In this notebook the precomputing of the indicators' values will be explored to try to accelerate the computations in the simulations

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# Basic imports
import os
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import datetime as dt
import scipy.optimize as spo
import sys
from time import time
from sklearn.metrics import r2_score, median_absolute_error

%matplotlib inline

%pylab inline
pylab.rcParams['figure.figsize'] = (20.0, 10.0)

%load_ext autoreload
%autoreload 2

sys.path.append('../../')


    

    




Populating the interactive namespace from numpy and matplotlib

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from recommender import simulator as sim


    

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SYMBOL = 'SPY'
total_data_train_df = pd.read_pickle('../../data/data_train_val_df.pkl').stack(level='feature')
data_train_df = total_data_train_df[SYMBOL].unstack()


    

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indicators = sim.create_indicators(data_train_df)


    

In [9]:

    

len(indicators['rsi'].q_levels)


    

    
Out[9]:





10

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