The Improvement Paradox
Your system will need updates. Markets change. Bugs emerge. Better ideas arise. But every change risks breaking what works or overfitting to recent data.
Continuous improvement requires balancing evolution with stability.
When to Change
Good Reasons to Update:
- •Bug fixes (something is broken)
- •Infrastructure improvements (reliability, not strategy)
- •New edge deployment (validated through proper process)
- •Risk management refinements (based on live experience)
Bad Reasons to Update:
- •Recent losses (might be variance)
- •Someone elses system did better (irrelevant)
- •Gut feeling (not data)
- •Boredom (dangerous motivation)
The Change Process
Every significant change should follow a process:
- •Identify - What specific problem are you solving?
- •Hypothesize - What change will fix it?
- •Validate - Test the change without risking capital
- •Deploy - Implement with ability to rollback
- •Monitor - Verify the change helped
Skip steps and you introduce risk.
Types of Changes
Tier 1: Critical Fixes Must deploy immediately. System is broken or losing money unexpectedly.
Process: Fix, test minimally, deploy, monitor closely.
Tier 2: Non-Critical Bugs System works but something is wrong.
Process: Fix, full test, deploy during low-activity period.
Tier 3: Improvements System works fine but could be better.
Process: Full validation cycle. No rush.
Tier 4: New Strategies Adding new edges or major changes.
Process: Complete validation as if building from scratch.
Avoiding Overfitting to Recent Data
The biggest danger in improvement: optimizing for recent performance.
Scenario: Last month had losses. You tweak parameters to improve that month. Next month has different conditions and your tweaks make things worse.
Protection:
- •Never optimize on recent data alone
- •Validate changes on out-of-sample periods
- •Require statistical significance, not just improvement
- •Be skeptical of parameters that seem perfect
Versioning Your System
Track every change with version numbers.
Version Components:
- •Major: Significant strategy changes (2.0, 3.0)
- •Minor: New features or edges (2.1, 2.2)
- •Patch: Bug fixes (2.1.1, 2.1.2)
Benefits:
- •Rollback to known-good version
- •Track when performance changed
- •Correlate issues with changes
A/B Testing in Production
Test changes against the current system:
- •Run new version on portion of capital
- •Compare performance over meaningful period
- •Require statistical significance before full deployment
Small sample sizes lie. Dont declare victory too early.
Monitoring for Degradation
Performance will degrade over time. Markets change, edges decay.
Watch For:
- •Gradual win rate decline
- •Increasing slippage
- •Reduced signal frequency
- •Longer drawdown recoveries
Response:
- •Investigate cause
- •Determine if edge is dying or variance
- •Either fix or retire
Edge Lifecycle Management
Edges dont live forever.
Lifecycle Stages:
- •Discovery - Finding the edge
- •Validation - Confirming it works
- •Production - Trading live
- •Monitoring - Watching for decay
- •Retirement - Removing when dead
Plan for retirement. What criteria will cause you to stop trading an edge?
Documentation
Document every change:
- •What changed
- •Why it changed
- •When it deployed
- •Expected impact
- •Actual impact
Future you will thank present you.
The Stability Bias
When things are working, bias toward not changing.
Every change has risk. A working system has value. Dont mess with success unless theres a clear reason.
Takeaway
Improve methodically. Validate thoroughly. Document everything.
The goal is a system that gets better over time without breaking what already works.
Next: scaling up when youre ready for serious capital.