AN ARTIFICIAL INTELLIGENCE -BASED FOREX TRADING PREDICTION SOLUTION FOR KENYA
A INDIVIDUAL CAPSTONE PROJECT FOR MASTERS IN DATA SCIENCE AND ARTIFICIAL INTELLIGENCE
Keywords:
Data ScienceAbstract
In the realm of forex trading, accurate currency price predictions are paramount for
successful trading strategies. However, the challenge of inaccurate predictions persists,
particularly in regions like Kenya. This academic report delves into the extent of this
problem in the Kenyan context, exploring the domain of time series forecasting and
predictive analytics. The proposed solution employs supervised learning, chosen for its
ability to learn patterns from labelled historical data. The report outlines the specific
machine learning algorithm employed, focusing on developing a predictive model for
currency price movements using historical forex data and economic indicators.
Challenges in data acquisition are discussed, alongside the steps involved in data
access, incorporation of economic indicators, and dataset requirements. Detailed
explanations of data processing techniques, including time series analysis, feature
engineering, and data normalization, are provided, along with insights into the use of
time series forecasting models and classification models, such as LSTM, and their
suitability for the task. Deployment considerations, including the deployment platform
and how traders can utilize the model to inform their trading strategies, are explored,
along with challenges and limitations in real-world scenarios.
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