Module 1: Foundations of Systematic Trading

• Introduction to Systematic Trading:
  • What is a trading system?
  • Advantages of systematic trading over discretionary trading (objectivity, consistency, backtesting).
  • Different types of trading systems (trend-following, mean-reversion, arbitrage, etc.).
  • The trading system development lifecycle.
• Understanding Market Data:
  • Types of market data (price, volume, open interest, etc.).
  • Data sources and APIs.
  • Data cleaning and preprocessing.
  • Timeframes and their impact on trading systems.
• Basic Statistical Concepts for Trading:
  • Mean, standard deviation, variance.
  • Distributions (normal, skewed).
  • Correlation and covariance.
  • Introduction to probability and risk.

Module 2: Technical Analysis for System Development 

• Core Technical Indicators:
  • Moving Averages (SMA, EMA, WMA).
  • Momentum Indicators (RSI, MACD, Stochastic Oscillator).
  • Volatility Indicators (Bollinger Bands, ATR).
  • Volume Indicators (On-Balance Volume, Volume Price Trend).
• Chart Patterns and Their Systematic Application:
  • Identifying and codifying chart patterns for entry and exit signals.
  • Combining chart patterns with technical indicators.
• Price Action Analysis for System Rules:
  • Identifying key price levels (support, resistance).
  • Candlestick patterns and their interpretation in a systematic context.
• Developing Rules-Based Trading Logic:
  • Translating technical analysis concepts into specific, testable rules.
  • Defining entry and exit criteria.
  • Setting stop-loss and take-profit levels systematically.

Module 3: Building the Trading System 

• Defining System Objectives and Parameters:
  • Identifying the target market and timeframe.
  • Setting realistic profit targets and risk tolerance.
  • Determining capital allocation per trade.
• Developing Entry and Exit Rules:
  • Combining multiple indicators and conditions for robust signals.
  • Implementing different order types (market, limit, stop).
• Implementing Position Sizing Strategies:
  • Fixed fractional, fixed ratio, Kelly criterion (introduction).
  • Risk-based position sizing.
• Incorporating Risk Management:
  • Setting maximum risk per trade and per day.
  • Using stop-loss orders effectively.
  • Managing drawdowns.

Module 4: Backtesting and Performance Evaluation 

• Introduction to Backtesting:
  • The importance of backtesting.
  • Choosing appropriate backtesting software or tools (Python libraries, dedicated platforms).
  • Data requirements and potential biases.
• Setting Up a Robust Backtesting Framework:
  • Defining clear backtesting parameters.
  • Handling slippage and commission costs.
  • Avoiding look-ahead bias.
• Analyzing Backtesting Results:
  • Key performance metrics: win rate, average win/loss ratio, profit factor, maximum drawdown, Sharpe ratio, Sortino ratio.
  • Interpreting performance reports and identifying strengths and weaknesses.
  • Statistical significance of backtesting results.
• Curve Fitting and Over-Optimization:
  • Understanding the dangers of optimizing parameters too closely to historical data.
  • Techniques to mitigate over-optimization (walk-forward testing, out-of-sample testing).

Module 5: System Implementation and Automation 

• Choosing a Brokerage Platform with API Access:
  • Understanding API capabilities for automated trading.
  • Factors to consider when selecting a broker.
• Introduction to Algorithmic Trading:
  • Basic programming concepts for trading (Python is often preferred).
  • Libraries for data analysis and trading (e.g., Pandas, NumPy, TA-Lib, backtrader, MetaTrader).
  • Developing simple trading bots.
• Manual vs. Automated Execution:
  • Advantages and disadvantages of each approach.
  • Setting up alerts and notifications for manual execution.
• Order Management and Execution Strategies:
  • Understanding different order types and their use in automated systems.
  • Managing order flow and potential execution issues.

Module 6: System Monitoring and Adaptation 

• Live Trading Considerations:
  • Psychological aspects of live trading.
  • Dealing with unexpected market events.
  • Managing emotions and sticking to the system.
• Performance Monitoring and Tracking:
  • Setting up real-time performance dashboards.
  • Tracking key metrics in a live environment.
• System Maintenance and Adjustment:
  • Identifying when a system is no longer performing as expected.
  • The process of re-evaluating and potentially modifying system rules.
  • Adapting to changing market conditions.
• Continuous Learning and Improvement:
  • Staying updated with market trends and new research.
  • The importance of a trading journal.