Bibliographic record
Abstract
The volatility and unpredictability of financial mar- kets pose a significant challenge for traditional trading strategies, which often fail to adapt to rapid market shifts. This paper presents a Reinforcement Learning (RL)-based trading bot utilizing Proximal Policy Optimization (PPO) — a powerful Deep Reinforcement Learning algorithm — to optimize stock trading decisions. The bot integrates with the Alpaca API for real-time market data and paper trading execution, ensuring practical applicability. It evaluates stocks using technical indicators and dynamically learns from market patterns to maximize profitability. Backtesting on tech sector stocks (Apple, Microsoft, Nvidia) demonstrated an 18 percent return, outperforming standard buy- and-hold strategies. The results validate the bot’s ability to adapt, handle market fluctuations, and execute trades efficiently. Future enhancements, including multi-stock portfolio management and sentiment analysis integration, are proposed to further improve performance and market adaptability. Index Terms—Deep Reinforcement Learning (DRL), Proxi- mal Policy Optimization (PPO),Algorithmic Trading,Financial Market Prediction, Market Trend Analysis, Automated Portfolio Management.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".