Part II · Trading equities · M04A · week 2 · 14 h
Reading markets: charts, volume, breadth and trade management
The practitioner's eye, made rigorous: candlesticks and timeframes, support and resistance, volume, gaps, market breadth and participant data; stops, trailing stops, scaling and time stops derived from first principles; the daily routine and the event calendar.
Builds on M01 Market structureM02 Statistics of returnsM03 Kelly and ruinM04 Systematic research
Prerequisites M01 (sessions, auctions, order types, costs), M02 (returns, volatility clustering, heavy tails), M03 (expectancy, R-multiples, gambler's ruin), M04 (indicators as functions of data, Donchian levels, swing pivots, testing a rule honestly). M09 · 9.2 (the range of Brownian motion, Parkinson's estimator) is used in 4A.7; it can be read alongside. Mathematics: Brownian motion with drift, the reflection principle, optional stopping for continuous martingales, Itô integrals at the level of ; all are stated in the Brownian toolkit of M02 · 2.11, and Itô's lemma is developed in M08 · 8.5. Time: ~14 hours (notes 8, problems 4, code 2). Primary texts: M00 · 0.3 for chart vocabulary (candlesticks, support and resistance, volume); Karlin and Taylor, A First Course in Stochastic Processes (2nd ed., Academic Press, 1975), ch. 7 (Brownian motion, first passage); Lo, Mamaysky and Wang (2000); Osler (2000, 2003); the NSE and SEBI pages cited in each section.
A trader with ten years of screen time reads a chart, a volume bar, a breadth table and a participant-OI file in a few seconds, and manages a position with stops, trailing stops and scaling rules that she rarely writes down. This module does two things with that craft. First, it turns each reading skill into a definition precise enough to compute and a hypothesis precise enough to reject (the programme M04 started). Second, it treats trade management as what it is mathematically: a stopping problem for a diffusion. The second half has the module's main theorem in several forms: exits and sizing rules cannot create expectancy where the price has no drift; they only reshape the distribution of outcomes. Everything a practitioner says about stops is true or false relative to that theorem.
Learning outcomes
- Write any candle, bar aggregation, swing pivot, support/resistance cluster, gap, relative-volume or VWAP rule as an explicit, look-ahead-free function of OHLCV data, and state the null distribution it must beat under a random walk.
- Derive, by optional stopping and the scale function, the probability that a price hits a target before a stop, with and without drift, and the expected time to exit (Propositions 4A.6–4A.8).
- Prove that for a driftless price no combination of stop, target, trailing stop or scaling rule has non-zero expectancy, and that with drift the expectancy equals times expected exposure-time (Propositions 4A.9 and 4A.12).
- Compute stop-out probabilities over a horizon by the reflection principle, correct them for discrete (closing-basis) monitoring, and size stops in ATR units so that the probability is the same across stocks.
- Derive the law of the peak and the expected give-back of a trailing stop under Brownian motion (Proposition 4A.11), and check it by simulation.
- Explain with a model why widening a stop after entry is negative-expectancy behaviour when the adverse move carries information, and connect it to prospect theory (M14).
- Read NSE breadth, participant-wise open interest, delivery and bulk/block-deal data, and say exactly what each can and cannot tell you.
- Lay out a pre-market routine and an event calendar for the Indian market, with the session times and rules in force on 6 Nov 2026, and estimate how event days change volatility.
Sections ≈ 3 h of study
- Foundation 7 sections · ≈ 1 h 25 min
- Core 8 sections · ≈ 1 h 25 min
- Advanced 1 section · ≈ 10 min
- Learning outcomes Foundation 5′
- 4A.1 Bars, candles and timeframes Foundation 15′
- 4A.2 Support and resistance Foundation 15′
- 4A.3 Volume, relative volume and VWAP Core 10′
- 4A.4 Gaps Core 10′
- Lab Nifty opening gaps and gap fills
- 4A.5 Breadth, participant data, delivery and deals Foundation 15′
- 4A.6 Stops and targets as barrier problems Core 20′
- Figure Stops and targets as a barrier problem
- 4A.7 Volatility-scaled stops: ATR Foundation 10′
- Figure How often is a stop hit on noise alone?
- 4A.8 Trailing stops Core 10′
- Figure Trailing stops: the law of the peak
- 4A.9 Scaling in, scaling out and pyramiding Advanced 10′
- 4A.10 Time stops, and why widening a stop is negative-expectancy behaviour Core 10′
- 4A.11 The daily routine, the session and the event calendar Foundation 15′
- 4A.12 Case studies Core 15′
- Summary of key results Foundation 10′
- Readings and resources Core 5′
- Facts that change Core 5′