Coding for Doji

    from tvDatafeed import TvDatafeed, Interval
import mplfinance as mpf
from datetime import datetime, timedelta
import pandas as pd
import numpy as np



# Doji detection functions
def is_doji(row, threshold=0.1):
    """Standard Doji - Open and Close are nearly equal"""
    body_size = abs(row['Close'] - row['Open'])
    total_range = row['High'] - row['Low']
    return body_size <= total_range * threshold


def is_long_legged_doji(row, threshold=0.05, min_wick_ratio=0.4):
    """Long-legged Doji - Small body with long upper and lower shadows"""
    body_size = abs(row['Close'] - row['Open'])
    total_range = row['High'] - row['Low']
    upper_wick = row['High'] - max(row['Open'], row['Close'])
    lower_wick = min(row['Open'], row['Close']) - row['Low']

    return (body_size <= total_range * threshold and
            upper_wick >= total_range * min_wick_ratio and
            lower_wick >= total_range * min_wick_ratio)


def is_gravestone_doji(row, threshold=0.05):
    """Gravestone Doji - Open and Close at low with long upper shadow"""
    body_size = abs(row['Close'] - row['Open'])
    total_range = row['High'] - row['Low']
    upper_wick = row['High'] - max(row['Open'], row['Close'])
    lower_wick = min(row['Open'], row['Close']) - row['Low']

    return (body_size <= total_range * threshold and
            upper_wick >= total_range * 0.5 and
            lower_wick <= total_range * 0.1)


def is_dragonfly_doji(row, threshold=0.05):
    """Dragonfly Doji - Open and Close at high with long lower shadow"""
    body_size = abs(row['Close'] - row['Open'])
    total_range = row['High'] - row['Low']
    upper_wick = row['High'] - max(row['Open'], row['Close'])
    lower_wick = min(row['Open'], row['Close']) - row['Low']

    return (body_size <= total_range * threshold and
            lower_wick >= total_range * 0.5 and
            upper_wick <= total_range * 0.1)


def detect_dojis(data):
    """Detect all types of dojis in the dataframe"""
    data['Doji'] = data.apply(is_doji, axis=1)
    data['LongLeggedDoji'] = data.apply(is_long_legged_doji, axis=1)
    data['GravestoneDoji'] = data.apply(is_gravestone_doji, axis=1)
    data['DragonflyDoji'] = data.apply(is_dragonfly_doji, axis=1)
    return data


# Custom ggplot style
def get_ggplot_style():
    """Returns the custom ggplot style for mplfinance"""
    return mpf.make_mpf_style(
        base_mpl_style='ggplot',
        marketcolors=mpf.make_marketcolors(
            up='#9ababb', down='#ea751d',
            wick={'up': '#9ababb', 'down': '#ea751d'},
            edge={'up': '#9ababb', 'down': '#ea751d'},
            volume='in'
        ),
        gridstyle='',
        facecolor='#5b6a7a',
        figcolor='#5b6a7a',
        rc={
            'axes.labelcolor': '#c2c5ca',
            'xtick.color': '#c2c5ca',
            'ytick.color': '#c2c5ca',
            'axes.edgecolor': '#c2c5ca',
            'text.color': '#c2c5ca',
            'axes.titlepad': 10,
        }
    )


# Main function
def analyze_stock(ticker, exchange, interval=Interval.in_1_minute, n_bars=100):
    """Fetch data and analyze dojis"""
    tv = TvDatafeed()

    # Fetch data
    data = tv.get_hist(
        symbol=ticker,
        exchange=exchange,
        interval=interval,
        n_bars=n_bars,
        extended_session=False
    )

    # Convert to proper DataFrame format
    data.index = pd.to_datetime(data.index)
    data = data.rename(columns={
        'open': 'Open',
        'high': 'High',
        'low': 'Low',
        'close': 'Close',
        'volume': 'Volume'
    })

    # Detect dojis
    data = detect_dojis(data)

    # Print doji counts
    print(f"\nDoji Analysis for {ticker} ({interval.name.replace('_', ' ')} chart):")
    print(f"Standard Dojis: {data['Doji'].sum()}")
    print(f"Long-legged Dojis: {data['LongLeggedDoji'].sum()}")
    print(f"Gravestone Dojis: {data['GravestoneDoji'].sum()}")
    print(f"Dragonfly Dojis: {data['DragonflyDoji'].sum()}")

    # Create marker plots - THE CRITICAL FIX
    # In your analyze_stock function, modify the marker creation section:

    # Create marker plots - with adjusted height
    add_doji_markers = []
    marker_offset = 0.001  # Adjust this value to control how far above the candles the markers appear

    for doji_type, marker, color in [
        ('Doji', '*', 'white'),
        ('LongLeggedDoji', '^', 'blue'),
        ('GravestoneDoji', 'v', 'red'),
        ('DragonflyDoji', 'o', 'yellow')
    ]:
        if data[doji_type].any():
            # Create a series with values only at doji points, positioned above the high
            marker_values = data['High'].where(data[doji_type]) * (1 + marker_offset)
            add_doji_markers.append(
                mpf.make_addplot(marker_values,
                                 scatter=True,
                                 markersize=20,
                                 marker=marker,
                                 color=color,
                                 label=doji_type)
            )
    # Plot candlestick chart
    mpf.plot(
        data,
        type='candle',
        style=get_ggplot_style(),
        title=f'{ticker} - Doji Analysis',
        ylabel='Price ($)',
        volume=False,
        figsize=(12, 8),
        datetime_format='%b %d %H:%M',
        addplot=add_doji_markers if add_doji_markers else None,
        savefig=f'{ticker}_doji_analysis.png'
    )

    return data




# Example usage
if __name__ == "__main__":
    ticker = 'INTC'
    exchange = 'NASDAQ'

    # Analyze 5-minute chart
    data_5min = analyze_stock(ticker, exchange, Interval.in_1_minute)

    # Analyze daily chart for comparison
    #data_daily = analyze_stock(ticker, exchange, Interval.in_daily)