207 lines
8.2 KiB
Python
207 lines
8.2 KiB
Python
import argparse
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import logging
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import os
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import sys
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import sqlite3
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import pandas as pd
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import json
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from datetime import datetime, timezone, timedelta
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import time
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# Assuming logging_utils.py is in the same directory
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from logging_utils import setup_logging
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class Resampler:
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"""
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Reads 1-minute candle data directly from the SQLite database, resamples
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it to various timeframes, and stores the results back in the database.
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This script is designed to run continuously as a self-scheduling service.
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"""
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def __init__(self, log_level: str, coins: list, timeframes: dict):
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setup_logging(log_level, 'Resampler')
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self.db_path = os.path.join("_data", "market_data.db")
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self.status_file_path = os.path.join("_data", "resampling_status.json")
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self.coins_to_process = coins
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self.timeframes = timeframes
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self.aggregation_logic = {
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'open': 'first',
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'high': 'max',
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'low': 'min',
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'close': 'last',
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'volume': 'sum',
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'number_of_trades': 'sum'
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}
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self.resampling_status = {}
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def _execute_resampling_job(self):
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"""
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Main execution function to process all configured coins and update the database.
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"""
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if not os.path.exists(self.db_path):
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logging.error(f"Database file '{self.db_path}' not found. "
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"Please run the data fetcher script first.")
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return # Don't exit, just wait for the next cycle
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# Load the latest status file at the start of each job
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self.resampling_status = self._load_existing_status()
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with sqlite3.connect(self.db_path) as conn:
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conn.execute("PRAGMA journal_mode=WAL;")
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logging.info(f"Processing {len(self.coins_to_process)} coins: {', '.join(self.coins_to_process)}")
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for coin in self.coins_to_process:
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source_table_name = f"{coin}_1m"
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logging.info(f"--- Processing {coin} ---")
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try:
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df = pd.read_sql(f'SELECT * FROM "{source_table_name}"', conn)
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if df.empty:
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logging.warning(f"Source table '{source_table_name}' is empty or does not exist. Skipping.")
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continue
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df['datetime_utc'] = pd.to_datetime(df['datetime_utc'])
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df.set_index('datetime_utc', inplace=True)
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for tf_name, tf_code in self.timeframes.items():
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logging.info(f" Resampling to {tf_name}...")
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resampled_df = df.resample(tf_code).agg(self.aggregation_logic)
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resampled_df.dropna(how='all', inplace=True)
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if coin not in self.resampling_status:
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self.resampling_status[coin] = {}
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if not resampled_df.empty:
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target_table_name = f"{coin}_{tf_name}"
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resampled_df.to_sql(
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target_table_name,
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conn,
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if_exists='replace',
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index=True
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)
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last_timestamp = resampled_df.index[-1].strftime('%Y-%m-%d %H:%M:%S')
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num_candles = len(resampled_df)
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self.resampling_status[coin][tf_name] = {
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"last_candle_utc": last_timestamp,
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"total_candles": num_candles
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}
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else:
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logging.info(f" -> No data to save for '{coin}_{tf_name}'.")
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self.resampling_status[coin][tf_name] = {
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"last_candle_utc": "N/A",
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"total_candles": 0
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}
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except pd.io.sql.DatabaseError as e:
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logging.warning(f"Could not read source table '{source_table_name}': {e}")
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except Exception as e:
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logging.error(f"Failed to process coin '{coin}': {e}")
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self._save_status()
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logging.info("--- Resampling job complete ---")
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def run_periodically(self):
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"""Runs the resampling job at every 5-minute mark of the hour (00, 05, 10...)."""
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logging.info("Resampler started. Waiting for the first scheduled run...")
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while True:
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# 1. Calculate sleep time
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now = datetime.now(timezone.utc)
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# Calculate how many minutes past the last 5-minute mark we are
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minutes_past_mark = now.minute % 5
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seconds_past_mark = minutes_past_mark * 60 + now.second + (now.microsecond / 1_000_000)
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# The total interval is 5 minutes (300 seconds)
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sleep_duration = 300 - seconds_past_mark
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# Add a small buffer to ensure the candle data is ready
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sleep_duration += 5
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logging.info(f"Next resampling run in {sleep_duration:.2f} seconds.")
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time.sleep(sleep_duration)
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# 2. Execute the job
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logging.info("Scheduled time reached. Starting resampling job...")
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self._execute_resampling_job()
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def _load_existing_status(self) -> dict:
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"""Loads the existing status file if it exists, otherwise returns an empty dict."""
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if os.path.exists(self.status_file_path):
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try:
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with open(self.status_file_path, 'r', encoding='utf-8') as f:
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return json.load(f)
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except (IOError, json.JSONDecodeError) as e:
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logging.warning(f"Could not read existing status file. Starting fresh. Error: {e}")
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return {}
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def _save_status(self):
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"""Saves the final resampling status to a JSON file."""
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if not self.resampling_status:
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logging.warning("No data was resampled, skipping status file creation.")
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return
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self.resampling_status['last_completed_utc'] = datetime.now(timezone.utc).strftime('%Y-%m-%d %H:%M:%S')
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try:
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with open(self.status_file_path, 'w', encoding='utf-8') as f:
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json.dump(self.resampling_status, f, indent=4, sort_keys=True)
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logging.info(f"Successfully saved resampling status to '{self.status_file_path}'")
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except IOError as e:
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logging.error(f"Failed to write resampling status file: {e}")
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def parse_timeframes(tf_strings: list) -> dict:
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"""Converts a list of timeframe strings into a dictionary for pandas."""
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tf_map = {}
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for tf_str in tf_strings:
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numeric_part = ''.join(filter(str.isdigit, tf_str))
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unit = ''.join(filter(str.isalpha, tf_str)).lower()
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code = ''
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if unit == 'm':
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code = f"{numeric_part}min"
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elif unit == 'w':
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code = f"{numeric_part}W"
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elif unit in ['h', 'd']:
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code = f"{numeric_part}{unit}"
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else:
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code = tf_str
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logging.warning(f"Unrecognized timeframe unit in '{tf_str}'. Using as-is.")
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tf_map[tf_str] = code
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return tf_map
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if __name__ == "__main__":
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# The script now runs as a long-running service, loading its config from a file.
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CONFIG_FILE = "resampler_conf.json"
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try:
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with open(CONFIG_FILE, 'r') as f:
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config = json.load(f)
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coins = config.get("coins", [])
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timeframes_list = config.get("timeframes", [])
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except (FileNotFoundError, json.JSONDecodeError) as e:
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print(f"FATAL: Could not load '{CONFIG_FILE}'. Please ensure it exists and is valid. Error: {e}")
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sys.exit(1)
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# Use a basic log level until the class is initialized
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setup_logging('normal', 'Resampler')
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timeframes_dict = parse_timeframes(timeframes_list)
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resampler = Resampler(
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log_level='normal',
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coins=coins,
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timeframes=timeframes_dict
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)
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try:
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resampler.run_periodically()
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except KeyboardInterrupt:
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logging.info("Resampler process stopped.")
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