VWAP strategy backtest results

The short answer

Answers here: Does trading the VWAP reclaim work? · What is its win rate? · 1-minute or 5-minute charts?

What the script does

VWAP Reclaim Strategy, from the strategy library.

The numbers, run by run

Net and holding the symbol are after costs over the same period. Held-out is the verdict on the last third of the history judged alone: survived, decayed, did not survive, or too few trades to judge.

Year by year

2-candle confirm, 1.5 ATR stop on SPY, long and short, the same rule long only, and SPY itself, each calendar year after costs. A partial first or last year counts the sessions it has.

Every trade

The newest 400 of the 2919 trades of 2-candle confirm, 1.5 ATR stop on SPY, long and short, oldest first: side, entry and exit days, fill prices after slippage, and the return after both commissions. The other runs' trades open below.

The script

Pine v6, exactly as it ran for 2-candle confirm, 1.5 ATR stop on SPY, long and short. The variants change only the inputs listed in the table.

//@version=6
strategy("VWAP Reclaim Strategy", overlay=true, default_qty_type=strategy.percent_of_equity, default_qty_value=100, initial_capital=100000, commission_type=strategy.commission.percent, commission_value=0.05, slippage=1, margin_long=0, margin_short=0)

src        = input.source(hlc3, "VWAP source", group="Setup")
confirmN   = input.int(2, "Bars on the far side before a reclaim counts", minval=1, group="Setup")
exitHour   = input.int(15, "Flat at hour (exchange time)", minval=0, maxval=23, group="Session")
exitMinute = input.int(55, "Flat at minute", minval=0, maxval=59, group="Session")
useLong    = input.bool(true, "Trade longs", group="Direction")
useShort   = input.bool(true, "Trade shorts", group="Direction")
atrLen     = input.int(14, "ATR length", minval=1, group="Risk")
stopMult   = input.float(1.5, "Stop (ATR multiples, 0 = off)", minval=0, step=0.5, group="Risk")

// the session VWAP, restarted every day
newSession = timeframe.change("D")
var float sumPV = 0.0
var float sumV  = 0.0
if newSession
    sumPV := 0.0
    sumV  := 0.0
sumPV += src * volume
sumV  += volume
vwap = sumV > 0 ? sumPV / sumV : na
atr  = ta.atr(atrLen)

// how many bars price has spent on each side of VWAP
var int belowN = 0
var int aboveN = 0
belowN := close < vwap ? belowN + 1 : 0
aboveN := close > vwap ? aboveN + 1 : 0
intraday = timeframe.isintraday
// the exchange clock, never GMT
flatTime = hour == exitHour and minute >= exitMinute

longSig   = intraday and ta.crossover(close, vwap) and belowN[1] >= confirmN and barstate.isconfirmed
shortSig  = intraday and ta.crossunder(close, vwap) and aboveN[1] >= confirmN and barstate.isconfirmed
exitLong  = ta.crossunder(close, vwap) and barstate.isconfirmed
exitShort = ta.crossover(close, vwap) and barstate.isconfirmed

plot(vwap, "VWAP", color=close > vwap ? color.new(#16A56A, 0) : color.new(#E5484D, 0), linewidth=2)

if longSig and useLong and not flatTime
    strategy.entry("Long", strategy.long)
    strategy.exit("Long stop", "Long", stop=stopMult > 0 ? close - stopMult * atr : na)
if shortSig and useShort and not flatTime
    strategy.entry("Short", strategy.short)
    strategy.exit("Short stop", "Short", stop=stopMult > 0 ? close + stopMult * atr : na)
if exitLong
    strategy.close("Long")
if exitShort
    strategy.close("Short")
if flatTime
    strategy.close_all()

How it was tested

Alpaca 5-minute data from the last 600 sessions, and 1-minute data from the last 120 sessions, 0.05% commission and 0.025% slippage on every fill, the last third of the history held out, textbook settings and nothing tuned. The engine, fills and what it does not model are written out once, in data and methodology.

Questions