3. 7 Enhance chart functionality with time range controls and stability improvements

- Updated `app_new.py` to run the application in debug mode for stability.
- Introduced a new time range control panel in `dashboard/components/chart_controls.py`, allowing users to select predefined time ranges and custom date ranges.
- Enhanced chart callbacks in `dashboard/callbacks/charts.py` to handle time range inputs, ensuring accurate market statistics and analysis based on user selections.
- Implemented logic to preserve chart state during updates, preventing resets of zoom/pan settings.
- Updated market statistics display to reflect the selected time range, improving user experience and data relevance.
- Added a clear button for custom date ranges to reset selections easily.
- Enhanced documentation to reflect the new time range features and usage guidelines.

components/charts/__init__.py

@@ -260,33 +260,50 @@ def get_supported_timeframes():
     
     return ['5s', '1m', '15m', '1h']  # Fallback
 
-def get_market_statistics(symbol: str, timeframe: str = "1h"):
-    """Calculate market statistics from recent data."""
+def get_market_statistics(symbol: str, timeframe: str = "1h", days_back: int = 1):
+    """Calculate market statistics from recent data over a specified period."""
     builder = ChartBuilder()
-    candles = builder.fetch_market_data(symbol, timeframe, days_back=1)
+    candles = builder.fetch_market_data(symbol, timeframe, days_back=days_back)
     
     if not candles:
-        return {'Price': 'N/A', '24h Change': 'N/A', '24h Volume': 'N/A', 'High 24h': 'N/A', 'Low 24h': 'N/A'}
+        return {'Price': 'N/A', f'Change ({days_back}d)': 'N/A', f'Volume ({days_back}d)': 'N/A', f'High ({days_back}d)': 'N/A', f'Low ({days_back}d)': 'N/A'}
     
     import pandas as pd
     df = pd.DataFrame(candles)
     latest = df.iloc[-1]
     current_price = float(latest['close'])
     
-    # Calculate 24h change
+    # Calculate change over the period
     if len(df) > 1:
-        price_24h_ago = float(df.iloc[0]['open'])
-        change_percent = ((current_price - price_24h_ago) / price_24h_ago) * 100
+        price_period_ago = float(df.iloc[0]['open'])
+        change_percent = ((current_price - price_period_ago) / price_period_ago) * 100
     else:
         change_percent = 0
     
     from .utils import format_price, format_volume
+    
+    # Determine label for period (e.g., "24h", "7d", "1h")
+    if days_back == 1/24:
+        period_label = "1h"
+    elif days_back == 4/24:
+        period_label = "4h"
+    elif days_back == 6/24:
+        period_label = "6h"
+    elif days_back == 12/24:
+        period_label = "12h"
+    elif days_back < 1: # For other fractional days, show as hours
+        period_label = f"{int(days_back * 24)}h"
+    elif days_back == 1:
+        period_label = "24h" # Keep 24h for 1 day for clarity
+    else:
+        period_label = f"{days_back}d"
+
     return {
         'Price': format_price(current_price, decimals=2),
-        '24h Change': f"{'+' if change_percent >= 0 else ''}{change_percent:.2f}%",
-        '24h Volume': format_volume(df['volume'].sum()),
-        'High 24h': format_price(df['high'].max(), decimals=2),
-        'Low 24h': format_price(df['low'].min(), decimals=2)
+        f'Change ({period_label})': f"{'+' if change_percent >= 0 else ''}{change_percent:.2f}%",
+        f'Volume ({period_label})': format_volume(df['volume'].sum()),
+        f'High ({period_label})': format_price(df['high'].max(), decimals=2),
+        f'Low ({period_label})': format_price(df['low'].min(), decimals=2)
     }
 
 def check_data_availability(symbol: str, timeframe: str):
@@ -472,4 +489,8 @@ def create_chart_with_indicators(symbol: str, timeframe: str,
     builder = ChartBuilder()
     return builder.create_chart_with_indicators(
         symbol, timeframe, overlay_indicators, subplot_indicators, days_back, **kwargs
-    ) 
+    ) 
+
+def initialize_indicator_manager():
+    # Implementation of initialize_indicator_manager function
+    pass