Spaces:
Running
Running
File size: 7,104 Bytes
da0ce7b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 | #!/usr/bin/env python3
"""
ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
β β
β π LIVE ECOSYSTEM MONITORING DASHBOARD π β
β β
β Real-time tracking of: β
β β’ Trade data collection progress β
β β’ Frequency distribution of trades β
β β’ Node performance tracking β
β β’ Market sweep validation β
β β’ Probability matrix correlation β
β β
ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
"""
from aureon_baton_link import link_system as _baton_link; _baton_link(__name__)
import os
import json
import time
from pathlib import Path
from collections import defaultdict
import statistics
def monitor_logs(log_dir="/tmp/aureon_trade_logs"):
"""Monitor trade logs in real-time"""
log_dir = Path(log_dir)
if not log_dir.exists():
print("β Log directory not found")
return
# Find the latest trades file
trades_files = sorted(log_dir.glob("trades_*.jsonl"))
if not trades_files:
print("β No trade logs found yet")
return
latest_trades_file = trades_files[-1]
latest_exits_file = Path(str(latest_trades_file).replace("trades_", "exits_"))
latest_sweep_file = Path(str(latest_trades_file).replace("trades_", "market_sweep_"))
print("\n" + "="*80)
print("π LIVE ECOSYSTEM MONITORING DASHBOARD".center(80))
print("="*80)
# Read trades
trades = []
if latest_trades_file.exists():
with open(latest_trades_file) as f:
for line in f:
try:
trades.append(json.loads(line))
except:
pass
# Read exits
exits = []
if latest_exits_file.exists():
with open(latest_exits_file) as f:
for line in f:
try:
exits.append(json.loads(line))
except:
pass
# Read market sweeps
sweeps = []
if latest_sweep_file.exists():
with open(latest_sweep_file) as f:
for line in f:
try:
sweeps.append(json.loads(line))
except:
pass
print(f"\nβ±οΈ Last Updated: {time.strftime('%H:%M:%S')}")
print(f"π Log File: {latest_trades_file.name}")
# Trade Statistics
print(f"\nπ TRADE COLLECTION PROGRESS:")
print(f" β’ Trades Entered: {len(trades)}")
print(f" β’ Trades Exited: {len(exits)}")
print(f" β’ Pending Trades: {len(trades) - len(exits)}")
if exits:
total_pnl = sum(e.get('net_pnl', 0) for e in exits)
wins = sum(1 for e in exits if e.get('net_pnl', 0) > 0)
win_rate = (wins / len(exits) * 100) if exits else 0
print(f"\nπ° PROFITABILITY:")
print(f" β’ Total P&L: ${total_pnl:+.2f}")
print(f" β’ Win Rate: {win_rate:.1f}% ({wins}/{len(exits)})")
print(f" β’ Avg P&L per trade: ${total_pnl/len(exits):+.2f}")
# Frequency Analysis
if trades:
freq_dist = defaultdict(int)
for trade in trades:
freq = trade.get('hnc_frequency', 0)
freq_dist[freq] += 1
print(f"\nπ HNC FREQUENCY DISTRIBUTION:")
for freq in sorted(freq_dist.keys()):
count = freq_dist[freq]
bar = "β" * min(20, count)
print(f" {freq:4.0f}Hz: {count:3d} trades {bar}")
# Node Performance
if trades:
node_dist = defaultdict(lambda: {'count': 0, 'coherence': []})
for trade in trades:
node = trade.get('dominant_node', 'Unknown')
node_dist[node]['count'] += 1
node_dist[node]['coherence'].append(trade.get('coherence', 0.5))
print(f"\nπ― NODE DISTRIBUTION:")
for node in sorted(node_dist.keys()):
data = node_dist[node]
avg_coherence = statistics.mean(data['coherence']) if data['coherence'] else 0
print(f" {node:15s}: {data['count']:3d} trades | Ξ_avg: {avg_coherence:.2f}")
# Market Sweep Statistics
if sweeps:
total_found = sum(s.get('total_opportunities_found', 0) for s in sweeps)
total_entered = sum(s.get('opportunities_entered', 0) for s in sweeps)
total_rejected = sum(s.get('opportunities_rejected', 0) for s in sweeps)
avg_coherence = statistics.mean([s.get('average_coherence', 0.5) for s in sweeps])
print(f"\nπ MARKET SWEEP ANALYSIS:")
print(f" β’ Total Opportunities Found: {total_found}")
print(f" β’ Opportunities Entered: {total_entered}")
print(f" β’ Opportunities Rejected: {total_rejected}")
print(f" β’ Entry Rate: {(total_entered/total_found*100) if total_found > 0 else 0:.1f}%")
print(f" β’ Average Coherence: {avg_coherence:.2f}")
print(f" β’ Sweeps Completed: {len(sweeps)}")
# Progress toward 50+ trades goal
target_trades = 50
current_trades = len(trades)
progress_pct = (current_trades / target_trades * 100) if target_trades > 0 else 0
bar_length = 30
filled = int(bar_length * progress_pct / 100)
bar = "β" * filled + "β" * (bar_length - filled)
print(f"\nπ― PROGRESS TOWARD 50-TRADE COLLECTION TARGET:")
print(f" [{bar}] {progress_pct:.1f}% ({current_trades}/{target_trades})")
if current_trades > 0:
time_per_trade = 0.5 # Rough estimate
remaining_trades = max(0, target_trades - current_trades)
eta_seconds = remaining_trades * time_per_trade
eta_minutes = eta_seconds / 60
print(f" β±οΈ ETA: ~{eta_minutes:.1f} minutes ({remaining_trades} trades remaining)")
print("\n" + "="*80)
print("\n⨠Dashboard updated successfully")
if __name__ == '__main__':
monitor_logs()
|