Shadow before replacement
V2 components ran beside the established path so outputs could be compared before the user-facing result changed.
MarketMood
A multi-market research system rebuilt from V1 to V2 through shadow execution, parity tests, feature flags, and controlled cutover.
Preserve trusted behavior while replacing the architecture underneath it.
1,200passing tests at cutover
77test files at audit
4markets covered
V1→V2staged migration
THE CHALLENGE
Why this was hard
01
Raw bullish-versus-bearish counts ignore analyst quality and market context.
02
A large first-generation application had accumulated tightly coupled responsibilities.
03
A rewrite could silently change behavior relied upon by reports and background jobs.
MY ROLE
What I owned
- Defined sentiment, idea-quality, analyst-rating, confidence, and historical-comparison rules.
- Directed integration of TradingView ideas, price context, AI memory/RAG, and Telegram reporting.
- Required behavior parity and rollback paths before V2 components replaced V1.
- Reviewed fallbacks, feature flags, model costs, and failure handling.
KEY DECISIONS
The choices behind the system.
Flags limit blast radius
New paths activated incrementally. Fallbacks and rollback behavior protected the system from mismatches and external-service failures.
Parity is a release gate
Tests preserved intended behavior rather than implementation details, allowing modular replacement without an uncontrolled rewrite.
VERIFICATION
Claims tied to evidence.
Every figure carries its scope. Strong evidence includes the boundary around what it does—and does not—prove.
- Exactly 1,200 passing tests recorded at the final reviewed cutover.
- A repository of 297 files, including 194 Python files and 77 test files at audit.
- Documented shadow runtime, feature flags, fallbacks, parity tests, and rollback paths.
- Separate paper simulation kept educational evaluation distinct from live financial claims.
OUTCOME
What changed
MarketMood moved toward a modular V2 system through measurable substitutions instead of a high-risk rewrite, while preserving decision logic and failure recovery.
Scope note
This is an educational decision-support project, not investment advice or a claim of trading performance.
REFLECTION
What I learned
A rewrite is safer when treated as a sequence of substitutions.
Decision products must separate signal, confidence, context, and outcome.
AI interpretation needs deterministic inputs and explicit fallbacks.
PythonSQLiteTradingView dataLLM APIsOpenRouterRAGTelegram