Drift Detection
Purpose
Drift Detection identifies behavioral and execution drift across your trades.
It analyzes how your emotional state, conviction, and plan adherence correlate with actual outcomes over time, and surfaces repeatable patterns — both strengths and risk areas — without coaching or judgment.
This view exists to answer one question:
Where does my execution reliably hold — and where does it drift?
What Drift Detection Analyzes
Drift Detection works by aggregating recorded behavior across many trades and comparing it to results.
Emotional State Patterns
TradeMonkey analyzes the emotional tags you recorded at entry and exit, such as:
Calm, Confident
Fear, FOMO, Greed, Revenge
Regret, Frustration, Relief, Satisfaction
Emotions can be combined (e.g. Confident + Impatient, Fear + Calm) to reflect how you actually felt.
Drift Detection then evaluates:
Win rate and P&L by emotional state
Which emotions correlate with stability vs degradation
Which emotional combinations tend to precede drift
Conviction Patterns (1–10)
Conviction scores are evaluated across trades:
Entry conviction — how strong the setup felt when you entered
Exit conviction — how confident you were when closing
Drift Detection highlights:
Performance by conviction level
Conviction gaps (exit minus entry)
Situations where conviction reliably collapses or holds
This surfaces confidence erosion patterns, not opinions.
Plan Adherence vs Outcome
Drift Detection compares outcomes based on how closely you followed your plan:
Followed
Partial
Not followed
This makes it possible to see, in aggregate:
How execution quality affects results
Where discretionary behavior consistently helps or hurts
What Drift Detection Produces
Drift Detection outputs are descriptive signals, not instructions.
Strengths
Patterns where your execution shows consistency, stability, or edge
(e.g. "When calm and confident, win rate remains elevated.")
Areas to Watch
Patterns where drift or degradation appears
(e.g. "Rule violations increase after conviction drops.")
Detected Patterns
AI-assisted summaries that highlight repeatable behavioral correlations across your selected time range.
All findings follow the same format:
When X behavior appears, Y outcome tends to follow.
How Drift Detection Fits Into the System
Drift Detection pulls from:
Emotional state
Conviction
Plan adherence
Rule outcomes
Trade results
It then feeds insight into:
Behavioral Recap (single-trade context)
Threads (timeline evidence)
Rules vs Reality
AI Diagnostics and Deep Dive tools (optional)
Think of Drift Detection as the wide-angle lens, while Threads and Recaps provide the close-up footage.
How to Use Drift Detection
Choose a time scope
Select the range you want to analyze:
Last N trades
Recent days or weeks
All time
Custom range
Review high-level signals first
Start with:
Net P&L
Win rate
Profit factor
Average trade
These anchor interpretation in outcomes, not feelings.
Scan strengths and risk areas
Review:
Strengths worth protecting
Areas where drift begins to appear
Patterns that repeat across multiple trades
Use filters (execution, rules, AI analysis) to isolate specific dimensions.
Drill down when needed
When a pattern stands out:
Click through to related trades
Open Behavioral Recaps or Threads
Review the underlying events and context
Drift Detection points to where to look, not what to change.
Important Notes
Drift Detection is descriptive, not prescriptive.
It shows correlations between behavior and outcome. Interpretation and correction remain yours.
Signal quality improves with volume.
Reliable patterns emerge over dozens of trades, not a handful.
Consistency beats precision.
Simple, honest tagging produces clearer signals than overthinking.
All data comes from what you log.
No emotions or conviction → no behavioral signal.
AI features are optional.
Core drift detection works without AI; AI enhances pattern surfacing when used.
Support
If Drift Detection looks incorrect or you have questions, contact
hello@trademonkey.app