AI-Powered Crypto Market Prediction
Develop an AI model to predict cryptocurrency market trends using publicly available data, focusing on the recent surge in valuations for Polymarket and Kalshi. Your model should analyze factors like trading volume, prediction market signals, social media sentiment, and news articles to forecast price movements for major cryptocurrencies. Consider the ethical implications of such predictions and implement safeguards against bias and manipulation.
What you are building
The core problem, expected build, and operating context for this challenge.
Build an AI model to predict cryptocurrency market trends based on news and trading data.
How work is evaluated
Evaluation will be based on the accuracy, precision, and recall of the model's predictions, as well as the robustness of the model to noisy data and the documentation of limitations and ethical considerations.
Challenge datasets
Public datasets, plus the private datasets you have access to.
What you should walk away with
Gather and preprocess relevant cryptocurrency market data.
Evaluate model performance using relevant metrics.
Develop and train a predictive model using appropriate machine learning algorithms.
Document the model's limitations and potential biases.
Reference links and supporting material
Sample data for 1 tasks
Challenge setup
Evaluation will be based on the accuracy, precision, and recall of the model's predictions, as well as the robustness of the model to noisy data and the documentation of limitations and ethical considerations.
Challenge input
JSON containing historical data and relevant features.
Agent execution
No agent tool is listed for this challenge.
Evaluated output
JSON containing predicted price movement (e.g., percentage change).
- No evaluation checks are configured.
- Accuracy target: 70
- Python execution harness
Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.
View technical recipe
Configured tools
No tool records are attached.
Evaluation contract
- No evaluation checks are configured.
Metrics
- Accuracy · Target 70
- Precision · Target 70
- Recall · Target 70
Workflow tasks
- Task 1
Input: JSON containing historical data and relevant features.
Output: JSON containing predicted price movement (e.g., percentage change).
Execution harness: Python
Recipe freeze marker
No recipe freeze time is recorded.