examples: fix and harmonize the strategy backtests across all languages (#324)
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).
This commit is contained in:
kingchenc
@@ -49,3 +49,43 @@ 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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@@ -1,24 +1,69 @@
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# Breakout: when Bollinger bandwidth is tight (a "squeeze") and price closes above
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# the upper band, go long with an ATR(14) trailing stop.
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library(wickra)
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# Strategy example: Bollinger-squeeze breakout with an ATR(14) trailing stop.
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#
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# Enters long when Bollinger bandwidth makes a new SQUEEZE_LOOKBACK low (a
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# volatility squeeze) and price closes above the upper band; exits on an ATR(14)
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# trailing stop or when the upper band falls back below the entry. 0.1% fees per
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# trade. The R counterpart of examples/python/strategy_bollinger_squeeze.py,
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# printing the same summary. Uses the checked-in examples/data/btcusdt-1d.csv
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# dataset (pass a CSV path to override).
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suppressPackageStartupMessages(library(wickra))
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source("_common.R")
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FEE <- 0.001
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ATR_STOP_MULT <- 2.0
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SQUEEZE_LOOKBACK <- 180
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args <- commandArgs(trailingOnly = TRUE)
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bars <- if (length(args) >= 1) load_ohlcv_csv(args[1]) else synthetic_candles(2000)
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bars <- if (length(args) >= 1) load_ohlcv_csv(args[1]) else bundled_candles("btcusdt-1d.csv")
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opens <- bars$open; highs <- bars$high; lows <- bars$low
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closes <- bars$close; vols <- bars$volume; ts <- bars$timestamp
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n_bars <- length(closes)
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bb <- BollingerBands(20, 2.0); atr <- Atr(14)
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returns <- numeric(0); trades <- 0L; in_pos <- FALSE; entry <- 0; stop <- 0
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for (i in seq_len(nrow(bars))) {
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b <- bars[i, ]
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band <- update(bb, b$close)
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atr_value <- update(atr, b$open, b$high, b$low, b$close, b$volume, b$timestamp)
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in_pos <- FALSE; entry_price <- 0; stop_level <- 0
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closed <- numeric(0); equity <- 1; equity_curve <- numeric(n_bars)
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bw_window <- numeric(0)
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for (i in seq_len(n_bars)) {
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band <- update(bb, closes[i])
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atr_value <- update(atr, opens[i], highs[i], lows[i], closes[i], vols[i], ts[i])
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price <- closes[i]
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equity_curve[i] <- if (in_pos) equity * (price / entry_price) else equity
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if (is.na(band[["middle"]]) || !is.finite(atr_value)) next
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bandwidth <- if (band[["middle"]] != 0) (band[["upper"]] - band[["lower"]]) / band[["middle"]] else .Machine$double.xmax
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if (!in_pos && bandwidth < 0.06 && b$close > band[["upper"]]) {
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in_pos <- TRUE; entry <- b$close; stop <- b$close - 2 * atr_value; trades <- trades + 1L
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} else if (in_pos) {
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stop <- max(stop, b$close - 2 * atr_value)
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if (b$close < stop) { returns <- c(returns, (b$close - entry) / entry); in_pos <- FALSE }
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middle <- band[["middle"]]
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if (abs(middle) <= 1e-12) next
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upper <- band[["upper"]]; lower <- band[["lower"]]
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bandwidth <- (upper - lower) / middle
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bw_window <- c(bw_window, bandwidth)
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if (length(bw_window) > SQUEEZE_LOOKBACK) {
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bw_window <- bw_window[(length(bw_window) - SQUEEZE_LOOKBACK + 1):length(bw_window)]
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}
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if (length(bw_window) < SQUEEZE_LOOKBACK) next
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min_bw <- min(bw_window)
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if (in_pos) {
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if (price < stop_level || upper < entry_price) {
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trade_ret <- price / entry_price - 1
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closed <- c(closed, trade_ret)
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equity <- equity * (1 + trade_ret) * (1 - FEE)
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in_pos <- FALSE
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}
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} else {
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is_new_low <- abs(bandwidth - min_bw) < 1e-12
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if (is_new_low && price > upper) {
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entry_price <- price; stop_level <- price - ATR_STOP_MULT * atr_value
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equity <- equity * (1 - FEE); in_pos <- TRUE
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}
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}
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}
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print_equity("Bollinger squeeze", summarize_equity(returns, trades))
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if (in_pos) {
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trade_ret <- closes[n_bars] / entry_price - 1
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closed <- c(closed, trade_ret)
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equity <- equity * (1 + trade_ret) * (1 - FEE)
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}
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print_summary("Bollinger Squeeze Breakout (1d, BTCUSDT)",
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closes[1], closes[n_bars], n_bars, closed, equity, equity_curve)
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@@ -1,24 +1,54 @@
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# Trend follower: enter long on a MACD histogram cross up, but only when ADX(14) > 20
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# confirms a trend; exit when the histogram crosses back below zero.
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library(wickra)
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# Strategy example: MACD crossover with ADX trend-strength filter.
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#
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# Enters long on a MACD histogram cross up (the histogram turns positive) while
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# ADX(14) > 20 (a directional market); exits on the opposite MACD crossover
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# regardless of ADX. 0.1% fees per trade. The R counterpart of
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# examples/python/strategy_macd_adx.py, printing the same summary. Uses the
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# checked-in examples/data/btcusdt-1h.csv dataset (pass a CSV path to override).
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suppressPackageStartupMessages(library(wickra))
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source("_common.R")
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FEE <- 0.001
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ADX_FLOOR <- 20
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args <- commandArgs(trailingOnly = TRUE)
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bars <- if (length(args) >= 1) load_ohlcv_csv(args[1]) else synthetic_candles(2000)
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bars <- if (length(args) >= 1) load_ohlcv_csv(args[1]) else bundled_candles("btcusdt-1h.csv")
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opens <- bars$open; highs <- bars$high; lows <- bars$low
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closes <- bars$close; vols <- bars$volume; ts <- bars$timestamp
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n_bars <- length(closes)
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macd <- MacdIndicator(12, 26, 9); adx <- Adx(14)
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returns <- numeric(0); trades <- 0L; in_pos <- FALSE; entry <- 0; prev_hist <- NA_real_
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for (i in seq_len(nrow(bars))) {
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b <- bars[i, ]
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m <- update(macd, b$close)
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a <- update(adx, b$open, b$high, b$low, b$close, b$volume, b$timestamp)
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in_pos <- FALSE; entry_price <- 0; closed <- numeric(0); equity <- 1
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equity_curve <- numeric(n_bars); have_prev <- FALSE; prev_sign <- FALSE
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for (i in seq_len(n_bars)) {
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m <- update(macd, closes[i])
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a <- update(adx, opens[i], highs[i], lows[i], closes[i], vols[i], ts[i])
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price <- closes[i]
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equity_curve[i] <- if (in_pos) equity * (price / entry_price) else equity
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if (is.na(m[["macd"]]) || is.na(a[["adx"]])) next
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trending <- a[["adx"]] > 20
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if (!in_pos && trending && is.finite(prev_hist) && prev_hist <= 0 && m[["histogram"]] > 0) {
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in_pos <- TRUE; entry <- b$close; trades <- trades + 1L
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} else if (in_pos && m[["histogram"]] < 0) {
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returns <- c(returns, (b$close - entry) / entry); in_pos <- FALSE
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hist_sign <- m[["histogram"]] > 0
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cross_up <- have_prev && !prev_sign && hist_sign
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cross_down <- have_prev && prev_sign && !hist_sign
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have_prev <- TRUE; prev_sign <- hist_sign
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if (!in_pos && cross_up && a[["adx"]] > ADX_FLOOR) {
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entry_price <- price; equity <- equity * (1 - FEE); in_pos <- TRUE
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} else if (in_pos && cross_down) {
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trade_ret <- price / entry_price - 1
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closed <- c(closed, trade_ret)
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equity <- equity * (1 + trade_ret) * (1 - FEE)
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in_pos <- FALSE
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}
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prev_hist <- m[["histogram"]]
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}
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print_equity("MACD + ADX trend", summarize_equity(returns, trades))
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if (in_pos) {
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trade_ret <- closes[n_bars] / entry_price - 1
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closed <- c(closed, trade_ret)
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equity <- equity * (1 + trade_ret) * (1 - FEE)
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}
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print_summary("MACD + ADX Trend Filter (1h, BTCUSDT)",
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closes[1], closes[n_bars], n_bars, closed, equity, equity_curve)
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@@ -1,20 +1,47 @@
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# Mean reversion: go long when RSI(14) drops below 30, exit when it recovers above 50.
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library(wickra)
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# Strategy example: RSI(14) mean-reversion.
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#
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# Go long when RSI(14) drops below 30 (oversold), exit when it recovers above 70
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# (overbought). 0.1% fees per trade. The R counterpart of
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# examples/python/strategy_rsi_mean_reversion.py, printing the same summary. Uses
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# the checked-in examples/data/btcusdt-1h.csv dataset (pass a CSV path to override).
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suppressPackageStartupMessages(library(wickra))
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source("_common.R")
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FEE <- 0.001
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OVERSOLD <- 30
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OVERBOUGHT <- 70
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args <- commandArgs(trailingOnly = TRUE)
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bars <- if (length(args) >= 1) load_ohlcv_csv(args[1]) else synthetic_candles(2000)
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bars <- if (length(args) >= 1) load_ohlcv_csv(args[1]) else bundled_candles("btcusdt-1h.csv")
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closes <- bars$close
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n_bars <- length(closes)
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rsi <- Rsi(14)
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returns <- numeric(0); trades <- 0L; in_pos <- FALSE; entry <- 0
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for (i in seq_len(nrow(bars))) {
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cl <- bars$close[i]
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value <- update(rsi, cl)
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in_pos <- FALSE; entry_price <- 0; closed <- numeric(0); equity <- 1
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equity_curve <- numeric(n_bars)
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for (i in seq_len(n_bars)) {
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value <- update(rsi, closes[i])
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price <- closes[i]
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equity_curve[i] <- if (in_pos) equity * (price / entry_price) else equity
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if (!is.finite(value)) next
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if (!in_pos && value < 30) {
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in_pos <- TRUE; entry <- cl; trades <- trades + 1L
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} else if (in_pos && value > 50) {
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returns <- c(returns, (cl - entry) / entry); in_pos <- FALSE
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if (!in_pos && value < OVERSOLD) {
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entry_price <- price; equity <- equity * (1 - FEE); in_pos <- TRUE
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} else if (in_pos && value > OVERBOUGHT) {
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trade_ret <- price / entry_price - 1
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closed <- c(closed, trade_ret)
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equity <- equity * (1 + trade_ret) * (1 - FEE)
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in_pos <- FALSE
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}
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}
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print_equity("RSI mean-reversion", summarize_equity(returns, trades))
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if (in_pos) {
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trade_ret <- closes[n_bars] / entry_price - 1
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closed <- c(closed, trade_ret)
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equity <- equity * (1 + trade_ret) * (1 - FEE)
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}
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print_summary("RSI Mean-Reversion (1h, BTCUSDT)",
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closes[1], closes[n_bars], n_bars, closed, equity, equity_curve)
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