COB working
This commit is contained in:
Dobromir Popov
@ -4,8 +4,10 @@ Manages the formatting and creation of dashboard components
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"""
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from dash import html, dcc
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import dash_bootstrap_components as dbc
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from datetime import datetime
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import logging
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import numpy as np
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logger = logging.getLogger(__name__)
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@ -245,139 +247,108 @@ class DashboardComponentManager:
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logger.error(f"Error formatting system status: {e}")
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return [html.P(f"Error: {str(e)}", className="text-danger small")]
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def _format_cnn_pivot_prediction(self, model_info):
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"""Format CNN pivot prediction for display"""
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try:
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pivot_prediction = model_info.get('pivot_prediction')
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if not pivot_prediction:
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return html.Div()
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pivot_type = pivot_prediction.get('pivot_type', 'UNKNOWN')
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predicted_price = pivot_prediction.get('predicted_price', 0)
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confidence = pivot_prediction.get('confidence', 0)
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time_horizon = pivot_prediction.get('time_horizon_minutes', 0)
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# Color coding for pivot types
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if 'RESISTANCE' in pivot_type:
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pivot_color = "text-danger"
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pivot_icon = "fas fa-arrow-up"
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elif 'SUPPORT' in pivot_type:
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pivot_color = "text-success"
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pivot_icon = "fas fa-arrow-down"
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else:
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pivot_color = "text-warning"
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pivot_icon = "fas fa-arrows-alt-h"
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return html.Div([
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html.Div([
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html.I(className=f"{pivot_icon} me-1 {pivot_color}"),
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html.Span("Next Pivot: ", className="text-muted small"),
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html.Span(f"${predicted_price:.2f}", className=f"small fw-bold {pivot_color}")
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], className="mb-1"),
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html.Div([
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html.Span(f"{pivot_type.replace('_', ' ')}", className=f"small {pivot_color}"),
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html.Span(f" ({confidence:.0%}) in ~{time_horizon}m", className="text-muted small")
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])
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], className="mt-1 p-1", style={"backgroundColor": "rgba(255,255,255,0.02)", "borderRadius": "3px"})
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except Exception as e:
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logger.debug(f"Error formatting CNN pivot prediction: {e}")
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return html.Div()
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def format_cob_data(self, cob_snapshot, symbol):
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"""Format COB data for display"""
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"""Format COB data into a ladder display with volume bars"""
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try:
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if not cob_snapshot:
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return [html.P("No COB data", className="text-muted small")]
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if not cob_snapshot or not hasattr(cob_snapshot, 'stats'):
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return html.Div([
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html.H6(f"{symbol} COB", className="mb-2"),
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html.P("No COB data available", className="text-muted small")
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])
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# Real COB data display
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cob_info = []
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stats = cob_snapshot.stats if hasattr(cob_snapshot, 'stats') else {}
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mid_price = stats.get('mid_price', 0)
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spread_bps = stats.get('spread_bps', 0)
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total_bid_liquidity = stats.get('total_bid_liquidity', 0)
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total_ask_liquidity = stats.get('total_ask_liquidity', 0)
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imbalance = stats.get('imbalance', 0)
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bids = getattr(cob_snapshot, 'consolidated_bids', [])
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asks = getattr(cob_snapshot, 'consolidated_asks', [])
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if mid_price == 0 or not bids or not asks:
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return html.Div([
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html.H6(f"{symbol} COB", className="mb-2"),
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html.P("Awaiting valid order book data...", className="text-muted small")
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])
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# Header with summary stats
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imbalance_text = f"Bid Heavy ({imbalance:.3f})" if imbalance > 0 else f"Ask Heavy ({imbalance:.3f})"
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imbalance_color = "text-success" if imbalance > 0 else "text-danger"
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# Symbol header
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cob_info.append(html.Div([
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html.Strong(f"{symbol}", className="text-info"),
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html.Span(" - COB Snapshot", className="small text-muted")
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], className="mb-2"))
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# Check if we have a real COB snapshot object
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if hasattr(cob_snapshot, 'volume_weighted_mid'):
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# Real COB snapshot data
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mid_price = getattr(cob_snapshot, 'volume_weighted_mid', 0)
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spread_bps = getattr(cob_snapshot, 'spread_bps', 0)
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bid_liquidity = getattr(cob_snapshot, 'total_bid_liquidity', 0)
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ask_liquidity = getattr(cob_snapshot, 'total_ask_liquidity', 0)
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imbalance = getattr(cob_snapshot, 'liquidity_imbalance', 0)
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bid_levels = len(getattr(cob_snapshot, 'consolidated_bids', []))
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ask_levels = len(getattr(cob_snapshot, 'consolidated_asks', []))
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# Price and spread
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cob_info.append(html.Div([
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html.Div([
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html.I(className="fas fa-dollar-sign text-success me-2"),
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html.Span(f"Mid: ${mid_price:.2f}", className="small fw-bold")
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], className="mb-1"),
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html.Div([
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html.I(className="fas fa-arrows-alt-h text-warning me-2"),
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html.Span(f"Spread: {spread_bps:.1f} bps", className="small")
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], className="mb-1")
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header = html.Div([
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html.H6(f"{symbol} - COB Ladder", className="mb-1"),
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html.Div([
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html.Span(f"Mid: ${mid_price:,.2f}", className="me-3"),
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html.Span(f"Spread: {spread_bps:.1f} bps", className="me-3"),
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html.Span(f"Imbalance: ", className="small"),
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html.Span(imbalance_text, className=f"fw-bold {imbalance_color}")
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], className="small text-muted")
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], className="mb-2")
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# --- Ladder Creation ---
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bucket_size = 10 # $10 price buckets
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num_levels = 5 # 5 levels above and below
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# Aggregate bids and asks into buckets
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def aggregate_buckets(orders, mid_price, bucket_size):
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buckets = {}
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for order in orders:
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price = order.get('price', 0)
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size = order.get('size', 0)
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if price > 0:
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bucket_key = round(price / bucket_size) * bucket_size
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if bucket_key not in buckets:
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buckets[bucket_key] = 0
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buckets[bucket_key] += size * price # Volume in quote currency (USD)
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return buckets
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bid_buckets = aggregate_buckets(bids, mid_price, bucket_size)
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ask_buckets = aggregate_buckets(asks, mid_price, bucket_size)
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all_volumes = list(bid_buckets.values()) + list(ask_buckets.values())
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max_volume = max(all_volumes) if all_volumes else 1
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# Determine ladder price levels
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center_bucket = round(mid_price / bucket_size) * bucket_size
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ask_levels = [center_bucket + i * bucket_size for i in range(1, num_levels + 1)]
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bid_levels = [center_bucket - i * bucket_size for i in range(num_levels)]
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# Create ladder rows
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ask_rows = []
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for price in sorted(ask_levels, reverse=True):
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volume = ask_buckets.get(price, 0)
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progress = (volume / max_volume) * 100
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ask_rows.append(html.Tr([
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html.Td(f"${price:,.2f}", className="text-danger price-level"),
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html.Td(f"${volume:,.0f}", className="volume-level"),
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html.Td(dbc.Progress(value=progress, color="danger", className="vh-25"), className="progress-cell")
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]))
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# Liquidity info
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total_liquidity = bid_liquidity + ask_liquidity
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bid_pct = (bid_liquidity / total_liquidity * 100) if total_liquidity > 0 else 0
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ask_pct = (ask_liquidity / total_liquidity * 100) if total_liquidity > 0 else 0
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cob_info.append(html.Div([
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html.Div([
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html.I(className="fas fa-layer-group text-info me-2"),
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html.Span(f"Liquidity: ${total_liquidity:,.0f}", className="small")
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], className="mb-1"),
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html.Div([
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html.Span(f"Bids: {bid_pct:.0f}% ", className="small text-success"),
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html.Span(f"Asks: {ask_pct:.0f}%", className="small text-danger")
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], className="mb-1")
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]))
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# Order book depth
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cob_info.append(html.Div([
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html.Div([
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html.I(className="fas fa-list text-secondary me-2"),
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html.Span(f"Levels: {bid_levels} bids, {ask_levels} asks", className="small")
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], className="mb-1")
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]))
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# Imbalance indicator
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imbalance_color = "text-success" if imbalance > 0.1 else "text-danger" if imbalance < -0.1 else "text-muted"
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imbalance_text = "Bid Heavy" if imbalance > 0.1 else "Ask Heavy" if imbalance < -0.1 else "Balanced"
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cob_info.append(html.Div([
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html.I(className="fas fa-balance-scale me-2"),
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html.Span(f"Imbalance: ", className="small text-muted"),
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html.Span(f"{imbalance_text} ({imbalance:.3f})", className=f"small {imbalance_color}")
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], className="mb-1"))
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else:
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# Fallback display for other data formats
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cob_info.append(html.Div([
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html.Div([
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html.I(className="fas fa-chart-bar text-success me-2"),
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html.Span("Order Book: Active", className="small")
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], className="mb-1"),
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html.Div([
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html.I(className="fas fa-coins text-warning me-2"),
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html.Span("Liquidity: Good", className="small")
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], className="mb-1"),
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html.Div([
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html.I(className="fas fa-balance-scale text-info me-2"),
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html.Span("Imbalance: Neutral", className="small")
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])
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bid_rows = []
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for price in sorted(bid_levels, reverse=True):
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volume = bid_buckets.get(price, 0)
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progress = (volume / max_volume) * 100
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bid_rows.append(html.Tr([
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html.Td(f"${price:,.2f}", className="text-success price-level"),
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html.Td(f"${volume:,.0f}", className="volume-level"),
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html.Td(dbc.Progress(value=progress, color="success", className="vh-25"), className="progress-cell")
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]))
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return cob_info
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# Mid-price separator
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mid_row = html.Tr([
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html.Td(f"${mid_price:,.2f}", colSpan=3, className="text-center fw-bold text-white bg-secondary")
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])
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ladder_table = html.Table([
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html.Thead(html.Tr([html.Th("Price (USD)"), html.Th("Volume (USD)"), html.Th("Total")])),
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html.Tbody(ask_rows + [mid_row] + bid_rows)
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], className="table table-sm table-dark cob-ladder-table")
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return html.Div([header, ladder_table])
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except Exception as e:
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logger.error(f"Error formatting COB data: {e}")
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return [html.P(f"Error: {str(e)}", className="text-danger small")]
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logger.error(f"Error formatting COB data ladder: {e}")
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return html.P(f"Error: {str(e)}", className="text-danger small")
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def format_cob_data_with_buckets(self, cob_snapshot, symbol, price_buckets, memory_stats, bucket_size=1.0):
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"""Format COB data with price buckets for high-frequency display"""
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@ -692,4 +663,43 @@ class DashboardComponentManager:
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except Exception as e:
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logger.error(f"Error formatting training metrics: {e}")
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return [html.P(f"Error: {str(e)}", className="text-danger small")]
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return [html.P(f"Error: {str(e)}", className="text-danger small")]
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def _format_cnn_pivot_prediction(self, model_info):
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"""Format CNN pivot prediction for display"""
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try:
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pivot_prediction = model_info.get('pivot_prediction')
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if not pivot_prediction:
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return html.Div()
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pivot_type = pivot_prediction.get('pivot_type', 'UNKNOWN')
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predicted_price = pivot_prediction.get('predicted_price', 0)
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confidence = pivot_prediction.get('confidence', 0)
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time_horizon = pivot_prediction.get('time_horizon_minutes', 0)
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# Color coding for pivot types
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if 'RESISTANCE' in pivot_type:
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pivot_color = "text-danger"
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pivot_icon = "fas fa-arrow-up"
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elif 'SUPPORT' in pivot_type:
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pivot_color = "text-success"
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pivot_icon = "fas fa-arrow-down"
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else:
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pivot_color = "text-warning"
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pivot_icon = "fas fa-arrows-alt-h"
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return html.Div([
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html.Div([
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html.I(className=f"{pivot_icon} me-1 {pivot_color}"),
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html.Span("Next Pivot: ", className="text-muted small"),
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html.Span(f"${predicted_price:.2f}", className=f"small fw-bold {pivot_color}")
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], className="mb-1"),
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html.Div([
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html.Span(f"{pivot_type.replace('_', ' ')}", className=f"small {pivot_color}"),
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html.Span(f" ({confidence:.0%}) in ~{time_horizon}m", className="text-muted small")
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])
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], className="mt-1 p-1", style={"backgroundColor": "rgba(255,255,255,0.02)", "borderRadius": "3px"})
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except Exception as e:
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logger.debug(f"Error formatting CNN pivot prediction: {e}")
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return html.Div()
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