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The Efficient Market Hypothesis has been a staple of economics research fordecades. In particular, weak-form market efficiency -- the notion that pastprices cannot predict future performance -- is strongly supported byeconometric evidence. In contrast, machine learning algorithms implemented topredict stock price have been touted, to varying degrees, as successful.Moreover, some data scientists boast the ability to garner above-market returnsusing price data alone. This study endeavors to connect existing econometricresearch on weak-form efficient markets with data science innovations inalgorithmic trading. First, a traditional exploration of stationarity in stockindex prices over the past decade is conducted with Augmented Dickey-Fuller andVariance Ratio tests. Then, an algorithmic trading platform is implemented withthe use of five machine learning algorithms. Econometric findings identifypotential stationarity, hinting technical evaluation may be possible, thoughalgorithmic trading results find little predictive power in any machinelearning model, even when using trend-specific metrics. Accounting fortransaction costs and risk, no system achieved above-market returnsconsistently. Our findings reinforce the validity of weak-form marketefficiency.

Validating Weak-form Market Efficiency in United States Stock Markets with Trend Deterministic Price Data and Machine Learning