cc56b72df2
CRITICAL FIX (Nov 30, 2025):
- Dashboard showed 'idle' despite 22+ worker processes running
- Root cause: SSH-based worker detection timing out
- Solution: Check database for running chunks FIRST
Changes:
1. app/api/cluster/status/route.ts:
- Query exploration database before SSH detection
- If running chunks exist, mark workers 'active' even if SSH fails
- Override worker status: 'offline' → 'active' when chunks running
- Log: '✅ Cluster status: ACTIVE (database shows running chunks)'
- Database is source of truth, SSH only for supplementary metrics
2. app/cluster/page.tsx:
- Stop button ALREADY EXISTS (conditionally shown)
- Shows Start when status='idle', Stop when status='active'
- No code changes needed - fixed by status detection
Result:
- Dashboard now shows 'ACTIVE' with 2 workers (correct)
- Workers show 'active' status (was 'offline')
- Stop button automatically visible when cluster active
- System resilient to SSH timeouts/network issues
Verified:
- Container restarted: Nov 30 21:18 UTC
- API tested: Returns status='active', activeWorkers=2
- Logs confirm: Database-first logic working
- Workers confirmed running: 22+ processes on worker1, workers on worker2
69 lines
2.4 KiB
Python
69 lines
2.4 KiB
Python
#!/usr/bin/env python3
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"""Debug MA gap scoring to understand parameter insensitivity."""
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import sys
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from pathlib import Path
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import numpy as np
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import pandas as pd
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# Add backtester to path
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sys.path.insert(0, str(Path(__file__).parent.parent))
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from backtester.data_loader import load_csv
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from backtester.indicators.money_line import MoneyLineInputs, ema
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def analyze_ma_gap_distribution(csv_path: str):
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"""Analyze MA gap score distribution to understand parameter behavior."""
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print("=" * 80)
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print("MA GAP DISTRIBUTION ANALYSIS")
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print("=" * 80)
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# Load data
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data_slice = load_csv(Path(csv_path), "SOL-PERP", "5m")
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df = data_slice.data # DataSlice.data is the DataFrame
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print(f"Loaded {len(df)} bars\n")
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# Calculate EMAs
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df["ema_fast"] = ema(df["close"], 50)
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df["ema_slow"] = ema(df["close"], 200)
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# Calculate raw MA gap percentage
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ma_gap = 100.0 * (df["ema_fast"] - df["ema_slow"]) / df["close"]
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print("RAW MA GAP (%) DISTRIBUTION:")
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print(f" Min: {ma_gap.min():.4f}%")
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print(f" 25th: {ma_gap.quantile(0.25):.4f}%")
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print(f" Median: {ma_gap.median():.4f}%")
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print(f" 75th: {ma_gap.quantile(0.75):.4f}%")
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print(f" Max: {ma_gap.max():.4f}%")
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print(f" Std: {ma_gap.std():.4f}%\n")
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# Test different thresholds
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for threshold in [0.2, 0.3, 0.35, 0.4, 0.5]:
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gap_score = np.tanh(ma_gap / threshold)
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print(f"\nTHRESHOLD = {threshold} (default: 0.35):")
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print(f" gap_score min: {gap_score.min():.6f}")
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print(f" gap_score max: {gap_score.max():.6f}")
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print(f" gap_score median: {gap_score.median():.6f}")
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# Check flip_threshold comparisons
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for flip_pct in [0.4, 0.5, 0.6, 0.7]:
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flip_threshold = flip_pct / 100.0 # Convert to decimal
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# Count how many bars exceed threshold
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long_signals = (gap_score > flip_threshold).sum()
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short_signals = (gap_score < -flip_threshold).sum()
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total_signals = long_signals + short_signals
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print(f" flip_threshold={flip_pct}% → {total_signals} potential signals")
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print(f" (LONG: {long_signals}, SHORT: {short_signals})")
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if __name__ == "__main__":
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if len(sys.argv) != 2:
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print("Usage: python debug_ma_gap.py <csv_path>")
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sys.exit(1)
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analyze_ma_gap_distribution(sys.argv[1])
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