From 0c4dec54fefe2c234324e6b42a3f877b1c745672 Mon Sep 17 00:00:00 2001 From: Peter Newell Date: Tue, 22 May 2018 20:20:23 +0800 Subject: [PATCH] Update README.md --- README.md | 14 ++++++++------ 1 file changed, 8 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index ac29e5d..2ff3058 100644 --- a/README.md +++ b/README.md @@ -1,20 +1,20 @@ -# V20pyPro +# Introduction +This project provides several examples of common machine learning models applied to financial market predictions using TensorFlow, Keras, and Sci-kit Learn. All these models use the past 500 days of data for a given forex pairs, with a number of technical indicators added to the DataFrame. All of these models are standard, i.e. there hasn't been a major effort to optimize them and they could all be improved up in one way or another. - -# Machine Learning Examples - -Exploring pricing data using a variety of machine learning libraries and algorithms. All examples use data from the AUD_JPY csv that contains OHLC and technical indicators' data. +Ideally, this project would help make these tools more accessible for those learning to apply machine learning to financial markets. ## Ensemble Research Using a basic list of six standard Sci-kit Learn ensemble methods, we can explore the effectiveness of these off-the-shelf models. Out of the box, these models can achieve 65-75% accuracy but could be improved by manipulating learning rates, increasing the number of estimators, or for some models, including a base estimator; i.e. another predictive model that improves the Booster's reliability. *AdaBoostRegressor* + ![AdaBoost png](/graphs/model1.png) *BaggingRegressor* -![BaggingRegressor png][/graphs/model2.png) + +![BaggingRegressor png](/graphs/model2.png) ## LSTM with Multiple Inputs @@ -30,9 +30,11 @@ This model could be improved in a number of ways. Increasing the number of layer Linear regression models are natural candidates for time series analysis. Using the standard Sci-kit learn Ridge and Linear Regression models, we can achieve roughly 80% accuracy on a single currency pair before manipulating any of the parameters. *Ridge* + ![Ridge png](/graphs/Ridge 0.80991178871757.png) *LinearRegression* + ![LinearRegression png](/graphs/LinearRegression 0.8099495670315746.png) The Autoregressive Integrated Moving Average (ARIMA) is not exactly a machine learning algorithm, but a linear model used in econometric analysis that can be applied to financial markets to make predictions. A quick build of this model can also produce 78% accurate predictions on the test data.