Week of September 16, 2024

• Kicked off senior project; clarified problem statement and success metrics.
• Surveyed recent literature on financial time‑series forecasting and portfolio optimization.
• Collected candidate open‑source datasets (Yahoo Finance, Kaggle equities data).
• Outlined experimental roadmap for traditional ML vs. deep‑learning approaches.

Week of September 23, 2024

• Performed exploratory data analysis—examined stationarity, seasonality, and outliers.
• Implemented a baseline Random Forest Regressor with default parameters.
• Set up experiment‑tracking in Weights & Biases; logged initial metrics (RMSE, MAE).
• Identified need for feature engineering (lag windows, technical indicators).

Week of September 30, 2024

• Engineered lag‑based features (t‑1…t‑30) and volatility measures.
• Ran grid search on Random Forest (n_estimators, max_depth, min_samples_split).
• Achieved 9% RMSE improvement over baseline.
• Documented findings and pushed notebooks to repo.

Week of October 7, 2024

• Researched recurrent neural networks for time‑series; selected GRU for trials.
• Implemented GRU baseline in PyTorch (1 hidden layer, 64 units).
• Set up time‑series cross‑validation (sliding window) for deep‑learning pipeline.
• Compared training times vs. Random Forest; noted GPU acceleration benefits.

Week of October 14, 2024

• Hyper‑parameter tuning for GRU (hidden_size, num_layers, dropout).
• Implemented early‑stopping and learning‑rate schedulers.
• GRU (2 layers, 128 units) surpassed Random Forest by 4 % on validation RMSE.
• Held sprint review; decided to pursue ensemble strategy.

Week of October 21, 2024

• Experimented with stacking: GRU + Random Forest meta‑learner.
• Implemented feature‑scaling pipeline (StandardScaler, MinMax for deep models).
• Benchmarked ensembles—observed marginal gains, increased complexity.
• Drafted technical blog post summarizing October experiments.

Week of October 28, 2024

• Refactored data‑loading pipeline using PyTorch DataLoader for efficiency.
• Automated hyper‑parameter sweeps with Optuna (50 trials, parallelized).
• Recorded best GRU config: 3 layers, 96 units, dropout 0.2, LR 1e‑3.
• Pushed experiment artifacts and updated README with results.

Week of November 4, 2024

• Introduced comprehensive metric dashboard (RMSE, MAPE, Sharpe).
• Added early‑stopping callbacks to reduce overfitting on GRU.
• Performed ablation study on feature windows (7, 14, 28 days).
• Selected 14‑day window as sweet‑spot for bias‑variance trade‑off.

Week of November 11, 2024

• Computed feature importances for Random Forest; applied SHAP to GRU embeddings.
• Discovered volatility and momentum indicators most predictive.
• Presented interpretability findings to advisors.
• Logged experiment documentation into wiki.

Week of November 18, 2024

• Enhanced preprocessing: missing‑value imputation via forward fill & KNN.
• Standardized scaling step across ML and DL pipelines.
• Retested models; confirmed stability of performance improvements.
• Created unit tests for preprocessing utilities.

Week of November 25, 2024

• Implemented incremental (online) training for GRU to handle data drift.
• Simulated drift scenarios; evaluated model robustness.
• Added drift‑detection alerts to monitoring plan.
• Updated project timeline for upcoming deployment work.

Week of December 2, 2024

• Benchmarked alternative models (Prophet, XGBoost) for comparison.
• XGBoost lag‑feature model under‑performed GRU; documented rationale to drop.
• Cleaned codebase; enforced PEP‑8 linting across notebooks.
• Prepared dataset versioning strategy using DVC.

Week of December 9, 2024

• Consolidated experiment results into master spreadsheet.
• Wrote draft of methodology section for final report.
• Set up CI checks for automatic unit‑test execution on PRs.
• Planned hand‑off to deployment track starting December 23.

Week of December 16, 2024

• Finalized GRU hyper‑parameters for production candidate.
• Tagged model artifact v1.0 and stored in model registry.
• Held technical deep dive with backend team on serving architecture.
• Scheduled sprint for main‑page database automatization work.

Week of December 23, 2024

• Researched financial forecasting models and methodologies.
• Developed initial data validation and quality assurance workflows.
• Created project testing framework and continuous integration setup.

Week of December 30, 2024

• Implemented automated data quality checks for financial datasets.
• Designed evaluation metrics for forecasting model performance.
• Developed integration testing suite for API endpoints.
• Created documentation standards for project codebase.

Week of January 6, 2025

• Finalized main page database automatization (PLUT‑56).
• Completed model feature elimination tests (PLUT‑31).
• Kicked off model deployment preparations (PLUT‑93).
• Held sprint retrospective and planned upcoming backend tasks.

Week of January 13, 2025

• Deployed predictive model to staging environment (PLUT‑93).
• Began frontend package installation & UI beautification (PLUT‑96).
• Reviewed backend completion requirements (PLUT‑97).
• Set up initial monitoring dashboards for staging cluster.

Week of January 20, 2025

• Integrated stock‑price data via Yahoo Finance API (PLUT‑106).
• Established secure Azure database connection (PLUT‑92).
• Implemented payment micro‑service (deadline 21 Jan) (PLUT‑57).
• Conducted unit tests for new backend endpoints.

Week of January 27, 2025

• Completed core backend functionality (PLUT‑97).
• Performed code review & refactoring across services.
• Upgraded CI/CD pipelines for faster feedback loops.
• Documented API contract changes for frontend team.

Week of February 3, 2025

• Executed backend deployment to production cluster (PLUT‑100).
• Configured rollout strategy for frontend deployment (PLUT‑101).
• Introduced blue‑green deployment workflow for risk mitigation.
• Began PageGuard backend service design (PLUT‑103).

Week of February 10, 2025

• Completed frontend deployment pipeline & release (PLUT‑101).
• Implemented PageGuard backend endpoints (PLUT‑103).
• Ran performance benchmarks on API gateway.
• Addressed security audit findings in data layer.

Week of February 17, 2025

• Built PageGuard frontend module (PLUT‑104).
• Conducted end‑to‑end user acceptance testing on PageGuard.
• Optimized lazy‑loading strategy for React components.
• Gathered stakeholder feedback for UI/UX improvements.

Week of February 24, 2025

• Performed backend performance optimization under load.
• Implemented connection pooling & query caching.
• Reduced average API latency by 38%.
• Updated technical documentation for optimizations.

Week of March 3, 2025

• Conducted security hardening & penetration tests.
• Added rate‑limiting & enhanced input sanitation.
• Patched identified vulnerabilities across microservices.
• Reviewed test coverage for critical security paths.

Week of March 10, 2025

• Implemented real‑time monitoring and alerting dashboards.
• Integrated Prometheus metrics with Grafana visualizations.
• Configured alert thresholds for SLO compliance.
• Trained team on incident‑response playbooks.

Week of March 17, 2025

• Prepared deployment procedure documentation.
• Created runbooks for common operational tasks.
• Facilitated knowledge‑transfer workshops for new interns.
• Established coding‑style guidelines organization‑wide.

Week of March 24, 2025

• Executed scalability testing at 5× projected load.
• Identified & resolved memory‑leak issues in service layer.
• Improved horizontal autoscaling policies.
• Presented findings to advisory board for feedback.

Week of March 31, 2025

• Implemented feedback from scalability review.
• Fixed outstanding bugs from QA backlog.
• Optimized SQL queries reducing cost by 22%.
• Updated user documentation & API reference.

Week of April 7, 2025

• Drafted final presentation slide deck & speaker notes.
• Rehearsed demo workflow and fail‑over scenarios.
• Collected final KPIs for project performance report.
• Aligned team on deliverable checklist for graduation review.

Week of April 14, 2025

• Conducted full dress‑rehearsal of presentation.
• Refined UI visuals based on stakeholder feedback.
• Completed dataset licensing compliance review.
• Locked feature set for release candidate.

Week of April 21, 2025

• Reviewed final deliverables with faculty advisor.
• Submitted draft of final report for feedback.
• Added glossary & appendix to documentation.
• Performed final accessibility audits.

Week of April 28, 2025

• Conducted final system integration testing and validation.
• Completed comprehensive test coverage documentation.
• Finalized code quality metrics and performance benchmarks.
• Participated in final project presentation and demonstration.

Week of May 4, 2025

• Submitted the final project report, codebase, and documentation bundle.
• Archived repositories and set up long‑term maintenance guidelines.
• Reflected on project outcomes and lessons learned for future work.
• Celebrated successful completion of the senior project with the team.