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corr.py
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corr.py
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import numpy as np
import pandas as pd
import pandas_datareader.data as web
from pandas_datareader.nasdaq_trader import get_nasdaq_symbols
import plotly.graph_objects as go
import matplotlib.pyplot as plt
import seaborn as sns
import scipy.optimize as sco
import pickle
# Basically all this does is take a stock icon and then finds all the positively correlated stocks (table1) and
# negatively correlated stocks (table2). Right now it displays the top 5. The stock index data is taken from some
# index pickle file which I've generated so this should work as is.
def get_pos_corr_stocks(pickle_filename="corr_pairs.pickle", key=""):
#loading pickle object:
pickle_in = open(pickle_filename,"rb")
corr_data = pickle.load(pickle_in)
table = corr_data.sort_values(ascending=False, axis=0, by='Correlation')
table = table[3363:]
output_table = pd.DataFrame(columns = ['stock1', 'stock2', 'Correlation'])
if key == "":
return table[:50]
else:
count=0
pick_in = open("stock_index", "rb")
stock_index = pickle.load(pick_in)
check = 0
for x in stock_index['name']:
if x == key:
check = 1
break;
if check == 0:
print("Key not found")
return 0
for index, row in table.iterrows():
if row['stock1'] == key:
output_table = output_table.append(row)
count = count +1
if count == 5:
break;
return output_table;
def get_neg_corr_stocks(pickle_filename="corr_pairs.pickle", key=""):
#loading pickle object:
pickle_in = open(pickle_filename,"rb")
corr_data = pickle.load(pickle_in)
output_table = pd.DataFrame(columns = ['stock1', 'stock2', 'Correlation'])
if key == "":
return corr_data[:50]
else:
count=0
pick_in = open("stock_index", "rb")
stock_index = pickle.load(pick_in)
check = 0
for x in stock_index['name']:
if x == key:
check = 1
break;
if check == 0:
print("Key not found")
return 0
for index, row in corr_data.iterrows():
if row['stock1'] == key:
output_table = output_table.append(row)
count = count +1
if count == 5:
break
else:
continue
return output_table;
def get_correlated_pairs(backtest_window_s="2018-08-01",backtest_window_e="2019-01-01", positive_corr_bound=0.9,
negative_corr_bound=-0.9,pickle_filename="dict.pickle", key=""):
positively_correlated_stocks = get_pos_corr_stocks(key=key)#Get +ve Corr
#print("positive table made")
negatively_correlated_stocks = get_neg_corr_stocks(key=key)#Get -ve Corr
#print("negative table made")
MSG="INVALID QUERY"
try:
if positively_correlated_stocks == 0:
return MSG, MSG
except:
return positively_correlated_stocks, negatively_correlated_stocks
table1, table2 = get_correlated_pairs(key="ICON")