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Dobromir Popov
2025-07-17 02:15:24 +03:00
parent b4e592b406
commit 6b9482d2be
6 changed files with 874 additions and 21 deletions
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.kiro/specs/multi-modal-trading-system/requirements.md
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@ -12,6 +12,7 @@ The Multi-Modal Trading System is an advanced algorithmic trading platform that
#### Acceptance Criteria
0. NEVER USE GENERATED/SYNTHETIC DATA or mock implementations and UI. If somethings is not implemented yet, it should be obvious.
1. WHEN the system starts THEN it SHALL collect and process data for both ETH and BTC symbols.
2. WHEN collecting data THEN the system SHALL store the following for the primary symbol (ETH):
- 300 seconds of raw tick data - price and COB snapshot for all prices +- 1% on fine reslolution buckets (1$ for ETH, 10$ for BTC)
@ -24,7 +25,7 @@ The Multi-Modal Trading System is an advanced algorithmic trading platform that
7. IF tick data is not available THEN the system SHALL substitute with the lowest available timeframe data.
8. WHEN normalizing data THEN the system SHALL normalize to the max and min of the highest timeframe to maintain relationships between different timeframes.
9. data is cached for longer (let's start with double the model inputs so 600 bars) to support performing backtesting when we know the current predictions outcomes so we can generate test cases.
10. In general all models have access to the whole data we collect in a central data provider implementation. only some are specialized.
10. In general all models have access to the whole data we collect in a central data provider implementation. only some are specialized. All models should also take as input the last output of evey other model (also cached in the data provider). there should be a room for adding more models in the other models data input so we can extend the system without having to loose existing models and trained W&B
### Requirement 2: CNN Model Implementation

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.kiro/specs/multi-modal-trading-system/tasks.md
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@ -3,6 +3,8 @@
## Data Provider and Processing
- [ ] 1. Enhance the existing DataProvider class
- Extend the current implementation in core/data_provider.py
- Ensure it supports all required timeframes (1s, 1m, 1h, 1d)
- Implement better error handling and fallback mechanisms