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name: trader-psychology-analysis description: 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

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

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

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

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

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

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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

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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.