imort CSV files

_data/strategies.json

@@ -1,11 +1,11 @@
 {
-    "rsi_strategy_eth": {
+    "sma_125d_eth": {
         "enabled": true,
         "script": "strategy_template.py",
         "parameters": {
             "coin": "ETH",
-            "timeframe": "5m",
-            "rsi_period": 14
+            "timeframe": "1D",
+            "sma_period": 125
         }
     },
     "ma_cross_btc": {

fix_timestamps.py

@@ -0,0 +1,118 @@
+import argparse
+import logging
+import os
+import sys
+import sqlite3
+import pandas as pd
+# script to fix missing millisecond timestamps in the database after import from CSVs (this is already fixed in import_csv.py)
+# Assuming logging_utils.py is in the same directory
+from logging_utils import setup_logging
+
+class DatabaseFixer:
+    """
+    Scans the SQLite database for rows with missing millisecond timestamps
+    and updates them based on the datetime_utc column.
+    """
+
+    def __init__(self, log_level: str, coin: str):
+        setup_logging(log_level, 'TimestampFixer')
+        self.coin = coin
+        self.table_name = f"{self.coin}_1m"
+        self.db_path = os.path.join("_data", "market_data.db")
+
+    def run(self):
+        """Orchestrates the entire database update and verification process."""
+        logging.info(f"Starting timestamp fix process for table '{self.table_name}'...")
+
+        if not os.path.exists(self.db_path):
+            logging.error(f"Database file not found at '{self.db_path}'. Exiting.")
+            sys.exit(1)
+
+        try:
+            with sqlite3.connect(self.db_path) as conn:
+                conn.execute("PRAGMA journal_mode=WAL;")
+                
+                # 1. Check how many rows need fixing
+                rows_to_fix_count = self._count_rows_to_fix(conn)
+                if rows_to_fix_count == 0:
+                    logging.info(f"No rows with missing timestamps found in '{self.table_name}'. No action needed.")
+                    return
+                
+                logging.info(f"Found {rows_to_fix_count:,} rows with missing timestamps to update.")
+
+                # 2. Process the table in chunks to conserve memory
+                updated_count = self._process_in_chunks(conn)
+
+                # 3. Provide a final summary
+                self._summarize_update(rows_to_fix_count, updated_count)
+
+        except Exception as e:
+            logging.error(f"A critical error occurred: {e}")
+
+    def _count_rows_to_fix(self, conn) -> int:
+        """Counts the number of rows where timestamp_ms is NULL."""
+        try:
+            return pd.read_sql(f'SELECT COUNT(*) FROM "{self.table_name}" WHERE timestamp_ms IS NULL', conn).iloc[0, 0]
+        except pd.io.sql.DatabaseError:
+            logging.error(f"Table '{self.table_name}' not found in the database. Cannot fix timestamps.")
+            sys.exit(1)
+
+    def _process_in_chunks(self, conn) -> int:
+        """Reads, calculates, and updates timestamps in manageable chunks."""
+        total_updated = 0
+        chunk_size = 50000  # Process 50,000 rows at a time
+        
+        # We select the special 'rowid' column to uniquely identify each row for updating
+        query = f'SELECT rowid, datetime_utc FROM "{self.table_name}" WHERE timestamp_ms IS NULL'
+        
+        for chunk_df in pd.read_sql_query(query, conn, chunksize=chunk_size):
+            if chunk_df.empty:
+                break
+                
+            logging.info(f"Processing a chunk of {len(chunk_df)} rows...")
+            
+            # Calculate the missing timestamps
+            chunk_df['datetime_utc'] = pd.to_datetime(chunk_df['datetime_utc'])
+            chunk_df['timestamp_ms'] = (chunk_df['datetime_utc'].astype('int64') // 10**6)
+            
+            # Prepare data for the update command: a list of (timestamp, rowid) tuples
+            update_data = list(zip(chunk_df['timestamp_ms'], chunk_df['rowid']))
+            
+            # Use executemany for a fast bulk update
+            cursor = conn.cursor()
+            cursor.executemany(f'UPDATE "{self.table_name}" SET timestamp_ms = ? WHERE rowid = ?', update_data)
+            conn.commit()
+            
+            total_updated += len(chunk_df)
+            logging.info(f"Updated {total_updated} rows so far...")
+            
+        return total_updated
+
+    def _summarize_update(self, expected_count: int, actual_count: int):
+        """Prints a final summary of the update process."""
+        logging.info("--- Timestamp Fix Summary ---")
+        print(f"\n{'Status':<25}: COMPLETE")
+        print("-" * 40)
+        print(f"{'Table Processed':<25}: {self.table_name}")
+        print(f"{'Rows Needing Update':<25}: {expected_count:,}")
+        print(f"{'Rows Successfully Updated':<25}: {actual_count:,}")
+        
+        if expected_count == actual_count:
+            logging.info("Verification successful: All necessary rows have been updated.")
+        else:
+            logging.warning("Verification warning: The number of updated rows does not match the expected count.")
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Fix missing millisecond timestamps in the SQLite database.")
+    parser.add_argument("--coin", default="BTC", help="The coin symbol for the table to fix (e.g., BTC).")
+    parser.add_argument(
+        "--log-level",
+        default="normal",
+        choices=['off', 'normal', 'debug'],
+        help="Set the logging level for the script."
+    )
+    args = parser.parse_args()
+    
+    fixer = DatabaseFixer(log_level=args.log_level, coin=args.coin)
+    fixer.run()

import_csv.py

@@ -23,6 +23,7 @@ class CsvImporter:
             
         self.csv_path = csv_path
         self.coin = coin
+        # --- FIX: Corrected the f-string syntax for the table name ---
         self.table_name = f"{self.coin}_1m"
         self.db_path = os.path.join("_data", "market_data.db")
         self.column_mapping = {
@@ -86,6 +87,10 @@ class CsvImporter:
         # Clean and format the data
         df.rename(columns=self.column_mapping, inplace=True)
         df['datetime_utc'] = pd.to_datetime(df['datetime_utc'])
+
+        # --- FIX: Calculate the millisecond timestamp from the datetime column ---
+        # This converts the datetime to nanoseconds and then to milliseconds.
+        df['timestamp_ms'] = (df['datetime_utc'].astype('int64') // 10**6)
         
         # Filter the data to find only rows that are outside the range of what's already in the DB
         if db_oldest and db_newest:
@@ -145,3 +150,5 @@ if __name__ == "__main__":
     
     importer = CsvImporter(log_level=args.log_level, csv_path=args.file, coin=args.coin)
     importer.run()
+
+