1a95fe1caa
- Delete obsolete files: data_fetcher_old.py, market_old.py, base_strategy.py (root), strategy_sma_cross.py, and old architecture remnants (address_monitor.py, position_monitor.py, trade_log.py, wallet_data.py, whale_tracker.py) - Delete zero-byte Docker artifacts and runtime files (clp_hedger.log, clp_hedger/hedge_status.json) - Move one-off utility scripts to scripts/ directory - Move example/template files to .temp/ directory - Update .gitignore: add entries for clp_hedger.log, clp_hedger/hedge_status.json, Docker layer hash files, Using, Running, and backups/ - Update .dockerignore: add clp_hedger.log, clp_hedger/hedge_status.json, backups/ - Create example config files: _data/strategies.json.example, _data/backtesting_conf.json.example, _data/coin_precision.json.example - Update GEMINI.md: remove outdated session summaries and duplicate review section - Update review.md: add cleanup status section, update remaining recommendations - Update MIGRATION_PLAN.md: mark completed phases, update file references - Update DOCKER_MIGRATION_GUIDE.md: update import_csv.py path reference
242 lines
11 KiB
Python
242 lines
11 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 warnings
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import db
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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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warnings.filterwarnings("ignore", message="pandas only supports SQLAlchemy")
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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 new 1-minute candle data from the PostgreSQL database, resamples it to
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various timeframes, and upserts the new candles to the corresponding tables,
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preventing data duplication.
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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.environ.get("PG_CONN_STR", "postgresql://hyper:hyper@localhost:5432/hyper")
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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 = self._load_existing_status()
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self.job_start_time = None
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self._ensure_tables_exist()
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def _ensure_tables_exist(self):
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"""
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Ensures all resampled tables exist with the correct schema.
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Uses db.create_candle_table() which is idempotent.
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"""
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conn = db.get_connection()
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for coin in self.coins_to_process:
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for tf_name in self.timeframes.keys():
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table_name = db.sanitize_table_name(coin, tf_name)
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db.create_candle_table(conn, table_name)
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conn.close()
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logging.info("All resampled table schemas verified.")
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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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logging.debug(f"Loading existing status from '{self.status_file_path}'")
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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 run(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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self.job_start_time = datetime.now(timezone.utc)
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logging.info(f"--- Resampling job started at {self.job_start_time.strftime('%Y-%m-%d %H:%M:%S %Z')} ---")
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if '1m' in self.timeframes:
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logging.debug("Ignoring '1m' timeframe as it is the source resolution.")
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del self.timeframes['1m']
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if not self.timeframes:
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logging.warning("No timeframes to process after filtering. Exiting job.")
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return
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conn = db.get_connection()
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try:
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logging.debug(f"Processing {len(self.coins_to_process)} coins...")
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for coin in self.coins_to_process:
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logging.info(f"--- Processing {coin} ---")
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try:
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for tf_name, tf_code in self.timeframes.items():
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target_table_name = db.sanitize_table_name(coin, tf_name)
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source_table_name = db.sanitize_table_name(coin, "1m")
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logging.info(f" Resampling {coin} -> {tf_name}")
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last_timestamp_ms = self._get_last_timestamp(conn, target_table_name)
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query = f'SELECT * FROM "{source_table_name}"'
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params = ()
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if last_timestamp_ms:
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query += ' WHERE timestamp_ms >= %s'
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# Go back one interval to rebuild the last (potentially partial) candle
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try:
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interval_delta_ms = pd.to_timedelta(tf_code).total_seconds() * 1000
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except ValueError:
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# Fall back to a safe 32-day lookback for special timeframes
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interval_delta_ms = timedelta(days=32).total_seconds() * 1000
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query_start_ms = last_timestamp_ms - interval_delta_ms
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params = (query_start_ms,)
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df_1m = pd.read_sql(query, conn, params=params, parse_dates=['datetime_utc'])
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if df_1m.empty:
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logging.debug(f" -> No new 1-minute data for {tf_name}. Table is up to date.")
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continue
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df_1m.set_index('datetime_utc', inplace=True)
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resampled_df = df_1m.resample(tf_code).agg(self.aggregation_logic)
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resampled_df.dropna(how='all', inplace=True)
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if not resampled_df.empty:
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records_to_upsert = []
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for index, row in resampled_df.iterrows():
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records_to_upsert.append((
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index.strftime('%Y-%m-%d %H:%M:%S'),
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int(index.timestamp() * 1000), # Generate timestamp_ms
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float(row['open']), float(row['high']), float(row['low']), float(row['close']),
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float(row['volume']), int(row['number_of_trades'])
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))
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db.upsert_candles(conn, target_table_name, records_to_upsert)
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logging.debug(f" -> Upserted {len(resampled_df)} candles into '{target_table_name}'.")
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if coin not in self.resampling_status: self.resampling_status[coin] = {}
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total_candles = int(self._get_table_count(conn, target_table_name))
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self.resampling_status[coin][tf_name] = {
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"last_candle_utc": resampled_df.index[-1].strftime('%Y-%m-%d %H:%M:%S'),
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"total_candles": total_candles
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}
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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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finally:
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conn.close()
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self._log_summary()
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self._save_status()
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logging.info(f"--- Resampling job finished at {datetime.now(timezone.utc).strftime('%Y-%m-%d %H:%M:%S %Z')} ---")
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def _log_summary(self):
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"""Logs a summary of the total candles for each timeframe."""
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logging.info("--- Resampling Job Summary ---")
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timeframe_totals = {}
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for coin, tfs in self.resampling_status.items():
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if not isinstance(tfs, dict): continue
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for tf_name, tf_data in tfs.items():
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total = tf_data.get("total_candles", 0)
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if tf_name not in timeframe_totals:
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timeframe_totals[tf_name] = 0
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timeframe_totals[tf_name] += total
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if not timeframe_totals:
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logging.info("No candles were resampled in this run.")
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return
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logging.info("Total candles per timeframe across all processed coins:")
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for tf_name, total in sorted(timeframe_totals.items()):
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logging.info(f" - {tf_name:<10}: {total:,} candles")
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def _get_last_timestamp(self, conn, table_name):
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"""Gets the millisecond timestamp of the last entry in a table."""
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try:
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# --- FIX: Query for the integer timestamp_ms, not the text datetime_utc ---
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timestamp_ms = pd.read_sql(f'SELECT MAX(timestamp_ms) FROM "{table_name}"', conn).iloc[0, 0]
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return int(timestamp_ms) if pd.notna(timestamp_ms) else None
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except (pd.io.sql.DatabaseError, IndexError):
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return None
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def _get_table_count(self, conn, table_name):
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"""Gets the total row count of a table."""
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try:
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return pd.read_sql(f'SELECT COUNT(*) FROM "{table_name}"', conn).iloc[0, 0]
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except (pd.io.sql.DatabaseError, IndexError):
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return 0
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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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return
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stop_time = datetime.now(timezone.utc)
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self.resampling_status['job_start_time_utc'] = self.job_start_time.strftime('%Y-%m-%d %H:%M:%S')
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self.resampling_status['job_stop_time_utc'] = stop_time.strftime('%Y-%m-%d %H:%M:%S')
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self.resampling_status.pop('last_completed_utc', None)
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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)) # Keep case for 'M'
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key = tf_str
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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.lower() == 'w':
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code = f"{numeric_part}W-MON"
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elif unit == 'M':
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code = f"{numeric_part}MS"
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key = f"{numeric_part}month"
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elif unit.lower() in ['h', 'd']:
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code = f"{numeric_part}{unit.lower()}"
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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[key] = code
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return tf_map
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="Resample 1-minute candle data from PostgreSQL to other timeframes.")
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parser.add_argument("--coins", nargs='+', required=True, help="List of coins to process.")
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parser.add_argument("--timeframes", nargs='+', required=True, help="List of timeframes to generate.")
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parser.add_argument("--log-level", default="normal", choices=['off', 'normal', 'debug'])
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args = parser.parse_args()
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timeframes_dict = parse_timeframes(args.timeframes)
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resampler = Resampler(log_level=args.log_level, coins=args.coins, timeframes=timeframes_dict)
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resampler.run()
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