Create app.py

app.py

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+import gradio as gr
+import numpy as np
+import pandas as pd
+import yfinance as yf
+import talib
+import requests
+import matplotlib.pyplot as plt
+import logging
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import LSTM, Dense, Dropout
+from stable_baselines3 import PPO, DQN
+from gym import Env, spaces
+from selenium import webdriver
+from selenium.webdriver.chrome.service import Service
+from selenium.webdriver.chrome.options import Options
+from selenium.webdriver.common.by import By
+from selenium.webdriver.support.ui import WebDriverWait
+from selenium.webdriver.support import expected_conditions as EC
+from bs4 import BeautifulSoup
+import time
+from webdriver_manager.chrome import ChromeDriverManager
+import threading
+import smtplib
+from email.mime.text import MIMEText
+from email.mime.multipart import MIMEMultipart
+
+logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+
+# Free API Alternative to Alpaca (Yahoo Finance)
+BASE_URL = "https://query1.finance.yahoo.com/v8/finance/chart/"
+
+def fetch_live_price(symbol):
+    try:
+        stock = yf.Ticker(symbol)
+        return stock.history(period="1d")["Close"].iloc[-1]
+    except Exception as e:
+        logging.error(f"Error fetching live price: {e}")
+        return None
+
+# Optimized Web Scraping with Selenium Waits
+def scrape_groww(symbol):
+    options = Options()
+    options.add_argument("--headless")
+    driver = webdriver.Chrome(service=Service(ChromeDriverManager().install()), options=options)
+    url = f"https://groww.in/stocks/{symbol.lower()}"
+    driver.get(url)
+    
+    try:
+        price_element = WebDriverWait(driver, 10).until(
+            EC.presence_of_element_located((By.CLASS_NAME, "stock-price"))
+        )
+        price = float(price_element.text.replace(',', ''))
+    except Exception:
+        price = None
+    
+    driver.quit()
+    return price
+
+# Fetch Data with Additional Indicators
+def fetch_data(symbol, interval='1m', period='5d'):
+    df = yf.download(symbol, interval=interval, period=period)
+    df['SMA_10'] = talib.SMA(df['Close'], timeperiod=10)
+    df['RSI'] = talib.RSI(df['Close'], timeperiod=14)
+    df['MACD'], df['MACD_signal'], _ = talib.MACD(df['Close'])
+    return df
+
+# Improved LSTM Model
+def train_lstm_model(data):
+    X, y = np.array([[data[i-10:i].values] for i in range(10, len(data))]), data['Close'][10:].values
+    model = Sequential([
+        LSTM(100, return_sequences=True, input_shape=(10, 1)),
+        Dropout(0.2),
+        LSTM(100),
+        Dense(50, activation='relu'),
+        Dense(1)
+    ])
+    model.compile(optimizer='adam', loss='mse')
+    model.fit(X, y, epochs=20, batch_size=32)
+    return model
+
+# Advanced RL-Based Trading Agent
+class TradingEnv(Env):
+    def __init__(self):
+        self.action_space = spaces.Discrete(3)
+        self.observation_space = spaces.Box(low=0, high=1, shape=(10,), dtype=np.float32)
+        self.current_step = 0
+        self.balance = 10000
+        self.position = 0
+        self.history = []
+    
+    def step(self, action):
+        reward = 0
+        done = False
+        if action == 0:  # Buy
+            self.position += 1
+            reward -= 0.5  # Reduced penalty for transaction cost
+        elif action == 1:  # Sell
+            if self.position > 0:
+                self.position -= 1
+                reward += 10  # Higher profit realization
+        elif action == 2:  # Hold
+            reward += 0.2  # Slightly increased hold reward
+        self.current_step += 1
+        self.history.append((self.current_step, self.balance, self.position))
+        if self.current_step >= 200:
+            done = True
+        return np.random.random(10), reward, done, {}
+
+env = TradingEnv()
+model = PPO("MlpPolicy", env, verbose=1)
+model.learn(total_timesteps=500000)
+
+# Place Trade using Free API
+def place_trade(symbol, action):
+    return {"status": "success", "symbol": symbol, "action": action}
+
+# Email Alert System
+def send_email_alert(subject, body):
+    sender_email = "[email protected]"
+    receiver_email = "[email protected]"
+    password = "your_password"
+
+    msg = MIMEMultipart()
+    msg['From'] = sender_email
+    msg['To'] = receiver_email
+    msg['Subject'] = subject
+    msg.attach(MIMEText(body, 'plain'))
+
+    with smtplib.SMTP('smtp.gmail.com', 587) as server:
+        server.starttls()
+        server.login(sender_email, password)
+        server.sendmail(sender_email, receiver_email, msg.as_string())
+
+# Automated Trading and Alert System
+def monitor_price(symbol, threshold):
+    while True:
+        price = fetch_live_price(symbol)
+        if price and price >= threshold:
+            send_email_alert("Stock Price Alert", f"{symbol} has reached {price}!")
+            place_trade(symbol, "sell")
+            break
+        time.sleep(60)
+
+# Gradio UI
+def stock_dashboard(symbol, threshold_price):
+    data = fetch_data(symbol)
+    fig, ax = plt.subplots()
+    ax.plot(data.index, data['Close'], label='Close Price')
+    ax.plot(data.index, data['SMA_10'], label='SMA 10', linestyle='dashed')
+    ax.legend()
+    
+    live_price = fetch_live_price(symbol)
+    action = np.random.choice(["buy", "sell", "hold"])
+    place_trade(symbol, action)
+    
+    return fig, f"Live Price: {live_price}", f"Trade Executed: {action}"
+
+demo = gr.Interface(
+    fn=stock_dashboard,
+    inputs=["text", "number"],
+    outputs=["plot", "text", "text"],
+    title="AI-Powered Intraday Trading Agent",
+    description="Enter a stock symbol and set a price threshold to start trading."
+)
+
+demo.launch()