This webinar presentation covers: - Overview of artificial intelligence and machine learning and practical applications in trading - Best practices and common pitfalls - Pattern recognition algorithms - Association rule learning - Building a live strategy in TRAIDE

1. Leveraging Artificial Intelligence
to Build Algorithmic Strategies
Brought to you by:

2. Welcome
Tad Slaff
CEO/Co-founder
Inovance
• Math background
• 6+ years experience
applying AI and ML to
algo trading
• Main expertise in forex

3. Overview
1. Terminology
2. Best Practices and Common Pitfalls
3. Strategy Development Applications
1. Indicator Selection
2. Pattern Recognition Algorithms
3. Association Rule Learning
4. Building a live strategy with TRAIDE

4. Terminology
• Artificial Intelligence
• The study and design of intelligent
agents.
• Intelligent agent: a system that perceive
its environment and takes action to
maximize its chance of success [1].
• Machine Learning
• Algorithms that can learn and make
predictions without being explicitly
programmed [2].
• Pattern Recognition
• Branch of machine learning focused on
uncovering patterns in data [3].
• Association Rule Learning
• Discovering interesting relationships,
usually formatted as “if/then”
statements [4].
• Big Data
• The Four V’s: Volume, Velocity, Variety,
Veracity [5].

5. Source: https://inovancetech.com/buzzwords.html

6. Best Practices and Common Pitfalls
1 ) Defining Success
• What are you trying to achieve?
2 ) Simplicity
• Do you really need to use AI or
machine-learning techniques?
3 ) Focus on Process
• Where are the assumptions or
uncertainties in your workflow?
4 ) Skepticism
• Are you confident in your results?

7. Indicator Selection
How do I know which indicators to use when
building my strategy?

8. Indicator Selection: Machine-Learning
Algorithms
Decision Trees or Random Forests
• Splitting the data set using recursive partitioning.

9. Indicator Selection: Machine Learning
Techniques
Wrappers
• Leveraging a machine-learning algorithm to select and evaluate
indicator subsets.

10. Indicator Selection: Machine Learning
Techniques
Ensemble Learning
• Combining multiple,
uncorrelated classifiers to
generate a singular, more
robust signal.

11. Pattern Recognition and
Association Rule Learning
How do I find robust entry and exit signals
based on the indicators I’ve selected?

12. Pattern Recognition and Association Rule
Learning
Pattern Recognition
• Branch of machine learning focused on uncovering patterns and
regularities in data.
Association Rule Learning
• Discovering interesting relationships, usually formatted as “if/then”
statements.

13. Pattern Recognition and Association Rule
Learning
1) Recognize Patterns
RSI(3)
Open -
SMA(50)
Long
Between 50
and 70
Open over 20
pips above
SMA(50)
Short Less than 25
Open between
10 and 5 pips
below SMA(50)
2) Association Rules

14. Pattern Recognition and Association Rule
Learning
Advantages
• Leverage machine-learning
algorithms
• Transparent
• Easily manually implemented
• Leverage human intuition
Disadvantages
• Difficult with high complexity
• Large number of indicators
• More complex algos or ensembles
• Not inherently easy to automate
• Requires human input
(subjective analyses)

15. Let’s build a strategy!
Easily apply pattern recognition and association rule learning
techniques to build your strategy.
Live Strategy:
https://traide.inovancetech.com/#/simulation/56f183a8e4b0aa46bd79ae82
TRAIDE

16. To Learn Automated Trading
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