Migrate data pipeline from SQLite to PostgreSQL + Docker setup

- Add db.py PostgreSQL abstraction layer (connection, upsert, table mgmt)
- Replace sqlite3 with psycopg2 in: live_candle_fetcher, resampler,
  data_fetcher, fetch_history, import_csv, indicators, base_strategy
- Sanitize table names (colons -> underscores) for PostgreSQL compat
- Replace INSERT OR REPLACE with ON CONFLICT upserts
- Replace pandas to_sql() with batch upsert_candles()
- Add scripts: resampler_loop, gap_detector, backup_runner, cron_scheduler
- Add migrate_sqlite_to_pg.py for one-time data migration
- Add Dockerfile, docker-compose.yml, supervisord.conf
- Add postgres/postgresql.conf tuned for 4GB RAM (Synology DS1513+)
- Add .dockerignore, .env.docker.example, secrets template
- Update requirements.txt (psycopg2-binary), .gitignore
- Add MIGRATION_PLAN.md with full plan and todo list

strategies/base_strategy.py

@@ -4,7 +4,7 @@ import json
 import os
 import logging
 from datetime import datetime, timezone
-import sqlite3
+import psycopg2
 import multiprocessing
 import time
 
@@ -27,7 +27,7 @@ class BaseStrategy(ABC):
         
         self.coin = params.get("coin", "N/A")
         self.timeframe = params.get("timeframe", "N/A")
-        self.db_path = os.path.join("_data", "market_data.db")
+        self.db_path = os.environ.get("PG_CONN_STR", "postgresql://hyper:hyper@localhost:5432/hyper")
         self.status_file_path = os.path.join("_data", f"strategy_status_{self.strategy_name}.json")
 
         self.current_signal = "INIT"
@@ -38,19 +38,23 @@ class BaseStrategy(ABC):
 
     def load_data(self) -> pd.DataFrame:
         """Loads historical data for the configured coin and timeframe."""
-        table_name = f"{self.coin}_{self.timeframe}"
+        table_name = f"{self.coin.replace(':', '_')}_{self.timeframe}"
         
         periods = [v for k, v in self.params.items() if 'period' in k or '_ma' in k or 'slow' in k or 'fast' in k]
         limit = max(periods) + 50 if periods else 500
 
         try:
-            with sqlite3.connect(f"file:{self.db_path}?mode=ro", uri=True) as conn:
+            conn = psycopg2.connect(self.db_path)
+            conn.set_session(readonly=True)
+            try:
                 query = f'SELECT * FROM "{table_name}" ORDER BY datetime_utc DESC LIMIT {limit}'
                 df = pd.read_sql(query, conn, parse_dates=['datetime_utc'])
                 if df.empty: return pd.DataFrame()
                 df.set_index('datetime_utc', inplace=True)
                 df.sort_index(inplace=True)
                 return df
+            finally:
+                conn.close()
         except Exception as e:
             logging.error(f"Failed to load data from table '{table_name}': {e}")
             return pd.DataFrame()