459 lines
18 KiB
Python
459 lines
18 KiB
Python
from .widgets import JupyterChart
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from .util import (
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is_vbt_indicator, get_next_color
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)
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import pandas as pd
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def append_or_extend(target_list, value):
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if isinstance(value, list):
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target_list.extend(value) # Extend if it's a list
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else:
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target_list.append(value) # Append if it's a single value
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def extend_kwargs(ohlcv, right, left, middle1, middle2, histogram, kwargs):
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"""
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Mutate lists based on kwargs for accessor.
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Used when user added additional series to kwargs when using accessor.
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"""
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if 'ohlcv' in kwargs:
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ohlcv = kwargs['ohlcv'] #ohlcv is only a tuple
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if 'left' in kwargs:
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append_or_extend(left, kwargs['left'])
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if 'right' in kwargs:
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append_or_extend(right, kwargs['right'])
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if 'histogram' in kwargs:
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append_or_extend(histogram, kwargs['histogram'])
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if 'middle1' in kwargs:
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append_or_extend(middle1, kwargs['middle1'])
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if 'middle2' in kwargs:
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append_or_extend(middle1, kwargs['middle2'])
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return ohlcv #as tuple is immutable
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# Register the custom accessor
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@pd.api.extensions.register_series_accessor("lw")
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class PlotSRAccessor:
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"""
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Custom plot accessor for pandas series. Quickly displays series values as line on the single pane.
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Also additional priceseries can be added on top of them. They can be added
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for each scale in the correct format - either as tuple(OHLCV) or as list of tuple (others)
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# input parameter / expected format:
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# ohlcv=(), #(series, entries, exits, other_markers)
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# histogram=[], # [(series, name, "rgba(53, 94, 59, 0.6)", opacity)]
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# right=[],
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# left=[], #[(series, name, entries, exits, other_markers)]
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# middle1=[],
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# middle2=[],
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Usage: s
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series.lw.plot() #plot series as line
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series.lw.plot(size="m") #on medium panesize
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series.lw.plot(histogram=(trade_series, "trades")) #plot histogram with trades on top of that
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"""
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def __init__(self, pandas_obj):
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self._obj = pandas_obj
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def plot(self, **kwargs):
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if "size" not in kwargs:
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kwargs["size"] = "xs"
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ohlcv = ()
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right = []
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left = []
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middle1 = []
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middle2 = []
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histogram = []
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#if there are additional series in kwargs add them too
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#ohlcv is returned as it is tuple thus immutable
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ohlcv = extend_kwargs(ohlcv, right, left, middle1, middle2, histogram, kwargs)
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right.append((self._obj,"line"))
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pane1 = Panel(
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ohlcv=ohlcv,
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histogram=histogram,
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right=right,
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left=left,
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middle1=middle1,
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middle2=middle2
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)
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ch = chart([pane1], **kwargs)
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@pd.api.extensions.register_dataframe_accessor("lw")
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class PlotDFAccessor:
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"""
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Custom plot accessor for dataframe. Quickly displays all columns on the single pane.
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Series type is automatically extracted for each column based on following setting:
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scale / columns
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ohlcv = ['close', 'volume', 'open', 'high', 'low']
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right = ['vwap']
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left = ['rsi']
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middle1 = []
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middle2 = []
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histogram = ['buyvolume', 'sellvolume', 'trades']
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Also additional priceseries can be added on top of them as parameters. They can be added
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for each scale in the correct format - either as tuple(OHLCV) or as list of tuple (others)
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# input parameter / expected format:
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# ohlcv=(), #(series, entries, exits, other_markers)
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# histogram=[], # [(series, name, "rgba(53, 94, 59, 0.6)", opacity)]
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# right=[],
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# left=[], #[(series, name, entries, exits, other_markers)]
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# middle1=[],
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# middle2=[],
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Usage:
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ohlcv_df.lw.plot()
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ohlcv_df.lw.plot(size="m")
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ohlcv_df.lw.plot(right=(rsi_series, "rsi"))
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ohlcv_df.lw.plot(right=[(rsi_series, "rsi"), (angle_series, "angle")])
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basic_data.data[SYMBOL].lw.plot(histogram=(basic_data.data[SYMBOL].close, "close"), size="m")
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"""
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def __init__(self, pandas_obj):
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self._obj = pandas_obj
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def plot(self, **kwargs):
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if "size" not in kwargs:
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kwargs["size"] = "xs"
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#default settings for each pricescale
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ohlcv_cols = ['close', 'volume', 'open', 'high', 'low']
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right_cols = ['vwap']
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left_cols = ['rsi']
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middle1_cols = []
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middle2_cols = []
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histogram_cols = ['buyvolume', 'sellvolume', 'trades']
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ohlcv = ()
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right = []
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left = []
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middle1 = []
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middle2 = []
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histogram = []
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for col in self._obj.columns:
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if col in right_cols:
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right.append((self._obj[col],col,))
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if col in histogram_cols:
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histogram.append((self._obj[col],col,))
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if col in left_cols:
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left.append((self._obj[col],col,))
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if col in middle1_cols:
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middle1_cols.append((self._obj[col],col,))
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if col in middle2_cols:
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middle2_cols.append((self._obj[col],col,))
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ohlcv = (self._obj[ohlcv_cols],)
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#if there are additional series in kwargs add them too
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ohlcv = extend_kwargs(ohlcv, right, left, middle1, middle2, histogram, kwargs)
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pane1 = Panel(
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ohlcv=ohlcv,
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histogram=histogram,
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right=right,
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left=left,
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middle1=middle1,
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middle2=middle2
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)
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ch = chart([pane1], **kwargs)
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# pane1 = Panel(
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# ohlcv=(), #(series, entries, exits, other_markers)
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# histogram=[], # [(series, name, "rgba(53, 94, 59, 0.6)", opacity)]
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# right=[],
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# left=[], #[(series, name, entries, exits, other_markers)]
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# middle1=[],
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# middle2=[],
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# )
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class Panel:
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"""
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A class to represent a panel in a chart.
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Attributes
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----------
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* ohlcv : tuple optional\n
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(series, entries, exits, other_markers).\n
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entries, exits and other_markers can be sr/df or lists of sr/df or list of tuples (if you want to specify color)
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* histogram : list of tuples, optional.\n
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[(series, name, color, opacity)]
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* title : str, optional
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The title of the panel. Default is None.
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* right : list of tuples, optional
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A list of line tuples in given scale.\n
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[(series, name, entries, exits, other_markers)]
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* left : list of tuples, optional
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* middle1 : list of tuples, optional
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* middle2 : list of tuples, optional
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* xloc : str or slice, optional. Vectorbt indexing. Default is None.
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* precision: int, optional. The number of digits after the decimal point. Apply to all lines on this pane. Default is None.
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Examples
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-------
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# Simple example
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```
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pane1 = Panel(
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ohlcv=(), #(series, entries, exits, other_markers)
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histogram=[], # [(series, name, "rgba(53, 94, 59, 0.6)", opacity)]
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right=[],
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left=[], #[(series, name, entries, exits, other_markers)]
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middle1=[],
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middle2=[],
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)
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ch = chart([pane1])
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# Synced example
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pane1 = Panel(
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ohlcv=(t1data.data["BAC"],), #(series, entries, exits, other_markers)
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histogram=[(order_imbalance_allvolume, "oivol")], # [(series, name, "rgba(53, 94, 59, 0.6)", opacity)]
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right=[], # [(series, name, entries, exits, other_markers)]
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left=[(sma, "sma", short_signals, short_exits)],
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middle1=[],
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middle2=[],
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xloc="2024-02-12 09:30",
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precision=3
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)
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pane2 = Panel(
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ohlcv=(t1data.data["BAC"],),
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right=[],
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left=[(sma, "sma_below", short_signals, short_exits)],
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middle1=[],
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middle2=[],
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histogram=[(order_imbalance_sma, "oisma")],
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)
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ch = chart([pane1, pane2], sync=True, title="neco", size="m", xloc=slice("2024-02-12 09:30","2024-02-12 16:00"))
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#Markers examples
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#assume i want to display simple entries or exits on series or ohlcv
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#based on tuple positions it determines entries or exits (and set colors and shape accordingly)
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pane1 = Panel(
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ohlcv=(ohlcv_df, clean_long_entries, clean_short_entries)
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)
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ch = chart([pane1], title="Chart with Entry/Exit Markers", session=None, size="s")
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#if you want to display more entries or exits, use tuples with their colors
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# Create Panel with OHLC data and entry signals
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pane1 = Panel(
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ohlcv=(data.ohlcv.get(),
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[(clean_long_entries, "yellow"), (clean_short_entries, "pink")], #list of entries tuples with color
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[(clean_long_exits, "yellow"), (clean_short_exits, "pink")]), #list of exits tuples with color
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)
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# # Create the chart with the panel
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ch = chart([pane1], title="Chart with EntryShort/ExitShort (yellow) and EntryLong/ExitLong markers (pink)", sync=True, session=None, size="s")
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```
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"""
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def __init__(self, ohlcv=None, right=None, left=None, middle1=None, middle2=None, histogram=None, title=None, xloc=None, precision=None):
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self.ohlcv = ohlcv if ohlcv is not None else ()
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self.right = right if right is not None else []
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self.left = left if left is not None else []
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self.middle1 = middle1 if middle1 is not None else []
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self.middle2 = middle2 if middle2 is not None else []
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self.histogram = histogram if histogram is not None else []
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self.title = title
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self.xloc = xloc
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self.precision = precision
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def chart(panes: list[Panel], sync=False, title='', size="m", xloc=None, session: str="9:30:00, 09:30:05", precision=None, **kwargs):
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"""
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Function to fast render a chart with multiple panes. This function manipulates graphical
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output or interfaces with an external framework to display charts with synchronized
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scales if required.
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Args:
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-----
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* panes (List[Pane]): A list of Pane instances to be rendered in the chart. Each Pane
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can include various data series and configurations such as OHLCV,
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histograms, and indicators positioned on left, right, or middle scales.
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* sync (bool): If True, synchronize scales of all panes to have the same scale limits.
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Default is False.
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* title (str): Title of the chart. If not specified, defaults to an empty string.
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* size (str): The size designation, which can be 's', 'm', or 'xl'. Defaults to'm'.
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Expected values:
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- 'xs' for extra-small
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- 's' for small
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- 'm' for medium
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- 'xl' for extra large
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* session (str): Draw session vertical divider at that time. Defaults to '9:30'. Can be any time in 24-hour format or xloc slice
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* precision (int): The number of digits after the decimal point. Defaults to None. Applies to lines on all panes, if not overriden by pane-specific precision.
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* xloc (str): xloc advanced filtering of vbt.xloc accessor. Defaults to None. Applies to all panes.
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Might be overriden by pane-specific xloc.
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XLOC Examples:
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xloc["2020-01-01 17:30"]
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xloc["2020-01-01"]
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xloc["2020-01"]
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xloc["2020"]
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xloc["2020-01-01":"2021-01-01"]
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xloc["january":"april"]
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xloc["monday":"saturday"]
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xloc["09:00":"16:00"]
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xloc["16:00":"09:00"]
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xloc["monday 09:00":"friday 16:00"]
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Returns:
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None: This function does not return a value. It performs operations to render graphical content.
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Examples:
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---------
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```
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pane1 = Pane(
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ohlcv=(t1data.data["BAC"],),
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histogram=[(order_imbalance_allvolume, "oivol")]
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right=[],
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left=[(sma, "sma", short_signals, short_exits)],
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middle1=[],
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middle2=[],
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xloc=slice("2024-02-12 09:30","2024-02-12 16:00"),
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precision=4
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)
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pane2 = Pane(
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ohlcv=(t1data.data["BAC"],),
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right=[],
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left=[(sma, "sma_below", short_signals, short_exits)],
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middle1=[],
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middle2=[],
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histogram=[(order_imbalance_sma, "oisma")]
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)
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ch = chart([pane1, pane2], sync=True, title="Chart", size="l", xloc=slice("2024-02-12 09:30","2024-02-12 16:00"), session="9:30")
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```
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Notes:
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------
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"""
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def xloc_me(dfsr, xloc):
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if xloc is None:
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return dfsr
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else:
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return dfsr.vbt.xloc[xloc].obj
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size_to_dimensions = {
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'xs': (600, 200),
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's': (800, 400),
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'm': (1000, 600),
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'l': (1300, 800)}
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width, height = size_to_dimensions.get(size, (1000, 600))
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height_ratio = 1 / len(panes)
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main_title_set = False
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output_series = None
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for index, pane in enumerate(panes):
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subchartX = None
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if index == 0:
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chartX = JupyterChart(width=width, height=height, inner_width=1, inner_height=height_ratio, leftScale=bool(pane.left))
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active_chart = chartX
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else:
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subchartX = chartX.create_subchart(position='right', width=1, height=height_ratio, sync=sync, leftScale=bool(pane.left))
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active_chart = subchartX
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xloc = pane.xloc if pane.xloc is not None else xloc
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def display_markers(active_chart: JupyterChart, markers, type= None, xloc=None):
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color = None
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if isinstance(markers, list):
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for markerset in markers:
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if isinstance(markerset, tuple):
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markerset,color = markerset
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active_chart.markers_set(markers=xloc_me(markerset, xloc), type=type, color=color if color is not None else None)
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else:
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if isinstance(markers, tuple):
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markers,color = markers
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active_chart.markers_set(markers=xloc_me(markers, xloc), type=type, color=color if color is not None else None)
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if pane.ohlcv != ():
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series, entries, exits, markers = (pane.ohlcv + (None,) * 4)[:4]
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if series is None:
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raise ValueError("OHLCV series cannot be None. Omit the OHLCV tuple.")
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active_chart.set(xloc_me(series, xloc))
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if entries is not None:
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display_markers(active_chart=active_chart, markers=entries, type="entries", xloc=xloc)
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if exits is not None:
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display_markers(active_chart=active_chart, markers=exits, type="exits", xloc=xloc)
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if markers is not None:
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display_markers(chart=active_chart, markers=markers, xloc=xloc)
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for tup in pane.histogram:
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series, name, color, opacity, _ = (tup + (None, None, None, None, None))[:5]
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if series is None:
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continue
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#conditionally include color
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kwargs = {'name': name}
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if color is not None:
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kwargs['color'] = color
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if opacity is not None:
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kwargs['opacity'] = opacity
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tmp = active_chart.create_histogram(**kwargs) #green transparent "rgba(53, 94, 59, 0.6)"
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tmp.set(xloc_me(series, xloc))
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if pane.title is not None:
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active_chart.topbar.textbox("title",pane.title)
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main_title_set = True if index==0 else False
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#iterate over keys - they are all priceScaleId except of these
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for att_name, att_value_tuple in vars(pane).items():
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if att_name in ["ohlcv","histogram","title","xloc","precision"]:
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continue
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for tup in att_value_tuple:
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series, name, entries, exits, markers = (tup + (None, None, None, None, None))[:5]
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if series is None:
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continue
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#pokud jde o vbt indikator vytahneme vsechny output a predpokladame, ze jde o lines a vykreslime je
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if is_vbt_indicator(series):
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series = series.xloc[xloc] if xloc is not None else series
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for output in series.output_names:
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output_series = getattr(series, output)
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output = name + ':' + output if name is not None else output
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tmp = active_chart.create_line(name=output, priceScaleId=att_name)#, color="blue")
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tmp.set(output_series)
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else:
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if name is None:
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name = "no_name"
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tmp = active_chart.create_line(name=name, priceScaleId=att_name)#, color="blue")
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tmp.set(xloc_me(series, xloc))
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if pane.precision is not None or precision is not None:
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tmp.precision(pane.precision if pane.precision is not None else precision)
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if entries is not None:
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display_markers(active_chart=tmp, markers=entries, type="entries", xloc=xloc)
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if exits is not None:
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display_markers(active_chart=tmp, markers=exits, type="exits", xloc=xloc)
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if markers is not None:
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display_markers(active_chart=tmp, markers=markers, xloc=xloc)
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active_chart.legend(True)
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active_chart.fit()
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if session is not None and session:
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last_used_series = output_series if is_vbt_indicator(series) else series #pokud byl posledni series vbt, pak pouzijeme jeho outputy
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active_chart.vertical_span(start_time=xloc_me(last_used_series, xloc).vbt.xloc[session].obj.index.to_list(), color="rgba(252, 255, 187, 0.42)")
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if not main_title_set:
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chartX.topbar.textbox("title",title)
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chartX.legend(True)
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chartX.fit()
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chartX.load()
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return chartX
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