OptiNod Academy
Trading Psychology
Browse all 14 parts in the Trading Psychology series and start from the section you need.
Why a backtest works but your trades do not — The gap between signals and execution
A backtest's positive expectancy belongs to its entry rules. Live results depend on how consistently those rules are followed, and the gap can be measured in R.
Prospect theory and loss aversion — Why losses feel more than twice as strong as gains
People feel gains and losses relative to a reference price, and losses have a steeper psychological slope. Average winning and losing R and holding time reveal the asymmetry.
The disposition effect — Taking gains early while letting losses run
A high win rate can coexist with a shrinking account when exit habits are asymmetric. Average holding time and realized-gain versus realized-loss rates reveal the pattern.
Anchoring and averaging down — Fixating on entry price pushes a valid stop away
Delayed stops and averaging down often come from treating your entry price as the market's reference. Three figures in trade records reveal the cost.
FOMO chasing — A later entry erodes reward-to-risk before the trade starts
A chased entry has a cost even when the directional call is right. Measure the distance from the signal price in R to see how much reward-to-risk was lost.
Revenge trading and overtrading — Extra trades after a loss can turn expectancy negative
After a loss, entry standards may loosen and trade count rise. Per-trade costs can then turn even a positive-expectancy system negative in practice.
Gambler's fallacy and the hot hand — Mistaking independent trades for a streak with meaning
Raising bets after losses and becoming overconfident after wins both assume that a sequence predicts the next independent outcome. Test the sequence with a runs test.
Herding and social proof — Extreme positioning is not automatically a reversal signal
Betting against a crowded extreme may work near a reversal and fail repeatedly during a trend. Test conditional expectancy at extremes before treating sentiment as a contrarian signal.
Confirmation bias — More supporting indicators can strengthen conviction without improving direction
Gathering more chart evidence does not necessarily improve a forecast. Compare in-sample with out-of-sample expectancy and count whether extra reasons improve realized R.
Recency bias — Mistaking a normal losing streak for a broken system
Even a tested positive-expectancy system can have long losing streaks. Compare them with expected streak length and track system changes before concluding the strategy failed.
Hindsight bias — The finished chart looks clear, but its right edge does not
A completed backtest chart looks obvious because its outcome is already known. Compare decisions in bar replay with hindsight marks and count rules added after losses to measure the bias.
Overconfidence and the illusion of control — Small winning samples lead to bigger bets
Treating a short winning run as skill and feeling in control of the market can push bets beyond prudent Kelly limits. Sample size and variation in position risk reveal the pattern.
Turn your trade journal into a dataset — Your biases remain in the fills
Feeling that you take profits too early is not evidence. Tag every fill with its setup and planned 1R to measure bias through expectancy and realization rates by setup.
Precommitment and pass criteria — Set the rules before judgment hardens
Fix entry, exit, sizing and rejection criteria before a trade begins. That removes discretion at execution time and lets you measure its cost through rule adherence.