Consider the seven major currency pairs, sampled hourly over the last six months. We calculate the pairwise Pearson correlation coefficients to determine the degree with which each pair “moves” together: Values near one or negative one indicate high correlation, values with lower absolute value less so. Positive values indicate movement in the same direction; negative […]

# Tag: FOREX

## machine learning in FOREX (part one: establishing a performance baseline)

Introduction We’ve been applying machine learning to FOREX price prediction. The performance of our models varies widely, so to establish a baseline we created a simple linear regression model with which we can compare performance of more sophisticated models against. What We Are Trying To Do Given a time-series of 26 four-hour price samples, we […]

## autocorrelation in FOREX

To inform the construction of a machine learning-based price prediction algorithm, we want to understand how many lags prove statistically significant with regard to autocorrelation in the seven major FOREX pairs. So we first choose 10,000 random time points between January 1, 2000 and January 1, 2017 for each of the seven pairs. Then we […]

## pseudo-harmonic FOREX prediction with machine learning (part one)

“Harmonic” trading methods seek patterns in the relationships between neighboring peaks and valleys in the time series. Particularly, harmonic traders seek pre-specified ratios in the price differences among a series of peaks and valleys. For example, a trader might observe the following pattern: Let A, B, C, D, and E be the points in the […]