Lowered signal tresholds so low that we got signals flowing. Few debug scripts to see way signals were not firing. Fix analyst.py indicator calculation to use TA-lib.
This commit is contained in:
Kalzu Rekku
158
signals/analysis_inspector.py
Executable file
158
signals/analysis_inspector.py
Executable file
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#!/usr/bin/env python3
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"""
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Analysis Database Inspector
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Check what indicators are actually populated
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"""
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import sqlite3
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import json
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def load_config():
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with open("config.json", "r") as f:
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return json.load(f)
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def inspect_database(db_path):
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"""Inspect analysis database schema and data"""
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print(f"\n📊 Inspecting: {db_path}")
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print("=" * 70)
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try:
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conn = sqlite3.connect(f"file:{db_path}?mode=ro", uri=True)
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cursor = conn.cursor()
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# Get table schema
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cursor.execute("PRAGMA table_info(analysis)")
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columns = cursor.fetchall()
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print("\n📋 TABLE SCHEMA:")
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print(f"{'Column Name':<20} {'Type':<15} {'Not Null':<10}")
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print("-" * 50)
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for col in columns:
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print(f"{col[1]:<20} {col[2]:<15} {'YES' if col[3] else 'NO':<10}")
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# Get row count
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cursor.execute("SELECT COUNT(*) FROM analysis")
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total_rows = cursor.fetchone()[0]
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print(f"\n📊 Total rows: {total_rows}")
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# Check data availability per timeframe
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cursor.execute("SELECT DISTINCT timeframe FROM analysis ORDER BY timeframe")
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timeframes = [row[0] for row in cursor.fetchall()]
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print("\n⏱️ DATA BY TIMEFRAME:")
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for tf in timeframes:
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cursor.execute(f"SELECT COUNT(*) FROM analysis WHERE timeframe = ?", (tf,))
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count = cursor.fetchone()[0]
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print(f" {tf}: {count} rows")
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# Check for NULL values in key indicators
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print("\n🔍 NULL VALUE CHECK (latest 10 rows per timeframe):")
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indicator_cols = [
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'ema_9', 'ema_21', 'sma_50', 'sma_200',
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'rsi_14', 'macd', 'macd_signal', 'macd_hist',
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'bb_upper', 'bb_middle', 'bb_lower', 'bb_squeeze',
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'volume_ma_20'
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]
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for tf in timeframes:
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print(f"\n Timeframe: {tf}")
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# Get latest row
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cursor.execute(f"""
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SELECT * FROM analysis
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WHERE timeframe = ?
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ORDER BY timestamp DESC
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LIMIT 1
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""", (tf,))
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row = cursor.fetchone()
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col_names = [desc[0] for desc in cursor.description]
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if row:
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row_dict = dict(zip(col_names, row))
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null_indicators = []
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present_indicators = []
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for ind in indicator_cols:
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if ind in row_dict:
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if row_dict[ind] is None:
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null_indicators.append(ind)
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else:
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present_indicators.append(ind)
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else:
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null_indicators.append(f"{ind} (MISSING COLUMN)")
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if present_indicators:
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print(f" ✓ Present: {', '.join(present_indicators[:5])}")
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if len(present_indicators) > 5:
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print(f" {', '.join(present_indicators[5:])}")
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if null_indicators:
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print(f" ❌ NULL/Missing: {', '.join(null_indicators)}")
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# Show sample values
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print(f"\n Sample values from latest row:")
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print(f" Timestamp: {row_dict.get('timestamp')}")
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for ind in ['ema_9', 'ema_21', 'rsi_14', 'bb_upper']:
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if ind in row_dict:
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val = row_dict[ind]
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if val is not None:
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print(f" {ind}: {val}")
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else:
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print(f" {ind}: NULL")
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else:
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print(f" ❌ No data found")
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# Check if buy_volume exists in candles
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print("\n\n📊 Checking candles table for buy_volume...")
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cursor.execute("SELECT name FROM sqlite_master WHERE type='table' AND name='candles'")
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if cursor.fetchone():
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cursor.execute("PRAGMA table_info(candles)")
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candles_cols = [col[1] for col in cursor.fetchall()]
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if 'buy_volume' in candles_cols:
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print(" ✓ buy_volume column exists in candles table")
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# Check if it has data
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cursor.execute("SELECT COUNT(*) FROM candles WHERE buy_volume IS NOT NULL")
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count = cursor.fetchone()[0]
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print(f" ✓ {count} rows with buy_volume data")
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else:
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print(" ❌ buy_volume column MISSING from candles table")
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print(" Available columns:", ', '.join(candles_cols))
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conn.close()
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except sqlite3.OperationalError as e:
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print(f" ❌ Database error: {e}")
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except Exception as e:
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print(f" ❌ Error: {e}")
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def main():
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config = load_config()
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print("🔍 ANALYSIS DATABASE INSPECTOR")
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print("=" * 70)
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inspect_database(config["analysis_db"])
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print("\n\n💡 NEXT STEPS:")
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print("=" * 70)
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print("If indicators are missing:")
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print(" 1. Check your analysis pipeline is running")
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print(" 2. Verify the analysis script calculates these indicators:")
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print(" - rsi_14, bb_upper, bb_lower, bb_middle, bb_squeeze")
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print(" 3. Re-run analysis on existing candle data")
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print("\nIf buy_volume is missing:")
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print(" 1. Update your candles table schema")
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print(" 2. Modify your data ingestion to capture buy_volume")
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print(" 3. Or set buy_volume = volume/2 as approximation")
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if __name__ == "__main__":
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main()
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