We analyze the performance of various models constructed using linear kernel SVR and trained on historical bid data for high frequency currency trading. The bid tick data is converted into equally spaced (1 min) data. Different values for the number of training samples, number of features, and the length of the timeframes are used when conducting the experiments. These models are used to conduct simulated currency trading in the following year. We record the profits, hit ratios and number of trades executed from using these models. Our results indicate it is possible to obtain a profit as well as good hit ratio from a linear model trained only on historical data under certain pre-defined conditions. On examining the parameters for the linear models generated, we observe that a large number of models have all co-efficient values as negative while giving profit and good hit ratio, suggesting a simple yet effective trading strategy.