Model Type | time-series prediction, financial analysis |
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Use Cases |
Areas: | financial trading, investment analysis |
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Applications: | trading bot development, market trend analysis |
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Primary Use Cases: | predicting Bitcoin price movements, generating trading signals |
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Limitations: | not suitable for real-time trading, does not account for market manipulations |
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Considerations: | Recommended to use alongside expert financial strategies for best results. |
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Additional Notes | Model is designed for educational and experimental purposes only. |
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Training Details |
Data Sources: | historical Bitcoin price data, financial news articles, market sentiment analysis |
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Data Volume: | |
Methodology: | transfer learning on top of Tinyllama |
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Hardware Used: | 8 x NVIDIA Tesla V100 GPUs |
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Model Architecture: | Transformer-based with special modules for time series analysis |
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Responsible Ai Considerations |
Fairness: | Model does not prioritize any region or market segment. |
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Transparency: | Model predictions are explainable via saliency maps. |
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Accountability: | Crypto AI Labs is accountable for model errors leading to financial losses. |
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Mitigation Strategies: | Periodic updates to incorporate new data and market shifts. |
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Input Output |
Input Format: | JSON formatted financial time series data |
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Accepted Modalities: | |
Output Format: | Predicted price movement (up, down, stable) |
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Performance Tips: | Ensure data is pre-processed for accurate prediction results. |
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Release Notes |
Version: | |
Date: | |
Notes: | Initial release with basic trading prediction features. |
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