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Copy pathUS_StockMarket_ML_Feb21_R1.py
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US_StockMarket_ML_Feb21_R1.py
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# -*- coding: utf-8 -*-
"""
Created on Mon Feb 8 11:14:35 2021
@author: Q & A.I
"""
class ML_Reg:
def __int__(self):
pass
def ml_regration_auto():
print("\n")
#Importing the libraries
import numpy as np
import pandas as pd
import math
from datetime import datetime,date
import matplotlib.pyplot as plt
import yfinance as yf # for data of intex
#Vareus date formats req. in program
Starttime = datetime.now().strftime("%Y-%m-%d %H:%M:%S") #program data pull start time
pdate = datetime.now().strftime("%d-%m-%Y")
textdate = datetime.now().strftime("%d%m%Y")
yahoo_date = datetime.now().strftime("%Y-%m-%d")
Day = datetime.now().strftime("%A")
Daydate = datetime.now().strftime("%d")
## Import from yahoo finance python lab function
nasdaq = yf.download("NDX", start="2016-01-01", end=yahoo_date)
dowJones = yf.download("DJI", start="2016-01-01", end=yahoo_date)
# Updating / adjusting data
cust_nq_index = pd.Series(range(len(nasdaq)))
nqdataset = nasdaq.set_index(cust_nq_index) # Replacing date to simple index number
nqdataset['pdAvg'] = (nqdataset['Open']+nqdataset['High']+nqdataset['Low']+nqdataset['Close'])/4
nqdataset['YClose'] = nqdataset['Close'].shift(1)
nqdataset['pdAvg'] = nqdataset['pdAvg'].shift(2)
## slicing to input and out put
nqx_o_h = nqdataset.iloc[2:, [0,1,6,7]].values #values # Taking x as open and high & avrage of previous values & yclose value
nqx_o_l = nqdataset.iloc[2:, [0,2,6,7]].values #values # Taking x as open and Low & avrage of previous values & yclose value
nqy_l = nqdataset.iloc[2:, 2].values # Taking y as low value
nqy_h = nqdataset.iloc[2:, 1].values # Taking y as high value
## For Dow ## Updating / adjusting data
cust_dj_index = pd.Series(range(len(dowJones)))
djdataset = dowJones.set_index(cust_dj_index) # Replacing date to simple index number
djdataset['pdAvg'] = (djdataset['Open']+djdataset['High']+djdataset['Low']+djdataset['Close'])/4
djdataset['YClose'] = djdataset['Close'].shift(1)
djdataset['pdAvg'] = djdataset['pdAvg'].shift(2)
djx_o_h = djdataset.iloc[2:, [0,1,6,7]].values # Taking x as open,high & avrage of previous values & yclose value
djx_o_l = djdataset.iloc[2:, [0,2,6,7]].values # Taking x as open,Low & avrage of previous values & yclose value
djy_l = djdataset.iloc[2:, 2].values # Taking y as low value
djy_h = djdataset.iloc[2:, 1].values # Taking y as high value
## For test data use ##x_test1.reshape(3,1) if needed
## For Current values Importing the dataset from file NQ_DJ_ML.csv from "in.investing.com"
## Manual process need to automated
#COLUMN_NAMES = ['Name', 'Symbol', 'Last', 'Open', 'High', 'Low', 'Chg.', 'Chg. %','Vol.', 'Time']
ML_file = 'NQ_DJ_ML_Watchlist_'+textdate+'.csv'
cvdataset = pd.read_csv(ML_file) # CSV file
df_cvdataset1 = cvdataset.replace('\,','', regex=True)
df_cvdataset = df_cvdataset1.replace('\%','', regex=True)
df_cvdataset['Chg. %'].astype(float) # converting in to float
df_cvdataset['Chg.'].astype(float) # converting in to float
def nqcurrentvalue(CurrentLOC):
CurrentValue = (float(df_cvdataset.iloc[CurrentLOC][2])+float(df_cvdataset.iloc[CurrentLOC][3])+float(df_cvdataset.iloc[CurrentLOC][4])+float(df_cvdataset.iloc[CurrentLOC][5]))/4
return CurrentValue
#nasdaq values from Watchlist file
nqltpv = float(df_cvdataset.iloc[0][2])
nqov = float(df_cvdataset.iloc[0][3])
nqhv = float(df_cvdataset.iloc[0][4])
nqlv = float(df_cvdataset.iloc[0][5])
nqycv = float(nqdataset.iloc[-1][3])
nqpdAvg = (nqov+nqhv+nqlv+nqltpv)/4
#Dow
djltpv = float(df_cvdataset.iloc[1][2])
djov = float(df_cvdataset.iloc[1][3])
djhv = float(df_cvdataset.iloc[1][4])
djlv = float(df_cvdataset.iloc[1][5])
djycv = float(djdataset.iloc[-1][3])
djpdAvg = (djov+djhv+djlv+djltpv)/4
for mlploop in range(1):
if mlploop == 0: # taking ML values for predicting nasdaq 50 Resistance
x = nqx_o_l # input as low and open values
y = nqy_h # output as high values act as Resistance
##offline current data
x_test1 = np.array([[nqov,nqhv,nqycv,nqpdAvg]])
elif mlploop == 1: # taking ML values for predicting nasdaq 50 Support
x = nqx_o_h # input as high and open values
y = nqy_l # output as low values act as support
##offline current data
x_test1 = np.array([[nqov,nqlv,nqycv,nqpdAvg]])
elif mlploop == 2: # taking ML values for predicting Dow nasdaq Resistance
x = djx_o_l # input as low and open values
y = djy_h # output as high values act as Resistance
##offline current data
x_test1 = np.array([[djov,djhv,djycv,djpdAvg]])
elif mlploop == 3: # taking ML values for predicting Dow nasdaq Support
x = djx_o_h # input as high and open values
y = djy_l # output as low values act as support
##offline current data
x_test1 = np.array([[djov,djlv,djycv,djpdAvg]])
# # # Splitting the dataset into the Training set and Test set ** if need to test accuresy
# from sklearn.model_selection import train_test_split
# x, x_test, y, y_test = train_test_split(x, y, test_size = 0.20, random_state = 10)
## Simple Linear Regression
# Fitting Simple Linear Regression to the Training set
from sklearn.linear_model import LinearRegression
lr_regressor = LinearRegression()
lr_regressor.fit(x, y)
# Predicting the Test set results
y_pred_lr = lr_regressor.predict(x_test1)
#y_pred_lr = lr_regressor.predict(x_test)
# # Use score method to get accuracy of model
# score = lr_regressor.score(x_test, y_pred_lr)
# print(score)
##Polynomial Regression
# Fitting Polynomial Regression to the dataset
from sklearn.preprocessing import PolynomialFeatures
poly_reg = PolynomialFeatures(degree = 4)
x_poly = poly_reg.fit_transform(x)
poly_reg.fit(x_poly, y)
# New Fitting Linear Regression model to fit Polynomial Regression object
pr_regressor = LinearRegression()
pr_regressor.fit(x_poly, y)
# Predicting the Test set results
#y_pred_ploy = pr_regressor.predict(poly_reg.fit_transform(x_test))
y_pred_ploy = pr_regressor.predict(poly_reg.fit_transform(x_test1))
## Decision Tree Regression
# Fitting Decision Tree Regression to the dataset
from sklearn.tree import DecisionTreeRegressor
dtr_regressor = DecisionTreeRegressor(random_state = 0)
dtr_regressor.fit(x, y)
# Predicting a new result
y_pred_dtr = dtr_regressor.predict(x_test1)
#y_pred_dtr = dtr_regressor.predict(x_test)
## RandomFores Regressor Regression
# Fitting RandomFores Regressor Regression to the dataset
from sklearn.ensemble import RandomForestRegressor
rf_regressor = RandomForestRegressor(n_estimators= 300,random_state = 0)
rf_regressor.fit(x, y)
# Predicting a new result
y_pred_rf = rf_regressor.predict(x_test1)
#y_pred_rf = rf_regressor.predict(x_test)
## Total prediction:
y_predn = (y_pred_lr+y_pred_ploy+y_pred_dtr+y_pred_rf)/4
y_pred = format(y_predn[0],'.2f') #Converting array to string
#Print function
if mlploop == 0:
print('nasdaq , Resistance , {} '.format(y_pred))
nq_y_pred_r = y_pred
elif mlploop == 1:
print('nasdaq , Support , {} '.format(y_pred))
print("\n")
nq_y_pred_s = y_pred
elif mlploop == 2:
print('Dow , Resistance , {} '.format(y_pred))
dj_y_pred_r = y_pred
elif mlploop == 3:
print('Dow , Support , {} '.format(y_pred))
print("\n")
dj_y_pred_s = y_pred
# Calculate the Pchange with respect to yesterday close price
nq_y_pred_r = float(nq_y_pred_r) # Conerting to float
nq_y_pred_s = float(nq_y_pred_s)
dj_y_pred_r = float(dj_y_pred_r)
dj_y_pred_s = float(dj_y_pred_s)
nqPChange_R_ML1 = ((nq_y_pred_r-nqycv)/nqycv)*100
nqPChange_S_ML1 = ((nq_y_pred_s-nqycv)/nqycv)*100
djPChange_R_ML1 = ((dj_y_pred_r-djycv)/djycv)*100
djPChange_S_ML1 = ((dj_y_pred_s-djycv)/djycv)*100
# Converting to print format
nqPChange_R_ML = format(nqPChange_R_ML1,'.2f')
nqPChange_S_ML = format(nqPChange_S_ML1,'.2f')
djPChange_R_ML = format(djPChange_R_ML1,'.2f')
djPChange_S_ML = format(djPChange_S_ML1,'.2f')
#Append the Range in CSV file and write it to data frame
print(('NASDAQ_ML,{},{},{},{},{},{}'.format(pdate,nq_y_pred_s,nqov,nq_y_pred_r,nqPChange_S_ML,nqPChange_R_ML)),file=open("nq_Support_Resistance_data.csv", "a"))
print(('Dow_ML,{},{},{},{},{},{}'.format(pdate,dj_y_pred_s,djov,dj_y_pred_r,djPChange_S_ML,djPChange_R_ML)),file=open("dj_Support_Resistance_data.csv", "a"))
if nqPChange_R_ML1>1.5 or djPChange_R_ML1>1.5 or nqPChange_S_ML1< -1.5 or djPChange_S_ML1<-1.5 :
print("Best chance for Trading")
# Taking close value of previous day
nq_y_close = nqdataset.iloc[-2][3]
dj_y_close = djdataset.iloc[-2][3]
#Open High Low export for classification bot
print(('NASDAQ_ML,{},{},{},{},{}'.format(pdate,nqov,nq_y_pred_r,nq_y_pred_s,nq_y_close)),file=open("nq_Classification_input_data.csv", "a"))
print(('Dow_ML,{},{},{},{},{}'.format(pdate,djov,dj_y_pred_r,dj_y_pred_s,dj_y_close)),file=open("dj_Classification_input_data.csv", "a"))
#Function call
ml_reg_auto = ml_regration_auto()