feat: Automated multi-timeframe price tracking system

Implemented comprehensive price tracking for multi-timeframe signal analysis.

**Components Added:**
- lib/analysis/blocked-signal-tracker.ts - Background job tracking prices
- app/api/analytics/signal-tracking/route.ts - Status/metrics endpoint

**Features:**
- Automatic price tracking at 1min, 5min, 15min, 30min intervals
- TP1/TP2/SL hit detection using ATR-based targets
- Max favorable/adverse excursion tracking (MFE/MAE)
- Analysis completion after 30 minutes
- Background job runs every 5 minutes
- Entry price captured from signal time

**Database Changes:**
- Added entryPrice field to BlockedSignal (for price tracking baseline)
- Added maxFavorablePrice, maxAdversePrice fields
- Added maxFavorableExcursion, maxAdverseExcursion fields

**Integration:**
- Auto-starts on container startup
- Tracks all DATA_COLLECTION_ONLY signals
- Uses same TP/SL calculation as live trades (ATR-based)
- Calculates profit % based on direction (long vs short)

**API Endpoints:**
- GET /api/analytics/signal-tracking - View tracking status and metrics
- POST /api/analytics/signal-tracking - Manually trigger update (auth required)

**Purpose:**
Enables data-driven multi-timeframe comparison. After 50+ signals per
timeframe, can analyze which timeframe (5min vs 15min vs 1H vs 4H vs Daily)
has best win rate, profit potential, and signal quality.

**What It Tracks:**
- Price at 1min, 5min, 15min, 30min after signal
- Would TP1/TP2/SL have been hit?
- Maximum profit/loss during 30min window
- Complete analysis of signal profitability

**How It Works:**
1. Signal comes in (15min, 1H, 4H, Daily) → saved to BlockedSignal
2. Background job runs every 5min
3. Queries current price from Pyth
4. Calculates profit % from entry
5. Checks if TP/SL thresholds crossed
6. Updates MFE/MAE if new highs/lows
7. After 30min, marks analysisComplete=true

**Future Analysis:**
After 50+ signals per timeframe:
- Compare TP1 hit rates across timeframes
- Identify which timeframe has highest win rate
- Determine optimal signal frequency vs quality trade-off
- Switch production to best-performing timeframe

User requested: "i want all the bells and whistles. lets make the
powerhouse more powerfull. i cant see any reason why we shouldnt"

prisma/schema.prisma

@@ -181,9 +181,12 @@ model BlockedSignal {
   indicatorVersion      String?  // Pine Script version (v5, v6, etc.)
   
   // Block reason
-  blockReason      String   // "QUALITY_SCORE_TOO_LOW", "DUPLICATE", "COOLDOWN", etc.
+  blockReason      String   // "QUALITY_SCORE_TOO_LOW", "DUPLICATE", "COOLDOWN", "DATA_COLLECTION_ONLY", etc.
   blockDetails     String?  // Human-readable details
   
+  // Entry tracking (for multi-timeframe analysis)
+  entryPrice       Float    @default(0) // Price at signal time
+  
   // For later analysis: track if it would have been profitable
   priceAfter1Min   Float?   // Price 1 minute after (filled by monitoring job)
   priceAfter5Min   Float?   // Price 5 minutes after
@@ -192,6 +195,13 @@ model BlockedSignal {
   wouldHitTP1      Boolean? // Would TP1 have been hit?
   wouldHitTP2      Boolean? // Would TP2 have been hit?
   wouldHitSL       Boolean? // Would SL have been hit?
+  
+  // Max favorable/adverse excursion (mirror Trade model)
+  maxFavorablePrice     Float?   // Price at max profit
+  maxAdversePrice       Float?   // Price at max loss
+  maxFavorableExcursion Float?   // Best profit % during tracking
+  maxAdverseExcursion   Float?   // Worst loss % during tracking
+  
   analysisComplete Boolean  @default(false) // Has post-analysis been done?
   
   @@index([symbol])