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November 28, 2025In high-frequency trading, milliseconds matter – and so do forgotten algorithms
After twelve years building trading systems, I’ve made a costly mistake more than once: abandoning algorithms too soon. Like coin collectors who sell rare pieces only to watch their value explode, quants often discard strategies right before they’d shine. Let me share how treating trading code like numismatic treasures could save you from future regret.
When Collectors and Quants Face the Same Regret
Picture this: a collector sells a 1917 Walker half-dollar in its original holder, needing quick cash. Years later, that same coin sells for millions after being re-graded. We quants do something similar when we:
- Retire “old” strategies chasing newer models
- Delete code that feels outdated
- Overlook the hidden wisdom in previous market conditions
The parallel is clearer than a freshly polished silver dollar – we’re both losing historical value for short-term gains.
Three Rules for Smart Algorithm Collecting
- Keep the original packaging: Preserve your first code version like an OGH coin holder – it contains market DNA
- Don’t over-polish: That “optimized” algorithm might be as fragile as an over-cleaned coin
- Check the attic regularly: Old strategies often outperform fancy new ones during market shocks
How HFT Systems Become Your Strategy Museum
Modern trading tech gives us perfect tools for preserving algorithms. Here’s how I approach it:
Git: Your Digital Coin Capsules
Tag strategy versions like rare specimens:
# Saving a strategy's original state
git tag -a v1.3_OGH -m "2020 crash pattern catcher"
Keep Your Old Market-Makers Handy
During March 2020’s madness, something surprising happened:
Basic 2012-era latency strategies beat shiny new ML models by 14%
Now I always keep legacy algo “backups” active.
Financial Models Need Conservation, Not Constant Remodels
Coin grading taught me an important lesson – sometimes “original” beats “improved.” Our team now follows:
The Strict Upgrade Rules
- New versions must triple Sharpe ratios to replace old ones
- Original code stays live for at least a year after updates
- Every change gets tested against the original market data
Coding Like a Coin Collector
Here’s how we bake preservation into our Python finance code:
# Strategy museum class
class AlgorithmVault:
def __init__(self, original):
self.og = original # Never touched
self.tweaks = [] # Potential upgrades
def add_version(self, new_code, metrics):
# New strategies must significantly outperform
if metrics['sharpe'] > 1.2 * self.current_sharpe:
self.tweaks.append(new_code)
Backtesting: Your Market Time Machine
Historical data is our trading archaeology site. We treat it like rare coin provenance:
Our Validation Process
- Original test environments saved in Docker “time capsules”
- Monthly runs of old strategies against new market data
- Grading system inspired by numismatic standards:
| Strategy Condition | What It Takes |
|---|---|
| Mint Original (OGH) | Untouched since creation day |
| Market Gem (MS65+) | Beats benchmarks for 5+ years |
Don’t Be That Regretful Collector
The lesson from coins is clear: preservation creates lasting value. For algorithmic trading, this means:
- Guard original code like rare artifacts
- Apply coin grading discipline to strategy performance
- Innovate without erasing your quant history
Next time you’re about to delete an “old” algorithm, remember the 1917 Walker half-dollar. That code you’re discarding might just be your future million-dollar strategy – if you keep its original holder intact.
Related Resources
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