CMT Level I · 3-Day Cram Guide

Comprehensive TL;DRs, deep dives, and 200+ Q&A flashcards covering every Level I unit (Welcome to Level I plus Units I through XII). Read top to bottom for full coverage, or jump to a specific section using the menu.

3-Day Cram Plan

If you only have 72 hours, fight smart. Cover everything, then practice retrieval, then triage weak spots.

Day 1 · Build the map

  • Read every TL;DR on this page (~90 minutes).
  • Take the 132-question placement exam once (2 hours).
  • Note your three weakest sections from the results.
  • Skim the deep dive of the top-weighted sections: Trend, Patterns, Indicators, and Comparative Market Analysis.

Day 2 · Repair weak spots

  • Re-read the full deep dive plus every Q&A for your three weakest sections.
  • Re-read the TL;DR for the remaining sections.
  • Re-take the placement exam.
  • Re-test only the sections you missed using the “Common questions” toggles on this page.

Day 3 · Lock it in

  • Read every Q&A out loud, answering before opening the toggle.
  • Memorize the numbers in the Final 24-hour Checklist (RSI 14, MACD 12/26/9, BBands 20/2, ADX 25, 68-95-99.7, and so on).
  • Stop introducing new material at least 3 hours before sleep.
  • Sleep at least 7 hours; arrive 30 minutes early with a valid photo ID.

Mindset rules

Level I rewards breadth, not depth. Most items test definitions, recognition, and one-step reasoning. When stuck, eliminate absolutist answers (“always,” “never,” “guarantees,” “automatically”) first — they are almost always wrong.

Pace math

132 questions in 120 minutes equals about 54 seconds per question. Plan four passes: (1) a fast pass answering every easy item; (2) work the medium items; (3) tackle the hard items; (4) review flagged items in the final ~10 minutes. Never leave a question blank — there is no guessing penalty.

0. Welcome to Level I — CMT Program, Charter & Ethics

~5% of the exam · The easiest points on the test if you actually read this section.

TL;DR:

The CMT Association is a not-for-profit credentialing body founded in the 1960s, advancing technical analysis through credentialing, ethics, education, and advocacy. The Chartered Market Technician (CMT) designation is earned only after (1) passing Levels I, II, and III; (2) becoming a Member in good standing; (3) completing three years of qualifying professional experience in technical analysis; (4) sponsorship; and (5) agreeing to abide by the CFA Institute Code of Ethics and Standards of Professional Conduct, which the CMT Association has adopted for its membership. Ethics is tested on all three levels.

Level I exam format: 132 multiple-choice questions, 2 hours (120 minutes), computer-based, four answer choices per item. It tests core terminology, definitions, basic tools, and concept recognition. Mathematics is conceptual rather than calculation-intensive. There is no guessing penalty — answer every question.

Level I emphasis: breadth across 12 numbered units plus the frontmatter. You are expected to recognize tools, interpret simple charts, and apply definitions correctly. Level II then tests application, and Level III tests integration, ethics scenarios, and portfolio construction.

The 7 Standards of Professional Conduct

The CMT Association adopts the CFA Institute Standards. Memorize the top-level structure:

StdTitleKey obligations
IProfessionalismKnowledge of the law (A); independence and objectivity (B); misrepresentation (C); misconduct (D); competence (E); responsibilities of supervisors (F, in newer editions).
IIIntegrity of Capital MarketsMaterial nonpublic information (A); market manipulation (B).
IIIDuties to ClientsLoyalty, prudence & care (A); fair dealing (B); suitability (C); performance presentation (D); preservation of confidentiality (E).
IVDuties to EmployersLoyalty (A); additional compensation arrangements (B); responsibilities of supervisors (C).
VInvestment Analysis, Recommendations, ActionsDiligence and reasonable basis (A); communication with clients (B); record retention (C).
VIConflicts of InterestDisclosure of conflicts (A); priority of transactions (B); referral fees (C).
VIIResponsibilities as a Member or CandidateConduct in CFA Program (A); reference to CFA designation (B). Mirrored for CMT Association by extension.

Ethics scenarios you will see

  • Material nonpublic info: cannot trade or recommend on it; the mosaic theory (combining nonmaterial public + nonpublic data into a material conclusion) is allowed.
  • Suitability (III-C): recommendations must fit the client’s objectives, constraints, and risk tolerance — not just be a “good idea.”
  • Priority of transactions (VI-B): client trades come first, then employer, then personal.
  • Soft dollars / gifts: must be disclosed; cannot impair independence.
  • Performance presentation (III-D): fair, accurate, complete; show gross/net, time period, methodology.
  • Misrepresentation (I-C): includes plagiarism — cite sources.

CMT Charter requirements (memorize)

  1. Pass Level I, Level II, and Level III exams.
  2. Become a Member of the CMT Association in good standing.
  3. Three years (36 months) of qualifying professional experience in a role that applies technical analysis.
  4. Provide professional references; sponsorship by existing Members.
  5. Sign and abide by the CFA Institute Code of Ethics and Standards of Professional Conduct.
  6. Maintain dues and membership in good standing.

Curriculum & exam logistics

  • Three volumes of curriculum, one per level. Level I focuses on foundations.
  • Exam is delivered at Prometric centers (or via approved online proctoring in many regions) on a multi-week window in Spring and Fall.
  • Approved calculators: TI BA II Plus and HP 12C. Level I uses calculator sparingly.
  • Photo ID required. No personal items in the testing room.
  • Score reporting: pass/fail with topic-level performance bands (above / at / below band).
Common pitfalls: Don’t confuse “passing Level III” with becoming a charterholder — membership, experience, sponsorship, and conduct agreement are all required. Don’t skip ethics; it appears on every level. Don’t assume soft-dollar arrangements are fine without disclosure.

Common questions

What is the format of the Level I exam?
132 multiple-choice questions, 2 hours, four options each, no guessing penalty, computer-based delivery.
What is the primary purpose of the CMT Association?
A not-for-profit professional association advancing the discipline of technical analysis through credentialing, ethics, education, and advocacy.
On how many CMT levels does ethics appear?
All three levels (Level I, II, and III).
Whose Code of Ethics does the CMT Association use?
The CFA Institute Code of Ethics and Standards of Professional Conduct.
How many years of qualifying experience are required to earn the CMT charter?
Three years (36 months) of professional experience in a role applying technical analysis.
Under the Standards, in what order should trades be executed?
Clients first, then the employer, then personal accounts (Standard VI-B, Priority of Transactions).
Is the mosaic theory permitted?
Yes — combining public and non-material non-public information into a material conclusion is permitted; trading on material non-public information is not.
What does suitability require?
A recommendation must fit the client’s objectives, constraints, and risk tolerance, not just be a generally attractive idea (Standard III-C).
If a question stem says “always” or “guarantees,” what should you do?
Eliminate first. Technical analysis is probabilistic; the curriculum rarely uses absolutist language.
Is there a penalty for guessing?
No. Answer every question.
Which approved calculators may a candidate use?
The Texas Instruments BA II Plus and the Hewlett-Packard 12C.
What is the difference between Level I, II, and III in posture?
Level I = recognition and definitions; Level II = application and analysis; Level III = integration, portfolio construction, and ethics scenarios (constructed-response).

I. Theory and History of Technical Analysis

~7% of exam · Heavy on Dow Theory, EMH debate, anomalies, and named historical figures.

TL;DR:

Technical analysis (TA) is the study of market action — primarily price and volume — to forecast future price direction. It rests on three core assumptions: (1) market action discounts everything (price reflects every known piece of fundamental, economic, and psychological information); (2) prices move in trends (a trend in motion is more likely to continue than reverse until clear evidence of reversal); and (3) history repeats itself (price patterns and crowd psychology recur because human behavior is consistent).

Dow Theory, the foundation of modern TA, was articulated by Charles H. Dow (founder of the WSJ, late 1800s) and codified by William Hamilton and Robert Rhea. Its six tenets describe how primary, secondary, and minor trends interact, the three phases of a primary bull/bear market (accumulation, public participation, distribution/panic), the principle that the major averages must confirm each other (originally Industrials and Rails), the requirement that volume confirms the trend, and the rule that a trend is assumed in force until a definitive reversal signal proves otherwise.

The Efficient Market Hypothesis (EMH) — weak, semi-strong, and strong forms — challenges TA by claiming past prices (weak), public info (semi-strong), or all info (strong) is already in price. Technicians answer with documented anomalies (momentum, value, size, low-volatility, post-earnings drift, calendar effects), behavioral evidence (overreaction, underreaction, herding), the arcsine law (which shows random walks visually produce trend-like runs, so the existence of trends is consistent with imperfect efficiency), and the practical track records of disciplined technicians. TA does not require markets to be inefficient; it requires that some exploitable patterns exist often enough to manage risk-adjusted return.

Recognition milestones: the formation of the Market Technicians Association (MTA) in 1973 (renamed CMT Association); SEC and FINRA acceptance of TA-based research; academic publication of behavioral finance (Kahneman/Tversky, Shiller, Thaler); and integration with quantitative finance.

The Three Assumptions in Detail

  1. Price discounts everything: any information that could affect a stock is already reflected in its price. This frees the technician from chasing earnings, geopolitics, or supply chains directly — the price tape integrates them. The corollary: focus on price, because that is the market’s aggregate verdict.
  2. Prices move in trends: a trend is more likely to continue than reverse. The technician’s job is to identify the trend, ride it, and detect when it reverses. This drives entry/exit philosophy and risk management.
  3. History repeats: chart patterns reflect crowd psychology — greed, fear, hope, denial — which recur consistently. This is why pattern recognition (e.g., head & shoulders) has decades of empirical longevity.

Dow Theory — The Six Tenets

  1. The Averages Discount Everything. Both the Industrial Average and the Rail (Transportation) Average reflect all known information.
  2. The Market Has Three Trends.
    • Primary trend: lasts months to years; the “tide.”
    • Secondary trend: lasts weeks to months; corrections against the primary; typically retrace 33%–66% (often ~50%) of the prior primary leg.
    • Minor trend: lasts days to weeks; noise — the “ripples.”
  3. Major Trends Have Three Phases.
    • Bull market: accumulation (informed buying near the lows) → public participation (sustained advance, broad media coverage) → distribution (informed selling to the late-arriving public).
    • Bear market: distributionpanic (sharp decline) → despondency (long grinding bottom with discouraged sellers).
  4. The Averages Must Confirm. A new primary trend signal requires confirmation: both the Industrial and Transportation averages must move in the same direction. A non-confirmation is a warning, not a reversal.
  5. Volume Must Confirm the Trend. Volume expands in the direction of the primary trend and contracts on counter-trend moves. Declining volume on advances is a red flag in a bull market.
  6. A Trend is Assumed In Force Until Definitively Reversed. This is the Dow analogue of Newton’s first law: don’t pre-empt a reversal call. Wait for evidence (a failed swing, structural break, or non-confirmation).

EMH & the Random Walk

Weak form: All past prices and volume are reflected in price. Direct challenge to TA — if true, charts cannot predict.
Semi-strong form: All publicly available information is in price. Also challenges fundamental analysis based on public reports.
Strong form: All information (public + private/insider) is in price. Empirically weakest form; insider trading studies show insiders can earn excess returns.
Random walk: Successive price changes are independent and identically distributed. Implies past returns do not predict future returns.
Arcsine law: For a random walk, the fraction of time spent above the starting level follows an arcsine distribution — long stretches at extremes are common. Random series therefore look trended. The existence of visual trends is not, by itself, evidence of inefficiency.
Joint hypothesis problem: Any test of EMH is simultaneously a test of EMH and the pricing model used. You cannot reject one without questioning the other.

Documented Anomalies (used to argue against strict EMH)

  • Momentum: past 3–12 month winners outperform losers over the next 3–12 months.
  • Value: low price-to-book / low P/E stocks historically outperform high-multiple stocks (Fama-French).
  • Size: small-cap stocks historically outperform large-cap, adjusted for risk (Banz, Fama-French).
  • Low-volatility: low-vol stocks have outperformed high-vol stocks on a risk-adjusted basis.
  • Post-earnings announcement drift (PEAD): stocks continue to drift in the direction of earnings surprises for weeks.
  • Calendar effects: January effect (small-cap January outperformance), turn-of-the-month effect, “Sell in May.”
  • Reversal at long horizons: 3–5 year prior losers tend to outperform prior winners (De Bondt & Thaler).

Historical Mileposts (names you should recognize)

  • Charles H. Dow (1851–1902): co-founder Dow Jones & Company and WSJ; published the editorials that became Dow Theory.
  • William Peter Hamilton: WSJ editor who systematized Dow’s editorials into a coherent theory (“The Stock Market Barometer,” 1922).
  • Robert Rhea: codified Dow Theory in “The Dow Theory” (1932), formalizing the six tenets.
  • Richard Schabacker (1930s): “Technical Analysis and Stock Market Profits” — first comprehensive pattern catalog.
  • Edwards & Magee (1948): “Technical Analysis of Stock Trends” — the canonical pattern textbook still in print today.
  • Ralph Nelson Elliott (1930s–40s): Elliott Wave Theory (5 impulse + 3 corrective wave structure).
  • W. D. Gann (early 1900s): time/price geometry, the Gann angle (1×1 = 45°).
  • Richard Wyckoff (early 1900s): accumulation/distribution method, the Composite Operator concept.
  • J. M. Hurst (1970): cycle theory and the Hurst principles.
  • Steve Nison (1990s): introduced Japanese candlesticks to Western audiences.
  • John Bollinger (1980s): Bollinger Bands.
  • J. Welles Wilder (1978): “New Concepts in Technical Trading Systems” — RSI, ADX, Parabolic SAR, ATR.
  • Peter Steidlmayer (1980s): Market Profile.
  • MTA founded 1973 → renamed CMT Association; later achieved regulatory recognition for technical analysis as a research discipline.
Pitfall: Don’t conflate “trends exist” with “markets are inefficient.” The arcsine law shows even random walks display trend-like behavior. Don’t confuse the secondary trend (a correction within the primary) with a new primary trend; classic Dow Theory waits for confirmation across averages before declaring a primary reversal.

Common questions

What three averages or indexes did Dow Theory originally use to confirm trends?
Originally the Dow Jones Industrial Average and the Dow Jones Railroad Average (now Transportation Average). They must move in the same direction to confirm a primary trend.
What are the three phases of a primary bull market under Dow Theory?
Accumulation, public participation, and distribution.
Which form of EMH most directly challenges technical analysis?
The weak form — it claims past price and volume data have no predictive value.
What is the arcsine law’s implication for technicians?
Random series can look strongly trended, so seeing trends is not by itself proof that markets are inefficient.
What is the joint hypothesis problem?
Any test of EMH is also a test of the asset-pricing model used. Rejecting EMH might just mean the pricing model is wrong.
What is the typical retracement range for a Dow secondary correction?
Roughly 33%–66% of the prior primary move, with about 50% being common.
Who wrote “Technical Analysis of Stock Trends” (1948)?
Robert D. Edwards and John Magee — the classical pattern analysis text.
Who introduced Japanese candlestick charting to the West?
Steve Nison, in publications during the early 1990s.
What does J. Welles Wilder’s 1978 book contribute?
RSI, ADX, Parabolic SAR, and the ATR concept (“New Concepts in Technical Trading Systems”).
What is the momentum anomaly?
Past 3–12 month winners tend to outperform past losers over the next 3–12 months, contradicting weak-form EMH.
What is the post-earnings announcement drift (PEAD)?
Stocks tend to drift for weeks in the direction of their earnings surprises — a documented under-reaction.
What does Dow Theory say about volume in a primary uptrend?
Volume should expand on rallies and contract on pullbacks; declining volume on rallies is a warning.
When is a Dow primary trend considered reversed?
Only after a definitive reversal signal (a failed swing in one average, confirmed by a similar move in the other).
What did the De Bondt-Thaler long-horizon study find?
3–5 year prior losers tend to outperform prior winners — evidence of long-horizon overreaction and mean reversion.
Name three major behavioral phenomena cited against EMH.
Overreaction, underreaction, and herding (also overconfidence, recency, anchoring).

II. Charts: Market Price Data

~10% of exam · Know every chart type, the difference between scales, all data intervals, and how volume + open interest behave.

TL;DR:

A chart is the visual representation of price (and often volume) over a chosen interval. The interval can be time-based (1-minute, 5-minute, 60-minute, daily, weekly, monthly) or activity-based (tick charts after every N trades, volume bars after every N shares/contracts, range bars after each N-point move, point-and-figure, Renko, Kagi).

The two most important chart-type distinctions: (1) line (close-only) vs bar/candle (OHLC); (2) time-axis charts (line/bar/candle) vs price-only charts (point-and-figure, Renko, Kagi). Candlesticks encode the same OHLC data as bars but make psychology visible through colored real bodies and shadows.

The two most important scale choices: arithmetic (linear) — equal vertical distance equals equal dollar change; and logarithmic / semi-log — equal vertical distance equals equal percentage change. Use semi-log for long-history charts and assets that have traded over wide ranges (a 10→20 move and a 100→200 move both look equal, because both are 100%).

Volume is the count of shares (equities) or contracts (futures/options) traded in the interval. Open interest (futures/options only) is the count of contracts currently outstanding. Together they reveal the participation and sponsorship behind a price move. A breakout on heavy volume is more reliable than the same move on thin volume.

Key data-quality concerns: split/dividend adjustment for equities, contract roll for futures (back-adjusted vs continuous), survivorship bias in historical universes, and exchange holiday gaps.

Chart Types — What Each Encodes

Line: Connects closing prices. Cleanest for trend recognition; ignores intraday range and open.
Bar (OHLC): Vertical bar = high–low range; left tick = open, right tick = close.
Candlestick: Same OHLC data as a bar, but the body (open↔close) is filled/colored. Hollow/green = close > open; filled/red = close < open. Shadows (wicks) above and below the body show high and low.
Point & Figure (P&F): X = rising price columns, O = falling. Time is removed. Box size (price unit) and reversal amount (typically 3 boxes) drive plotting. Reveals support/resistance and offers count-based price targets.
Renko: Bricks of fixed price size placed at 45°. New brick only when price moves at least one brick size in either direction. Time ignored.
Kagi: Vertical lines that switch thickness only on reversal beyond a threshold (yang line = thick/bullish, yin line = thin/bearish). Time ignored.
Equivolume: Each bar’s width is proportional to volume. Wide-body breakouts get a visual signal.
Market Profile: Plots Time-Price Opportunity (TPO) letters across the trading day to build a distribution. Identifies value area (70% of volume), point of control (POC, the most-traded price), and value-area high/low.
Volume Profile: Histogram of volume traded at each price level over a chosen window (independent of time-distribution semantics).

Arithmetic vs Logarithmic Scaling

  • Arithmetic (linear): equal vertical = equal dollar move. Useful for short timeframes and narrow ranges. A trendline drawn on an arithmetic chart implies equal dollar gains.
  • Semi-log / logarithmic: equal vertical = equal percentage move. Required for long-term charts of high-growth assets. A trendline on a log chart implies equal percentage gains.
  • Worked example: 10 → 20 is +100%; 100 → 200 is +100%. On an arithmetic chart, the second looks 10× bigger; on a log chart they look identical.
  • Best practice: long-term charts (multi-year, multi-decade) — log. Intraday and short-term — arithmetic is fine.

Intervals: Time-Based vs Activity-Based

  • Time-based: 1m, 5m, 15m, 30m, 60m, 4h, daily, weekly, monthly. Each bar represents the OHLC over that fixed clock window.
  • Tick charts: a new bar after every N trades. Activity-driven; quiet periods produce fewer bars.
  • Volume bars: a new bar after every N shares/contracts. Each bar represents equal participation, not equal time.
  • Range bars: a new bar after each N-point price move. Each bar has equal price travel.
  • P&F / Renko / Kagi: pure price-driven; time removed.
  • Shorter intervals = more noise, more signals; longer intervals = more signal, slower response. Multiple time frames are recommended.

Volume & Open Interest

  • Volume: number of shares (equities) or contracts (futures/options) traded in an interval. Reveals the participation behind a move.
  • Open interest (OI): futures/options only. Number of contracts outstanding. A new buyer + new seller = OI rises by 1. Two existing holders offsetting = OI falls by 1.
  • Key futures/options rules:
    • Price up + Volume up + OI up ⇒ strong bullish (new longs).
    • Price up + Volume down + OI down ⇒ weak rally (short covering).
    • Price down + Volume up + OI up ⇒ strong bearish (new shorts).
    • Price down + Volume down + OI down ⇒ weak decline (long liquidation).
  • Volume divergence with price (price up, volume falling for many days) warns of fading participation.

Data quality & gotchas

  • Split/dividend adjustment: equity charts should be adjusted for splits and dividends for accurate long-term analysis.
  • Futures contract roll: continuous contracts must be back-adjusted to remove gaps at expiration roll; otherwise pattern analysis is distorted.
  • Survivorship bias: historical equity universes that exclude delisted or merged companies inflate measured returns.
  • Holidays, half-days, and time-zone shifts create gaps that are not informational.
  • Tick vs trade vs quote data: granularity differences matter for high-frequency analysis.
Pitfall: Don’t use arithmetic scaling on multi-year charts of compounders — old percentage moves get visually crushed. Don’t treat bars and candlesticks as different data; only the visualization differs. Don’t ignore the futures roll — it creates fake gaps that pattern analysis will misread.

Common questions

Which chart type ignores time entirely?
Point-and-figure (P&F). Renko and Kagi are also price-driven and effectively ignore time.
When is a logarithmic price scale preferred?
For long time horizons and wide price ranges, because it equalizes percentage moves visually.
What information does a candlestick add beyond a line chart?
Open, high, low, and close, plus body color showing whether the close was above or below the open.
What does open interest measure?
The total number of outstanding futures or options contracts not yet offset. Rising OI in the direction of the trend supports the trend.
What is the bullish interpretation of: price up, volume up, OI up?
Strong uptrend driven by new long positions — the most bullish combination in futures.
What does price up + volume down + OI down imply?
A weak rally driven by short covering, not new long demand.
What is a 3-box-reversal P&F chart?
A P&F construction requiring price to reverse by 3 box-sizes before plotting a new column in the opposite direction.
What are the value area and point of control in Market Profile?
Value area = the price range containing ~70% of the day’s volume; point of control (POC) = the single price at which the most volume traded.
Which chart type best shows the closing-price trend with minimum noise?
A line chart of closing prices.
What does a bar chart’s left tick represent?
The opening price of the bar’s interval. The right tick = the close.
What is equivolume charting?
A chart in which each bar’s width is proportional to that period’s volume, so heavy-participation days produce visually wider bars.
What is the main risk of using unadjusted historical equity data?
Splits and dividends create artificial price jumps that distort pattern analysis and long-term return measurement.
How does a Renko brick get plotted?
A new brick is added in either direction only after price has moved at least one brick’s size beyond the prior close, regardless of time.
What does a yang (thick) Kagi line represent?
A bullish reversal in price exceeding the chart’s reversal threshold.

III. Trend Analysis

~13% of exam · The single highest-yield section. Master this first.

TL;DR:

A trend is the directional bias of price. An uptrend = sequence of higher highs (HH) and higher lows (HL). A downtrend = sequence of lower highs (LH) and lower lows (LL). A sideways / trading range oscillates between defined support and resistance with no directional bias.

Support = a price area where demand has historically been strong enough to halt declines. Resistance = a price area where supply has historically been strong enough to halt advances. They are zones, not exact prices. Once decisively broken, support becomes resistance and resistance becomes support — the polarity principle — because the trapped traders at the broken level want to exit on a return.

Trendlines connect successive higher lows (uptrend) or lower highs (downtrend). Two touches define a line; three touches confirm it. A break requires a meaningful close beyond the line, ideally on expanding volume. Channels are formed by drawing a parallel line through the opposite swings of the trendline.

Common retracement levels use Fibonacci ratios: 23.6%, 38.2%, 50%, 61.8%, 78.6%. A normal pullback in a healthy trend retraces 38.2% to 61.8% of the prior leg. Deeper retracements (>61.8%) warn the trend may be ending.

Volume confirms trend: in an uptrend, volume expands on rallies and contracts on pullbacks. Multiple time frames are mandatory: use higher time frames for context (where am I in the bigger trend?), lower time frames for execution (precise entry). Signals aligned across time frames are highest quality.

Trends end through (1) structural failure — a lower low in an uptrend or a higher high in a downtrend; (2) momentum exhaustion — bearish/bullish divergences; (3) breadth deterioration — participation narrows; (4) climax volume — capitulative bar; (5) major support/resistance break.

Trend Definitions & Classification

  • Primary trend: months to years (Dow Theory tide).
  • Intermediate / secondary trend: weeks to months; corrections within the primary; retrace 33–66% typically.
  • Short-term / minor trend: days to weeks; noise on the primary trend.
  • An uptrend requires both HH and HL. A pullback that does not undercut the prior HL keeps the uptrend intact.
  • An uptrend is suspect when the last HL gets broken; it is broken when a LH and LL form (a new downtrend).

Support & Resistance — Strength Factors

  • Number of touches: more touches = more visible level = more reactive.
  • Time held: a level that has held for years is more meaningful than one that held for days.
  • Volume at the level: high-volume touches anchor the level in memory.
  • Age: older levels often have larger trapped audiences; their reactions can be stronger.
  • Round numbers and psychological levels: $100, $50, integers in indexes (S&P 5000, DJIA 40,000) act as soft support/resistance.
  • Prior all-time high / low: often a major battleground.
  • Polarity principle: a broken support becomes resistance (sellers who bought at that level want to break even); a broken resistance becomes support.

Trendlines & Channels — Drawing Rules

  • Up trendline: connect successive higher lows. Touch points should not pierce the line.
  • Down trendline: connect successive lower highs.
  • Two touches define, three touches confirm, break requires a decisive close beyond (not just intrabar) — ideally with rising volume.
  • Steeper trendlines break more easily than shallow ones; very steep trendlines are typically unsustainable.
  • Channel: draw a parallel line through the opposite swings. Price oscillates within the channel; touches of the upper line in an uptrend are profit-taking zones, lower line is buy-the-dip.
  • Internal vs external trendlines: external use extreme highs/lows; internal pass through the body of price action.

Fibonacci Retracements & Extensions

  • Retracements (within prior swing): 23.6%, 38.2%, 50%, 61.8%, 78.6%.
  • Extensions (beyond the prior swing): 127.2%, 161.8%, 261.8%.
  • The 61.8% (golden ratio) is the most-watched retracement; a hold there is bullish for the prior trend.
  • A retracement deeper than 78.6% often signals a trend change.

Volume + Trend Rules

  • In a healthy uptrend: volume expands on rallies, contracts on pullbacks.
  • In a healthy downtrend: volume expands on declines, contracts on bounces.
  • Divergence: price up + volume down for several days = warning.
  • Climax volume on a single bar can mark the end of a move (selling/buying exhaustion).

Multiple Time Frames (MTF)

  • Top-down approach: monthly → weekly → daily → intraday.
  • Trade only in the direction of the higher time frame trend; use the lower time frame for execution.
  • A signal aligned across at least two time frames is higher quality than a single-time-frame signal.

The Four Trades (Dow-style decision framework)

  1. Buy in an established uptrend on a pullback to support / rising trendline / 38.2–61.8% retracement.
  2. Add to longs on a continuation breakout above prior resistance with volume.
  3. Sell or short when an uptrend reverses and a new downtrend is confirmed (LH + LL).
  4. Add to shorts on a continuation breakdown below prior support with volume.

Stop Placement Conventions

  • Structural stops: just beyond the most recent swing low (long) or swing high (short).
  • Volatility stops: distance defined as N×ATR from entry, where N is typically 1.5–3.
  • Trailing stops: ratchet stop up (long) as new HL form; Parabolic SAR or chandelier stops are common.
  • Round-number caution: don’t place stops precisely at the round numbers everyone else uses; allow some buffer.
Pitfall: A single touch is not a trendline. Breakouts without volume can be traps (bull/bear traps). Don’t mistake a counter-trend bounce in a downtrend for a new uptrend until you see HH and HL. Don’t take a single Fibonacci touch as a buy signal — look for confluence (Fib + support + moving average + reversal candle).

Common questions

How is an uptrend defined?
A sequence of higher highs and higher lows.
What is the polarity principle?
Broken support becomes resistance, and broken resistance becomes support.
How many touches confirm a trendline?
Two touches define it, a third touch confirms it.
What is a false breakout?
A move that briefly breaks a level then reverses back inside it — often trapping traders who acted on the break.
How should volume behave in a healthy uptrend?
Volume expands on rallies and contracts on pullbacks.
Why use multiple time frames?
To align short-term action with the larger-trend context, improving signal quality and avoiding counter-trend trades.
What are the most commonly watched Fibonacci retracement levels?
23.6%, 38.2%, 50%, 61.8%, and 78.6%.
Which Fibonacci level is considered the most important?
61.8% — the “golden ratio.”
What is a channel?
Two parallel lines — the trendline plus a parallel through the opposite swings — that contain price action.
What signals trend exhaustion?
Momentum divergence, narrowing breadth, climax volume, and structural failure (e.g., a lower low in an uptrend).
Where do structural stops go for a long position?
Just beyond the most recent swing low.
How is a volatility stop sized?
N times the Average True Range (ATR), typically 1.5–3 times.
What is the typical retracement for a healthy pullback in an uptrend?
38.2% to 61.8% of the prior leg.
What confirms a trendline break?
A decisive close beyond the line, preferably on expanding volume; intraday penetrations alone are not enough.
What is the “trapped traders” explanation for polarity?
Traders who bought at the broken support are underwater; they often sell when price returns to break-even, creating new resistance at the former support level.
How does a trendline’s steepness affect its reliability?
Steeper trendlines break more easily; shallow trendlines are more durable. Very steep trendlines are typically unsustainable.

IV. Chart Pattern Analysis

~16% of exam · The largest single section. Recognize every pattern, the volume signature, and how targets are measured.

TL;DR:

Chart patterns are visual evidence of supply, demand, and crowd psychology. They divide into two families:

  • Reversal patterns — appear at the end of a trend and signal a change in direction. Examples: head & shoulders, inverse head & shoulders, double top/bottom (M/W), triple top/bottom, rounding top/bottom (saucer), V-spike top/bottom, broadening top.
  • Continuation patterns — appear within a trend as a pause and signal that the trend will likely resume. Examples: symmetrical triangle, ascending triangle, descending triangle, flag, pennant, rectangle, wedge (context-dependent).

Pattern lifecycle: formation → breakout (decisive close beyond the pattern’s key line) → optional throwback (pullback) to the broken line (now polarity-flipped) → measured move.

Target measurement rule of thumb: project the pattern’s vertical height (or the prior “pole” in flags/pennants) from the breakout point in the direction of the break. These are minimum objectives, not guarantees.

Volume signature: classic patterns typically show declining volume during formation (consolidation) and expanding volume on breakout. A breakout without volume confirmation is suspect.

Gaps are price discontinuities between one bar’s close and the next bar’s open. Four main types: common (no signal), breakaway (out of a base, new trend), runaway/measuring (mid-trend, often the halfway point), exhaustion (near the end of a trend, often filled rapidly). An island reversal = exhaustion gap followed by a breakaway gap in the opposite direction.

Japanese candlesticks are bar-chart equivalents that emphasize the open-to-close real body. Reversal patterns: doji, hammer, hanging man, shooting star, inverted hammer, engulfing, harami, morning/evening star, three white soldiers / three black crows, tweezers, dark cloud cover, piercing line. Continuation patterns: rising/falling three methods, marubozu, separating lines.

Reversal Patterns — In Detail

PatternTrend beforeConfirmationTarget measurementVolume signature
Head & Shoulders TopUptrendClose below neckline (drawn through the two intervening lows)Distance from head to neckline, projected downward from the neckline breakHighest volume on left shoulder, lower on head, lowest on right shoulder; expansion on breakdown
Inverse Head & ShouldersDowntrendClose above necklineDistance from head to neckline, projected upwardMirror image; volume should expand on breakout
Double Top (“M”)UptrendClose below the intervening lowDistance from peak to trough, projected downwardSecond top often shows lower volume than first
Double Bottom (“W”)DowntrendClose above the intervening highDistance from trough to peak, projected upwardVolume usually expands on the second low’s rally and on breakout
Triple Top / BottomUp / DownBreak of the support/resistance shared by the three extremesHeight of pattern projected from breakoutStronger than double; failures at the level on diminishing volume
Rounding Top (saucer)UptrendGradual rollover; break of long-term supportOften unmeasured; major top signalVolume bowl-shaped, low at the apex of the curve
Rounding Bottom (saucer)DowntrendGradual base and breakout above the saucer rimOften unmeasured; major bottom signalVolume low at the bottom of the saucer; expands on emergence
V Top / BottomEitherSingle-bar reversal; often news or capitulation-drivenNone; reversal-confirmation onlyClimax-volume bar typical
Broadening Top (megaphone)Late uptrendHigher highs + lower lows wideningVolatile and unreliable; warns of distributionVolume is erratic; sign of disorderly market

Continuation Patterns — In Detail

PatternSetupBiasTarget measurementVolume signature
Symmetrical TriangleLower highs + higher lows convergingDirection usually = prior trend (~60–70% of the time)Height of the widest part projected from the breakoutDeclining volume during formation; expansion on breakout
Ascending TriangleFlat resistance, rising supportBullish biasTriangle height projected upward from breakoutVolume declines as it forms; expands on breakout
Descending TriangleFlat support, falling resistanceBearish biasTriangle height projected downward from breakdownVolume declines; expands on breakdown
FlagSharp move (the “pole”) followed by a small parallel counter-trend channelContinuationLength of the pole projected from the breakout pointVolume highest on the pole, low in the flag, expands on breakout
PennantSharp move (pole) followed by a small symmetrical triangleContinuationLength of the pole projected from breakoutSame as flag
RectangleHorizontal range between defined support and resistanceContinuation in direction of prior trend, with breakoutHeight of rectangle projected in breakout directionVolume often subdued; expansion on breakout
Rising WedgeBoth lines rising but convergingBearish in uptrends (reversal); continuation rarelyMove back to wedge origin or beyondVolume often declines as wedge narrows
Falling WedgeBoth lines falling but convergingBullish in downtrends (reversal); continuation rarelyMove back to wedge origin or beyondVolume often declines as wedge narrows
Cup & HandleU-shaped base + small consolidation handleBullish continuationCup depth projected upward from breakoutVolume bowl in cup; pullback in handle; expansion on breakout

Gap Taxonomy

Common (area) gap: Inside a trading range, no information value, fills quickly.
Breakaway gap: Out of a consolidation base on heavy volume; often starts a new trend; usually does not get filled in the near term.
Runaway / measuring gap: Mid-trend on continued strong volume; statistically often marks the midpoint of the entire move.
Exhaustion gap: Near the end of a trend; volume often heavy initially then fades; typically gets filled rapidly.
Island reversal: Exhaustion gap + breakaway gap in opposite direction; leaves an isolated “island” of bars.

Japanese Candlesticks — Essential Patterns

Single-candle

  • Doji: open ≈ close. Indecision. Becomes meaningful at trend extremes. Variants: long-legged, gravestone (long upper shadow), dragonfly (long lower shadow).
  • Hammer: small body at the top, long lower shadow (≥ 2× body). Bullish reversal at lows.
  • Hanging Man: same shape as hammer, but at the top of an uptrend. Bearish.
  • Shooting Star: small body at the bottom, long upper shadow. Bearish reversal at highs.
  • Inverted Hammer: same shape as shooting star, but at the bottom of a downtrend. Bullish.
  • Marubozu: no shadows; entire bar is body. Strong trending bar.
  • Spinning top: small body, both shadows. Indecision.

Two-candle

  • Bullish engulfing: down bar followed by an up bar whose real body fully engulfs the prior body. Bullish reversal at lows.
  • Bearish engulfing: up bar followed by a down bar whose real body fully engulfs the prior body. Bearish reversal at highs.
  • Harami (bullish/bearish): a small body fully contained within the prior large opposite-color body. Indecision / potential reversal.
  • Piercing line (bullish): a down bar, then a strong up bar opening below the prior low and closing past the midpoint of the prior body.
  • Dark cloud cover (bearish): an up bar, then a strong down bar opening above the prior high and closing past the midpoint of the prior body.
  • Tweezer top / bottom: two candles with matching highs (top) or lows (bottom) at the trend extreme.

Three-candle

  • Morning star: down bar → small-body indecision bar (often a doji) → strong up bar. Bullish reversal at lows.
  • Evening star: up bar → small-body indecision bar → strong down bar. Bearish reversal at highs.
  • Three white soldiers: three consecutive strong up bars with small wicks. Bullish continuation / reversal.
  • Three black crows: three consecutive strong down bars. Bearish.
  • Rising/falling three methods: continuation patterns — a strong directional bar, three small counter-trend bars contained inside the first bar’s range, then another strong directional bar.

Pattern reliability tips

  • Bigger patterns project bigger moves. A 6-month base > a 3-week base.
  • Patterns formed at strong support/resistance confluences are more reliable.
  • Patterns confirmed by volume expansion on breakout are more reliable.
  • Patterns that retest the breakout line (throwback) and hold the polarity flip are higher-quality entries.
Pitfall: A pattern is not confirmed until the decisive break (with volume) of its key line. Targets are minimum objectives, not guarantees. Don’t front-run formations; many “potential” head-and-shoulders never confirm. Symmetric triangles can break either direction; do not assume bias before the break.

Common questions

What is the measured target for a head and shoulders top?
The vertical distance from the head to the neckline, projected downward from the neckline breakout point.
Are triangles reversal or continuation patterns?
Usually continuation. Symmetrical triangles can break either way but break in the direction of the prior trend ~60–70% of the time; ascending triangles carry bullish bias, descending triangles carry bearish bias.
What confirms a double bottom?
A decisive close above the intervening swing high (the “neckline” between the two lows), preferably on rising volume.
What does an exhaustion gap signal?
A potential end to the prevailing trend. It often gets filled quickly and can lead to an island reversal.
What is the measurement target for a flag pattern?
The length of the prior pole projected from the flag breakout point in the direction of the breakout.
What is a bullish engulfing candle?
A green/hollow candle whose real body completely engulfs the prior down candle’s real body, occurring after a downtrend; signals potential bullish reversal.
Why does volume matter to pattern confirmation?
Volume reflects participation. Patterns supported by expanding volume on the breakout are more reliable than thin moves.
What is the difference between a flag and a pennant?
A flag is a small parallel counter-trend channel after a sharp move; a pennant is a small symmetrical triangle after a sharp move. Both signal continuation.
What is an island reversal?
A cluster of price action isolated by an exhaustion gap on one side and a breakaway gap in the opposite direction on the other side; a reversal signal.
What is the typical volume pattern during a symmetrical triangle?
Declining volume as the triangle forms, with an expansion on the breakout.
What does a doji at the top of an uptrend suggest?
Indecision after a sustained rally; potential reversal, especially if confirmed by a subsequent down bar.
What is the target of a cup and handle pattern?
The depth of the cup projected upward from the handle breakout.
What does an evening star indicate?
A bearish three-candle reversal at the top: a strong up bar, a small-body indecision bar (often a doji), and a strong down bar.
What is a throwback or pullback after a breakout?
A retest of the broken line (now polarity-flipped) before the trend resumes. A successful retest improves the trade’s reliability.
What is a measuring gap and what does it suggest about the trend?
A mid-trend gap typically marking approximately the halfway point of the entire move — useful for extrapolating likely targets.
What distinguishes a rising wedge from a flag?
A rising wedge has converging upward-sloping lines and is typically bearish in an uptrend (potential reversal). A flag is a parallel counter-trend channel after a pole and is a continuation pattern.
Why is the volume on a head & shoulders top typically lower on the right shoulder?
Diminishing volume on the right shoulder reflects waning buying enthusiasm and is a key piece of evidence that distribution is occurring.

V. Technical Indicators

~14% of exam · Know every indicator’s formula intuition, default settings, signal logic, and how it can fail.

TL;DR:

Indicators are mathematical transformations of price (and sometimes volume) designed to expose information that the eye might miss — trend direction, trend strength, momentum, volatility, money flow, or divergence. Almost all indicators are lagging because they are computed from past prices; they confirm rather than predict.

Four families to memorize: trend (moving averages, MACD, ADX, Parabolic SAR, Ichimoku), momentum / oscillators (RSI, Stochastics, Williams %R, CCI, ROC), volume / money-flow (OBV, A/D Line, CMF, MFI, VWAP), and volatility bands (Bollinger Bands, Keltner Channels, Donchian Channels, ATR).

Default settings to memorize: SMA/EMA 50 and 200 (intermediate/long), RSI 14, Stochastics 14/3/3 (or 5/3/3), MACD 12/26/9, Bollinger Bands 20-period SMA ± 2σ, ADX 14, ATR 14, CCI 20, Williams %R 14, Ichimoku 9/26/52.

Key concepts: crossover (e.g., golden cross = 50 MA crossing above 200 MA), overbought/oversold (RSI > 70 / < 30; Stochastics > 80 / < 20), divergence (price makes new extreme but indicator does not — warns of momentum loss), centerline (oscillator crosses zero or 50), and signal-line (MACD crossing its 9-EMA signal line).

Combining indicators is only valuable when they measure different things. Three momentum oscillators built from the same price input do not provide independent confirmation — they create false confidence. The strongest setups feature confluence of price action + volume + indicator.

Moving Averages

  • Simple Moving Average (SMA): arithmetic mean of N closes. SMA = (P1 + P2 + ... + Pn) / N. Equal weight to each period.
  • Weighted Moving Average (WMA): linear weights favor recent data. Recent period weight = N, oldest weight = 1.
  • Exponential Moving Average (EMA): weights decay exponentially. EMA_t = α · P_t + (1 − α) · EMA_{t-1} where α = 2 / (N + 1). Responds faster than SMA of the same length.
  • Common lengths: 10, 20, 50, 100, 200 days.
  • Golden cross: short MA (commonly 50-day) crosses above long MA (commonly 200-day). Bullish intermediate/long-term signal.
  • Death cross: short MA crosses below long MA. Bearish.
  • MAs act as dynamic support (uptrend) and dynamic resistance (downtrend).
  • Price relative to the 200-day SMA is a common bull-vs-bear regime filter.

Momentum & Oscillators

IndicatorDefaultRangeOB / OSKey signal
RSI (Wilder)140–100>70 / <30Divergence + level + centerline (50)
Stochastics (%K / %D)14, 3, 30–100>80 / <20%K vs %D crossover; divergence
Williams %R140 to −100> −20 OB / < −80 OSInverted stochastic
CCI20unbounded>+100 / <−100Trend identification + extreme reversion
ROC10 or 12unbounded %Zero-line crosses; magnitudePure momentum
MACD12, 26, 9unboundedMACD vs signal-line crossover; zero-line; histogram; divergence
  • RSI formula intuition: RSI = 100 − 100 / (1 + RS), where RS = avg gain / avg loss over N periods.
  • MACD: MACD line = EMA(12) − EMA(26); Signal = EMA(9) of MACD; Histogram = MACD − Signal.
  • Divergence: bullish = price makes a lower low, oscillator makes a higher low (momentum fading on the downside). Bearish = price higher high, oscillator lower high.
  • In strong trends, oscillators can remain overbought / oversold for long stretches. Don’t fade strong trends purely on RSI > 70.

Trend-Strength Indicators

  • ADX (Average Directional Index): 0–100. Direction-agnostic measure of trend strength. ADX > 25 = trending; < 20 = trendless/range. Trend direction read from +DI vs −DI (when +DI > −DI, uptrend; opposite for downtrend).
  • Parabolic SAR: trailing stop indicator. Dots appear above price in downtrends, below in uptrends. Flips on penetration.
  • Ichimoku Cloud: combines five lines (Tenkan-sen 9, Kijun-sen 26, Senkou Span A and B, Chikou Span). Cloud (Kumo) above price = bullish regime; below = bearish.

Volume & Money-Flow Indicators

  • On-Balance Volume (OBV): cumulative running total: add volume on up-close days, subtract on down-close days. Divergence between OBV and price warns of weak participation.
  • Accumulation/Distribution (A/D) Line: volume weighted by where the close sits in the day’s range. Close near high = positive contribution; near low = negative.
  • Chaikin Money Flow (CMF): A/D summed over 20 periods, normalized by 20-period volume. Above 0 = buying pressure; below 0 = selling pressure.
  • Money Flow Index (MFI): volume-weighted RSI. 0–100; >80 OB, <20 OS.
  • Volume-Weighted Average Price (VWAP): intraday benchmark; running cumulative price×volume / cumulative volume. Institutions execute around VWAP.
  • Force Index (Elder): (Close − Prior Close) × Volume. Combines direction, magnitude, and volume.

Volatility Bands & Envelopes

  • Bollinger Bands: middle = 20-period SMA; upper/lower = middle ± 2σ of price over 20 periods. Squeeze (bands contracting) precedes expansion. Price tagging the upper band in a strong uptrend is normal — not automatically a sell.
  • Keltner Channels: middle = EMA(20); upper/lower = EMA(20) ± multiplier × ATR. Smoother than Bollinger.
  • Donchian Channels: highest high / lowest low over N periods (e.g., 20). Used in classic trend-following systems (Turtle).
  • ATR: Wilder’s smoothed average of true range over 14 periods. TR = max(H−L, |H−PrevClose|, |L−PrevClose|). Used for volatility-adjusted stops, position sizing, and breakout filters.

Pivot Points & Other Tools

  • Pivot Points: PP = (H + L + C) / 3; S1, S2, R1, R2 derived from PP. Common in intraday futures and FX.
  • Fibonacci retracements/extensions: 23.6, 38.2, 50, 61.8, 78.6% (covered in Trend section).
  • Linear regression channel: best-fit line through prices with ±2 standard-error bands.

Signal Generation Patterns to Recognize

  • Crossover: MA cross (golden/death); MACD vs signal line; Stochastic %K vs %D.
  • Centerline: oscillator crosses zero (MACD, ROC) or 50 (RSI).
  • Divergence: bullish/bearish, regular/hidden. Regular divergence signals reversal; hidden divergence signals continuation.
  • Overbought/oversold reversion: most reliable in range-bound markets, weakest in strong trends.
  • Breakout: Bollinger squeeze release, Donchian breakout.
Pitfall: Stacking multiple indicators built from the same data (RSI + Stochastics + Williams %R) is redundant, not confirming. Indicators lag — in fast moves they will not save you. Don’t blindly fade OB/OS readings in strong trends. The strongest signals require multi-dimensional confluence: price action + volume + indicator divergence + level (e.g., support/resistance or Fib).

Common questions

What is the default period for RSI?
14 periods (Wilder’s original).
What RSI levels are considered overbought and oversold?
Above 70 = overbought; below 30 = oversold. In strong trends, RSI can remain extreme for long stretches.
What is a golden cross?
A short-term MA (commonly 50-day) crossing above a long-term MA (commonly 200-day). Bullish intermediate-term signal.
Why are moving averages considered lagging?
They are computed from past prices, so they confirm a change in trend only after price has already moved.
What does ADX measure?
The strength of the trend, regardless of direction. ADX above 25 typically indicates a trending market; below 20 indicates a range.
What are the MACD’s three components?
The MACD line (EMA12 − EMA26), the signal line (EMA9 of MACD), and the histogram (MACD − Signal).
What is a Bollinger Band squeeze?
A period of unusually low volatility where the bands contract tightly; often precedes a sharp expansion in volatility (breakout).
How does On-Balance Volume work?
It adds the day’s volume on up-close days and subtracts it on down-close days, producing a running cumulative line that attempts to measure buying versus selling pressure.
What is bullish divergence?
Price makes a lower low but the oscillator makes a higher low — momentum is fading on the downside, suggesting potential reversal.
What is bearish divergence?
Price makes a higher high but the oscillator makes a lower high — momentum is fading on the upside, warning of reversal.
What is the default Bollinger Band setting?
20-period SMA with bands at ±2 standard deviations.
What is the formula for the True Range?
The greatest of: today’s high minus today’s low, the absolute value of today’s high minus the prior close, or the absolute value of today’s low minus the prior close.
What does VWAP represent?
The Volume-Weighted Average Price — cumulative price×volume divided by cumulative volume over the session.
What is the α for an EMA of length N?
α = 2 / (N + 1).
What signal does +DI crossing above −DI give?
A bullish directional signal (within the ADX system); the trend has shifted upward in direction.
Why is combining many oscillators sometimes misleading?
If they are derived from the same price input, they will tend to give correlated signals, creating an illusion of independent confirmation.
What does Parabolic SAR do?
Plots trailing-stop dots that “parabolically” tighten toward price; flips above/below price when price penetrates the dots, signaling trend reversal.
What is the Money Flow Index?
A volume-weighted version of RSI; readings above 80 are overbought and below 20 are oversold.

VI. Statistics for Technicians

~6% of exam · Conceptual rather than computational, but you must know every term.

TL;DR:

Statistics gives technicians the language to describe data and reason about uncertainty. The two big buckets:

  • Descriptive statistics: summarize a dataset. Central tendency (mean, median, mode), dispersion (range, variance, standard deviation, mean absolute deviation), and shape (skewness, kurtosis).
  • Inferential statistics: draw conclusions about a population from a sample. Confidence intervals, hypothesis testing, p-values, type I (false positive) and type II (false negative) errors.

Normal distribution is symmetric, bell-shaped, and fully described by its mean and standard deviation. The 68-95-99.7 rule: 68% of observations fall within ±1σ, 95% within ±2σ, 99.7% within ±3σ. Financial returns are not normal — they exhibit fat tails (high kurtosis / leptokurtic) and often negative skew. Models that assume normality (e.g., classic VaR) systematically underestimate tail risk.

Correlation (r, −1 to +1) measures linear association. Covariance is the unscaled version. Regression fits a line to data; is the proportion of variance explained.

Backtest biases to memorize: overfitting/curve-fitting, survivorship bias, look-ahead bias, data-snooping (multiple comparisons), selection bias, time-period bias, ignoring transaction costs/slippage. Defenses: parsimonious models, out-of-sample testing, walk-forward analysis, sensitivity analysis, Monte Carlo.

Central Tendency

  • Arithmetic mean: Σx / n. Sensitive to outliers.
  • Median: the middle value when sorted. Robust to outliers.
  • Mode: the most frequent value. Useful for categorical data and identifying multimodal distributions.
  • Geometric mean: (∏(1 + r_i))^(1/n) − 1. Correct for averaging multi-period returns (compounded).
  • Harmonic mean: used for averaging rates (e.g., average P/E across funds).

Dispersion

  • Range: max − min.
  • Variance (σ²): average of squared deviations from the mean. Σ(x − μ)² / N (population) or / (n − 1) (sample — Bessel’s correction).
  • Standard deviation (σ): square root of variance; same units as data. The workhorse of risk measurement.
  • Mean Absolute Deviation (MAD): Σ|x − mean| / n. Robust alternative to std dev.
  • Coefficient of variation (CV): σ / μ. Unitless measure of relative variability.

Shape: Skewness & Kurtosis

  • Skewness = asymmetry. Positive skew = long right tail (more occasional large gains; common in lottery-like assets). Negative skew = long left tail (occasional large losses; common in credit and short-vol strategies).
  • Kurtosis = tail heaviness. Normal distribution has kurtosis = 3 (or excess kurtosis = 0). Leptokurtic = excess kurtosis > 0, fat tails (typical of equity returns). Platykurtic = excess kurtosis < 0, thin tails.
  • Financial returns are leptokurtic and often negatively skewed. Extreme moves happen far more often than a normal distribution predicts.

Distributions

Normal: Symmetric bell. Defined by mean & std dev. 68/95/99.7 rule.
Lognormal: Logs are normally distributed; used for prices (which cannot go below zero).
Binomial: Number of successes in N independent Bernoulli trials.
Poisson: Number of events in fixed time at known rate (e.g., trade arrivals).
Uniform: All outcomes equally likely (e.g., random number generator).
Student’s t: Like normal but with fatter tails; used in small-sample inference.

Probability Essentials

  • Probabilities sum to 1.
  • Independent events: P(A ∩ B) = P(A) × P(B).
  • Conditional: P(A | B) = P(A ∩ B) / P(B).
  • Bayes: P(A | B) = P(B | A) · P(A) / P(B).
  • Expected value: E[X] = Σ x_i · P(x_i).

Correlation, Covariance, Regression

  • Covariance: cov(x,y) = Σ(x − μ_x)(y − μ_y) / (n − 1). Sign tells direction; magnitude depends on scale.
  • Correlation: r = cov(x,y) / (σ_x σ_y). Bounded in [−1, +1].
  • Linear regression: y = a + bx + ε. Best-fit line minimizes squared residuals (Ordinary Least Squares).
  • : proportion of variance in y explained by x. R² = r² in simple regression.
  • Correlation ≠ causation; correlations can be spurious or regime-dependent.

Hypothesis Testing

  • Set up a null hypothesis (H₀) and an alternative (H₁).
  • Compute a test statistic; compare to a critical value (or compute a p-value).
  • p < α (typically 0.05) ⇒ reject H₀.
  • Type I error = false positive (reject true H₀). Type II error = false negative (fail to reject false H₀).
  • Power = 1 − P(type II error).

Backtesting Biases & Defenses

BiasWhat it isDefense
Overfitting / curve-fittingTuning many parameters until backtest looks great; captures noiseParsimony, out-of-sample test, regularization
Survivorship biasUniverse excludes failed/delisted assets, inflating returnsUse point-in-time databases including delisted securities
Look-ahead biasUsing info not available at decision time (e.g., restated earnings)Strict point-in-time data; lag fundamental data
Data-snooping / multiple comparisonsTesting many strategies; one looks great by chanceBonferroni correction, deflated Sharpe ratio, hold out a final OOS
Selection bias in samplePicking a flattering historical windowTest over full cycle including bear and bull phases
Time-period biasResults dependent on specific datesWalk-forward; rolling windows
Ignoring costsSlippage and commissions destroy edge in high-turnover strategiesRealistic cost model; capacity analysis
Pitfall: Correlation does not mean causation. Standard deviation assumes symmetry; financial returns don’t cooperate — tails are fat and often negatively skewed. A backtest is a research output, not proof of edge.

Common questions

What does standard deviation measure?
Dispersion or variability around the mean.
What does a correlation of −1 imply?
A perfect inverse linear relationship between the two series.
What is the 68-95-99.7 rule?
In a normal distribution, ~68% of observations fall within ±1σ, ~95% within ±2σ, and ~99.7% within ±3σ of the mean.
What is positive skew?
A distribution with a long right tail — occasional very large positive values.
What is leptokurtosis?
Excess kurtosis greater than zero — fat-tailed distribution. Extreme moves are more common than a normal distribution predicts.
Why use the geometric mean for returns?
Because returns compound multiplicatively over time; the geometric mean correctly represents the average compound growth rate.
What is overfitting?
Tuning a model so tightly to historical data that it captures noise, producing strong backtest results that fail out of sample.
What is survivorship bias?
Using a dataset that only includes assets/funds that survived the period, inflating measured returns.
What does R² in a regression measure?
The proportion of variance in the dependent variable explained by the independent variable(s).
What is a Type I error?
A false positive — rejecting a true null hypothesis.
What is a Type II error?
A false negative — failing to reject a false null hypothesis.
What is the formula for the coefficient of variation?
Standard deviation divided by the mean (CV = σ / μ). Unitless measure of relative variability.
What is the difference between descriptive and inferential statistics?
Descriptive statistics summarize observed data (mean, std dev, charts); inferential statistics draw conclusions about a population from a sample (hypothesis tests, confidence intervals).
Why is financial-returns analysis sensitive to fat tails?
Because risk models assuming normality (e.g., classic VaR) underestimate the probability of extreme moves, leading to under-reserved risk in crises.
What is data-snooping bias?
Trying many strategies until one looks great by chance; the more rules you test, the higher the probability of a false discovery.
What does Bessel’s correction (n−1) address?
The downward bias in the variance estimate when using a sample rather than the full population; dividing by n−1 yields an unbiased estimator.

VII. Behavioral Finance

~3% of exam · Memorize biases (one-line definitions are enough) and the prospect theory framework.

TL;DR:

Investors are not perfectly rational. Behavioral finance documents the cognitive and emotional biases that produce predictable patterns of mispricing, trend, and reversal. The foundational model is Prospect Theory (Daniel Kahneman & Amos Tversky, 1979), which replaced expected-utility theory’s assumption of pure rationality with three key empirical findings:

  1. Reference dependence: people evaluate outcomes relative to a reference point (often the purchase price), not absolute wealth.
  2. Loss aversion: losses hurt roughly 2× as much as equivalent gains feel good. The utility function is steeper for losses.
  3. Probability weighting: people overweight small probabilities (buy lottery tickets and insurance) and underweight large probabilities. The utility function is concave for gains and convex for losses (S-shaped).

Biases are usually split into cognitive (belief / information processing) and emotional categories. Cognitive biases are easier to correct with education and process. Emotional biases require restraint and rules.

Behavioral finance is the theoretical foundation for why technical analysis works: trends persist because investors herd, anchor, and follow momentum; reversals at extremes occur because emotional capitulation produces predictable crowd behavior; support and resistance form because anchoring and loss aversion create memory at those prices.

Prospect Theory in Detail

  • Value function: S-shaped — concave for gains (diminishing sensitivity), convex for losses (also diminishing sensitivity, but loss side is steeper).
  • Loss-aversion ratio: ~2 (a $100 loss feels like a $200 gain in reverse).
  • Reference dependence: gains and losses are measured relative to a reference (often purchase price, or a recent high).
  • Probability weighting function: people overweight low-probability events (lotteries, crashes) and underweight high-probability ones.
  • Implications for trading: the disposition effect (sell winners early, hold losers), narrow framing (treating each trade in isolation), and mental accounting all flow from prospect theory.

Cognitive (Belief & Information) Biases

Anchoring: Over-weighting the first piece of information (e.g., entry price) when making subsequent judgments.
Confirmation: Seeking evidence that supports existing beliefs; ignoring contradictions.
Hindsight: Believing past events were predictable in retrospect (“I knew it all along”).
Representativeness: Judging probability by resemblance to a stereotype rather than by base rates.
Availability: Over-weighting recent or vivid events when estimating probability (e.g., overestimating airline crash risk after a news story).
Conservatism: Sticking to prior beliefs too long when new evidence arrives.
Mental accounting: Treating money differently based on its source/label (windfall money vs. salary).
Framing: Different decisions for the same problem framed differently (gain frame vs. loss frame).
Recency: Assuming the recent trend will continue indefinitely.
Illusion of control: Believing one has more control over random outcomes than is actually possible.
Narrative fallacy: Constructing simple causal stories around complex or random events.

Emotional Biases

Loss aversion: Losses hurt ~2× more than equivalent gains feel good.
Overconfidence: Overestimating skill or information edge. Leads to excessive turnover, concentration, and risk-taking.
Regret aversion: Avoiding decisions because of the pain of being wrong (often manifests as status-quo bias).
Endowment effect: Valuing things more highly once owned.
Self-attribution: Crediting wins to skill, blaming losses on bad luck or external factors.
Disposition effect: Selling winners too early and holding losers too long.
Herding: Following the crowd; amplifies trends, bubbles, and panics.
Fear of missing out (FOMO): Late-cycle chasing as the trend matures and the public arrives.

How Behavioral Biases Drive TA

  • Trends persist because anchoring, recency, herding, and momentum chasing keep order flow aligned for longer than fundamentals would suggest.
  • Support and resistance form because loss aversion and anchoring create memory at specific prices (people sell at break-even on a level they bought, creating supply).
  • Reversals at extremes happen because the disposition effect, capitulation, and emotional exhaustion eventually flip the participant base.
  • Bubbles emerge when herding and overconfidence dominate; panics emerge when loss aversion and availability dominate.
Pitfall: Biases apply to you, not just “everyone else.” Defend with explicit rules, pre-defined exits, position-sizing systems, journals, and process reviews. Don’t try to argue your way out of an emotional bias in the middle of a trade.

Common questions

Who developed prospect theory?
Daniel Kahneman and Amos Tversky.
What is loss aversion?
The tendency for losses to feel roughly twice as painful as equivalent gains feel pleasurable.
What is the disposition effect?
Selling winners too quickly to lock in gains while holding losers too long, hoping to avoid realizing the loss.
What is herding behavior?
The tendency for investors to follow the actions or beliefs of a group, amplifying trends and contributing to bubbles and panics.
What is anchoring?
Relying too heavily on the first piece of information encountered when making subsequent judgments.
What is overconfidence in trading?
Overestimating one’s skill or information edge, leading to excessive turnover, concentration, and risk-taking.
What does the prospect theory value function look like?
S-shaped: concave for gains, convex for losses, with the loss side steeper than the gain side (loss aversion).
What is availability bias?
Overweighting recent or vivid events when estimating their probability.
What is the framing effect?
Different decisions for the same underlying problem depending on whether outcomes are framed as gains or losses.
What is mental accounting?
Treating money differently depending on its source or label (e.g., gambling winnings vs. salary).
What is confirmation bias?
Seeking out and over-weighting information that supports a held belief while discounting contradictory evidence.
What is representativeness?
Judging the probability of an event by how closely it resembles a stereotype, ignoring base rates.
What is recency bias?
Assuming that the most recent trend will continue indefinitely.
Why is behavioral finance considered foundational to TA?
Because the predictable crowd behavior it documents is what creates the trends, support/resistance levels, and reversal patterns technical analysis attempts to identify and exploit.

VIII. Sentiment

~7% of exam · Know every indicator, who it measures, what extremes look like, and when it’s contrarian vs confirmatory.

TL;DR:

Sentiment indicators measure the mood, opinion, and positioning of market participants. Their value comes mostly at extremes: when bullish or bearish opinion becomes one-sided, the marginal buyer or seller is exhausted, and reversal probability rises. At extremes, sentiment is typically contrarian; in the middle of a trend, sentiment is usually confirmatory and uninformative.

Sentiment indicators fall into four families:

  • Survey-based: AAII (retail investors), Investors Intelligence (newsletter writers), Consensus Inc. / Market Vane (futures traders), NAAIM (active managers).
  • Derivative / positioning: put/call ratio, VIX (as fear gauge), CFTC Commitments of Traders (COT), short interest, NAAIM exposure.
  • Flow / activity: money market fund assets, mutual fund and ETF flows, margin debt, IPO supply and first-day pops, insider buy/sell transactions.
  • Anecdotal / narrative: magazine covers, mainstream media coverage, taxi/Uber driver tip-giving.

Key interpretation rules:

  • High put/call (fear) = bullish contrarian. Low put/call (complacency) = bearish contrarian.
  • VIX spikes mark fear (often near lows). Sustained low VIX = complacency.
  • Rising money market assets = sidelined cash = potential future buying power (bullish at extremes).
  • Heavy insider selling at indices’ highs = late-cycle warning.
  • Magazine covers mark consensus — classic contrarian.

Survey-Based Sentiment

  • AAII (American Association of Individual Investors): weekly bull/bear/neutral survey of retail investors. Watch the bull-bear spread. Extremes (>40% bulls or >40% bears for several weeks) are contrarian.
  • Investors Intelligence (II): weekly survey of investment newsletter writers. Bull/Bear ratio > 3 = extreme optimism; < 1 = extreme pessimism. Historically reliable at extremes.
  • Consensus Inc. / Market Vane: futures-trader bullish consensus, %. Extremes > 70% or < 30% are watched.
  • NAAIM Exposure Index: average equity exposure of active managers (range −200% to +200%). Highs and lows are contrarian.
  • University of Michigan / Conference Board consumer confidence: macro mood; extremes precede recessions and recoveries.

Derivatives & Positioning

  • Put/Call Ratio (P/C): total put volume / total call volume on an exchange (CBOE). Equity-only P/C < 0.5 = complacent, > 1.0 = fearful. Index P/C is structurally higher because of hedging.
  • VIX (CBOE Volatility Index): 30-day implied vol of S&P 500 options. Heuristic ranges: <12 calm/complacent; 12–20 normal; 20–30 stress; >30 fear; >40 crisis. Spike + reversal candle on SPX often marks intermediate lows.
  • VVIX: vol of the VIX itself; signals volatility-of-volatility regime.
  • Commitments of Traders (COT): weekly CFTC report on futures positioning by commercials (hedgers), large speculators (managed money / funds), and small speculators. Extreme commercial vs. speculator positioning often precedes turns. Commercials tend to be right at extremes; small specs tend to be wrong.
  • Short interest: total short shares / float. Rising short interest = bearish positioning. Very high short interest can lead to a short squeeze if price reverses.
  • Short interest ratio (days to cover): short interest / average daily volume.

Flow & Activity

  • Money market fund (MMF) assets: cash held outside risk assets. Rising MMF assets = potential future buying power (bullish at extremes).
  • Mutual fund / ETF flows: net subscriptions vs redemptions. Large inflows often coincide with tops (the public arrives late).
  • Margin debt: investor borrowing. Record highs reflect high risk appetite and leverage; subsequent contraction typically accompanies bear markets.
  • IPO activity: heavy IPO supply with big first-day pops = late-cycle euphoria.
  • Insider transactions: net insider buying = bullish; broad-based insider selling = bearish (though selling can be liquidity-driven so weight broad buying more).
  • Cash levels in mutual funds: low cash = fully invested = limited dry powder; high cash = defensive positioning.

Anecdotal Sentiment

  • Magazine covers: a story reaching mainstream cover status (Time, Newsweek, BusinessWeek, The Economist) typically marks late-stage consensus — a classic contrarian signal.
  • “Cocktail party” / taxi-driver tip-giving: when non-professionals enthusiastically give stock tips, late-cycle behavior is signaled.
  • Bookstore section size: too many books on a single sector or strategy = late-cycle.

Composite Sentiment Tools

  • CNN Fear & Greed Index: composite of put/call, VIX, junk bond demand, market momentum, breadth, safe-haven demand, and stock-price strength. 0 = extreme fear; 100 = extreme greed. Extremes > 75 or < 25 are watched.
  • SKEW Index: measures the perceived tail-risk priced into out-of-the-money SPX puts. Rising SKEW = fear of crash.
Pitfall: Sentiment is a context tool, not a precise timing signal. Extremes can persist and become more extreme (“markets can remain irrational longer than you can remain solvent”). Use sentiment with price action, breadth, and trend.

Common questions

Why are sentiment indicators considered contrarian?
Because extremes of optimism or pessimism often appear near major turning points; when consensus is one-sided, marginal buyers or sellers are exhausted.
What does a high put/call ratio suggest?
High fear among options participants; treated as bullish at extremes (contrarian).
What does a low put/call ratio suggest?
Complacency / extreme optimism; treated as bearish at extremes (contrarian).
What does rising short interest indicate?
Increasing bearish positioning. At extremes can fuel a short squeeze if price reverses upward.
How are rising money market fund assets interpreted?
As potential buying power sitting in cash that may rotate into risk assets — bullish at extremes.
Why are magazine covers used as sentiment?
A theme reaching mainstream cover status reflects late-stage consensus, often near major tops or bottoms.
What does the COT report show?
Weekly futures positioning broken down by commercials (hedgers), large speculators (funds), and small speculators. Extremes are watched for sentiment context.
In the COT, which group tends to be right at extremes?
Commercials (hedgers) tend to be right at extremes; small speculators tend to be wrong.
How is the VIX used as a sentiment tool?
VIX spikes mark fear and often coincide with market lows; sustained low VIX suggests complacency.
What does heavy insider buying suggest?
Insiders accumulating shares with their own money is typically bullish, especially when broad-based across companies.
What is the Investors Intelligence bull/bear ratio extreme that is watched?
Above ~3 = extreme optimism (bearish contrarian); below ~1 = extreme pessimism (bullish contrarian).
What is the AAII survey and who does it measure?
The American Association of Individual Investors weekly survey measures retail-investor sentiment (bullish, neutral, bearish percentages).
What is the SKEW Index?
A measure of tail-risk priced into out-of-the-money SPX puts. Rising SKEW indicates rising perceived crash risk.
What does margin debt at record highs typically indicate?
High investor risk appetite and leverage — a late-cycle warning. Subsequent contraction often accompanies bear markets.
What is the difference between equity-only and total put/call ratios?
Total includes index options (heavily used for hedging) and runs structurally higher; equity-only strips out index hedging activity for a cleaner read on directional speculation.

IX. Cycle Analysis

~4% of exam · Memorize named cycles, the principles, and the basic anatomy of a cycle.

TL;DR:

Cycle analysis looks for recurring rhythms in market data. A cycle has four characteristics: amplitude (height from peak to trough), period (length of one cycle), phase (where in the cycle we currently are), and frequency (cycles per unit of time, = 1/period).

Cycles are tendencies, not guarantees. They can shift, fail, or shrink, and any observed cycle is the sum of multiple underlying cycles plus noise. Cycle analysis is most useful for timing context and should always be combined with price action and risk controls.

Key cycle principles (Hurst): summation, harmonicity, synchronicity, proportionality, variation, nominality.

Named cycles to recognize: Kitchin (~3-5 yr, inventory), Juglar (~7-11 yr, fixed investment / business cycle), Kuznets (~15-25 yr, infrastructure), Kondratiev (~45-60 yr, long wave), Presidential (4 yr U.S. equity), Decennial (10 yr U.S. equity), plus broad seasonality (“Sell in May,” Santa rally, January effect, turn-of-the-month effect).

Anatomy of a Cycle

  • Amplitude: vertical distance from trough to peak. Bigger amplitude = larger moves.
  • Period: time from trough to next trough (or peak to next peak).
  • Phase: where in the cycle we are now — rising, topping, falling, or basing.
  • Frequency: number of cycles per unit time (= 1 / period).

Hurst’s Cycle Principles

  • Summation: any observed cycle is the sum of many underlying cycles of different periods.
  • Harmonicity: cycles tend to be related by simple integer multiples (e.g., 2× or ½).
  • Synchronicity: cycles of similar length across different markets tend to bottom at the same time.
  • Proportionality: longer cycles have larger amplitudes.
  • Variation: real cycles vary in period and amplitude around an ideal mean.
  • Nominality: there is a “nominal” set of cycle periods that recur across markets (e.g., 80-day, 40-day, 20-day, 10-day cycles).

Named Cycles

CyclePeriodDriver
Kitchin~3–5 yearsInventory adjustments by firms
Juglar~7–11 yearsBusiness cycle, fixed-capital investment
Kuznets~15–25 yearsDemographics & infrastructure
Kondratiev (K-wave)~45–60 yearsLong-wave economic / technological
Presidential4 years (U.S.)Aligned with U.S. presidential term; historically weakest in year 2, strongest in year 3
Decennial10 years (U.S.)Years ending in 5 historically strong; years ending in 0 or 7 historically weak
Seasonal: Sell in May6 monthsNov–Apr historically strongest; May–Oct weaker
Santa rally~5 trading daysLate December / first 2 days of January
January effect~1 monthSmall-cap January outperformance (largely attenuated post-2000)
Turn-of-the-month~4 daysLast/first few trading days of month historically strong

Tools for Cycle Analysis

  • Visual identification: peak-to-peak, trough-to-trough measurement on the chart.
  • Detrended Price Oscillator (DPO): removes trend from price to isolate cyclical component. Plot of Close − SMA(N) shifted (N/2 + 1) periods back.
  • Centered moving averages: smooth out cycles to reveal underlying trend.
  • Spectral / Fourier analysis: decompose price into component frequencies. Identify dominant cycle periods.
  • Cycle channels / envelopes: bands constructed around an ideal cycle path.
  • MESA (Maximum Entropy Spectral Analysis): Ehlers’ adaptive cycle work.
Pitfall: Don’t force a cycle onto noisy data. Cycle periods shift with regime — what was a 40-day cycle may become a 30-day cycle. Combine cycles with trend and risk controls; never trade a cycle in isolation.

Common questions

What does cycle analysis attempt to identify?
Recurring rhythms or periodic tendencies in market data, useful for timing context.
What are the four properties of a cycle?
Amplitude, period, phase, and frequency.
What is the presidential cycle?
A four-year U.S. equity tendency aligned with the presidential term, historically weakest in year 2 and strongest in year 3.
What is the Kondratiev wave?
A long-wave economic cycle of approximately 45–60 years, driven by major technological / capital-formation shifts.
What is the principle of summation?
Observed price behavior is the sum of multiple underlying cycles of different periods.
What is the principle of synchronicity?
Cycles of similar period across different markets tend to bottom at the same time.
What is the principle of harmonicity?
Cycles tend to be related by simple integer multiples (e.g., 2× or ½).
What is seasonality?
A tendency for an asset to perform in a recurring way over a calendar period; a tendency, not a guarantee.
What is the “Sell in May” effect?
The historical tendency for U.S. equity returns to be stronger from November through April than from May through October.
What does a detrended price oscillator (DPO) show?
The cyclical component of price after removing the trend, by subtracting a centered moving average from price.
What is the biggest risk in cycle analysis?
Forcing a cycle pattern onto noisy data, or assuming a cycle period is fixed when it shifts with regime.
What is the Juglar cycle?
Approximately a 7–11 year business cycle tied to fixed-capital investment.

X. Comparative Market Analysis & Intermarket

~10% of exam · The largest curriculum unit. Master relative strength, sector rotation, breadth, and bond-curve basics.

TL;DR:

Comparative market analysis compares the performance of one security or group to another to identify leadership, laggards, and macro context. The core tool is the Relative Strength (RS) ratio — the price of an asset divided by a benchmark’s price (e.g., XLK / SPY). A rising RS line means the asset is outperforming; a falling RS line means it is underperforming. RS is distinct from RSI — RSI is a momentum oscillator on a single security, RS is a ratio between two securities.

Intermarket analysis studies the relationships between stocks, bonds, commodities, and currencies as a coherent macro system, originally popularized by John Murphy. The classic relationships (in normal regimes): stocks ↔ bonds (often correlated when growth is healthy, decorrelate in stress); bonds ↔ rates (inverse by construction); commodities ↔ dollar (typically inverse); commodities ↔ bonds (commodity inflation → rising rates → falling bonds); stocks ↔ commodities (cyclical interplay).

Sector rotation tracks how leadership shifts through the business cycle: early expansion — cyclicals (financials, consumer discretionary, technology, transports). Mid expansion — industrials, materials. Late expansion / peak — energy, materials, staples. Recession / contraction — defensive (utilities, staples, healthcare). The standard 11 GICS sectors are the trade vehicle.

Market breadth measures internal participation. Key tools: A/D line, new highs minus new lows, % of stocks above 50-day / 200-day MA, McClellan Oscillator and Summation Index, TRIN (Arms Index), up volume vs down volume. Healthy advances feature broad participation; narrow advances are warnings.

Bond market basics: bond prices and yields move inversely; duration measures price sensitivity to yield change; the yield curve (2s10s, 3m10y) is normal-upward in growth, inverted as a recession warning. Credit spreads (HY − Treasuries) widen in stress, tighten in risk-on.

Relative Strength (RS) — Not RSI

  • RS ratio = numerator / denominator (e.g., stock / sector, sector / market, country / world).
  • Trend in the RS line matters more than the absolute level.
  • Use to find leadership rotation (which sectors / countries / styles are outperforming), and to evaluate single-stock alpha vs its sector.
  • Common pairings: small cap (IWM) vs large cap (SPY); growth (IWF) vs value (IWD); cyclicals vs defensives; high-beta vs low-vol.
  • An RS line breaking out (new highs) before the absolute price line is a leadership signal.

Intermarket Relationships (general / long-run)

PairTypical relationshipNotes / regime caveat
Bond price ↔ YieldInverse by constructionHigher yields = lower bond prices.
Stocks ↔ BondsOften positively correlated in disinflation; negatively correlated in deflation/stressRegime-dependent. 2022 saw stocks and bonds fall together.
USD ↔ CommoditiesTypically inverseWeak dollar lifts commodities; strong dollar pressures.
Commodities ↔ BondsRising commodities → inflation → rising rates → falling bondsStrong relationship in inflationary cycles.
Stocks ↔ CommoditiesCyclical; both rise in growth expansions, diverge in stagflationDepends on whether growth or inflation is dominant.
Gold ↔ Real yieldsInverse (gold rises when real yields fall)Gold is a hedge against falling real rates and currency debasement.
Gold ↔ USDTypically inverseWeak dollar lifts gold.
Energy stocks ↔ Crude oilPositive (lagged)Earnings sensitivity to oil prices.

Sector Rotation Through the Business Cycle

  1. Early expansion (recovery): financials, consumer discretionary, technology, transports. Falling rates, rising risk appetite.
  2. Mid expansion: industrials, basic materials. Capital expenditure and demand rising.
  3. Late expansion / peak: energy, materials, consumer staples begin to outperform; inflation pressures rise.
  4. Recession / contraction: defensives lead — utilities, consumer staples, healthcare. Earnings stability matters.
  5. Early recovery: cyclicals reassert leadership; the cycle restarts.

The 11 GICS Sectors (the trading universe)

  • Communication Services (XLC), Consumer Discretionary (XLY), Consumer Staples (XLP), Energy (XLE), Financials (XLF), Health Care (XLV), Industrials (XLI), Information Technology (XLK), Materials (XLB), Real Estate (XLRE), Utilities (XLU).

Market Breadth Indicators

Advance/Decline Line: Running sum of (advancing issues − declining issues). Divergence with the index = warning of narrowing participation.
New Highs − New Lows: 52-week high count minus 52-week low count. Persistent positive readings support bull markets; persistent negative readings define bear markets.
% of stocks above 50-day / 200-day MA: Direct measure of how broadly stocks participate. Above 70% = broad participation; below 30% = washout / oversold breadth.
McClellan Oscillator: EMA(19) − EMA(39) of net advances; short-term breadth momentum.
McClellan Summation Index: Running cumulative total of the McClellan Oscillator; intermediate-term breadth trend.
TRIN (Arms Index)(Adv Issues / Dec Issues) ÷ (Adv Vol / Dec Vol). >1 = bearish, <1 = bullish. Extreme spikes can mark capitulation.
Up Volume vs Down Volume: Ratio of volume on advancing issues to volume on declining issues. 9:1 up days are bullish initiation; 9:1 down days are capitulative.

Bonds & Fixed Income (essentials)

  • Inverse relationship: bond prices fall when yields rise, and vice versa.
  • Duration: bond price sensitivity to a 1% change in yields. Longer maturity, lower coupon = higher duration.
  • Yield curve: plot of yield vs maturity. Normal = upward sloping; flat = late-cycle warning; inverted = recession warning (especially 2s10s, 3m10y).
  • Credit spreads: HY (high-yield) yield minus Treasury yield. Widening = stress; tightening = risk-on.
  • TED spread: 3-mo LIBOR minus 3-mo T-bill. Stress indicator (largely historical).
  • Bond market vs stock market: bond market is much larger; bond signals often precede equity turns.

Currencies (essentials)

  • DXY (U.S. Dollar Index): trade-weighted basket vs major currencies (EUR ~58%, JPY, GBP, CAD, SEK, CHF).
  • Dollar strength: typically pressures commodities, EM equities, USD-denominated foreign assets.
  • Carry trade: borrow in low-yielding currency (e.g., JPY), invest in high-yielding currency (e.g., MXN, BRL). Unwinds during risk-off (currency vol spikes).
Pitfall: Intermarket relationships are regime-dependent. The stock/bond correlation flipped between disinflation (~2000-2020) and the 2022 inflation regime. Always confirm intermarket signals with price action and breadth.

Common questions

What does relative strength (RS) compare?
The performance of one security or group against another (e.g., a stock vs. its sector, or a sector vs. the market).
How is relative strength different from RSI?
RS is a ratio between two securities (asset/benchmark). RSI is a momentum oscillator computed on a single security. They share the term “relative strength” but measure entirely different things.
What does a rising RS line indicate?
The numerator (asset being analyzed) is outperforming the denominator (benchmark).
Which sectors typically lead in early expansion?
Consumer discretionary, financials, technology, transports (cyclicals).
Which sectors typically lead during a recession?
Defensive sectors — utilities, consumer staples, and healthcare.
What does the Advance/Decline line measure?
Cumulative advancing minus declining issues — a breadth gauge of how broadly stocks are participating in the market move.
How do bond prices and yields relate?
Inversely — as yields rise, bond prices fall, and vice versa.
What is an inverted yield curve?
A yield curve in which short-term yields exceed long-term yields; historically a leading recession signal.
What is the typical relationship between the U.S. dollar and commodities?
Generally inverse — a stronger dollar weighs on commodity prices, and vice versa, though regime-dependent.
What does the McClellan Oscillator measure?
Short-term breadth momentum — EMA(19) minus EMA(39) of net advances (advancing minus declining issues).
What is TRIN (Arms Index)?
(Advancing issues / Declining issues) divided by (Advancing volume / Declining volume). Greater than 1 = bearish; less than 1 = bullish; extreme spikes mark capitulation.
Why analyze market breadth?
A rising index supported by broad participation is healthier than one driven by a narrow group of large stocks; breadth divergence warns of underlying weakness.
What is duration?
A measure of a bond’s price sensitivity to a 1% change in yields. Longer maturity and lower coupon = higher duration.
What are credit spreads?
The yield difference between higher-risk bonds (e.g., high yield) and risk-free Treasuries. Widening = stress; tightening = risk-on.
What is the DXY?
The U.S. Dollar Index — a trade-weighted basket of the dollar vs. major foreign currencies (EUR weights ~58%, plus JPY, GBP, CAD, SEK, CHF).
What is the typical gold-dollar relationship?
Generally inverse — a weaker dollar tends to lift gold, while a stronger dollar pressures it.
What does it mean if the RS line breaks out before the price line?
The asset is gaining leadership relative to its benchmark, often a positive precursor to absolute price strength.

XI. Volatility Analysis

~7% of exam · Distinguish realized vs implied vol cold; know VIX construction and behavior; understand skew, ATR, and BBands.

TL;DR:

Volatility measures the degree of price variability around a mean — not direction. It is the most important input to options pricing, risk budgeting, and position sizing.

Three flavors to distinguish:

  • Realized (historical / statistical) volatility — computed from past returns. Typically the annualized standard deviation of log returns: σ_annual = σ_daily × √252.
  • Implied volatility (IV) — the volatility input that makes an option pricing model (Black-Scholes) match the option’s market price. Reflects the market’s expectation of future volatility.
  • Forward / expected volatility — the volatility expected over a specific future window (e.g., the VIX is the 30-day expected SPX vol).

VIX (CBOE Volatility Index): 30-day implied volatility of S&P 500 index options, expressed as an annualized %. Calculated from a strip of out-of-the-money SPX puts and calls. Often called the “fear gauge” because it spikes when investors bid up options to hedge.

Key behaviors of volatility:

  • Clustering — high-vol periods follow high-vol periods (Mandelbrot, Engle ARCH/GARCH).
  • Mean reversion — volatility tends to return to its long-run average.
  • Asymmetry (leverage effect) — in equities, volatility typically spikes when prices fall; equity vol and equity returns are negatively correlated.
  • Non-directional — high vol does not by itself tell you the direction.

Volatility skew / smile: implied volatility differs across strikes. In equity indexes, out-of-the-money puts typically have higher IV than ATM or calls (the “skew”) because of crash hedging demand. In FX, IV is often more symmetric (the “smile”).

Realized Volatility

  • Standard deviation of log returns over a lookback window.
  • Annualization factor: √252 for daily; √52 for weekly; √12 for monthly.
  • Common windows: 20-day, 60-day, 252-day.
  • Realized vol is backward-looking; useful for risk reporting and as a baseline for comparing IV.

Implied Volatility (IV)

  • Derived from market option prices by inverting Black-Scholes.
  • Inputs that move IV: underlying expected variability, time to expiration, supply/demand for options, hedging activity.
  • IV is forward-looking; the market’s consensus view of future vol.
  • IV minus realized vol (vol premium): tends to be positive on average (options are slightly “rich”), but compresses or inverts in crises.

VIX in Depth

  • Calculated from a wide strip of out-of-the-money SPX puts and calls, weighted by strike and expiration.
  • Reports as an annualized percentage — e.g., VIX 20 means options imply ~20% annualized SPX vol over the next 30 days, or ~1.25% expected daily move (20 / √252 ≈ 1.26).
  • Heuristic ranges: <12 calm/complacent; 12–20 normal; 20–30 elevated; 30–40 stress; >40 crisis.
  • Historical extremes: dotcom bust ~46; 2008 GFC peak ~89; COVID March 2020 peak ~82.
  • VIX is negatively correlated with SPX ~−0.75 to −0.85; VIX usually spikes during market declines, not before.
  • VIX term structure: VIX front month vs VX futures further out. Normal = contango (front below back). Inverted = backwardation (front above back) = stress signal.
  • VVIX: vol of VIX; signals how nervous the vol market itself is.

ATR & Volatility-Based Stops

  • True Range: max(H−L, |H−PrevClose|, |L−PrevClose|).
  • ATR: Wilder’s smoothed average of TR over 14 periods.
  • Uses: position sizing (risk per trade = N×ATR), trailing stops (chandelier exit = high − 3×ATR), breakout filters.

Bollinger Bands & Volatility Squeeze

  • Middle = 20-period SMA; upper/lower = SMA ± 2σ of price over 20 periods.
  • Squeeze: bandwidth (upper−lower) at a low percentile of its history; signals coming expansion.
  • Tagging the upper band in a strong uptrend is not automatically a sell — it can be a sign of strength.
  • %B = (Price − Lower) / (Upper − Lower). >1 = above upper band; <0 = below lower band.
  • Bandwidth = (Upper − Lower) / Middle. Used to spot squeezes.

Volatility Skew & Term Structure

  • Skew: difference in IV across strikes at one expiration. Equity skew typically downside-tilted (OTM puts > ATM IV > OTM calls).
  • SKEW Index: CBOE measure of perceived crash risk from far OTM SPX puts.
  • Smile: more symmetric U-shaped IV (typical in FX).
  • Term structure: IV across expirations (e.g., 30-day vs 90-day). Steep upward sloping = calm; flat or inverted = stress.
Pitfall: Don’t confuse high VIX with “sell.” A high VIX usually accompanies a market that has already fallen. Volatility is for sizing and timing context, not directional signal alone.

Common questions

What does volatility measure?
The degree of price variability around a mean — not direction.
What is implied volatility?
The market’s expectation of future volatility embedded in option prices via an option pricing model.
What does the VIX measure?
The 30-day implied volatility of S&P 500 index options, annualized, computed from a wide strip of out-of-the-money SPX puts and calls.
What is the annualization factor for daily volatility?
√252 (the square root of the number of trading days per year).
What is ATR used for?
Volatility-adjusted stops, position sizing, and breakout filters. Measures average true range over N periods (typically 14).
What is volatility skew?
A pattern where implied volatility differs across strikes; in equity indexes, out-of-the-money puts often carry higher IV than ATM options or calls.
What does Bollinger Band expansion indicate?
Rising volatility — price dispersion around the moving average is increasing.
What is a Bollinger Band squeeze?
A period of unusually low volatility where bands contract tightly; often precedes a sharp expansion in volatility.
Why is the VIX called the “fear gauge”?
Because it spikes when traders bid up SPX options for protection during market stress.
What is volatility clustering?
The tendency for high-vol periods to be followed by more high-vol periods and low-vol periods by more low-vol periods.
What is the VIX term structure’s normal shape?
Contango — front-month VIX below the futures further out the curve. Inversion (backwardation) is a stress signal.
What is the leverage effect in volatility?
The empirical regularity that equity volatility rises when prices fall — equity returns and equity vol are negatively correlated.
What is the SKEW Index?
A CBOE index measuring perceived tail risk from far out-of-the-money SPX puts; rising SKEW indicates rising perceived crash risk.
What is the relationship between VIX and SPX?
Strongly negative correlation (typically around −0.75 to −0.85). VIX spikes coincide with SPX declines.
How is implied volatility typically distributed compared to realized volatility on average?
Implied vol tends to trade above realized vol on average (the “vol risk premium”), reflecting compensation for selling insurance.

XII. Systems & Quantitative Methods

~3% of exam · Know the quant process, pitfalls list, validation tools, and core risk metrics.

TL;DR:

A trading system is a rules-based process for entries, exits, position sizing, and risk control. Systematic / quantitative approaches remove discretion in favor of repeatability and measurement. The quantitative process — memorize the order:

  1. Hypothesis: a clear, testable idea grounded in market behavior or theory.
  2. Data: point-in-time, survivorship-bias-free, with proper corporate-action handling and futures roll adjustment.
  3. Model / signal construction: implement the hypothesis as deterministic rules.
  4. Backtest: realistic transaction costs, slippage, capacity, position sizing, and risk controls.
  5. Validation: out-of-sample test, walk-forward analysis, sensitivity to parameters, Monte Carlo robustness.
  6. Risk & portfolio context: drawdown limits, position limits, correlation across signals, leverage.
  7. Implementation: paper-trade, then deploy with small size, scale up.
  8. Monitoring: live performance attribution, regime checks, periodic re-validation.

Pitfalls to memorize: overfitting / curve-fitting, survivorship bias, look-ahead bias, data-snooping, ignoring transaction costs, regime change, capacity constraint, selection bias in sample window. The best defense is parsimony (few parameters), out-of-sample testing, walk-forward analysis, and Monte Carlo trade resampling.

Core risk metrics: Sharpe ratio (excess return / total vol), Sortino (excess return / downside vol), Calmar (annual return / max drawdown), Max Drawdown, MAR ratio, profit factor (gross profit / gross loss), expectancy ((win% × avg win) − (loss% × avg loss)).

Position sizing methods: fixed-fractional (risk a fixed % of equity per trade), fixed-dollar, volatility-adjusted (e.g., N units of ATR per trade), Kelly criterion (mathematically optimal fraction; typically used at fractional Kelly — e.g., half-Kelly — in practice).

Why Systematize?

  • Removes emotion and inconsistent execution.
  • Makes performance measurable, attributable, and repeatable.
  • Forces explicit risk control (stops, sizing, exposure limits).
  • Enables backtesting and parameter robustness checks.
  • Scales across instruments and time frames.

The Quantitative Process (in order)

  1. Hypothesis: e.g., “Stocks that are above their 200-day SMA and in the top 20% RS over 6 months outperform in the following month.” Must be falsifiable.
  2. Data: source equities universe (with delisted names), futures continuous contracts (back-adjusted), point-in-time fundamentals if relevant. Strip survivorship bias.
  3. Signal construction: code the rules. Include long/short/flat states.
  4. Backtest: realistic assumptions — bid/ask spread, commissions, market-impact model, capacity (% of ADV), borrow costs for shorts. Include warm-up period.
  5. Validation: in-sample / out-of-sample split (e.g., 70/30); walk-forward analysis (re-fit on rolling window, test on next window); sensitivity (vary parameters ±25%; results should be stable); Monte Carlo trade resampling.
  6. Risk overlay: max drawdown limit, max position concentration, portfolio vol target, leverage cap, correlation budget across signals.
  7. Paper trading / live with small size: catch implementation issues (order routing, data lag).
  8. Monitoring: live Sharpe vs backtested Sharpe, drawdown vs expected, regime classification, decay detection. Re-validate quarterly or after major regime shifts.

Common Pitfalls (memorize this list)

PitfallWhat it isDefense
Overfitting / curve-fittingToo many parameters tuned to noiseParsimony; few parameters; cross-validation
Survivorship biasExcluding delisted assetsUse point-in-time database including delistings
Look-ahead biasUsing info not available at decision timeStrict point-in-time data; lag fundamentals
Data-snoopingTrying many strategies; one wins by chanceDeflated Sharpe; reserved final OOS; multiple-testing correction
Selection bias / Time-period biasFlattering sample windowTest full cycle (bull + bear)
Ignoring costsSlippage and commissions erase edgeRealistic cost model; capacity test
Regime changeStrategy fails in new regimeRegime classifier; multi-strategy diversification
CapacityEdge disappears at scale due to market impactCap position size at % of ADV; market-impact model
Snooping the backtestIterating on the backtest until OOS is “clean”Strict pre-registration of rules; locked OOS

Validation Tools

  • In-sample vs out-of-sample split: e.g., 70% IS / 30% OOS. Fit on IS only; test on OOS.
  • Walk-forward analysis: re-fit parameters on a rolling in-sample window; test on the next out-of-sample window; advance and repeat.
  • Sensitivity analysis: vary parameters ±10–25%; results should remain stable. A strategy whose Sharpe collapses when a parameter moves slightly is overfit.
  • Monte Carlo: randomize trade order or returns to gauge robustness of drawdowns and equity curves.
  • Bootstrap: resample returns with replacement to derive confidence intervals on Sharpe, drawdown, etc.
  • Stress testing: simulate historical crisis windows (1987, 1998 LTCM, 2008 GFC, 2020 COVID, 2022 inflation shock).

Core Risk & Performance Metrics

MetricFormula intuitionWhat it measures
Sharpe ratio(Return − Rf) / σExcess return per unit of total volatility
Sortino ratio(Return − Rf) / downside σLike Sharpe but penalizes only downside vol
Calmar ratioAnnual return / Max DrawdownReturn per unit of max drawdown
MAR ratioCAGR / Max DrawdownSimilar to Calmar over longer horizons
Max Drawdown (MDD)Largest peak-to-trough %Pain measure / risk of ruin
Profit factorGross profit / Gross loss>1 = profitable; >1.5 typically considered good
Expectancy(win% × avg win) − (loss% × avg loss)Average dollars per trade
Win rateWinners / total tradesUseful only combined with payoff ratio
Payoff ratioAvg win / Avg lossMust offset low win rate for trend strategies

Position Sizing

  • Fixed dollar: risk $X per trade. Simple but ignores account growth.
  • Fixed fractional: risk a fixed % of equity (e.g., 1% per trade). Account compounds.
  • Volatility-adjusted: position size = (Risk $ per trade) / (N × ATR per share). Normalizes risk across instruments.
  • Kelly criterion: f* = (p × b − q) / b, where p = win%, q = 1−p, b = payoff ratio. Mathematically optimal; in practice, use fractional Kelly (e.g., half-Kelly) to reduce drawdown.
Pitfall: A backtest is a research output, not proof of edge. The lower the parameter count, the longer the holdout, and the more out-of-sample testing — the more credible the system. Beware survivor stories — selection bias in published case studies overstates expected outcomes.

Common questions

What is the first step of the quantitative process?
Forming a clear, testable hypothesis grounded in market behavior.
What is walk-forward analysis?
A validation technique that repeatedly fits parameters on a rolling in-sample window and tests on the next out-of-sample window.
What is the danger of optimizing many parameters?
Overfitting to historical noise, which inflates backtest performance and tends to fail in live trading.
Why must transaction costs be included in backtests?
Because real-world slippage, commissions, and bid-ask spreads can erase apparent edge, especially in high-turnover systems.
What does the Sharpe ratio measure?
Excess return per unit of total volatility — a risk-adjusted return measure.
What does the Sortino ratio measure?
Excess return per unit of downside volatility — only the “bad” volatility penalizes the ratio.
What does the Calmar ratio measure?
Annualized return divided by maximum drawdown — return per unit of worst peak-to-trough loss.
What does max drawdown measure?
The largest peak-to-trough loss in an equity curve, a key risk-of-ruin metric.
What is regime change in systematic trading?
A shift in the market environment (volatility, interest-rate regime, correlation structure) that can invalidate previously profitable rules.
Why does context matter in quantitative analysis?
Signals can behave very differently across asset classes, time periods, and macro regimes; robust strategies acknowledge this and test broadly.
What is the Kelly criterion?
f* = (p×b − q) / b, where p is win probability, q = 1−p, and b is the payoff ratio. The mathematically optimal fraction of capital to risk.
Why is fractional Kelly used in practice?
Full Kelly produces high drawdowns and is sensitive to estimation error in p and b; fractional Kelly (e.g., half-Kelly) reduces drawdown at the cost of some long-run growth.
What is the profit factor?
Gross profits divided by gross losses. Greater than 1 = profitable; greater than ~1.5 typically considered good.
What is expectancy?
Average dollars per trade: (win% × avg win) − (loss% × avg loss).
What is volatility-adjusted position sizing?
Sizing positions so that the dollar risk per trade is constant — e.g., position size = risk $ / (N × ATR per share), where N is the stop multiple.
Why use Monte Carlo simulation on backtests?
To randomize trade order or returns and gauge the robustness of drawdowns, equity curves, and confidence intervals on performance metrics.

Final 24-Hour Checklist

The night before: No new material. Re-read the TL;DR of every section. Memorize the numbers below. Sleep 7+ hours.

High-yield numbers and definitions to lock in

  1. Three core TA assumptions: discount, trend, repeat.
  2. Dow Theory’s six tenets and three trend phases (accumulation / public participation / distribution).
  3. Definitions: support, resistance, uptrend (HH/HL), downtrend (LH/LL), polarity (broken support becomes resistance).
  4. Identify on sight: H&S, inverse H&S, double top/bottom, triangles (sym/asc/desc), flag, pennant, rectangle, cup & handle.
  5. Default settings: RSI 14 (OB 70/OS 30), MACD 12/26/9, BBands 20/2, ADX 14 (trending > 25), Stochastics 14/3/3 (OB 80/OS 20), ATR 14, CCI 20.
  6. Golden cross (50 MA > 200 MA) / death cross definitions.
  7. Fibonacci retracements: 23.6, 38.2, 50, 61.8, 78.6%. Golden ratio = 61.8%.
  8. Normal-distribution rule (68/95/99.7) and what skew/kurtosis mean. Returns are leptokurtic, often negatively skewed.
  9. Loss aversion ratio ~2:1. Prospect theory: S-shaped value function.
  10. VIX = 30-day implied vol of SPX options. <12 complacent, 20+ stressed, 30+ fear, 40+ crisis. Annualization factor for daily vol = √252.
  11. Intermarket: bonds inverse to yields; dollar usually inverse to commodities; gold inverse to real yields.
  12. Sector rotation: early (cyclicals: discretionary, tech, financials) → mid (industrials, materials) → late (energy, staples) → recession (utilities, staples, healthcare).
  13. Sentiment extremes are contrarian. P/C high = bullish; magazine covers = late-stage consensus; MMF assets rising = sidelined cash.
  14. Backtest pitfalls: overfit, survivorship, look-ahead, data-snooping. Defense = walk-forward + parsimony + OOS.
  15. Risk metrics: Sharpe = excess / total vol; Sortino = excess / downside vol; Calmar = return / max DD; profit factor = gross profit / gross loss.
  16. Charter requirements: pass I/II/III + 3 yrs experience + Member in good standing + Code & Standards + sponsorship.
  17. Ethics: 7 Standards. Mosaic theory OK; trading on material non-public info NOT OK. Priority of transactions: clients > employer > personal.

Exam-day tactics

  • Pace = 132 questions in 120 minutes ≈ 54 seconds per question. Don’t camp on any single item more than ~90 seconds.
  • Plan 4 passes: (1) fast pass answering every easy item; (2) work the medium items; (3) tackle the hard items; (4) review flagged items in the final ~10 minutes.
  • Flag and move on whenever you’re uncertain. Return at the end.
  • Eliminate absolutist answers (“always,” “never,” “guarantees,” “automatically”) first.
  • If two options look right, pick the one that better matches the specific language of the CMT curriculum.
  • Trust your first instinct unless you find a clear contradiction.
  • Answer every question — there is no penalty for guessing.
  • Arrive 30 minutes early with valid photo ID. No personal items in the testing room.

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