dwker/lightweight-charts-python
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

df and sr accessor enhancements

Browse Source
This commit is contained in:
David Brazda
2024-10-04 11:54:24 +02:00
parent 7986aa9195
commit c4356bef3a
10 changed files with 195 additions and 20 deletions
Download Patch File Download Diff File Expand all files Collapse all files

165
lightweight_charts/helpers.py
View File

@ -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, 09:30:05", 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