Add file

.gitattributes

@@ -1,33 +0,0 @@
-*.7z filter=lfs diff=lfs merge=lfs -text
-*.arrow filter=lfs diff=lfs merge=lfs -text
-*.bin filter=lfs diff=lfs merge=lfs -text
-*.bz2 filter=lfs diff=lfs merge=lfs -text
-*.ftz filter=lfs diff=lfs merge=lfs -text
-*.gz filter=lfs diff=lfs merge=lfs -text
-*.h5 filter=lfs diff=lfs merge=lfs -text
-*.joblib filter=lfs diff=lfs merge=lfs -text
-*.lfs.* filter=lfs diff=lfs merge=lfs -text
-*.mlmodel filter=lfs diff=lfs merge=lfs -text
-*.model filter=lfs diff=lfs merge=lfs -text
-*.msgpack filter=lfs diff=lfs merge=lfs -text
-*.npy filter=lfs diff=lfs merge=lfs -text
-*.npz filter=lfs diff=lfs merge=lfs -text
-*.onnx filter=lfs diff=lfs merge=lfs -text
-*.ot filter=lfs diff=lfs merge=lfs -text
-*.parquet filter=lfs diff=lfs merge=lfs -text
-*.pb filter=lfs diff=lfs merge=lfs -text
-*.pickle filter=lfs diff=lfs merge=lfs -text
-*.pkl filter=lfs diff=lfs merge=lfs -text
-*.pt filter=lfs diff=lfs merge=lfs -text
-*.pth filter=lfs diff=lfs merge=lfs -text
-*.rar filter=lfs diff=lfs merge=lfs -text
-*.safetensors filter=lfs diff=lfs merge=lfs -text
-saved_model/**/* filter=lfs diff=lfs merge=lfs -text
-*.tar.* filter=lfs diff=lfs merge=lfs -text
-*.tflite filter=lfs diff=lfs merge=lfs -text
-*.tgz filter=lfs diff=lfs merge=lfs -text
-*.wasm filter=lfs diff=lfs merge=lfs -text
-*.xz filter=lfs diff=lfs merge=lfs -text
-*.zip filter=lfs diff=lfs merge=lfs -text
-*.zst filter=lfs diff=lfs merge=lfs -text
-*tfevents* filter=lfs diff=lfs merge=lfs -text

Procfile

@@ -0,0 +1 @@
+web: sh setup.sh && streamlit run apps.py

apps.py

@@ -0,0 +1,83 @@
+import warnings
+import numpy as np
+import pandas as pd
+import datetime as dt
+import streamlit as st
+import tensorflow as tf
+import pandas_datareader as pdr
+from tensorflow.keras import load_model
+from sklearn.preprocessing import MinMaxScaler
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Dense, Dropout, LSTM
+
+# warnings.filterwarnings('ignore')
+# plt.style.use('fivethirtyeight')
+
+st.set_page_config(
+        page_title="FANG Stock Prediction",
+        page_icon=":bitcoin:",
+        layout="wide"
+    )
+
+st.write("# Welcome to FANG Stock Prediction Dashboard :bitcoin:")
+
+company_ticker = ['FB', 'AAPL', 'TSLA', 'GOOG', 'NVDA']
+
+start_date = dt.datetime(2007, 1, 1)
+end_date = dt.datetime(2020, 12, 13)
+data_FB = pdr.DataReader(company_ticker[0], 'yahoo', start_date, end_date)
+# data_AAPL = pdr.DataReader(company_ticker[1], 'yahoo', start_date, end_date)
+# data_TSLA = pdr.DataReader(company_ticker[2], 'yahoo', start_date, end_date)
+data_GOOG = pdr.DataReader(company_ticker[3], 'yahoo', start_date, end_date)
+# data_NVDA = pdr.DataReader(company_ticker[4], 'yahoo', start_date, end_date)
+
+scaler = MinMaxScaler()
+scaled_data_FB = scaler.fit_transform(data_FB.filter(['Adj Close']).values.reshape(-1, 1))
+scaled_data_GOOG = scaler.fit_transform(data_GOOG.filter(['Adj Close']).values.reshape(-1, 1))
+prediction_days = 89
+
+test_start = dt.datetime(2020, 12, 31)
+test_end = dt.datetime.now()
+test_data_FB = pdr.DataReader(company_ticker[0], 'yahoo', test_start, test_end)
+# test_data_AAPL = pdr.DataReader(company_ticker[1], 'yahoo', test_start, test_end)
+# test_data_TSLA = pdr.DataReader(company_ticker[2], 'yahoo', test_start, test_end)
+test_data_GOOG = pdr.DataReader(company_ticker[3], 'yahoo', test_start, test_end)
+# test_data_NVDA = pdr.DataReader(company_ticker[4], 'yahoo', test_start, test_end)
+actual_prices = test_data_GOOG.filter(['Adj Close']).values
+
+total_dataset = pd.concat((data_GOOG['Adj Close'], test_data_GOOG['Adj Close']), axis=0)
+
+model_inputs = total_dataset[len(total_dataset) - len(test_data_GOOG) - prediction_days:].values
+model_inputs = model_inputs.reshape(-1, 1)
+model_inputs = scaler.fit_transform(model_inputs)
+
+def test_data_predict(lstm_model):
+  X_test = []
+
+  for x in range(prediction_days, len(model_inputs)):
+    X_test.append(model_inputs[x-prediction_days:x, 0])
+  
+  X_test = np.array(X_test)
+  X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))
+
+  prediction_prices = lstm_model.predict(X_test)
+  prediction_prices = scaler.inverse_transform(prediction_prices)
+
+  return prediction_prices
+
+stock_lstm = load_model("https://github.com/bills1912/stock-predicition/blob/main/fang_stock_prediction.h5")
+
+predict_prices = test_data_predict(lstm_model=stock_lstm)
+valid = test_data_GOOG.filter(['Adj Close'])
+valid['Predictions'] = predict_prices
+
+st.line_chart(data_GOOG.filter['Adj Close'])
+st.line_chart(valid)
+
+real_data = [model_inputs[len(model_inputs)+1 - prediction_days:len(model_inputs)+1, 0]]
+real_data = np.array(real_data)
+real_data = np.reshape(real_data, (real_data.shape[0], real_data.shape[1], 1))
+
+real_prediction = stock_lstm.predict(real_data)
+real_prediction = scaler.inverse_transform(real_prediction)
+st.text(f"Real prediction stock prices on Google is {real_prediction[0]}")

requirements.txt

@@ -0,0 +1,6 @@
+numpy>=1.17.3
+pandas==1.3.5
+streamlit==1.10.0
+tensorflow==2.6.0
+pandas-datareader==0.10.0
+sklearn==0.0

runtime.txt

@@ -0,0 +1 @@
+python-3.9.14