Now that we've discussed the causes and impact of simulation dislocations from live trading performance, let's explore possible approaches/solutions to those problems if the algorithmic trading strategies deployed to live markets do not match th anticipated performance.
Tweaking backtesting and strategies in response to live trading
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
Tweaking backtesting and strategies in response to live trading
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