ml_for_stock_trading.py

# # -*- coding: utf-8 -*-
# """ML for Stock Trading.ipynb
#
# Automatically generated by Colaboratory.
#
# Original file is located at
#     https://colab.research.google.com/drive/15Ergxt4zGHY3Z9QWzXOtkZASfRD13btk
#
# <h2> Machine Learning for Stocks Trading </h2>
#
# Step 1: Getting some historical data.
#
# Libraries or Modules:
#
# Yahoo Finance module.
# Create Charts
# Handle dataframes - Pandas
# We need datetime functions
# """
#
# import pandas as pd
# import matplotlib.pyplot as plt
# from datetime import date, time
#
#
# #Install Yahoo Finance Libraries
# try:
#   import yfinance
# except:
#   !pip install -q yfinance
#   import yfinance
# try:
#   import yahoofinancials
# except:
#   !pip install -q yahoofinancials
#   import yahoofinancials
#
# import yfinance as yf
# from yahoofinancials import YahooFinancials
#
# import warnings
# warnings.filterwarnings("ignore")
#
# #Download our Historical Data
# df = yf.download('AAPL',
#                  start = '2010-01-01',
#                  end =date.today(),
#                  progress=False,)
#
# # Create a simple chart
# df.plot(y='Close', title='APPLE STOCK')
# plt.show()
#
# """Creating Technical Analysis Indicators and we are going to also plot them"""
#
# try:
#   import pandas_ta as ta #create basic technical analysis
# except:
#   !pip install -q pandas_ta
#   import pandas_ta as ta
#
# df['RSI(2)'] = ta.rsi(df['Close'],length = 2)
# df['RSI(7)'] = ta.rsi(df['Close'],length = 7 )
# df['RSI(14)'] = ta.rsi(df['Close'],length = 14)
# df['CCI(30)'] = ta.cci(close= df['Close'],lenght = 30,high=df['High'],low=df['Low'])
# df['CCI(50)'] = ta.cci(close= df['Close'],lenght = 30,high=df['High'],low=df['Low'])
# df['CCI(100)'] = ta.cci(close= df['Close'],lenght = 30,high=df['High'],low=df['Low'])
#
# #Drop NaN Values
# df = df.dropna()
#
# #Create a plot showing some of our indicators
# df.plot(y='RSI(2)')
# df.plot(y='CCI(100)')
#
# #How's our current data frame going
# df.head()
#
# """Prepare our data labelling
#
# when we place our trade: Buy at Open (over the next day)
#
# Formula:
#
# Handling the returns this ways: Buy: Open(-1) - Closing: Open(-2)
#
# We want to label our data in the following day
#
# BUY SIGNAL: Open(-2) > Open (-1)
#
# SEEL SIGNAL: Open(-2) < Open(-1)
#
# Indicators the current situations, and our labels take a look ahead of time
#
#
# """
#
# import numpy as np
#
# df['LABEL'] = np.where(df['Open'].shift(-2).gt(df['Open'].shift(-1)),"1","0")
# df = df.dropna()
# df.head()
#
# """Creating a Neural Network
#
# SkLearn
# """
#
# import sklearn
# from sklearn.neural_network import MLPClassifier
# from sklearn.neural_network import MLPRegressor
# from sklearn.model_selection import train_test_split
# from sklearn.metrics import mean_squared_error
# from math import sqrt
# from sklearn.metrics import r2_score
#
#
# #Fitting - Indicators and our Labelling
#
# X = df[df.columns[6:-1]]
# y = df['LABEL'].values
#
# X_train , X_test, y_train, y_test = train_test_split(X,y,test_size = 0.3)
#
# mlp = MLPClassifier(hidden_layer_sizes=(8,8,8),activation='relu',solver='adam',max_iter = 1000)
# mlp.fit(X_train,y_train)
#
# predict_train = mlp.predict(X_train)
# predict_test = mlp.predict(X_test,)
#
# from sklearn.metrics import classification_report,confusion_matrix
#
# print('Train Data Accuracy')
# print(classification_report(y_train,predict_train))
#
# print( ' Testing Data Accuracy')
# print(classification_report(y_test,predict_test))
#
# """Measure the returns obtained by trading our model"""
#
# df['Prediction'] = np.append(predict_train,predict_test)
# df['Strategy Returns'] = np.where( df['Prediction'].eq("1"),df['Open'].shift(-2)-df['Open'].shift(-1),0)
#
# df['Strategy Returns'] = df['Strategy Returns'].cumsum()
#
# df.plot(y='Strategy Returns')
# plt.plot()
#
# prediction = df.iloc[-1]['Prediction']
#
# if prediction == "1":
#   print("Today's return forecast: UP")
# else:
#   print("Today's retrun forecas: DOWN")
#