candlestickchart.py

import pandas as pd
import numpy as np
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import os

# Load data
folder_path = r'C:\data\equity\NSE500_202509'
df = pd.read_csv(os.path.join(folder_path, 'DLF.csv'))

# Convert date to datetime
df['date'] = pd.to_datetime(df['date'])
df = df.sort_values('date')

# Calculate RSI
def calculate_rsi(data, period=14):
    delta = data.diff()
    gain = (delta.where(delta > 0, 0)).rolling(window=period).mean()
    loss = (-delta.where(delta < 0, 0)).rolling(window=period).mean()
    rs = gain / loss
    rsi = 100 - (100 / (1 + rs))
    return rsi

# Calculate Moving Averages
df['SMA_20'] = df['close'].rolling(window=20).mean()
df['SMA_50'] = df['close'].rolling(window=50).mean()
df['RSI'] = calculate_rsi(df['close'])

# TradingView color scheme
tv_bg = '#131722'  # Dark background
tv_grid = '#1e222d'  # Grid lines
tv_text = '#d1d4dc'  # Text color
tv_green = '#089981'  # Bull candle (TradingView green)
tv_red = '#f23645'  # Bear candle (TradingView red)
tv_blue = '#2962ff'  # Blue for indicators
tv_purple = '#9c27b0'  # Purple for indicators
tv_orange = '#ff6d00'  # Orange for indicators

# Create subplots with 3 rows
fig = make_subplots(
    rows=3, cols=1,
    shared_xaxes=True,
    vertical_spacing=0.02,
    row_heights=[0.6, 0.2, 0.2],
    subplot_titles=('DLF', 'Volume', 'RSI')
)

# Candlestick chart (TradingView style)
fig.add_trace(
    go.Candlestick(
        x=df['date'],
        open=df['open'],
        high=df['high'],
        low=df['low'],
        close=df['close'],
        name='DLF',
        increasing=dict(line=dict(color=tv_green, width=1), fillcolor=tv_green),
        decreasing=dict(line=dict(color=tv_red, width=1), fillcolor=tv_red)
    ),
    row=1, col=1
)

# Add SMA 20
fig.add_trace(
    go.Scatter(
        x=df['date'],
        y=df['SMA_20'],
        mode='lines',
        name='SMA 20',
        line=dict(color=tv_blue, width=2),
        opacity=0.8
    ),
    row=1, col=1
)

# Add SMA 50
fig.add_trace(
    go.Scatter(
        x=df['date'],
        y=df['SMA_50'],
        mode='lines',
        name='SMA 50',
        line=dict(color=tv_orange, width=2),
        opacity=0.8
    ),
    row=1, col=1
)

# Volume bars with TradingView colors
colors = [tv_green if row['close'] >= row['open'] else tv_red 
          for idx, row in df.iterrows()]

fig.add_trace(
    go.Bar(
        x=df['date'],
        y=df['volume'],
        name='Volume',
        marker=dict(
            color=colors,
            line=dict(width=0)
        ),
        showlegend=False,
        opacity=0.5
    ),
    row=2, col=1
)

# RSI line
fig.add_trace(
    go.Scatter(
        x=df['date'],
        y=df['RSI'],
        mode='lines',
        name='RSI',
        line=dict(color=tv_purple, width=2),
        fill='tozeroy',
        fillcolor='rgba(156, 39, 176, 0.1)'
    ),
    row=3, col=1
)

# RSI overbought line (70)
fig.add_trace(
    go.Scatter(
        x=df['date'],
        y=[70] * len(df),
        mode='lines',
        line=dict(color='rgba(255, 82, 82, 0.5)', width=1, dash='dash'),
        showlegend=False,
        hoverinfo='skip'
    ),
    row=3, col=1
)

# RSI oversold line (30)
fig.add_trace(
    go.Scatter(
        x=df['date'],
        y=[30] * len(df),
        mode='lines',
        line=dict(color='rgba(38, 166, 154, 0.5)', width=1, dash='dash'),
        showlegend=False,
        hoverinfo='skip'
    ),
    row=3, col=1
)

# RSI middle line (50)
fig.add_trace(
    go.Scatter(
        x=df['date'],
        y=[50] * len(df),
        mode='lines',
        line=dict(color='rgba(209, 212, 220, 0.2)', width=1, dash='dot'),
        showlegend=False,
        hoverinfo='skip'
    ),
    row=3, col=1
)

# Update layout with TradingView styling
fig.update_layout(
    title=dict(
        text='DLF - Technical Analysis',
        font=dict(color=tv_text, size=20),
        x=0.5,
        xanchor='center'
    ),
    plot_bgcolor=tv_bg,
    paper_bgcolor=tv_bg,
    font=dict(color=tv_text, family='Arial, sans-serif', size=12),
    xaxis_rangeslider_visible=False,
    height=900,
    showlegend=True,
    legend=dict(
        orientation="h",
        yanchor="top",
        y=0.99,
        xanchor="left",
        x=0.01,
        bgcolor='rgba(19, 23, 34, 0.8)',
        bordercolor=tv_grid,
        borderwidth=1,
        font=dict(color=tv_text, size=11)
    ),
    hovermode='x unified',
    hoverlabel=dict(
        bgcolor=tv_grid,
        font_size=12,
        font_family="Arial, sans-serif",
        font_color=tv_text
    ),
    margin=dict(l=60, r=30, t=80, b=40)
)

# Update all xaxes
for i in [1, 2, 3]:
    fig.update_xaxes(
        gridcolor=tv_grid,
        showgrid=True,
        gridwidth=1,
        zeroline=False,
        showline=True,
        linewidth=1,
        linecolor=tv_grid,
        row=i, col=1
    )

# Update all yaxes
for i in [1, 2, 3]:
    fig.update_yaxes(
        gridcolor=tv_grid,
        showgrid=True,
        gridwidth=1,
        zeroline=False,
        showline=True,
        linewidth=1,
        linecolor=tv_grid,
        row=i, col=1
    )

# Update y-axes labels
fig.update_yaxes(
    title_text="Price (₹)",
    title_font=dict(color=tv_text, size=12),
    row=1, col=1
)
fig.update_yaxes(
    title_text="Volume",
    title_font=dict(color=tv_text, size=12),
    row=2, col=1
)
fig.update_yaxes(
    title_text="RSI",
    title_font=dict(color=tv_text, size=12),
    range=[0, 100],
    row=3, col=1
)

# Update x-axis label
fig.update_xaxes(
    title_text="Date",
    title_font=dict(color=tv_text, size=12),
    row=3, col=1
)

# Update subplot titles styling
for annotation in fig['layout']['annotations']:
    annotation['font'] = dict(color=tv_text, size=14)
    annotation['xanchor'] = 'left'
    annotation['x'] = 0.01

# Show the plot
fig.show()

# Optional: Save to HTML file
# fig.write_html("DLF_tradingview_style.html")