machine_learning.py

from collections import Counter
import numpy as np
import pandas as pd
from sklearn import neighbors, svm
from sklearn.ensemble import VotingClassifier, RandomForestClassifier
from sklearn.model_selection import train_test_split


def process_data_for_labels(ticker):
	how_many_days = 7
	df = pd.read_csv('sp500_joined_adjusted_closes.csv', index_col=0)
	tickers = df.columns.values
	df.fillna(0, inplace=True)

	for i in range(1, how_many_days+1):
		df['{}_{}d'.format(ticker, i)] = (df[ticker].shift(-i) - df[ticker]) / df[ticker]
	df.fillna(0, inplace=True)
	return how_many_days, tickers, df

def buy_sell_hold(*args):
	cols = [c for c in args]
	requirement = 0.02
	for col in cols:
		if col > requirement:
			return 1
		if col < -requirement:
			return -1
	return 0

def extract_featuresets(ticker):
	how_many_days, tickers, df = process_data_for_labels(ticker)
	df['{}_target'.format(ticker)] = list(map(buy_sell_hold, *[df['{}_{}d'.format(ticker,i)] for i in range(1,how_many_days+1)]))
	vals = df['{}_target'.format(ticker)].values
	str_vals = [str(i) for i in vals]
	print('Data spread', Counter(str_vals))
	df.fillna(0, inplace=True)
	df = df.replace([np.inf, -np.inf], np.nan)
	df.dropna(inplace=True)
	df_vals = df[[ticker for ticker in tickers]].pct_change()
	df_vals = df_vals.replace([np.inf, -np.inf], 0)
	df_vals.fillna(0, inplace=True)
	X = df_vals.values 
	y = vals
	return X, y, df

def do_machine_learning(ticker):
	X, y, df = extract_featuresets(ticker)
	X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25)
	#classifier = neighbors.KNeighborsClassifier()
	classifier = VotingClassifier([('linearsvc', svm.LinearSVC()), ('knn', neighbors.KNeighborsClassifier()), ('rfor', RandomForestClassifier())])
	classifier.fit(X_train, y_train)
	confidence = classifier.score(X_test, y_test)
	print('Accuracy:', confidence)
	prediction = classifier.predict(X_test)
	print('Predicted spread:', Counter(prediction))
	return confidence