import argparse import logging import os import sys import sqlite3 import pandas as pd import json from datetime import datetime, timezone # Assuming logging_utils.py is in the same directory from logging_utils import setup_logging class Resampler: """ Reads new 1-minute candle data from the SQLite database, resamples it to various timeframes, and appends the new candles to the corresponding tables. """ def __init__(self, log_level: str, coins: list, timeframes: dict): setup_logging(log_level, 'Resampler') self.db_path = os.path.join("_data", "market_data.db") self.status_file_path = os.path.join("_data", "resampling_status.json") self.coins_to_process = coins self.timeframes = timeframes self.aggregation_logic = { 'open': 'first', 'high': 'max', 'low': 'min', 'close': 'last', 'volume': 'sum', 'number_of_trades': 'sum' } self.resampling_status = self._load_existing_status() self.job_start_time = None def _load_existing_status(self) -> dict: """Loads the existing status file if it exists, otherwise returns an empty dict.""" if os.path.exists(self.status_file_path): try: with open(self.status_file_path, 'r', encoding='utf-8') as f: logging.debug(f"Loading existing status from '{self.status_file_path}'") return json.load(f) except (IOError, json.JSONDecodeError) as e: logging.warning(f"Could not read existing status file. Starting fresh. Error: {e}") return {} def run(self): """ Main execution function to process all configured coins and update the database. """ self.job_start_time = datetime.now(timezone.utc) logging.info(f"--- Resampling job started at {self.job_start_time.strftime('%Y-%m-%d %H:%M:%S %Z')} ---") if not os.path.exists(self.db_path): logging.error(f"Database file '{self.db_path}' not found.") return with sqlite3.connect(self.db_path) as conn: conn.execute("PRAGMA journal_mode=WAL;") logging.debug(f"Processing {len(self.coins_to_process)} coins...") for coin in self.coins_to_process: source_table_name = f"{coin}_1m" logging.debug(f"--- Processing {coin} ---") try: # Load the full 1m history once per coin df_1m = pd.read_sql(f'SELECT * FROM "{source_table_name}"', conn, parse_dates=['datetime_utc']) if df_1m.empty: logging.warning(f"Source table '{source_table_name}' is empty. Skipping.") continue df_1m.set_index('datetime_utc', inplace=True) for tf_name, tf_code in self.timeframes.items(): target_table_name = f"{coin}_{tf_name}" logging.debug(f" Updating {tf_name} table...") last_timestamp = self._get_last_timestamp(conn, target_table_name) # Get the new 1-minute data that needs to be processed new_df_1m = df_1m[df_1m.index > last_timestamp] if last_timestamp else df_1m if new_df_1m.empty: logging.debug(f" -> No new 1-minute data for {tf_name}. Table is up to date.") continue resampled_df = new_df_1m.resample(tf_code).agg(self.aggregation_logic) resampled_df.dropna(how='all', inplace=True) if not resampled_df.empty: # Append the newly resampled data to the target table resampled_df.to_sql(target_table_name, conn, if_exists='append', index=True) logging.debug(f" -> Appended {len(resampled_df)} new candles to '{target_table_name}'.") if coin not in self.resampling_status: self.resampling_status[coin] = {} total_candles = int(self._get_table_count(conn, target_table_name)) self.resampling_status[coin][tf_name] = { "last_candle_utc": resampled_df.index[-1].strftime('%Y-%m-%d %H:%M:%S'), "total_candles": total_candles } except Exception as e: logging.error(f"Failed to process coin '{coin}': {e}") self._log_summary() self._save_status() logging.info(f"--- Resampling job finished at {datetime.now(timezone.utc).strftime('%Y-%m-%d %H:%M:%S %Z')} ---") def _log_summary(self): """Logs a summary of the total candles for each timeframe.""" logging.info("--- Resampling Job Summary ---") timeframe_totals = {} # Iterate through coins, skipping metadata keys for coin, tfs in self.resampling_status.items(): if not isinstance(tfs, dict): continue for tf_name, tf_data in tfs.items(): total = tf_data.get("total_candles", 0) if tf_name not in timeframe_totals: timeframe_totals[tf_name] = 0 timeframe_totals[tf_name] += total if not timeframe_totals: logging.info("No candles were resampled in this run.") return logging.info("Total candles per timeframe across all processed coins:") for tf_name, total in sorted(timeframe_totals.items()): logging.info(f" - {tf_name:<10}: {total:,} candles") def _get_last_timestamp(self, conn, table_name): """Gets the timestamp of the last entry in a table.""" try: return pd.read_sql(f'SELECT MAX(datetime_utc) FROM "{table_name}"', conn).iloc[0, 0] except (pd.io.sql.DatabaseError, IndexError): return None def _get_table_count(self, conn, table_name): """Gets the total row count of a table.""" try: return pd.read_sql(f'SELECT COUNT(*) FROM "{table_name}"', conn).iloc[0, 0] except (pd.io.sql.DatabaseError, IndexError): return 0 def _save_status(self): """Saves the final resampling status to a JSON file.""" if not self.resampling_status: return stop_time = datetime.now(timezone.utc) self.resampling_status['job_start_time_utc'] = self.job_start_time.strftime('%Y-%m-%d %H:%M:%S') self.resampling_status['job_stop_time_utc'] = stop_time.strftime('%Y-%m-%d %H:%M:%S') # Clean up old key if it exists from previous versions self.resampling_status.pop('last_completed_utc', None) try: with open(self.status_file_path, 'w', encoding='utf-8') as f: json.dump(self.resampling_status, f, indent=4, sort_keys=True) logging.info(f"Successfully saved resampling status to '{self.status_file_path}'") except IOError as e: logging.error(f"Failed to write resampling status file: {e}") def parse_timeframes(tf_strings: list) -> dict: """Converts a list of timeframe strings into a dictionary for pandas.""" tf_map = {} for tf_str in tf_strings: numeric_part = ''.join(filter(str.isdigit, tf_str)) unit = ''.join(filter(str.isalpha, tf_str)).lower() code = '' if unit == 'm': code = f"{numeric_part}min" elif unit == 'w': code = f"{numeric_part}W" elif unit in ['h', 'd']: code = f"{numeric_part}{unit}" else: code = tf_str tf_map[tf_str] = code return tf_map if __name__ == "__main__": parser = argparse.ArgumentParser(description="Resample 1-minute candle data from SQLite to other timeframes.") parser.add_argument("--coins", nargs='+', required=True, help="List of coins to process.") parser.add_argument("--timeframes", nargs='+', required=True, help="List of timeframes to generate.") parser.add_argument("--log-level", default="normal", choices=['off', 'normal', 'debug']) args = parser.parse_args() timeframes_dict = parse_timeframes(args.timeframes) resampler = Resampler(log_level=args.log_level, coins=args.coins, timeframes=timeframes_dict) resampler.run()