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prediction.py
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prediction.py
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import quandl, math, datetime
import numpy as np
from sklearn import preprocessing
from sklearn.linear_model import LinearRegression
from matplotlib import style
import time
from sklearn import svm, model_selection
import os
import threading
style.use ('ggplot')
def save_total_data(df):
df.rename(index=str, columns={"Adj. Open": "Open", "Adj. High": "High", "Adj. Low": "Low", "Adj. Close": "Close",
"Adj. Volume": "Volume"})
df.to_csv('/home/ajay/PycharmProjects/SDL-PROJ/static/' + 'data_full.csv', sep=',', encoding='utf-8')
def calculate(stock, start_date, end_date):
print(stock + ' from calculate')
#df = quandl.get("GOOG/NASDAQ_GOOGL", authtoken="sD9npaAKgpHsQdwVfZ5p")
stock = stock.upper()
df = quandl.get("WIKI/" + stock, trim_start=start_date, trim_end=end_date, authtoken="sD9npaAKgpHsQdwVfZ5p")
df = df[['Adj. Open', 'Adj. High', 'Adj. Low', 'Adj. Close', 'Adj. Volume', ]]
pf = df[['Adj. Open', 'Adj. High', 'Adj. Low', 'Adj. Close', 'Adj. Volume', ]]
pf.rename(index=str, columns={"Adj. Open": "Open", "Adj. High": "High", "Adj. Low": "Low", "Adj. Close": "Close",
"Adj. Volume": "Volume"})
pf.to_csv('/home/ajay/PycharmProjects/SDL-PROJ/static/' + 'data_full.csv', sep=',', encoding='utf-8')
df['HL_PCT'] = (df['Adj. High'] - df['Adj. Close']) / df['Adj. Close'] * 100.0
df['PCT_change'] = (df['Adj. Close'] - df['Adj. Open']) / df['Adj. Open'] * 100.0
# define a new data frame
df = df[['Adj. Close', 'HL_PCT', 'PCT_change', 'Adj. Volume']]
forecast_col = 'Adj. Close'
df.fillna(-99999, inplace=True)
forecast_out = int(math.ceil(0.01 * len(df)))
df['label'] = df[forecast_col].shift(-forecast_out)
X = np.array(df.drop(['label'], 1))
X = preprocessing.scale(X)
X_lately = X[-forecast_out:]
X = X[:-forecast_out:]
df.dropna(inplace=True)
y = np.array(df['label'])
X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.2)
#clf = svm.SVR()
clf = LinearRegression()
clf.fit(X_train, y_train)
accuracy = clf.score(X_test, y_test)
forecast_set = clf.predict(X_lately)
#print(forecast_set, accuracy, forecast_out)
df['Forecast'] = np.nan
last_date = df.iloc[-1].name
last_unix = time.mktime(last_date.timetuple())
one_day = 86400
next_unix = last_unix + one_day
for i in forecast_set:
next_date = datetime.datetime.fromtimestamp(next_unix)
next_unix += one_day
df.loc[next_date] = [np.nan for _ in range(len(df.columns) - 1)] + [i]
print(forecast_set)
fs = df[['Adj. Close', 'Forecast']]
fs.to_csv('/home/ajay/PycharmProjects/SDL-PROJ/static/forecast.csv', sep=',', encoding='utf-8')
#cwd = os.getcwd()
df['Adj. Close'].to_csv('/home/ajay/PycharmProjects/SDL-PROJ/static/temp.csv', sep=',', encoding='utf-8',
header={'date,close'})
try:
f = open("/home/ajay/PycharmProjects/SDL-PROJ/static/temp.csv", "r+")
fi = open( "/home/ajay/PycharmProjects/SDL-PROJ/static/data.csv", 'w+')
lines = f.readlines()
lines = lines[:-1]
for line in lines:
fi.write(line)
except FileNotFoundError:
print("file not found")
return (forecast_set, accuracy, forecast_out)