kingchenc
677ea37402
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.
52 lines
1.9 KiB
R
52 lines
1.9 KiB
R
# Shared helpers for the offline Wickra R examples: deterministic synthetic
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# market data, a small OHLCV CSV loader, and an equity-curve summary. Mirrors the
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# helpers used by the C, C# and Go example suites.
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synthetic_prices <- function(count, start = 100) {
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i <- seq_len(count) - 1
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start + 12 * sin(i * 0.05) + 5 * sin(i * 0.013) + i * 0.01
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}
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synthetic_candles <- function(count, start_ts = 0, step_ms = 3600000) {
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prices <- synthetic_prices(count + 1)
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k <- seq_len(count)
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i <- k - 1
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open <- prices[k]
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close <- prices[k + 1]
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data.frame(
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open = open,
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high = pmax(open, close) + 0.5 + abs(sin(i * 0.7)),
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low = pmin(open, close) - 0.5 - abs(cos(i * 0.7)),
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close = close,
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volume = 1000 + 500 * (1 + sin(i * 0.1)),
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timestamp = start_ts + i * step_ms
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)
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}
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load_ohlcv_csv <- function(path) {
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# Native CandleReader: header validation, BOM and field-whitespace tolerance.
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# read() returns an (n x 6) matrix of open, high, low, close, volume, timestamp.
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m <- read(CandleReader(paste(readLines(path, warn = FALSE), collapse = "\n")))
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data.frame(open = m[, "open"], high = m[, "high"], low = m[, "low"],
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close = m[, "close"], volume = m[, "volume"], timestamp = m[, "timestamp"])
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}
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summarize_equity <- function(returns, trades, periods_per_year = 252) {
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equity <- 1; peak <- 1; maxdd <- 0
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for (r in returns) {
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equity <- equity * (1 + r)
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peak <- max(peak, equity)
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maxdd <- max(maxdd, (peak - equity) / peak)
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}
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mean_r <- if (length(returns)) mean(returns) else 0
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sd_r <- if (length(returns) > 1) stats::sd(returns) else 0
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sharpe <- if (sd_r > 1e-12) mean_r / sd_r * sqrt(periods_per_year) else 0
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list(total_return_pct = (equity - 1) * 100, sharpe = sharpe,
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max_dd_pct = maxdd * 100, trades = trades)
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}
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print_equity <- function(name, r) {
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cat(sprintf("%-26s return=%8.2f%% sharpe=%6.2f maxDD=%6.2f%% trades=%d\n",
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name, r$total_return_pct, r$sharpe, r$max_dd_pct, r$trades))
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}
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