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"

lib/startup/init-position-manager.ts

@@ -9,6 +9,7 @@ import { getInitializedPositionManager } from '../trading/position-manager'
 import { initializeDriftService } from '../drift/client'
 import { getPrismaClient } from '../database/trades'
 import { getMarketConfig } from '../../config/trading'
+import { startBlockedSignalTracking } from '../analysis/blocked-signal-tracker'
 
 let initStarted = false
 
@@ -42,6 +43,10 @@ export async function initializePositionManagerOnStartup() {
     if (status.activeTradesCount > 0) {
       console.log(`📊 Monitoring: ${status.symbols.join(', ')}`)
     }
+    
+    // Start blocked signal price tracking
+    console.log('🔬 Starting blocked signal price tracker...')
+    startBlockedSignalTracking()
   } catch (error) {
     console.error('❌ Failed to initialize Position Manager on startup:', error)
   }