#!/usr/bin/env python
# coding: utf-8

# In[1]:


get_ipython().run_line_magic('load_ext', 'watermark')
get_ipython().run_line_magic('watermark', '')
from copy import deepcopy
import pickle
import os

import pandas as pd
import pandas_datareader.data as web
from pandas.tseries.offsets import Week
import numpy as np
import sklearn.mixture as mix
from sklearn.externals import joblib
import scipy.stats as scs
from math import ceil

import matplotlib as mpl
import matplotlib.pyplot as plt

get_ipython().run_line_magic('matplotlib', 'inline')
import seaborn as sns
sns.set(font_scale=1.25)
style_kwds = {'xtick.major.size': 3, 'ytick.major.size': 3,
              'font.family':'Courier Prime Code', 'legend.frameon': True} # 
sns.set_style('white', style_kwds)

import missingno as msno
from tqdm import tqdm
import affirm 
import warnings
warnings.filterwarnings("ignore")
import re
p=print
p()
get_ipython().run_line_magic('watermark', '-p pandas,pandas_datareader,numpy,scipy,sklearn,matplotlib,seaborn')


# In[2]:


df = pd.read_hdf('GMM_Results_TidyData.h5', 'table')


# In[3]:


# can view individual results
df.query('sym == "SPY"').groupby(['k', 'steps','lookback']).mean().T


# In[4]:


df.query('sym == "SPY"').groupby(['k', 'steps','lookback'])['sum_ratio'].mean().unstack()


# In[5]:


ser = df.groupby(['sym', 'lookback'])['median'].mean().unstack().mean(axis=1)
ser


# In[6]:


# median returns by K
df1 = df.groupby(['sym', 'k'])['median'].mean().unstack()#.mean(axis=1)
df1


# In[7]:


# median returns by step
df1 = df.groupby(['sym', 'steps'])['median'].mean().unstack()#.mean(axis=1)
df1


# In[8]:


# sum ratio by step
df2 = df.groupby(['sym', 'steps'])['sum_ratio'].mean().unstack()#.mean(axis=1)
df2


# In[9]:


# fun with facet grids
g = sns.FacetGrid(df, col="sym", hue="lookback", col_wrap=3)
g.map(plt.hist, "sum_ratio", alpha=0.5);
g.add_legend();
for ax in g.axes.flat:
    ax.axvline(1, color='k', lw=1, ls='--')
g.fig.savefig('facetgrid-lookback-steps-sum-ratio-hist.png', dpi=300, bbox_inches='tight') 


# In[10]:


g = sns.FacetGrid(df, col="sym", hue="lookback", col_wrap=3)
g.map(plt.hist, "median", alpha=0.5);
g.add_legend();
for ax in g.axes.flat:
    ax.axvline(0, color='k', lw=1, ls='--')
g.fig.savefig('facetgrid-lookback-steps-median-return-hist.png', dpi=300, bbox_inches='tight')  


# In[11]:


g = sns.FacetGrid(df, col="sym", hue="lookback", col_wrap=3)
g.map(sns.barplot, "steps", "median", ci=None, alpha=0.5)
g.add_legend();
for ax in g.axes.flat:
    ax.axhline(0, color='k', lw=1, ls='--')
g.fig.savefig('facetgrid-lookback-steps-median-return-barplot.png', dpi=300, bbox_inches='tight')    


# In[12]:


g = sns.FacetGrid(df, col="sym", hue="lookback", col_wrap=3)
g.map(sns.barplot, "steps", "sum_ratio", ci=None, alpha=0.5)
g.add_legend();
for ax in g.axes.flat:
    ax.axhline(1., color='k', lw=1, ls='--')
g.fig.savefig('facetgrid-lookback-steps-sum-ratio-barplot.png', dpi=300, bbox_inches='tight')


# In[13]:


g = sns.FacetGrid(df, col="sym", hue="lookback", col_wrap=3)
g.map(sns.barplot, "k", "median", ci=None, alpha=0.5)
g.add_legend();
for ax in g.axes.flat:
    ax.axhline(0, color='k', lw=1, ls='--')
g.fig.savefig('facetgrid-lookback-k-median-return-barplot.png', dpi=300, bbox_inches='tight')   


# In[14]:


g = sns.FacetGrid(df, col="sym", hue="lookback", col_wrap=3)
g.map(sns.barplot, "k", "sum_ratio", ci=None, alpha=0.5)
g.add_legend();
for ax in g.axes.flat:
    ax.axhline(1., color='k', lw=1, ls='--')
g.fig.savefig('facetgrid-lookback-k-sum-ratio-barplot.png', dpi=300, bbox_inches='tight')