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predictStocks.py
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predictStocks.py
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# currently it is linear regression, we need some error to measure accuracy
# clf = joblib.load('filename.pkl') # load classifier
# given ['Adj. Open','Adj. Close', 'Adj. Volume','Adj. High', 'Adj. Low'], we can predict 1% into future
# mean reversion for long term prediction
from __future__ import division # preventing division issue in 2.7
import pandas as pd
import quandl, shutil
import math
import numpy as np
from sklearn.externals import joblib
from sklearn import preprocessing
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.neural_network import MLPClassifier
# import matplotlib.pyplot as plt
# import pylab
from sklearn.metrics import f1_score
# from sklearn.ensemble import RandomForestClassifier
import time
# import os
# for tuning hyper parameters we use grid search
# from sklearn.grid_search import GridSearchCV #Perforing grid search
# import xgboost
import datetime
from yahoo_finance import Share
class predictStocks:
def __init__(self):
pass
def predictML(self,stocksDf, useRegression, symbol):
stocksDf = stocksDf.dropna(how='any')
if useRegression:
X = np.array(stocksDf.drop(['Future'],1))
else:
X = np.array(stocksDf.drop(['Decision'],1))
X = preprocessing.scale(X)
# X = X[:predict_index-2]
# print(X.shape)
# X_lately = X[-forecast_out:]
# X = X[:-forecast_out:]
# y = np.array()
if useRegression:
y = np.array(stocksDf['Future']) # y is set forecast out
else:
y = np.array(stocksDf['Decision'])
X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.5) # 50% training data, 50% testing
best_clf = LinearRegression(n_jobs=-1)
# best_clf = KNeighborsClassifier(n_neighbors=10, weights='uniform', algorithm='auto', leaf_size=30, p=2, metric='minkowski', metric_params=None, n_jobs=-1)
best_accuracy = 0.0
best_algo = 'RF'
num_runs = 1
for _ in xrange(num_runs):
if useRegression:
# use KNN or other binary classifiers
clf = LinearRegression(n_jobs=-1)
# print("Crunching...")
clf.fit(X_train,y_train)
# clf.fit(X,y) # all data till now
file_name = 'LinearRegressionClf_%s.pkl' %symbol
# joblib.dump(clf, file_name) # save the classifier to file
# clf = joblib.load('LinearRegressionClf.pkl')
# print clf
# do cross fold validation
accuracy = clf.score(X_test,y_test) # test on data not used for training, is around 95%
# print(accuracy)
# print clf.predict(predict_value) # give array of last 10 days to get 1% into each values future
# print clf.predict() # predict into 1% future given todays ['Adj. Open','Adj. Close','S&P Open', 'Adj. Volume','Adj. High', 'Adj. Low']
# y_true = y_test
# y_pred = clf.predict(X_test)
# print f1_score(y_true, y_pred, average='macro')
if accuracy > best_accuracy:
best_clf = clf
best_accuracy = accuracy
best_algo = 'Linear'
else:
X = np.array(stocksDf.drop(['Decision'],1))
X = preprocessing.scale(X)
y = np.array(stocksDf['Decision']) # y is the 1% forcast
# y = y[:predict_index-2] # to keep consistent
# to convert into numbers
# y = le_decision.fit_transform(y)
X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2) # 20% training data, 80% testing
# clf = RandomForestClassifier(min_samples_leaf=2, n_estimators=100)
# best is {'max_depth': 8, 'min_child_weight': 2}
# clf = xgboost.XGBClassifier(
# learning_rate =0.1,
# n_estimators=1000,
# max_depth=8,
# min_child_weight=2,
# gamma=0,
# subsample=0.8,
# colsample_bytree=0.8,
# objective= 'binary:logistic',
# nthread=4,
# seed=27)
# print("Crunching...")
# clf.fit(X_train,y_train)
# clf.fit(X_train,y_train) # all data till now
# clf = joblib.load('LinearRegressionClf.pkl')
# print clf
# accuracy = clf.score(X_test,y_test) # test on data not used for training, is around 95%
# print(accuracy)
# print clf.predict(predict_value) # give array of last 10 days to get 1% into each values future
# print clf.predict() # predict into 1% future given todays ['Adj. Open','Adj. Close','S&P Open', 'Adj. Volume','Adj. High', 'Adj. Low']
# f1 score
# y_true = y_test
# y_pred = clf.predict(X_test)
# print f1_score(y_true, y_pred, average='binary')
# if accuracy > best_accuracy:
# best_clf = clf
# best_accuracy = accuracy
# best_algo = 'XG'
# file_name = 'XGClf_%s.pkl' %symbol
# joblib.dump(best_clf, file_name) # save the classifier to file
return best_clf
# print 'best accuracy:'
# print best_accuracy
# predict using neural network
# need to retrain till we get a higher accuracy
# I will store best Neural Net to disk when I get
# access to Amazon S3
def predictNN(self,stocksDf, useRegression, symbol):
stocksDf = stocksDf.dropna(how='any')
X = np.array(stocksDf.drop(['Decision'],1))
X = preprocessing.scale(X)
y = np.array(stocksDf['Decision'])
X_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.5) # 50% training data, 50% testing
# clf = MLPClassifier(solver='lbfgs', alpha=1e-5,
# hidden_layer_sizes=(5, 2), random_state=1)
# tune, train and store on S3
mlp = MLPClassifier(activation='relu', alpha=1e-05, batch_size='auto',
beta_1=0.9, beta_2=0.999, early_stopping=False,
epsilon=1e-08, hidden_layer_sizes=(5, 2), learning_rate='constant',
learning_rate_init=0.001, max_iter=150, momentum=0.9,
nesterovs_momentum=True, power_t=0.5, random_state=1, shuffle=True,
solver='lbfgs', tol=0.0001, validation_fraction=0.1, verbose=False,
warm_start=False)
mlp.fit(X_train, y_train)
print mlp.score(X_test, y_test)
# y_true = y_test
# y_pred = clf.predict(X_test)
# print f1_score(y_true, y_pred, average='macro')
return mlp
def nerualNetClassify(self, stocksDf, symbol, predict_index, clf):
stocksDf = stocksDf.dropna(how='any')
X = np.array(stocksDf)
X = preprocessing.scale(X)
predicted_df = pd.DataFrame()
predicted_df['to_predict'] = stocksDf['Adj. Close'].tail(predict_index)
predicted_df = predicted_df.reset_index(drop=True)
predict_values = X[len(X)-predict_index:] # future for last set dates
temp_df = pd.DataFrame(clf.predict(predict_values), columns=['Predicted'])
frames = [predicted_df, temp_df]
result = pd.concat(frames, axis=1)
return temp_df
# def binaryClassifySaved(self,stocksDf, symbol, predict_index):
# # stocksDf = stocksDf.drop(['Decision'], axis=1)
# # predict_index = 14
# stocksDf = stocksDf.dropna(how='any')
# X = np.array(stocksDf)
# # use same preprocessing scale used while training
# # predicted_df = pd.DataFrame(clf.predict(to_predict), columns=['Predicted_Winner'])
# # predicted_df.to_csv('predictions.csv', encoding='utf-8')
# # print len(stocksDf['Adj. Close'].tail(predict_index))
# predicted_df = pd.DataFrame()
# predicted_df['to_predict'] = stocksDf['Adj. Close'].tail(predict_index)
# predicted_df = predicted_df.reset_index(drop=True)
# # print stocksDf['Adj. Close'].tail(predict_index)
# X = preprocessing.scale(X)
# predict_values = X[len(X)-predict_index:] # future for last set dates
# # print("Loading Classifier...")
# file_name = 'XGClf_%s.pkl' %symbol
# clf = joblib.load(file_name)
# # graph prediction and show dates of prediction
# # print clf.predict(predict_values) # give array of last 10 days to get 1% into each values future
# # predicted_df['Predicted'] = pd.DataFrame(clf.predict(predict_values))
# temp_df = pd.DataFrame(clf.predict(predict_values), columns=['Predicted'])
# # plot(stocksDf['Adj. Close'], "AAPL", "Date", "Prices")
# frames = [predicted_df, temp_df]
# result = pd.concat(frames, axis=1)
# # le.classes_ = np.load('Label_Encoder.npy')
# print result
# cur_path = os.getcwd()
# file_name = '/data/%s_predicted_classification.csv' %symbol
# abs_path = cur_path+file_name
# # print temp_df
# temp_df.to_csv(abs_path, encoding='utf-8')
# result = pd.concat(frames, axis=1)
# # print result
# return abs_path
# returns file name of the csv with predicted values
def regressionSaved(self, stocksDf, symbol, predict_index, clf):
# predict_index = 14
stocksDf = stocksDf.dropna(how='any')
X = np.array(stocksDf)
# print len(stocksDf['Adj. Close'].tail(predict_index))
predicted_df = pd.DataFrame()
predicted_df['to_predict'] = stocksDf['Adj. Close'].tail(predict_index)
predicted_df = predicted_df.reset_index(drop=True)
# print stocksDf['Adj. Close'].tail(predict_index)
X = preprocessing.scale(X)
predict_values = X[len(X)-predict_index:] # future for last predict_index dates
# print("Loading Classifier...")
# file_name = 'LinearRegressionClf_%s.pkl' %symbol
# clf = joblib.load(file_name)
temp_df = pd.DataFrame(clf.predict(predict_values), columns=['Predicted'])
frames = [predicted_df, temp_df]
# cur_path = os.getcwd()
# file_name = '/data/%s_predicted_values.csv' %symbol
# abs_path = cur_path+file_name
# temp_df.to_csv(abs_path, encoding='utf-8')
result = pd.concat(frames, axis=1)
# print result
return temp_df
def dailyReturn(self,data):
# make chart
# did price go up or down on a particular day
daily_returns = data.copy()
daily_returns = (data/data.shift(1)) - 1
daily_returns.ix[0] = 0 # set daily return for row 0 to 0
# plot(daily_returns, "Stock Analysis" ,"Date", "Daily Returns")
# print daily_returns
return daily_returns
def plot(self,data_frame, title_label, x_label, y_label):
ax = data_frame.plot(title=title_label)
ax.set_xlabel(x_label)
ax.set_ylabel(y_label)
# pylab.show()
# download and clean symbol data
def download_data(self,symbol):
# use current date
till_date = time.strftime("%Y-%m-%d")
# print 'Till Date:'
# print till_date
to_download = 'WIKI/%s' %symbol
df = quandl.get(to_download, authtoken="zzYfW2Zd_3J3Gt2o3Nz6", start_date="2010-12-12", end_date=till_date)
sp500_df_all = quandl.get("YAHOO/INDEX_GSPC", authtoken="zzYfW2Zd_3J3Gt2o3Nz6", start_date="2010-12-12", end_date=till_date)
df = df[['Adj. Open','Adj. Close', 'Adj. Volume','Adj. High', 'Adj. Low']]
# print sp500_df_all
sp500_df = sp500_df_all[['Open', 'Adjusted Close']]
frames = [df, sp500_df]
df = pd.concat(frames, axis=1) # concatenate column-wise, remove Nan Data
df.columns = ['Adj. Open','Adj. Close','S&P Open','Adj. High', 'Adj. Low', 'Adj. Volume', 'S&P Adj. Close']
# add moving average
df = df.dropna(how='any')
# print df
# file_name = 'data/%s_training.csv' %symbol
# use amazon s3 to store files
# df.to_csv(file_name, encoding='utf-8')
return df
def stocksRegression(self, stockName, forecast_out):
# only download if new data avaliable
read_df = self.download_data(stockName)
# file_name_training = 'data/%s_training.csv' %stockName
# read_df = pd.read_csv(file_name_training, index_col = "Date")
read_df['Daily Returns'] = self.dailyReturn(read_df['Adj. Close'])
to_predict_df = read_df.copy(deep=True)
read_df['Future'] = read_df['Adj. Close'].shift(-forecast_out)
read_df = read_df.dropna(how='any')
clf = self.predictML(read_df, True, stockName)
prediction_df = self.regressionSaved(to_predict_df, stockName, forecast_out, clf)
current_date = datetime.date.today()
prediction_dates = []
for i in range(forecast_out):
# do not count weekend
current_date = current_date + datetime.timedelta(days=1)
weekno = current_date.weekday()
while weekno>4:
current_date = current_date + datetime.timedelta(days=1)
weekno = current_date.weekday()
prediction_dates.append(current_date)
prediction_df['Date'] = prediction_dates
prediction_df = prediction_df[['Date','Predicted']]
prediction_df = prediction_df.rename(columns = {'Predicted':'Adj. Close'})
prediction_df = prediction_df.reset_index()
prediction_df = prediction_df.set_index(['Date'])
prediction_df = prediction_df[['Adj. Close']]
# print prediction_df
to_predict_df = to_predict_df[['Adj. Close']]
to_predict_df = to_predict_df.append(prediction_df)
to_predict_df = to_predict_df.tail(forecast_out)
print to_predict_df
return np.array(to_predict_df)
def stocksNeuralNet(self, stockName, forecast_out):
# TODO:only download if new data avaliable-base on date
read_df = self.download_data(stockName)
read_df['Daily Returns'] = self.dailyReturn(read_df['Adj. Close'])
read_df['Future'] = read_df['Adj. Close'].shift(-forecast_out)
to_predict_df = read_df.copy(deep=True)
to_predict_df = to_predict_df.dropna(how='any')
decisions = []
pe_ratio = []
for index, row in read_df.iterrows():
# floating point comparison careful
# if 1 % increase in two weeks, then classify as a buy
# another method is to get historical buy-sell ratings
if (round(row['Future'],3) > round((1.01*row['Adj. Close']),3)):
decisions.append('Buy')
elif (round(row['Future'],3) < ((-1.00*row['Adj. Close']),3)):
decisions.append('Sell')
else:
decisions.append('Hold')
read_df_binary = read_df.copy(deep=True)
read_df_binary['Decision'] = decisions
clf = self.predictNN(read_df_binary, True, stockName)
prediction_df = self.nerualNetClassify(to_predict_df, stockName, forecast_out, clf)
current_date = datetime.date.today()
prediction_dates = []
for i in range(forecast_out):
# do not count weekend
current_date = current_date + datetime.timedelta(days=1)
weekno = current_date.weekday()
while weekno>4:
current_date = current_date + datetime.timedelta(days=1)
weekno = current_date.weekday()
prediction_dates.append(current_date)
prediction_df['Date'] = prediction_dates
prediction_df = prediction_df[['Date','Predicted']]
prediction_df = prediction_df.rename(columns = {'Predicted':'Adj. Close'})
prediction_df = prediction_df.reset_index()
prediction_df = prediction_df.set_index(['Date'])
prediction_df = prediction_df[['Adj. Close']]
# print prediction_df
to_predict_df = to_predict_df[['Adj. Close']]
to_predict_df = to_predict_df.append(prediction_df)
to_predict_df = to_predict_df.tail(forecast_out)
print to_predict_df
return np.array(to_predict_df)
# using yahoo finance api to get current stock price
def getCurrentPrice(self, stockName):
stock = Share(stockName)
return stock.get_price()
def getDividendPayDate(self, stockName):
stock = Share(stockName)
return stock.get_dividend_pay_date()
if __name__ == "__main__":
predict = predictStocks()
symbol = 'GOOGL'
# print predict.stocksRegression(symbol, 14)
# print predict.stocksClassify(symbol, 14)
# print predict.getCurrentPrice(symbol)
print predict.stocksNeuralNet(symbol, 14)