df and sr accessor enhancements

README.md

@@ -1,17 +1,118 @@
-Fork of lightweight-charts with enhancements and supporting proprietary workflow
-* colored legends
-* self picking colors
-* support for left price scale
-* support from pd series as input to set series
+Fork of the original [lightweight-charts](louisnw01/lightweight-charts-python) with enhancements and supporting vectorbtpro workflow
+* legend color matching each line color
+* automatic color assignment if not provided
+* support for multiple scales (right, left, middle1, middle2, histogram)
+* accepts df,pd.series or vectorbtpro indicator object (including unpacking multi outputs)
 * new markers_set method allowing to set pd.series or dataframe as markers input
-* allows vbt indicators as input for set() method (extracting real values series from that)
+* for quick display supports simple df/sr accessors `close.lw.plot()` for quick visualization of single panel chart
+* df accessor unpacks dataframe columns and display them accordingly (ohlcv as ohlcv, vwap on the right, rsi on the left scale etc.)
+* multipanes support `ch = chart([pane1, pane2], sync=True, title="Title", size="m")` to quickly display chart with N panes (`Panels`). Also supports syncing the Panels `sync=True` or using xloc.
+  
 
 <img width="1005" alt="image" src="https://github.com/drew2323/lightweight-charts-python/assets/28433232/856c32aa-e0ff-4de0-b4a2-befc34adb571">
 
+## Instalation
+Directly from the repository
+
+```pip install git+https://github.com/drew2323/lightweight-charts-python.git```
+
+## Examples
+
+```python
+from lightweight_charts import chart, Panel, PlotSRAccessor, PlotDFAccessor
+
+#one liner, displays close series as line on single Panel
+close_sr.lw.plot()
+```
+![alt text](image-2.png)
+```python
+close_sr.lw.plot(size="m") #on medium panesize
+close_se.lw.plot(histogram=(trade_series, "trades")) #number of trades as histogram is displayed on top of that
+```
+![alt text](image-1.png)
+```python
+#one liner to display OHLCV 
+ohlcv_df.lw.plot() #if dataframe contains other columns they are displayed too - based on standard settings (rsi-on the left, vwap - right etc.)
+```
+![alt text](image-3.png)
+```python
+ohlcv_df.lw.plot(left=[(angle_series, "angle_momentum")]) #Another line is displayed on top of OHLCV on left scale
+```
+![alt text](image-4.png)
+```python
+ohlcv_complex_df.lw.plot() #df containing ohlcv and other columns
+```
+![alt text](image-5.png)
+```python
+#quick few liner, displays close series with label "close" on right pricescale and rsi on left price scale, all on single Panel
+pane1 = Panel(
+    right=[(close, "close")],
+    left=[(rsi,"rsi")]
+)
+ch = chart([pane1])
+
+# display two Panels
+# on first displays ohlcv data, orderimbalance volume as histogram with opacity, bbands on the right pricescale and
+# sma with short_signals and short_exits on the left pricescale
+pane1 = Panel(
+    ohlcv=(t1data.data["BAC"],),     #(series, entries, exits, other_markers)
+    histogram=[(order_imbalance_allvolume, "oivol",None, 0.3)],  # [(series, name, "rgba(53, 94, 59, 0.6)", opacity)]
+    
+    #following attributes assign lineseries series to different priceScaleIds, format: # [(series, name, entries, exits, other_markers)]
+    right=[(bbands)],   #multioutput indicator, outputs are autom.extracted
+    left=[(sma, "sma", short_signals, short_exits) #simple vbt indicator with markers in and outs
+          (supertrend.trend, "ST_trend") #explicitly just one output of multioutput indicator
+         ], 
+    middle1=[],
+    middle2=[],
+)
+#on second panel displays also ohlcv data, and sma on the left pricescale and histogram
+pane2 = Panel(
+    ohlcv=(t1data.data["BAC"],),
+    right=[],
+    left=[(sma, "sma_below", short_signals, short_exits)],
+    middle1=[],
+    middle2=[],
+    histogram=[(order_imbalance_sma, "oisma")],
+)
+
+#display both Panels, sync them and pick size, use xloc
+ch = chart([pane1, pane2], sync=True, title="Title", size="l", xloc=slice("1-1-2024","1-2-2024"))
+
+```
+
+## Example with markers
+
+```python
+
+#assume i want to display simple entries or exits on series or ohlcv 
+#based on tuple positions it determines entries or exits (and set colors and shape accordingly)
+pane1 = Panel(
+    ohlcv=(ohlcv_df, clean_long_entries, clean_short_entries)
+)
+ch = chart([pane1], title="Chart with Entry/Exit Markers", session=None, size="s")
+```
+<img width="798" alt="image" src="https://github.com/drew2323/lightweight-charts-python/assets/28433232/0cdc3930-b8b1-40f8-af95-55467879148b">
+
+```python
+#if you want to display more entries or exits, use tuples with their colors
+
+# Create Panel with OHLC data and entry signals
+pane1 = Panel(
+    ohlcv=(data.ohlcv.get(),
+           [(clean_long_entries, "yellow"), (clean_short_entries, "pink")], #list of entries tuples with color
+           [(clean_long_exits, "yellow"), (clean_short_exits, "pink")]), #list of exits tuples with color
+)
+
+# # Create the chart with the panel
+ch = chart([pane1], title="Chart with EntryShort/ExitShort (yellow) and EntryLong/ExitLong markers (pink)", session=None, size="s")
+```
+<img width="800" alt="image" src="https://github.com/drew2323/lightweight-charts-python/assets/28433232/0acde2bf-600e-4f45-8db0-5077822d6993">
+
 
 <div align="center">
     
-# lightweight-charts-python
+# lightweight-charts-python  - forked from
 
 [![PyPi Release](https://img.shields.io/pypi/v/lightweight-charts?color=32a852&label=PyPi)](https://pypi.org/project/lightweight-charts/)
 [![Made with Python](https://img.shields.io/badge/Python-3.8+-c7a002?logo=python&logoColor=white)](https://python.org "Go to Python homepage")

lightweight_charts/__init__.py

@@ -2,4 +2,4 @@ from .abstract import AbstractChart, Window
 from .chart import Chart
 from .widgets import JupyterChart
 from .polygon import PolygonChart
-from .helpers import chart, Panel, PlotAccessor
+from .helpers import chart, Panel, PlotSRAccessor, PlotDFAccessor

lightweight_charts/abstract.py

@@ -202,7 +202,7 @@ class SeriesCommon(Pane):
         self._set_interval(df)
         if not pd.api.types.is_datetime64_any_dtype(df['time']):
             df['time'] = pd.to_datetime(df['time'])
-        df['time'] = df['time'].astype('int64') / 10 ** 9 #removed integer divison // 10 ** 9 to keep subseconds precision
+        df['time'] = (df['time'].astype('int64') / 10 ** 9).astype(float)
 
         # if 'updated' in df.columns:
         #     df['updated'] = pd.to_datetime(df['updated']).astype('int64') / 10**9
@@ -213,7 +213,7 @@ class SeriesCommon(Pane):
             if col == 'time' or not pd.api.types.is_datetime64_any_dtype(df[col]):
                 continue
             # Convert datetime to timestamp with nanosecond precision after the decimal
-            df[col] = pd.to_datetime(df[col]).astype('int64') / 10**9
+            df[col] = (pd.to_datetime(df[col]).astype('int64') / 10**9).astype(float)
 
         return df
 

lightweight_charts/helpers.py

@@ -4,15 +4,54 @@ from .util import (
 )
 import pandas as pd
 
+def append_or_extend(target_list, value):
+    if isinstance(value, list):
+        target_list.extend(value)  # Extend if it's a list
+    else:
+        target_list.append(value)  # Append if it's a single value
+
+def extend_kwargs(ohlcv, right, left, middle1, middle2, histogram, kwargs):
+    """
+    Mutate lists based on kwargs for accessor.
+    Used when user added additional series to kwargs when using accessor.
+    """
+    if 'ohlcv' in kwargs:
+        ohlcv = kwargs['ohlcv'] #ohlcv is only a tuple
+    if 'left' in kwargs:
+        append_or_extend(left, kwargs['left'])
+    if 'right' in kwargs:
+        append_or_extend(right, kwargs['right'])
+    if 'histogram' in kwargs:
+        append_or_extend(histogram, kwargs['histogram'])
+    if 'middle1' in kwargs:
+        append_or_extend(middle1, kwargs['middle1'])         
+    if 'middle2' in kwargs:
+        append_or_extend(middle1, kwargs['middle2'])   
+
+    return ohlcv #as tuple is immutable
+
 # Register the custom accessor
 @pd.api.extensions.register_series_accessor("lw")
-class PlotAccessor:
+class PlotSRAccessor:
     """
-    Custom plot accessor for pandas series.
+    Custom plot accessor for pandas series. Quickly displays series values as line on the single pane.
+
+    Also additional priceseries can be added on top of them. They can be added
+    for each scale in the correct format - either as tuple(OHLCV) or as list of tuple (others)
+
+        # input parameter / expected format:
+        #     ohlcv=(), #(series, entries, exits, other_markers)
+        #     histogram=[], # [(series, name, "rgba(53, 94, 59, 0.6)", opacity)]
+        #     right=[],
+        #     left=[], #[(series, name, entries, exits, other_markers)]
+        #     middle1=[],
+        #     middle2=[],
+
 
     Usage: s
-    series.lw.plot()
-    series.lw.plot(size="m")
+    series.lw.plot() #plot series as line
+    series.lw.plot(size="m") #on medium panesize
+    series.lw.plot(histogram=(trade_series, "trades")) #plot histogram with trades on top of that
     """
     def __init__(self, pandas_obj):
         self._obj = pandas_obj
@@ -20,11 +59,123 @@ class PlotAccessor:
     def plot(self, **kwargs):
         if "size" not in kwargs:
             kwargs["size"] = "xs"
+
+        ohlcv = ()
+        right = []
+        left = []
+        middle1 = []
+        middle2 = []
+        histogram = []
+
+        #if there are additional series in kwargs add them too
+        #ohlcv is returned as it is tuple thus immutable
+        ohlcv = extend_kwargs(ohlcv, right, left, middle1, middle2, histogram, kwargs)
+
+        right.append((self._obj,"line"))
+
         pane1 = Panel(
-            right=[(self._obj, "line")],
-        )
+            ohlcv=ohlcv,
+            histogram=histogram,
+            right=right,
+            left=left,
+            middle1=middle1,
+            middle2=middle2
+            )
+
         ch = chart([pane1], **kwargs)
 
+@pd.api.extensions.register_dataframe_accessor("lw")
+class PlotDFAccessor:
+    """
+    Custom plot accessor for dataframe. Quickly displays all columns on the single pane.
+
+    Series type is automatically extracted for each column based on following setting:
+        scale / columns
+        ohlcv = ['close', 'volume', 'open', 'high', 'low']
+        right = ['vwap']
+        left = ['rsi']
+        middle1 = []
+        middle2 = []
+        histogram = ['buyvolume', 'sellvolume', 'trades']
+
+    Also additional priceseries can be added on top of them as parameters. They can be added
+    for each scale in the correct format - either as tuple(OHLCV) or as list of tuple (others)
+
+        # input parameter / expected format:
+        #     ohlcv=(), #(series, entries, exits, other_markers)
+        #     histogram=[], # [(series, name, "rgba(53, 94, 59, 0.6)", opacity)]
+        #     right=[],
+        #     left=[], #[(series, name, entries, exits, other_markers)]
+        #     middle1=[],
+        #     middle2=[],
+
+
+    Usage:
+    ohlcv_df.lw.plot()
+    ohlcv_df.lw.plot(size="m")
+    ohlcv_df.lw.plot(right=(rsi_series, "rsi"))
+    ohlcv_df.lw.plot(right=[(rsi_series, "rsi"), (angle_series, "angle")])
+    basic_data.data[SYMBOL].lw.plot(histogram=(basic_data.data[SYMBOL].close, "close"), size="m")
+    """
+    def __init__(self, pandas_obj):
+        self._obj = pandas_obj
+
+    def plot(self, **kwargs):
+        if "size" not in kwargs:
+            kwargs["size"] = "xs"
+
+        #default settings for each pricescale
+        ohlcv_cols = ['close', 'volume', 'open', 'high', 'low']
+        right_cols = ['vwap']
+        left_cols = ['rsi']
+        middle1_cols = []
+        middle2_cols = []
+        histogram_cols = ['buyvolume', 'sellvolume', 'trades']
+        
+        ohlcv = ()
+        right = []
+        left = []
+        middle1 = []
+        middle2 = []
+        histogram = []
+
+        for col in self._obj.columns:
+             if col in right_cols:
+                  right.append((self._obj[col],col,))
+             if col in histogram_cols:
+                  histogram.append((self._obj[col],col,))
+             if col in left_cols:
+                  left.append((self._obj[col],col,))
+             if col in middle1_cols:
+                  middle1_cols.append((self._obj[col],col,))
+             if col in middle2_cols:
+                  middle2_cols.append((self._obj[col],col,))
+
+        ohlcv = (self._obj[ohlcv_cols],)
+
+        #if there are additional series in kwargs add them too
+        ohlcv = extend_kwargs(ohlcv, right, left, middle1, middle2, histogram, kwargs)
+
+        pane1 = Panel(
+            ohlcv=ohlcv,
+            histogram=histogram,
+            right=right,
+            left=left,
+            middle1=middle1,
+            middle2=middle2
+            )
+
+        ch = chart([pane1], **kwargs)
+
+        # pane1 = Panel(
+        #     ohlcv=(), #(series, entries, exits, other_markers)
+        #     histogram=[], # [(series, name, "rgba(53, 94, 59, 0.6)", opacity)]
+        #     right=[],
+        #     left=[], #[(series, name, entries, exits, other_markers)]
+        #     middle1=[],
+        #     middle2=[],
+        # )
+
 class Panel:
     """
     A class to represent a panel in a chart.
@@ -118,7 +269,7 @@ class Panel:
         self.precision = precision
 
 
-def chart(panes: list[Panel], sync=False, title='', size="m", xloc=None, session: str="9:30:00", precision=None):
+def chart(panes: list[Panel], sync=False, title='', size="m", xloc=None, session: str="9:30:00, 09:30:05", precision=None, **kwargs):
     """
     Function to fast render a chart with multiple panes. This function manipulates graphical
     output or interfaces with an external framework to display charts with synchronized

setup.py

@@ -5,7 +5,7 @@ with open('README.md', 'r', encoding='utf-8') as f:
 
 setup(
     name='lightweight_charts',
-    version='2.1.4',
+    version='2.2.0',
     packages=find_packages(),
     python_requires='>=3.8',
     install_requires=[