Updated UI

main.py

@@ -1,43 +1,257 @@
 """
 Different teams for the project (see discord for the rest of the information)
-
 1. GUI (tkinter)
 2. Graphs, stats and data-analysis (statitstics and matplotlib for graphs)
 3. AI and predictive analysis (tensorflow and scikit)
-
 """
-
-
 import numpy as np
 import pandas as pd
 import tkinter as tk
 from tkinter import messagebox
-from datetime import datetime
+from matplotlib import pyplot as pp
+from datetime import datetime, date, timedelta
 import statistics
+from dateutil.relativedelta import relativedelta
+from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
+import tensorflow as tf
+from tensorflow import keras
+from sklearn import preprocessing
+from sklearn.preprocessing import MinMaxScaler
+from tensorflow.keras import layers
+from tensorflow.keras import callbacks
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LinearRegression
 
 
+# Windows settings
 root = tk.Tk()
-root.iconbitmap(r'icon.ico')
-root.geometry("500x500")
+#root.iconbitmap(r'icon.ico')
+root.geometry("600x500")
 root.resizable(True, True)
-root.title("Stock tracker")
-root.configure(bg = "#a2a2a2")
+root.title("Stock Tracker Portal")
+root.configure(bg = "#282C2F")
 
-data = ""
-def acceptFile():
-    fileName = fileInput.get()
+class point:
+    def __init__(self, colData):
+        dates, self.open, self.high, self.low, self.close, self.adjClose, self.volume = colData
+        year, month, day = dates.split('-')
+        self.date = date(int(year), int(month), int(day))
+
+# Reading the data files (format Datasets\File.csv)
+def acceptFile(fileName):
     try:
         data = pd.read_csv(fileName)
-    except Exception as e:
+        return data
+    except Exception:
         messagebox.showerror(title = "Invalid file", message = "Please ensure file is in current working directory and the correct name was entered!")
-        print(e)
 
-fileInput = tk.Entry(root, width = 55, bd = 2, justify = "left", font = "TkDefault 10")
-fileInput.grid(row = 0, column = 0)
 
-fileAccept = tk.Button(root, text = "Submit", font = "TkDefault 10", command = acceptFile)
-fileAccept.grid(row = 0, column = 1)
+def subtractDay(dateObj, num):
+    return dateObj - timedelta(num)
+
+def subtractMonth(dateObj, num):
+    return dateObj - relativedelta(months=num)
+
+def subtractYear(dateObj, num):
+    return dateObj - relativedelta(years=num)
+
+
+def searchTicker():
+    ticker = fileInput.get()
+    data = acceptFile('SPY_max.csv').transpose() # change to ticker at the end. Changed to  for testing
+    dateObj = []
+    for colNum, colData in data.iteritems():
+        obj = point(colData)
+        dateObj.append(obj)
+
+    dataDates = date.today()
+
+    dateSelection = optionList.get()
+    if dateSelection[1] == 'D':
+        dataDates = subtractDay(dataDates, int(dateSelection[0]))
+    elif dateSelection[1] == 'W':
+        dataDates = subtractDay(dataDates, int(dateSelection[0])*7)
+    elif dateSelection[1] == 'M':
+        dataDates = subtractMonth(dataDates, int(dateSelection[0]))
+    elif dateSelection[1] == 'Y':
+        dataDates = subtractYear(dataDates, int(dateSelection[0]))
+    else:
+        dataDates = date(1, 1, 1)
+
+    filteredData = set()
+    for obj in dateObj:
+        if obj.date >= dataDates:
+            filteredData.add(obj)
+
+
+# Frames for organization
+instructionFrame = tk.Frame(master = root)
+topFrame = tk.Frame(master = root)
+bodyFrame = tk.Frame(master = root)
+
+# User instructions
+instructions = tk.Label(
+    master = instructionFrame,
+    text = "1. Select timeframe of prices.\n2. Click Submit.\n3. Produce results!",
+    font = 'TkDefault 16',
+    foreground = 'white',
+    background = '#303030'
+    )
+instructions.grid(row=0, column = 0)
+instructionFrame.pack()
+
+# Text box for stock ticker
+fileInput = tk.Entry(master = topFrame, width = 55, bd = 2, justify = "left", font = "TkDefault 10")
+fileInput.grid(row = 1, column = 0)
+
+# Drop down box to add time frame
+timeOptions = ["1D", "1W", "1M", "3M", "6M", "1Y", "5Y", "max"]
+optionList = tk.StringVar(topFrame)
+optionList.set(timeOptions[0])
+timePeriod = tk.OptionMenu(topFrame, optionList, *timeOptions)
+timePeriod.config(width = 3, font = "TKDefault 10")
+timePeriod.grid(row = 1, column = 1)
+
+# Button to commence search for data
+fileAccept = tk.Button(master = topFrame, text = "Submit", font = "TkDefault 10", command = searchTicker)
+fileAccept.grid(row = 1, column = 2)
+topFrame.pack()
+
+# Immediate Predictions
+result1 = tk.Label(
+    master = bodyFrame,
+    text = "Price is going: [UP/DOWN]\n",
+    font = 'TkDefault 16',
+    foreground = 'white',
+    background = '#303030'
+    )
+result1.grid(row = 2, column = 0)
+result2 = tk.Label(
+    master = bodyFrame,
+    text = "Predicted Price: ",
+    font = 'TkDefault 12',
+    foreground = 'white',
+    background = '#303030'
+    )
+result2.grid(row = 3, column = 0)
+result3 = tk.Label(
+    master = bodyFrame,
+    text = "Percent change from yesterday: ",
+    font = 'TkDefault 12',
+    foreground = 'white',
+    background = '#303030'
+    )
+result3.grid(row = 4, column = 0)
+bodyFrame.pack()
+
+#random data to add to the GUI
+data1 = {'Year': [1993,1994,1995,1996,1997,1998,1999,2000],
+         'Opening_Price': [41,42,43,44,45,46,47,48.]
+        }  
+df1 = pd.DataFrame(data1,columns=['Year','Opening_Price'])
+
+#adds the graph to the GUI (under the topFrame and bodyFrame)
+figure1 = pp.Figure(figsize=(4,4), dpi=100)
+ax1 = figure1.add_subplot(111) # 111 represents how much of the whitespace that the graph fills
+line1 = FigureCanvasTkAgg(figure1, root)
+line1.get_tk_widget().pack(fill=tk.BOTH)
+df1 = df1[['Year','Opening_Price']].groupby('Year').sum()
+df1.plot(kind='line', legend=True, ax=ax1, color='r',marker='o', fontsize=10)
+ax1.set_title('Year Vs. Opening_Price')
+
 
 root.mainloop()
 
 
+
+
+
+
+
+def some_prep(data):
+    new_data = data[["Date", "Adj Close"]].shift(-1)
+    new_data[['Open', 'High', 'Low', 'Close', 'Volume']] =  data[
+        ['Open', 'High', 'Low', 'Close', 'Volume']]
+    to_predict = new_data.iloc[-1:]
+    return new_data, to_predict
+
+
+def get_data(data):
+    val_size = data.shape[0]
+    val_size = 2112
+    train_size = 4931
+    data = data.drop("Date", axis = 1)
+    cols = data.columns
+    scaler = MinMaxScaler(feature_range=(0, 1)) 
+    data = pd.DataFrame(scaler.fit_transform(data), columns = cols)
+    data_x = (data.copy()).drop("Adj Close", axis = 1)
+    data_y = data["Adj Close"]
+    X_train = data_x.iloc[:train_size]
+    X_val = data_x.iloc[val_size:]
+    Y_train =  data_y.iloc[:train_size]
+    Y_val = data_y.iloc[val_size:]
+    return X_train, X_val, Y_train, Y_val
+
+def model_deep(data):
+    # normalize the dataset 
+    X_train, X_valid, y_train, y_valid = get_data(data)
+    input_shape = [data.shape[1] - 1]
+    model = keras.Sequential([
+    layers.BatchNormalization(input_shape=input_shape),
+    layers.Dense(512, activation='relu'),
+    layers.BatchNormalization(),
+    layers.Dense(512, activation='relu'),
+    layers.BatchNormalization(),
+    layers.Dense(512, activation='relu'),
+    layers.BatchNormalization(),
+    layers.Dense(1),
+    ])
+    model.compile(
+    optimizer='sgd',
+    loss='mae',
+    metrics=['mae'],
+    )
+    EPOCHS = 100
+    history = model.fit(
+        X_train, y_train,
+        validation_data=(X_valid, y_valid),
+        batch_size=64,
+        epochs=EPOCHS,
+        verbose=0,
+        )
+
+    history_df = pd.DataFrame(history.history)
+    return history_df
+
+
+def linear_reg(data):
+    input_shape = [data.shape[1] - 1]
+    data_x = data["Date"]
+    data_y = data["Adj Close"]
+    model = keras.Sequential([layers.Dense(units=1, input_shape = [6])])
+    model(data_x)
+    weights = model.get_weights()
+    return weights
+
+#df['Date'] = pd.to_datetime(df.Date,format='%Y-%m-%d')
+
+
+
+
+
+
+
+"""
+# Temporary place for graphing utility
+# scope is a list of Day
+def graphData(scope):
+    x_values = []
+    y_values = []
+    for i in scope:
+        x_values.append(i.date)     # open price
+        y_values.append(i.open)
+        x_values.append(i.date)     # close price
+        y_values.append(i.close)
+    pp.plot(x_values, y_values)
+    pp.show()
+"""