Per-timeframe cache TTLs — D1 cached 4h, H1 30m, M15 5m

A single 5-minute refresh_interval applied to every timeframe wasted
API credits: a daily candle was being refetched every 5 minutes even
though it only changes once per day. Now each TF caches for roughly
its bar interval, so repeat scans within an hour skip D1 entirely
(saves ~60s of throttle wait) and within 30 min also skip H1.

config.py: new cache_ttl_per_tf dict on PlatformConfig with sensible
defaults for every timeframe; refresh_interval kept as fallback.

data_provider.py: _cache_ttl() helper used by both fetch_ohlcv and
prefetch_batch instead of the global refresh_interval.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

config.py

@@ -196,9 +196,25 @@ class PlatformConfig:
     # Twelve Data API key (overridden by TWELVE_DATA_API_KEY env var)
     twelve_data_api_key: str = "f563b22ffdfd4924955decbe1a8c8701"
 
-    # Dashboard refresh interval (seconds)
+    # Dashboard refresh interval (seconds) — used as a fallback when a
+    # timeframe isn't covered by cache_ttl_per_tf below.
     refresh_interval: int = 300  # 5 minutes
 
+    # Per-timeframe cache TTL (seconds). A 1-week candle changes less
+    # often than a 15-minute candle, so caching them all for the same
+    # 5 minutes wastes API credits. Tuned to roughly match each TF's
+    # bar interval so we re-fetch about once per new bar.
+    cache_ttl_per_tf: Dict[Timeframe, int] = field(default_factory=lambda: {
+        Timeframe.M1:  60,         # 1 minute
+        Timeframe.M5:  150,        # 2.5 minutes
+        Timeframe.M15: 300,        # 5 minutes
+        Timeframe.M30: 600,        # 10 minutes
+        Timeframe.H1:  1800,       # 30 minutes
+        Timeframe.H4:  7200,       # 2 hours
+        Timeframe.D1:  14400,      # 4 hours
+        Timeframe.W1:  86400,      # 24 hours
+    })
+
     # Logging
     log_level: str = "INFO"
     log_signals_to_file: bool = True