2026-06-09 19:18:40 +02:00
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# Resample a 1-minute series into higher timeframes and run an indicator per timeframe.
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library(wickra)
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source("_common.R")
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resample <- function(bars, factor) {
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if (factor <= 1) return(bars)
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2026-06-17 01:49:11 +02:00
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# Native Resampler: bucket by an absolute timeframe (synthetic bars step 60000 ms,
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# so factor minutes == factor*60000 ms). update() yields NA until a bucket closes;
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# flush() returns the final partial bucket. No hand-written bucketing.
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r <- Resampler(factor * 60000)
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out <- list()
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for (i in seq_len(nrow(bars))) {
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c <- update(r, bars$open[i], bars$high[i], bars$low[i], bars$close[i],
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bars$volume[i], bars$timestamp[i])
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if (!is.na(c[1])) out[[length(out) + 1L]] <- c
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}
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f <- flush(r)
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if (!is.null(f)) out[[length(out) + 1L]] <- f
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m <- do.call(rbind, out)
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data.frame(open = m[, 1], high = m[, 2], low = m[, 3], close = m[, 4],
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volume = m[, 5], timestamp = m[, 6])
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2026-06-09 19:18:40 +02:00
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}
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one_minute <- synthetic_candles(1200, step_ms = 60000)
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cat("EMA(20) of close across timeframes (resampled from 1-minute bars):\n")
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for (factor in c(1, 5, 15)) {
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bars <- resample(one_minute, factor)
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ema <- Ema(20); last <- NA_real_
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for (cl in bars$close) last <- update(ema, cl)
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cat(sprintf(" %2dm: %5d bars EMA(20) last = %.4f\n", factor, nrow(bars), last))
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}
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