week4: ai stock trading
This commit is contained in:
Omar Marie
483
week4/community-contributions/ai_stock_trading/tools/charting.py
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483
week4/community-contributions/ai_stock_trading/tools/charting.py
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"""
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Charting Module
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This module provides comprehensive charting and visualization capabilities
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for stock analysis with interactive dashboards using Plotly.
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"""
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import pandas as pd
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import numpy as np
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import plotly.graph_objects as go
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import plotly.express as px
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from plotly.subplots import make_subplots
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import streamlit as st
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from typing import Dict, List, Optional, Tuple
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import warnings
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warnings.filterwarnings('ignore')
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class StockChartGenerator:
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"""Enhanced stock chart generator with interactive dashboards"""
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def __init__(self):
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self.color_scheme = {
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'primary': '#1f77b4',
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'secondary': '#ff7f0e',
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'success': '#2ca02c',
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'danger': '#d62728',
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'warning': '#ff7f0e',
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'info': '#17a2b8',
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'background': '#f8f9fa'
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}
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def create_price_chart(self, data: pd.DataFrame, symbol: str, analysis: Dict = None) -> go.Figure:
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"""
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Create comprehensive price chart with technical indicators
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Args:
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data: Stock price data
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symbol: Stock symbol
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analysis: Technical analysis results
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Returns:
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Plotly figure object
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"""
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if data.empty:
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return self._create_empty_chart("No data available")
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# Create subplots
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fig = make_subplots(
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rows=3, cols=1,
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shared_xaxes=True,
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vertical_spacing=0.05,
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subplot_titles=(f'{symbol} Price Chart', 'Volume', 'Technical Indicators'),
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row_heights=[0.6, 0.2, 0.2]
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)
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# Main price chart (candlestick)
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fig.add_trace(
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go.Candlestick(
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x=data.index,
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open=data['Open'],
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high=data['High'],
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low=data['Low'],
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close=data['Close'],
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name='Price',
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increasing_line_color=self.color_scheme['success'],
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decreasing_line_color=self.color_scheme['danger']
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),
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row=1, col=1
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)
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# Add moving averages if available
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if 'SMA_20' in data.columns:
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fig.add_trace(
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go.Scatter(
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x=data.index,
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y=data['SMA_20'],
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mode='lines',
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name='SMA 20',
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line=dict(color=self.color_scheme['primary'], width=1)
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),
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row=1, col=1
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)
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if 'SMA_50' in data.columns:
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fig.add_trace(
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go.Scatter(
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x=data.index,
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y=data['SMA_50'],
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mode='lines',
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name='SMA 50',
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line=dict(color=self.color_scheme['secondary'], width=1)
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),
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row=1, col=1
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)
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# Volume chart
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colors = ['red' if close < open else 'green' for close, open in zip(data['Close'], data['Open'])]
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fig.add_trace(
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go.Bar(
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x=data.index,
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y=data['Volume'],
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name='Volume',
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marker_color=colors,
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opacity=0.7
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),
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row=2, col=1
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)
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# Technical indicators (RSI if available in analysis)
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if analysis and 'rsi' in analysis:
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# Create RSI line (simplified - would need full RSI calculation for time series)
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rsi_value = analysis['rsi']
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rsi_line = [rsi_value] * len(data)
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fig.add_trace(
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go.Scatter(
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x=data.index,
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y=rsi_line,
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mode='lines',
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name=f'RSI ({rsi_value:.1f})',
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line=dict(color=self.color_scheme['info'], width=2)
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),
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row=3, col=1
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)
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# Add RSI reference lines
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fig.add_hline(y=70, line_dash="dash", line_color="red", opacity=0.5, row=3, col=1)
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fig.add_hline(y=30, line_dash="dash", line_color="green", opacity=0.5, row=3, col=1)
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# Update layout
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fig.update_layout(
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title=f'{symbol} Stock Analysis Dashboard',
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xaxis_title='Date',
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yaxis_title='Price ($)',
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template='plotly_white',
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height=800,
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showlegend=True,
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hovermode='x unified'
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)
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# Remove rangeslider for cleaner look
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fig.update_layout(xaxis_rangeslider_visible=False)
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return fig
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def create_performance_chart(self, data: pd.DataFrame, symbol: str, analysis: Dict) -> go.Figure:
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"""
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Create performance analysis chart
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Args:
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data: Stock price data
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symbol: Stock symbol
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analysis: Analysis results with performance metrics
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Returns:
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Plotly figure object
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"""
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if data.empty:
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return self._create_empty_chart("No data available for performance analysis")
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# Calculate cumulative returns
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daily_returns = data['Close'].pct_change().fillna(0)
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cumulative_returns = (1 + daily_returns).cumprod() - 1
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fig = go.Figure()
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# Cumulative returns line
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fig.add_trace(
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go.Scatter(
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x=data.index,
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y=cumulative_returns * 100,
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mode='lines',
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name='Cumulative Returns (%)',
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line=dict(color=self.color_scheme['primary'], width=2),
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fill='tonexty',
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fillcolor='rgba(31, 119, 180, 0.1)'
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)
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)
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# Add benchmark line (0% return)
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fig.add_hline(y=0, line_dash="dash", line_color="gray", opacity=0.5)
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# Add performance annotations
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if analysis:
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total_return = analysis.get('total_return_pct', 0)
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fig.add_annotation(
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x=data.index[-1],
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y=total_return,
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text=f"Total Return: {total_return:.1f}%",
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showarrow=True,
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arrowhead=2,
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arrowcolor=self.color_scheme['primary'],
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bgcolor="white",
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bordercolor=self.color_scheme['primary']
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)
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fig.update_layout(
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title=f'{symbol} Performance Analysis',
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xaxis_title='Date',
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yaxis_title='Cumulative Returns (%)',
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template='plotly_white',
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height=500,
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hovermode='x'
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)
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return fig
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def create_risk_analysis_chart(self, analysis: Dict, symbol: str) -> go.Figure:
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"""
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Create risk analysis visualization
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Args:
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analysis: Analysis results with risk metrics
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symbol: Stock symbol
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Returns:
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Plotly figure object
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"""
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if not analysis or 'error' in analysis:
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return self._create_empty_chart("No risk data available")
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# Prepare risk metrics
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risk_metrics = {
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'Volatility (Annual)': analysis.get('volatility_annualized', 0),
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'Max Drawdown': abs(analysis.get('max_drawdown', 0)),
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'VaR 95%': abs(analysis.get('var_95', 0)),
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'VaR 99%': abs(analysis.get('var_99', 0))
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}
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# Create radar chart for risk metrics
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categories = list(risk_metrics.keys())
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values = list(risk_metrics.values())
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fig = go.Figure()
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fig.add_trace(go.Scatterpolar(
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r=values,
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theta=categories,
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fill='toself',
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name=f'{symbol} Risk Profile',
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line_color=self.color_scheme['danger'],
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fillcolor='rgba(214, 39, 40, 0.1)'
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))
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fig.update_layout(
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polar=dict(
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radialaxis=dict(
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visible=True,
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range=[0, max(values) * 1.2] if values else [0, 100]
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)
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),
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title=f'{symbol} Risk Analysis Chart',
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template='plotly_white',
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height=500
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)
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return fig
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def create_comparison_chart(self, data_dict: Dict[str, pd.DataFrame], symbols: List[str]) -> go.Figure:
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"""
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Create comparison chart for multiple stocks
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Args:
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data_dict: Dictionary of stock data {symbol: dataframe}
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symbols: List of stock symbols to compare
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Returns:
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Plotly figure object
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"""
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fig = go.Figure()
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colors = [self.color_scheme['primary'], self.color_scheme['secondary'],
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self.color_scheme['success'], self.color_scheme['danger']]
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for i, symbol in enumerate(symbols):
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if symbol in data_dict and not data_dict[symbol].empty:
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data = data_dict[symbol]
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# Normalize prices to start at 100 for comparison
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normalized_prices = (data['Close'] / data['Close'].iloc[0]) * 100
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fig.add_trace(
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go.Scatter(
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x=data.index,
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y=normalized_prices,
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mode='lines',
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name=symbol,
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line=dict(color=colors[i % len(colors)], width=2)
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)
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)
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fig.update_layout(
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title='Stock Price Comparison (Normalized to 100)',
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xaxis_title='Date',
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yaxis_title='Normalized Price',
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template='plotly_white',
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height=600,
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hovermode='x unified'
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)
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return fig
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def create_sector_analysis_chart(self, sector_data: Dict) -> go.Figure:
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"""
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Create sector analysis visualization
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Args:
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sector_data: Dictionary with sector analysis data
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Returns:
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Plotly figure object
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"""
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# This would typically show sector performance, P/E ratios, etc.
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# For now, create a placeholder
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fig = go.Figure()
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fig.add_annotation(
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x=0.5, y=0.5,
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text="Sector Analysis<br>Coming Soon",
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showarrow=False,
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font=dict(size=20),
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xref="paper", yref="paper"
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)
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fig.update_layout(
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title='Sector Analysis Dashboard',
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template='plotly_white',
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height=400,
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showticklabels=False
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)
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return fig
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def create_trading_signals_chart(self, data: pd.DataFrame, analysis: Dict, trading_decision: Dict, symbol: str) -> go.Figure:
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"""
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Create trading signals visualization
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Args:
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data: Stock price data
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analysis: Technical analysis results
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trading_decision: Trading recommendation
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symbol: Stock symbol
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Returns:
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Plotly figure object
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"""
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if data.empty:
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return self._create_empty_chart("No data available for trading signals")
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fig = go.Figure()
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# Price line
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fig.add_trace(
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go.Scatter(
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x=data.index,
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y=data['Close'],
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mode='lines',
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name='Price',
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line=dict(color=self.color_scheme['primary'], width=2)
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)
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)
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# Add trading signal
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recommendation = trading_decision.get('recommendation', 'HOLD')
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current_price = data['Close'].iloc[-1]
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signal_color = {
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'BUY': self.color_scheme['success'],
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'SELL': self.color_scheme['danger'],
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'HOLD': self.color_scheme['warning']
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}.get(recommendation, self.color_scheme['info'])
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fig.add_trace(
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go.Scatter(
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x=[data.index[-1]],
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y=[current_price],
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mode='markers',
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name=f'{recommendation} Signal',
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marker=dict(
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color=signal_color,
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size=15,
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symbol='triangle-up' if recommendation == 'BUY' else
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'triangle-down' if recommendation == 'SELL' else 'circle'
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)
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)
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)
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# Add price target if available
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price_target = trading_decision.get('price_target')
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if price_target:
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fig.add_hline(
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y=price_target,
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line_dash="dash",
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line_color=self.color_scheme['success'],
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annotation_text=f"Target: ${price_target:.2f}"
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)
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# Add stop loss if available
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stop_loss = trading_decision.get('stop_loss')
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if stop_loss:
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fig.add_hline(
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y=stop_loss,
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line_dash="dash",
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line_color=self.color_scheme['danger'],
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annotation_text=f"Stop Loss: ${stop_loss:.2f}"
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)
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fig.update_layout(
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title=f'{symbol} Trading Signals',
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xaxis_title='Date',
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yaxis_title='Price ($)',
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template='plotly_white',
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height=500,
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hovermode='x'
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)
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return fig
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def create_dashboard_summary(self, symbol: str, analysis: Dict, trading_decision: Dict, sharia_compliance: Dict) -> Dict:
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"""
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Create summary metrics for dashboard display
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Args:
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symbol: Stock symbol
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analysis: Technical analysis results
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trading_decision: Trading recommendation
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sharia_compliance: Sharia compliance results
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Returns:
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Dictionary with summary metrics
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"""
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summary = {
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'symbol': symbol,
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'current_price': analysis.get('current_price', 0),
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'total_return': analysis.get('total_return_pct', 0),
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'volatility': analysis.get('volatility_annualized', 0),
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'trading_recommendation': trading_decision.get('recommendation', 'HOLD'),
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'trading_confidence': trading_decision.get('confidence', 0) * 100,
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'sharia_ruling': sharia_compliance.get('ruling', 'UNCERTAIN'),
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'sharia_confidence': sharia_compliance.get('confidence', 0) * 100,
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'risk_level': trading_decision.get('risk_level', 'medium'),
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'trend_direction': analysis.get('trend_direction', 'unknown'),
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'rsi': analysis.get('rsi', 50),
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'max_drawdown': analysis.get('max_drawdown', 0)
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}
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return summary
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def _create_empty_chart(self, message: str) -> go.Figure:
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"""Create an empty chart with a message"""
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fig = go.Figure()
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fig.add_annotation(
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x=0.5, y=0.5,
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text=message,
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showarrow=False,
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font=dict(size=16),
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xref="paper", yref="paper"
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)
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fig.update_layout(
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template='plotly_white',
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height=400,
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showticklabels=False
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)
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return fig
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# Global instance for easy import
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chart_generator = StockChartGenerator()
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# Convenience functions
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def create_price_chart(data: pd.DataFrame, symbol: str, analysis: Dict = None) -> go.Figure:
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"""Convenience function to create price chart"""
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return chart_generator.create_price_chart(data, symbol, analysis)
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def create_performance_chart(data: pd.DataFrame, symbol: str, analysis: Dict) -> go.Figure:
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"""Convenience function to create performance chart"""
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return chart_generator.create_performance_chart(data, symbol, analysis)
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def create_trading_signals_chart(data: pd.DataFrame, analysis: Dict, trading_decision: Dict, symbol: str) -> go.Figure:
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"""Convenience function to create trading signals chart"""
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return chart_generator.create_trading_signals_chart(data, analysis, trading_decision, symbol)
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