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{
"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
} |