Create app.py

app.py

@@ -0,0 +1,109 @@
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from sklearn.model_selection import train_test_split
+from sklearn.linear_model import LinearRegression
+import streamlit as st
+from datetime import date
+import yfinance as yf 
+from plotly import graph_objs as go
+
+START = "2019-01-01"
+TODAY = date.today().strftime("%Y-%m-%d")
+
+st.title("Cryptocurrency Price Forecaster")
+html_temp = """
+    <marquee behavior="scroll" direction="left">ALL INVESTMENTS ARE SUBJECT TO PRICE FLUCTUATIONS AND OTHER MARKET RISKS..... </marquee>
+    """
+st.markdown(html_temp,unsafe_allow_html=True)
+
+currency=("ETH-USD","BTC-USD","BNB-USD","MATIC-USD","TRX-USD","DOGE-USD","SOL-USD","ATOM-USD")
+selected_currency=st.selectbox("select coin",currency)
+
+n_days = st.slider("Days of prediction",1,30)
+
+# period =n_years*365
+
+@st.cache_data(allow_output_mutation=True)
+
+# @st.cache
+def load_data(ticker):
+    data=yf.download(ticker, START, TODAY)
+    data.reset_index(inplace=True)
+    return data
+
+data_load_state=st.text("load data...")
+data=load_data(selected_currency)
+data_load_state.text("loading data....done")
+
+# st.subheader('Raw data')
+# st.write(data.head(7))
+# st.write(data.tail(7))
+
+def plot_raw_data():
+    fig=go.Figure()
+    fig.add_trace(go.Scatter(x=data['Date'], y=data['Open'], name='open'))
+    fig.add_trace(go.Scatter(x=data['Date'], y=data['Close'], name='close'))
+    fig.layout.update(title_text="Time Series Graph", xaxis_rangeslider_visible=True)
+    st.plotly_chart(fig)
+    
+# plot_raw_data()
+ 
+def get_data(ticker):
+    data=load_data(ticker)
+    data[str(n_days)+'_Day_Price_Forecast'] = data[['Close']].shift(-n_days)
+    X= np.array(data[['Close']])
+    X= X[:data.shape[0]-n_days]
+    y= np.array(data[str(n_days)+'_Day_Price_Forecast'])
+    y= y[:-n_days]
+    return X,y
+
+X, y= get_data(selected_currency)
+
+#linear regression
+def result(X,y):
+    X_train, X_test, y_train,y_test = train_test_split(X,y,test_size=0.2) 
+    linReg = LinearRegression()
+    linReg.fit(X_train,y_train)
+    x_projection = np.array(data[['Close']])[-n_days:]
+    linReg_prediction = linReg.predict(x_projection)
+    lr_acc = linReg.score(X_test,y_test)#r^2 test
+    return x_projection,linReg_prediction,lr_acc
+n, m, p= result(X,y)
+
+# st.write(n)
+# st.subheader('Predicted prices ')
+# st.write(m)
+
+r_list=list(range(1,n_days))
+def plot_result_data():
+    fig=go.Figure()
+    fig.add_trace(go.Scatter(x=r_list, y=m, name='predicted'))
+    fig.layout.update(title_text="Time Series Data", xaxis_rangeslider_visible=True)
+    st.plotly_chart(fig)
+# plot_result_data()
+if st.button("View Past data"):
+
+    #st.subheader('Raw data')
+    st.write(data.head(7))
+    st.write(data.tail(7))
+    plot_raw_data()
+
+if st.button("Predict future Prices"):
+    st.subheader('Predicted prices ')
+    st.write(m)
+    plot_result_data()
+if st.button("Accuracy check"):
+    st.write(p*100)
+if st.button('INR CONVERTER'):
+    st.write(m*82.56)
+    st.write(f'''
+        <a target="_blank" href="https://www.coinbase.com/learn/crypto-basics">
+            <button style = "background-color:#16767B; border-radius:7px;">
+                LEARN MORE
+            </button>
+        </a>
+        ''',
+        unsafe_allow_html=True
+    )
+