Datasets:
ArXiv:
License:
| { | |
| "name": "48_Stock_Trading_Simulation_PPO_HistoricalData_RL", | |
| "query": "Hey! I'm interested in developing a stock trading agent using the Proximal Policy Optimization (PPO) algorithm. The idea is to use historical market data for training and testing. A stock trading simulation environment should be implemented in `src/env.py`. The Proximal Policy Optimization (PPO) algorithm should be implemented in `src/train.py`. Please save the trained agent under `models/saved_models/`. Record all the trade decisions in `results/trade_decisions.txt` and save the total profit in `results/metrics/total_profit.txt`. Visualize the profit curve and save it as `results/figures/profit_curve.png`. Generate a report that covers the trading strategy, profit, and risk analysis, and save it as `results/trading_strategy_report.md`. Implement an interactive tool using Streamlit in `src/visualize.py` that allows users to try different parameters and run simulations.", | |
| "tags": [ | |
| "Financial Analysis", | |
| "Reinforcement Learning" | |
| ], | |
| "requirements": [ | |
| { | |
| "requirement_id": 0, | |
| "prerequisites": [], | |
| "criteria": "A stock trading simulation environment is implemented in `src/env.py`.", | |
| "category": "Dataset or Environment", | |
| "satisfied": null | |
| }, | |
| { | |
| "requirement_id": 1, | |
| "prerequisites": [ | |
| 0 | |
| ], | |
| "criteria": "Historical market data is used for training and testing.", | |
| "category": "Dataset or Environment", | |
| "satisfied": null | |
| }, | |
| { | |
| "requirement_id": 2, | |
| "prerequisites": [], | |
| "criteria": "The \"Proximal Policy Optimization (PPO)\" algorithm is implemented in `src/train.py`.", | |
| "category": "Machine Learning Method", | |
| "satisfied": null | |
| }, | |
| { | |
| "requirement_id": 3, | |
| "prerequisites": [ | |
| 1, | |
| 2 | |
| ], | |
| "criteria": "Trade decisions are recorded and saved as `results/trade_decisions.txt`.", | |
| "category": "Other", | |
| "satisfied": null | |
| }, | |
| { | |
| "requirement_id": 4, | |
| "prerequisites": [ | |
| 3 | |
| ], | |
| "criteria": "Total profit is saved as `results/metrics/total_profit.txt`.", | |
| "category": "Other", | |
| "satisfied": null | |
| }, | |
| { | |
| "requirement_id": 5, | |
| "prerequisites": [ | |
| 4 | |
| ], | |
| "criteria": "The profit curve is visualized and saved as `results/figures/profit_curve.png`.", | |
| "category": "Visualization", | |
| "satisfied": null | |
| }, | |
| { | |
| "requirement_id": 6, | |
| "prerequisites": [ | |
| 4 | |
| ], | |
| "criteria": "A report containing trading strategy, profit, and risk analysis is generated and saved as `results/trading_strategy_report.md`.", | |
| "category": "Other", | |
| "satisfied": null | |
| }, | |
| { | |
| "requirement_id": 7, | |
| "prerequisites": [ | |
| 1, | |
| 2 | |
| ], | |
| "criteria": "An interactive tool allowing users to try different parameters and run simulations using \"Streamlit\" is implemented in `src/visualize.py`.", | |
| "category": "Human Computer Interaction", | |
| "satisfied": null | |
| } | |
| ], | |
| "preferences": [ | |
| { | |
| "preference_id": 0, | |
| "criteria": "The profit curve visualization should highlight significant trades or events that impacted performance.", | |
| "satisfied": null | |
| }, | |
| { | |
| "preference_id": 1, | |
| "criteria": "The report should include insights on how parameter tuning affects the trading outcome.", | |
| "satisfied": null | |
| } | |
| ], | |
| "is_kaggle_api_needed": false, | |
| "is_training_needed": true, | |
| "is_web_navigation_needed": false | |
| } |