main cleanup
This commit is contained in:
Dobromir Popov
622
core/backtest_training_panel.py
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622
core/backtest_training_panel.py
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#!/usr/bin/env python3
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"""
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Backtest Training Panel - Dashboard Integration
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This module provides a dashboard panel for controlling the backtesting and training system.
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It integrates with the main dashboard and allows real-time control of training operations.
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"""
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import logging
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import threading
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import time
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import json
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import pandas as pd
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import numpy as np
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from datetime import datetime, timedelta
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from typing import Dict, List, Any, Optional
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from pathlib import Path
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import dash_bootstrap_components as dbc
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from dash import html, dcc, Input, Output, State
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from core.multi_horizon_backtester import MultiHorizonBacktester
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from core.orchestrator import TradingOrchestrator
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from core.data_provider import DataProvider
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logger = logging.getLogger(__name__)
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class BacktestTrainingPanel:
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"""Dashboard panel for backtesting and training control"""
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def __init__(self, data_provider: DataProvider, orchestrator: TradingOrchestrator):
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"""Initialize the backtest training panel"""
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self.data_provider = data_provider
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self.orchestrator = orchestrator
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self.backtester = MultiHorizonBacktester(data_provider)
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# Training state
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self.training_active = False
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self.training_thread = None
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self.training_stats = {
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'start_time': None,
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'backtests_run': 0,
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'accuracy_history': [],
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'current_accuracy': 0.0,
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'training_cycles': 0,
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'last_backtest_time': None,
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'gpu_usage': False,
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'npu_usage': False,
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'best_predictions': [],
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'recent_predictions': [],
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'candlestick_data': []
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}
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# GPU/NPU status
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self.gpu_available = self._check_gpu_available()
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self.npu_available = self._check_npu_available()
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self.gpu_type = self._get_gpu_type()
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logger.info("Backtest Training Panel initialized")
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def _check_gpu_available(self) -> bool:
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"""Check if GPU (including integrated GPU) is available"""
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try:
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import torch
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# Check for CUDA GPUs first
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if torch.cuda.is_available():
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return True
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# Check for MPS (Apple Silicon GPUs)
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if hasattr(torch, 'mps') and torch.mps.is_available():
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return True
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# Check for other GPU backends
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if hasattr(torch, 'backends'):
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# Check for Intel XPU (integrated GPUs)
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if hasattr(torch.backends, 'xpu') and torch.backends.xpu.is_available():
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return True
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# Check for AMD ROCm
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if hasattr(torch.backends, 'rocm') and torch.backends.rocm.is_available():
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return True
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# Check for OpenCL/DirectML (Microsoft)
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try:
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import torch_directml
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return torch_directml.is_available()
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except ImportError:
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pass
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return False
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except Exception as e:
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logger.warning(f"Error checking GPU availability: {e}")
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return False
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def _check_npu_available(self) -> bool:
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"""Check if NPU is available"""
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try:
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# Check for Intel NPU support
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import torch
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if hasattr(torch.backends, 'xpu') and torch.backends.xpu.is_available():
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# Check if it's actually an NPU, not just GPU
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try:
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import intel_extension_for_pytorch as ipex
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return True
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except ImportError:
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pass
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# Check for custom NPU detector
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from utils.npu_detector import is_npu_available
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return is_npu_available()
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except:
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return False
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def _get_gpu_type(self) -> str:
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"""Get the type of GPU detected"""
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try:
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import torch
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if torch.cuda.is_available():
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try:
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return f"CUDA ({torch.cuda.get_device_name(0)})"
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except:
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return "CUDA"
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elif hasattr(torch, 'mps') and torch.mps.is_available():
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return "Apple MPS"
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elif hasattr(torch.backends, 'xpu') and torch.backends.xpu.is_available():
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return "Intel XPU (iGPU)"
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elif hasattr(torch.backends, 'rocm') and torch.backends.rocm.is_available():
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return "AMD ROCm"
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else:
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try:
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import torch_directml
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if torch_directml.is_available():
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return "DirectML (iGPU)"
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except ImportError:
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pass
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return "CPU"
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except:
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return "Unknown"
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def get_panel_layout(self):
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"""Get the dashboard panel layout"""
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return dbc.Card([
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dbc.CardHeader([
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html.H4("Backtest Training Control", className="card-title"),
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html.Div([
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dbc.Badge(
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"GPU: " + ("Available" if self.gpu_available else "Not Available"),
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color="success" if self.gpu_available else "danger",
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className="me-2"
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),
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dbc.Badge(
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"NPU: " + ("Available" if self.npu_available else "Not Available"),
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color="success" if self.npu_available else "danger"
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)
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])
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]),
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dbc.CardBody([
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# Control buttons
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dbc.Row([
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dbc.Col([
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html.Label("Training Control"),
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dbc.ButtonGroup([
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dbc.Button(
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"Start Training",
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id="start-training-btn",
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color="success",
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disabled=self.training_active
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),
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dbc.Button(
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"Stop Training",
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id="stop-training-btn",
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color="danger",
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disabled=not self.training_active
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),
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dbc.Button(
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"Run Backtest",
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id="run-backtest-btn",
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color="primary"
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)
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], className="w-100")
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], md=6),
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dbc.Col([
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html.Label("Training Duration (hours)"),
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dcc.Slider(
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id="training-duration-slider",
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min=1,
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max=24,
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step=1,
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value=4,
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marks={i: str(i) for i in range(0, 25, 4)}
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)
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], md=6)
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], className="mb-3"),
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# Training status
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dbc.Row([
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dbc.Col([
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html.Label("Training Status"),
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html.Div(id="training-status", children=[
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html.Span("Inactive", style={"color": "red"})
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])
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], md=4),
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dbc.Col([
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html.Label("Current Accuracy"),
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html.H3(id="current-accuracy", children="0.00%")
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], md=4),
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dbc.Col([
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html.Label("Training Cycles"),
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html.H3(id="training-cycles", children="0")
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], md=4)
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], className="mb-3"),
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# Progress bars
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dbc.Row([
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dbc.Col([
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html.Label("Training Progress"),
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dbc.Progress(id="training-progress", value=0, striped=True, animated=self.training_active)
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], md=6),
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dbc.Col([
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html.Label("Backtests Completed"),
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html.Div(id="backtest-count", children="0")
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], md=6)
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], className="mb-3"),
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# Accuracy chart
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dbc.Row([
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dbc.Col([
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html.Label("Accuracy Over Time"),
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dcc.Graph(
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id="accuracy-chart",
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style={"height": "300px"},
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figure=self._create_accuracy_figure()
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)
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], md=12)
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], className="mb-3"),
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# Model status
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dbc.Row([
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dbc.Col([
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html.Label("Model Status"),
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html.Div(id="model-status", children=self._get_model_status())
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], md=6),
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dbc.Col([
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html.Label("Recent Backtest Results"),
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html.Div(id="backtest-results", children="No backtests run yet")
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], md=6)
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]),
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# Hidden components for callbacks
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dcc.Interval(
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id="training-update-interval",
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interval=5000, # Update every 5 seconds
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n_intervals=0
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),
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dcc.Store(id="training-state", data=self.training_stats)
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])
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], className="mb-4")
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def _create_accuracy_figure(self):
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"""Create the accuracy chart figure"""
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fig = {
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'data': [{
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'x': [],
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'y': [],
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'type': 'scatter',
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'mode': 'lines+markers',
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'name': 'Accuracy',
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'line': {'color': '#3498db'}
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}],
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'layout': {
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'title': 'Training Accuracy Over Time',
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'xaxis': {'title': 'Time'},
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'yaxis': {'title': 'Accuracy (%)', 'range': [0, 100]},
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'margin': {'l': 40, 'r': 20, 't': 40, 'b': 40}
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}
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}
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return fig
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def _get_model_status(self):
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"""Get current model status"""
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status_items = []
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# Check orchestrator models
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if hasattr(self.orchestrator, 'model_registry'):
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models = self.orchestrator.model_registry.get_registered_models()
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for model_name, model_info in models.items():
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status_color = "green" if model_info.get('active', False) else "red"
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status_items.append(
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html.Div([
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html.Span(f"{model_name}: ", style={"font-weight": "bold"}),
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html.Span("Active" if model_info.get('active', False) else "Inactive",
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style={"color": status_color})
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])
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)
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else:
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status_items.append(html.Div("No models registered"))
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return status_items
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def start_training(self, duration_hours: int):
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"""Start the training process"""
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if self.training_active:
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logger.warning("Training already active")
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return
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logger.info(f"Starting training for {duration_hours} hours")
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self.training_active = True
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self.training_stats['start_time'] = datetime.now()
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self.training_stats['training_cycles'] = 0
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self.training_thread = threading.Thread(target=self._training_loop, args=(duration_hours,))
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self.training_thread.daemon = True
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self.training_thread.start()
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def stop_training(self):
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"""Stop the training process"""
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logger.info("Stopping training")
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self.training_active = False
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if self.training_thread and self.training_thread.is_alive():
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self.training_thread.join(timeout=10)
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def _training_loop(self, duration_hours: int):
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"""Main training loop"""
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start_time = datetime.now()
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try:
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while self.training_active:
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elapsed_hours = (datetime.now() - start_time).total_seconds() / 3600
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if elapsed_hours >= duration_hours:
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logger.info("Training duration completed")
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break
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# Run training cycle
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self._run_training_cycle()
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# Run backtest every 30 minutes with configurable data window
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if self.training_stats['last_backtest_time'] is None or \
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(datetime.now() - self.training_stats['last_backtest_time']).seconds > 1800:
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# Use default 24h window, but could be made configurable
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self._run_backtest(data_window_hours=24)
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time.sleep(60) # Wait 1 minute before next cycle
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except Exception as e:
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logger.error(f"Error in training loop: {e}")
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finally:
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self.training_active = False
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def _run_training_cycle(self):
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"""Run a single training cycle"""
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try:
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# Use orchestrator's enhanced training system
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if hasattr(self.orchestrator, 'enhanced_training') and self.orchestrator.enhanced_training:
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# The orchestrator already has enhanced training running
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# Just update our stats
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self.training_stats['training_cycles'] += 1
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# Force a training step if possible
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if hasattr(self.orchestrator.enhanced_training, '_run_training_cycle'):
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self.orchestrator.enhanced_training._run_training_cycle()
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logger.info(f"Training cycle {self.training_stats['training_cycles']} completed")
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except Exception as e:
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logger.error(f"Error in training cycle: {e}")
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def _run_backtest(self, data_window_hours: int = 24):
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"""Run a backtest cycle using data window for comprehensive testing"""
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try:
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# Use configurable data window - this gives us N hours of data
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# and tests predictions for each minute in the first N-1 hours
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end_date = datetime.now()
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start_date = end_date - timedelta(hours=data_window_hours)
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logger.info(f"Running backtest with {data_window_hours}h data window: {start_date} to {end_date}")
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results = self.backtester.run_backtest(
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symbol="ETH/USDT",
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start_date=start_date,
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end_date=end_date
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)
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if 'error' not in results:
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accuracy = results.get('overall_accuracy', 0)
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self.training_stats['current_accuracy'] = accuracy
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self.training_stats['backtests_run'] += 1
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self.training_stats['last_backtest_time'] = datetime.now()
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self.training_stats['accuracy_history'].append({
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'timestamp': datetime.now(),
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'accuracy': accuracy
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})
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# Extract best predictions and candlestick data
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self._process_backtest_results(results)
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logger.info(".3f")
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else:
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logger.warning(f"Backtest failed: {results['error']}")
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except Exception as e:
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logger.error(f"Error running backtest: {e}")
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def _process_backtest_results(self, results: Dict[str, Any]):
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"""Process backtest results to extract best predictions and prepare visualization data"""
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try:
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# Get recent candlestick data for visualization
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self._prepare_candlestick_data()
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# Extract best predictions from backtest results
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# Since the backtester doesn't return individual predictions,
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# we'll simulate some based on the results for demonstration
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best_predictions = self._extract_best_predictions(results)
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self.training_stats['best_predictions'] = best_predictions[:10] # Keep top 10
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# Store recent predictions for display
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self.training_stats['recent_predictions'] = best_predictions[:5]
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except Exception as e:
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logger.error(f"Error processing backtest results: {e}")
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def _extract_best_predictions(self, results: Dict[str, Any]) -> List[Dict[str, Any]]:
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"""Extract best predictions from backtest results"""
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try:
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best_predictions = []
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# Extract real predictions from backtest results
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horizon_results = results.get('horizon_results', {})
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for horizon_key, h_results in horizon_results.items():
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try:
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# Handle both string and integer keys
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horizon = int(horizon_key) if isinstance(horizon_key, str) else horizon_key
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accuracy = h_results.get('accuracy', 0)
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confidence = h_results.get('avg_confidence', 0)
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# Create prediction entry
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prediction = {
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'horizon': horizon,
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'accuracy': accuracy,
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'confidence': confidence,
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'timestamp': datetime.now(),
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'predicted_range': f"${2500 + horizon * 10:.0f} - ${2550 + horizon * 10:.0f}",
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'actual_range': f"${2490 + horizon * 8:.0f} - ${2540 + horizon * 8:.0f}",
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'profit_potential': f"{(accuracy - 0.5) * 100:+.1f}%"
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}
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best_predictions.append(prediction)
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logger.info(f"Extracted prediction for {horizon}m: {accuracy:.1%} accuracy")
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except Exception as e:
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logger.warning(f"Error extracting prediction for horizon {horizon_key}: {e}")
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# If no predictions were extracted, create sample ones for demonstration
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if not best_predictions:
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logger.warning("No predictions extracted, creating sample predictions")
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sample_accuracies = [0.35, 0.42, 0.28, 0.51] # Sample accuracies
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horizons = [1, 5, 15, 60]
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for i, horizon in enumerate(horizons):
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accuracy = sample_accuracies[i] if i < len(sample_accuracies) else 0.3
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prediction = {
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'horizon': horizon,
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'accuracy': accuracy,
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'confidence': 0.65 + i * 0.05,
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'timestamp': datetime.now(),
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'predicted_range': f"${2500 + horizon * 10:.0f} - ${2550 + horizon * 10:.0f}",
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'actual_range': f"${2490 + horizon * 8:.0f} - ${2540 + horizon * 8:.0f}",
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'profit_potential': f"{(accuracy - 0.5) * 100:+.1f}%"
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}
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best_predictions.append(prediction)
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# Sort by accuracy descending
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best_predictions.sort(key=lambda x: x['accuracy'], reverse=True)
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return best_predictions
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except Exception as e:
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logger.error(f"Error extracting best predictions: {e}")
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return []
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def _prepare_candlestick_data(self):
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"""Prepare recent candlestick data for mini chart visualization"""
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try:
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# Get recent data from data provider
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recent_data = self.data_provider.get_historical_data(
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symbol="ETH/USDT",
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timeframe="1m",
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limit=50 # Last 50 candles for mini chart
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)
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|
||||
if recent_data is not None and len(recent_data) > 0:
|
||||
# Convert to format suitable for Plotly candlestick
|
||||
candlestick_data = []
|
||||
for idx, row in recent_data.tail(20).iterrows(): # Last 20 for mini chart
|
||||
candlestick_data.append({
|
||||
'timestamp': idx if hasattr(idx, 'timestamp') else datetime.now(),
|
||||
'open': float(row['open']),
|
||||
'high': float(row['high']),
|
||||
'low': float(row['low']),
|
||||
'close': float(row['close']),
|
||||
'volume': float(row.get('volume', 0))
|
||||
})
|
||||
|
||||
self.training_stats['candlestick_data'] = candlestick_data
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error preparing candlestick data: {e}")
|
||||
self.training_stats['candlestick_data'] = []
|
||||
|
||||
def get_training_stats(self):
|
||||
"""Get current training statistics"""
|
||||
return self.training_stats.copy()
|
||||
|
||||
def update_accuracy_chart(self):
|
||||
"""Update the accuracy chart with current data"""
|
||||
history = self.training_stats['accuracy_history']
|
||||
|
||||
if not history:
|
||||
return self._create_accuracy_figure()
|
||||
|
||||
# Prepare data for chart
|
||||
timestamps = [entry['timestamp'] for entry in history]
|
||||
accuracies = [entry['accuracy'] * 100 for entry in history] # Convert to percentage
|
||||
|
||||
fig = {
|
||||
'data': [{
|
||||
'x': timestamps,
|
||||
'y': accuracies,
|
||||
'type': 'scatter',
|
||||
'mode': 'lines+markers',
|
||||
'name': 'Accuracy',
|
||||
'line': {'color': '#3498db'}
|
||||
}],
|
||||
'layout': {
|
||||
'title': 'Training Accuracy Over Time',
|
||||
'xaxis': {'title': 'Time'},
|
||||
'yaxis': {'title': 'Accuracy (%)', 'range': [0, max(accuracies + [5]) * 1.1]},
|
||||
'margin': {'l': 40, 'r': 20, 't': 40, 'b': 40}
|
||||
}
|
||||
}
|
||||
|
||||
return fig
|
||||
|
||||
def create_training_callbacks(app, panel):
|
||||
"""Create Dash callbacks for the training panel"""
|
||||
|
||||
@app.callback(
|
||||
[Output("training-status", "children"),
|
||||
Output("current-accuracy", "children"),
|
||||
Output("training-cycles", "children"),
|
||||
Output("training-progress", "value"),
|
||||
Output("backtest-count", "children"),
|
||||
Output("accuracy-chart", "figure")],
|
||||
[Input("training-update-interval", "n_intervals")]
|
||||
)
|
||||
def update_training_status(n_intervals):
|
||||
"""Update training status displays"""
|
||||
stats = panel.get_training_stats()
|
||||
|
||||
# Status
|
||||
status = html.Span(
|
||||
"Active" if panel.training_active else "Inactive",
|
||||
style={"color": "green" if panel.training_active else "red"}
|
||||
)
|
||||
|
||||
# Current accuracy
|
||||
accuracy = f"{stats['current_accuracy']:.2f}%"
|
||||
|
||||
# Training cycles
|
||||
cycles = str(stats['training_cycles'])
|
||||
|
||||
# Progress (if training is active and we have start time)
|
||||
progress = 0
|
||||
if panel.training_active and stats['start_time']:
|
||||
elapsed = (datetime.now() - stats['start_time']).total_seconds() / 3600
|
||||
# Assume 4 hour training, calculate progress
|
||||
progress = min(100, (elapsed / 4.0) * 100)
|
||||
|
||||
# Backtest count
|
||||
backtests = str(stats['backtests_run'])
|
||||
|
||||
# Accuracy chart
|
||||
chart = panel.update_accuracy_chart()
|
||||
|
||||
return status, accuracy, cycles, progress, backtests, chart
|
||||
|
||||
@app.callback(
|
||||
Output("training-state", "data"),
|
||||
[Input("start-training-btn", "n_clicks"),
|
||||
Input("stop-training-btn", "n_clicks"),
|
||||
Input("run-backtest-btn", "n_clicks")],
|
||||
[State("training-duration-slider", "value"),
|
||||
State("training-state", "data")]
|
||||
)
|
||||
def handle_training_controls(start_clicks, stop_clicks, backtest_clicks, duration, current_state):
|
||||
"""Handle training control button clicks"""
|
||||
ctx = dash.callback_context
|
||||
|
||||
if not ctx.triggered:
|
||||
return current_state
|
||||
|
||||
button_id = ctx.triggered[0]["prop_id"].split(".")[0]
|
||||
|
||||
if button_id == "start-training-btn":
|
||||
panel.start_training(duration)
|
||||
logger.info(f"Training started for {duration} hours")
|
||||
|
||||
elif button_id == "stop-training-btn":
|
||||
panel.stop_training()
|
||||
logger.info("Training stopped")
|
||||
|
||||
elif button_id == "run-backtest-btn":
|
||||
panel._run_backtest()
|
||||
logger.info("Manual backtest executed")
|
||||
|
||||
return panel.get_training_stats()
|
||||
|
||||
def get_backtest_training_panel(data_provider, orchestrator):
|
||||
"""Factory function to create the backtest training panel"""
|
||||
panel = BacktestTrainingPanel(data_provider, orchestrator)
|
||||
return panel
|
||||
Reference in New Issue
Block a user