examples: migrate to the native data layer (drop ws/coder-websocket/jackson/jsonlite) (#316)

Stacked on #315 (the native Binance REST fetcher). Retarget to `main` once #315 merges.

Migrates the runnable examples off third-party data-I/O packages onto Wickra's
native data layer (`CandleReader`, `Resampler`, `BinanceFeed`, `fetch_*klines`).

## Third-party packages removed (the zero-dep selling point)
- **Node**: `ws` (live feed → BinanceFeed) — dropped from package.json + lockfile
- **Go**: `github.com/coder/websocket` — dropped from go.mod / go.sum (`go mod tidy`)
- **Java**: `jackson-databind` (live feed + REST fetch) — dropped from pom.xml
- **R**: `jsonlite` + `websocket` + `later` — dropped from the README notes

Each language's CSV loading now goes through `CandleReader`, manual resampling
through `Resampler`, the live feed through `BinanceFeed`, and (Java/R) the REST
download through the native fetcher.

## Verification
Ran the offline examples per language against the bundled data — backtest and
multi_timeframe produce identical output across Python / Node / Go / Java / R
(e.g. ATR(14) last 345.1010; 1h→5m resamples to 240 bars, →15m to 80 bars).

C# / C / WASM (stdlib-only, no third-party deps to remove) follow in this branch.

Note: the streaming `strategy_*` examples have pre-existing candle-indicator
runtime bugs (CI only syntax-smokes them); the CSV migration preserves their
shape and leaves those bugs for a separate fix.

examples/python/strategy_macd_adx.py

@@ -15,7 +15,6 @@ Uses the checked-in ``examples/data/btcusdt-1h.csv`` dataset.
 
 from __future__ import annotations
 
-import csv
 import math
 from pathlib import Path
 
@@ -26,18 +25,14 @@ ADX_FLOOR = 20.0
 
 
 def load_candles(path: Path) -> list[dict[str, float]]:
-    with path.open() as fh:
-        reader = csv.DictReader(fh)
-        return [
-            {
-                "open": float(r["open"]),
-                "high": float(r["high"]),
-                "low": float(r["low"]),
-                "close": float(r["close"]),
-                "volume": float(r["volume"]),
-            }
-            for r in reader
-        ]
+    # Native CandleReader: validates the header, tolerates a UTF-8 BOM and field
+    # whitespace, and raises ValueError on a malformed row. No third-party CSV.
+    candles = ta.CandleReader(path.read_text(encoding="utf-8")).read()
+    # CandleReader yields (open, high, low, close, volume, timestamp) tuples.
+    return [
+        {"open": o, "high": h, "low": l, "close": c, "volume": v}
+        for o, h, l, c, v, _ts in candles
+    ]
 
 
 def print_summary(