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).
92 lines
3.8 KiB
R
92 lines
3.8 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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# Loads one of the checked-in datasets under examples/data (the R examples run
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# from this directory, so ../data is examples/data).
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bundled_candles <- function(filename) {
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load_ohlcv_csv(file.path("..", "data", filename))
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}
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# Prints the per-trade backtest summary shared verbatim with the Rust, Python,
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# Node, Go, C and C# example suites (same labels, same numbers).
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print_summary <- function(name, first_price, last_price, bars, closed_trades, final_equity, equity_curve) {
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buy_hold <- last_price / first_price
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strat_return <- final_equity - 1
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bh_return <- buy_hold - 1
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n <- length(closed_trades)
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wins <- sum(closed_trades > 0)
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losses <- sum(closed_trades < 0)
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best <- if (n > 0) max(closed_trades) else 0
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worst <- if (n > 0) min(closed_trades) else 0
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mean_r <- if (n > 0) mean(closed_trades) else 0
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var_r <- if (n > 1) stats::var(closed_trades) else 0
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sharpe <- if (var_r > 0) mean_r / sqrt(var_r) else 0
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peak <- if (length(equity_curve) > 0) equity_curve[1] else 1
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maxdd <- 0
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for (eq in equity_curve) {
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if (eq > peak) peak <- eq
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dd <- (peak - eq) / peak
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if (dd > maxdd) maxdd <- dd
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}
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cat(sprintf("=== %s ===\n", name))
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cat(sprintf("%-23s%d\n", "Bars:", bars))
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cat(sprintf("%-23s%d (W%d / L%d)\n", "Trades:", n, wins, losses))
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cat(sprintf("%-23s%+.2f%%\n", "Strategy return:", strat_return * 100))
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cat(sprintf("%-23s%+.2f%%\n", "Buy & Hold return:", bh_return * 100))
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cat(sprintf("%-23s%+.2f%%\n", "Excess over BH:", (strat_return - bh_return) * 100))
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cat(sprintf("%-23s%.2f%%\n", "Max drawdown:", maxdd * 100))
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cat(sprintf("%-23s%.2f (mean %+.4f, stddev %.4f)\n", "Per-trade Sharpe:", sharpe, mean_r, sqrt(var_r)))
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cat(sprintf("%-23s%+.2f%% / %+.2f%%\n", "Best / worst trade:", best * 100, worst * 100))
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cat("\n")
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cat("NOTE: Educational example — fees, slippage, funding costs and tax effects are simplified or omitted. Past performance is not indicative of future results.\n")
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}
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