Automated Entry & Exit Signals w/ Python

import pandas as pd
from alpha_vantage.timeseries import TimeSeries
import matplotlib.pyplot as plt
import numpy as np
import datetime

api_key = [enter_your_api_key]

def get_stock_data(symbol, interval='5min', size='compact'):
    ts = TimeSeries(key=api_key, output_format='pandas')
    data, meta_data = ts.get_intraday(symbol=symbol, interval=interval, outputsize=size)
    df = data.rename(columns=lambda x: x.split(' ')[-1])  # Remove space in column names
    df.index = pd.to_datetime(df.index)
    return df

def calculate_superposition_bands(df, length=20, multiplier=1.5):
    df['Middle_Band'] = df['close'].rolling(window=length).mean()
    df['ATR'] = df['high'] - df['low']
    df['Upper_Band'] = df['Middle_Band'] + multiplier * df['ATR']
    df['Lower_Band'] = df['Middle_Band'] - multiplier * df['ATR']
    return df

def generate_signals(df, atr_multiplier=0.5):
    df['ATR'] = df['high'] - df['low']
    df['Buy_Signal'] = ((df['close'] > df['Lower_Band']) & (df['close'].shift(1) <= df['Lower_Band'].shift(1))).astype(int)
    df['Sell_Signal'] = ((df['close'] > df['Middle_Band']) & (df['close'].shift(1) <= df['Middle_Band'].shift(1))
                        & (df['close'] >= df['close'].shift(1) + atr_multiplier * df['ATR'].shift(1))).astype(int)
    return df

def plot_superposition_bands(df, symbol, threshold=10, rolling_window=5):
    df_smoothed = df.copy()
    df_smoothed['close'] = df['close'].rolling(window=rolling_window).mean()

    plt.figure(figsize=(12, 6))
    plt.plot(df_smoothed.index, df_smoothed['close'], label='Close Price (Smoothed)', color='black')
    plt.plot(df.index, df['Middle_Band'], label='Middle Band', color='blue')
    plt.plot(df.index, df['Upper_Band'], label='Upper Band', color='red', linestyle='--')
    plt.plot(df.index, df['Lower_Band'], label='Lower Band', color='green', linestyle='--')
    plt.scatter(df[df['Buy_Signal'] == 1].index, df[df['Buy_Signal'] == 1]['close'], label='Buy Signal', marker='^', color='green')
    plt.scatter(df[df['Sell_Signal'] == 1].index, df[df['Sell_Signal'] == 1]['close'], label='Sell Signal', marker='v', color='red')
    plt.title(f'Superposition Bands Analysis for {symbol}')
    plt.xlabel('Date (MST)')
    plt.ylabel('Price')
    plt.legend()

    # Set y-axis range with a threshold to exclude extreme values
    plt.ylim([df_smoothed['close'].min() - threshold, df_smoothed['close'].max() + threshold])

    # Display relevant trading hours on x-axis
    trading_hours = pd.date_range('09:30', '16:00', freq='30min')
    plt.xticks(trading_hours, trading_hours.strftime('%H:%M'))

    plt.show()

def monitor_stock(symbol):
    df = get_stock_data(symbol)
    df = calculate_superposition_bands(df)
    df = generate_signals(df)
    plot_superposition_bands(df, symbol)

# Example: Monitor the stock with the symbol 'TSLA'
monitor_stock('TSLA')