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https://github.com/aljazceru/hummingbot-dashboard.git
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83 lines
3.3 KiB
Python
83 lines
3.3 KiB
Python
import pandas as pd
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import pandas_ta as ta # noqa: F401
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import plotly.graph_objects as go
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from frontend.visualization import theme
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def get_bbands_traces(df, bb_length, bb_std):
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tech_colors = theme.get_color_scheme()
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df.ta.bbands(length=bb_length, std=bb_std, append=True)
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bb_lower = f'BBL_{bb_length}_{bb_std}'
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bb_middle = f'BBM_{bb_length}_{bb_std}'
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bb_upper = f'BBU_{bb_length}_{bb_std}'
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traces = [
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go.Scatter(x=df.index, y=df[bb_upper], line=dict(color=tech_colors['upper_band']),
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name='Upper Band'),
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go.Scatter(x=df.index, y=df[bb_middle], line=dict(color=tech_colors['middle_band']),
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name='Middle Band'),
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go.Scatter(x=df.index, y=df[bb_lower], line=dict(color=tech_colors['lower_band']),
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name='Lower Band'),
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]
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return traces
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def get_volume_trace(df):
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df.index = pd.to_datetime(df.timestamp, unit='s')
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return go.Bar(x=df.index, y=df['volume'], name="Volume", marker_color=theme.get_color_scheme()["volume"], opacity=0.7)
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def get_macd_traces(df, macd_fast, macd_slow, macd_signal):
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tech_colors = theme.get_color_scheme()
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df.ta.macd(fast=macd_fast, slow=macd_slow, signal=macd_signal, append=True)
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macd = f'MACD_{macd_fast}_{macd_slow}_{macd_signal}'
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macd_s = f'MACDs_{macd_fast}_{macd_slow}_{macd_signal}'
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macd_hist = f'MACDh_{macd_fast}_{macd_slow}_{macd_signal}'
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traces = [
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go.Scatter(x=df.index, y=df[macd], line=dict(color=tech_colors['macd_line']),
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name='MACD Line'),
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go.Scatter(x=df.index, y=df[macd_s], line=dict(color=tech_colors['macd_signal']),
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name='MACD Signal'),
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go.Bar(x=df.index, y=df[macd_hist], name='MACD Histogram',
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marker_color=df[f"MACDh_{macd_fast}_{macd_slow}_{macd_signal}"].apply(lambda x: '#FF6347' if x < 0 else '#32CD32'))
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]
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return traces
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def get_supertrend_traces(df, length, multiplier):
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tech_colors = theme.get_color_scheme()
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df.ta.supertrend(length=length, multiplier=multiplier, append=True)
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supertrend_d = f'SUPERTd_{length}_{multiplier}'
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supertrend = f'SUPERT_{length}_{multiplier}'
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df = df[df[supertrend] > 0]
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# Create segments for line with different colors
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segments = []
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current_segment = {"x": [], "y": [], "color": None}
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for i in range(len(df)):
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if i == 0 or df[supertrend_d].iloc[i] == df[supertrend_d].iloc[i - 1]:
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current_segment["x"].append(df.index[i])
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current_segment["y"].append(df[supertrend].iloc[i])
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current_segment["color"] = tech_colors['buy'] if df[supertrend_d].iloc[i] == 1 else tech_colors['sell']
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else:
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segments.append(current_segment)
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current_segment = {"x": [df.index[i - 1], df.index[i]],
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"y": [df[supertrend].iloc[i - 1], df[supertrend].iloc[i]],
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"color": tech_colors['buy'] if df[supertrend_d].iloc[i] == 1 else tech_colors['sell']}
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segments.append(current_segment)
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# Create traces from segments
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traces = [
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go.Scatter(
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x=segment["x"],
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y=segment["y"],
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mode='lines',
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line=dict(color=segment["color"], width=2),
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name='SuperTrend'
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) for segment in segments
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]
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return traces
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