Dynamic VCA with a Quantitative Trading Dashboard

A full-stack quantitative trading analysis platform that combines traditional technical indicators with AI/ML predictions for enhanced market analysis. Designed to Value Cost Average (VCA) ASX stocks. Developed to maximize long-term investment returns through systematic, data-driven decision making. (Rather than DCA like a noob)

Table of Contents

• Coming Soon!
• Overview
• Features
• Technical Indicators
• AI/ML Components
• Installation
• Usage

✨ Features Coming Soon!

• Interactive Backtesting Interface

  • Test trading strategies directly from the frontend
  • Visual backtesting results and performance metrics
  • Real-time strategy optimization
  • Custom parameter adjustment through UI

• Advanced AI Model Customization

  • Fine-tune ML models through the frontend interface
  • Customize training parameters
  • Adjust prediction timeframes
  • Model performance comparison tools

• Complete ASX Stock Integration

  • Access to all ASX-listed stocks via Yahoo Finance
  • Automated stock screening
  • Sector-based analysis
  • Market-wide trend detection

Overview

This platform integrates multiple layers of market analysis:

1. Traditional technical indicators
2. AI-driven price predictions
3. Risk management metrics
4. Market regime detection

Features

Market Analysis

• Real-time market data fetching using yfinance
• Comprehensive technical analysis
• Multiple timeframe analysis
• Volume profile analysis
• Risk metrics calculation
• Recommended VCA for the current month

AI/ML Predictions

• Prophet model for trend forecasting
• LSTM-based advanced predictions
• Parallel processing for multiple model consensus
• Confidence intervals for predictions

Risk Management

• Dynamic position sizing
• Volatility-adjusted recommendations
• Market regime-based risk scaling
• Automated stop-loss suggestions

Technical Indicators

Trend Indicators

1. Moving Averages

   • SMA (20, 50, 200 days)
   • EMA (100 days)
   • TEMA (Triple Exponential Moving Average)
   • HMA (Hull Moving Average)

2. MACD (Moving Average Convergence Divergence)

   • Signal Line
   • MACD Line
   • MACD Histogram
   • Used for trend direction and momentum

Momentum Indicators

1. RSI (Relative Strength Index)

   • Measures speed and magnitude of price changes
   • Overbought > 70
   • Oversold < 30

2. Stochastic Oscillator

   • Stoch %K (Fast)
   • Stoch %D (Slow)
   • Measures price position relative to high-low range

3. ROC (Rate of Change)

   • Measures percentage price change over time
   • Used for momentum confirmation

Volatility Indicators

1. Bollinger Bands

   • Upper Band (2 standard deviations)
   • Middle Band (20-day SMA)
   • Lower Band (2 standard deviations)
   • Used for volatility and potential reversal points

2. ATR (Average True Range)

   • Measures market volatility
   • Used for position sizing and stop-loss calculation

Volume Indicators

1. VWAP (Volume Weighted Average Price)

   • Intraday trading reference
   • Institutional trading benchmark

2. OBV (On-Balance Volume)

   • Cumulative volume indicator
   • Confirms price trends with volume

3. Accumulation/Distribution Line

   • Measures money flow
   • Helps identify divergences

AI/ML Components

Prophet Model (Basic Forecasting)

• Facebook's Prophet model for time series forecasting
• Features:
  • Trend detection
  • Seasonality patterns
  • Holiday effects
  • Changepoint detection

LSTM Model (Advanced Forecasting)

• Deep learning model for complex pattern recognition
• Architecture:

  LSTM(100) → Dropout(0.3) → LSTM(100) → Dropout(0.3) → LSTM(100) → Dense(1)
  

• Features:
  • Multi-feature input (price, volume, indicators)
  • Sequence learning
  • Non-linear pattern recognition

Parallel Processing System

• Multiple model instances run simultaneously
• Benefits:
  • Reduced prediction variance
  • Improved reliability
  • Confidence interval calculation
  • Model consensus

Backtesting Engine

• Historical performance testing
• Metrics:
  • MAE (Mean Absolute Error)
  • RMSE (Root Mean Square Error)
  • MAPE (Mean Absolute Percentage Error)
  • Custom performance metrics
  • Model back tests 5 years of data

Market Regime Detection

The system uses multiple factors to determine market regime:

1. Trend Analysis

   • SMA relationships
   • EMA crossovers
   • Price momentum

2. Volatility Analysis

   • ATR trends
   • Bollinger Band width
   • Price range analysis

3. Volume Analysis

   • OBV trends
   • Volume momentum
   • A/D line analysis

Installation

Prerequisites

• Python 3.8 or higher
• Node.js 14+ and npm
• Git

Backend Setup

1. Clone the repository:

git clone <repository-url>
cd quantitative-trading-dashboard

2. Create and activate a Python virtual environment:

# Windows
python -m venv venv
.\venv\Scripts\activate

# macOS/Linux
python3 -m venv venv
source venv/bin/activate

3. Install Python dependencies:

pip install -r requirements.txt

Frontend Setup

1. Navigate to the frontend directory:

cd frontend

2. Install Node.js dependencies:

npm install

Starting the Application

1. Start the Flask backend server (from the root directory):

# Windows
cd quant-dashboard/backend/app
python app.py

# macOS/Linux
export FLASK_APP=app.py
export FLASK_ENV=development
flask run

The backend server will start at http://localhost:5000

2. In a new terminal, start the frontend development server:

cd quant-dashboard/frontend
npm run dev

The frontend will be available at http://localhost:5173

Troubleshooting

If you encounter issues with package installation:

1. Prophet installation:

# Windows
conda install -c conda-forge prophet

# macOS/Linux
pip install prophet

2. TensorFlow installation:

pip install tensorflow

If you get SSL certificate errors with yfinance:

pip install --upgrade certifi

Usage

1. Access the dashboard at http://localhost:5173
2. Enter a stock ticker
3. Set your investment parameters:
   • Monthly target investment
   • Total investment target
   • Chose a AI modle to run

The system will provide:

• Current market analysis
• Investment recommendations
• AI-driven price predictions
• Risk assessment
• Position sizing suggestions

API Endpoints

1. /api/analyze

   • Basic market analysis
   • Technical indicators
   • Investment recommendations

2. /api/predict

   • Basic AI predictions
   • Prophet model forecasts
   • Trend analysis

3. /api/predict-advanced

   • LSTM model predictions
   • Confidence intervals
   • Multiple timeframe analysis

4. /api/backtest

   • Historical performance
   • Model accuracy metrics
   • Strategy validation

Development

• Frontend: React + Vite
• Backend: Flask
• AI/ML: Prophet, TensorFlow
• Data: yfinance, pandas

For development, use:

# Frontend
cd frontend
npm run dev

# Backend
cd backend
python app.py