Treffer: Algorithmic forex trading: developing a Python program for automated trading strategies

Intraday trading, or day trading, involves buying and selling financial instruments within the same trading day. This strategy capitalizes on short-term market movements and requires rigorous analysis and quick decision-making. Historically, trading was predominantly manual, relying on traders' intuition and experience. However, the advent of algorithmic trading has revolutionized the field by employing computer algorithms to execute trades at speeds and frequencies far beyond human capability. Algorithmic trading uses complex mathematical models and statistical techniques to identify and exploit market inefficiencies. The Forex (foreign exchange) market, the largest and most liquid financial market in the world, is particularly suited for intraday trading. With a daily trading volume exceeding $6 trillion, the Forex market offers ample opportunities for traders to profit from short-term price fluctuations in currency pairs. The high liquidity and round-the-clock trading of the Forex market make it an ideal environment for implementing and testing algorithmic trading strategies. Python has emerged as a powerful tool for algorithmic trading due to its simplicity, extensive libraries, and strong community support. Libraries such as NumPy and pandas facilitate data manipulation and analysis, while matplotlib and seaborn enable robust data visualization. Furthermore, Python's machine learning libraries provide the capabilities to develop predictive models that can enhance trading strategies. This master's thesis aims to develop and evaluate various intraday trading strategies using Python. The project covers the entire pipeline from data preprocessing to the implementation of trading algorithms and their backtesting. Strategies include the Simple Moving Average (SMA) crossover, scalping using price action, and advanced techniques combining multiple indicators like the Relative Strength Index (RSI) and support and resistance levels. The thesis also explores vectorized ba
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