<commit_before><p align="center"><img width=12.5% src="https://github.com/anfederico/Clairvoyant/blob/master/media/Logo.png"></p>
<p align="center"><img width=60% src="https://github.com/anfederico/Clairvoyant/blob/master/media/Clairvoyant.png"></p>

&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
![Python](https://img.shields.io/badge/python-v2.7%20%2F%20v3.6-blue.svg)
[![Build Status](https://travis-ci.org/anfederico/Clairvoyant.svg?branch=master)](https://travis-ci.org/anfederico/Clairvoyant)
![Dependencies](https://img.shields.io/badge/dependencies-up%20to%20date-brightgreen.svg)
[![GitHub Issues](https://img.shields.io/github/issues/badges/shields.svg)](https://github.com/altantur/algorithms/issues)
![Contributions welcome](https://img.shields.io/badge/contributions-welcome-orange.svg)
[![License](https://img.shields.io/badge/license-MIT-blue.svg)](https://opensource.org/licenses/MIT)

## Basic Overview

Using stock historical data, train a supervised learning algorithm with any combination of financial indicators. Rapidly backtest your model for accuracy and simulate investment portfolio performance. 
<p align="center"><img width=95% src="https://github.com/anfederico/Waldo/blob/master/media/Schematic.png"></p>

<br>

## Visualize the Learning Process
<img src="https://github.com/anfederico/Clairvoyant/blob/master/media/Learning.gif" width=40%>

<br>

## Install
```python
pip install clairvoyant
```

## Backtesting Signal Accuracy
During the testing period, the model signals to buy or sell based on its prediction for price
movement the following day. By putting your trading algorithm aside and testing for signal accuracy
alone, you can rapidly build and test more reliable models.<commit_msg>issues tracker added<commit_after><p align="center"><img width=12.5% src="https://github.com/anfederico/Clairvoyant/blob/master/media/Logo.png"></p>
<p align="center"><img width=60% src="https://github.com/anfederico/Clairvoyant/blob/master/media/Clairvoyant.png"></p>

&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;
![Python](https://img.shields.io/badge/python-v2.7%20%2F%20v3.6-blue.svg)
[![Build Status](https://travis-ci.org/anfederico/Clairvoyant.svg?branch=master)](https://travis-ci.org/anfederico/Clairvoyant)
![Dependencies](https://img.shields.io/badge/dependencies-up%20to%20date-brightgreen.svg)
[![GitHub Issues](https://img.shields.io/github/issues/altantur/algorithms.svg)](https://github.com/altantur/algorithms/issues)
![Contributions welcome](https://img.shields.io/badge/contributions-welcome-orange.svg)
[![License](https://img.shields.io/badge/license-MIT-blue.svg)](https://opensource.org/licenses/MIT)

## Basic Overview

Using stock historical data, train a supervised learning algorithm with any combination of financial indicators. Rapidly backtest your model for accuracy and simulate investment portfolio performance. 
<p align="center"><img width=95% src="https://github.com/anfederico/Waldo/blob/master/media/Schematic.png"></p>

<br>

## Visualize the Learning Process
<img src="https://github.com/anfederico/Clairvoyant/blob/master/media/Learning.gif" width=40%>

<br>

## Install
```python
pip install clairvoyant
```

## Backtesting Signal Accuracy
During the testing period, the model signals to buy or sell based on its prediction for price
movement the following day. By putting your trading algorithm aside and testing for signal accuracy
alone, you can rapidly build and test more reliable models.
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