Slippage is the difference between the price you intended to trade at and the price you actually got. It happens due to spreads, liquidity, volatility, and execution delays. This guide explains slippage with Indian market examples (NSE/BSE, equities/derivatives) and gives practical, battle-tested ways to measure, model, and reduce it.
Why Slippage Matters (Especially for Algos)
For discretionary traders, slippage eats into returns; for algorithmic traders, it can invalidate a backtest. Even a modest 4–10 bps average slippage can flip a strategy from profitable to loss-making after brokerage, taxes, and impact.
Quick formula:
Slippage (₹) = (Executed Price − Expected Price) × Quantity
Slippage (bps) = \(Executed − Expected\) ÷ Expected × 10,000
Example (NIFTY futures):
Expected 24,100; filled 24,106 for 2 lots (100 qty) → ₹600 slippage; ≈ 2.5 bps.
What Causes Slippage? (Market Microstructure in India)
1) Bid–Ask Spread
You cross the spread when using marketable orders. Wider spreads = higher slippage. Spreads widen around news, low-liquidity names, and off-peak times.
2) Market Impact & Order Size
Large orders “walk the book,” consuming multiple price levels. Impact increases non-linearly with size and volatility.
3) Latency & Queue Position
Even milliseconds matter. If many orders are ahead of you at the best price, you may partial-fill or miss fills, slipping into worse levels.
4) Volatility & Gaps
Sharp moves during pre-open/close auctions, events (RBI policy, earnings), or after sudden index rebalancing drive slippage higher.
5) Illiquidity & Fragmentation
Mid/small caps, far-dated futures/options, or deep OTM strikes often have thin order books, causing outsized price impact.
How to Measure Slippage (and Know If You’re Improving)
A) Implementation Shortfall (IS)
IS = (Execution VWAP − Decision Price) × Quantity
- Decision Price: when the signal triggers (or prior close for rebalances).
- Captures spread, impact, delays, and missed trades (opportunity cost).
B) Effective vs. Quoted Spread
- Quoted Spread: ask − bid at order arrival.
- Effective Spread: 2 × |Fill − Mid|. If effective < quoted → good execution.
C) Slippage Attribution
Break IS into:
- Delay Cost (signal to first slice)
- Market Impact (price moves caused by your own trading)
- Timing/Drift (market’s move independent of you)
- Fees & Taxes (brokerage, exchange, STT, GST, stamp duty)
Tip: Track by symbol, time bucket, order type, and liquidity regime. In India, also tag by lot size and strike moneyness for options.
Order Types & Relative Slippage Risk
| Order Type | Fill Certainty | Price Control | Typical Slippage Risk |
| Market / Marketable Limit | High | Low–Medium | High in thin/volatile markets |
| Passive Limit (post-only style) | Medium | High | Low (but risk of no fill) |
| IOC / FOK | Medium | High | Low (partial/miss risk) |
| Iceberg (multi-slice) | Medium | High | Lower impact per slice |
Guideline: Prefer passive or softly marketable limits; avoid pure market orders except for urgent exits or highly liquid index instruments during calm periods.
12 Practical Ways to Minimize Slippage
1) Right-Size Your Slices
- Break large parent orders into small child orders.
- Use an impact model: expected impact ≈ k × (slice size / ADV)^α × volatility. Keep the slice small vs Average Daily Volume (ADV).
2) Schedule with Liquidity
- Concentrate execution in high-liquidity windows (typically around open, post-lunch, and near close for large-caps).
- Avoid event windows (RBI policy, major earnings) unless the alpha depends on them.
3) Use Execution Algos
- VWAP/TWAP for benchmarked execution;
- POV (Participation) to cap market share;
- Liquidity-seeking algos to opportunistically cross hidden and displayed size.
4) Queue-Aware Limits
- Place limits at competitive prices but avoid chasing every tick. Track queue position (order book size ahead of you) and prefer resting over aggressive when alpha half-life allows.
5) Dynamic Limits with Price Bands
- Use limit price = signal price ± guard band (e.g., ± 3–5 ticks) to cap slippage. Widen bands in high vol; tighten in calm markets.
6) Avoid Forced Urgency
- Urgent market orders can be expensive. Escalate aggression gradually: passive → lightly marketable → market, based on remaining time and alpha decay.
7) Instrument Selection
- Prefer liquid futures over cash in large-cap baskets; choose near-month over far-month unless roll cost justifies.
- In options, target liquid strikes/expiries; avoid deep OTM strikes with 0×10 lots showing.
8) Smart Routing & Venue Nuances
- If your broker offers smart order routing or internalization with price improvement, test it. Measure post-trade effective spread vs quoted.
9) Control for News & Auctions
- Pre-open and close auctions can be noisy. For non-benchmark flows, trade in the continuous session unless benchmarking to the official close.
10) Latency Hygiene
- Minimize platform hops, use low-latency gateways, and keep your risk checks optimized. Even a few hundred ms can flip a passive fill to an aggressive one.
11) Backtest Like You Trade
- Simulate spread crossing and book depth (at least top-5 levels for derivatives).
- Calibrate slippage with historical microstructure:
- equities: 1–5 ticks typical in large caps;
- index futures: test 0.5–2 ticks baseline;
- options: use % of premium or min(ticks, % of mid).
- Include rejects, partial fills, and cancels.
12) Review Broker & Infra
- Compare commission tiers, margin usage, API stability, and throttle limits. Cheaper brokerage is pointless if API outages spike slippage.
A Simple Slippage Model You Can Start With
- Baseline (by symbol/segment):
- Equities (large caps): max(1 tick, 2 bps)
- Index futures: max(0.5 tick, 1 bp)
- Options: max(1–2 ticks, 10–25 bps of premium)
- Volatility Uplift: multiply by 1+σ1dσ60d1 + \frac{\sigma_{1d}}{\sigma_{60d}}.
- Size Uplift: multiply by 1+c×Slice/ADV1 + c \times \sqrt{\text{Slice}/\text{ADV}} (start with c=0.5c = 0.5).
- Urgency Uplift: +25–100% if must finish within short window.
Then, re-fit coefficients monthly using your live fills vs model (minimize mean absolute error).
Execution Playbooks (Indian Examples)
- Index Futures Rebalance (NIFTY/Bank Nifty):
- Use POV 5–10% during liquid periods; cap aggression near data releases.
- Add price-banded limits; pause if impact > 2× baseline.
- Single-Stock Momentum (Large Caps):
- Start passive at best bid/ask; if unfilled for X seconds or price drifts beyond guard band, step one tick.
- Avoid trading right through earnings unless strategy is event-driven.
- Options Roll (Near-Month to Next):
- Prefer calendar spreads to reduce leg risk.
- For legs with wide spreads, work passively; only cross when inventory risk dominates.
Troubleshooting Checklist
- Are you crossing the spread more than planned?
- Are fills clustering just after price jumps (you’re chasing)?
- Is avg slice size too large for current ADV?
- Are rejects/timeouts spiking (API/network issues)?
- Do slippage spikes correlate with events or auction periods?
- Is your backtest slippage model consistently under-estimating certain symbols/segments?
Featured Snippet: Slippage vs Requotes vs Impact
- Slippage: Difference between expected and executed price.
- Requote: Broker declines price; you re-submit (common in OTC/CFD; rare on exchange).
- Market Impact: Price moves because of your order (a component of slippage).
FAQs
Q1. Is zero slippage possible?
No. You can reduce it (tight limits, passive posting, smaller slices) but cannot eliminate it, especially in volatile or illiquid markets.
Q2. Market or limit—what’s better for algos?
Limits with intelligent price bands generally win over time. Use market only when urgency or risk requires immediate exit.
Q3. How should I set a realistic backtest slippage?
Start with tick-based minima by segment and add volatility/size uplifts. Recalibrate with live post-trade data monthly.
Q4. Why is my live slippage worse than backtest?
Backtests often ignore queue position, cancels, rejects, and child-order logic. Add these, plus latency and event filters, to close the gap.
Conclusion: Treat Execution as an Alpha Source
For Indian markets—whether NIFTY futures, liquid large-caps, or index options—slippage control can add 20–50 bps of annual performance versus naïve execution. Think of execution as a strategy overlay: measure it (IS), model it (tick/vol/size), and keep iterating.
Disclaimer: Examples are educational and not investment advice. Live trading involves risk; consult your broker and review exchange circulars for current rules and product specifications.