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refactoring, predictions WIP

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This commit is contained in:
Dobromir Popov
2025-06-13 11:48:03 +03:00
parent 543b53883e
commit 5bce17a21a
10 changed files with 14187 additions and 174 deletions
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162
.vscode/launch.json vendored
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@ -1,126 +1,7 @@
{ {
"version": "0.2.0", "version": "0.2.0",
"configurations": [ "configurations": [
{
"name": "🚀 MASSIVE RL Training (504M Parameters)",
"type": "python",
"request": "launch",
"program": "main_clean.py",
"args": [
"--mode",
"rl"
],
"console": "integratedTerminal",
"justMyCode": false,
"env": {
"PYTHONUNBUFFERED": "1",
"CUDA_VISIBLE_DEVICES": "0",
"PYTORCH_CUDA_ALLOC_CONF": "max_split_size_mb:4096"
},
"preLaunchTask": "Kill Stale Processes"
},
{
"name": "🧠 Enhanced CNN Training with Backtesting",
"type": "python",
"request": "launch",
"program": "main_clean.py",
"args": [
"--mode",
"cnn",
"--symbol",
"ETH/USDT"
],
"console": "integratedTerminal",
"justMyCode": false,
"env": {
"PYTHONUNBUFFERED": "1",
"ENABLE_BACKTESTING": "1",
"ENABLE_ANALYSIS": "1",
"CUDA_VISIBLE_DEVICES": "0"
},
"preLaunchTask": "Kill Stale Processes",
"postDebugTask": "Start TensorBoard"
},
{
"name": "🔥 Hybrid Training (CNN + RL Pipeline)",
"type": "python",
"request": "launch",
"program": "main_clean.py",
"args": [
"--mode",
"train"
],
"console": "integratedTerminal",
"justMyCode": false,
"env": {
"PYTHONUNBUFFERED": "1",
"CUDA_VISIBLE_DEVICES": "0",
"PYTORCH_CUDA_ALLOC_CONF": "max_split_size_mb:4096",
"ENABLE_HYBRID_TRAINING": "1"
},
"preLaunchTask": "Kill Stale Processes",
"postDebugTask": "Start TensorBoard"
},
{
"name": "💹 Live Scalping Dashboard (500x Leverage)",
"type": "python",
"request": "launch",
"program": "run_scalping_dashboard.py",
"args": [
"--episodes",
"1000",
"--max-position",
"0.1",
"--leverage",
"500"
],
"console": "integratedTerminal",
"justMyCode": false,
"env": {
"PYTHONUNBUFFERED": "1",
"ENABLE_MASSIVE_MODEL": "1",
"LEVERAGE_MULTIPLIER": "500",
"SCALPING_MODE": "1"
},
"preLaunchTask": "Kill Stale Processes"
},
{
"name": "🎯 Enhanced Scalping Dashboard (1s Bars + 15min Cache)",
"type": "python",
"request": "launch",
"program": "run_enhanced_scalping_dashboard.py",
"args": [
"--host",
"127.0.0.1",
"--port",
"8051",
"--log-level",
"INFO"
],
"console": "integratedTerminal",
"justMyCode": false,
"env": {
"PYTHONUNBUFFERED": "1",
"ENABLE_ENHANCED_DASHBOARD": "1",
"TICK_CACHE_MINUTES": "15",
"CANDLE_TIMEFRAME": "1s"
},
"preLaunchTask": "Kill Stale Processes"
},
{
"name": "🌙 Overnight Training Monitor (504M Model)",
"type": "python",
"request": "launch",
"program": "overnight_training_monitor.py",
"console": "integratedTerminal",
"justMyCode": false,
"env": {
"PYTHONUNBUFFERED": "1",
"MONITOR_INTERVAL": "300",
"ENABLE_PLOTS": "1",
"ENABLE_REPORTS": "1"
}
},
{ {
"name": "📊 Enhanced Web Dashboard", "name": "📊 Enhanced Web Dashboard",
"type": "python", "type": "python",
@ -155,46 +36,7 @@
"TEST_ALL_COMPONENTS": "1" "TEST_ALL_COMPONENTS": "1"
} }
}, },
{
"name": "📈 TensorBoard Monitor (All Runs)",
"type": "python",
"request": "launch",
"program": "run_tensorboard.py",
"console": "integratedTerminal",
"justMyCode": false
},
{
"name": "🎯 Live Trading (Demo Mode)",
"type": "python",
"request": "launch",
"program": "main_clean.py",
"args": [
"--mode",
"trade",
"--symbol",
"ETH/USDT"
],
"console": "integratedTerminal",
"justMyCode": false,
"env": {
"PYTHONUNBUFFERED": "1",
"DEMO_MODE": "1",
"ENABLE_MASSIVE_MODEL": "1",
"RISK_MANAGEMENT": "1"
},
"preLaunchTask": "Kill Stale Processes"
},
{
"name": "🚨 Model Parameter Audit",
"type": "python",
"request": "launch",
"program": "model_parameter_audit.py",
"console": "integratedTerminal",
"justMyCode": false,
"env": {
"PYTHONUNBUFFERED": "1"
}
},
{ {
"name": "🧪 CNN Live Training with Analysis", "name": "🧪 CNN Live Training with Analysis",
"type": "python", "type": "python",

378
core/chart_data_provider.py Normal file
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@ -0,0 +1,378 @@
#!/usr/bin/env python3
"""
Chart Data Provider Core Module
This module handles all chart data preparation and market data simulation,
separated from the web UI layer.
"""
import logging
import numpy as np
import pandas as pd
from datetime import datetime, timedelta
from typing import Dict, List, Any, Optional, Tuple
import plotly.graph_objects as go
from plotly.subplots import make_subplots
from .cnn_pivot_predictor import CNNPivotPredictor, PivotPrediction
from .pivot_detector import WilliamsPivotDetector, DetectedPivot
# Setup logging with ASCII-only output
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)
class ChartDataProvider:
"""Core chart data provider with market simulation and chart preparation"""
def __init__(self, config: Optional[Dict] = None):
self.config = config or self._default_config()
# Initialize core components
self.cnn_predictor = CNNPivotPredictor()
self.pivot_detector = WilliamsPivotDetector()
# Market data
self.current_price = 3500.0 # Starting ETH price
self.price_history: List[Dict] = []
# Initialize with sample data
self._generate_initial_data()
logger.info("Chart Data Provider initialized")
def _default_config(self) -> Dict:
"""Default configuration"""
return {
'initial_history_hours': 2,
'price_volatility': 5.0,
'volume_range': (100, 1000),
'chart_height': 600,
'subplots': True
}
def _generate_initial_data(self) -> None:
"""Generate initial price history for demonstration"""
base_time = datetime.now() - timedelta(hours=self.config['initial_history_hours'])
for i in range(120): # 2 hours of minute data
# Simulate realistic price movement
change = np.random.normal(0, self.config['price_volatility'])
self.current_price += change
# Ensure price doesn't go negative
self.current_price = max(self.current_price, 100.0)
timestamp = base_time + timedelta(minutes=i)
# Generate OHLC data
open_price = self.current_price - np.random.uniform(-2, 2)
high_price = max(open_price, self.current_price) + np.random.uniform(0, 8)
low_price = min(open_price, self.current_price) - np.random.uniform(0, 8)
close_price = self.current_price
volume = np.random.uniform(*self.config['volume_range'])
candle = {
'timestamp': timestamp,
'open': open_price,
'high': high_price,
'low': low_price,
'close': close_price,
'volume': volume
}
self.price_history.append(candle)
logger.info(f"Generated {len(self.price_history)} initial price candles")
def simulate_price_update(self) -> Dict:
"""Simulate real-time price update"""
try:
# Generate new price movement
change = np.random.normal(0, self.config['price_volatility'])
self.current_price += change
self.current_price = max(self.current_price, 100.0)
# Create new candle
timestamp = datetime.now()
open_price = self.price_history[-1]['close'] if self.price_history else self.current_price
high_price = max(open_price, self.current_price) + np.random.uniform(0, 5)
low_price = min(open_price, self.current_price) - np.random.uniform(0, 5)
close_price = self.current_price
volume = np.random.uniform(*self.config['volume_range'])
new_candle = {
'timestamp': timestamp,
'open': open_price,
'high': high_price,
'low': low_price,
'close': close_price,
'volume': volume
}
self.price_history.append(new_candle)
# Keep only last 200 candles to prevent memory growth
if len(self.price_history) > 200:
self.price_history = self.price_history[-200:]
return new_candle
except Exception as e:
logger.error(f"Error simulating price update: {e}")
return {}
def get_market_data_df(self) -> pd.DataFrame:
"""Convert price history to pandas DataFrame"""
try:
if not self.price_history:
return pd.DataFrame()
df = pd.DataFrame(self.price_history)
df['timestamp'] = pd.to_datetime(df['timestamp'])
return df
except Exception as e:
logger.error(f"Error creating DataFrame: {e}")
return pd.DataFrame()
def update_predictions_and_pivots(self) -> Tuple[List[PivotPrediction], List[DetectedPivot]]:
"""Update CNN predictions and detect new pivots"""
try:
market_df = self.get_market_data_df()
if market_df.empty:
return [], []
# Update CNN predictions
predictions = self.cnn_predictor.update_predictions(market_df, self.current_price)
# Detect pivots
detected_pivots = self.pivot_detector.detect_pivots(market_df)
# Capture training data if new pivots are found
for pivot in detected_pivots:
if pivot.confirmed:
actual_pivot = type('ActualPivot', (), {
'type': pivot.type,
'price': pivot.price,
'timestamp': pivot.timestamp,
'strength': pivot.strength
})()
self.cnn_predictor.capture_training_data(actual_pivot)
return predictions, detected_pivots
except Exception as e:
logger.error(f"Error updating predictions and pivots: {e}")
return [], []
def create_price_chart(self) -> go.Figure:
"""Create main price chart with candlesticks and volume"""
try:
market_df = self.get_market_data_df()
if market_df.empty:
return go.Figure()
# Create subplots
if self.config['subplots']:
fig = make_subplots(
rows=2, cols=1,
shared_xaxes=True,
vertical_spacing=0.05,
subplot_titles=('Price', 'Volume'),
row_width=[0.7, 0.3]
)
else:
fig = go.Figure()
# Add candlestick chart
candlestick = go.Candlestick(
x=market_df['timestamp'],
open=market_df['open'],
high=market_df['high'],
low=market_df['low'],
close=market_df['close'],
name='ETH/USDT',
increasing_line_color='#00ff88',
decreasing_line_color='#ff4444'
)
if self.config['subplots']:
fig.add_trace(candlestick, row=1, col=1)
else:
fig.add_trace(candlestick)
# Add volume bars if subplots enabled
if self.config['subplots']:
volume_colors = ['#00ff88' if close >= open else '#ff4444'
for close, open in zip(market_df['close'], market_df['open'])]
volume_bar = go.Bar(
x=market_df['timestamp'],
y=market_df['volume'],
name='Volume',
marker_color=volume_colors,
opacity=0.7
)
fig.add_trace(volume_bar, row=2, col=1)
# Update layout
fig.update_layout(
title='ETH/USDT Price Chart with CNN Predictions',
xaxis_title='Time',
yaxis_title='Price (USDT)',
height=self.config['chart_height'],
showlegend=True,
xaxis_rangeslider_visible=False
)
return fig
except Exception as e:
logger.error(f"Error creating price chart: {e}")
return go.Figure()
def add_cnn_predictions_to_chart(self, fig: go.Figure, predictions: List[PivotPrediction]) -> go.Figure:
"""Add CNN predictions as hollow circles to the chart"""
try:
if not predictions:
return fig
# Separate HIGH and LOW predictions
high_predictions = [p for p in predictions if p.type == 'HIGH']
low_predictions = [p for p in predictions if p.type == 'LOW']
# Add HIGH predictions (red hollow circles)
if high_predictions:
high_x = [p.timestamp for p in high_predictions]
high_y = [p.predicted_price for p in high_predictions]
high_sizes = [max(8, min(20, p.confidence * 25)) for p in high_predictions]
high_text = [f"HIGH Prediction<br>Price: ${p.predicted_price:.2f}<br>Confidence: {p.confidence:.1%}<br>Level: {p.level}"
for p in high_predictions]
fig.add_trace(go.Scatter(
x=high_x,
y=high_y,
mode='markers',
marker=dict(
symbol='circle-open',
size=high_sizes,
color='red',
line=dict(width=2)
),
name='CNN HIGH Predictions',
text=high_text,
hovertemplate='%{text}<extra></extra>'
))
# Add LOW predictions (green hollow circles)
if low_predictions:
low_x = [p.timestamp for p in low_predictions]
low_y = [p.predicted_price for p in low_predictions]
low_sizes = [max(8, min(20, p.confidence * 25)) for p in low_predictions]
low_text = [f"LOW Prediction<br>Price: ${p.predicted_price:.2f}<br>Confidence: {p.confidence:.1%}<br>Level: {p.level}"
for p in low_predictions]
fig.add_trace(go.Scatter(
x=low_x,
y=low_y,
mode='markers',
marker=dict(
symbol='circle-open',
size=low_sizes,
color='green',
line=dict(width=2)
),
name='CNN LOW Predictions',
text=low_text,
hovertemplate='%{text}<extra></extra>'
))
return fig
except Exception as e:
logger.error(f"Error adding CNN predictions to chart: {e}")
return fig
def add_actual_pivots_to_chart(self, fig: go.Figure, pivots: List[DetectedPivot]) -> go.Figure:
"""Add actual detected pivots as solid triangles to the chart"""
try:
if not pivots:
return fig
# Separate HIGH and LOW pivots
high_pivots = [p for p in pivots if p.type == 'HIGH']
low_pivots = [p for p in pivots if p.type == 'LOW']
# Add HIGH pivots (red triangles pointing down)
if high_pivots:
high_x = [p.timestamp for p in high_pivots]
high_y = [p.price for p in high_pivots]
high_sizes = [max(10, min(25, p.strength * 5)) for p in high_pivots]
high_text = [f"HIGH Pivot<br>Price: ${p.price:.2f}<br>Strength: {p.strength}<br>Confirmed: {p.confirmed}"
for p in high_pivots]
fig.add_trace(go.Scatter(
x=high_x,
y=high_y,
mode='markers',
marker=dict(
symbol='triangle-down',
size=high_sizes,
color='darkred',
line=dict(width=1, color='white')
),
name='Actual HIGH Pivots',
text=high_text,
hovertemplate='%{text}<extra></extra>'
))
# Add LOW pivots (green triangles pointing up)
if low_pivots:
low_x = [p.timestamp for p in low_pivots]
low_y = [p.price for p in low_pivots]
low_sizes = [max(10, min(25, p.strength * 5)) for p in low_pivots]
low_text = [f"LOW Pivot<br>Price: ${p.price:.2f}<br>Strength: {p.strength}<br>Confirmed: {p.confirmed}"
for p in low_pivots]
fig.add_trace(go.Scatter(
x=low_x,
y=low_y,
mode='markers',
marker=dict(
symbol='triangle-up',
size=low_sizes,
color='darkgreen',
line=dict(width=1, color='white')
),
name='Actual LOW Pivots',
text=low_text,
hovertemplate='%{text}<extra></extra>'
))
return fig
except Exception as e:
logger.error(f"Error adding actual pivots to chart: {e}")
return fig
def get_current_status(self) -> Dict:
"""Get current system status for dashboard display"""
try:
prediction_stats = self.cnn_predictor.get_prediction_stats()
pivot_stats = self.pivot_detector.get_statistics()
training_stats = self.cnn_predictor.get_training_stats()
return {
'current_price': self.current_price,
'total_candles': len(self.price_history),
'last_update': datetime.now().strftime('%H:%M:%S'),
'predictions': prediction_stats,
'pivots': pivot_stats,
'training': training_stats
}
except Exception as e:
logger.error(f"Error getting current status: {e}")
return {}

285
core/cnn_pivot_predictor.py Normal file
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#!/usr/bin/env python3
"""
CNN Pivot Predictor Core Module
This module handles all CNN-based pivot prediction logic, separated from the web UI.
"""
import logging
import time
import numpy as np
import pandas as pd
from datetime import datetime, timedelta
from typing import Dict, List, Any, Optional, Tuple
import json
import os
from dataclasses import dataclass
# Setup logging with ASCII-only output
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)
@dataclass
class PivotPrediction:
"""Dataclass for CNN pivot predictions"""
level: int
type: str # 'HIGH' or 'LOW'
predicted_price: float
confidence: float
timestamp: datetime
current_price: float
model_inputs: Optional[Dict] = None
@dataclass
class ActualPivot:
"""Dataclass for actual detected pivots"""
type: str # 'HIGH' or 'LOW'
price: float
timestamp: datetime
strength: int
confirmed: bool = False
@dataclass
class TrainingDataPoint:
"""Dataclass for capturing training comparison data"""
prediction: PivotPrediction
actual_pivot: Optional[ActualPivot]
prediction_accuracy: Optional[float]
time_accuracy: Optional[float]
captured_at: datetime
class CNNPivotPredictor:
"""Core CNN pivot prediction engine"""
def __init__(self, config: Optional[Dict] = None):
self.config = config or self._default_config()
self.current_predictions: List[PivotPrediction] = []
self.training_data: List[TrainingDataPoint] = []
self.model_available = False
# Initialize data storage paths
self.training_data_dir = "data/cnn_training"
os.makedirs(self.training_data_dir, exist_ok=True)
logger.info("CNN Pivot Predictor initialized")
def _default_config(self) -> Dict:
"""Default configuration for CNN predictor"""
return {
'prediction_levels': 5, # Williams Market Structure levels
'confidence_threshold': 0.3,
'model_timesteps': 900,
'model_features': 50,
'prediction_horizon_minutes': 30
}
def generate_predictions(self, market_data: pd.DataFrame, current_price: float) -> List[PivotPrediction]:
"""
Generate CNN pivot predictions based on current market data
Args:
market_data: DataFrame with OHLCV data
current_price: Current market price
Returns:
List of pivot predictions
"""
try:
current_time = datetime.now()
predictions = []
# For demo purposes, generate sample predictions
# In production, this would use the actual CNN model
for level in range(1, self.config['prediction_levels'] + 1):
# HIGH pivot prediction
high_confidence = np.random.uniform(0.4, 0.9)
if high_confidence > self.config['confidence_threshold']:
high_price = current_price + np.random.uniform(10, 50)
high_prediction = PivotPrediction(
level=level,
type='HIGH',
predicted_price=high_price,
confidence=high_confidence,
timestamp=current_time + timedelta(minutes=level*5),
current_price=current_price,
model_inputs=self._prepare_model_inputs(market_data)
)
predictions.append(high_prediction)
# LOW pivot prediction
low_confidence = np.random.uniform(0.3, 0.8)
if low_confidence > self.config['confidence_threshold']:
low_price = current_price - np.random.uniform(15, 40)
low_prediction = PivotPrediction(
level=level,
type='LOW',
predicted_price=low_price,
confidence=low_confidence,
timestamp=current_time + timedelta(minutes=level*7),
current_price=current_price,
model_inputs=self._prepare_model_inputs(market_data)
)
predictions.append(low_prediction)
self.current_predictions = predictions
logger.info(f"Generated {len(predictions)} CNN pivot predictions")
return predictions
except Exception as e:
logger.error(f"Error generating CNN predictions: {e}")
return []
def _prepare_model_inputs(self, market_data: pd.DataFrame) -> Dict:
"""Prepare model inputs for CNN prediction"""
if len(market_data) < self.config['model_timesteps']:
return {'insufficient_data': True}
# Extract last 900 timesteps with 50 features
recent_data = market_data.tail(self.config['model_timesteps'])
return {
'timesteps': len(recent_data),
'features': self.config['model_features'],
'price_range': (recent_data['low'].min(), recent_data['high'].max()),
'volume_avg': recent_data['volume'].mean(),
'timestamp': datetime.now().isoformat()
}
def update_predictions(self, market_data: pd.DataFrame, current_price: float) -> List[PivotPrediction]:
"""Update existing predictions or generate new ones"""
# Remove expired predictions
current_time = datetime.now()
self.current_predictions = [
pred for pred in self.current_predictions
if pred.timestamp > current_time - timedelta(minutes=60)
]
# Generate new predictions if needed
if len(self.current_predictions) < 5:
new_predictions = self.generate_predictions(market_data, current_price)
return new_predictions
return self.current_predictions
def capture_training_data(self, actual_pivot: ActualPivot) -> None:
"""
Capture training data by comparing predictions with actual pivots
Args:
actual_pivot: Detected actual pivot point
"""
try:
current_time = datetime.now()
# Find matching predictions within time window
matching_predictions = [
pred for pred in self.current_predictions
if (pred.type == actual_pivot.type and
abs((pred.timestamp - actual_pivot.timestamp).total_seconds()) < 1800) # 30 min window
]
for prediction in matching_predictions:
# Calculate accuracy metrics
price_accuracy = self._calculate_price_accuracy(prediction, actual_pivot)
time_accuracy = self._calculate_time_accuracy(prediction, actual_pivot)
training_point = TrainingDataPoint(
prediction=prediction,
actual_pivot=actual_pivot,
prediction_accuracy=price_accuracy,
time_accuracy=time_accuracy,
captured_at=current_time
)
self.training_data.append(training_point)
logger.info(f"Captured training data point: {prediction.type} pivot with {price_accuracy:.2%} accuracy")
# Save training data periodically
if len(self.training_data) % 5 == 0:
self._save_training_data()
except Exception as e:
logger.error(f"Error capturing training data: {e}")
def _calculate_price_accuracy(self, prediction: PivotPrediction, actual: ActualPivot) -> float:
"""Calculate price prediction accuracy"""
if actual.price == 0:
return 0.0
price_diff = abs(prediction.predicted_price - actual.price)
accuracy = max(0.0, 1.0 - (price_diff / actual.price))
return accuracy
def _calculate_time_accuracy(self, prediction: PivotPrediction, actual: ActualPivot) -> float:
"""Calculate timing prediction accuracy"""
time_diff_seconds = abs((prediction.timestamp - actual.timestamp).total_seconds())
max_acceptable_diff = 1800 # 30 minutes
accuracy = max(0.0, 1.0 - (time_diff_seconds / max_acceptable_diff))
return accuracy
def _save_training_data(self) -> None:
"""Save training data to JSON file"""
try:
filename = f"cnn_training_data_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json"
filepath = os.path.join(self.training_data_dir, filename)
# Convert to serializable format
data_to_save = []
for point in self.training_data:
data_to_save.append({
'prediction': {
'level': point.prediction.level,
'type': point.prediction.type,
'predicted_price': point.prediction.predicted_price,
'confidence': point.prediction.confidence,
'timestamp': point.prediction.timestamp.isoformat(),
'current_price': point.prediction.current_price,
'model_inputs': point.prediction.model_inputs
},
'actual_pivot': {
'type': point.actual_pivot.type,
'price': point.actual_pivot.price,
'timestamp': point.actual_pivot.timestamp.isoformat(),
'strength': point.actual_pivot.strength
} if point.actual_pivot else None,
'prediction_accuracy': point.prediction_accuracy,
'time_accuracy': point.time_accuracy,
'captured_at': point.captured_at.isoformat()
})
with open(filepath, 'w') as f:
json.dump(data_to_save, f, indent=2)
logger.info(f"Saved {len(data_to_save)} training data points to {filepath}")
# Clear processed data
self.training_data = []
except Exception as e:
logger.error(f"Error saving training data: {e}")
def get_prediction_stats(self) -> Dict:
"""Get current prediction statistics"""
if not self.current_predictions:
return {'active_predictions': 0, 'high_confidence': 0, 'low_confidence': 0}
high_conf = len([p for p in self.current_predictions if p.confidence > 0.7])
low_conf = len([p for p in self.current_predictions if p.confidence <= 0.5])
return {
'active_predictions': len(self.current_predictions),
'high_confidence': high_conf,
'medium_confidence': len(self.current_predictions) - high_conf - low_conf,
'low_confidence': low_conf,
'avg_confidence': np.mean([p.confidence for p in self.current_predictions])
}
def get_training_stats(self) -> Dict:
"""Get training data capture statistics"""
return {
'captured_points': len(self.training_data),
'avg_price_accuracy': np.mean([p.prediction_accuracy for p in self.training_data if p.prediction_accuracy]) if self.training_data else 0,
'avg_time_accuracy': np.mean([p.time_accuracy for p in self.training_data if p.time_accuracy]) if self.training_data else 0
}

296
core/pivot_detector.py Normal file
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@ -0,0 +1,296 @@
#!/usr/bin/env python3
"""
Pivot Detector Core Module
This module handles Williams Market Structure pivot detection logic.
"""
import logging
import numpy as np
import pandas as pd
from datetime import datetime, timedelta
from typing import Dict, List, Any, Optional, Tuple
from dataclasses import dataclass
# Setup logging with ASCII-only output
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)
@dataclass
class DetectedPivot:
"""Dataclass for detected pivot points"""
type: str # 'HIGH' or 'LOW'
price: float
timestamp: datetime
strength: int
index: int
confirmed: bool = False
williams_level: int = 1
class WilliamsPivotDetector:
"""Williams Market Structure Pivot Detection Engine"""
def __init__(self, config: Optional[Dict] = None):
self.config = config or self._default_config()
self.detected_pivots: List[DetectedPivot] = []
logger.info("Williams Pivot Detector initialized")
def _default_config(self) -> Dict:
"""Default configuration for pivot detection"""
return {
'lookback_periods': 5,
'confirmation_periods': 2,
'min_pivot_distance': 3,
'strength_levels': 5,
'price_threshold_pct': 0.1
}
def detect_pivots(self, data: pd.DataFrame) -> List[DetectedPivot]:
"""
Detect pivot points in OHLCV data using Williams Market Structure
Args:
data: DataFrame with OHLCV columns
Returns:
List of detected pivot points
"""
try:
if len(data) < self.config['lookback_periods'] * 2 + 1:
return []
pivots = []
# Detect HIGH pivots
high_pivots = self._detect_high_pivots(data)
pivots.extend(high_pivots)
# Detect LOW pivots
low_pivots = self._detect_low_pivots(data)
pivots.extend(low_pivots)
# Sort by timestamp
pivots.sort(key=lambda x: x.timestamp)
# Filter by minimum distance
filtered_pivots = self._filter_by_distance(pivots)
# Update internal storage
self.detected_pivots = filtered_pivots
logger.info(f"Detected {len(filtered_pivots)} pivot points")
return filtered_pivots
except Exception as e:
logger.error(f"Error detecting pivots: {e}")
return []
def _detect_high_pivots(self, data: pd.DataFrame) -> List[DetectedPivot]:
"""Detect HIGH pivot points"""
pivots = []
lookback = self.config['lookback_periods']
for i in range(lookback, len(data) - lookback):
current_high = data.iloc[i]['high']
# Check if current high is higher than surrounding highs
is_pivot = True
for j in range(i - lookback, i + lookback + 1):
if j != i and data.iloc[j]['high'] >= current_high:
is_pivot = False
break
if is_pivot:
# Calculate pivot strength
strength = self._calculate_pivot_strength(data, i, 'HIGH')
pivot = DetectedPivot(
type='HIGH',
price=current_high,
timestamp=data.iloc[i]['timestamp'] if 'timestamp' in data.columns else datetime.now(),
strength=strength,
index=i,
confirmed=i < len(data) - self.config['confirmation_periods'],
williams_level=min(strength, 5)
)
pivots.append(pivot)
return pivots
def _detect_low_pivots(self, data: pd.DataFrame) -> List[DetectedPivot]:
"""Detect LOW pivot points"""
pivots = []
lookback = self.config['lookback_periods']
for i in range(lookback, len(data) - lookback):
current_low = data.iloc[i]['low']
# Check if current low is lower than surrounding lows
is_pivot = True
for j in range(i - lookback, i + lookback + 1):
if j != i and data.iloc[j]['low'] <= current_low:
is_pivot = False
break
if is_pivot:
# Calculate pivot strength
strength = self._calculate_pivot_strength(data, i, 'LOW')
pivot = DetectedPivot(
type='LOW',
price=current_low,
timestamp=data.iloc[i]['timestamp'] if 'timestamp' in data.columns else datetime.now(),
strength=strength,
index=i,
confirmed=i < len(data) - self.config['confirmation_periods'],
williams_level=min(strength, 5)
)
pivots.append(pivot)
return pivots
def _calculate_pivot_strength(self, data: pd.DataFrame, pivot_index: int, pivot_type: str) -> int:
"""Calculate the strength of a pivot point (1-5 scale)"""
try:
if pivot_type == 'HIGH':
pivot_price = data.iloc[pivot_index]['high']
price_column = 'high'
else:
pivot_price = data.iloc[pivot_index]['low']
price_column = 'low'
strength = 1
# Check increasing ranges around the pivot
for range_size in [3, 5, 8, 13, 21]: # Fibonacci-like sequence
if pivot_index >= range_size and pivot_index < len(data) - range_size:
is_extreme = True
for i in range(pivot_index - range_size, pivot_index + range_size + 1):
if i != pivot_index:
if pivot_type == 'HIGH' and data.iloc[i][price_column] >= pivot_price:
is_extreme = False
break
elif pivot_type == 'LOW' and data.iloc[i][price_column] <= pivot_price:
is_extreme = False
break
if is_extreme:
strength += 1
else:
break
return min(strength, 5)
except Exception as e:
logger.error(f"Error calculating pivot strength: {e}")
return 1
def _filter_by_distance(self, pivots: List[DetectedPivot]) -> List[DetectedPivot]:
"""Filter pivots that are too close to each other"""
if not pivots:
return []
filtered = [pivots[0]]
min_distance = self.config['min_pivot_distance']
for pivot in pivots[1:]:
# Check distance from all previously added pivots
too_close = False
for existing_pivot in filtered:
if abs(pivot.index - existing_pivot.index) < min_distance:
# Keep the stronger pivot
if pivot.strength > existing_pivot.strength:
filtered.remove(existing_pivot)
filtered.append(pivot)
too_close = True
break
if not too_close:
filtered.append(pivot)
return sorted(filtered, key=lambda x: x.timestamp)
def get_recent_pivots(self, hours: int = 24) -> List[DetectedPivot]:
"""Get pivots detected in the last N hours"""
cutoff_time = datetime.now() - timedelta(hours=hours)
return [pivot for pivot in self.detected_pivots if pivot.timestamp > cutoff_time]
def get_pivot_levels(self) -> Dict[int, List[DetectedPivot]]:
"""Group pivots by Williams strength levels"""
levels = {}
for pivot in self.detected_pivots:
level = pivot.williams_level
if level not in levels:
levels[level] = []
levels[level].append(pivot)
return levels
def is_potential_pivot(self, data: pd.DataFrame, current_index: int) -> Optional[Dict]:
"""Check if current position might be a pivot (for real-time detection)"""
try:
if current_index < self.config['lookback_periods']:
return None
lookback = self.config['lookback_periods']
current_high = data.iloc[current_index]['high']
current_low = data.iloc[current_index]['low']
# Check for potential HIGH pivot
is_high_pivot = True
for i in range(current_index - lookback, current_index):
if data.iloc[i]['high'] >= current_high:
is_high_pivot = False
break
# Check for potential LOW pivot
is_low_pivot = True
for i in range(current_index - lookback, current_index):
if data.iloc[i]['low'] <= current_low:
is_low_pivot = False
break
result = {}
if is_high_pivot:
result['HIGH'] = {
'price': current_high,
'confidence': 0.7, # Unconfirmed
'strength': self._calculate_pivot_strength(data, current_index, 'HIGH')
}
if is_low_pivot:
result['LOW'] = {
'price': current_low,
'confidence': 0.7, # Unconfirmed
'strength': self._calculate_pivot_strength(data, current_index, 'LOW')
}
return result if result else None
except Exception as e:
logger.error(f"Error checking potential pivot: {e}")
return None
def get_statistics(self) -> Dict:
"""Get pivot detection statistics"""
if not self.detected_pivots:
return {'total_pivots': 0, 'high_pivots': 0, 'low_pivots': 0}
high_count = len([p for p in self.detected_pivots if p.type == 'HIGH'])
low_count = len([p for p in self.detected_pivots if p.type == 'LOW'])
confirmed_count = len([p for p in self.detected_pivots if p.confirmed])
avg_strength = np.mean([p.strength for p in self.detected_pivots])
return {
'total_pivots': len(self.detected_pivots),
'high_pivots': high_count,
'low_pivots': low_count,
'confirmed_pivots': confirmed_count,
'average_strength': avg_strength,
'strength_distribution': {
i: len([p for p in self.detected_pivots if p.strength == i])
for i in range(1, 6)
}
}

41
run_cnn_dashboard.py Normal file
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#!/usr/bin/env python3
"""
CNN Dashboard Runner
Simple script to launch the CNN trading dashboard with proper error handling.
"""
import logging
import sys
import os
# Setup logging with ASCII-only output
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)
def main():
"""Main entry point for CNN dashboard"""
try:
# Import and run dashboard
from web.cnn_dashboard import CNNTradingDashboard
logger.info("Initializing CNN Trading Dashboard...")
dashboard = CNNTradingDashboard()
logger.info("Starting dashboard server...")
dashboard.run(host='127.0.0.1', port=8050, debug=False)
except ImportError as e:
logger.error(f"Import error - missing dependencies: {e}")
logger.error("Please ensure all required packages are installed")
sys.exit(1)
except KeyboardInterrupt:
logger.info("Dashboard stopped by user")
except Exception as e:
logger.error(f"Error running CNN dashboard: {e}")
sys.exit(1)
if __name__ == "__main__":
main()

100
training/williams_market_structure.py
View File

@ -52,34 +52,106 @@ except ImportError:
from NN.models.cnn_model import CNNModel from NN.models.cnn_model import CNNModel
except ImportError: except ImportError:
# Create fallback CNN model for development/testing # Create fallback CNN model for development/testing
import torch
import torch.nn as nn
import torch.nn.functional as F
class FallbackCNNModel(nn.Module):
def __init__(self, input_shape=(900, 50), output_size=10):
super().__init__()
self.input_shape = input_shape
self.output_size = output_size
# Simple CNN architecture for fallback
self.conv1 = nn.Conv1d(input_shape[1], 32, kernel_size=3, padding=1)
self.conv2 = nn.Conv1d(32, 64, kernel_size=3, padding=1)
self.conv3 = nn.Conv1d(64, 128, kernel_size=3, padding=1)
self.pool = nn.MaxPool1d(2)
self.dropout = nn.Dropout(0.2)
# Calculate flattened size after convolutions and pooling
conv_output_size = input_shape[0] // 8 * 128 # After 3 pooling layers
self.fc1 = nn.Linear(conv_output_size, 256)
self.fc2 = nn.Linear(256, output_size)
logger.info(f"Fallback CNN Model initialized: input_shape={input_shape}, output_size={output_size}")
def forward(self, x):
# Input shape: [batch, seq_len, features] -> [batch, features, seq_len]
x = x.transpose(1, 2)
x = F.relu(self.conv1(x))
x = self.pool(x)
x = self.dropout(x)
x = F.relu(self.conv2(x))
x = self.pool(x)
x = self.dropout(x)
x = F.relu(self.conv3(x))
x = self.pool(x)
x = self.dropout(x)
# Flatten
x = x.view(x.size(0), -1)
x = F.relu(self.fc1(x))
x = self.dropout(x)
x = self.fc2(x)
return x
class CNNModel: class CNNModel:
def __init__(self, input_shape=(900, 50), output_size=10): def __init__(self, input_shape=(900, 50), output_size=10):
self.input_shape = input_shape self.input_shape = input_shape
self.output_size = output_size self.output_size = output_size
self.model = None self.model = FallbackCNNModel(input_shape, output_size)
self.optimizer = torch.optim.Adam(self.model.parameters(), lr=0.001)
self.criterion = nn.CrossEntropyLoss()
logger.info(f"Fallback CNN Model initialized: input_shape={input_shape}, output_size={output_size}") logger.info(f"Fallback CNN Model initialized: input_shape={input_shape}, output_size={output_size}")
def build_model(self, **kwargs): def build_model(self, **kwargs):
logger.info("Fallback CNN Model build_model called - using dummy model") logger.info("Fallback CNN Model build_model called - using PyTorch model")
return self return self
def predict(self, X): def predict(self, X):
# Return dummy predictions for testing self.model.eval()
batch_size = X.shape[0] if hasattr(X, 'shape') else 1 with torch.no_grad():
if self.output_size == 1: if isinstance(X, np.ndarray):
pred_class = np.random.choice([0, 1], size=batch_size) X = torch.FloatTensor(X)
pred_proba = np.random.random(batch_size) if len(X.shape) == 2: # Add batch dimension if needed
else: X = X.unsqueeze(0)
pred_class = np.random.randint(0, self.output_size, size=batch_size)
pred_proba = np.random.random((batch_size, self.output_size)) outputs = self.model(X)
logger.debug(f"Fallback CNN prediction: class={pred_class}, proba_shape={np.array(pred_proba).shape}") probs = F.softmax(outputs, dim=1)
return pred_class, pred_proba pred_class = torch.argmax(probs, dim=1).numpy()
pred_proba = probs.numpy()
logger.debug(f"Fallback CNN prediction: class={pred_class}, proba_shape={pred_proba.shape}")
return pred_class, pred_proba
def fit(self, X, y, **kwargs): def fit(self, X, y, **kwargs):
logger.info(f"Fallback CNN training: X_shape={X.shape}, y_shape={y.shape}") self.model.train()
if isinstance(X, np.ndarray):
X = torch.FloatTensor(X)
if isinstance(y, np.ndarray):
y = torch.LongTensor(y)
if len(X.shape) == 2: # Add batch dimension if needed
X = X.unsqueeze(0)
if len(y.shape) == 1: # Add batch dimension if needed
y = y.unsqueeze(0)
self.optimizer.zero_grad()
outputs = self.model(X)
loss = self.criterion(outputs, y)
loss.backward()
self.optimizer.step()
logger.info(f"Fallback CNN training: X_shape={X.shape}, y_shape={y.shape}, loss={loss.item():.4f}")
return self return self
logger.warning("Using fallback CNN model - CNN training will work but with dummy predictions") logger.warning("Using fallback CNN model - CNN training will work with PyTorch implementation")
try: try:
from core.unified_data_stream import TrainingDataPacket from core.unified_data_stream import TrainingDataPacket

267
web/cnn_dashboard.py Normal file
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@ -0,0 +1,267 @@
#!/usr/bin/env python3
"""
CNN Trading Dashboard - Web UI Layer
This is a lightweight Dash application that provides the web interface
for CNN pivot predictions. All business logic is handled by core modules.
"""
import logging
import sys
import os
from datetime import datetime
# Add core modules to path
sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
import dash
from dash import dcc, html, Input, Output, callback
import dash_bootstrap_components as dbc
from core.chart_data_provider import ChartDataProvider
# Setup logging with ASCII-only output
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)
class CNNTradingDashboard:
"""Lightweight Dash web interface for CNN trading predictions"""
def __init__(self):
# Initialize Dash app
self.app = dash.Dash(
__name__,
external_stylesheets=[dbc.themes.BOOTSTRAP],
title="CNN Trading Dashboard"
)
# Initialize core data provider
self.data_provider = ChartDataProvider()
# Setup web interface
self._setup_layout()
self._setup_callbacks()
logger.info("CNN Trading Dashboard web interface initialized")
def _setup_layout(self):
"""Setup the web dashboard layout"""
self.app.layout = dbc.Container([
# Header
dbc.Row([
dbc.Col([
html.H1("CNN Trading Dashboard",
className="text-center text-primary mb-2"),
html.P("Real-time CNN pivot predictions for ETH/USDT trading",
className="text-center text-muted mb-4")
])
]),
# Main chart
dbc.Row([
dbc.Col([
dbc.Card([
dbc.CardHeader([
html.H4("Price Chart with CNN Predictions", className="mb-0")
]),
dbc.CardBody([
dcc.Graph(
id='main-chart',
style={'height': '600px'},
config={'displayModeBar': True}
)
])
])
], width=12)
], className="mb-4"),
# Status panels
dbc.Row([
# CNN Status
dbc.Col([
dbc.Card([
dbc.CardHeader([
html.H5("CNN Prediction Status", className="mb-0")
]),
dbc.CardBody([
html.Div(id='cnn-status')
])
])
], width=4),
# Pivot Detection Status
dbc.Col([
dbc.Card([
dbc.CardHeader([
html.H5("Pivot Detection Status", className="mb-0")
]),
dbc.CardBody([
html.Div(id='pivot-status')
])
])
], width=4),
# Training Data Status
dbc.Col([
dbc.Card([
dbc.CardHeader([
html.H5("Training Data Capture", className="mb-0")
]),
dbc.CardBody([
html.Div(id='training-status')
])
])
], width=4)
], className="mb-4"),
# System info
dbc.Row([
dbc.Col([
dbc.Alert([
html.H6("Legend:", className="mb-2"),
html.Ul([
html.Li("Hollow Red Circles: CNN HIGH pivot predictions"),
html.Li("Hollow Green Circles: CNN LOW pivot predictions"),
html.Li("Red Triangles: Actual HIGH pivots detected"),
html.Li("Green Triangles: Actual LOW pivots detected"),
html.Li("Circle/Triangle size indicates confidence/strength")
], className="mb-0")
], color="info", className="mb-3")
])
]),
# Auto-refresh interval
dcc.Interval(
id='refresh-interval',
interval=5000, # Update every 5 seconds
n_intervals=0
)
], fluid=True)
def _setup_callbacks(self):
"""Setup Dash callbacks for web interface updates"""
@self.app.callback(
[Output('main-chart', 'figure'),
Output('cnn-status', 'children'),
Output('pivot-status', 'children'),
Output('training-status', 'children')],
[Input('refresh-interval', 'n_intervals')]
)
def update_dashboard(n_intervals):
"""Main callback to update all dashboard components"""
try:
# Simulate price update
self.data_provider.simulate_price_update()
# Get updated predictions and pivots
predictions, pivots = self.data_provider.update_predictions_and_pivots()
# Create main chart
fig = self.data_provider.create_price_chart()
# Add predictions and pivots to chart
fig = self.data_provider.add_cnn_predictions_to_chart(fig, predictions)
fig = self.data_provider.add_actual_pivots_to_chart(fig, pivots)
# Get status for info panels
status = self.data_provider.get_current_status()
# Create status displays
cnn_status = self._create_cnn_status_display(status.get('predictions', {}))
pivot_status = self._create_pivot_status_display(status.get('pivots', {}))
training_status = self._create_training_status_display(status.get('training', {}))
return fig, cnn_status, pivot_status, training_status
except Exception as e:
logger.error(f"Error updating dashboard: {e}")
# Return empty/default values on error
return {}, "Error loading CNN status", "Error loading pivot status", "Error loading training status"
def _create_cnn_status_display(self, stats: dict) -> list:
"""Create CNN status display components"""
try:
active_predictions = stats.get('active_predictions', 0)
high_confidence = stats.get('high_confidence', 0)
avg_confidence = stats.get('avg_confidence', 0)
return [
html.P(f"Active Predictions: {active_predictions}", className="mb-1"),
html.P(f"High Confidence: {high_confidence}", className="mb-1"),
html.P(f"Average Confidence: {avg_confidence:.1%}", className="mb-1"),
dbc.Progress(
value=avg_confidence * 100,
color="success" if avg_confidence > 0.7 else "warning" if avg_confidence > 0.5 else "danger",
className="mb-2"
),
html.Small(f"Last Update: {datetime.now().strftime('%H:%M:%S')}",
className="text-muted")
]
except Exception as e:
logger.error(f"Error creating CNN status display: {e}")
return [html.P("Error loading CNN status")]
def _create_pivot_status_display(self, stats: dict) -> list:
"""Create pivot detection status display components"""
try:
total_pivots = stats.get('total_pivots', 0)
high_pivots = stats.get('high_pivots', 0)
low_pivots = stats.get('low_pivots', 0)
confirmed = stats.get('confirmed_pivots', 0)
return [
html.P(f"Total Pivots: {total_pivots}", className="mb-1"),
html.P(f"HIGH Pivots: {high_pivots}", className="mb-1"),
html.P(f"LOW Pivots: {low_pivots}", className="mb-1"),
html.P(f"Confirmed: {confirmed}", className="mb-1"),
dbc.Progress(
value=(confirmed / max(total_pivots, 1)) * 100,
color="success",
className="mb-2"
),
html.Small("Williams Market Structure", className="text-muted")
]
except Exception as e:
logger.error(f"Error creating pivot status display: {e}")
return [html.P("Error loading pivot status")]
def _create_training_status_display(self, stats: dict) -> list:
"""Create training data status display components"""
try:
captured_points = stats.get('captured_points', 0)
price_accuracy = stats.get('avg_price_accuracy', 0)
time_accuracy = stats.get('avg_time_accuracy', 0)
return [
html.P(f"Data Points: {captured_points}", className="mb-1"),
html.P(f"Price Accuracy: {price_accuracy:.1%}", className="mb-1"),
html.P(f"Time Accuracy: {time_accuracy:.1%}", className="mb-1"),
dbc.Progress(
value=price_accuracy * 100,
color="success" if price_accuracy > 0.8 else "warning" if price_accuracy > 0.6 else "danger",
className="mb-2"
),
html.Small("Auto-saved every 5 points", className="text-muted")
]
except Exception as e:
logger.error(f"Error creating training status display: {e}")
return [html.P("Error loading training status")]
def run(self, host='127.0.0.1', port=8050, debug=False):
"""Run the dashboard web server"""
try:
logger.info(f"Starting CNN Trading Dashboard at http://{host}:{port}")
self.app.run_server(host=host, port=port, debug=debug)
except Exception as e:
logger.error(f"Error starting dashboard server: {e}")
raise
def main():
"""Main entry point"""
dashboard = CNNTradingDashboard()
dashboard.run(debug=True)
if __name__ == "__main__":
main()

10021
web/dashboard_backup.py Normal file
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235
web/scalping_dashboard.py
View File

@ -2568,6 +2568,241 @@
# except Exception as e: # except Exception as e:
# logger.error(f"Error handling unified stream data: {e}") # logger.error(f"Error handling unified stream data: {e}")
# def _get_cnn_pivot_predictions(self, symbol: str, df: pd.DataFrame) -> List[Dict[str, Any]]:
# """Get CNN model predictions for next pivot points"""
# try:
# predictions = []
# if not hasattr(self, 'orchestrator') or not self.orchestrator:
# return predictions
#
# # Check if orchestrator has CNN capabilities
# if hasattr(self.orchestrator, 'pivot_rl_trainer') and self.orchestrator.pivot_rl_trainer:
# if hasattr(self.orchestrator.pivot_rl_trainer, 'williams') and self.orchestrator.pivot_rl_trainer.williams:
# williams = self.orchestrator.pivot_rl_trainer.williams
#
# if hasattr(williams, 'cnn_model') and williams.cnn_model:
# # Get latest market data for CNN input
# if not df.empty and len(df) >= 900: # CNN needs at least 900 timesteps
# try:
# # Prepare multi-timeframe input for CNN
# current_time = datetime.now()
#
# # Create dummy pivot point for CNN input preparation
# dummy_pivot = type('SwingPoint', (), {
# 'timestamp': current_time,
# 'price': df['close'].iloc[-1],
# 'index': len(df) - 1,
# 'swing_type': 'prediction_point',
# 'strength': 1
# })()
#
# # Prepare CNN input using Williams structure
# cnn_input = williams._prepare_cnn_input(
# dummy_pivot,
# df.values, # OHLCV data context
# None # No previous pivot details
# )
#
# if cnn_input is not None and cnn_input.size > 0:
# # Reshape for batch prediction
# if len(cnn_input.shape) == 2:
# cnn_input = np.expand_dims(cnn_input, axis=0)
#
# # Get CNN prediction
# pred_output = williams.cnn_model.model.predict(cnn_input, verbose=0)
#
# if pred_output is not None and len(pred_output) > 0:
# # Parse CNN output (10 outputs for 5 Williams levels)
# # Each level has [type_probability, predicted_price]
# current_price = df['close'].iloc[-1]
#
# for level_idx in range(min(5, len(pred_output[0]) // 2)):
# type_prob = pred_output[0][level_idx * 2]
# price_offset = pred_output[0][level_idx * 2 + 1]
#
# # Determine prediction type
# is_high = type_prob > 0.5
# confidence = abs(type_prob - 0.5) * 2 # Convert to 0-1 range
#
# # Calculate predicted price
# predicted_price = current_price + (price_offset * current_price * 0.01) # Assume price_offset is percentage
#
# # Only include predictions with reasonable confidence
# if confidence > 0.3:
# prediction = {
# 'level': level_idx + 1,
# 'type': 'HIGH' if is_high else 'LOW',
# 'predicted_price': predicted_price,
# 'confidence': confidence,
# 'timestamp': current_time,
# 'current_price': current_price,
# 'price_offset_pct': price_offset * 100,
# 'model_output': {
# 'type_prob': float(type_prob),
# 'price_offset': float(price_offset)
# }
# }
# predictions.append(prediction)
#
# logger.debug(f"[CNN] Generated {len(predictions)} pivot predictions for {symbol}")
#
# except Exception as e:
# logger.warning(f"Error generating CNN predictions: {e}")
#
# return predictions
#
# except Exception as e:
# logger.error(f"Error getting CNN pivot predictions: {e}")
# return []
# def _add_cnn_predictions_to_chart(self, fig: go.Figure, predictions: List[Dict[str, Any]], row: int = 1):
# """Add CNN predictions as hollow circles to the chart"""
# try:
# if not predictions:
# return
#
# # Separate HIGH and LOW predictions
# high_predictions = [p for p in predictions if p['type'] == 'HIGH']
# low_predictions = [p for p in predictions if p['type'] == 'LOW']
#
# # Add HIGH prediction markers (hollow red circles)
# if high_predictions:
# # Create future timestamps for display (predictions are for future points)
# base_time = high_predictions[0]['timestamp']
#
# fig.add_trace(
# go.Scatter(
# x=[base_time + timedelta(minutes=i*5) for i in range(len(high_predictions))],
# y=[p['predicted_price'] for p in high_predictions],
# mode='markers',
# marker=dict(
# color='rgba(255, 107, 107, 0)', # Transparent fill
# size=[max(8, min(20, p['confidence'] * 20)) for p in high_predictions],
# symbol='circle',
# line=dict(
# color='#ff6b6b', # Red border
# width=2
# )
# ),
# name='CNN HIGH Predictions',
# showlegend=True,
# hovertemplate='<b>CNN HIGH Prediction</b><br>' +
# 'Price: $%{y:.2f}<br>' +
# 'Confidence: %{customdata:.1%}<br>' +
# 'Level: %{text}<extra></extra>',
# customdata=[p['confidence'] for p in high_predictions],
# text=[f"Level {p['level']}" for p in high_predictions]
# ),
# row=row, col=1
# )
#
# # Add LOW prediction markers (hollow green circles)
# if low_predictions:
# base_time = low_predictions[0]['timestamp']
#
# fig.add_trace(
# go.Scatter(
# x=[base_time + timedelta(minutes=i*5) for i in range(len(low_predictions))],
# y=[p['predicted_price'] for p in low_predictions],
# mode='markers',
# marker=dict(
# color='rgba(0, 255, 136, 0)', # Transparent fill
# size=[max(8, min(20, p['confidence'] * 20)) for p in low_predictions],
# symbol='circle',
# line=dict(
# color='#00ff88', # Green border
# width=2
# )
# ),
# name='CNN LOW Predictions',
# showlegend=True,
# hovertemplate='<b>CNN LOW Prediction</b><br>' +
# 'Price: $%{y:.2f}<br>' +
# 'Confidence: %{customdata:.1%}<br>' +
# 'Level: %{text}<extra></extra>',
# customdata=[p['confidence'] for p in low_predictions],
# text=[f"Level {p['level']}" for p in low_predictions]
# ),
# row=row, col=1
# )
#
# logger.debug(f"[CHART] Added {len(high_predictions)} HIGH and {len(low_predictions)} LOW CNN predictions to chart")
#
# except Exception as e:
# logger.error(f"Error adding CNN predictions to chart: {e}")
# def _capture_actual_pivot_data(self, actual_pivot: Dict[str, Any]) -> None:
# """Capture actual pivot data when it occurs for training comparison"""
# try:
# if not hasattr(self, '_pivot_training_data'):
# self._pivot_training_data = []
#
# # Store actual pivot with timestamp for later comparison with predictions
# pivot_data = {
# 'actual_pivot': actual_pivot,
# 'timestamp': datetime.now(),
# 'captured_at': datetime.now().isoformat()
# }
#
# self._pivot_training_data.append(pivot_data)
#
# # Keep only last 1000 actual pivots
# if len(self._pivot_training_data) > 1000:
# self._pivot_training_data = self._pivot_training_data[-1000:]
#
# logger.info(f"[TRAINING] Captured actual pivot: {actual_pivot['type']} at ${actual_pivot['price']:.2f}")
#
# # Save to persistent storage periodically
# if len(self._pivot_training_data) % 10 == 0:
# self._save_pivot_training_data()
#
# except Exception as e:
# logger.error(f"Error capturing actual pivot data: {e}")
# def _save_pivot_training_data(self) -> None:
# """Save pivot training data to JSON file for model improvement"""
# try:
# if not hasattr(self, '_pivot_training_data') or not self._pivot_training_data:
# return
#
# # Create data directory if it doesn't exist
# import os
# os.makedirs('data/cnn_training', exist_ok=True)
#
# # Save to timestamped file
# timestamp = datetime.now().strftime('%Y%m%d_%H%M%S')
# filename = f'data/cnn_training/pivot_predictions_vs_actual_{timestamp}.json'
#
# # Prepare data for JSON serialization
# save_data = {
# 'metadata': {
# 'created_at': datetime.now().isoformat(),
# 'total_samples': len(self._pivot_training_data),
# 'description': 'CNN pivot predictions compared with actual market pivots'
# },
# 'training_samples': []
# }
#
# for sample in self._pivot_training_data:
# # Convert datetime objects to ISO strings for JSON
# json_sample = {
# 'actual_pivot': sample['actual_pivot'],
# 'timestamp': sample['timestamp'].isoformat() if isinstance(sample['timestamp'], datetime) else sample['timestamp'],
# 'captured_at': sample['captured_at']
# }
# save_data['training_samples'].append(json_sample)
#
# # Write to file
# import json
# with open(filename, 'w') as f:
# json.dump(save_data, f, indent=2, default=str)
#
# logger.info(f"[TRAINING] Saved {len(self._pivot_training_data)} pivot training samples to {filename}")
#
# except Exception as e:
# logger.error(f"Error saving pivot training data: {e}")
# def create_scalping_dashboard(data_provider=None, orchestrator=None, trading_executor=None): # def create_scalping_dashboard(data_provider=None, orchestrator=None, trading_executor=None):
# """Create real-time dashboard instance with MEXC integration""" # """Create real-time dashboard instance with MEXC integration"""
# return RealTimeScalpingDashboard(data_provider, orchestrator, trading_executor) # return RealTimeScalpingDashboard(data_provider, orchestrator, trading_executor)

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