Deep Reinforcement Learning for Quantitative Finance: Time Series Forecasting using Proximal Policy Optimization
Bibliographic record
Abstract
Investment and financial management has existed as a field of study for quite some time, with roots in practical application. One of the approaches used in order to assist with investment decision making is the broad area of modelling. Modelling approaches can be found within multiple distinct, but still related, unique fields. These include statistics, machine learning, deep learning, reinforcement learning, discrete mathematics, dynamic programming, and many more. Not only are there multiple modelling approaches belonging to multiple different fields, but there are also different unique problems in investment management. In this work, the focus is on price prediction and concurrent strategy building. The modelling approach chosen for this is of the deep reinforcement learning type, and actor-critic class. Specifically, in this work the proximal policy optimization (PPO) architecture is employed individually on each stock's market history in order to try and solve the price prediction problem.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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 teacher head, 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".