implement

README.md

@@ -1,13 +1,10 @@
 ---
 title: StockOracle
-emoji: 🐨
-colorFrom: yellow
-colorTo: pink
+emoji: 💻
+colorFrom: green
+colorTo: blue
 sdk: streamlit
 sdk_version: 1.26.0
 app_file: app.py
 pinned: false
-license: gpl-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---

app.py

@@ -0,0 +1,191 @@
+import pandas as pd
+import pandas_datareader as data
+import numpy as np
+import plotly.graph_objects as go
+import streamlit as st
+import yfinance as yf
+import datetime as dt
+
+from pandas_datareader import data as pdr
+
+from keras.models import load_model
+from sklearn.preprocessing import MinMaxScaler
+from tensorflow.python import tf2
+
+from datetime import timedelta
+
+default_ticker = 'NVDA'
+
+yf.pdr_override()
+
+st.set_page_config(
+    page_title="Future Stock Price Prediction", 
+    initial_sidebar_state="auto",
+    page_icon=":computer:",
+    layout="wide", 
+)
+
+today = dt.date.today() 
+
+def create_dataset(df, days):
+    x = []
+    y = []
+    for i in range(days, df.shape[0]):
+        x.append(df[i-days:i, 0])
+        y.append(df[i, 0])
+    x = np.array(x)
+    y = np.array(y)
+    return x,y
+
+def predict(model_file, x_data, y_data):
+    model = load_model(model_file)
+    predictions = model.predict(x_data)
+    predictions = scaler.inverse_transform(predictions)
+    y_data_scaled = scaler.inverse_transform(y_data.reshape(-1, 1))
+
+    df_y_data_scaled = pd.DataFrame(y_data_scaled, columns = ['Close'])
+    df_predictions = pd.DataFrame(predictions, columns = ['Close'])
+
+    return df_y_data_scaled, df_predictions
+
+
+def prediction_chart(model_file, x_data, original_y_data, predicted_y_data):
+    chart = go.Figure()
+    chart.add_trace(go.Scatter(x = x_data, y = original_y_data.Close, name='Price', 
+                             mode='lines', marker_color='black'))
+    
+    chart.add_trace(go.Scatter(x = x_data, y = predicted_y_data.Close, name='Prediction', 
+                             mode='lines', marker_color='red'))
+    
+    chart.update_layout(title='Stock Price vs Predicted Price with loaded model: ' + model_file,
+                        xaxis_title='Date', 
+                        yaxis_title='Price')    
+    
+    chart.show()
+    st.plotly_chart(chart, use_container_width=True)
+
+def show_prediction(model_file, x_data, dataset_test, days):
+    #Creating dataset
+    x_test, y_test = create_dataset(dataset_test, days)
+    x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))
+
+    #Load model and predict
+    original_y_data, predicted_y_data = predict(model_file, x_test, y_test)
+    prediction_chart(model_file, x_data[days:], original_y_data, predicted_y_data)
+
+with st.sidebar:
+    user_input = st.text_input("Ticker", default_ticker)
+    user_date = st.date_input("Prediction start date", dt.date(2021, 1, 1))
+    
+    ticker=True
+    try:
+        df = pdr.get_data_yahoo(user_input, start=user_date, end=today).reset_index()
+        current_price = df['Close'].tail(1)
+    except:
+        ticker=False
+    if ticker==True:
+        st.header(current_price.iloc[0].round(2))     
+    else: 
+        st.write("Wrong ticker. Select again")
+    st.markdown("""---""")    
+    
+    st.title("S&P FUTURES")
+    spval=True
+    try:
+        sp = pdr.get_data_yahoo('ES=F', start=today - timedelta(7), end=today)['Close'].tail(1)
+    except:
+        spval=False
+    if spval==True:
+        st.header(sp.iloc[0].round(2))
+    else: 
+        st.write("Can't load right now")
+    st.markdown("""---""")    
+    
+    st.title("NASDAQ")
+    nasval=True
+    try:
+        nas = pdr.get_data_yahoo('NQ=F', start=today - timedelta(7), end=today)['Close'].tail(1)
+    except:
+        nasval=False
+    if nasval==True:
+        st.header(nas.iloc[0].round(2))
+    else: 
+        st.write("Can't load right now")
+    st.markdown("""---""")
+    
+    st.title("DOW")
+    dowval=True
+    try:
+        dow = pdr.get_data_yahoo('YM=F', start=today - timedelta(7), end=today)['Close'].tail(1)
+    except:
+        dowval=False
+    if dowval==True:
+        st.header(dow.iloc[0].round(2))
+    else: 
+        st.write("Can't load right now")
+    st.markdown("""---""")
+    
+    st.title("GOLD")
+    goldval=True
+    try:
+        gold = pdr.get_data_yahoo('GC=F', start=today - timedelta(7), end=today)['Close'].tail(1)
+    except:
+        goldval=False
+    if goldval==True:
+        st.header(gold.iloc[0].round(2))
+    else: 
+        st.write("Can't load right now")
+    st.markdown("""---""")
+    
+    st.title("CRUDE OIL")
+    oilval=True
+    try:
+        oil = pdr.get_data_yahoo('CL=F', start=today - timedelta(7), end=today)['Close'].tail(1)
+    except:
+        oilval=False
+    if oilval==True:
+        st.header(oil.iloc[0].round(2))
+    else: 
+        st.write("Can't load right now")
+    st.markdown("""---""")
+
+if ticker==True:
+    
+    date = df.Date
+    close = df.Close.fillna(method='ffill')
+    
+    fig = go.Figure()
+    fig.add_trace(go.Scatter(x = date, y = close, name='Price', 
+                             mode='lines', marker_color='black'))
+    
+    ma1 = close.ewm(span=100, adjust=False).mean()
+    fig.add_trace(go.Scatter(x = date, y = ma1, name='MA 100', 
+                             mode='lines', marker_color='red'))
+    
+    ma2 = close.ewm(span=365, adjust=False).mean()
+    fig.add_trace(go.Scatter(x = date, y = ma2, name='MA 365',
+                             mode='lines', marker_color='blue'))
+    
+    fig.update_layout(title='Stock Price vs Moving averages',
+                      xaxis_title='Date',
+                      yaxis_title='Price')
+    fig.show()
+    st.plotly_chart(fig, use_container_width=True)
+    
+    #Start prediction
+
+    data_training = pd.DataFrame(close[0:int(len(close)*0.7)])
+    data_testing = pd.DataFrame(close[int(len(close)*0.7):int(len(close))])
+    x_data = date[int(len(date)*0.7):int(len(date))].reset_index(drop=True)
+    
+    #normalising data  
+    scaler = MinMaxScaler(feature_range=(0,1))
+
+    dataset_train = scaler.fit_transform(data_training)
+    dataset_test = scaler.transform(data_testing)
+
+    show_prediction('stock_prediction.h5', x_data, dataset_test, 50)
+    show_prediction('stock_prediction_test.h5', x_data, dataset_test, 7)
+    show_prediction('stock_prediction_longer_train.h5', x_data, dataset_test, 7)
+
+    

requirements.txt

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+numpy==1.24.3
+pandas==2.0.2
+pandas-datareader==0.10.0
+seaborn==0.12.2
+yfinance==0.2.24
+scikit-learn==1.2.2
+keras==2.13.1
+streamlit==1.22.0
+tensorflow
+plotly==5.9.0

stock_prediction.h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:230bec19c7ac3b71d78e29f788fa7155b55e6c5836872c26c1c809de4c14f463
+size 3195144

stock_prediction_longer_train.h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2d805c5ef443a6a065d072d6fca5f00925081e5faa24241b5547eee7ebd52ecd
+size 1302600

stock_prediction_test.h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1ca16b6c20f59ed16dff39dc332af7351ba4638ece8632c34fa0e02185965335
+size 1302584