NEW FEATURE: Trendlines, Rays and the Toolbox

- Added `trend_line` and `ray_line` to the Common Methods.

- Added the `toolbox` parameter to chart declaration. This allows horizontal lines, trend lines and rays to be drawn on the chart using hotkeys and buttons.
    - cmd-Z will delete the last drawing.
    - Drawings can be moved by clicking and dragging.

- Added the `render_drawings` parameter to `set`, which will keep and re-render the drawings displayed on the chart (useful for multiple timeframes!)

Horizontal Lines
- The `horizontal_line` method now returns a HorizontalLine object, containing the methods `update` and `delete`.
- Added the `interactive` parameter to `horizontal_line`, allowing for callbacks to be emitted to the `on_horizontal_line_move` callback method when the line is dragged to a new price (stop losses, limit orders, etc.).

Enhancements:
- added the `precision` method to the Common Methods, allowing for the number of decimal places shown on the price scale to be declared.
- Lines displayed on legends now have toggle switches, allowing for their visibility to be controlled directly within the chart window.
- when using `set`, the column names can now be capitalised, and the `date` column can be the index.

Changes:
- Merged the `title` method into the `price_line` method.

examples/4_line_indicators/line_indicators.py

@@ -8,19 +8,20 @@ def calculate_sma(data: pd.DataFrame, period: int = 50):
     result = []
     for i in range(period - 1, len(data)):
         val = avg(data.iloc[i - period + 1:i])
-        result.append({'time': data.iloc[i]['date'], 'value': val})
+        result.append({'time': data.iloc[i]['date'], f'SMA {period}': val})
     return pd.DataFrame(result)
 
 
 if __name__ == '__main__':
 
-    chart = Chart()
+    chart = Chart(debug=True)
+    chart.legend(visible=True)
 
     df = pd.read_csv('ohlcv.csv')
     chart.set(df)
 
     line = chart.create_line()
-    sma_data = calculate_sma(df)
-    line.set(sma_data)
+    sma_data = calculate_sma(df, period=50)
+    line.set(sma_data, name='SMA 50')
 
     chart.show(block=True)