import pandas as pd
import numpy as np
import yfinance as yf
import streamlit as st
from datetime import datetime, timedelta
import pytz




# Function to fetch stock data
def fetch_stock_data(ticker, start_datetime, end_datetime):
    stock_data = yf.download(ticker, start=start_datetime, end=end_datetime)
    return stock_data

def fetch_stock_data(ticker, start_datetime, end_datetime):
    # Fetch live data using yfinance
    stock_data = yf.download(ticker, start=start_datetime, end=end_datetime)

    # Convert DatetimeIndex to a column
    df.reset_index(inplace=True)

    return stock_data

# Function to detect head and shoulder patterns
def detect_head_shoulder(df, window=3):
    roll_window = window
    df['high_roll_max'] = df['High'].rolling(window=roll_window).max()
    df['low_roll_min'] = df['Low'].rolling(window=roll_window).min()
    mask_head_shoulder = (
        (df['high_roll_max'] > df['High'].shift(1)) &
        (df['high_roll_max'] > df['High'].shift(-1)) &
        (df['High'] < df['High'].shift(1)) &
        (df['High'] < df['High'].shift(-1))
    )
    mask_inv_head_shoulder = (
        (df['low_roll_min'] < df['Low'].shift(1)) &
        (df['low_roll_min'] < df['Low'].shift(-1)) &
        (df['Low'] > df['Low'].shift(1)) &
        (df['Low'] > df['Low'].shift(-1))
    )
    df['head_shoulder_pattern'] = np.nan
    df.loc[mask_head_shoulder, 'head_shoulder_pattern'] = 'Head and Shoulder'
    df.loc[mask_inv_head_shoulder, 'head_shoulder_pattern'] = 'Inverse Head and Shoulder'
    return df

# Function to detect multiple tops and bottoms
def detect_multiple_tops_bottoms(df, window=3):
    roll_window = window
    df['high_roll_max'] = df['High'].rolling(window=roll_window).max()
    df['low_roll_min'] = df['Low'].rolling(window=roll_window).min()
    df['close_roll_max'] = df['Close'].rolling(window=roll_window).max()
    df['close_roll_min'] = df['Close'].rolling(window=roll_window).min()
    mask_top = (df['high_roll_max'] >= df['High'].shift(1)) & (df['close_roll_max'] < df['Close'].shift(1))
    mask_bottom = (df['low_roll_min'] <= df['Low'].shift(1)) & (df['close_roll_min'] > df['Close'].shift(1))
    df['multiple_top_bottom_pattern'] = np.nan
    df.loc[mask_top, 'multiple_top_bottom_pattern'] = 'Multiple Top'
    df.loc[mask_bottom, 'multiple_top_bottom_pattern'] = 'Multiple Bottom'
    return df

# Function to calculate support and resistance levels
def calculate_support_resistance(df, window=3):
    roll_window = window
    std_dev = 2
    df['high_roll_max'] = df['High'].rolling(window=roll_window).max()
    df['low_roll_min'] = df['Low'].rolling(window=roll_window).min()
    mean_high = df['High'].rolling(window=roll_window).mean()
    std_high = df['High'].rolling(window=roll_window).std()
    mean_low = df['Low'].rolling(window=roll_window).mean()
    std_low = df['Low'].rolling(window=roll_window).std()
    df['support'] = mean_low - std_dev * std_low
    df['resistance'] = mean_high + std_dev * std_high
    return df

# Function to detect triangle patterns
def detect_triangle_pattern(df, window=3):
    roll_window = window
    df['high_roll_max'] = df['High'].rolling(window=roll_window).max()
    df['low_roll_min'] = df['Low'].rolling(window=roll_window).min()
    mask_asc = (
        (df['high_roll_max'] >= df['High'].shift(1)) &
        (df['low_roll_min'] <= df['Low'].shift(1)) &
        (df['Close'] > df['Close'].shift(1))
    )
    mask_desc = (
        (df['high_roll_max'] <= df['High'].shift(1)) &
        (df['low_roll_min'] >= df['Low'].shift(1)) &
        (df['Close'] < df['Close'].shift(1))
    )
    df['triangle_pattern'] = np.nan
    df.loc[mask_asc, 'triangle_pattern'] = 'Ascending Triangle'
    df.loc[mask_desc, 'triangle_pattern'] = 'Descending Triangle'
    return df

# Function to detect wedge patterns
def detect_wedge(df, window=3):
    roll_window = window
    df['high_roll_max'] = df['High'].rolling(window=roll_window).max()
    df['low_roll_min'] = df['Low'].rolling(window=roll_window).min()
    df['trend_high'] = df['High'].rolling(window=roll_window).apply(lambda x: 1 if (x[-1]-x[0]) > 0 else -1 if (x[-1]-x[0]) < 0 else 0)
    df['trend_low'] = df['Low'].rolling(window=roll_window).apply(lambda x: 1 if (x[-1]-x[0]) > 0 else -1 if (x[-1]-x[0]) < 0 else 0)
    mask_wedge_up = (
        (df['high_roll_max'] >= df['High'].shift(1)) &
        (df['low_roll_min'] <= df['Low'].shift(1)) &
        (df['trend_high'] == 1) &
        (df['trend_low'] == 1)
    )
    mask_wedge_down = (
        (df['high_roll_max'] <= df['High'].shift(1)) &
        (df['low_roll_min'] >= df['Low'].shift(1)) &
        (df['trend_high'] == -1) &
        (df['trend_low'] == -1)
    )
    df['wedge_pattern'] = np.nan
    df.loc[mask_wedge_up, 'wedge_pattern'] = 'Wedge Up'
    df.loc[mask_wedge_down, 'wedge_pattern'] = 'Wedge Down'
    return df

# Function to detect channel patterns
def detect_channel(df, window=3):
    roll_window = window
    channel_range = 0.1
    df['high_roll_max'] = df['High'].rolling(window=roll_window).max()
    df['low_roll_min'] = df['Low'].rolling(window=roll_window).min()
    df['trend_high'] = df['High'].rolling(window=roll_window).apply(lambda x: 1 if (x[-1]-x[0]) > 0 else -1 if (x[-1]-x[0]) < 0 else 0)
    df['trend_low'] = df['Low'].rolling(window=roll_window).apply(lambda x: 1 if (x[-1]-x[0]) > 0 else -1 if (x[-1]-x[0]) <0 else 0)
    mask_channel_up = (
        (df['high_roll_max'] >= df['High'].shift(1)) &
        (df['low_roll_min'] <= df['Low'].shift(1)) &
        (df['high_roll_max'] - df['low_roll_min'] <= channel_range * (df['high_roll_max'] + df['low_roll_min'])/2) &
        (df['trend_high'] == 1) &
        (df['trend_low'] == 1)
    )
    mask_channel_down = (
        (df['high_roll_max'] <= df['High'].shift(1)) &
        (df['low_roll_min'] >= df['Low'].shift(1)) &
        (df['high_roll_max'] - df['low_roll_min'] <= channel_range * (df['high_roll_max'] + df['low_roll_min'])/2) &
        (df['trend_high'] == -1) &
        (df['trend_low'] == -1)
    )
    df['channel_pattern'] = np.nan
    df.loc[mask_channel_up, 'channel_pattern'] = 'Channel Up'
    df.loc[mask_channel_down, 'channel_pattern'] = 'Channel Down'
    return df

# Function to detect double top and bottom patterns
def detect_double_top_bottom(df, window=3, threshold=0.05):
    roll_window = window
    range_threshold = threshold
    df['high_roll_max'] = df['High'].rolling(window=roll_window).max()
    df['low_roll_min'] = df['Low'].rolling(window=roll_window).min()
    mask_double_top = (
        (df['high_roll_max'] >= df['High'].shift(1)) &
        (df['high_roll_max'] >= df['High'].shift(-1)) &
        (df['High'] < df['High'].shift(1)) &
        (df['High'] < df['High'].shift(-1)) &
        ((df['High'].shift(1) - df['Low'].shift(1)) <= range_threshold * (df['High'].shift(1) + df['Low'].shift(1))/2) &
        ((df['High'].shift(-1) - df['Low'].shift(-1)) <= range_threshold * (df['High'].shift(-1) + df['Low'].shift(-1))/2)
    )
    mask_double_bottom = (
        (df['low_roll_min'] <= df['Low'].shift(1)) &
        (df['low_roll_min'] <= df['Low'].shift(-1)) &
        (df['Low'] > df['Low'].shift(1)) &
        (df['Low'] > df['Low'].shift(-1)) &
        ((df['High'].shift(1) - df['Low'].shift(1)) <= range_threshold * (df['High'].shift(1) + df['Low'].shift(1))/2) &
        ((df['High'].shift(-1) - df['Low'].shift(-1)) <= range_threshold * (df['High'].shift(-1) + df['Low'].shift(-1))/2)
    )
    df['double_pattern'] = np.nan
    df.loc[mask_double_top, 'double_pattern'] = 'Double Top'
    df.loc[mask_double_bottom, 'double_pattern'] = 'Double Bottom'
    return df

# Function to detect trendlines
def detect_trendline(df, window=2):
    roll_window = window
    df['slope'] = np.nan
    df['intercept'] = np.nan

    for i in range(window, len(df)):
        x = np.array(range(i-window, i))
        y = df['Close'][i-window:i]
        A = np.vstack([x, np.ones(len(x))]).T
        m, c = np.linalg.lstsq(A, y, rcond=None)[0]
        df.at[df.index[i], 'slope'] = m
        df.at[df.index[i], 'intercept'] = c

    mask_support = df['slope'] > 0
    mask_resistance = df['slope'] < 0
    df['support'] = np.nan
    df['resistance'] = np.nan
    df.loc[mask_support, 'support'] = df['Close'] * df['slope'] + df['intercept']
    df.loc[mask_resistance, 'resistance'] = df['Close'] * df['slope'] + df['intercept']

    return df

# Function to find pivots
def find_pivots(df):
    high_diffs = df['High'].diff()
    low_diffs = df['Low'].diff()
    higher_high_mask = (high_diffs > 0) & (high_diffs.shift(-1) < 0)
    lower_low_mask = (low_diffs < 0) & (low_diffs.shift(-1) > 0)
    lower_high_mask = (high_diffs < 0) & (high_diffs.shift(-1) > 0)
    higher_low_mask = (low_diffs > 0) & (low_diffs.shift(-1) < 0)
    df['signal'] = ''
    df.loc[higher_high_mask, 'signal'] = 'HH'
    df.loc[lower_low_mask, 'signal'] = 'LL'
    df.loc[lower_high_mask, 'signal'] = 'LH'
    df.loc[higher_low_mask, 'signal'] = 'HL'
    return df

# Streamlit App
def main():
    st.title('Live Stock Pattern Detection App')
    ticker = st.text_input('Enter Stock Ticker:', 'AAPL')
    
    # Placeholder for start and end date
    start_date_placeholder = st.empty()
    end_date_placeholder = st.empty()

    start_date = start_date_placeholder.date_input('Start Date', pd.to_datetime('2022-01-01'))
    end_date = end_date_placeholder.date_input('End Date', pd.to_datetime('2022-02-01'))

    st.info("Select Preferred Timezone:")
    preferred_timezone = st.selectbox('Timezone', list(pytz.all_timezones))

    # Time selection components
    start_hour, start_minute, start_second = st.slider('Select Start Time', 0, 23, 0), st.slider('', 0, 59, 0), st.slider('', 0, 59, 0)
    end_hour, end_minute, end_second = st.slider('Select End Time', 0, 23, 23), st.slider('', 0, 59, 59), st.slider('', 0, 59, 59)

    start_datetime = datetime(start_date.year, start_date.month, start_date.day, start_hour, start_minute, start_second)
    end_datetime = datetime(end_date.year, end_date.month, end_date.day, end_hour, end_minute, end_second)

    start_datetime = pytz.timezone(preferred_timezone).localize(start_datetime)
    end_datetime = pytz.timezone(preferred_timezone).localize(end_datetime)

    start_date_placeholder.info(f"Start Date (UTC): {start_datetime.strftime('%Y-%m-%d %H:%M:%S %Z')}")
    end_date_placeholder.info(f"End Date (UTC): {end_datetime.strftime('%Y-%m-%d %H:%M:%S %Z')}")

    if st.button('Detect Patterns'):
        stock_data = fetch_stock_data(ticker, start_datetime, end_datetime)
        stock_data = detect_head_shoulder(stock_data)
        stock_data = detect_multiple_tops_bottoms(stock_data)
        stock_data = calculate_support_resistance(stock_data)
        stock_data = detect_triangle_pattern(stock_data)
        stock_data = detect_wedge(stock_data)
        stock_data = detect_channel(stock_data)
        stock_data = detect_double_top_bottom(stock_data)
        stock_data = detect_trendline(stock_data)
        stock_data = find_pivots(stock_data)

        st.write(stock_data)

if __name__ == "__main__":
    main()