Martingale Lab is an educational indicator that tests position sizing models, including flat stake, martingale, and anti-martingale, on simulated trade results from an EMA crossover strategy.
1Copy the scriptPress Copy script above, or the copy button on the code below.
2Open the Pine EditorOpen a TradingView chart and choose Pine Editor at the bottom of the screen.
3Paste over the templateCreate a new indicator, select all of the template text and paste. Press Save and give it a name.
4Add it to the chartPress Add to chart, then adjust the settings with the gear icon next to its name.
Source Code
174 lines of Pine Script v6 from scripts/martingale-truth.pine.txt, published under MPL-2.0. Keep the author's credit and licence lines when you reuse it.
martingale-truth.pine.txt
//@version=6
indicator("Martingale Lab", overlay = true)
// 1. Settings
fastLen = input.int(20, "Fast average")
slowLen = input.int(50, "Slow average")
atrMult = input.float(2.0, "Stop = ATR x", step = 0.5)
rr = input.float(2.0, "Target = R x", step = 0.5)
baseRisk = input.float(1.0, "Base risk per trade (%)", step = 0.25) / 100
maxSteps = input.int(6, "How many doublings a rule may take", minval = 1)
sims = input.int(300, "Alternative orderings to test", minval = 50, maxval = 800)
seed = input.int(11, "Random seed")
// 2. The strategy. It is ordinary on purpose: the sizing rule is the experiment.
// ta.* calls stay at the top level so they run on every bar.
atr = ta.atr(14)
fast = ta.ema(close, fastLen)
slow = ta.ema(close, slowLen)
long = ta.crossover(fast, slow)
plot(fast, "Fast", color.aqua)
plot(slow, "Slow", color.orange)
// 3. Record every trade as an R multiple, keeping the books by hand.
// One open trade at a time: a loser is -1 R, a winner is +rr R, so the
// history is a clean list of R values with no engine assumptions in it.
var float entry = na
var float stop = na
var float targ = na
var array<float> rs = array.new<float>()
opened = false
hitStop = false
hitTarg = false
if na(entry)
if long and atr > 0
entry := close
stop := close - atrMult * atr
targ := close + atrMult * atr * rr
opened := true
else
if low <= stop
array.push(rs, -1.0)
entry := na
hitStop := true
else if high >= targ
array.push(rs, rr)
entry := na
hitTarg := true
plotshape(opened, "Entry", shape.triangleup, location.belowbar, color.teal)
plotshape(hitTarg, "Target", shape.triangledown, location.abovebar, color.green)
plotshape(hitStop, "Stop", shape.xcross, location.abovebar, color.red)
// 4. Random numbers, so we can reorder the same trades (state in a one-slot array)
var array<int> rngBox = array.new_int(1, seed)
nextRand() =>
st = (1103515245 * array.get(rngBox, 0) + 12345) % 2147483648
array.set(rngBox, 0, st)
math.abs(st) / 2147483648.0
// 5. THE EXPERIMENT. One function, three rules, identical trades.
// rule 0 = flat, 1 = martingale (double after a loss),
// 2 = anti-martingale (double after a win).
// Returns where the account finished, its worst drawdown, and whether it was ruined.
replay(src, rule) =>
eq = 1.0
peak = 1.0
dd = 0.0
mult = 1.0
ruined = false
for i = 0 to array.size(src) - 1
r = array.get(src, i)
stake = math.min(baseRisk * mult, 1.0)
eq := eq * (1 + stake * r)
peak := math.max(peak, eq)
dd := math.max(dd, 1 - eq / peak)
if eq <= 0.2 and not ruined
ruined := true
mult := rule == 0 ? 1.0 :
rule == 1 ? (r < 0 ? math.min(mult * 2, math.pow(2, maxSteps)) : 1.0) :
(r > 0 ? math.min(mult * 2, math.pow(2, maxSteps)) : 1.0)
[eq, dd, ruined]
// 6. A shuffled copy of the trade list (Fisher-Yates)
shuffled(src) =>
work = array.copy(src)
for i = array.size(work) - 1 to 1
j = math.floor(nextRand() * (i + 1))
tmp = array.get(work, i)
array.set(work, i, array.get(work, j))
array.set(work, j, tmp)
work
// 7. The two tables
var table t = table.new(position.bottom_left, 4, 5, border_width = 1)
head(c, s) =>
table.cell(t, c, 0, s, text_color = color.white, text_size = size.normal,
bgcolor = color.new(color.blue, 20))
var table h = table.new(position.middle_right, 3, 5, border_width = 1)
blocks(v, pk) =>
w = int(math.round(22.0 * v / math.max(pk, 0.0001)))
s = ""
// a Pine for-loop with a start above its end counts DOWNWARDS,
// so an empty bin would print two blocks instead of none
if w > 0
for i = 1 to w
s += "█"
s
// 8. Run all three rules on the real order, then on hundreds of reorderings
if barstate.islast and array.size(rs) > 20
n = array.size(rs)
// the longest losing streak in the real history: this is what kills a martingale
streak = 0
longest = 0
for i = 0 to n - 1
streak := array.get(rs, i) < 0 ? streak + 1 : 0
longest := math.max(longest, streak)
ruin = array.new_float(3, 0.0)
for s = 0 to sims - 1
order = shuffled(rs)
for rule = 0 to 2
[_e, _d, wiped] = replay(order, rule)
if wiped
array.set(ruin, rule, array.get(ruin, rule) + 1)
// 9. The comparison table: same trades, three rules
head(0, "SAME " + str.tostring(n) + " TRADES")
head(1, "ends at")
head(2, "worst drawdown")
head(3, "ruined in " + str.tostring(sims) + " orders")
names = array.from("flat size", "martingale", "anti-martingale")
for rule = 0 to 2
[e, d, _w] = replay(rs, rule)
pctRuin = 100.0 * array.get(ruin, rule) / sims
table.cell(t, 0, rule + 1, array.get(names, rule), text_color = color.white,
bgcolor = color.new(color.gray, 20), text_halign = text.align_left,
text_size = size.normal)
table.cell(t, 1, rule + 1, str.tostring(e, "#.00") + "x",
text_color = color.white, text_size = size.normal,
bgcolor = e < 1 ? color.new(color.red, 25) : color.new(color.teal, 25))
table.cell(t, 2, rule + 1, str.tostring(100 * d, "#.0") + "%",
text_color = color.white, text_size = size.normal,
bgcolor = d > 0.5 ? color.new(color.red, 25) : color.new(color.gray, 25))
table.cell(t, 3, rule + 1, str.tostring(pctRuin, "#.0") + "%",
text_color = color.white, text_size = size.normal,
bgcolor = pctRuin > 0 ? color.new(color.red, 20) :
color.new(color.green, 30))
table.cell(t, 0, 4, "longest losing streak", text_color = color.white,
bgcolor = color.new(color.gray, 20), text_halign = text.align_left)
table.cell(t, 1, 4, str.tostring(longest) + " in a row",
text_color = color.white,
bgcolor = color.new(color.orange, 25))
table.cell(t, 2, 4, "a martingale needs", text_color = color.white,
bgcolor = color.new(color.gray, 20))
table.cell(t, 3, 4, str.tostring(math.pow(2, longest - 1), "#") + "x stake",
text_color = color.white, bgcolor = color.new(color.red, 20))
// 10. Risk of ruin, drawn as a bar so the difference is impossible to miss
table.cell(h, 0, 0, "rule", text_color = color.white, text_size = size.small,
bgcolor = color.new(color.blue, 20))
table.cell(h, 1, 0, "RISK OF RUIN", text_color = color.white,
text_size = size.small,
bgcolor = color.new(color.blue, 20))
table.cell(h, 2, 0, "", bgcolor = color.new(color.blue, 20))
for rule = 0 to 2
p = 100.0 * array.get(ruin, rule) / sims
table.cell(h, 0, rule + 1, array.get(names, rule), text_color = color.white,
bgcolor = color.new(color.gray, 30), text_size = size.tiny,
text_halign = text.align_left)
table.cell(h, 1, rule + 1, blocks(p, 100), text_color = color.red,
bgcolor = color.new(color.black, 0), text_size = size.tiny,
text_halign = text.align_left)
table.cell(h, 2, rule + 1, str.tostring(p, "#.0") + "%",
text_color = color.white,
bgcolor = color.new(color.black, 0), text_size = size.tiny)
Overview
Martingale Lab logs trade returns in R-multiples using ATR-based stop losses and take profit targets, then runs Monte Carlo simulations by shuffling trade sequences. It plots moving averages and entry or exit shapes on the price chart while displaying summary tables for final equity, max drawdown, and ruin risk.
The inputs you can change from the gear icon on the chart. Defaults are the author's.
Setting
Type
Default
Variable
Fast average
int
20
fastLen
Slow average
int
50
slowLen
Stop = ATR x
float
2.0
atrMult
Target = R x
float
2.0
rr
Base risk per trade (%)
float
1.0
baseRisk
How many doublings a rule may take
int
6
maxSteps
Alternative orderings to test
int
300
sims
Random seed
int
11
seed
Third-party code. ForexR did not write, test or endorse this script, and the summary above was generated automatically from its code and README. Indicators describe the past, and a strategy's backtest can look better than live trading, especially when a script repaints. Check it in the Strategy Tester and on a demo account before you rely on it.
This overlay indicator plots a Supertrend from an ATR length and factor, four exponential moving averages, and a selectable VWAP or TWAP taken from a chosen timeframe on the price chart. It also shows MACD and stochastic with adjustable lengths, smoothing, ver
This indicator plots a 200-period weighted moving average (WMA) on the main chart and monitors price deviations across multiple timeframes. It retrieves daily, 15-minute, 5-minute, and 2-minute price data to track distance from the 200 WMA relative to key sess
TradersPost Trading Checklist is an overlay indicator that evaluates a suite of popular technical conditions for long and short trade setups. It checks session-anchored VWAP, fast and slow EMA alignments and crosses, 200 SMA, RSI overbought or oversold conditi
The 8zz Contrarian Indicator plots reverse sentiment trading signals directly on the price chart based on tracked retail post activity. It generates bullish signals during retail panic and bearish signals during market euphoria. The indicator features adjustab
This indicator plots five moving averages of a chosen source on the price chart, defaulting to EMA lengths 10, 20, 30, 40 and 50. The user can switch the average type to SMA, WMA or HMA and recolor each line. Wide translucent glows sit under the lines, and sof
This indicator builds a table of simple moving averages and places it on the price chart. It calculates a user-chosen number of SMAs whose periods start at a base length and increase by a fixed step. On the last bar it lists each period and its SMA value, colo