Scan only M15 and H1; keep D1 as confirmation, drop W1 entirely

Trades are intraday (M15/H1) only. D1 stays as a confirmation TF
for both so higher-timeframe trend context still gates A/A+ grades.
W1 is no longer used anywhere — removes one batch call.

Scan now: 3 batch calls (M15, H1, D1) × 7 pairs = 21 credits, ~2 min.
Was:      4 batch calls (M15, H1, D1, W1) = 28 credits, ~3 min.

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

LOGIC.md

@@ -43,30 +43,30 @@ After every fetch the data goes through `_validate_and_clean`:
 
 ### Per scan or /signal command
 
-The bot does **one shared prefetch** before spawning the 4 TF analyses.
+The bot does **one shared prefetch** before spawning TF analyses.
-It collects all unique timeframes needed across all scans, then makes
+It collects all unique timeframes needed across scans, then makes
 one batch API call per timeframe (all pairs in a single request).
 
-Current SCAN_TIMEFRAMES: M15, H1, D1, W1
+Current SCAN_TIMEFRAMES: M15, H1 (only intraday trade signals)
 Confirmations:
 - M15 uses: H1, D1
 - H1  uses: M15, D1
-- D1  uses: H1, W1
-- W1  uses: D1
 
-Unique timeframes: M15, H1, D1, W1 = **4 batch calls**
+D1 is kept as a confirmation for higher-timeframe trend context but
+no D1 trade signals are produced. W1 has been dropped entirely.
+
+Unique timeframes: M15, H1, D1 = **3 batch calls**
 
 Each call: 7 pairs × 1 credit = **7 credits per call**
 
-Total per scan: **4 calls × 7 credits = 28 credits**
+Total per scan: **3 calls × 7 credits = 21 credits**
 
 With the 8 credits/minute limit:
 - Call 1 (7 credits): fires immediately — 1 credit remaining
 - Call 2 (7 credits): must wait ~60s for window to clear
 - Call 3 (7 credits): waits another ~60s
-- Call 4 (7 credits): waits another ~60s
 
-**Minimum scan time: ~3 minutes.**
+**Minimum scan time: ~2 minutes.**
 
 After the shared prefetch, the 4 concurrent TF analyses read from
 the in-memory cache and make **zero additional API calls**.
@@ -204,13 +204,12 @@ Telegram /scan or /signal
         ▼
 _analyze_all_tfs(pairs)
         │
-        ├── prefetch_batch(pairs, [M15, H1, D1, W1])
+        ├── prefetch_batch(pairs, [M15, H1, D1])
         │       ├── Batch call 1: all pairs @ M15  (7 credits, wait if needed)
         │       ├── Batch call 2: all pairs @ H1   (7 credits, wait if needed)
-        │       ├── Batch call 3: all pairs @ D1   (7 credits, wait if needed)
+        │       └── Batch call 3: all pairs @ D1   (7 credits, wait if needed)
-        │       └── Batch call 4: all pairs @ W1   (7 credits, wait if needed)
         │
-        └── 4 threads (one per TF), all reading from warm cache
+        └── 2 threads (M15 + H1), all reading from warm cache
                 │
                 └── per thread: SignalAggregator.analyze_all_pairs()
                         │

telegram_bot.py

@@ -40,20 +40,19 @@ logger = logging.getLogger(__name__)
 
 # ── Config ──────────────────────────────────────────────────────────
 
-SCAN_TIMEFRAMES = ["M15", "H1", "D1", "W1"]
+SCAN_TIMEFRAMES = ["M15", "H1"]
 
 _TF_MAP = {
     "M5":  Timeframe.M5,  "M15": Timeframe.M15,
     "M30": Timeframe.M30, "H1":  Timeframe.H1,
     "H4":  Timeframe.H4,  "D1":  Timeframe.D1,
-    "W1":  Timeframe.W1,
 }
 
+# D1 kept as confirmation for higher-timeframe trend context.
+# W1 dropped — only the D1 scan used it, and we no longer scan D1.
 _TF_CONFIRMATIONS = {
     "M15": [Timeframe.H1,  Timeframe.D1],
     "H1":  [Timeframe.M15, Timeframe.D1],
-    "D1":  [Timeframe.H1,  Timeframe.W1],
-    "W1":  [Timeframe.D1],
 }
 
 _GRADE_RANK      = {"A+": 0, "A": 1, "B": 2, "C": 3, "D": 4, "F": 5}