Compare commits
3 Commits
b404191ffa
...
kiro
| Author | SHA1 | Date | |
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d49a473ed6 | ||
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fc1ac2061f | ||
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300cf3eb2c |
24
NN/training/samples/curl.txt
Normal file
24
NN/training/samples/curl.txt
Normal file
@@ -0,0 +1,24 @@
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so, curl example:
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curl http://localhost:1234/v1/chat/completions \
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-H "Content-Type: application/json" \
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-d '{
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"model": "deepseek-r1-0528-qwen3-8b",
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"messages": [
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{ "role": "system", "content": "what will be the next row in this sequence:
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symbol MAIN SYMBOL (ETH) REF1 (BTC) REF2 (SPX) REF3 (SOL)
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timeframe 1s 1m 1h 1d 1s 1s 1s
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datapoint O H L C V Timestamp O H L C V Timestamp O H L C V Timestamp O H L C V Timestamp O H L C V Timestamp O H L C V Timestamp O H L C V Timestamp
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2025-01-15T10:00:00Z 3421.5 3421.75 3421.25 3421.6 125.4 2025-01-15T10:00:00Z 3422.1 3424.8 3420.5 3423.25 1245.7 2025-01-15T10:00:00Z 3420 3428.5 3418.75 3425.1 12847.2 2025-01-15T10:00:00Z 3415.25 3435.6 3410.8 3430.4 145238.6 2025-01-15T10:00:00Z 97850.2 97852.4 97848.1 97851.3 8.7 2025-01-15T10:00:00Z 5925.4 5926.1 5924.8 5925.7 0 2025-01-15T10:00:00Z 191.22 191.45 191.08 191.35 1247.3
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2025-01-15T10:00:01Z 3421.6 3421.85 3421.45 3421.75 98.2 2025-01-15T10:01:00Z 3423.25 3425.9 3421.8 3424.6 1189.3 2025-01-15T11:00:00Z 3425.1 3432.2 3422.4 3429.8 11960.5 2025-01-16T10:00:00Z 3430.4 3445.2 3425.15 3440.85 138947.1 2025-01-15T10:00:01Z 97851.3 97853.8 97849.5 97852.9 9.1 2025-01-15T10:00:01Z 5925.7 5926.3 5925.2 5925.9 0 2025-01-15T10:00:01Z 191.35 191.58 191.15 191.48 1156.7
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2025-01-15T10:00:02Z 3421.75 3421.95 3421.55 3421.8 110.6 2025-01-15T10:02:00Z 3424.6 3427.15 3423.4 3425.9 1356.8 2025-01-15T12:00:00Z 3429.8 3436.7 3427.2 3434.5 13205.9 2025-01-17T10:00:00Z 3440.85 3455.3 3438.9 3450.75 142568.3 2025-01-15T10:00:02Z 97852.9 97855.2 97850.7 97854.6 7.9 2025-01-15T10:00:02Z 5925.9 5926.5 5925.4 5926.1 0 2025-01-15T10:00:02Z 191.48 191.72 191.28 191.61 1298.4
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2025-01-15T10:00:03Z 3421.8 3422.05 3421.65 3421.9 87.3 2025-01-15T10:03:00Z 3425.9 3428.4 3424.2 3427.1 1423.5 2025-01-15T13:00:00Z 3434.5 3441.8 3432.1 3438.2 14087.6 2025-01-18T10:00:00Z 3450.75 3465.4 3448.6 3460.2 149825.7 2025-01-15T10:00:03Z 97854.6 97857.1 97852.3 97856.8 8.4 2025-01-15T10:00:03Z 5926.1 5926.7 5925.6 5926.3 0 2025-01-15T10:00:03Z 191.61 191.85 191.42 191.74 1187.9
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2025-01-15T10:00:04Z 3421.9 3422.15 3421.75 3422.0 134.7 2025-01-15T10:04:00Z 3427.1 3429.6 3425.8 3428.3 1298.2 2025-01-15T14:00:00Z 3438.2 3445.6 3436.4 3442.1 12734.8 2025-01-19T10:00:00Z 3460.2 3475.8 3457.4 3470.6 156742.4 2025-01-15T10:00:04Z 97856.8 97859.4 97854.9 97858.2 9.2 2025-01-15T10:00:04Z 5926.3 5926.9 5925.8 5926.5 0 2025-01-15T10:00:04Z 191.74 191.98 191.55 191.87 1342.6
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2025-01-15T10:00:05Z 3422.0 3422.25 3421.85 3422.1 156.8 2025-01-15T10:05:00Z 3428.3 3430.8 3426.9 3429.5 1467.9 2025-01-15T15:00:00Z 3442.1 3449.3 3440.7 3446.8 11823.4 2025-01-20T10:00:00Z 3470.6 3485.2 3467.9 3480.1 163456.8 2025-01-15T10:00:05Z 97858.2 97860.7 97856.4 97859.8 8.8 2025-01-15T10:00:05Z 5926.5 5927.1 5926.0 5926.7 0 2025-01-15T10:00:05Z 191.87 192.11 191.68 192.0 1278.5
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" },
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{ "role": "user", "content": "2025-01-15T10:00:06Z" }
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],
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"temperature": 0.7,
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"max_tokens": -1,
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"stream": false
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}'
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@@ -9,3 +9,8 @@ datapoint O H L C V Timestamp O H L C V Timestamp O H L C V Timestamp O H L C V
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2025-01-15T10:00:03Z 3421.8 3422.05 3421.65 3421.9 87.3 2025-01-15T10:03:00Z 3425.9 3428.4 3424.2 3427.1 1423.5 2025-01-15T13:00:00Z 3434.5 3441.8 3432.1 3438.2 14087.6 2025-01-18T10:00:00Z 3450.75 3465.4 3448.6 3460.2 149825.7 2025-01-15T10:00:03Z 97854.6 97857.1 97852.3 97856.8 8.4 2025-01-15T10:00:03Z 5926.1 5926.7 5925.6 5926.3 0 2025-01-15T10:00:03Z 191.61 191.85 191.42 191.74 1187.9
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2025-01-15T10:00:04Z 3421.9 3422.15 3421.75 3422.0 134.7 2025-01-15T10:04:00Z 3427.1 3429.6 3425.8 3428.3 1298.2 2025-01-15T14:00:00Z 3438.2 3445.6 3436.4 3442.1 12734.8 2025-01-19T10:00:00Z 3460.2 3475.8 3457.4 3470.6 156742.4 2025-01-15T10:00:04Z 97856.8 97859.4 97854.9 97858.2 9.2 2025-01-15T10:00:04Z 5926.3 5926.9 5925.8 5926.5 0 2025-01-15T10:00:04Z 191.74 191.98 191.55 191.87 1342.6
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2025-01-15T10:00:05Z 3422.0 3422.25 3421.85 3422.1 156.8 2025-01-15T10:05:00Z 3428.3 3430.8 3426.9 3429.5 1467.9 2025-01-15T15:00:00Z 3442.1 3449.3 3440.7 3446.8 11823.4 2025-01-20T10:00:00Z 3470.6 3485.2 3467.9 3480.1 163456.8 2025-01-15T10:00:05Z 97858.2 97860.7 97856.4 97859.8 8.8 2025-01-15T10:00:05Z 5926.5 5927.1 5926.0 5926.7 0 2025-01-15T10:00:05Z 191.87 192.11 191.68 192.0 1278.5
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4
NN/training/samples/txt/market_data_20250826_2129.txt
Normal file
4
NN/training/samples/txt/market_data_20250826_2129.txt
Normal file
@@ -0,0 +1,4 @@
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symbol MAIN SYMBOL (ETH) REF1 (BTC) REF2 (SPX) REF3 (SOL)
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timeframe 1s 1m 1h 1d 1s 1s 1s
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datapoint O H L C V Timestamp O H L C V Timestamp O H L C V Timestamp O H L C V Timestamp O H L C V Timestamp O H L C V Timestamp O H L C V Timestamp
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2025-08-26T21:29:44Z 0 0 0 0 0 2025-08-26T21:29:44Z 0 0 0 0 0 2025-08-26T21:29:44Z 0 0 0 0 0 2025-08-26T21:29:44Z 0 0 0 0 0 2025-08-26T21:29:44Z 0 0 0 0 0 2025-08-26T21:29:44Z 5500.00 5520.00 5495.00 5510.00 1000000.0 2025-08-26T21:29:44Z 0 0 0 0 0 2025-08-26T21:29:44Z
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@@ -302,12 +302,13 @@ class EnhancedRewardCalculator:
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normalized_mse = min(mse / max_mse, 1.0)
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mse_reward = np.exp(-5 * normalized_mse) # Exponential decay, range [exp(-5), 1]
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# Direction accuracy bonus/penalty
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# Direction accuracy bonus/penalty (stronger punishment for wrong direction)
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direction_correct = (prediction.predicted_direction == actual_direction)
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direction_bonus = 0.5 if direction_correct else -0.5
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# Increase wrong-direction penalty; reduce correct-direction bonus slightly
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direction_bonus = 0.25 if direction_correct else -1.0
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# Confidence scaling
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confidence_weight = prediction.confidence
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# Confidence scaling (apply floor to avoid near-zero scaling)
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confidence_weight = max(prediction.confidence, 0.2)
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# Final reward calculation
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base_reward = mse_reward + direction_bonus
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@@ -7042,7 +7042,9 @@ class TradingOrchestrator:
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'main_symbol': self.symbol,
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'ref1_symbol': self.ref_symbols[0] if self.ref_symbols else 'BTC/USDT',
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'ref2_symbol': 'SPX', # Default to SPX for now
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'export_dir': 'NN/training/samples/txt'
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'ref3_symbol': 'SOL/USDT',
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'export_dir': 'NN/training/samples/txt',
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'export_format': 'PIPE'
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}
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self.text_export_manager.export_config.update(export_config)
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@@ -44,7 +44,9 @@ class TextDataExporter:
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export_dir: str = "NN/training/samples/txt",
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main_symbol: str = "ETH/USDT",
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ref1_symbol: str = "BTC/USDT",
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ref2_symbol: str = "SPX"):
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ref2_symbol: str = "SPX",
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ref3_symbol: str = "SOL/USDT",
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export_format: str = "PIPE"):
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"""
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Initialize text data exporter
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@@ -60,6 +62,8 @@ class TextDataExporter:
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self.main_symbol = main_symbol
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self.ref1_symbol = ref1_symbol
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self.ref2_symbol = ref2_symbol
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self.ref3_symbol = ref3_symbol
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self.export_format = export_format.upper() if isinstance(export_format, str) else "PIPE"
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# Timeframes to export
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self.timeframes = ['1s', '1m', '1h', '1d']
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@@ -77,7 +81,7 @@ class TextDataExporter:
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os.makedirs(self.export_dir, exist_ok=True)
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logger.info(f"Text Data Exporter initialized - Export dir: {self.export_dir}")
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logger.info(f"Symbols: MAIN={main_symbol}, REF1={ref1_symbol}, REF2={ref2_symbol}")
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logger.info(f"Symbols: MAIN={main_symbol}, REF1={ref1_symbol}, REF2={ref2_symbol}, REF3={ref3_symbol}")
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def start(self):
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"""Start the data export process"""
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@@ -140,7 +144,8 @@ class TextDataExporter:
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symbols = [
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("MAIN", self.main_symbol),
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("REF1", self.ref1_symbol),
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("REF2", self.ref2_symbol)
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("REF2", self.ref2_symbol),
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("REF3", self.ref3_symbol)
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]
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for symbol_type, symbol in symbols:
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@@ -168,11 +173,27 @@ class TextDataExporter:
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def _get_latest_data(self, symbol: str, timeframe: str) -> Optional[MarketDataPoint]:
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"""Get latest market data for symbol/timeframe"""
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try:
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if not hasattr(self.data_provider, 'get_latest_candle'):
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return None
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# Try to get latest candle data
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candle = self.data_provider.get_latest_candle(symbol, timeframe)
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candle = None
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# Try direct method
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if hasattr(self.data_provider, 'get_latest_candle'):
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candle = self.data_provider.get_latest_candle(symbol, timeframe)
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# Fallback to historical last row
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if (not candle) and hasattr(self.data_provider, 'get_historical_data'):
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try:
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df = self.data_provider.get_historical_data(symbol, timeframe, limit=1)
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if df is not None and not df.empty:
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latest = df.iloc[-1]
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ts = df.index[-1]
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candle = {
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'open': latest.get('open', 0),
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'high': latest.get('high', 0),
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'low': latest.get('low', 0),
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'close': latest.get('close', 0),
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'volume': latest.get('volume', 0),
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'timestamp': ts.to_pydatetime() if hasattr(ts, 'to_pydatetime') else ts
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}
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except Exception as _ex:
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logger.debug(f"hist fallback failed for {symbol} {timeframe}: {_ex}")
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if not candle:
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return None
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@@ -205,8 +226,10 @@ class TextDataExporter:
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grouped_data = self._group_data_by_symbol(export_data)
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with open(filepath, 'w', encoding='utf-8') as txtfile:
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# Write in the format specified in readme.md sample
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self._write_tab_format(txtfile, grouped_data)
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if self.export_format == 'TAB':
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self._write_tab_format(txtfile, grouped_data)
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else:
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self._write_pipe_format(txtfile, grouped_data)
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logger.debug(f"Exported {len(export_data)} data points to {filepath}")
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@@ -280,56 +303,105 @@ class TextDataExporter:
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rows.append(row)
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return rows
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def _write_tab_format(self, txtfile, grouped_data: Dict[str, Dict[str, Dict[str, Any]]]):
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def _write_tab_format(self, txtfile, grouped_data: Dict[str, Dict[str, Dict[str, Any]]] ):
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"""Write data in tab-separated format like readme.md sample"""
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# Write header structure
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txtfile.write("symbol\tMAIN SYMBOL (ETH)\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tREF1 (BTC)\t\t\t\t\t\tREF2 (SPX)\t\t\t\t\t\tREF3 (SOL)\n")
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txtfile.write("symbol\tMAIN SYMBOL (ETH)\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tREF1 (BTC)\t\t\t\t\t\t\tREF2 (SPX)\t\t\t\t\t\t\tREF3 (SOL)\n")
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txtfile.write("timeframe\t1s\t\t\t\t\t\t1m\t\t\t\t\t\t1h\t\t\t\t\t\t1d\t\t\t\t\t\t1s\t\t\t\t\t\t1s\t\t\t\t\t\t1s\n")
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txtfile.write("datapoint\tO\tH\tL\tC\tV\tTimestamp\tO\tH\tL\tC\tV\tTimestamp\tO\tH\tL\tC\tV\tTimestamp\tO\tH\tL\tC\tV\tTimestamp\tO\tH\tL\tC\tV\tTimestamp\tO\tH\tL\tC\tV\tTimestamp\tO\tH\tL\tC\tV\tTimestamp\n")
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# Write data row
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row_parts = []
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current_time = datetime.now()
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# Build up to 300 rows using historical 1s data when available
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# Collect timestamps from MAIN 1s historical data
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timestamps: List[Any] = []
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hist_df = None
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try:
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if hasattr(self.data_provider, 'get_historical_data'):
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hist_df = self.data_provider.get_historical_data(self.main_symbol, '1s', limit=300)
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if hist_df is not None and not hist_df.empty:
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timestamps = list(hist_df.index[-300:])
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except Exception as _e:
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logger.debug(f"hist 1s not available: {_e}")
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if not timestamps:
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timestamps = [datetime.utcnow()]
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# Timestamp first
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row_parts.append(current_time.strftime("%Y-%m-%dT%H:%M:%SZ"))
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# ETH data for all timeframes (1s, 1m, 1h, 1d)
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# Fetch snapshots for non-1s
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main_data = grouped_data.get('MAIN', {})
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for timeframe in ['1s', '1m', '1h', '1d']:
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data_point = main_data.get(timeframe)
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if data_point:
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main_1m = main_data.get('1m')
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main_1h = main_data.get('1h')
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main_1d = main_data.get('1d')
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ref1_1s = grouped_data.get('REF1', {}).get('1s')
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ref2_1s = grouped_data.get('REF2', {}).get('1s')
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ref3_1s = grouped_data.get('REF3', {}).get('1s')
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for ts in timestamps:
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try:
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ts_str = ts.strftime("%Y-%m-%dT%H:%M:%SZ") if hasattr(ts, 'strftime') else str(ts)
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except Exception:
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ts_str = ""
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row_parts = [ts_str]
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# MAIN 1s from hist row if possible
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if hist_df is not None and ts in hist_df.index:
|
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r = hist_df.loc[ts]
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row_parts.extend([
|
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f"{data_point['open']:.2f}",
|
||||
f"{data_point['high']:.2f}",
|
||||
f"{data_point['low']:.2f}",
|
||||
f"{data_point['close']:.2f}",
|
||||
f"{data_point['volume']:.1f}",
|
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data_point['timestamp'].strftime("%Y-%m-%dT%H:%M:%SZ")
|
||||
f"{float(r.get('open', 0) or 0):.2f}",
|
||||
f"{float(r.get('high', 0) or 0):.2f}",
|
||||
f"{float(r.get('low', 0) or 0):.2f}",
|
||||
f"{float(r.get('close', 0) or 0):.2f}",
|
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f"{float(r.get('volume', 0) or 0):.1f}",
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ts_str
|
||||
])
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||||
else:
|
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row_parts.extend(["0", "0", "0", "0", "0", current_time.strftime("%Y-%m-%dT%H:%M:%SZ")])
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snap = main_data.get('1s')
|
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if snap:
|
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row_parts.extend([
|
||||
f"{snap['open']:.2f}", f"{snap['high']:.2f}", f"{snap['low']:.2f}", f"{snap['close']:.2f}", f"{snap['volume']:.1f}", ts_str
|
||||
])
|
||||
else:
|
||||
row_parts.extend(["0", "0", "0", "0", "0", ts_str])
|
||||
|
||||
# REF1 (BTC), REF2 (SPX), REF3 (SOL) - 1s timeframe only
|
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for ref_type in ['REF1', 'REF2']: # REF3 will be added by LLM proxy
|
||||
ref_data = grouped_data.get(ref_type, {})
|
||||
data_point = ref_data.get('1s')
|
||||
if data_point:
|
||||
row_parts.extend([
|
||||
f"{data_point['open']:.2f}",
|
||||
f"{data_point['high']:.2f}",
|
||||
f"{data_point['low']:.2f}",
|
||||
f"{data_point['close']:.2f}",
|
||||
f"{data_point['volume']:.1f}",
|
||||
data_point['timestamp'].strftime("%Y-%m-%dT%H:%M:%SZ")
|
||||
])
|
||||
else:
|
||||
row_parts.extend(["0", "0", "0", "0", "0", current_time.strftime("%Y-%m-%dT%H:%M:%SZ")])
|
||||
# MAIN 1m/1h/1d snapshots
|
||||
for snap in [main_1m, main_1h, main_1d]:
|
||||
if snap:
|
||||
row_parts.extend([
|
||||
f"{snap['open']:.2f}", f"{snap['high']:.2f}", f"{snap['low']:.2f}", f"{snap['close']:.2f}", f"{snap['volume']:.1f}", snap['timestamp'].strftime("%Y-%m-%dT%H:%M:%SZ")
|
||||
])
|
||||
else:
|
||||
row_parts.extend(["0", "0", "0", "0", "0", ts_str])
|
||||
|
||||
# Add placeholder for REF3 (SOL) - will be filled by LLM proxy
|
||||
row_parts.extend(["0", "0", "0", "0", "0", current_time.strftime("%Y-%m-%dT%H:%M:%SZ")])
|
||||
# REF1/REF2/REF3 1s snapshots
|
||||
for snap in [ref1_1s, ref2_1s, ref3_1s]:
|
||||
if snap:
|
||||
row_parts.extend([
|
||||
f"{snap['open']:.2f}", f"{snap['high']:.2f}", f"{snap['low']:.2f}", f"{snap['close']:.2f}", f"{snap['volume']:.1f}", snap['timestamp'].strftime("%Y-%m-%dT%H:%M:%SZ")
|
||||
])
|
||||
else:
|
||||
row_parts.extend(["0", "0", "0", "0", "0", ts_str])
|
||||
|
||||
txtfile.write("\t".join(row_parts) + "\n")
|
||||
txtfile.write("\t".join(row_parts) + "\n")
|
||||
|
||||
def _write_pipe_format(self, txtfile, grouped_data: Dict[str, Dict[str, Dict[str, Any]]] ):
|
||||
"""Write data in pipe-delimited console-friendly grid."""
|
||||
from io import StringIO
|
||||
buffer = StringIO()
|
||||
self._write_tab_format(buffer, grouped_data)
|
||||
content = buffer.getvalue().splitlines()
|
||||
if not content:
|
||||
return
|
||||
headers = [line.split('\t') for line in content[:3]]
|
||||
data_rows = [line.split('\t') for line in content[3:]]
|
||||
|
||||
def to_pipe(row: List[str]) -> str:
|
||||
return "|" + "|".join(str(col) for col in row) + "|"
|
||||
|
||||
# Render header rows with separators
|
||||
for hdr in headers:
|
||||
txtfile.write(to_pipe(hdr) + "\n")
|
||||
txtfile.write("|" + "|".join(["-" * max(3, len(str(c))) for c in hdr]) + "|\n")
|
||||
|
||||
# Render data
|
||||
for row in data_rows:
|
||||
txtfile.write(to_pipe(row) + "\n")
|
||||
|
||||
def get_current_filename(self) -> Optional[str]:
|
||||
"""Get current export filename"""
|
||||
|
||||
@@ -33,7 +33,9 @@ class TextExportManager:
|
||||
'main_symbol': 'ETH/USDT',
|
||||
'ref1_symbol': 'BTC/USDT',
|
||||
'ref2_symbol': 'SPX', # Will need to be mapped to available data
|
||||
'export_dir': 'NN/training/samples/txt'
|
||||
'ref3_symbol': 'SOL/USDT',
|
||||
'export_dir': 'NN/training/samples/txt',
|
||||
'export_format': 'PIPE' # PIPE (default) or TAB
|
||||
}
|
||||
|
||||
def initialize_exporter(self, config: Optional[Dict[str, Any]] = None):
|
||||
@@ -54,7 +56,9 @@ class TextExportManager:
|
||||
export_dir=self.export_config['export_dir'],
|
||||
main_symbol=self.export_config['main_symbol'],
|
||||
ref1_symbol=self.export_config['ref1_symbol'],
|
||||
ref2_symbol=self.export_config['ref2_symbol']
|
||||
ref2_symbol=self.export_config['ref2_symbol'],
|
||||
ref3_symbol=self.export_config.get('ref3_symbol', 'SOL/USDT'),
|
||||
export_format=self.export_config.get('export_format', 'PIPE')
|
||||
)
|
||||
|
||||
logger.info("Text data exporter initialized successfully")
|
||||
@@ -198,16 +202,8 @@ class EnhancedDataProviderWrapper:
|
||||
|
||||
def _get_spx_data(self) -> Optional[Dict[str, Any]]:
|
||||
"""Get SPX data - placeholder for now"""
|
||||
# For now, return mock SPX data
|
||||
# In production, this would connect to a stock data provider
|
||||
return {
|
||||
'open': 5500.0,
|
||||
'high': 5520.0,
|
||||
'low': 5495.0,
|
||||
'close': 5510.0,
|
||||
'volume': 1000000,
|
||||
'timestamp': datetime.now()
|
||||
}
|
||||
# No synthetic data allowed; return None if not available
|
||||
return None
|
||||
|
||||
# Integration helper functions
|
||||
def setup_text_export(data_provider=None, orchestrator=None, config: Optional[Dict[str, Any]] = None) -> TextExportManager:
|
||||
|
||||
@@ -1921,6 +1921,86 @@ class CleanTradingDashboard:
|
||||
logger.error(f"Error closing position manually: {e}")
|
||||
return [html.I(className="fas fa-times me-1"), "CLOSE"]
|
||||
|
||||
# Text Export Controls
|
||||
@self.app.callback(
|
||||
Output('text-export-status', 'children'),
|
||||
[Input('start-text-export-btn', 'n_clicks'),
|
||||
Input('stop-text-export-btn', 'n_clicks')],
|
||||
prevent_initial_call=True
|
||||
)
|
||||
def handle_text_export_controls(start_clicks, stop_clicks):
|
||||
"""Handle text export start/stop buttons"""
|
||||
ctx = dash.callback_context
|
||||
if not ctx.triggered:
|
||||
raise PreventUpdate
|
||||
|
||||
button_id = ctx.triggered[0]['prop_id'].split('.')[0]
|
||||
|
||||
try:
|
||||
if button_id == 'start-text-export-btn' and start_clicks:
|
||||
success = self.orchestrator.start_text_export()
|
||||
if success:
|
||||
logger.info("Text export started from dashboard")
|
||||
return "Export: Running"
|
||||
else:
|
||||
logger.error("Failed to start text export")
|
||||
return "Export: Failed to start"
|
||||
|
||||
elif button_id == 'stop-text-export-btn' and stop_clicks:
|
||||
success = self.orchestrator.stop_text_export()
|
||||
if success:
|
||||
logger.info("Text export stopped from dashboard")
|
||||
return "Export: Stopped"
|
||||
else:
|
||||
logger.error("Failed to stop text export")
|
||||
return "Export: Failed to stop"
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error in text export controls: {e}")
|
||||
return f"Export: Error - {str(e)}"
|
||||
|
||||
raise PreventUpdate
|
||||
|
||||
# LLM Proxy Controls
|
||||
@self.app.callback(
|
||||
Output('llm-status', 'children'),
|
||||
[Input('start-llm-btn', 'n_clicks'),
|
||||
Input('stop-llm-btn', 'n_clicks')],
|
||||
prevent_initial_call=True
|
||||
)
|
||||
def handle_llm_controls(start_clicks, stop_clicks):
|
||||
"""Handle LLM proxy start/stop buttons"""
|
||||
ctx = dash.callback_context
|
||||
if not ctx.triggered:
|
||||
raise PreventUpdate
|
||||
|
||||
button_id = ctx.triggered[0]['prop_id'].split('.')[0]
|
||||
|
||||
try:
|
||||
if button_id == 'start-llm-btn' and start_clicks:
|
||||
success = self.orchestrator.start_llm_proxy()
|
||||
if success:
|
||||
logger.info("LLM proxy started from dashboard")
|
||||
return "LLM: Running"
|
||||
else:
|
||||
logger.error("Failed to start LLM proxy")
|
||||
return "LLM: Failed to start"
|
||||
|
||||
elif button_id == 'stop-llm-btn' and stop_clicks:
|
||||
success = self.orchestrator.stop_llm_proxy()
|
||||
if success:
|
||||
logger.info("LLM proxy stopped from dashboard")
|
||||
return "LLM: Stopped"
|
||||
else:
|
||||
logger.error("Failed to stop LLM proxy")
|
||||
return "LLM: Failed to stop"
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error in LLM controls: {e}")
|
||||
return f"LLM: Error - {str(e)}"
|
||||
|
||||
raise PreventUpdate
|
||||
|
||||
# Leverage slider callback
|
||||
@self.app.callback(
|
||||
Output('leverage-display', 'children'),
|
||||
@@ -2899,7 +2979,7 @@ class CleanTradingDashboard:
|
||||
hold_predictions = []
|
||||
|
||||
for pred in dqn_predictions[-30:]: # Last 30 DQN predictions
|
||||
action = pred.get('action', 2) # 0=BUY, 1=SELL, 2=HOLD
|
||||
action = pred.get('action', 2) # 0=SELL, 1=HOLD, 2=BUY
|
||||
confidence = pred.get('confidence', 0)
|
||||
timestamp = pred.get('timestamp', datetime.now())
|
||||
price = pred.get('price', 0)
|
||||
@@ -2916,9 +2996,10 @@ class CleanTradingDashboard:
|
||||
'q_values': pred.get('q_values', [0, 0, 0])
|
||||
}
|
||||
|
||||
if action == 0: # BUY
|
||||
# Correct mapping: 2 -> BUY, 0 -> SELL, 1 -> HOLD
|
||||
if action == 2: # BUY
|
||||
buy_predictions.append(pred_data)
|
||||
elif action == 1: # SELL
|
||||
elif action == 0: # SELL
|
||||
sell_predictions.append(pred_data)
|
||||
else: # HOLD
|
||||
hold_predictions.append(pred_data)
|
||||
@@ -3329,10 +3410,6 @@ class CleanTradingDashboard:
|
||||
try:
|
||||
predictions = []
|
||||
|
||||
# Generate sample predictions if needed (for display purposes)
|
||||
if hasattr(self.orchestrator, 'generate_sample_predictions_for_display'):
|
||||
self.orchestrator.generate_sample_predictions_for_display(symbol)
|
||||
|
||||
# Get REAL predictions from orchestrator
|
||||
if hasattr(self.orchestrator, 'recent_dqn_predictions'):
|
||||
predictions.extend(list(self.orchestrator.recent_dqn_predictions.get(symbol, [])))
|
||||
|
||||
@@ -320,6 +320,33 @@ class DashboardLayoutManager:
|
||||
html.I(className="fas fa-arrows-rotate me-1"),
|
||||
"Sync Positions/Orders"
|
||||
], id="manual-sync-btn", className="btn btn-primary btn-sm w-100 mt-2"),
|
||||
|
||||
# Text Export Controls
|
||||
html.Hr(className="my-2"),
|
||||
html.Small("Text Export & LLM", className="text-muted d-block mb-1"),
|
||||
html.Div([
|
||||
html.Button([
|
||||
html.I(className="fas fa-file-export me-1"),
|
||||
"Start Text Export"
|
||||
], id="start-text-export-btn", className="btn btn-success btn-sm me-1", style={"fontSize": "10px"}),
|
||||
html.Button([
|
||||
html.I(className="fas fa-stop me-1"),
|
||||
"Stop"
|
||||
], id="stop-text-export-btn", className="btn btn-danger btn-sm", style={"fontSize": "10px"})
|
||||
], className="d-flex mb-2"),
|
||||
html.Div([
|
||||
html.Button([
|
||||
html.I(className="fas fa-robot me-1"),
|
||||
"Start LLM"
|
||||
], id="start-llm-btn", className="btn btn-info btn-sm me-1", style={"fontSize": "10px"}),
|
||||
html.Button([
|
||||
html.I(className="fas fa-stop me-1"),
|
||||
"Stop"
|
||||
], id="stop-llm-btn", className="btn btn-warning btn-sm", style={"fontSize": "10px"})
|
||||
], className="d-flex mb-2"),
|
||||
html.Small(id="text-export-status", children="Export: Stopped", className="text-muted d-block"),
|
||||
html.Small(id="llm-status", children="LLM: Stopped", className="text-muted d-block"),
|
||||
|
||||
html.Hr(className="my-2"),
|
||||
html.Small("System Status", className="text-muted d-block mb-1"),
|
||||
html.Div([
|
||||
|
||||
Reference in New Issue
Block a user