remove emojis from console

ANNOTATE/TRAINING_DATA_FORMAT.md

@@ -3,9 +3,9 @@
 ## 🎯 Overview
 
 The ANNOTATE system generates training data that includes **±5 minutes of market data** around each trade signal. This allows models to learn:
-- ✅ **WHERE to generate signals** (at entry/exit points)
-- ✅ **WHERE NOT to generate signals** (before entry, after exit)
-- ✅ **Context around the signal** (what led to the trade)
+-  **WHERE to generate signals** (at entry/exit points)
+-  **WHERE NOT to generate signals** (before entry, after exit)
+-  **Context around the signal** (what led to the trade)
 
 ---
 
@@ -259,11 +259,11 @@ for timestamp, label in zip(timestamps, labels):
 ```
 
 **Model Learns:**
-- ✅ Don't signal during consolidation
-- ✅ Signal at breakout confirmation
-- ✅ Hold during profitable move
-- ✅ Exit at target
-- ✅ Don't signal after exit
+-  Don't signal during consolidation
+-  Signal at breakout confirmation
+-  Hold during profitable move
+-  Exit at target
+-  Don't signal after exit
 
 ---
 
@@ -290,16 +290,16 @@ print(f"EXIT: {labels.count(3)}")
 
 ---
 
-## 🚀 Summary
+##  Summary
 
 The ANNOTATE system generates **production-ready training data** with:
 
-✅ **±5 minutes of context** around each signal  
-✅ **Training labels** for each timestamp  
-✅ **Negative examples** (where NOT to signal)  
-✅ **Positive examples** (where TO signal)  
-✅ **All 4 timeframes** (1s, 1m, 1h, 1d)  
-✅ **Complete market state** (OHLCV data)  
+ **±5 minutes of context** around each signal  
+ **Training labels** for each timestamp  
+ **Negative examples** (where NOT to signal)  
+ **Positive examples** (where TO signal)  
+ **All 4 timeframes** (1s, 1m, 1h, 1d)  
+ **Complete market state** (OHLCV data)  
 
 This enables models to learn:
 - **Precise timing** of entry/exit signals