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