Turn a yearly return goal into a daily game plan, size positions like a prop desk, and stress-test your edge with expected value, Kelly, risk-of-ruin, drawdown and Monte-Carlo simulators.
Every tool above runs entirely in your browser — nothing is uploaded and no login is required. Here is what each one computes and how to read the answer.
Position sizing answers one question: how many shares or lots can you buy so that being wrong costs a fixed, survivable amount. The calculation is position size = (account equity × risk per trade %) ÷ (entry price − stop-loss price). On a ₹10,00,000 account risking 1% (₹10,000) with an entry at ₹500 and a stop at ₹480, the per-share risk is ₹20, so the position is 500 shares — ₹2,50,000 of exposure, or 25% of the account, to put ₹10,000 at risk. For F&O the same arithmetic runs on the contract's lot size, so the result is rounded down to whole lots. The output that matters is the rupee loss at the stop, not the notional value of the position.
Expected value is the average rupee outcome of one trade repeated many times: EV = (win rate × average win) − (loss rate × average loss). A system that wins only 40% of the time but makes ₹3,000 on winners and loses ₹1,000 on losers has an EV of (0.40 × 3,000) − (0.60 × 1,000) = +₹600 per trade. A system winning 70% of the time for ₹500 while losing ₹1,500 has an EV of (0.70 × 500) − (0.30 × 1,500) = −₹100 and loses money despite the high hit rate. Win rate alone tells you nothing; the payoff ratio decides whether an edge exists.
The Kelly criterion gives the bet size that maximises the long-run growth rate of capital: f* = W − (1 − W) ÷ R, where W is the win probability and R is the ratio of average win to average loss. With a 50% win rate and a 2:1 payoff, f* = 0.5 − 0.5 ÷ 2 = 0.25, meaning full Kelly stakes 25% of equity per trade. Full Kelly is far too volatile for discretionary trading because the inputs are estimates from a limited sample; most traders use half-Kelly or quarter-Kelly, which keeps roughly three-quarters of the growth rate at a fraction of the drawdown. A negative f* means the edge is negative and the correct size is zero.
Risk of ruin is the probability that a string of losses reduces your account below a level you have defined as failure, given your win rate, payoff ratio and risk per trade. It is the reason position sizing matters more than entry accuracy: a profitable edge risked at 10% per trade can still be wiped out by a normal losing streak, while the same edge risked at 1% is close to unruinable. The calculator's usefulness is comparative — hold the edge fixed, vary risk per trade, and watch how sharply the ruin probability collapses as size comes down.
A drawdown is the peak-to-trough fall in account equity, and recovery is asymmetric: a 20% drawdown needs a 25% gain to get back to even, a 50% drawdown needs 100%, and a 70% drawdown needs 233%. The simulator shows the losing streaks a given win rate produces by chance — a 55% win-rate system will still see runs of eight or more consecutive losses over a few hundred trades. Knowing the drawdown your own parameters generate is what stops you abandoning a working system during an ordinary bad patch.
A single backtest is one ordering of your trades; Monte Carlo reshuffles that sequence thousands of times to show the range of outcomes the same edge could have produced. It replaces a single equity curve with a distribution — median return, worst-case drawdown, and the share of runs that end below the starting balance. If a strategy looks excellent on one path but a meaningful fraction of shuffles end in ruin, the original result was ordering luck rather than edge.
The North Star tool works backwards from a yearly return goal to what it demands day by day. A 50% annual target on ₹10,00,000 is ₹5,00,000, which across roughly 250 trading days is ₹2,000 a day — and if your average winning trade nets ₹4,000 at a 50% win rate, that is one qualifying setup per day, every day. Stated that way, most return goals turn out to require either a trade frequency or a position size the account cannot support, which is the point of building the plan before the year rather than after it.
Scope: these are arithmetic tools, not forecasts. They compute the consequences of the assumptions you type in — win rate, payoff ratio and risk per trade are your estimates, and the output is only as good as they are.