Project Overview: Building a System for Consistent Daily Profits

Build a system that can generate a return of 10-20% in a month. If the target is met within the first week(s), stop trading; otherwise, rebalance or exit positions by the month’s end.

Step 1: Set Up a Trading System Based on Market Regimes

Goal: Classify the Current Market Regime

Objective: Determine whether the market is in a bullish, bearish, or sideways (neutral) regime. This will help you identify the ideal trading conditions for your strategy.
How to Implement:
- Use the Market Regime Detection Model (using Hidden Markov Models, clustering, etc.) to identify the current market regime based on historical stock price movements, indicators, and economic conditions.
- Bullish Regime: Focus on long positions (buying stocks).
- Bearish Regime: Focus on short positions or avoiding new long positions.
- Sideways Regime: Implement mean-reversion or range-bound strategies.

Step 2: Predict Stock Movements (Up or Down) Using Classification Models

Goal: Predict Stock Price Movements

Objective: Use a Bayesian Classifier to predict whether a stock will go up or down in the next day or week.
How to Implement:
- For each stock in your universe (e.g., 50 stocks), use historical price action data (OHLCV, trades executed, etc.) to train the Bayesian model.
- This model will give you the probability of the stock going up or down on the next trading day/week.
- Decision Logic: Only select stocks that have a high probability of going up if the regime is bullish or a high probability of going down if the regime is bearish.

Step 3: Forecast the Magnitude of Stock Movement (Regression Model)

Goal: Predict the Magnitude of Price Movement

Objective: Use a regression model to predict how much a stock will move if it is expected to go up or down.
How to Implement:
- Use regression models (such as Bayesian Regression, LSTMs, or ARIMA) to predict the percentage or magnitude of stock price movement for the next time frame (e.g., next day or week).
- The output can help you assess how much potential return each stock is expected to offer.

Step 4: Portfolio Construction & Optimization

Goal: Optimize Portfolio Allocation Based on Predictions

Objective: Allocate capital to stocks predicted to have the highest returns while managing risk.
How to Implement:
- Use Mean-Variance Optimization or Black-Litterman models to allocate your capital across different stocks.
- Factors to consider:
  - Predicted probability of stock movement (from Bayesian classifier).
  - Predicted magnitude of return (from the regression model).
  - Stock volatility (from historical data or volatility models like GARCH).
- Maximize Risk-Adjusted Returns by ensuring that the portfolio weights align with the stocks with the highest predicted returns and lowest risk.

Step 5: Define Entry and Exit Conditions Based on Market Regimes and Forecasts

Goal: Set Entry & Exit Triggers

Objective: Implement a strategy for when to enter or exit positions.
How to Implement:
- Entry Conditions:
  - For a bullish regime, enter long positions on stocks with a high probability of moving up (as predicted by the classification model).
  - For a bearish regime, enter short positions on stocks predicted to go down.
  - Consider using technical indicators like Bollinger Bands, RSI Divergence, and MACD to time entries more precisely.
- Exit Conditions:
  - Set stop-loss limits and take-profit levels to lock in profits or limit losses.
  - Exit a position if the stock hits the predefined target percentage return (e.g., 10-20% in a month).
  - Rebalance portfolio if the cumulative return target is achieved before the month ends.

Step 6: Backtest the Strategy

Goal: Simulate the Strategy on Historical Data

Objective: Validate the strategy and assess its effectiveness using historical data.
How to Implement:
- Backtest the entire strategy on past stock data, using the predictions from the classification and regression models, portfolio optimization techniques, and entry/exit rules.
- Monitor metrics like Sharpe Ratio, Maximum Drawdown, Win Rate, and Annualized Return.
- Assess whether the strategy consistently achieves the 10-20% return target.

Step 7: Implement Real-Time Execution and Monitoring

Goal: Deploy the Strategy in Live Markets

Objective: Execute trades in real-time while continuously monitoring performance.
How to Implement:
- Execution System: Set up the execution system via platforms like Interactive Brokers or Alpaca. This will connect your models to actual trading accounts to place buy/sell orders based on the strategy’s predictions.
- Continuous Monitoring: Keep track of portfolio performance in real-time to ensure that the strategy is working as expected.
- Adjustment & Rebalancing: If the strategy achieves 10-20% returns early in the month, stop trading or rebalance based on updated predictions.

Step 8: Iterate & Improve the System

Goal: Refine the Strategy

Objective: Continuously improve the models and strategies to optimize performance.
How to Implement:
- Use model performance feedback (e.g., backtest results, real-time performance) to adjust the parameters of the Bayesian classifier, regression models, and portfolio optimization techniques.
- Experiment with new technical indicators, market regimes, and other data sources (e.g., sentiment analysis, macroeconomic data).
- Continuously test new strategies, for example, incorporating more advanced machine learning models (like XGBoost or reinforcement learning) as needed.

Summary of Action Plan to Achieve the Goal:

Market Regime Detection: Classify market conditions and adapt strategies based on whether the market is bullish, bearish, or sideways.
Stock Price Prediction: Use Bayesian classifiers to predict the trend (up/down) of stocks for the next time period.
Magnitude Prediction: Use regression models to forecast the magnitude of stock movement.
Portfolio Optimization: Allocate capital efficiently based on predicted returns and risk profiles.
Entry/Exit Strategy: Define clear entry and exit rules for stocks based on market conditions and predictions.
Backtest & Optimize: Test the strategy on historical data and adjust for improved performance.
Real-Time Execution: Set up execution systems to trade in live markets based on strategy predictions.
Iterate & Improve: Continuously refine models and strategies to improve long-term performance.

How This Helps Achieve the 10-20% Monthly Target:

By combining market regime analysis with precise stock movement predictions, portfolio optimization, and strategic entry/exit rules, this approach maximizes the chances of achieving consistent returns. The key is managing risk, monitoring performance, and adjusting when the strategy hits the 10-20% target, either by stopping trading early or rebalancing positions effectively.

Enhanced Trading Strategy Framework Analysis

Key Optimisations and Improvements

1. Market Regime Detection Enhancements

Replace Hidden Markov Models with Transformer-based Architecture
- HMMs can miss complex, non-linear market patterns
- Modern transformer models can better capture long-term dependencies and market transitions
- Consider using Time-Series Transformer (TST) or Temporal Fusion Transformer (TFT)
- Include attention mechanisms to weight different market indicators dynamically

2. Advanced Classification Models

Replace Simple Bayesian Classifier with Ensemble Approach
- Implement Stacking Ensemble combining:
  - XGBoost (for handling non-linear relationships)
  - LightGBM (for speed and handling categorical features)
  - Neural Networks (for complex pattern recognition)
  - CatBoost (for robust handling of categorical variables)
- Use Bayesian Optimization for hyperparameter tuning
- Implement feature importance analysis for better feature selection

3. Enhanced Regression Models

Hybrid Forecasting System
- Replace single regression model with a combination of:
  - Prophet for trend and seasonality decomposition
  - LSTM-CNN hybrid for capturing both sequential and local patterns
  - Gaussian Process Regression for uncertainty quantification
  - Quantile Regression for risk assessment
- Implement Dynamic Time Warping (DTW) for better pattern matching

4. Advanced Portfolio Optimization

Replace Traditional Mean-Variance with Modern Approaches
- Implement Hierarchical Risk Parity (HRP) algorithm
  - More robust to estimation errors
  - Better handles market turbulence
- Add Kelly Criterion for position sizing
- Incorporate Factor Investing principles
  - Include momentum, value, and quality factors
  - Use PCA for factor decomposition
- Implement Conditional Value at Risk (CVaR) optimization

5. Enhanced Entry/Exit Strategy

Add Dynamic Position Management
- Implement Adaptive Stop-Loss using:
  - Volatility-adjusted thresholds
  - Machine learning-based exit signals
- Add multi-timeframe momentum analysis
- Include order flow analysis
- Implement options-based hedging strategies

6. Improved Backtesting Framework

Event-Driven Backtesting System
- Include realistic transaction costs
- Account for market impact
- Implement walk-forward optimization
- Add Monte Carlo simulation for robustness testing
- Include regime-specific performance analysis

7. Real-Time Execution Enhancements

Add Smart Order Routing
- Implement adaptive execution algorithms
- Include dark pool access strategies
- Add anti-gaming logic
- Implement transaction cost analysis (TCA)

8. Risk Management Improvements

Enhanced Risk Controls
- Implement portfolio-level circuit breakers
- Add correlation-based position limits
- Include sector exposure limits
- Add drawdown-based position scaling
- Implement VaR-based position sizing

Potential of this framework

Market Regime Detection Framework
- This is powerful because it helps you adapt to different market conditions
- You can detect bull markets, bear markets, high volatility, low volatility, trending, or ranging markets
- This knowledge can be applied to any trading strategy, not just for 10-20% returns
Classification Models (Predicting Direction)
- Using machine learning to predict if prices will go up or down
- This can be applied to any timeframe (minutes, days, weeks)
- Can be used for any tradeable asset (stocks, crypto, forex, commodities)
Magnitude Prediction (How Much Movement)
- Predicts the size of potential moves
- Helps identify which opportunities have the best potential
- Can be used to filter trades or size positions
Portfolio Optimization
- Helps distribute risk efficiently
- Can be used for any portfolio size or type
- Adaptable to different risk tolerances
Entry/Exit Framework
- Systematic approach to entering and exiting positions
- Can be modified for different strategies (trend following, mean reversion, etc.)
- Adaptable to different timeframes and goals

This framework could be applied to various goals like:

Building a long-term investment portfolio
Day trading strategies
Swing trading systems
Market-neutral strategies
Sector rotation strategies
Multi-asset trading systems

🪴 Prakritidev Verma

Explorer

"Introduction"