Files
wickra/examples/r/strategy_macd_adx.R
kingchenc 75eefbbd08 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).
2026-06-17 17:56:22 +02:00

55 lines
2.0 KiB
R

# Strategy example: MACD crossover with ADX trend-strength filter.
#
# Enters long on a MACD histogram cross up (the histogram turns positive) while
# ADX(14) > 20 (a directional market); exits on the opposite MACD crossover
# regardless of ADX. 0.1% fees per trade. The R counterpart of
# examples/python/strategy_macd_adx.py, printing the same summary. Uses the
# checked-in examples/data/btcusdt-1h.csv dataset (pass a CSV path to override).
suppressPackageStartupMessages(library(wickra))
source("_common.R")
FEE <- 0.001
ADX_FLOOR <- 20
args <- commandArgs(trailingOnly = TRUE)
bars <- if (length(args) >= 1) load_ohlcv_csv(args[1]) else bundled_candles("btcusdt-1h.csv")
opens <- bars$open; highs <- bars$high; lows <- bars$low
closes <- bars$close; vols <- bars$volume; ts <- bars$timestamp
n_bars <- length(closes)
macd <- MacdIndicator(12, 26, 9); adx <- Adx(14)
in_pos <- FALSE; entry_price <- 0; closed <- numeric(0); equity <- 1
equity_curve <- numeric(n_bars); have_prev <- FALSE; prev_sign <- FALSE
for (i in seq_len(n_bars)) {
m <- update(macd, closes[i])
a <- update(adx, opens[i], highs[i], lows[i], closes[i], vols[i], ts[i])
price <- closes[i]
equity_curve[i] <- if (in_pos) equity * (price / entry_price) else equity
if (is.na(m[["macd"]]) || is.na(a[["adx"]])) next
hist_sign <- m[["histogram"]] > 0
cross_up <- have_prev && !prev_sign && hist_sign
cross_down <- have_prev && prev_sign && !hist_sign
have_prev <- TRUE; prev_sign <- hist_sign
if (!in_pos && cross_up && a[["adx"]] > ADX_FLOOR) {
entry_price <- price; equity <- equity * (1 - FEE); in_pos <- TRUE
} else if (in_pos && cross_down) {
trade_ret <- price / entry_price - 1
closed <- c(closed, trade_ret)
equity <- equity * (1 + trade_ret) * (1 - FEE)
in_pos <- FALSE
}
}
if (in_pos) {
trade_ret <- closes[n_bars] / entry_price - 1
closed <- c(closed, trade_ret)
equity <- equity * (1 + trade_ret) * (1 - FEE)
}
print_summary("MACD + ADX Trend Filter (1h, BTCUSDT)",
closes[1], closes[n_bars], n_bars, closed, equity, equity_curve)