Tesla Falls 1.42 as $56.95B Volume Tops U.S. Trading Amid Production Delays and Regulatory Scrutiny

Tesla (TSLA) fell 1.42% on October 3, 2025, with a trading volume of $56.95 billion, ranking first in market activity. The decline occurred amid mixed signals from production challenges and evolving regulatory scrutiny.

Reports highlighted ongoing bottlenecks in battery supply chain logistics, particularly for 4680 cells, as production ramp-up efforts in Texas and Berlin faced delays. Analysts noted these constraints could pressure near-term delivery guidance, though long-term demand for EVs remains robust. Concurrently, regulatory agencies intensified investigations into data privacy practices related to vehicle software updates, adding short-term uncertainty for investors.

Competitive dynamics also played a role, with traditional automakers accelerating EV platform launches. However, Tesla’s Q3 earnings demonstrated improved gross margins despite lower vehicle production, suggesting resilience in pricing strategies. Market participants are closely monitoring upcoming guidance for Q4 production targets and potential cost-cutting measures.

To run this back-test rigorously I need to pin down a few practical details and confirm that the tooling matches the scope of what you have in mind: 1. Universe • “Top 500 stocks by daily trading volume” – do you mean: a) All U.S. listed common stocks each day, ranked by that day’s dollar volume? b) Another universe (e.g., S&P 500 constituents, Nasdaq stocks only, etc.)? 2. Trade mechanics • Entry price: use the same-day Close and exit at next-day Close (Close→Close), or enter at next-day Open and exit at next-day Close (Close→Open/Close), or something else? • Position size: equal-weight across the 500 names each day? • Transaction costs / slippage assumptions (e.g., 2 bps per side)? 3. Tool limitation disclosure • The built-in back-testing engine currently evaluates one ticker at a time. • For a cross-sectional strategy we’ll calculate the daily portfolio return separately (outside the single-ticker engine) and then feed the resulting equity curve into the visualization module. • That requires pulling daily price & volume for the tradable universe, ranking each day, building the portfolio, and aggregating returns – all doable, but the data pull and transformation steps will be scripted rather than handled in one “strategy_backtest_engine” call. If that workflow is acceptable, please let me know (and clarify points 1 & 2). Once I have those details I’ll proceed to: 1. Retrieve the necessary price/volume data. 2. Compute daily top-500 sets and the 1-day-hold portfolio returns. 3. Feed the equity curve into the visualization module for performance statistics and charts. Just confirm or adjust the parameters and I’ll start.