5.1 The intraday landscape · M05 Intraday and execution

Motivation

Before modelling anything, be clear about the environment. An intraday trader in India trades in a limit order book (M01 · 1.4) dominated by algorithms, under a cost stack that is fixed by law, inside a session that opens and closes with call auctions.

Who is on the other side SEBI's FY26 F&O study reports that individuals lost about ₹72,000 crore gross while proprietary traders gained about ₹44,000 crore and FPIs about ₹14,000 crore, and that about 99% of the prop and FPI gross profit came from algorithmic entities (SEBI FY26 study; M01 · 1.11 Case 1). In cash-market intraday trading, SEBI's FY23 study counted 68.9 lakh individual intraday traders, 4.6 times the FY19 number (PR 13/2024). A retail intraday order is, with high probability, matched against a machine that has seen the same information sooner.

The shape of the day Trading volume and volatility are higher at the open and close than at midday: the intraday "U-shape". Admati and Pfleiderer (1988, Review of Financial Studies 1(1)) give a model in which discretionary liquidity traders bunch together to reduce their costs, and informed traders follow them. In India the shape is now pinned by the pre-open auction (09:00–09:15), the closing auction (live since 3 Aug 2026; 15:30–15:40 from 7 Dec 2026), and derivative expiry settlement on the last 30 minutes' VWAP (from 7 Dec 2026). M01 · 1.2 has the details and dates.

Measuring the cost of one trade Let $m_t$ be the mid just before a trade at price $p_t$ with sign $q_t = +1$ (buyer-initiated) or $-1$ (seller-initiated), and let $m_{t+\Delta}$ be the mid a short time later (say 5 minutes). Define, all as signed half-spreads:

\underbrace{q_t(p_t - m_t)}_{\text{effective half-spread}} = \underbrace{q_t(p_t - m_{t+\Delta})}_{\text{realised half-spread}} + \underbrace{q_t(m_{t+\Delta} - m_t)}_{\text{price impact}}. \tag{5.1}

The identity is trivial (add and subtract $m_{t+\Delta}$ ), but it is the standard way exchanges and regulators decompose costs. The realised spread is what the liquidity provider keeps; the price impact is what the market learned from the trade. The models in 5.3–5.4 explain why the second term is positive on average.

Practitioner notes

Retail orders are small, so for them the spread and the statutory charges dominate impact. Impact matters for anyone whose orders are a visible fraction of volume, and it explains how institutions schedule their orders, which shapes the intraday patterns retail traders try to exploit.
The effective spread (5.1) is measured against the mid at the time of the trade. Quoted spreads at midday overstate what patient traders pay and understate what impatient traders pay at the open.