Multi-Timeframe Algorithmic Trading Bots Using Thick Data Heuristics with Deep Reinforcement Learning
Bibliographic record
Abstract
This article presents an augmented Artificial Intelligence (AI) algorithmic trading approach that combines Thick Data Heuristic (TDH), with Deep Reinforcement Learning (DRL), to successfully learn trading execution timing policies. Combining the augmented AI human trader's intuition and heuristics with DRL techniques to provide more focused drivers for trading order execution timing is explored in this study. In this research, the goal is to solve the sequential decision-making problem of AI for profitable day and swing trading order timing executions. Enabling trading bots with cognitive intelligence and common-sense heuristics will offer traders including automatic traders an insight to understand the day-to-day swing trading timeframes indicators and arrive at mature trading decision-making. This article examines the performance of bots with Nasdaq and NYSE stocks that have a strong catalyst (info. which increases directional momentum) to find that they outperform benchmark algorithmic trading approaches. The research illustrates to the reader how to combine TDH and Deep Q-networks (DQN) into a TDH-DQN augmented AI trading bot. The bot learns through test data to predict the optimal timing of order executions autonomously on idealized trading time series data.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".