Create app.py

app.py

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+import streamlit as st
+import yfinance as yf
+from statsmodels.tsa.arima.model import ARIMA
+import pandas as pd
+import matplotlib.pyplot as plt
+
+# Title and description
+st.title("Algorithmic Trading Bot with ARIMA")
+st.write("This app simulates an algorithmic trading bot using the ARIMA model for price prediction.")
+
+# Sidebar for user inputs
+st.sidebar.title("Settings")
+ticker = st.sidebar.text_input("Stock Ticker (e.g., AAPL, TSLA, ^GSPC):", value="^GSPC")
+start_date = st.sidebar.date_input("Start Date", value=pd.to_datetime("2015-01-01"))
+end_date = st.sidebar.date_input("End Date", value=pd.to_datetime("2023-12-31"))
+n_days = st.sidebar.slider("Prediction Horizon (days)", min_value=1, max_value=30, value=7)
+initial_balance = st.sidebar.number_input("Initial Balance (USD):", value=10000.0)
+arima_order = st.sidebar.text_input("ARIMA Order (p, d, q):", value="5,1,0")
+
+# Parse ARIMA order
+try:
+    p, d, q = map(int, arima_order.split(","))
+except ValueError:
+    st.error("Invalid ARIMA order. Please enter in the format 'p,d,q'.")
+
+# Fetch historical data
+st.write("### Historical Data")
+try:
+    data = yf.download(ticker, start=start_date, end=end_date)
+    data = data["Close"]
+    st.line_chart(data)
+except Exception as e:
+    st.error(f"Error fetching data: {e}")
+
+# Trading Bot Simulation
+if st.button("Run Trading Bot"):
+    st.write("### Trading Bot Simulation")
+
+    if len(data) < 30:
+        st.error("Not enough data to train the model. Please select a longer date range.")
+    else:
+        try:
+            # Train ARIMA model
+            model = ARIMA(data, order=(p, d, q))
+            fitted_model = model.fit()
+
+            # Predict future prices
+            future_index = pd.date_range(start=data.index[-1], periods=n_days + 1, freq="B")[1:]
+            forecast = fitted_model.forecast(steps=n_days)
+            forecast_df = pd.DataFrame({"Date": future_index, "Predicted Price": forecast}).set_index("Date")
+
+            # Simulate trading
+            balance = initial_balance
+            position = 0  # Number of shares held
+            trades = []
+
+            for i in range(1, len(forecast)):
+                if forecast[i] > forecast[i - 1]:  # Buy signal
+                    if position == 0:
+                        position = balance / forecast[i]
+                        balance = 0
+                        trades.append((future_index[i], "BUY", forecast[i]))
+                elif forecast[i] < forecast[i - 1]:  # Sell signal
+                    if position > 0:
+                        balance = position * forecast[i]
+                        position = 0
+                        trades.append((future_index[i], "SELL", forecast[i]))
+
+            # Final balance
+            final_balance = balance + (position * forecast[-1] if position > 0 else 0)
+            profit = final_balance - initial_balance
+
+            # Show results
+            st.write(f"### Final Balance: ${final_balance:,.2f}")
+            st.write(f"### Total Profit: ${profit:,.2f}")
+            trades_df = pd.DataFrame(trades, columns=["Date", "Action", "Price"])
+            st.write("### Trade History")
+            st.write(trades_df)
+
+            # Plot results
+            plt.figure(figsize=(10, 6))
+            plt.plot(data, label="Historical Data (USD/share)")
+            plt.plot(forecast_df, label="Predicted Data (USD/share)", linestyle="--")
+            for trade in trades:
+                plt.scatter(trade[0], trade[2], label=trade[1], color="green" if trade[1] == "BUY" else "red")
+            plt.legend()
+            plt.title("Algorithmic Trading Bot Simulation")
+            plt.xlabel("Date")
+            plt.ylabel("Price (USD/share)")
+            st.pyplot(plt)
+
+        except Exception as e:
+            st.error(f"Error in trading bot simulation: {e}")