>> Models 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 add integration of the checkpoint manager to all training pipelines 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) . remove wandb integration from the training pipeline do we load the best model for each model type? or we do a cold start each time? >> UI we stopped showing executed trades on the chart. let's add them back . update chart every second as well. the list with closed trades is not updated. clear session button does not clear all data. 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) >> Training how effective is our training? show current loss and accuracy on the chart. also show currently loaded models for each model type >> Training 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 allow models to be dynamically loaded and unloaded from the webui (orchestrator) show cob data in the dashboard over ws report and audit rewards and penalties in the RL training pipeline >> clean dashboard 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 :) 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 ### **✅ Trading Integration** - [ ] Recent signals show with confidence levels - [ ] Manual BUY/SELL buttons work - [ ] Executed vs blocked signals displayed - [ ] Current position shows correctly - [ ] Session P&L updates in real-time ### **✅ COB Integration** - [ ] System status shows "COB: Active" - [ ] ETH/USDT COB data displays - [ ] BTC/USDT COB data displays - [ ] Order book metrics update ### **✅ Training Pipeline** - [ ] CNN model status shows "Active" - [ ] RL model status shows "Training" - [ ] Training metrics update - [ ] Model performance data available ### **✅ Performance** - [ ] Chart updates every second - [ ] No flickering or data loss - [ ] WebSocket connection stable - [ ] Memory usage reasonable 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 also, adjust our bybit api so we trade with usdt futures - where we can have up to 50x leverage. on spots we can have 10x max -------------- 1. on the dash buy/sell buttons do not open/close positions in live mode . 2. we also need to fix our Current Order Book data shown on the dash - it is not consistent ande definitely not fast/low latency. let's store all COB data aggregated to 1S buckets and 0.2s sec ticks. show COB datasource updte rate 3. we don't calculate the COB imbalance correctly - we have MA with 4 time windows. 4. we have some more work on the models statistics and overview but we can focust there later when we fix the other issues 5. audit and backtest if calculate_williams_pivot_points works correctly. show pivot points on the dash on the 1m candlesticks can we enhance our RL reward/punish to promote closing loosing trades and keep winning ones taking into account the predicted price direction and conviction. For example the more loosing a open position is the more we should be biased to closing it. but if the models predict with high certainty that there will be a big move up we will be more tolerant to a drawdown. and the opposite - we should be inclined to close winning trades but keep them as long as the price goes up and we project more upside. Do you think there is a smart way to implement that in the current RL and other training pipelines? I want it more to be a part of a proper rewardfunction bias rather than a algorithmic calculation on the post signal processing as I prefer that this is a behaviour the moedl learns and is adapted to the current condition without hard bowndaries. THINK REALY HARD do we evaluate and reward/punish each model at each reference? in our realtime Reinforcement learning training how do we calculate the score (reward/penalty?) Let's use the mean squared difference between the prediction and the empirical outcome. We should do a training run at each inference which will use the last inference's prediction and the current price as outcome. do that up to 6 last predictions and calculating accuracity separately to have a better picture of the ability to predict couple of timeframes in the future. additionally to the frequent inference every 1 or 5s (i forgot the curent CNN rate) do an inference at each new timeframe interval. model should get the full data (multi timeframe - ETH (main) 1s 1m 1h 1d and 1m for BTC, SPX and one more) but should also know on what timeframe it is predicting. we predict only on the main symbol - so in 4 timeframes. bur on every hour we will do 4 inferences - one for each timeframe