Case Study · Problem → Solution → KPI/ROI
Realtime U.S. Stock Analysis Program
An internal tool for realtime analysis and prediction across 5,000+ U.S. listed stocks and ETFs, with 15m / 1h multi-timeframe support.
This case explains historical validation and a stop decision; it is not investment advice or a performance guarantee.
TL;DR
Role & Impact
- Designed the end-to-end UX of a data product: collect → analyze → realtime inference → share.
- Kept a KPI/ROI board visible to support a 10-minute time-to-insight on an i9-13900K baseline.
- Validated signal quality during the 2024.11-2025.01 live-test period, then stopped after a market-regime change.
Technical History
- Core stack: Python and MySQL.
- Model path: Regression → LSTM → SVM → Transformer → SVR → XGBoost → Dask → cosine similarity.
- Improved speed through cosine similarity and condition tuning: 48x vs Regression, 3-4x vs Dask.
- Improved MySQL performance about 3x through connection-pool and thread tuning.
- Built CPU-based formula pipelines for preprocessing and postprocessing.
Problem
User & Context
- Needed automation for large-universe, multi-timeframe analysis across U.S. stocks/ETFs.
- Needed one screen for signal validation, realtime deployment, and output sharing.
Needs & Pain Points
- Compare signal quality and risk KPIs across 15m/1h timeframes.
- Expose cause and ROI impact when a run fails.
- Detect and handle missing values and outliers in realtime data.
Core Solution Strategy
1) One-screen workflow
Collect → analyze → realtime inference → preview in a single flow.
2) Multi-timeframe realtime flow
Process 15m/1h configurable windows with realtime inference streams.
3) Cosine-similarity engine
Managed historical validation and post-regime-shift degradation separately.
4) Recovery and risk
Separated pre/post-event regimes with step logs, cancellation, restart, and rollback flows.
Design-to-Code Mapping
Collect 5,000+ U.S. stock/ETF time series at 15m/1h intervals.
Cosine-similarity pattern matching and tuning metrics.
Realtime comparison/prediction charts and file export.
KPI & ROI Frame
KPI Definition
- Coverage: 5,000+ U.S. stocks/ETFs.
- Signal Quality: directional signal quality in historical validation.
- TTI: collect → preview = 10 minutes on Intel i9-13900K.
- Win Rate: about 95% in the historical test context.
- Reproducibility: no variance under identical historical-data conditions.
Sensitivity, Not Investment Claims
The scenario explains the historical test period and does not represent future returns.
- Live test: 2024.11-2025.01.
- Interpretation: validation numbers are for model-quality inspection.
- Stop decision: strategy stopped after tariff-related market-regime changes in 2025.
Includes no fees, slippage, taxes, execution failure, or FX effects; this is a recruiting risk-management case.
Core Flow
Data refresh → analysis → sharing
- Select SourceChoose the analysis universe.
- CollectRefresh data and handle errors.
- ConfigureReview analysis parameters.
- AnalyzeRun, show progress, and allow cancellation.
- PreviewReview result charts and tables.
- ShareExport spreadsheets and rerun.
Impact & Result
Metrics
- Coverage: 5,000+ symbols.
- Signal Quality: confirmed in the historical validation period.
- Risk Outcome: strategy stopped after tariff-driven regime changes.
Business Result
- 2024-05: explored commercialization from backtest performance.
- 2025-04: accuracy dropped after the Trump administration and tariff war context, so the strategy was discarded.
Confirmed the limits of a similarity-based pattern strategy under regime shifts.
Measurements were reported from logs, trade records, and Excel metadata; public materials clearly separate historical validation from future performance.