Subjectivity and Objectivity in Technical Analysis

1. Overview

The tension between subjectivity and objectivity is a core dilemma every technical analyst faces. Technical analysis is often perceived as an "objective tool," but in practice, the analyst's subjectivity is deeply embedded in the selection of tools, parameter settings, and the interpretation process. Individual measurements can be objectively precise, but subjectivity arises when choosing among multiple alternatives.

Moreover, the reliability of technical signals is not fixed. Through the Self-Fulfilling Prophecy effect, signals operate within a dynamic cyclical structure — and paradoxically, the more widely a particular signal becomes known, the more its effectiveness deteriorates. This chapter explores these structural characteristics and presents practical strategies to improve analytical consistency and reliability.

2. Core Rules and Principles

2.1 The Subjective Objectivity Paradox

Fundamental Principle:

The paradox of technical analysis lies in "making subjective judgments with objective tools." This paradox manifests in the following structure:

• Individual measurements are objectively accurate — whether RSI is below 30 or whether price has broken a moving average yields the same result regardless of who observes it
• Subjectivity arises in the selection process when multiple alternatives exist — which moving average period to use and which indicator combination to apply varies from analyst to analyst
• Completely different interpretations are possible from the same data — one analyst may identify a bull flag while another sees a descending wedge on the same chart
• Even when rules themselves are clear, the choice of rules is subjective — "buy when price breaks above the 20-day moving average" is an unambiguous rule, but the rationale for choosing 20 days depends on the analyst's experience and preference

Application by Analysis Domain:

Analysis Domain	Objective Elements	Subjective Elements
Trendlines	Whether a specific trendline has been broken	Determining which trendline among several represents a valid breakout
Pattern Recognition	Location of highs and lows, raw price data	Timing of pattern completion, classification of pattern type
Support/Resistance	Number of bounces or rejections at a specific price level	Choosing which level to designate as key support/resistance
Indicator Settings	Mathematical calculation output of the indicator	Period settings, overbought/oversold threshold selection
Timeframe Selection	Candlestick data for each timeframe	Deciding which primary timeframe to use for analysis

Practical Note: Due to the significantly higher volatility in cryptocurrency markets compared to traditional markets, interpretation differences among analysts tend to be far more extreme on the same chart. Even something as simple as drawing a trendline connecting Bitcoin's major highs and lows can lead to entirely different conclusions depending on whether wicks or candle bodies are used as anchor points.

2.2 The 6-Stage Self-Fulfilling Prophecy Cycle

A self-fulfilling prophecy occurs when enough market participants believe a particular signal or pattern "works" and act accordingly, causing the prediction to actually materialize. However, this effect is not permanent — it follows a 6-stage lifecycle.

Stage 1: Initial Signal Discovery

• A technical signal is first identified
• Only a small number of pioneering traders recognize the signal
• Signal reliability is at its highest, and the risk-reward ratio is maximized
• Example: Discovery of a novel correlation between a specific on-chain metric and a price pattern

Stage 2: Signal Diffusion

• A growing number of traders begin to recognize the signal
• Trading volume associated with the signal gradually increases
• The self-fulfilling effect begins operating in earnest, maintaining high profitability
• Example: The strategy starts being shared through social media and trading communities

Stage 3: Widespread Recognition

• The signal becomes "common knowledge" among market participants
• It is universally referenced in educational materials, YouTube, Twitter, and similar platforms
• Front-running begins — attempts to enter positions before the signal fully completes become increasingly common
• Example: The narrative "always buy the golden cross" becomes conventional wisdom

Stage 4: Front-Running Intensifies

• Traders compete aggressively to get ahead of each other
• Building positions well before signal completion becomes widespread
• The signal's predictive power visibly deteriorates
• "By the time the signal appears, it's already too late" becomes the prevailing sentiment

Stage 5: Signal Reliability Breakdown

• Excessive front-running distorts the signal
• Outcomes that diverge from expectations occur frequently
• Counter-signal phenomena may emerge, where price moves in the opposite direction of the signal
• Trader confidence in the signal drops sharply

Stage 6: Reversion to Original State

• Interest in the signal fades
• The number of traders using the signal decreases substantially
• As market efficiency is restored, the signal begins to regain validity
• The seeds of a new cycle are planted

Cryptocurrency Market Specifics: Because information spreads extremely rapidly in crypto markets and social media influence is outsized, this 6-stage cycle progresses far more quickly than in traditional markets. A cycle that might take years in traditional finance can complete in months — or even weeks — in crypto.

3. Chart Verification Methods

3.1 Subjectivity Verification Checklist

Multi-Method Analysis:

• Draw multiple trendlines on the same chart and prioritize the one with the most touchpoints
• Verify whether the same pattern is confirmed across different timeframes (e.g., 4-hour, daily, weekly) — the more consistent the signal across timeframes, the lower the likelihood of subjective bias
• Compare opinions from multiple analysts, but avoid blindly conforming to the majority view

Establishing Objective Criteria:

• Set explicit numerical criteria for entries, exits, and stop-losses in advance (e.g., "Consider buying when RSI drops below 30 AND price touches the lower Bollinger Band")
• For conditions that cannot be quantified, define criteria in the most specific descriptive terms possible
• Validate criteria through backtesting against historical performance, while guarding against overfitting

Trading Journal Utilization:

• Record the rationale behind every analytical decision
• Compare results after the fact to identify recurring patterns of subjective bias
• Explicitly documenting "why I chose this trendline" and "why I interpreted this as that pattern" is particularly effective for improving self-awareness

3.2 Assessing Self-Fulfilling Prophecy Stage

Signal Maturity Evaluation:

Evaluation Criteria	Early Stage (1–2)	Mid Stage (3–4)	Late Stage (5–6)
Awareness Level	Known to few	Widely known	Declining interest
Social Media Mention Frequency	Low	High to very high	Decreasing
Reaction Speed After Signal	Slow (hours to days)	Immediate	No reaction or inverse reaction
Evidence of Front-Running	Nearly absent	Clearly visible	Front-running itself diminishes
Profitability	High	Moderate to declining	Low or negative

Market Reaction Pattern Analysis:

• If price begins moving before the signal is generated, this is evidence that front-running is underway
• If volatility drops sharply at the moment of signal generation, most participants may have already established their positions
• If the frequency of price moving opposite to the signal increases, this suggests entry into Stage 5–6

4. Common Mistakes and Pitfalls

4.1 Subjectivity-Related Mistakes

Excessive Confirmation Bias:

• Selectively accepting only information that supports your analysis while ignoring or downplaying contradictory evidence
• A classic example: after forming a bullish thesis, searching only for bullish signals and dismissing bearish ones as "noise"
• Prevention: For every analysis, deliberately construct a Devil's Advocate scenario. Always ask: "If my analysis is wrong, what evidence would appear?"

Blind Trust in Analysis Tools:

• Relying on a single indicator or pattern makes it impossible to adapt when market conditions change
• For example, trend-following indicators (moving averages) generate consecutive false signals during ranging markets
• Prevention: Combine analysis tools of different natures, such as pairing trend-following indicators with oscillators

Anchoring Bias:

• Clinging excessively to an initial conclusion and refusing to revise your view even when new information emerges
• This bias intensifies when the analyst has publicly shared their analysis
• Prevention: When the market reaches a predefined invalidation level, mechanically trigger a reassessment of your thesis

4.2 Self-Fulfilling Prophecy-Related Mistakes

Misjudging Signal Maturity:

• Frequently mistaking a signal already in Stage 3–4 for a fresh Stage 1–2 opportunity
• If posts claiming "this pattern has a 90% historical win rate" are circulating on social media, the signal has likely already reached high maturity
• Prevention: Check how frequently the signal is being mentioned on social media and whether major influencers have already taken positions

Excessive Front-Running Attempts:

• Entering far too early before signal completion creates significant exposure to false signals
• Whipsaw price action is especially common in cryptocurrency markets, amplifying the risk of premature entries
• Prevention: Make entry after signal confirmation your default principle. Accept the cost of waiting for confirmation (a slightly less favorable entry price). Always set a stop-loss in advance

Ignoring the Cycle:

• Assuming a strategy that once worked will continue to work indefinitely
• Every signal has a lifecycle, and its effectiveness changes over time
• Prevention: Regularly review strategy performance and track the win rate and profit/loss ratio of the most recent N trades on a rolling basis

5. Practical Application Tips

5.1 Strategies to Minimize Subjectivity

Building a Systematic Analysis Framework:

• Create a checklist to complete before every analysis (e.g., ① Confirm trend direction → ② Identify key support/resistance → ③ Check indicators → ④ Verify volume → ⑤ Determine entry/exit levels)
• Standardize chart settings — maintaining consistency in indicators, periods, and even color schemes improves decision-making consistency
• Prioritize quantitative criteria while not entirely excluding qualitative judgment

Multi-Angle Verification:

• Execute high-conviction trades only when at least 3 independent analytical factors converge (e.g., price pattern + volume confirmation + indicator signal alignment)
• Perform Multi-Timeframe Analysis as a mandatory step — distinguish between the trend direction on higher timeframes and entry timing on lower timeframes
• Where possible, also check for alignment with fundamental/structural analysis such as on-chain data, funding rates, and open interest

"Blind Analysis" Practice:

• Occasionally practice analyzing charts with the asset name and timeframe hidden
• This helps reduce the influence of pre-existing biases toward specific assets on your analysis

5.2 Strategies for Leveraging the Self-Fulfilling Prophecy

Tracking Signal Lifecycle:

• When a new signal is discovered, immediately record the date, conditions, and market context
• Regularly monitor how frequently the signal is mentioned across social media, communities, and news outlets
• Track the signal's win rate and average return over time to detect early signs of declining effectiveness

Stage-Based Response Strategy:

Stage	Strategy	Position Size	Stop-Loss Approach
Stage 1–2	Actively utilize; high-conviction trades	Standard or above	Set relatively wide
Stage 3–4	Approach with caution; watch for front-running	50–75% of standard	Set tight
Stage 5–6	Avoid the signal or consider contrarian application	Minimize or do not trade	Set very tight

Key Insight: In Stages 5–6, acting contrary to the crowd's expectations can actually be effective. For example, if everyone anticipates buying on a golden cross, selling pressure may emerge immediately after the cross occurs. However, such contrarian strategies require sufficient experience and strict risk management as prerequisites.

Risk Management:

• Scale position size according to signal maturity
• Never bet your entire portfolio on a single signal; diversify at the portfolio level
• Specify and adhere to concrete conditions under which the expected scenario becomes invalidated

5.3 Integration with Other Analytical Tools

Combination Strategies to Reduce Subjectivity:

• Price Action + Volume: Supplement the subjectivity of pattern interpretation with the objective data of volume. If a breakout is not accompanied by volume, reduce confidence in the interpretation
• Chart Patterns + Oscillators: Reinforce the subjectivity of pattern recognition with numerical confirmation from RSI, MACD, and similar indicators
• Technical Analysis + On-Chain Analysis: When technical signals align with on-chain data (whale movements, exchange inflow volumes, etc.), the probability of subjective bias is reduced

Supplementary Tools for Identifying Self-Fulfilling Prophecies:

• Funding Rates / Open Interest: If positions are excessively skewed in one direction, this is evidence the signal has already entered the front-running stage
• Social Sentiment Indicators: Reference the Fear & Greed Index, Twitter/Telegram mention volume, and similar metrics to gauge signal maturity
• Options Market Data (where available): Check whether expectations around specific price levels are reflected in options positioning

5.4 Balancing Subjectivity and Objectivity

Completely eliminating subjectivity from technical analysis is neither possible nor necessarily desirable. Intuition born from experience holds inherent value, and a fully mechanical system cannot adapt to exceptional market conditions.

Practical Principles for Achieving Balance:

• 80/20 Rule: Base 80% of decisions on objective, quantitative criteria and supplement the remaining 20% with experiential judgment
• Always set risk limits on subjective judgments — for instance, never allocate more than 5% of total capital to a position based on the reasoning "I have a gut feeling it will go up"
• Make post-trade review a habit — use data to verify whether subjective judgments produced better outcomes than objective criteria alone

Continuous Learning and Improvement:

• Review your analysis records at least once a month to identify recurring bias patterns
• Practice distinguishing whether a failed trade resulted from "my analysis being subjective" or "the market behaving exceptionally"
• When market structure changes (e.g., regulatory shifts, influx of new participants), proactively update your analytical framework

Acknowledging and managing the subjectivity inherent in technical analysis, combined with understanding the cyclical structure of self-fulfilling prophecies — it is only when these two elements come together that a sustainable edge in the market can be achieved.