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+# ATR-Based Position Scaling Guide
+
+## Overview
+
+This guide explains how the trading bot uses Average True Range (ATR) for position management decisions, including scaling in/out of positions, dynamic stop losses, and take profit targets.
+
+---
+
+## Current Architecture: Entry ATR Storage
+
+### How ATR is Captured
+
+```
+TradingView Signal → Contains ATR value (e.g., 2.15)
+  ↓
+Bot receives signal via n8n webhook
+  ↓
+Stores ATR in database: atrAtEntry = 2.15
+  ↓
+Position Manager uses stored ATR for entire trade lifecycle
+```
+
+**Key Point:** ATR value is "frozen" at entry time and stored in the `Trade.atrAtEntry` field.
+
+### Current Data Flow
+
+```typescript
+// Entry signal from TradingView (via n8n)
+{
+  "symbol": "SOLUSDT",
+  "direction": "long",
+  "atr": 2.15,      // Sent once at entry
+  "adx": 28,
+  "rsi": 62,
+  "volumeRatio": 1.3,
+  "pricePosition": 45
+}
+
+// Stored in database
+Trade {
+  atrAtEntry: 2.15,
+  entryPrice: 186.50,
+  // ... other fields
+}
+
+// Used by Position Manager (every 2 seconds)
+const atr = trade.atrAtEntry || 2.0  // Fallback if missing
+```
+
+---
+
+## Approach 1: Static ATR (Entry Value)
+
+**Status:** Recommended for Phases 1-3 (Current Implementation)
+
+### How It Works
+
+```typescript
+// In position-manager.ts monitoring loop
+async checkTargets(trade: ActiveTrade, currentPrice: number) {
+  // Use ATR from entry signal (static for entire trade)
+  const atr = trade.atrAtEntry || 2.0
+  
+  // Calculate bands using ENTRY ATR
+  const directionMultiplier = trade.direction === 'long' ? 1 : -1
+  
+  const band_05x = trade.entryPrice + (atr * 0.5 * directionMultiplier)
+  const band_1x = trade.entryPrice + (atr * 1.0 * directionMultiplier)
+  const band_15x = trade.entryPrice + (atr * 1.5 * directionMultiplier)
+  const band_2x = trade.entryPrice + (atr * 2.0 * directionMultiplier)
+  
+  // Check current price against bands
+  if (currentPrice >= band_1x && !trade.band1xCrossed) {
+    console.log('🎯 Price crossed 1×ATR band')
+    trade.band1xCrossed = true
+    // Trigger actions (e.g., adjust trailing stop)
+  }
+}
+```
+
+### Use Cases
+
+**1. Dynamic Take Profit Targets (Phase 2)**
+```typescript
+// Instead of fixed +1.5% and +3.0%
+const tp1Price = trade.entryPrice + (atr * 1.5 * directionMultiplier)
+const tp2Price = trade.entryPrice + (atr * 3.0 * directionMultiplier)
+
+// If ATR = 2.0 and entry = $100:
+// TP1 = $103 (3% move)
+// TP2 = $106 (6% move)
+
+// If ATR = 0.5 and entry = $100:
+// TP1 = $100.75 (0.75% move)
+// TP2 = $101.50 (1.5% move)
+```
+
+**2. ATR-Based Trailing Stop (Phase 5)**
+```typescript
+// Instead of fixed 0.3% trailing stop
+const trailingStopDistance = atr * 1.5  // Trail by 1.5×ATR
+
+// Calculate trailing stop price
+const trailingStopPrice = trade.direction === 'long'
+  ? trade.peakPrice - trailingStopDistance
+  : trade.peakPrice + trailingStopDistance
+
+// If ATR = 2.0 (high volatility):
+// Trailing stop = 3% below peak (gives room to breathe)
+
+// If ATR = 0.5 (low volatility):
+// Trailing stop = 0.75% below peak (tighter protection)
+```
+
+**3. Scaling In Decisions (Phase 6+)**
+```typescript
+// Scale in on healthy pullback (0.5×ATR from peak)
+const scaleInTrigger = trade.direction === 'long'
+  ? trade.peakPrice - (atr * 0.5)  // Long: pullback from high
+  : trade.peakPrice + (atr * 0.5)  // Short: rally from low
+
+// Conditions for scaling in
+const qualityHigh = trade.signalQualityScore >= 80
+const pullbackHealthy = trade.direction === 'long' 
+  ? currentPrice >= scaleInTrigger && currentPrice < trade.peakPrice
+  : currentPrice <= scaleInTrigger && currentPrice > trade.peakPrice
+const notAlreadyScaled = !trade.hasScaledIn
+const withinRiskLimits = trade.positionSize * 1.5 <= maxPositionSize
+
+if (qualityHigh && pullbackHealthy && notAlreadyScaled && withinRiskLimits) {
+  await scaleIntoPosition(trade, 0.5) // Add 50% more size
+}
+```
+
+### Advantages
+
+- ✅ **Simple:** No additional infrastructure needed
+- ✅ **Consistent:** Uses same volatility context as entry decision
+- ✅ **No sync issues:** No need to track TradingView state
+- ✅ **Good for short-duration trades:** Entry ATR valid for 30min-2 hour timeframes
+
+### Limitations
+
+- ❌ **Stale data:** ATR from entry may be outdated hours later
+- ❌ **No adaptation:** If volatility changes mid-trade, targets don't adjust
+- ❌ **Example:** Enter with ATR=2.0, but 3 hours later ATR drops to 0.8
+  - Bot still uses 2.0 for calculations
+  - May give too much room to runner (3% trailing stop instead of 1.2%)
+
+---
+
+## Approach 2: Real-Time ATR Updates
+
+**Status:** Future enhancement (Phase 5+)
+
+### Option A: TradingView Periodic Updates
+
+**Pine Script sends ATR updates while position is open:**
+
+```pine
+//@version=5
+strategy("ATR Monitor with Updates", overlay=true)
+
+// Your entry logic
+longSignal = yourLongCondition()
+shortSignal = yourShortCondition()
+
+if longSignal
+    strategy.entry("Long", strategy.long)
+if shortSignal
+    strategy.entry("Short", strategy.short)
+
+// Send ATR updates every candle close if position open
+atr = ta.atr(14)
+if strategy.position_size != 0 and barstate.isconfirmed
+    message = "POSITION_UPDATE" +
+              " | SYMBOL:" + syminfo.ticker +
+              " | ATR:" + str.tostring(atr, "#.##") +
+              " | PRICE:" + str.tostring(close, "#.####") +
+              " | DIRECTION:" + (strategy.position_size > 0 ? "long" : "short")
+    
+    alert(message, alert.freq_once_per_bar_close)
+```
+
+**Bot receives updates:**
+```json
+{
+  "type": "POSITION_UPDATE",
+  "symbol": "SOLUSDT",
+  "atr": 2.35,  // Current ATR (updated)
+  "price": 188.50,
+  "direction": "long"
+}
+```
+
+**New API endpoint:**
+```typescript
+// app/api/trading/position-update/route.ts
+export async function POST(request: NextRequest) {
+  const body = await request.json()
+  
+  // Find active trade by symbol and direction
+  const positionManager = await getInitializedPositionManager()
+  const trade = positionManager.findTradeBySymbol(body.symbol, body.direction)
+  
+  if (trade) {
+    // Update current ATR
+    trade.currentATR = body.atr
+    
+    // Recalculate bands with fresh ATR
+    const band_1x = trade.entryPrice + (body.atr * 1.0)
+    const band_2x = trade.entryPrice + (body.atr * 2.0)
+    
+    // Update trailing stop distance dynamically
+    trade.trailingStopDistance = body.atr * 1.5
+    
+    console.log(`📊 ATR updated: ${trade.atrAtEntry} → ${body.atr}`)
+  }
+  
+  return NextResponse.json({ success: true })
+}
+```
+
+**Position Manager logic:**
+```typescript
+// In position-manager.ts
+interface ActiveTrade {
+  // ... existing fields
+  atrAtEntry: number      // Original ATR from entry
+  currentATR?: number     // Updated ATR (if receiving updates)
+  trailingStopDistance?: number  // Dynamic trailing stop
+}
+
+async checkTargets(trade: ActiveTrade, currentPrice: number) {
+  // Use current ATR if available, fallback to entry ATR
+  const atr = trade.currentATR || trade.atrAtEntry || 2.0
+  
+  // Rest of logic uses current ATR
+  const band_1x = trade.entryPrice + (atr * 1.0)
+  // ...
+}
+```
+
+### Advantages
+
+- ✅ **Always current:** Uses latest volatility data
+- ✅ **Adapts to market:** If volatility spikes, targets widen automatically
+- ✅ **More accurate:** Trailing stops adjust to current conditions
+
+### Limitations
+
+- ❌ **Complex:** Requires new endpoint and TradingView webhook setup
+- ❌ **Webhook spam:** Sends updates every 5-15 minutes (candle close frequency)
+- ❌ **Sync issues:** TradingView doesn't know if bot actually has position open
+  - If bot closes position, TradingView keeps sending updates
+  - Need to handle "position not found" gracefully
+- ❌ **API rate limits:** More webhook calls to your server
+
+### Implementation Checklist
+
+- [ ] Create `/api/trading/position-update` endpoint
+- [ ] Add `currentATR` field to `ActiveTrade` interface
+- [ ] Update Position Manager to use `currentATR || atrAtEntry`
+- [ ] Modify Pine Script to send periodic ATR updates
+- [ ] Add n8n workflow node to parse ATR updates
+- [ ] Test with position open/close sync
+- [ ] Add database field to track ATR history: `atrUpdates: Json[]`
+
+---
+
+### Option B: Bot Calculates ATR Itself
+
+**Bot fetches historical candles and calculates ATR:**
+
+```typescript
+// lib/indicators/atr.ts
+export function calculateATR(candles: OHLC[], period: number = 14): number {
+  if (candles.length < period) {
+    throw new Error(`Need at least ${period} candles for ATR`)
+  }
+  
+  const trueRanges: number[] = []
+  
+  for (let i = 1; i < candles.length; i++) {
+    const high = candles[i].high
+    const low = candles[i].low
+    const prevClose = candles[i - 1].close
+    
+    const tr = Math.max(
+      high - low,                    // Current high-low
+      Math.abs(high - prevClose),    // Current high - previous close
+      Math.abs(low - prevClose)      // Current low - previous close
+    )
+    
+    trueRanges.push(tr)
+  }
+  
+  // Simple Moving Average of True Range
+  const atr = trueRanges.slice(-period).reduce((a, b) => a + b, 0) / period
+  
+  return atr
+}
+
+// In position-manager.ts
+async updateATR(trade: ActiveTrade) {
+  // Fetch last 14 candles from price feed
+  const candles = await fetchRecentCandles(trade.symbol, 14, '5m')
+  const currentATR = calculateATR(candles)
+  
+  trade.currentATR = currentATR
+  
+  console.log(`📊 Calculated ATR: ${currentATR.toFixed(2)}`)
+}
+```
+
+### Advantages
+
+- ✅ **Autonomous:** No TradingView dependency
+- ✅ **Always fresh:** Calculate on-demand
+- ✅ **No webhooks:** No additional API calls to your server
+
+### Limitations
+
+- ❌ **Complex:** Need to implement ATR calculation in TypeScript
+- ❌ **Data source:** Need reliable OHLC candle data
+  - Pyth Network: Primarily provides spot prices, may not have full OHLC
+  - Drift SDK: May have orderbook data but not historical candles
+  - Alternative: Fetch from Binance/CoinGecko API
+- ❌ **API calls:** Need to fetch candles every update cycle (rate limits)
+- ❌ **Performance:** Additional latency for fetching + calculating
+
+---
+
+## Recommended Implementation Path
+
+### Phase 1-3: Use Entry ATR (Current) ✅
+
+**What to do:**
+- Store `atrAtEntry` from TradingView signals (already implemented)
+- Use static ATR for all calculations during trade
+- Validate that scaling strategies work with entry ATR
+
+**Configuration:**
+```typescript
+// config/trading.ts
+export interface TradingConfig {
+  // ... existing config
+  useATRTargets: boolean           // Enable ATR-based TP1/TP2
+  atrMultiplierTP1: number         // 1.5×ATR for TP1
+  atrMultiplierTP2: number         // 3.0×ATR for TP2
+  atrMultiplierTrailing: number    // 1.5×ATR for trailing stop
+  atrFallback: number              // Default ATR if missing (2.0)
+}
+```
+
+### Phase 4: Add ATR Normalization
+
+**Analyze collected data:**
+```sql
+-- What's typical ATR range for SOL-PERP?
+SELECT 
+  ROUND(MIN("atrAtEntry")::numeric, 2) as min_atr,
+  ROUND(AVG("atrAtEntry")::numeric, 2) as avg_atr,
+  ROUND(MAX("atrAtEntry")::numeric, 2) as max_atr,
+  ROUND(STDDEV("atrAtEntry")::numeric, 2) as stddev_atr
+FROM "Trade"
+WHERE "atrAtEntry" IS NOT NULL AND "atrAtEntry" > 0;
+
+-- Result example:
+-- min_atr: 0.5, avg_atr: 2.0, max_atr: 3.5, stddev: 0.8
+```
+
+**Implement normalization:**
+```typescript
+function normalizeATR(atr: number, baseline: number = 2.0): number {
+  // Returns factor relative to baseline
+  return atr / baseline
+}
+
+// Usage
+const atrFactor = normalizeATR(trade.atrAtEntry, 2.0)
+const tp1Price = trade.entryPrice + (baseline_tp1_percent * atrFactor)
+
+// If ATR = 3.0 (high volatility):
+// atrFactor = 1.5, TP1 = entry + (1.5% × 1.5) = entry + 2.25%
+
+// If ATR = 1.0 (low volatility):
+// atrFactor = 0.5, TP1 = entry + (1.5% × 0.5) = entry + 0.75%
+```
+
+### Phase 5+: Consider Real-Time Updates
+
+**Decision gate:**
+- ✅ Do trades last > 2 hours frequently?
+- ✅ Does ATR change significantly during typical trade duration?
+- ✅ Would dynamic updates improve performance measurably?
+
+**If YES to all three:**
+- Implement Option A (TradingView updates) OR Option B (Bot calculates)
+- A/B test: 20 trades with real-time ATR vs 20 with entry ATR
+- Compare: Win rate, avg P&L, trailing stop effectiveness
+
+**If NO:**
+- Stay with entry ATR (simpler, good enough)
+
+---
+
+## Troubleshooting
+
+### Problem: ATR values are 0 or missing
+
+**Cause:** TradingView not sending ATR or n8n not extracting it
+
+**Solution:**
+1. Check TradingView alert message: Should include `ATR:{{plot_0}}` or similar
+2. Check n8n "Parse Signal Enhanced" node: Should extract `atr` field
+3. Verify webhook payload in n8n execution log
+4. Ensure Pine Script has `atr = ta.atr(14)` and plots it
+
+### Problem: ATR seems too high/low
+
+**Cause:** Using wrong timeframe or different calculation method
+
+**TradingView ATR calculation:**
+```pine
+atr = ta.atr(14)  // 14-period ATR
+```
+
+**Bot should use same period (14) if calculating itself.**
+
+**Typical ATR ranges for SOL-PERP:**
+- 5-minute chart: 0.3 - 1.5 (low to high volatility)
+- 15-minute chart: 0.8 - 3.0 (low to high volatility)
+- Daily chart: 3.0 - 10.0 (low to high volatility)
+
+### Problem: Trailing stop too tight/loose with ATR
+
+**Cause:** Wrong ATR multiplier
+
+**Solution:**
+```typescript
+// Test different multipliers
+const trailingStopDistance = atr * 1.5  // Start here
+// Too tight? Increase to 2.0
+// Too loose? Decrease to 1.0
+
+// Log and analyze
+console.log(`ATR: ${atr}, Trailing: ${trailingStopDistance} (${(trailingStopDistance/trade.entryPrice*100).toFixed(2)}%)`)
+```
+
+---
+
+## Database Schema
+
+### Current Fields
+
+```prisma
+model Trade {
+  // ... other fields
+  atrAtEntry        Float?  // ATR% when trade opened
+  adxAtEntry        Float?  // ADX trend strength
+  rsiAtEntry        Float?  // RSI momentum
+  volumeAtEntry     Float?  // Volume relative to MA
+  pricePositionAtEntry Float? // Price position in range
+}
+```
+
+### Future Enhancement (Real-Time Updates)
+
+```prisma
+model Trade {
+  // ... existing fields
+  atrHistory        Json?   // Array of ATR updates: [{time, atr, price}]
+}
+
+// Example atrHistory value:
+// [
+//   {"time": "2025-10-31T10:00:00Z", "atr": 2.15, "price": 186.50},
+//   {"time": "2025-10-31T10:15:00Z", "atr": 2.28, "price": 188.20},
+//   {"time": "2025-10-31T10:30:00Z", "atr": 2.45, "price": 189.10}
+// ]
+```
+
+---
+
+## Key Takeaways
+
+1. **Entry ATR is sufficient for Phases 1-3** - Don't overcomplicate early
+2. **Real-time ATR updates are optional** - Only add if data proves benefit
+3. **Test with data** - Run analysis queries to validate ATR effectiveness
+4. **Start simple, optimize later** - Use entry ATR → Analyze results → Then enhance
+
+**Most important:** Let the system collect data first. Implement ATR-based logic AFTER you have 20-50 trades with real ATR values to validate the approach!
+
+---
+
+## Related Documentation
+
+- `POSITION_SCALING_ROADMAP.md` - 6-phase optimization plan
+- `.github/copilot-instructions.md` - Architecture overview
+- `docs/guides/TESTING.md` - How to test ATR-based features
+- `config/trading.ts` - ATR configuration options