Update app.py

app.py

@@ -1,59 +1,44 @@
-import streamlit as st
-import yfinance as yf
 import pandas as pd
-import ta
-import plotly.graph_objects as go
-
-# Function to load stock data using yfinance
-def get_stock_data(symbol, start_date, end_date, interval='1h'):
-    stock_data = yf.download(symbol, start=start_date, end=end_date, interval=interval)
-    return stock_data
-
-# Function to detect Double Top pattern
-def detect_double_top(data, lookback=20):
-    signals = pd.DataFrame(index=data.index)
-    signals['signal'] = 0.0
-
-    # Identify potential double top pattern
-    signals['double_top'] = ta.patterns.double_top(data['High'], data['Low'], data['Close'], lookback=lookback, threshold=0.02)
-
-    # Generate trading orders
-    signals['positions'] = signals['double_top'].diff()
-
-    return signals
-
-# Streamlit App
-def main():
-    st.title("Hourly Stock Price Analysis and Double Top Pattern Detection")
-
-    # Load stock data using Streamlit sidebar
-    symbol = st.sidebar.text_input("Enter Stock Symbol", value='AAPL')
-    start_date = st.sidebar.text_input("Enter Start Date", value='2022-01-01')
-    end_date = st.sidebar.text_input("Enter End Date", value='2022-02-01')
-    stock_prices = get_stock_data(symbol, start_date, end_date, interval='1h')
-
-    # Detect Double Top Pattern
-    signals = detect_double_top(stock_prices)
-
-    # Plotting Historical Stock Prices with Double Top Pattern Signals
-    st.subheader("Hourly Historical Stock Prices with Double Top Pattern Signals")
-    fig = go.Figure()
-
-    fig.add_trace(go.Candlestick(x=stock_prices.index,
-                                 open=stock_prices['Open'],
-                                 high=stock_prices['High'],
-                                 low=stock_prices['Low'],
-                                 close=stock_prices['Close'],
-                                 name='Candlesticks'))
-
-    # Plot Double Top pattern signals
-    fig.add_trace(go.Scatter(x=signals.index[signals['double_top'] == 1],
-                             y=stock_prices['High'][signals['double_top'] == 1],
-                             mode='markers',
-                             marker=dict(color='red', size=10),
-                             name='Double Top Signal'))
-
-    st.plotly_chart(fig)
-
-if __name__ == "__main__":
-    main()
+import yfinance as yf
+from ta import add_all_ta_features
+from ta.utils import dropna
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import accuracy_score
+
+# Load historical stock data using yfinance
+symbol = 'AAPL'
+start_date = '2021-01-01'
+end_date = '2022-01-01'
+stock_data = yf.download(symbol, start=start_date, end=end_date)
+
+# Clean NaN values
+stock_data = dropna(stock_data)
+
+# Add all ta features
+stock_data = add_all_ta_features(
+    stock_data, open="Open", high="High", low="Low", close="Close", volume="Volume")
+
+# Define target variable (1 for pattern occurrence, 0 otherwise)
+stock_data['DoubleTop'] = stock_data['close'].shift(-1) > stock_data['close']
+
+# Drop NaN values introduced by the shift
+stock_data = dropna(stock_data)
+
+# Features and target
+X = stock_data.drop(['DoubleTop'], axis=1)
+y = stock_data['DoubleTop']
+
+# Split into train and test sets
+X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+# Train a simple RandomForestClassifier (you may need a more sophisticated model)
+clf = RandomForestClassifier()
+clf.fit(X_train, y_train)
+
+# Predictions
+y_pred = clf.predict(X_test)
+
+# Evaluate the model
+accuracy = accuracy_score(y_test, y_pred)
+print(f"Model Accuracy: {accuracy}")