Annualized return projection from strategy stats
The single most-asked question in retail trading is "if my strategy works, how much do I make a year?" The textbook answer is expectancy × trades per year × risk per trade, compounded over the run. The honest answer is that's only the median outcome. The actual annual return for any single year is drawn from a distribution, and the spread between the 5th and 95th percentiles is wider than most traders expect.
The calculator below does both: the deterministic expectancy projection, plus a thousand-path Monte-Carlo simulation that shows the percentile bands. Same inputs, two answers — one for "what should I plan around" (the median) and one for "what range am I actually signing up for" (the 5th to 95th).
Annual return projection
What the calculator is doing
Two passes on the same inputs. The first is the deterministic projection: per-trade expectancy in R, multiplied by the number of trades, compounded with the risk-per-trade fraction. This gives a single annual-return number that's the "if everything happens at the average" answer. It's useful as a sanity check.
The second is the Monte-Carlo simulation: 1,000 independent paths through the year, each path simulating every individual trade as a coin flip with the chosen win probability. Sort the outcomes; pick the 5th, 50th, and 95th percentiles. The median is the typical year. The 5th is roughly the worst 1-in-20 year. The 95th is roughly the best 1-in-20 year. The width of the band between them is what most retail traders never see — and what causes the most psychological grief when a real year happens to land in the lower tail of an honest-but-noisy strategy.
The third number — probability of profitable year — is the share of paths that finished above zero. For a robust strategy, this should be above 90%. For a borderline strategy with thin edge and many trades, it can be above 50% with the median still small enough to be unimpressive. For a coin-flip strategy at zero edge, it's roughly 50% as expected.
Why the spread matters
A strategy with median +30% per year and 5th-percentile −10% per year is not the same product as a strategy with median +30% per year and 5th-percentile +10% per year. Both have the same expected value; the second is meaningfully easier to live with. The Monte-Carlo spread is the difference.
Most retail traders evaluate strategies on expected value alone and discover the variance only after a year of trading reveals it. The calculator surfaces both up front. If the 5th percentile of your strategy's outcome distribution is more negative than you can tolerate emotionally or financially, the strategy is wrong-sized for your situation — even though the math works out positive over many years.
The fix is usually one of two things: lower the per-trade risk percentage (which scales the spread linearly with the median), or pick a strategy with higher per-trade edge (which shifts the entire distribution to the right). Trading frequency cuts the variance proportional to √N — doubling trades-per-year shrinks the relative spread by about 1.4×.
How the inputs interact
- Win rate up, R:R down (or vice-versa). As long as expectancy stays positive, the spread shape changes: high-win/low-R:R produces a tight band, low-win/high-R:R produces a wider band. Both can yield the same median; the second is harder to stay with through losing streaks.
- Trades per year up. The median rises (more compounding events), and the percentile band tightens relative to the median (more samples = less per-year variance). The cost-floor caveat: above ~500 trades/year, fees become a real drag on the median that this pre-cost calculator ignores.
- Risk per trade up. The median rises roughly linearly, but the bad-tail percentile drops faster than linearly because compounding penalises drawdowns harder than equivalent gains help. Doubling risk-per-trade is not the same as doubling expected return at the cost of doubled variance — it's worse on the downside.
For the broader picture of how risk-per-trade interacts with the strategy's underlying edge, the risk-of-ruin tool covers the extreme-tail math (probability of going to zero), and the break-even-rr tool covers the win-rate-and-R:R relationship for the floor. For the prose version of the same topic — why the deterministic projection over-states what compounding actually delivers — see the posts on profit factor vs expectancy and what 60 trades at 80% win rate actually look like.
FAQ
Why is the median sometimes different from the deterministic projection?
The deterministic projection compounds at the per-trade arithmetic mean, but Monte-Carlo paths compound at the actual sequence of wins and losses. Because losses scale equity multiplicatively and wins scale equity multiplicatively, the geometric mean of outcomes is lower than the arithmetic — the "volatility drag" effect. The Monte-Carlo median tracks the geometric mean, which is the more honest projection.
How accurate is a 1,000-path Monte-Carlo?
Reasonable for the central percentiles (50th is stable to within ~1 percentage point on most inputs). Less accurate for the extreme tails (5th and 95th wobble more between re-rolls — that's why there's a re-roll button). For institutional precision you'd run 10,000+ paths; for getting an order-of-magnitude feel for the spread, 1,000 is more than enough.
Does this account for sequence risk?
Yes — Monte-Carlo naturally captures it. Sequence risk is "the order of wins and losses matters when compounding," and the simulation runs each path with a different random sequence. Bad-luck paths cluster losses early and never recover; good-luck paths get wins early and compound. The 5th-95th band reflects this directly.
What about real-market clustering and regime change?
Not modeled — the Monte-Carlo treats trades as independent draws from a fixed distribution. Real markets have fat tails, regime shifts, and clustering, all of which widen the actual outcome distribution beyond what an i.i.d. simulation produces. Treat the spread shown as a lower bound on real-world variance. The honest framing: "the spread is at least this wide; in practice probably 10-30% wider."
How do I compare this output to my actual track record?
Run the calculator with your strategy's measured stats from a real sample (300+ trades — see the win-rate-confidence tool for the sample-size discussion). Compare your actual annual return to the median. If you're inside the 5-95 band, your strategy is performing as expected given its statistical profile. Outside it (one direction or the other) for multiple years suggests either luck running out, the strategy genuinely changing, or your input estimates being off.