kingchenc
75eefbbd08
The strategy_* examples were only syntax-smoked in CI, never run, which hid two classes of problem: 1. Python strategy_macd_adx / strategy_bollinger_squeeze passed three separate arguments to the candle indicators ADX/ATR, whose .update() takes a single candle — a TypeError at runtime — and read the ADX tuple at index 0 (plus_di) instead of 2 (adx). Both fixed. 2. The Go / C# / R / Java strategies defaulted to synthetic data and used a different (annualised) one-line summary, so they printed wildly different numbers from the Rust/Python/Node/C/WASM suite. Rewrite them to the shared per-trade backtest (load the bundled BTCUSDT CSV by default, same entry/exit logic, same print_summary output). All nine runnable bindings now print byte-identical backtest summaries on the same data (MACD+ADX 246 trades / -47.19%, RSI 37 / -17.84%, Bollinger 1 / -7.82%), verified by diffing each language's output against the Python reference. WASM shares the same logic and bundled dataset (browser-rendered).
Wickra examples — Java
Runnable examples for the Wickra Java binding. Each
example is a small main class that uses the org.wickra:wickra artifact and
resolves the native library automatically (from target/release during local
development, or the bundled per-platform library when packaged).
Build the native library and install the binding to your local Maven repo
first, then run any example with the exec plugin:
cargo build -p wickra-c --release
mvn -f bindings/java install -DskipTests
mvn -f examples/java compile
mvn -f examples/java exec:exec -Dexec.mainClass=org.wickra.examples.Streaming
(The exec:exec goal forks a JVM with --enable-native-access=ALL-UNNAMED, the
flag the FFM API needs.)
| Example | What it does | Main class |
|---|---|---|
streaming |
Feed a synthetic price series through SMA / EMA / RSI / MACD tick by tick. | org.wickra.examples.Streaming |
backtest |
Compute a basket of indicators over an OHLCV series and print a summary. | org.wickra.examples.Backtest |
multi_timeframe |
Resample a 1-minute series into 5m / 15m and print an indicator per timeframe. | org.wickra.examples.MultiTimeframe |
parallel_assets |
SMA(20) batch over a panel of assets, serial vs parallel streams, with speedup. | org.wickra.examples.ParallelAssets |
strategy_rsi_mean_reversion |
RSI(14) mean-reversion with a PnL / Sharpe / max-DD summary. | org.wickra.examples.StrategyRsiMeanReversion |
strategy_macd_adx |
MACD crossover entries gated by ADX(14) > 20. | org.wickra.examples.StrategyMacdAdx |
strategy_bollinger_squeeze |
Bollinger-squeeze breakout with an ATR(14) trailing stop. | org.wickra.examples.StrategyBollingerSqueeze |
fetch_btcusdt |
Download real BTCUSDT klines from the Binance REST API into a CSV. | org.wickra.examples.FetchBtcusdt |
live_binance |
Stream live Binance klines through EMA(20) over a WebSocket. | org.wickra.examples.LiveBinance |
fetch_btcusdt and live_binance require network access; the rest run offline
on deterministic synthetic data. Shared helpers (synthetic data, CSV loader,
equity summary) live in MarketData and Equity.