wip
This commit is contained in:
Dobromir Popov
@@ -16,7 +16,7 @@ sys.path.insert(0, str(parent_dir))
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from flask import Flask, render_template, request, jsonify, send_file
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from dash import Dash, html
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import logging
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from datetime import datetime, timezone
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from datetime import datetime, timezone, timedelta
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from typing import Optional, Dict, List, Any
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import json
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import pandas as pd
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@@ -2254,11 +2254,19 @@ class AnnotationDashboard:
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if cnn_preds:
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predictions['cnn'] = cnn_preds[-1]
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# Transformer predictions
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# Transformer predictions with next_candles for ghost candles
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# First check if there are stored predictions from the inference loop
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if hasattr(self.orchestrator, 'recent_transformer_predictions') and symbol in self.orchestrator.recent_transformer_predictions:
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transformer_preds = list(self.orchestrator.recent_transformer_predictions[symbol])
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if transformer_preds:
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# Use the most recent stored prediction (from inference loop)
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predictions['transformer'] = transformer_preds[-1]
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logger.debug(f"Using stored prediction: {list(transformer_preds[-1].keys())}")
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else:
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# Fallback: generate new prediction if no stored predictions
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transformer_pred = self._get_live_transformer_prediction(symbol)
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if transformer_pred:
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predictions['transformer'] = transformer_pred
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if predictions:
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response['prediction'] = predictions
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@@ -2497,6 +2505,110 @@ class AnnotationDashboard:
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self._live_update_thread = threading.Thread(target=live_update_worker, daemon=True)
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self._live_update_thread.start()
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def _get_live_transformer_prediction(self, symbol: str = 'ETH/USDT'):
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"""
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Generate live transformer prediction with next_candles for ghost candle display
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"""
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try:
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if not self.orchestrator:
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logger.debug("No orchestrator - cannot generate predictions")
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return None
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if not hasattr(self.orchestrator, 'primary_transformer'):
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logger.debug("Orchestrator has no primary_transformer - enable training first")
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return None
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transformer = self.orchestrator.primary_transformer
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if not transformer:
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logger.debug("primary_transformer is None - model not loaded yet")
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return None
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transformer.eval()
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# Get recent market data
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price_data_1s = self.data_provider.get_ohlcv(symbol, '1s', limit=200) if self.data_provider else None
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price_data_1m = self.data_provider.get_ohlcv(symbol, '1m', limit=150) if self.data_provider else None
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price_data_1h = self.data_provider.get_ohlcv(symbol, '1h', limit=24) if self.data_provider else None
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price_data_1d = self.data_provider.get_ohlcv(symbol, '1d', limit=14) if self.data_provider else None
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btc_data_1m = self.data_provider.get_ohlcv('BTC/USDT', '1m', limit=150) if self.data_provider else None
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if not price_data_1m or len(price_data_1m) < 10:
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return None
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import torch
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import numpy as np
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device = next(transformer.parameters()).device
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def ohlcv_to_tensor(data, limit=None):
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if not data:
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return None
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data = data[-limit:] if limit and len(data) > limit else data
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arr = np.array([[d['open'], d['high'], d['low'], d['close'], d['volume']] for d in data], dtype=np.float32)
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return torch.from_numpy(arr).unsqueeze(0).to(device)
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inputs = {
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'price_data_1s': ohlcv_to_tensor(price_data_1s, 200),
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'price_data_1m': ohlcv_to_tensor(price_data_1m, 150),
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'price_data_1h': ohlcv_to_tensor(price_data_1h, 24),
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'price_data_1d': ohlcv_to_tensor(price_data_1d, 14),
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'btc_data_1m': ohlcv_to_tensor(btc_data_1m, 150)
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}
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# Forward pass
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with torch.no_grad():
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outputs = transformer(**inputs)
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# Extract next_candles
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next_candles = outputs.get('next_candles', {})
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if not next_candles:
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return None
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# Convert to JSON-serializable format
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predicted_candle = {}
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for tf, candle_tensor in next_candles.items():
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if candle_tensor is not None:
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candle_values = candle_tensor.squeeze(0).cpu().numpy().tolist()
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predicted_candle[tf] = candle_values
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current_price = price_data_1m[-1]['close']
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predicted_1m_close = predicted_candle.get('1m', [0,0,0,current_price,0])[3]
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price_change = (predicted_1m_close - current_price) / current_price
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if price_change > 0.001:
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action = 'BUY'
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elif price_change < -0.001:
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action = 'SELL'
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else:
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action = 'HOLD'
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confidence = 0.7
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if 'confidence' in outputs:
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conf_tensor = outputs['confidence']
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confidence = float(conf_tensor.squeeze(0).cpu().numpy()[0])
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prediction = {
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'timestamp': datetime.now().isoformat(),
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'symbol': symbol,
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'action': action,
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'confidence': confidence,
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'predicted_price': predicted_1m_close,
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'current_price': current_price,
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'price_change': price_change,
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'predicted_candle': predicted_candle, # This is what frontend needs!
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'type': 'transformer_prediction'
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}
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# Store for tracking
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self.orchestrator.store_transformer_prediction(symbol, prediction)
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logger.debug(f"Generated transformer prediction with {len(predicted_candle)} timeframes for ghost candles")
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return prediction
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except Exception as e:
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logger.error(f"Error generating live transformer prediction: {e}", exc_info=True)
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return None
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def _train_on_validated_prediction(self, timeframe: str, timestamp: str, predicted: list,
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actual: list, errors: dict, direction_correct: bool, accuracy: float):
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"""
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