Sophie Z

Outlined the narrative structure for the story behind the CYA game, with chapters signaling different phases of the game. Really added to and elaborated on the details of the game’s story aspect.

Set up the basic outline for the text-based CYA game using React and .jsx files. The start of the storyline for the adventure has been established, along with the initial choices that the player can make and the initial consequences that they will face from their actions/decisions. More stylistic/formatting elements will be added later for visual appeal.

Main Game Enhancements

• Smooth Transitions: Implemented cinematic phase changes with overlay effects between phases
• Audio System: Integrated soothing ambient background music and mellow click sounds for a better atmosphere
  Gameplay Mechanics
• User-Controlled Progression: Replaced automatic phase transitions with manual navigation
  • Conditional Advancement: Forward buttons only appear when requirements are met
  • Full Navigation: Users can freely move between phases backward/forward at will

Phase 3 Character ASCII Art Enhancement

• Replaced all 4 character visuals (Scholar, Gardener, Architect, Echo) with enhanced versions from the showcase
  • Scholar: Updated to display detailed book-focused ASCII art
  • Gardener: Updated to display complex plant/nature ASCII art
  • Architect: Updated to display multi-level building blueprint ASCII art
  • Echo: Refined to display recursive reflection figure ASCII art

4 major bugs fixed (text visibility, coherence depletion, fragment displays)

• 22+ debug logging points implemented for diagnostic capability
• All Phase 2 and Phase 3 visual elements enhanced
• Comprehensive documentation created

Enhanced Phase 1 Interactivity - Added cursor tracking with a 300px influence radius, color-cycling orbs, and glow effects for visual richness
Text Stability Fixes - Implemented debouncing and interval tracking to prevent rapid clicking from corrupting the typewriter animation
Phase 3 Layout Optimization - Improved character story panel visibility with scrollable content and adjusted flex properties to prevent text overlap
Color-Changing Dynamics - Orbs now smoothly transition between five colors, with connection lines also adopting contextual colors based on nearby elements

Packaged and uploaded the project to PyPl: https://pypi.org/project/stock-market-screener/0.1.2/

I started by setting up the main infrastructure for my stock screener by importing pandas and numpy for data handling, yfinance for market data, TA-Lib for technical indicators, and matplotlib/seaborn for visualization.
I used the 30 Dow Jones Industrial Average stocks and yfinance to download one year of daily OHLCV data (from January 1, 2025 to January 7, 2026), which I inspected to confirm data quality and structure before analysis.
I then built three custom scoring functions. The MA50 score combined three elements: the percentage distance of price from its 50-day moving average (50% weight), the recent slope of the MA50 over five periods (30%), and a long-term regime filter based on the 200-day moving average (20%). All components were normalized to a -1 to +1 range. The RSI momentum score measured changes in RSI, normalized by twice the rolling standard deviation, to capture strengthening or weakening momentum. The volume score combined current volume relative to the 20-day average with price direction to assess whether volume confirmed the price move.
With these metrics defined, I ran a full analysis across all 30 stocks, calculated each individual score, and averaged them into a composite final score. I stored all results in a pandas DataFrame for review.
To visualize the output, I created a horizontal bar chart ranking stocks from best to worst by final score. I also built a heatmap with a dynamically sized grid and a red-yellow-green color scale centered at zero, providing a quick visual snapshot of bullish and bearish signals.
I spot-checked Apple’s price data to validate the inputs, then finalized the workflow by sorting tickers alphabetically and rerunning the analysis to ensure consistent, reproducible results. This produced clean tables and visualizations ready for further analysis.
Overall, I built a systematic multi-indicator stock screener that integrates trend, momentum, and volume into a single quantitative signal for large-cap stocks.