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/r/_common.R

@@ -24,14 +24,11 @@ synthetic_candles <- function(count, start_ts = 0, step_ms = 3600000) {
 }
 
 load_ohlcv_csv <- function(path) {
-  df <- utils::read.csv(path, header = TRUE, stringsAsFactors = FALSE)
-  if (ncol(df) >= 6) {
-    data.frame(open = df[[2]], high = df[[3]], low = df[[4]],
-               close = df[[5]], volume = df[[6]], timestamp = df[[1]])
-  } else {
-    data.frame(open = df[[1]], high = df[[2]], low = df[[3]],
-               close = df[[4]], volume = df[[5]], timestamp = seq_len(nrow(df)))
-  }
+  # Native CandleReader: header validation, BOM and field-whitespace tolerance.
+  # read() returns an (n x 6) matrix of open, high, low, close, volume, timestamp.
+  m <- read(CandleReader(paste(readLines(path, warn = FALSE), collapse = "\n")))
+  data.frame(open = m[, "open"], high = m[, "high"], low = m[, "low"],
+             close = m[, "close"], volume = m[, "volume"], timestamp = m[, "timestamp"])
 }
 
 summarize_equity <- function(returns, trades, periods_per_year = 252) {