triple-barrier 0.4.5rc0

Back-testing and machine learning pipelines trades labeling utility

WARNING:

This is a work in progress DO NOT USE IT FOR PRODUCTION PURPOSES

Overview

Triple Barrier is a trade labeler that can be used for algorithmic trading back-testing or machine learning training and validating pipelines. It records for each trade when, at what price level and why a position was closed.

It includes features to plot the triple for a particular trade and the closing event:

Other features:

• Built upon pandas, numpy, matplotlib mplfinance.
• It can be used to label single trades or semi vectorized, meaning that can be used with a pandas apply function.

Why?

This project emerges from a repeated trading strategy back-testing process where I was caught again and again copying and pasting from previous pipelines the code to perform a vectorized (semi-vectorized to be more accurate) labeling of trades. To avoid this DRY (Do not Repeat Yourself) routine, that is why I decided to move this code to a library.

Before moving further into the library details a little bit of context.

Trading Strategies

A trading strategy describes the logic for opening, managing and closing positions.

Depending on the trading strategy, once a position is opened, four events can determine how the position ends:

1. Stop loss hit: The price hit the stop loss, which is the maximum tolerable loss (Limit Order)
2. Take profit hit: The price hit the take profit, which is the estimated maximum profit the position can reach (Limit Order)
3. Expiration time reached: The position has reached a specific expiration time (Good Til Time or GTT orders)
4. A custom condition: Any custom condition that can trigger the position closing. These conditions depend on price action while the position is opened

To determine if a strategy is potentially profitable before live trading, it is necessary to collect a large sample of trades to analyze the effectiveness of the strategy in terms of profits, mean profits, profits distributions, drawdowns or any metric you prefer.

Doing this analysis process manually is not recommended at all, considering the amount of data you need to analyze, the volume of trades required to determine if the strategy is significantly profitable and the human error, to name a few reasons.

Algorithmic Trading and Triple Barrier

In simple terms, algorithmic trading is using software to automate all the processes described above: Open, manage and Close trading positions.

But before running live a trading algorithm that implements a trading model you need to perform some research and analyze the algorithm behavior on historic data to understand whether the algorithm is able to generalize well and behave similarly on unseen or future data, this process is called back-testing.

Is in back-testing where you need to identify: when positions were opened, when and why they were closed, so you can calculate all required performance metrics and that is why Triple Barrier was built for.

How?

How to install

pip install triple-barrier==0.4.3rc0

Triple barrier test

Or to this Jupyter Notebook that combines triple barrier calculation with plotting.

Triple Barrier Jupyter Notebook

Examples

This is a work in progress, but you can find some examples here:

You can see use case examples in this folder

TODO

This project is its final stages of testing, documentation and CLEANing.

Besides that, there are some identified tasks that need to be done before the first release.

• Add string representations for some classes
• Provide an out-of-the-box function that can be easily used by pandas apply . Currently, you need to build one.
• Refactor list of barriers hits (OrderBoxHits.barriers) as dictionary, currently is a list which is not much actionable.
• Plotting: Add possibility to plot oscillators in a panel below
• Add trailing stops

Other Documentation

Uml models

References

Why numpy is fast