From f367668a4ea765ac951904d9a6c458df369c9a1d Mon Sep 17 00:00:00 2001
From: Gabs <76870170+privacyrespected@users.noreply.github.com>
Date: Tue, 21 Feb 2023 13:34:06 +0000
Subject: [PATCH] Update main.py

---
 main.py | 79 ++++++++++++++++++++++++++++++++++++++++-----------------
 1 file changed, 56 insertions(+), 23 deletions(-)

diff --git a/main.py b/main.py
index 3f5ba7e..e375f99 100644
--- a/main.py
+++ b/main.py
@@ -1,28 +1,61 @@
-import sys
-import time
-import pyfiglet
-def slowprint(s):
-	for c in s + '\n':
-		sys.stdout.write(c)
-		sys.stdout.flush()
-		time.sleep(1./1000)
-def ascii(input):
-    result= pyfiglet.figlet_format(input)
-    return result
-### formatting functions
-results = ascii("Omega")
-print(results)
-slowprint("Version 1.0 Alpha")
-##########################################################
 import pandas as pd
+from sklearn.linear_model import LinearRegression
+from sklearn.model_selection import train_test_split
 import yfinance as yf
-import matplotlib.pyplot as plt
-import ta
-import pandas_ta as pta
-from finta import TA
+from datetime import datetime, timedelta
+# Set the ticker and the start and end dates
+ticker = 'TSLA'
+start_date = '2010-02-09'
+end_date = '2023-02-17'
 
+# Use the Ticker object to download the data
+ticker_data = yf.Ticker(ticker)
+df = ticker_data.history(start=start_date, end=end_date)
 
-df = yf.download("AAPL", start="2020-01-01", end="2020-12-31")
+# Save the DataFrame to a CSV file
+df.to_csv('stock_data.csv', index=True)
+
+# Load the data into a Pandas DataFrame
+df = pd.read_csv('stock_data.csv')
+
+# Create a new DataFrame with only the closing prices
+prices = df[['Close']]
+
+# Shift the prices so that the first row corresponds to today's price and the second row corresponds to yesterday's price
+prices_shifted = prices.shift(1)
+
+# Add a column with tomorrow's closing price
+prices_shifted['Tomorrow'] = prices['Close']
+
+# Drop the first row, which has a null value for yesterday's price
+prices_shifted = prices_shifted.dropna()
+
+# Separate the target variable (tomorrow's closing price) from the input features (yesterday's closing price)
+X = prices_shifted[['Close']]
+y = prices_shifted['Tomorrow']
+
+# Split the data into training and test sets
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Create a Linear Regression model
+model = LinearRegression()
+
+# Train the model on the training data
+model.fit(X_train, y_train)
+
+# Make predictions on the test data
+y_pred = model.predict(X_test)
+
+# Calculate the test score
+test_score = model.score(X_test, y_test)
+print(f'Test score: {test_score:.2f}')
+n=0
+# Use the model to predict tomorrow's closing price based on today's closing price
+today_close = df['Close'].iloc[-1]
+tomorrow_close_prediction=today_close
+for i in range(1,31):
+    tomorrow_close_prediction = model.predict([[tomorrow_close_prediction]])[n]
+    print(tomorrow_close_prediction)
+
+print('#'*80)
 
-df.plot()
-plt.show()