# vectorbt

**Repository Path**: yutiansut/vectorbt

## Basic Information

- **Project Name**: vectorbt
- **Description**: Vectorized library for backtesting and trade optimization
- **Primary Language**: Unknown
- **License**: GPL-3.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2019-08-18
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# vectorbt

#### Superfast backtesting and trade optimization

From indicators, signals, positions, equity, returns, to performance metrics and grid search: This package shares the same idea as behind the [Quantopian](https://www.quantopian.com) and [Zipline](https://www.zipline.io), but designed especially for fast strategy tuning. The code was made blazingly fast by using full vectorization with NumPy and multiprocessing.

The motivation behind this package is simple: *"How to select the optimal windows for a SMA crossover strategy? Which strategy has the lowest risk? Which strategy yields the highest return? Are technical indicators of any use at all?"*

Resulted in some cool animations such as how optimal hyperparameters change over time.

#### Installation
```
pip install git+https://github.com/polakowo/vector-bt.git
```

#### Tutorial
Tutorial is provided through [Jupyter nbviewer](http://nbviewer.jupyter.org/github/polakowo/vector-bt/blob/master/example.ipynb)

#### Bitcoin example
Exhaustive grid search over Bitcoin price. 

Parameters: 1 year of 2h-period OHLC data, dual SMA crossover strategy with filter of 0.05 * STD and transaction fees of 0.0015 (Poloniex exchange). Heatmap below visualizes grid of SMA windows and the respective rates.

![SMA-heatmap](SMA-heatmap.png)

Distribution of expectancy rates in SMA strategy. 

![SMA-dist](SMA-dist.png)

For comparison, here is the distribution of expectancy rates in random strategy.

![random-dist](random-dist.png)