Forex Trading Tutorial

Forex Trading Tutorial | Learn to Use Machine Learning and Systematic Trading in Forex

Christos Gklinavos teaches how to build a Random Forest regression model for Forex trading using price indicators and a sentiment indicator and Marti Castany digs deep into the Systematic G10 FX index.

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Additional Reading:
✅Basics Of Forex Trading For Beginners
https://blog.quantinsti.com/basics-forex-trading-beginners/

✅9 Factors Affecting Forex Market Trading
https://blog.quantinsti.com/forex-market-trading-factors/

✅Automated Forex Trading: Introduction, Strategies and Platforms
https://blog.quantinsti.com/automated-forex-trading/

✅Carry Trade Strategy In Forex
https://blog.quantinsti.com/carry-trade-strategy-forex/
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Chapters:
00:00 – Christos Gklinavos Introduction
03:36 – Project Introduction
05:35 – Motivation behind persuing Ml for Forex
06:18 – Approach
09:33 – Steps Involved to Build the Strategy
12:04 – Challenges and Support
15:46 – Key Leanings
17:19 – Resources
17:58 – Q&A
32:05 – Martí C Introduction
33:18 – Project Introduction
35:10 – How to Build an FX Index
37:07 – Intrest Rates (Carry)
39:19 – Purchasing Power – Value Factor
41:16 – Momentum – Trend
44:10 – Putting it All Together
45:40 – FX Universe
47:26 – Working with Data
50:11 – Plots and Graphs
53:40 – Conclusion
54:52 – Q&A

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About the Tutorial
1: Building a Random Forest regression model for Forex trading using price indicators and a sentiment indicator
This tutorial goes into building a machine learning model that could predict the next day’s currency close price based on previous days’ OHLC data, EMA, RSI, OBV indicators, and a Twitter sentiment indicator. It is based on a Random forests Regressor because it combines the benefits of trees’ predictive power and avoidance of overfitting. Also, the introduction of a sentiment indicator balances the limitations of technical analysis and draws information from the domain of behavioral economics.

2: Systematic G10 FX index
This tutorial is about the construction of an FX Index that can be used as a benchmark to proxy the G10 FX market performance in a systematic manner. To accomplish it, we will be using both technical and macroeconomic factors such as momentum, interest rates and purchasing power.

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Experts:

Christos Gklinavos (Independent Software Developer and Robotics Educator)
Christos Gklinavos is an enthusiast of AI, Robotics, Drones and FinTech. He studied Economics, Business Administration, and Informatics. He holds a Master of Science in ICT and several certifications in programming and machine learning from Udacity, Kaggle and Google. In July 2019, he gained the Certificate of Excellence for successfully completing the Executive Programme in Algorithmic Trading offered by QuantInsti Ltd.

He had worked for two 2 years as an Independent Forex Trader using various platforms like MT4 with EAs, QuantConnect, Blueshift and InteractiveBrokers. Currently, he is living in Ioannina, Greece, teaching Educational Robotics and he is married with one kid.

Marti Castany (Founder & Portfolio Manager)
Marti is a quantitative researcher focused on the development of low and medium frequency systematic trading models. His experience comprises derivatives pricing and trading, OTC liquidity infrastructure and consulting services for the sell and buy side. He is also the Founder and Portfolio Manager of KomaLogic, a proprietary systematic trading firm.

Don’t forget to ‘Like’ and ‘Share’ this video and be sure to ‘Subscribe’ to our YouTube channel!
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Most useful links:
Join EPAT – Executive Programme in Algorithmic Trading: https://goo.gl/3Oyf2B
Visit us at: https://www.quantinsti.com/
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