Create app.py

app.py

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+import ccxt
+import pandas as pd
+from datetime import datetime, timedelta
+from prophet import Prophet
+import matplotlib.pyplot as plt
+import streamlit as st
+
+# Initialize the exchange
+binance = ccxt.bitget()
+symbol = "BTC/USDT"
+
+def fetch_btc_usdt_data(start_date, end_date):
+    since = int(datetime.strptime(start_date, '%Y-%m-%d').timestamp() * 1000)
+    end = int(datetime.strptime(end_date, '%Y-%m-%d').timestamp() * 1000)
+    timeframe = '1d'  # Daily data
+    data = []
+
+    while since < end:
+        ohlcv = binance.fetch_ohlcv(symbol, timeframe, since, limit=1000)
+        if not ohlcv:
+            break
+        since = ohlcv[-1][0] + 1
+        data.extend(ohlcv)
+
+    # Convert data to DataFrame
+    columns = ['timestamps', 'open', 'high', 'low', 'close', 'volume']
+    df = pd.DataFrame(data, columns=columns)
+    df['timestamps'] = pd.to_datetime(df['timestamps'], unit='ms')
+    df.set_index('timestamps', inplace=True)
+
+    # Return the DataFrame
+    return df
+
+def train_and_forecast(df):
+    # Prepare the data for Prophet
+    df_prophet = df[['close']].reset_index()
+    df_prophet.rename(columns={'timestamps': 'ds', 'close': 'y'}, inplace=True)
+
+    # Train the Prophet model
+    model = Prophet(daily_seasonality=True)
+    model.fit(df_prophet)
+
+    # Make a future dataframe for predictions (e.g., next 30 days)
+    future = model.make_future_dataframe(periods=30, freq='D')
+
+    # Get the forecast
+    forecast = model.predict(future)
+
+    return forecast, model
+
+def plot_forecast(forecast, model):
+    # Plot the forecast
+    fig, ax = plt.subplots(figsize=(10, 6))
+    model.plot(forecast, ax=ax)
+    
+    # Plot the components
+    fig2, ax2 = plt.subplots(figsize=(10, 6))
+    model.plot_components(forecast)
+
+    return fig, fig2
+
+# Streamlit UI
+st.title("BTC/USDT Price Forecasting")
+st.markdown("""
+This app uses Facebook's Prophet model to forecast the future prices of BTC/USDT based on historical data from Bitget. 
+You can select the start and end dates to get a prediction for the next 30 days.
+""")
+
+# Date input
+start_date = st.date_input('Start Date', datetime.today() - timedelta(days=365))
+end_date = st.date_input('End Date', datetime.today())
+
+if start_date and end_date:
+    # Fetch the data
+    df = fetch_btc_usdt_data(start_date.strftime('%Y-%m-%d'), end_date.strftime('%Y-%m-%d'))
+    
+    # Train and forecast
+    forecast, model = train_and_forecast(df)
+    
+    # Plot the forecast and components
+    st.subheader("Forecast Plot")
+    fig1, fig2 = plot_forecast(forecast, model)
+    
+    st.pyplot(fig1)  # Forecast plot
+    st.pyplot(fig2)  # Components plot