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from hmmlearn import hmm
import plotly.graph_objects as go
import streamlit as st
original_map = {'max': 2, 'mid': 1, 'low': 0}
@st.cache
def GetMood(indval):
if indval <= 1/3:
return "Greed"
elif 1/3 < indval < 2/3:
return "Holding Steady"
else:
return "Fear"
@st.cache
def ProcessVixData(vix_df):
X = vix_df[['Close']].copy()
model = hmm.GaussianHMM(n_components=3, covariance_type="full", n_iter=100)
model.fit(X)
res = X.copy()
res['Class'] = model.predict(X)
resmap = res.groupby('Class').agg({'Close': 'mean'}).sort_values(by='Close')
resmap = dict(zip(resmap.index, ['low', 'mid', 'max']))
res['Class_name'] = res.Class.apply(lambda x:resmap[x])
res['Class_proc'] = res.Class_name.apply(lambda x:original_map[x])
res['Norm_Class'] = (res['Class_proc']-res['Class_proc'].min())/(res['Class_proc'].max()-res['Class_proc'].min())
return res
@st.cache
def GetIndicatorChart(res):
value = res.Norm_Class[-30:].mean()
fig = go.Figure(go.Indicator(
domain={'x': [0, 1], 'y': [0, 1]},
value=value,
mode="gauge+number",
title={'text': f"VIX Analysis\nMood : {GetMood(value)}"},
gauge={'axis': {'range': [None, res.Norm_Class.max()]},
'steps': [
{'range': [0, 1/3], 'color': "green"},
{'range': [1/3, 2/3], 'color': "gray"},
{'range': [2/3, 1], 'color': "red"}],
'threshold': {'line': {'color': "black", 'width': 4}, 'thickness': 0.75, 'value': value}}))
return fig
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