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Peter Newell
2018-05-22 20:20:23 +08:00
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# 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
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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.