# Trader Psychology Analysis > Use when analysing retail vs institutional trader behaviour, modelling cognitive biases in market data, detecting FOMO/FUD cycles, identifying contrarian signals, or implementing behavioural finance models for trading decisions - Skill: `joogy06/trader-psychology-analysis` (Agent Skill) - Install (CLI): `npx skillmds@latest add joogy06/trader-psychology-analysis` - Raw SKILL.md: https://api.skillmd.com/api/skills/joogy06/trader-psychology-analysis/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: joogy06 (https://skillmd.com/u/joogy06) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/joogy06/trader-psychology-analysis --- # Trader Psychology Analysis ## Overview Markets are driven by human psychology as much as fundamentals. Understanding **cognitive biases**, **herding dynamics**, and **fear/greed cycles** allows you to anticipate price movements caused by irrational behaviour. This skill maps behavioural finance theory to quantifiable trading signals. ## When to Use - Modelling retail vs. institutional behaviour from order flow or positioning data - Building contrarian indicators from extreme sentiment - Detecting FOMO-driven momentum and FUD-driven capitulation - Interpreting sentiment extremes as mean-reversion signals - Explaining anomalous price movements through behavioural lens ## Key Cognitive Biases and Market Impact | Bias | Description | Market Effect | Signal | |------|-------------|---------------|--------| | **Loss Aversion** | Losses hurt ~2× more than gains | Reluctance to cut losers → trend prolongation | Large open loss concentration = reversal risk | | **Recency Bias** | Overweight recent events | Momentum overshoot | High recent returns → mean reversion expectation | | **Anchoring** | Over-rely on reference price | Support/resistance at round numbers | Volume clusters at .00 prices | | **Herding** | Follow the crowd | Bubble formation, panics | Extreme long/short positioning | | **Overconfidence** | Overestimate skill | Excessive trading, risk-taking | High retail volume in bull markets | | **Disposition Effect** | Sell winners too early, hold losers | Suppressed upside momentum | Low volume on new highs | | **FOMO** | Fear of missing out | Late-cycle volume spikes | Volume surge + price acceleration | | **FUD** | Fear, uncertainty, doubt | Overcorrection on bad news | Price gap down on moderate news | ## Prospect Theory Model ```python import numpy as np def prospect_theory_value(outcome: float, reference: float, lambda_loss: float = 2.25, alpha: float = 0.88) -> float: """ Kahneman-Tversky prospect theory utility function. lambda_loss: loss aversion coefficient (default 2.25 from original paper) alpha: diminishing sensitivity (default 0.88) Reference: Kahneman & Tversky (1979) """ delta = outcome - reference if delta >= 0: return delta ** alpha else: return -lambda_loss * ((-delta) ** alpha) def aggregate_portfolio_pain( positions: list[dict], # [{entry_price, current_price, size}] ) -> dict: """ Estimates aggregate psychological pain of a portfolio. High pain = elevated sell pressure likely. """ total_pain = 0.0 losing_positions = 0 for pos in positions: pnl = (pos['current_price'] - pos['entry_price']) * pos['size'] pv = prospect_theory_value(pos['current_price'], pos['entry_price']) total_pain += pv if pnl < 0: losing_positions += 1 return { 'aggregate_utility': total_pain, 'losing_pct': losing_positions / len(positions) if positions else 0, 'sell_pressure_estimate': 'HIGH' if total_pain < -50 else 'MEDIUM' if total_pain < 0 else 'LOW', } ``` ## Fear & Greed Cycle Detection ```python import pandas as pd import numpy as np def fear_greed_index( price_data: pd.DataFrame, # OHLCV volume_data: pd.Series, funding_rate: pd.Series, # crypto perpetual funding long_short_ratio: pd.Series, lookback: int = 30, ) -> pd.Series: """ Composite Fear & Greed index [0-100]. 0 = Extreme Fear, 100 = Extreme Greed. """ def normalise(series, window=lookback): rolling_min = series.rolling(window).min() rolling_max = series.rolling(window).max() return (series - rolling_min) / (rolling_max - rolling_min + 1e-8) * 100 # Component signals price_momentum = price_data['close'].pct_change(7) # 7-day return volatility = price_data['close'].pct_change().rolling(30).std() volume_trend = volume_data.rolling(7).mean() / volume_data.rolling(30).mean() # Funding rate: positive = greed (longs paying shorts) funding_norm = normalise(funding_rate) # Long/short ratio: >1.5 = crowded longs (greed) ls_norm = normalise(long_short_ratio) # Composite (equal weights) composite = ( normalise(price_momentum) * 0.25 + (100 - normalise(volatility)) * 0.15 + # low vol = greed normalise(volume_trend) * 0.20 + funding_norm * 0.25 + ls_norm * 0.15 ) return composite def detect_fomo_signal(price: pd.Series, volume: pd.Series, price_threshold: float = 0.05, volume_threshold: float = 2.0) -> pd.Series: """ FOMO = price up >5% in 24h with volume >2× 30-day average. Returns boolean series. """ price_surge = price.pct_change(24) > price_threshold volume_spike = volume > (volume.rolling(720).mean() * volume_threshold) return price_surge & volume_spike ``` ## Retail vs Institutional Behaviour ```python def classify_order_flow(trades_df: pd.DataFrame) -> pd.DataFrame: """ Classify trades as likely retail (small) or institutional (large). Uses Kyle's lambda and trade size distribution. """ # Median trade size median_size = trades_df['size'].median() trades_df['type'] = 'retail' trades_df.loc[trades_df['size'] > median_size * 10, 'type'] = 'institutional' # Retail typically trades in round numbers trades_df['is_round_number'] = ( (trades_df['price'] * 100).round(0) == (trades_df['price'] * 100) ) return trades_df def institutional_accumulation_signal(ohlcv: pd.DataFrame, window: int = 20) -> pd.Series: """ Detect institutional accumulation: price flat/rising on above-average volume without breakout. Classic Wyckoff accumulation signature. """ price_range = (ohlcv['high'] - ohlcv['low']) / ohlcv['close'] avg_volume = ohlcv['volume'].rolling(window).mean() high_volume = ohlcv['volume'] > avg_volume * 1.5 tight_range = price_range < price_range.rolling(window).median() return (high_volume & tight_range).astype(int) ``` ## Contrarian Indicators ```python def contrarian_signal(fear_greed: pd.Series, extreme_greed_threshold: float = 80, extreme_fear_threshold: float = 20) -> pd.Series: """ Contrarian signal: buy extreme fear, sell extreme greed. Returns: -1 (sell), 0 (neutral), +1 (buy) """ signal = pd.Series(0, index=fear_greed.index) signal[fear_greed <= extreme_fear_threshold] = 1 # buy the fear signal[fear_greed >= extreme_greed_threshold] = -1 # sell the greed return signal def herd_intensity(long_short_ratio: pd.Series, window: int = 14) -> pd.Series: """ Measures deviation from 50/50 positioning. High values = dangerous herding (setup for short squeeze or flush). """ deviation = (long_short_ratio - 1.0).abs() return deviation / deviation.rolling(window).std() ``` ## Behavioural Patterns Quick Reference | Pattern | Setup | Trade Direction | |---------|-------|----------------| | **Capitulation** | Volume spike down + fear >90 | Buy (bottom fishing) | | **Blow-off top** | Volume spike up + greed >90 | Sell (distribution) | | **FOMO chase** | Price up 10%+ with retail surge | Fade (short) | | **Smart money divergence** | Price rising but institutional selling | Short | | **Panic selling** | Price falls >15% in 24h, LSR <0.5 | Buy (oversold) | | **Wyckoff accumulation** | Flat price, high volume, low volatility | Buy breakout | ## Common Mistakes 1. **Trading against trends prematurely** — contrarian signals need confirmation; wait for reversal candles 2. **Ignoring macro context** — fear in a bull market is different from fear in a bear market 3. **Using single indicator** — combine fear/greed + positioning + volume for robust signals 4. **Anthropomorphising** — markets don't "feel" anything; biases are statistical tendencies, not certainties 5. **Overweighting retail sentiment** — institutional flow dominates; retail sentiment is noise until extreme --- ## Anti-Patterns | Anti-Pattern | Why It Fails | Correct Approach | |---|---|---| | Modelling traders as purely rational agents | Ignores loss aversion, anchoring, herding, and overconfidence that systematically drive market behaviour | Incorporate behavioural finance models; calibrate against empirical data on bias magnitudes | | Using put/call ratio in isolation as a sentiment indicator | Hedging activity, market-making, and structural flows contaminate the signal; raw ratio is misleading | Combine put/call with VIX term structure, fund flows, and positioning data for multi-factor sentiment | | Treating all retail traders as a monolithic group | Reddit retail, institutional retail (wealth management), and day traders have very different behaviours | Segment retail by platform and behaviour: Reddit/social traders vs systematic retail vs passive retail | | Going contrarian against every extreme sentiment reading | Sometimes the crowd is right; strong trends persist through extreme sentiment for weeks or months | Use contrarian signals only at structural levels (support/resistance); combine with technical confirmation | | Ignoring institutional positioning data (COT reports) | Retail sentiment without institutional context misses the dominant market force | Overlay CFTC Commitment of Traders data; institutional positioning often leads retail sentiment shifts by days | --- **See also:** this skill models the market CROWD — for logging and reviewing YOUR OWN executed trades and personal tilt/mistake tagging, use `trade-journaling-and-review`.