dash_app.py

import pandas as pd
import pandas_ta as ta
import dash
from dash import dcc, html
from dash.dependencies import Input, Output, State
import plotly.graph_objects as go
from plotly.subplots import make_subplots

# Load sample data (replace with actual data)
data = pd.read_csv('data/brent_futures.csv')
data['Date'] = pd.to_datetime(data['Date'])
data.sort_values('Date', inplace=True)

# Initialize Dash app
app = dash.Dash(__name__)

# Dash layout
app.layout = html.Div([
    html.H1("OHLCV Chart with Technical Indicators"),
    
    # Dropdown for selecting moving averages and other technical indicators
    html.Div([
        html.Label("Select Moving Averages"),
        dcc.Dropdown(
            id='moving-avg-choice',
            options=[
                {'label': 'MA 4', 'value': 'MA_4'},
                {'label': 'MA 10', 'value': 'MA_10'},
                {'label': 'MA 30', 'value': 'MA_30'}
            ],
            multi=True,
            value=['MA_4', 'MA_10']  # Default selected
        ),
        html.Br(),
        
        # Sliders for lengths of technical indicators
        html.Label("MA 4 Length"),
        dcc.Slider(id='ma4-length', min=2, max=20, value=4, marks={i: str(i) for i in range(2, 21)}),
        html.Br(),
        
        html.Label("MA 10 Length"),
        dcc.Slider(id='ma10-length', min=5, max=50, value=10, marks={i: str(i) for i in range(5, 51, 5)}),
        html.Br(),
        
        html.Label("MA 30 Length"),
        dcc.Slider(id='ma30-length', min=10, max=100, value=30, marks={i: str(i) for i in range(10, 101, 10)}),
        html.Br(),
        
        dcc.Checklist(
            id='indicator-choice',
            options=[
                {'label': 'Bollinger Bands', 'value': 'BB'},
                {'label': 'RSI', 'value': 'RSI'},
                {'label': 'MACD', 'value': 'MACD'}
            ],
            value=['BB', 'RSI'],  # Default checked
            labelStyle={'display': 'inline-block'}
        )
    ], style={'width': '30%', 'display': 'inline-block', 'padding': '20px'}),
    
    # Div for rendering plotly chart
    html.Div([dcc.Graph(id='technical-chart')], style={'width': '68%', 'display': 'inline-block'}),
])

# Callback to update chart based on selected technical analyses and lengths
@app.callback(
    Output('technical-chart', 'figure'),
    Input('moving-avg-choice', 'value'),
    Input('ma4-length', 'value'),
    Input('ma10-length', 'value'),
    Input('ma30-length', 'value'),
    Input('indicator-choice', 'value')
)
def update_chart(moving_avg_choice, ma4_length, ma10_length, ma30_length, indicators):
    # Reset calculations for indicators
    data['MA_4'] = ta.sma(data['Close'], length=ma4_length) if 'MA_4' in moving_avg_choice else None
    data['MA_10'] = ta.sma(data['Close'], length=ma10_length) if 'MA_10' in moving_avg_choice else None
    data['MA_30'] = ta.sma(data['Close'], length=ma30_length) if 'MA_30' in moving_avg_choice else None
    
    if 'BB' in indicators:
        bbands = ta.bbands(data['Close'], length=20)
        data['BB_upper'], data['BB_middle'], data['BB_lower'] = bbands.iloc[:, 2], bbands.iloc[:, 1], bbands.iloc[:, 0]
    if 'RSI' in indicators:
        data['RSI'] = ta.rsi(data['Close'], length=8)
    if 'MACD' in indicators:
        macd = ta.macd(data['Close'], fast=12, slow=26, signal=9)
        data['MACD'], data['MACD_Signal'], data['MACD_Hist'] = macd.iloc[:, 0], macd.iloc[:, 1], macd.iloc[:, 2]
    
    # Create subplots
    fig = make_subplots(
        rows=4 if 'MACD' in indicators else 3, cols=1,
        shared_xaxes=True,
        vertical_spacing=0.05,
        row_heights=[0.6, 0.2, 0.2, 0.2] if 'MACD' in indicators else [0.6, 0.2, 0.2],
        subplot_titles=('OHLC with Technical Indicators', 'RSI', 'MACD', 'Volume') if 'MACD' in indicators else ('OHLC with Technical Indicators', 'RSI', 'Volume')
    )

    # Plot OHLC candlestick
    fig.add_trace(go.Candlestick(x=data['Date'], open=data['Open'], high=data['High'], low=data['Low'], close=data['Close'], name="OHLC"), row=1, col=1)

    # Plot Moving Averages
    if 'MA_4' in moving_avg_choice:
        fig.add_trace(go.Scatter(x=data['Date'], y=data['MA_4'], mode='lines', name=f'MA {ma4_length}', line=dict(color='blue')), row=1, col=1)
    if 'MA_10' in moving_avg_choice:
        fig.add_trace(go.Scatter(x=data['Date'], y=data['MA_10'], mode='lines', name=f'MA {ma10_length}', line=dict(color='orange')), row=1, col=1)
    if 'MA_30' in moving_avg_choice:
        fig.add_trace(go.Scatter(x=data['Date'], y=data['MA_30'], mode='lines', name=f'MA {ma30_length}', line=dict(color='green')), row=1, col=1)

    # Plot Bollinger Bands
    if 'BB' in indicators:
        fig.add_trace(go.Scatter(x=data['Date'], y=data['BB_upper'], mode='lines', name='BB Upper', line=dict(color='purple')), row=1, col=1)
        fig.add_trace(go.Scatter(x=data['Date'], y=data['BB_middle'], mode='lines', name='BB Middle', line=dict(color='gray')), row=1, col=1)
        fig.add_trace(go.Scatter(x=data['Date'], y=data['BB_lower'], mode='lines', name='BB Lower', line=dict(color='purple')), row=1, col=1)

    # Plot RSI
    if 'RSI' in indicators:
        fig.add_trace(go.Scatter(x=data['Date'], y=data['RSI'], mode='lines', name='RSI', line=dict(color='magenta')), row=2, col=1)

    # Plot MACD
    if 'MACD' in indicators:
        fig.add_trace(go.Scatter(x=data['Date'], y=data['MACD'], mode='lines', name='MACD', line=dict(color='blue')), row=3, col=1)
        fig.add_trace(go.Scatter(x=data['Date'], y=data['MACD_Signal'], mode='lines', name='MACD Signal', line=dict(color='red')), row=3, col=1)
        fig.add_trace(go.Bar(x=data['Date'], y=data['MACD_Hist'], name='MACD Hist', marker=dict(color='green')), row=3, col=1)

    # Plot Volume
    fig.add_trace(go.Bar(x=data['Date'], y=data['Volume'], name='Volume', marker=dict(color='darkgreen')), row=4 if 'MACD' in indicators else 3, col=1)

    # Customize layout
    fig.update_layout(
        height=1000,
        showlegend=True,
        xaxis_rangeslider_visible=False,
        dragmode='drawline'  # Allow drawing lines on the chart
    )

    # Enable dynamic range for y-axes
    fig.update_yaxes(autorange=True)

    return fig

# Run the app
if __name__ == '__main__':
    app.run_server(debug=True)