84 lines
3.2 KiB
Markdown
84 lines
3.2 KiB
Markdown
>> Models
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how we manage our training W&B checkpoints? we need to clean up old checlpoints. for every model we keep 5 checkpoints maximum and rotate them. by default we always load te best, and during training when we save new we discard the 6th ordered by performance
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add integration of the checkpoint manager to all training pipelines
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skip creating examples or documentation by code. just make sure we use the manager when we run our main training pipeline (with the main dashboard/📊 Enhanced Web Dashboard/main.py)
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remove wandb integration from the training pipeline
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do we load the best model for each model type? or we do a cold start each time?
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>> UI
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we stopped showing executed trades on the chart. let's add them back
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update chart every second as well.
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the list with closed trades is not updated. clear session button does not clear all data.
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fix the dash. it still flickers every 10 seconds for a second. update the chart every second. maintain zoom and position of the chart if possible. set default chart to 15 minutes, but allow zoom out to the current 5 hours (keep the data cached)
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>> Training
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how effective is our training? show current loss and accuracy on the chart. also show currently loaded models for each model type
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>> Training
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what are our rewards and penalties in the RL training pipeline? reprt them so we can evaluate them and make sure they are working as expected and do improvements
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allow models to be dynamically loaded and unloaded from the webui (orchestrator)
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show cob data in the dashboard over ws
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report and audit rewards and penalties in the RL training pipeline
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>> clean dashboard
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initial dash loads 180 historical candles, but then we drop them when we get the live ones. all od them instead of just the last. so in one minute we have a 2 candles chart :)
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use existing checkpoint manager if it;s not too bloated as well. otherwise re-implement clean one where we keep rotate up to 5 checkpoints - best if we can reliably measure performance, otherwise latest 5
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### **✅ Trading Integration**
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- [ ] Recent signals show with confidence levels
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- [ ] Manual BUY/SELL buttons work
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- [ ] Executed vs blocked signals displayed
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- [ ] Current position shows correctly
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- [ ] Session P&L updates in real-time
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### **✅ COB Integration**
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- [ ] System status shows "COB: Active"
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- [ ] ETH/USDT COB data displays
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- [ ] BTC/USDT COB data displays
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- [ ] Order book metrics update
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### **✅ Training Pipeline**
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- [ ] CNN model status shows "Active"
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- [ ] RL model status shows "Training"
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- [ ] Training metrics update
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- [ ] Model performance data available
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### **✅ Performance**
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- [ ] Chart updates every second
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- [ ] No flickering or data loss
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- [ ] WebSocket connection stable
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- [ ] Memory usage reasonable
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we should load the models in a way that we do a back propagation and other model specificic training at realtime as training examples emerge from the realtime data we process. we will save only the best examples (the realtime data dumps we feed to the models) so we can cold start other models if we change the architecture. if it's not working, perform a cleanup of all traininn and trainer code to make it easer to work withm to streamline latest changes and to simplify and refactor it |