🧠 Turning Your Research Into Objective Rankings
This is the final piece that makes your system behave like a real portfolio research desk.
Right now you generate insights.
Now you convert those insights into:
consistent, comparable, decision-grade scores
🎯 GOAL
Turn every analysis into:
- a numeric score
- a rankable system
- a portfolio decision input
So instead of:
“This looks good…”
You get:
“This scores 7.8/10 and ranks #2 across all strategies.”
🧠 CORE IDEA
You are NOT scoring based on “profit.”
You are scoring based on:
robustness, consistency, and behavior across regimes
🧱 1. SCORING CATEGORIES (STANDARDIZED)
Every strategy is scored across 5 dimensions:
🟢 1. PERFORMANCE QUALITY (0–10)
Measures:
- Sharpe-like behavior
- consistency of returns
- stability of gains
🟡 2. DRAWDOWN CONTROL (0–10)
Measures:
- max drawdown severity
- recovery time
- loss clustering
🔵 3. REGIME ROBUSTNESS (0–10)
Measures:
- performance across:
- bull
- bear
- high volatility
- crisis
🟣 4. CONSISTENCY (0–10)
Measures:
- stability across time periods
- walk-forward behavior
- variance in results
🔴 5. RISK STRUCTURE (0–10)
Measures:
- exposure to volatility
- tail risk behavior
- failure mode severity
📊 2. FINAL SCORE FORMULA
Final Score =
(Performance * 0.25) +
(Drawdown * 0.20) +
(Regime Robustness * 0.25) +
(Consistency * 0.15) +
(Risk Structure * 0.15)
🧠 3. SCORING TEMPLATE (USED IN PHASE 4 OUTPUT)
You will append this to every analysis:
📊 STRATEGY SCORECARD
Strategy: [Name]
Performance Quality: X/10
Drawdown Control: X/10
Regime Robustness: X/10
Consistency: X/10
Risk Structure: X/10
FINAL SCORE: X.X / 10
Confidence Level: High / Medium / Low
🧾 4. PROMPT UPGRADE (IMPORTANT)
Update your Phase 4 prompt:
Add at the end:
7. Strategy Scorecard:
Assign scores (0–10) for:
- Performance Quality
- Drawdown Control
- Regime Robustness
- Consistency
- Risk Structure
Then compute final weighted score.
🧠 5. HOW YOU USE THIS IN PRACTICE
BEFORE (old system)
- qualitative insights
- subjective conclusions
AFTER (scored system)
You can now:
- rank all strategies
- compare objectively
- track improvement over time
- build portfolio decisions
📈 6. STRATEGY RANKING TABLE
You will build:
| Strategy | Score | Rank |
|---|---|---|
| Strategy A | 8.2 | #1 |
| Strategy B | 7.6 | #2 |
| Strategy C | 6.9 | #3 |
🧠 7. PORTFOLIO DECISION LAYER
Now you can answer:
- Which strategies deserve capital?
- Which should be removed?
- Which need improvement?
🔁 8. SCORE EVOLUTION (POWERFUL)
Each time you:
- refine strategy
- adjust rules
- re-test
You re-score it.
You now track:
strategy evolution over time
🚫 IMPORTANT LIMITATION
Scores are:
- derived from historical data
- dependent on your dataset quality
- influenced by prompt consistency
So:
they are decision tools — not absolute truth
🧠 9. FINAL SYSTEM EVOLUTION
You now have:
🧱 FOUNDATION
- data
- RAG system
🧠 INTELLIGENCE
- LLM reasoning engine
🖥️ INTERFACE
- dashboard
🔁 WORKFLOW
- research templates
📊 DECISION LAYER
- scoring system
💡 FINAL ONE-LINE SUMMARY
The scoring system converts your AI-driven research into objective, repeatable, and rankable strategy evaluations for real decision-making.
🏁 YOU NOW HAVE A COMPLETE SYSTEM
What you built is no longer:
“a local LLM project”
It is:
a structured AI-powered trading research platform with scoring, workflows, and decision frameworks.
🚀 FINAL STEP (OPTIONAL BUT POWERFUL)
If you want to push this to the highest level, next would be:
👉 “Automation Layer”
(auto-run templates across all strategies, auto-score everything, and generate a daily/weekly research report without manual prompting)
Just say 👍