. Thousands of symbols update in parallel, turning the whole market into a crash early-warning seismograph that price-only tools never see.

Because the alert is data, not code, the exact same output crosses the C ABI and WASM unchanged — and stays byte-for-byte identical between the parallel (rayon) and sequential (the WASM fallback) builds. The core is exposed as a JSON-over-C-ABI data API (Radar::command) in Rust, Python, Node.js, WASM, C, C++, C#, Go, Java and R, with a command-line reference consumer.

• OI delta — a burst in open interest over a rolling window.
• Funding flip — funding rate crossing zero (longs ↔ shorts pay).
• Book imbalance — resting bid/ask liquidity skew.
• Liquidation cluster — liquidation events bunching in a short window.
• OI / price divergence — open interest rising while price stalls or falls.

# Scan a perp universe from a spec + an event batch, raw RadarReport JSON
# (the same bytes every binding returns):
cargo run -p wickra-radar -- --spec golden/specs/composite.json --stdin --format json < golden/events.json

# Human-readable table of alerts:
cargo run -p wickra-radar -- --spec golden/specs/composite.json --stdin < golden/events.json

Status

0.1.3 — the current release. The core, the CLI, all ten language bindings, the byte-exact golden corpus, property + fuzz tests, benchmarks and one runnable example per language are in place and green across the full CI matrix (10 languages × 3 OS). Track progress in ROADMAP.md.

Documentation

• Architecture — the core, the data-driven boundary, the binding surface.
• Signal & spec reference and per-binding quickstarts under docs/; one runnable example per language under examples/.
• ROADMAP.md · BENCHMARKS.md · THREAT_MODEL.md · SECURITY.md.

Quickstart

The --spec file is a RadarSpec; events are read either from --stdin (one JSON object {"SYMBOL":[event, …], …}) or from --events <dir>, a directory of per-symbol <SYMBOL>.jsonl files (one JSON Event per line). --limit and --threshold override the spec.

RadarSpec / signals

A spec is a JSON (or TOML) document: a list of signals, an optional severity threshold, and an optional top-N limit. Each signal names a kind, its numeric params, and an optional weight (default 1.0). The report scores every symbol, keeps those at or above threshold, and returns the top limit sorted by severity (descending), then symbol (ascending).

{
  "signals": [
    { "kind": "oi_delta", "params": [2.0, 0.1], "weight": 1.0 },
    { "kind": "funding_flip", "params": [0.0005], "weight": 2.0 },
    { "kind": "book_imbalance", "params": [1.0], "weight": 1.0 },
    { "kind": "liq_cluster", "params": [5.0, 30.0], "weight": 1.5 },
    { "kind": "oi_price_divergence", "params": [2.0, 0.1], "weight": 3.0 }
  ],
  "threshold": 0.2,
  "limit": 3
}

• Signals (kind): oi_delta, funding_flip, book_imbalance, liq_cluster, oi_price_divergence.
• Alert — RadarAlert { symbol, severity, factors, ts }; factors is the per-signal score map plus the aggregated severity, so every alert explains itself. The report is RadarReport { alerts, scanned }.

Streaming, and why it is deterministic

scan folds a whole batch at once; feed / feed_batch drive the same per-symbol state incrementally and alerts reads the report at any point — the streaming path and the batch path go through one shared report_from_states, so they return byte-identical JSON. The parallel (rayon) and sequential builds agree bit-for-bit too: alerts sort by a total order (f64::total_cmp on severity, then symbol), never a partial float compare.

Use in any language

The same Radar handle — construct from a JSON spec, drive with command(json) -> json, read version — is reachable from every binding:

from wickra_radar import Radar
r = Radar('{"signals":[{"kind":"funding_flip","params":[0.0005]}],"threshold":0.0}')
report = r.command('{"cmd":"scan","events":{"AAA":['
                   '{"kind":"derivatives","ts":1,"open_interest":1.0,"funding_rate":0.0003,"mark_price":50.0},'
                   '{"kind":"derivatives","ts":2,"open_interest":1.0,"funding_rate":-0.0004,"mark_price":50.0}]}}')
# report is a JSON RadarReport: {"alerts":[{"symbol":"AAA","severity":1.0,...}],"scanned":1}

The C ABI hub (bindings/c) backs C, C++, C#, Go, Java and R; Rust, Python, Node.js and WASM are native. See each bindings/<lang>/README.md and the runnable examples/.

Project layout

crates/radar-core     the data-driven core (RadarSpec, Universe, signals, aggregate, scan, command_json)
crates/radar-cli      the CLI (bin: wickra-radar)
crates/radar-bench    criterion benchmarks
bindings/{python,node,wasm,c,go,csharp,java,r}   the ten-language surface
golden/               a deterministic event universe, specs, and byte-exact expected reports
fuzz/                 cargo-fuzz targets (spec_parse, command_json, scan)
examples/             one runnable "scan a universe" example per language

Building everything from source

cargo build --workspace
cargo test  --workspace --all-features
cargo test  --workspace --no-default-features   # sequential build path
cargo clippy --workspace --all-targets --all-features -- -D warnings
cargo run -p wickra-radar -- --spec golden/specs/composite.json --stdin --format json < golden/events.json

Each binding builds from its own directory — see the per-binding READMEs under bindings/.

Testing

Run the suites with the commands in Building everything from source.

• wickra-radar-core — unit tests per signal, the scoring and aggregation path, the parallel-versus-sequential parity, property tests over the event stream and the command envelope. The golden fixtures in golden/ are the anchor: the same (spec, events) pair must scan to the same report bytes here as in every binding.
• Every binding asserts the same golden bytes. That is the whole cross-language claim, so it is checked the same way in each one rather than approximated per language: Python with pytest (and a plain runner on 3.9), Node with node --test, WASM through the nodejs build, C and C++ through ctest, C# with dotnet test, Go with go test, Java with JUnit, and R with the shipped tests/smoke.R plus the repository's run_tests.R.
• Examples — every example under examples/ runs in CI and is held to the version and the alerts it prints.
• Fuzz — fuzz/ holds libFuzzer targets over spec parsing, the command envelope and the scan; CI runs each for a short smoke.

Requirements

• Rust 1.86+ — the workspace MSRV; the Node binding needs Rust 1.88.
• Python 3.9+ — the Python binding.
• Node 22+ — the Node binding.
• Go 1.23+ — the Go binding.
• Java 22+ — the Java binding.
• R 4.1+ — the R package.
• .NET 8+ — the C# binding.
• A C11 / C++17 compiler with CMake 3.15+ for the C and C++ examples.

See each bindings/<lang>/README.md for the per-language build and install.

Benchmarks

crates/radar-bench measures scan scaling by universe size and events per symbol, parallel vs sequential. See BENCHMARKS.md.

Ecosystem

Part of the Wickra family — each one a data-driven core with a CLI and the same ten-language binding surface:

• wickra — main library (Rust core + Python / Node.js / WASM bindings + a C ABI for C / C++ / C# / Go / Java / R)
• wickra-playground — a polyglot strategy playground: one StrategySpec live side by side in Python, Rust, JS and Go, entirely in the browser
• wickra-exchange — unified market-data + execution across ten crypto exchanges
• wickra-backtest — event-driven backtester over the Wickra core
• wickra-terminal — the trading terminal: a TUI and a browser renderer over the stack
• wickra-screener — parallel multi-symbol screening over 514 streaming indicators
• wickra-xray — market-microstructure explorer: footprint, order-book heatmap, liquidation map, funding/OI divergence
• wickra-copilot — local market copilot grounded in real order-book, liquidation and funding microstructure
• wickra-shazam — match an asset's current microstructure fingerprint against its entire history
• wickra-benchmark — reproducible, golden-verified benchmark suite — recompute any (strategy, dataset, report) in ten languages and confirm it byte-for-byte
• wickra-strategy-ci — Jest for trading strategies: golden-pin the report, catch regressions in CI, property-test against fuzzed data
• wickra-verify — confirm or refute a claimed backtest report against its strategy and data, in ten languages
• wickra-proof — Proof-of-Backtest: deterministic (spec, data) → report + blake3 hash, recomputable byte-for-byte in ten languages
• wickra-zk — prove a backtest zero-knowledge — on-chain-verifiable performance without revealing the data or the strategy
• wickra-impact — the backtester that knows you would have moved the market: agent-based fills on the real historical L2 order book
• wickra-darwin — evolutionary strategy search at millions of backtests per second, mutating and crossing JSON specs across the 514-indicator space
• wickra-gym — a Gymnasium-compatible, microstructure-aware backtest environment with O(1) steps for deterministic RL rollouts
• wickra-feature-store — OHLCV and microstructure streams into ML-ready feature matrices over 514 O(1) streaming indicators
• wickra-genome — a vector database of the whole market: every asset a 514-dim live vector, for similarity search, clustering and anomaly detection
• wickra-timemachine — scrub the whole market like a video — every symbol, full order book, rewound to any moment via deterministic re-fold
• wickra-synth — deterministic synthetic market microstructure: OHLCV, order book, trades and funding from a single seed
• wickra-compile — compile a strategy spec into a standalone deployable: a WASM module, a self-contained binary, or a no_std artifact
• wickra-embed — allocation-free, no_std streaming indicators for bare-metal and HFT, byte-for-byte identical to the core
• wickra-pico — the O(1) indicator core running bare-metal on a $5 Raspberry Pi Pico — the LED blinks on the EMA cross

Docs at docs.wickra.org; the marketing site and in-browser demo at wickra.org.

Contributing

See CONTRIBUTING.md and CODE_OF_CONDUCT.md. Commits are signed and in English; open a PR against main.

Security

See SECURITY.md and THREAT_MODEL.md. Report vulnerabilities privately — never in a public issue.

License

Licensed under either of

• Apache License, Version 2.0 (LICENSE-APACHE or http://www.apache.org/licenses/LICENSE-2.0)
• MIT license (LICENSE-MIT or http://opensource.org/licenses/MIT)

at your option. Use it, fork it, modify it, redistribute it — commercially or not — file issues, send pull requests; all welcome.

Contribution

Unless you explicitly state otherwise, any contribution intentionally submitted for inclusion in the work by you, as defined in the Apache-2.0 license, shall be dual licensed as above, without any additional terms or conditions.

Disclaimer

Wickra Radar is analysis software: it computes early-warning signals over historical and live market data. It is provided "as is", without warranty of any kind, and is not financial advice — it places no orders. Trading carries risk of loss; review the code and use at your own discretion.

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Built on Wickra. If it saved you time, the cheapest way to say thanks is to ⭐ the repo.