Create app.py

app.py

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+import streamlit as st
+import yfinance as yf
+import pandas as pd
+from sklearn.linear_model import LinearRegression
+from sklearn.ensemble import RandomForestRegressor
+from sklearn.preprocessing import StandardScaler
+from sklearn.model_selection import train_test_split
+from datetime import datetime, timedelta
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Function to compute RSI
+def compute_rsi(data, window):
+    diff = data.diff(1).dropna()
+    gain = diff.where(diff > 0, 0)
+    loss = -diff.where(diff < 0, 0)
+    avg_gain = gain.rolling(window=window, min_periods=1).mean()
+    avg_loss = loss.rolling(window=window, min_periods=1).mean()
+    rs = avg_gain / avg_loss
+    rsi = 100 - (100 / (1 + rs))
+    return rsi
+
+# Set up the Streamlit app
+st.title("Bitcoin Price Prediction")
+st.write("This app uses historical data to predict future Bitcoin prices.")
+
+# Fetch Bitcoin historical data using yfinance
+btc_data = yf.download('BTC-USD', start='2020-01-01', end=datetime.today().strftime('%Y-%m-%d'))
+btc_data.reset_index(inplace=True)
+
+# Display the historical data
+st.write("Historical Bitcoin Data")
+st.dataframe(btc_data.tail())
+
+# Feature engineering
+btc_data['MA_10'] = btc_data['Close'].rolling(window=10).mean()
+btc_data['MA_50'] = btc_data['Close'].rolling(window=50).mean()
+btc_data['RSI'] = compute_rsi(btc_data['Close'], window=14)
+btc_data['Return'] = btc_data['Close'].pct_change()
+btc_data.dropna(inplace=True)
+
+# Prepare features and target variable
+X = btc_data[['Open', 'High', 'Low', 'Volume', 'MA_10', 'MA_50', 'RSI', 'Return']]
+y = btc_data['Close']
+
+# Split the data
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Scale the features
+scaler = StandardScaler()
+X_train_scaled = scaler.fit_transform(X_train)
+X_test_scaled = scaler.transform(X_test)
+
+# Train the Linear Regression model
+lr_model = LinearRegression()
+lr_model.fit(X_train_scaled, y_train)
+
+# Train the Random Forest model
+rf_model = RandomForestRegressor(n_estimators=100, random_state=42)
+rf_model.fit(X_train_scaled, y_train)
+
+# Predict future prices using ensemble method
+future_dates = [btc_data['Date'].iloc[-1] + timedelta(days=x) for x in range(1, 15)]
+future_df = pd.DataFrame(index=future_dates, columns=X.columns)
+future_df = future_df.fillna(method='ffill')
+
+future_X_scaled = scaler.transform(future_df)
+lr_predictions = lr_model.predict(future_X_scaled)
+rf_predictions = rf_model.predict(future_X_scaled)
+
+# Combine predictions (average)
+combined_predictions = (lr_predictions + rf_predictions) / 2
+
+# Display predictions
+predictions_df = pd.DataFrame({'Date': future_dates, 'Predicted Close': combined_predictions})
+predictions_df.set_index('Date', inplace=True)
+st.write("Future Bitcoin Price Predictions")
+st.dataframe(predictions_df)
+
+# Plot the predictions
+st.line_chart(predictions_df['Predicted Close'])