Interactive Visual Concept Diagram
Calculating Expected Value: EV = (Win Rate × Win Size) - (Loss Rate × Loss Size)
The core mathematical formula determining long-term profitability.
### What is Expected Value (EV)?
Expected Value (EV) is the average amount an investor expects to win or lose per trade over a sample of 100+ trades.
The EV Equation: $$EV = (P_{win} \times W) - (P_{loss} \times L)$$
Where: - $P_{win}$ = Probability of winning trade (Win Rate %) - $W$ = Average gain size in ₹ - $P_{loss}$ = Probability of losing trade (1 - Win Rate) - $L$ = Average loss size in ₹
EV Example: Suppose a setup has a **40% Win Rate**, average win of **₹15,000**, and average loss of **₹5,000**: $$EV = (0.40 \times 15000) - (0.60 \times 5000) = 6000 - 3000 = +₹3,000\text{ per trade}$$
Even though you lose 6 out of 10 trades, you generate **+₹3,000 per trade** on average!
- •A system with positive EV (+EV) is mathematically guaranteed to grow capital over a large sample of trades.
- •Focus on maximizing average win size rather than chasing unrealistically high win rates.
Structuring Trades with Minimum 1:2 or 1:3 R:R Ratio
Why asymmetric payout ratios create an unshakeable statistical edge.
### The Power of Asymmetric Payoffs
Risk-to-Reward (R:R) ratio compares potential loss (Risk) against potential gain (Reward).
Break-Even Win Rate Matrix: - **1:1 R:R**: Requires > 50.0% Win Rate to break even. - **1:2 R:R**: Requires > 33.3% Win Rate to break even. - **1:3 R:R**: Requires > 25.0% Win Rate to break even. - **1:5 R:R**: Requires > 16.7% Win Rate to break even.
By targeting a minimum **1:2.5 or 1:3 R:R**, you can be wrong 60% of the time and still build significant wealth!
- •Never enter a trade where the risk exceeds potential reward.
- •Always calculate Risk-to-Reward *before* executing an order.
Why 40% Win-Rate with 1:2.5 R:R Outperforms 80% Win-Rate with Poor Risk
Debunking the high win-rate fallacy.
### The High Win-Rate Trap
Many beginner traders seek 80–90% win rate systems. However, high win-rate strategies often suffer from asymmetric loss profiles—winning 8 small trades of ₹1,000 (+₹8,000) but wiping out everything on 1 unmanaged loss of ₹10,000 (-₹10,000).
Institutional Comparison: - **Trader A**: 80% Win Rate, 1:0.3 R:R → Net Result: -₹2,000 per 10 trades. - **Trader B**: 40% Win Rate, 1:2.5 R:R → Net Result: +₹4,000 per 10 trades.
- •Professional quants optimize for Positive Expected Value, not Win Rate.
- •A single unmanaged loss can destroy months of high win-rate gains.
Stop-Loss Placement based on Volatility (ATR)
Using Average True Range (ATR) to place stops outside market noise.
### Dynamic Volatility-Based Stops
Placing fixed arbitrary point stops (e.g. fixed 20 points on NIFTY) leads to premature stop-outs during high volatility regimes.
Average True Range (ATR) Solution: - Measure 14-period ATR on your operational timeframe. - Set Stop-Loss = **Entry - (1.5 × ATR)** for long trades. - This ensures your stop-loss adapts dynamically to current market noise.
- •Widen stop distances during high VIX / high ATR market regimes.
- •Never move a stop-loss further away once a trade is live.
Recommended Reading & Academic Literature
The Mathematics of Money Management
By Ralph Vince
Advanced mathematical principles of optimal position sizing and EV.