Martingale and Grid Strategies Explained: Why They Look Great Until They Don't

A martingale strategy increases the position size after every loss, usually doubling it, so that a single win recovers all previous losses plus a small profit; a grid strategy opens additional positions at fixed price intervals as the market moves against it and closes the whole basket on a small bounce. Both produce very high win rates and smooth equity curves for long stretches, but neither changes the expected value of the underlying trades: they swap many small wins for a rare loss large enough to erase them, and trading costs make the average outcome negative.

In short: the strategy shares its name with the probability concept of a martingale, a fair game, and Doob’s optional stopping theorem says no stopping rule can turn a fair game into a winning one when your bankroll is finite. Forex adds two twists. First, spreads are paid on ever-larger positions, so a cycle that “wins” on its fifth or sixth doubling can still lose money. Second, margin limits cap how deep the doubling can go. In our simulation, a $10,000 account running a classic doubling system on EUR/USD won 98% of its cycles and had positive results in 69% of months, yet lost 14.5% on average over a year after a 1-pip spread, with 40% of accounts down more than 20%. A flat grid at 0.1 lots won 99% of its baskets and was liquidated within a year in 73% of simulated paths. On the real EUR/USD decline of 2014, the same grid never closed a single basket before liquidation.

Martingale and grid logic is common inside retail expert advisors and “high win rate” signal services. The simpler version of the argument, with a $1,000 example, is in why win rate is misleading. This piece goes through the mathematics, the forex-specific mechanics and the simulations.

The pitch: “95% winning trades, no losing months”

The typical sales page shows an equity curve that rises almost in a straight line, a win rate above 90%, and a closed-trade history with almost no losses. The fine print, when there is any, mentions “recovery” or “averaging” logic, or that the system “does not use a stop loss because positions always come back.”

Three features should immediately suggest martingale or grid mechanics:

Win rate near 100% with small average wins. A genuine edge rarely looks like that; a strategy that sells tail risk always does.
Position size that grows after losses. Look at the lot sizes in the trade history. A sequence like 0.01, 0.02, 0.04, 0.08 is a martingale; many open trades in the same direction spaced a fixed number of pips apart is a grid.
Closed-trade drawdown far smaller than equity drawdown. Losing positions stay open, so the closed-trade record looks clean while floating losses build. (See drawdown and risk of ruin.)

The mechanics

Classic martingale

Pick a trade with a fixed take-profit and stop-loss of equal size, say 20 pips each. Start at a base size. After a loss, double the size on the next trade. After a win, return to the base size. A cycle is the run of trades from one base-size trade to the first win.

If the cycle wins on trade k, the win on that trade is 2^(k-1) units and the previous losses were 1 + 2 + 4 + … + 2^(k-2) = 2^(k-1) minus 1 units, so the cycle nets exactly +1 unit, whatever k is. That is the whole appeal: every completed cycle makes the same small profit. The problem is the cycle that does not complete because you run out of money or margin.

Grid

A grid needs no stop loss and no forecast. A long-only grid buys at the current price, then buys again every N pips lower, and closes every open position when the price recovers to some target above the average entry. Variants include:

Flat grids, which add the same lot size at each level.
Geometric (martingale) grids, which multiply the lot size at each level, often by 1.3 to 2.
Hedged grids, which hold buy and sell grids at once. US retail forex dealers cannot offer these: NFA Compliance Rule 2-43(b) states that “Forex Dealer Members may not carry offsetting positions in a customer account but must offset them on a first-in, first-out basis.” Expert advisors written for hedging-enabled offshore accounts therefore behave differently, or fail, on US accounts. How EAs execute is covered in expert advisors explained.

A flat grid is not a martingale in lot size, but it is one in exposure: every level adds to a losing position, so total size grows exactly when the trade is going wrong.

The mathematics

A fair game stays fair

If each trade is a fair bet (a 50% chance of +20 pips and a 50% chance of -20 pips, before costs), the expected profit of any single trade is zero. Doob’s optional stopping theorem formalizes the intuition that changing the size of fair bets, or choosing when to stop, cannot make the expected total positive as long as the strategy cannot run forever with unlimited credit. A martingale does not create an edge. It reshapes the distribution of outcomes: many small gains and an occasional catastrophic loss, with the same average.

For a cycle that can survive at most N consecutive losses, at a 50% win probability:

the probability that the cycle completes with +1 unit is 1 - 0.5^N;
the probability that it fails is 0.5^N, and the loss is 2^N - 1 units;
the expected value is (1 - 0.5^N) x 1 - 0.5^N x (2^N - 1) = 0, exactly.

Gambler’s ruin for a grid

A grid is easiest to understand as a barrier problem. If a price follows a driftless random walk, the probability that it rises Y pips before it falls X pips is X / (X + Y). A grid that takes profit 20 pips above its entry and can absorb a 500-pip fall before liquidation wins with probability 500 / 520, or 96.2%, and loses the accumulated position the other 3.8% of the time. The profit target is small and close; the loss barrier is large and distant. That arithmetic, not any market insight, is where the high win rate comes from. (In practice the take-profit moves closer as averaging lowers the break-even price, which pushes the win rate even higher and the eventual loss even larger.)

Costs turn zero into negative

Every trade pays a spread, and in a martingale the spread is paid on doubled and redoubled size. With a 20-pip target and stop and a 1-pip spread, a win nets 19 pips and a loss costs 21. Our ladder below shows the effect: by the fifth doubling, a cycle that wins still loses money overall.

Worked example 1: a doubling ladder on EUR/USD

Assumptions: a $10,000 account, EUR/USD at 1.10, $10 per pip per standard lot, 20-pip stop and target, base size 0.10 lots, and the US margin requirement of 2% of notional on a major pair (50:1). The account cannot open a trade whose margin exceeds its remaining equity.

Trade in cycle	Lot size	Margin needed	Equity before the trade	Cycle result if this trade wins, no spread	Same, 1-pip spread
1	0.10	$220	$10,000	+$20	+$19
2	0.20	$440	$9,980	+$20	+$17
3	0.40	$880	$9,940	+$20	+$13
4	0.80	$1,760	$9,860	+$20	+$5
5	1.60	$3,520	$9,700	+$20	-$11
6	3.20	$7,040	$9,380	+$20	-$43
7	6.40	$14,080	$8,740	cannot be opened	cannot be opened

Two things stand out. The margin limit stops the ladder after six trades, so six straight losses, which happen with probability 0.5^6 = 1/64 per cycle, end the cycle with a $1,260 loss (about $1,323 with spreads) against a $20 target. And with a 1-pip spread, cycles that win on the fifth or sixth trade are losers. Including the spread, the expected result of each cycle is -$6.00, about a third of the nominal $19 target, every cycle.

A smaller base size buys depth, not safety. At 0.01 lots the same account can fund nine trades before margin runs out. The cycle target falls to $2, the probability of nine straight losses falls to 1/512, and the loss when it happens is $1,022: 511 cycles of profit at once. The median number of cycles before the first failure is about 355, more than a year at one cycle a trading day. That is long enough to build a convincing track record.

Worked example 2: a year of martingale trading, simulated

We simulated, in Python, 200,000 accounts of $10,000 running the ladder above for 250 cycles (about one a trading day), each trade a 50/50 coin flip. After a failed cycle the account restarts at the base size if it can still afford the margin.

Design	Cycle win rate	Months with a profit	Accounts with no failed cycle	Mean end equity	Median end equity	Accounts below $8,000
0.10 base, no spread	98.3%	70%	1.9%	$10,001	$9,880	22.5%
0.10 base, 1-pip spread	98.1%	69%	2.0%	$8,554	$8,464	39.5%
0.01 base, no spread	99.8%	96%	61.3%	$10,000	$10,500	1.3%
0.01 base, 1-pip spread	99.8%	90%	61.2%	$9,774	$10,257	1.4%

The table shows the trap precisely. Without costs the mean is exactly the starting $10,000, as the optional stopping theorem says it must be, yet the typical account looks profitable for most of the year. With the smaller base size, 61% of accounts never fail in the year and finish about 3% to 5% up, with profitable results in 90% to 96% of months. The other 39% pay for them, mostly with a single $1,022 loss each, which is why so few fall below $8,000 in one year: the damage is spread thinly across many accounts rather than concentrated in a few. A spread of 1 pip turns the frictionless zero into a loss of 14.5% a year at the larger size.

Now imagine a signal seller or EA vendor who runs a few dozen such accounts and advertises the best one. With 61% of accounts surviving a year without a single failed cycle, there will always be a flawless record to show. That is survivorship bias built into the strategy, and it is why the statistical tests in Monte Carlo simulation for trading systems matter more than any equity curve.

Worked example 3: grids on simulated and real EUR/USD

Simulated paths. We generated 4,000 one-year hourly price paths for EUR/USD as a driftless random walk with 8% annual volatility (the H.10 daily data from the Federal Reserve give realized EUR/USD volatility of 7.9% for 2015-2025). The grid buys every 25 pips lower and closes the basket when the bid is 20 pips above the average entry, paying a 1-pip spread on each entry. Under US rules (2% margin), positions are liquidated when equity falls to the margin requirement, an assumption since stop-out levels vary by dealer. A second set uses 500:1 leverage with a 50% stop-out, conditions typical of offshore accounts.

Grid design	Leverage, stop-out	Basket win rate	Months positive (while trading)	Liquidated within a year	Median end equity	Mean end equity
Flat, 0.10 lots per level	50:1, 100%	99.0%	71%	73%	$5,423	$8,954
Flat, 0.05 lots per level	50:1, 100%	99.5%	66%	44%	$10,857	$9,321
x1.5 per level from 0.01	50:1, 100%	98.8%	95%	94%	$8,381	$9,259
Flat, 0.10 lots per level	500:1, 50%	99.4%	67%	52%	$348	$8,688
x1.5 per level from 0.01	500:1, 50%	99.6%	94%	65%	$1,483	$7,359

“Months positive” counts only months the account finished still trading; the month in which an account was liquidated is excluded, which is how a vendor’s monthly table would usually look after a restart. Counting the liquidation month as a losing month lowers the figures to roughly 63% for the flat grids and 73% to 85% for the geometric ones. Every design wins more than 98% of its baskets, and every design has a mean below $10,000, which is the cost of the spreads. The differences are in the shape of the losses:

The flat 0.10-lot grid is liquidated in 73% of paths, typically about four months in, leaving the median account with $5,423.
The geometric grid under US margin rules is closed out early in almost every path, because its lot sizes outgrow the 2% margin requirement after about a dozen levels. The margin rule acts as a circuit breaker and the typical loss is moderate.
At 500:1 the same designs can keep adding levels, so when liquidation comes it takes most of the account: the median flat-grid account ends the year with $348. Hourly moves jumped through the stop-out level often enough that about 9% of flat-grid paths and 17% of geometric-grid paths ended with a negative balance, the same mechanism that hit retail accounts in the Swiss franc shock of 2015.

A real path. Simulations assume no trend. Real exchange rates trend for months. We ran the same grids on the Federal Reserve’s daily EUR/USD fixings starting May 8, 2014, at 1.3864. The euro then fell, with few meaningful rallies, to 1.0524 by March 13, 2015, more than 3,300 pips.

The flat 0.10-lot grid under US margin rules was liquidated on July 30, 2014, at 1.3378, after 20 levels, with about half the account gone. It never closed a single winning basket.
The x1.5 geometric grid was closed out on June 10, 2014, at 1.3543, after 13 levels.
At 500:1 with a 50% stop-out, the flat grid survived until August 28, 2014, at 1.3178, and ended with $214 of the original $10,000.

None of these grids was “wrong” about EUR/USD eventually rebounding; it recovered to 1.158 by August 2015. They simply ran out of capital first, which is the gambler’s ruin result in practice.

Reading a statement for the signature

The mathematics above leaves fingerprints in an account statement, and none of them requires access to the strategy’s code.

Lot size against the previous result. In a martingale, the size of each trade depends on whether the previous one lost. Sort the trade list by open time and compare each lot size with the outcome of the trade before it. If sizes jump after losses and reset after wins, the ratio between the largest and smallest lot tells you how deep the ladder has gone: a ratio of 32 means at least five consecutive doublings have already happened.
Clusters of same-direction entries. A grid shows up as several positions in the same pair and direction opened at regular price intervals, all closed within minutes of each other. The closing time is the basket take-profit; the spacing between entry prices is the grid step.
Average win against the largest open loss. Closed-trade averages hide the mechanism, so compare the typical winning basket with the deepest floating loss on the equity curve. A system averaging $20 per win that has spent time $3,000 underwater is paying out small premiums on a large insurance policy it has written.
Exposure against equity over time. Total open lots divided by equity rises in a drawdown for both designs. In a fixed fractional system it falls.

All four checks need nothing more than the trade list and an equity curve, and all four look at the shape of the bet rather than its recent outcome.

Why the track records look so good

The losses are rare by design. A strategy that fails once every 64 or 512 cycles will produce long, flawless runs. A three-month or six-month verified record tells you almost nothing about it.
Closed-trade statistics hide open losses. A grid with 20 losing positions open shows no losing trades until it is liquidated. Ask for equity (mark-to-market) drawdown, not balance drawdown.
Win rate is the wrong metric. A 99% basket win rate with a 1% chance of losing half the account is a negative-expectancy bet once costs are included.
Survivorship. With many accounts or many vendors running similar logic, the visible ones are the survivors.
Leverage hides the risk. At high leverage the strategy appears to need little capital, which flatters the return on margin and makes the eventual loss larger.

What can go wrong

Trends. Grids and martingales assume mean reversion. A sustained trend, like EUR/USD from 1.39 to 1.05 in 2014-2015 or USD/JPY from 115 to almost 150 in 2022, is exactly the scenario they cannot survive.
Gaps and stop-outs. Weekend gaps and sudden moves can push equity through the stop-out level, and in extreme cases below zero.
Rising costs at depth. Spreads widen in fast markets, exactly when the largest positions are opened. Overnight financing is charged on the whole accumulated position.
Margin changes. Dealers can raise margin requirements in volatile markets, triggering liquidation earlier than a backtest assumed.
Regulatory mismatch. Hedged grids built for offshore platforms cannot run as designed under the NFA’s first-in, first-out rule for US accounts.
The restart illusion. After a blow-up, a vendor can restart the EA on a new account and show a fresh, flawless track record.

The short version

Martingale and grid strategies do not create an edge; they rearrange a zero-expectancy bet into many small wins and a rare, large loss, and costs make the average negative. In our simulations, a classic doubling system on a $10,000 EUR/USD account won 98% of its cycles and still lost 14.5% a year on average after a 1-pip spread, and with smaller sizing 61% of accounts went a full year without a failed cycle, which is why such systems always have a perfect-looking record to advertise. A flat grid won 99% of its baskets and was liquidated within a year on 73% of random-walk paths, and on the real 2014 euro decline it never closed a winning basket. US margin limits cap how deep these systems can go; offshore leverage removes the cap and enlarges the final loss. This explains the mechanics and the evidence; it is not a recommendation about any strategy or product.