What Makes a Good Quantitative Signal Source? The QTGS Framework #2
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We've been thinking a lot about what separates reliable quantitative signal sources from noise. Here's a framework we use internally — sharing it because we think the community could benefit.
The QTGS Framework (4 dimensions)
1. Forward Tracking Integrity
Key question: Are all signals timestamped and immutable from the moment they're published, including losses?
Backtested performance is meaningless — anyone can overfit a beautiful equity curve. The only credible evidence is forward-tracked signals where every entry is recorded at publish time and can't be modified after the fact.
We use Git commit history as our audit trail. Every signal is a commit with an immutable timestamp. Anyone can verify: was this signal published before or after the market moved?
2. Strategy Transparency
Key question: Can you explain in one sentence what the strategy profits from?
"We use deep learning on 1000 factors" is not transparency — it's a black box.
Real transparency: "This strategy exploits the lagged correlation between offshore CNH exchange rate and A-share indices — when CNH depreciates sharply, CSI300 typically follows within 1-2 weeks."
3. Custody Risk
Key question: Are user funds always under user control?
A signal source never touches your money. Zero custody = zero run-away risk. You execute in your own brokerage account.
4. Factor Robustness
Key question: Is the alpha source a durable economic phenomenon, or a data-mined coincidence?
Macro factors (FX cycles, liquidity rotation, sentiment extremes) have clear causal chains rooted in economic behavior. VIX mean reversion at extremes isn't a statistical accident — it's driven by the market overpricing fear during panics.
Compare this to: "this factor had p < 0.05 over the last 10 years." That's a paper result, not a trading edge.
How QuantToGo Scores
We'd love to hear how others think about evaluating signal quality. What dimensions would you add to this framework?
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