Backtesting is how traders check if a strategy would have worked in the past. Simulations extend that idea by testing many scenarios. But a backtest that ignores real trading costs gives a false picture. Two of the most important costs are slippage and commissions. Ignoring them can turn a seemingly profitable system into a losing one. This article explains why they matter in the Indian context and how to model them realistically.
Slippage is the difference between the expected price of a trade and the actual executed price. It happens because markets move, orders take time to fill, and liquidity varies. For a liquid Nifty futures trade, slippage may be a few paise per lot. For a thinly traded small-cap share, slippage can be several rupees per share. Slippage grows with order size, market volatility, and when using market orders or trading near major news.
Commissions are the fees brokers charge per trade. In India, these vary widely: flat-fee brokers may charge ₹20–₹50 per order, while percentage-based brokers might charge 0.01%–0.05% of turnover. There are also exchange transaction charges, GST, SEBI fees, stamp duty and clearing fees. All of these add up and must be included in your simulation as a round-trip cost (entry plus exit).
Why these two matter:
- They reduce net profit. Even a small per-trade cost compounds over many trades, especially for high-frequency or scalping strategies.
- They change break-even win rate. A strategy with 60% wins at zero cost may need 70% wins after realistic costs.
- They affect position sizing. Net expectancy per trade falls, so optimal position size should shrink.
- They impact drawdown and risk of ruin. Higher costs increase the chance a losing streak wipes out capital.
- They influence strategy selection. Strategies that look great on gross returns may be poor after costs.
How to model slippage and commissions in simulations
A simple example in rupees:
If your strategy makes ₹5,000 gross per month with 50 trades, and average round-trip cost per trade is ₹40 (broker + taxes + slippage), total cost is 50 × ₹40 = ₹2,000. Net profit is ₹3,000 — a 40% drop. If slippage doubles during volatile months, profits can quickly disappear.
Practical tips
- Use realistic, market-derived slippage values, not optimistic estimates.
- Differentiate by stock and time frame. One-size-fits-all undermines accuracy.
- Update models regularly as fees and market structure change.
- Consider limit orders and smart order routing in the simulator to reflect real execution choices.
- Finally, always show both gross and net performance in reports so you and others understand the real expected outcome.
Including slippage and commissions makes backtests honest. It reduces curve-fitting, reveals true risk, and prepares you for real trading behavior in Indian markets.
Slippage is the difference between the expected price of a trade and the actual executed price. It happens because markets move, orders take time to fill, and liquidity varies. For a liquid Nifty futures trade, slippage may be a few paise per lot. For a thinly traded small-cap share, slippage can be several rupees per share. Slippage grows with order size, market volatility, and when using market orders or trading near major news.
Commissions are the fees brokers charge per trade. In India, these vary widely: flat-fee brokers may charge ₹20–₹50 per order, while percentage-based brokers might charge 0.01%–0.05% of turnover. There are also exchange transaction charges, GST, SEBI fees, stamp duty and clearing fees. All of these add up and must be included in your simulation as a round-trip cost (entry plus exit).
Why these two matter:
- They reduce net profit. Even a small per-trade cost compounds over many trades, especially for high-frequency or scalping strategies.
- They change break-even win rate. A strategy with 60% wins at zero cost may need 70% wins after realistic costs.
- They affect position sizing. Net expectancy per trade falls, so optimal position size should shrink.
- They impact drawdown and risk of ruin. Higher costs increase the chance a losing streak wipes out capital.
- They influence strategy selection. Strategies that look great on gross returns may be poor after costs.
How to model slippage and commissions in simulations
- Start by collecting typical broker commission schedules and all statutory levies. Model commissions as either fixed per-order or percentage of turnover, and include GST and exchange charges. Remember to apply costs both on entry and exit.
- Estimate slippage by asset and time frame. Use historical tick or minute data to measure the average difference between the intended price (signal price) and actual trade fills. Group instruments into liquidity buckets (large-cap, mid-cap, small-cap) and assign slippage per share or per lot accordingly.
- Make slippage variable. Use a distribution rather than a single number: for example, median slippage 20 paise for a liquid stock, with 90th percentile 70 paise under stress. For intraday or high-volume trades, scale slippage with order size as a percentage of average daily volume.
- Include spread and impact cost. For limit orders, model probability of getting filled and sometimes paying the spread. For market orders in low liquidity, add an impact cost proportional to trade size relative to volume.
- Run sensitivity tests. Simulate performance across a range of slippage and commission values to see how fragile the strategy is.
- Validate with forward testing. After including costs, paper trade or use a small live account to compare simulated vs. real results and tune your slippage model.
A simple example in rupees:
If your strategy makes ₹5,000 gross per month with 50 trades, and average round-trip cost per trade is ₹40 (broker + taxes + slippage), total cost is 50 × ₹40 = ₹2,000. Net profit is ₹3,000 — a 40% drop. If slippage doubles during volatile months, profits can quickly disappear.
Practical tips
- Use realistic, market-derived slippage values, not optimistic estimates.
- Differentiate by stock and time frame. One-size-fits-all undermines accuracy.
- Update models regularly as fees and market structure change.
- Consider limit orders and smart order routing in the simulator to reflect real execution choices.
- Finally, always show both gross and net performance in reports so you and others understand the real expected outcome.
Including slippage and commissions makes backtests honest. It reduces curve-fitting, reveals true risk, and prepares you for real trading behavior in Indian markets.