Technical Analysis Classification Framework

1. Overview

Technical analysis encompasses a vast array of tools and methodologies. With countless indicators and techniques available, analysts can easily lose direction in this flood of tools without a systematic classification. This chapter divides the entire domain of technical analysis into four major branches and establishes a hierarchy of market data used in analysis, ranked by importance.

The classification framework presented in Mark Andrew Lim's The Handbook of Technical Analysis is not merely an academic taxonomy — it is a practical decision-making framework that addresses which tools to examine first and in what order to form judgments. It provides analysts with essential criteria for selecting analytical methods suited to their personal style and the prevailing market regime.

Understanding this classification framework offers the following practical advantages:

• When conflicting signals arise, you can make decisions based on clear priorities
• You can identify blind spots in your analysis and address them
• You can dynamically adjust weightings according to market conditions

2. Core Rules and Principles

2.1 The Four Branches of Technical Analysis

All tools and techniques in technical analysis fall into four broad categories. Each branch views the market from a different perspective, and they serve complementary roles. No single branch can capture the complete market picture, so balanced use of all four is ideal.

Classical Analysis

This is the most traditional approach, interpreting visual patterns on price charts. Originating from Charles Dow's theory, it encompasses empirical techniques accumulated over more than a century.

• Chart Patterns: Identifies recurring price structures such as head and shoulders, double tops/bottoms, triangles, and flags
• Support and Resistance: Analyzes horizontal price levels where price repeatedly bounces or faces rejection
• Trendlines and Channels: Visually defines price direction and establishes trading ranges within trends
• Gap Analysis: Interprets discontinuous price movements to capture sudden shifts in market sentiment
• Dow Theory: Provides foundational principles for defining, confirming, and identifying trend reversals

Practical Note: Classical analysis is often criticized for its subjectivity, but because many market participants recognize and act on the same patterns, a self-fulfilling prophecy effect exists. Mastery of key patterns is essential.

Statistical Analysis

This approach processes price data objectively using mathematical and statistical formulas. Its primary advantage is minimizing subjective judgment and providing clear numerical criteria.

• Moving Averages: SMA, EMA, and other smoothing techniques identify trend direction and support/resistance levels
• Oscillators: RSI, Stochastic, CCI, and others measure overbought/oversold conditions and momentum shifts
• Volatility Indicators: Bollinger Bands, ATR, standard deviation, and others quantify the range and intensity of price fluctuations
• Trend-Following Indicators: MACD, ADX, Parabolic SAR, and others objectively measure trend direction and strength

Practical Note: Statistical indicators are inherently lagging. Since they are derived from processed price data, they reflect past information rather than predict the future. Recognizing this limitation is essential — always use them in conjunction with other branches of analysis.

Sentiment Analysis

This approach measures the collective psychology and emotional state of market participants. It is particularly useful for capturing counter-trend reversal signals when the market reaches extremes of optimism or pessimism.

• Contrary Opinion Indicators: Anticipate reversals when the majority opinion skews to one extreme
• Investor Sentiment Surveys: Direct sentiment measurement tools such as the AAII Investor Sentiment Survey and Investors Intelligence reports
• Fear & Greed Index: Composite indicators synthesizing various sentiment measures including VIX (volatility index) and put/call ratios
• Media and News Analysis: Captures emotional extremes through the tone and frequency of news headlines

Practical Note: Sentiment indicators are environmental assessment tools, not timing tools. When extreme readings are reached, do not enter immediately — wait for technical confirmation of a price reversal. In cryptocurrency markets especially, extreme sentiment conditions can persist far longer than expected.

Behavioral Analysis

This approach observes what market participants have actually done. While sentiment analysis measures "how they feel," behavioral analysis tracks "how they acted."

• Volume Analysis: Validates the authenticity of price movements by examining accompanying volume changes
• Open Interest (OI) Analysis: Assesses new capital inflow by tracking changes in outstanding contracts in futures and options markets
• Money Flow Analysis: Combines price and volume through indicators like OBV, MFI, and CMF to measure the actual direction of buying and selling pressure
• Large Trader Positioning: Tracks position changes of institutional and commercial participants through reports such as the COT (Commitments of Traders) report

Practical Note: In cryptocurrency markets, trading volume varies significantly across exchanges, and volume distortion from wash trading is prevalent. Use data from reliable exchanges as your baseline, and supplement with on-chain data (active addresses, exchange inflows/outflows) to improve the accuracy of behavioral analysis.

2.2 Comparative Summary of the Four Branches

Branch	Core Question	Representative Tools	Characteristics
Classical	What patterns is price forming?	Chart patterns, trendlines, support/resistance	Subjective, visual, experience-dependent
Statistical	What is the market's numerical state?	Moving averages, RSI, Bollinger Bands	Objective, lagging, systematic
Sentiment	How do market participants feel?	VIX, put/call ratio, surveys	Contrarian, extreme-capturing, leading
Behavioral	What have market participants actually done?	Volume, OI, money flow	Confirmatory, empirical, coincident/leading

2.3 Market Data Hierarchy

Not all market data used in technical analysis carries equal value. A clear priority ranking exists among data types, and when higher-ranked data conflicts with lower-ranked data, the higher-ranked data takes precedence. Understanding this hierarchy dramatically reduces analytical confusion.

Rank 1: Price Data

• OHLC (Open, High, Low, Close) data is the highest-priority information in technical analysis
• Price is the final output reflecting all market information — fundamentals, sentiment, and supply/demand
• "Price is king" — no other indicator can override price itself
• Applicable in the same form across all timeframes (1-minute to monthly)

Rank 2: Volume

• The essential tool for validating the authenticity and strength of price movements
• Rising price + increasing volume → healthy advance; rising price + declining volume → suspect advance
• Volume tends to lead price, serving as an early warning of trend reversals
• A breakout without volume has a high probability of being a false breakout

Rank 3: Open Interest (OI)

• Primarily used in futures and options markets, expressing participants' commitment to positions
• Price rise + OI increase → new long entries, strong uptrend
• Price rise + OI decrease → short covering, potential trend weakening
• In cryptocurrency perpetual futures, analyzing OI alongside funding rates enables more accurate assessment

Rank 4: Sentiment Indicators

• Secondary indicators that quantify the emotional state of market participants
• Provide meaningful signals only when reaching extreme readings
• In mid-range territory, use as reference only
• In cryptocurrency markets, social media sentiment and the Crypto Fear & Greed Index are representative examples

Rank 5: Market Breadth

• Measures the participation scope and internal health of the overall market
• Composed of advance/decline ratios, new highs/new lows counts, and similar metrics
• If the index is rising but market breadth is narrowing → the advance is distorted by a few names, warranting caution
• In cryptocurrency markets, the Altcoin Season Index and BTC dominance changes serve similar roles

Rank 6: Money Flow

• Tracks the actual capital movements of institutional investors and large participants
• Analyzes capital inflow/outflow patterns across sectors and asset classes
• Serves as a leading indicator for long-term trend changes, but is unsuitable for short-term timing
• In cryptocurrency markets, exchange BTC/ETH inflow/outflow volumes and stablecoin supply changes are used as money flow indicators

2.4 Market Data Hierarchy Summary

Rank	Data Type	Role	When to Use
1	Price (OHLC)	Primary analysis subject	Always first
2	Volume	Confirm/validate price action	Immediately after price analysis
3	Open Interest	Assess trend sustainability	When analyzing futures markets
4	Sentiment Indicators	Capture extreme conditions	During overheated/panic zones
5	Market Breadth	Evaluate internal market health	During broad market assessment
6	Money Flow	Detect long-term trend shifts	When building medium/long-term strategies

3. Chart Verification Methods

3.1 Verification Across the Four Branches

To improve analytical accuracy, conduct independent verification within each of the four branches, then synthesize the results to form a final judgment.

Classical Analysis Verification

1. Confirm pattern completeness: Check whether the pattern meets minimum duration and minimum touch-count requirements
2. Volume confirmation: Verify whether volume increased meaningfully above average at the breakout point
3. Calculate price targets after pattern completion: Set target prices using the measured move technique and assess achievement probability
4. Observe for throwback/pullback: Watch whether price retests the former support/resistance level after breakout

Statistical Analysis Verification

1. Check indicator overbought/oversold zones: Treat only signals from extreme zones as high-confidence
2. Multi-timeframe analysis: Confirm whether signals align across higher and lower timeframes
3. Divergence screening: When price and indicator diverge, evaluate the probability of a trend reversal
4. Win rate and risk-reward verification through backtesting: Confirm historical performance in the specific market and timeframe

Sentiment Analysis Verification

1. When extreme readings are reached, check historical counter-trend reversal probability: Compare market reactions at similar levels in the past
2. Analyze asymmetric price reactions to news: Capture instances where price doesn't fall on bad news or doesn't rise on good news
3. Confirm consistency across multiple sentiment indicators: Verify that several sentiment indicators simultaneously point to extremes, not just one

Behavioral Analysis Verification

1. Volume-price relationship consistency: Confirm that volume increases with trend-direction moves and decreases with counter-trend moves
2. Track institutional and large trader position changes: Analyze COT reports, whale wallet movements on exchanges, etc.
3. Behaviorally-derived indicators such as put/call ratios and funding rates: Confirm market bias using indicators derived from actual trading activity

3.2 Market Data Hierarchy Verification

Priority-Based Verification Process

1. Price-first principle: Always analyze OHLC data first, then confirm findings with lower-ranked data
2. Higher rank prevails in conflicts: When divergence occurs between higher and lower-ranked data, adopt the signal from the higher-ranked data
3. Timeframe consistency: Verify OHLC data consistency across all timeframes to filter out distorted data
4. Confirmatory role of lower-ranked data: Lower-ranked data serves the supporting role of either "confirming" or "questioning" the scenario presented by higher-ranked data

4. Common Mistakes and Pitfalls

4.1 Classification Framework Mistakes

Over-Reliance on a Single Branch

• It is extremely common to stack statistical indicators while ignoring structural price patterns (support/resistance, trendlines)
• Focusing only on classical patterns while neglecting volume and open interest analysis leads to repeated false breakout traps
• Relying solely on sentiment indicators leads to the "being too early" trap
• Solution: Before every trading decision, develop the habit of checking at least one tool from each of the four branches

Errors in Handling Cross-Branch Conflicts

• When different branches produce opposing signals, guard against confirmation bias — the tendency to select only the signal that supports your existing view
• Assigning equal weight to all branches invariably leads to confusion — adjust weightings according to the market regime
• In trending markets, increase the weight of classical and behavioral analysis; in ranging markets, increase the weight of statistical and sentiment analysis

4.2 Data Hierarchy Mistakes

Ignoring Priority Rankings

• A frequent mistake is entering counter-trend trades based solely on extreme sentiment readings while ignoring the prevailing price trend
• Overvaluing price movements not accompanied by volume leads to frequent entries on false breakouts
• Attempting to time short-term trades using only lower-ranked data such as market breadth or money flow is risky
• Core principle: When lower-ranked indicators conflict with higher-ranked indicators, follow the higher-ranked indicators

Data Quality Issues

• Failing to verify OHLC data consistency across timeframes can lead to recognizing false patterns
• Overlooking the disruption of data continuity during gaps causes distorted indicator readings
• Using unadjusted futures data (without rollover adjustments) creates severe distortions on long-term charts
• In cryptocurrency markets, always be mindful of data reliability issues including price discrepancies across exchanges and volume inflation from wash trading

4.3 Limitations of the Classification Framework Itself

• Some tools span two or more categories (e.g., OBV is both statistical and behavioral). Rather than fixating on classification, focus on the tool's essential function
• The classification framework is a starting point for analysis, not the final answer. Flexible application is necessary in practice

5. Practical Application Tips

5.1 Classification Framework Strategies

Balanced Analysis Checklist

Before making a trading decision, work through the following checklist sequentially:

Order	Branch	Items to Check (Examples)
1	Classical	Key support/resistance levels? Developing chart patterns? Trendline status?
2	Statistical	RSI/Stochastic position? Moving average alignment? MACD signal?
3	Sentiment	Fear & Greed Index level? Social media sentiment? Any extreme bias?
4	Behavioral	Volume trend? Open interest changes? Whale wallet activity?

Select 2–3 key indicators per branch and monitor them consistently. Using too many indicators can lead to analysis paralysis.

Weighting Adjustments Based on Personal Style

• Systematic/quantitative style: Center on statistical analysis, confirm with behavioral analysis
• Intuitive/experience-based style: Center on classical analysis, supplement with sentiment analysis
• Counter-trend trading style: Center on sentiment analysis, use classical analysis for entry timing
• Regardless of style, the price-data-first principle remains constant

5.2 Data Hierarchy Strategies

Step-by-Step Analysis Process (6 Stages)

1. Stage 1 — Price Action Analysis: Use OHLC data to identify trend direction, key support/resistance, and chart patterns. Establish the basic trade scenario (long/short/flat) at this stage.
2. Stage 2 — Volume Confirmation: Verify whether volume accompanies the price movement. Moves unsupported by volume receive reduced confidence.
3. Stage 3 — Open Interest Analysis: Check OI changes in the futures market to distinguish between new capital inflows and existing position liquidations.
4. Stage 4 — Sentiment Check: Determine whether the market is in a state of extreme fear or greed. If not extreme, use as reference only.
5. Stage 5 — Market Breadth Check: Assess whether the current move is broad-based across the market or confined to a few names.
6. Stage 6 — Money Flow Review: From a medium-to-long-term perspective, perform a final check on whether the direction of capital flow aligns with your trade scenario.

Principles for Resolving Conflicts

• Higher-ranked data prevails: If price is in an uptrend but sentiment indicators signal overheating, prioritize the price trend. However, manage risk by reducing position size or tightening stop-losses.
• Divergence is a warning signal: Divergence between higher and lower-ranked data is not an immediate action signal — it is a warning to raise your alert level.
• Weighted evaluation approach: Rather than simple majority rule, apply weighted evaluation based on data ranking. When Rank 1–2 data agrees, opposing signals from Rank 3–6 data serve only as supplementary warnings.

5.3 Integrated Approach Strategies

Multi-Timeframe Analysis

Different timeframes call for different emphasis across the classification framework:

Timeframe	Primary Analysis Branches	Purpose
Long-term (Weekly/Monthly)	Classical + Behavioral	Identify major trend direction and key structures
Medium-term (Daily)	Statistical + Behavioral	Determine entry zones and trend strength
Short-term (4H/1H)	Statistical + Sentiment	Capture precise entry and exit timing

The fundamental principle is the top-down approach: establish direction on higher timeframes, then find entry timing on lower timeframes.

Confluence Analysis

A point where signals from multiple branches simultaneously point in the same direction is called a confluence zone — trades at these points offer the highest expected win rates.

• 4-branch agreement: All branches align → highest-confidence trade opportunity (occurs rarely)
• 3-branch agreement: Strong trade signal → enter with normal position size
• 2-branch agreement: Moderate confidence → enter with reduced position size, additional confirmation needed
• 1-branch signal only: A standalone signal is insufficient as a trade basis → stand aside or allow only very small entries

Dynamic Weighting

Because the effectiveness of each branch varies with market conditions, adjust weightings dynamically:

• High volatility / trending markets: Increase weight on statistical analysis (trend-following indicators) and behavioral analysis (volume)
• Market turning points / extreme zones: Increase weight on classical analysis (pattern completion) and sentiment analysis (extreme sentiment)
• Low volatility / ranging markets: Increase weight on statistical analysis (oscillators) and classical analysis (support/resistance range)
• Mature trend phase: Focus on behavioral analysis (declining volume, OI changes) to capture trend exhaustion signals

Final Summary: The technical analysis classification framework and market data hierarchy are not about "which tools to use" but rather about "in what order and with what weight to form judgments." Internalizing this framework enables consistent decision-making even in confusing situations where signals conflict.