The dual-moving average trading strategy places a buy order when the short moving average crosses the long moving average in an upward direction and will place a sell order when the cross happens on the other side. This section will present the backtesting implementation of the dual-moving average strategy. We will present the implementation of a for-loop backtester and an event-based backtester.
Backtesting the dual-moving average trading strategy
by Sourav Ghosh, Sebastien Donadio
Learn Algorithmic Trading
- Title Page
- Copyright and Credits
- About Packt
- Contributors
- Preface
- Section 1: Introduction and Environment Setup
- Algorithmic Trading Fundamentals
- Why are we trading?
- Basic concepts regarding the modern trading setup
- Understanding algorithmic trading concepts
- From intuition to algorithmic trading
- Components of an algorithmic trading system
- Why Python?
- Summary
- Section 2: Trading Signal Generation and Strategies
- Deciphering the Markets with Technical Analysis
- Designing a trading strategy based on trend- and momentum-based indicators
- Creating trading signals based on fundamental technical analysis
- Implementing advanced concepts, such as seasonality, in trading instruments
- Summary
- Predicting the Markets with Basic Machine Learning
- Section 3: Algorithmic Trading Strategies
- Classical Trading Strategies Driven by Human Intuition
- Sophisticated Algorithmic Strategies
- Creating a trading strategy that adjusts for trading instrument volatility
- Creating a trading strategy for economic events
- Understanding and implementing basic statistical arbitrage trading strategies
- Summary
- Managing the Risk of Algorithmic Strategies
- Section 4: Building a Trading System
- Building a Trading System in Python
- Connecting to Trading Exchanges
- Creating a Backtester in Python
- Section 5: Challenges in Algorithmic Trading
- Adapting to Market Participants and Conditions
- Strategy performance in backtester versus live markets
- Continued profitability in algorithmic trading
- Profit decay in algorithmic trading strategies
- Signal decay due to lack of optimization
- Signal decay due to absence of leading participants
- Signal discovery by other participants
- Profit decay due to exit of losing participants
- Profit decay due to discovery by other participants
- Profit decay due to changes in underlying assumptions/relationships
- Seasonal profit decay
- Adapting to market conditions and changing participants
- Profit decay in algorithmic trading strategies
- Summary
- Final words
- Other Books You May Enjoy
Backtesting the dual-moving average trading strategy
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