My trading bot


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After this not-so-friendly introduction, I started to study how the technology behind cryptocurrencies works, and I fell in love with it. I was already interested in the stock market, so I my trading bot the familiar stock market with the novel crypto.

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This worked for a while, but I quickly realized that I should - and could - make the bot a lot better. Now, the project that I started as a hobby has a capital management system, a combination of technical indicators, and sentiment analysis powered by machine learning. Between 10 March and 10 Augustmy bot resulted in a success rate of About my project I needed a bot that gave me a high-performance, scalable way to calculate technical indicators and process sentiment data in real-time.

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The machine learning sentiment analysis started as a simple experiment to see if external news affected the market. For example: if a famous person in the crypto ecosystem tweeted earnings on the Internet 100 dollars per day a big exchange my trading bot hacked, the price will probably fall and affect the whole market.

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Likewise, very good news should impact the price in a positive way. I calculated sentiment analysis scores in real-time, as soon as new data was ingested from sources like Twitter, Reddit, RSS feeds, and etc.

Then, using these scores, I could determine market conditions at the moment.

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Current trading bot dashboard where I track all my ongoing trades and results; In this particular case, filtered for the period of March to August Quick breakdown of my results and success rates week-over-week; In this particular case, filtered for the period of March to August I started looking for an alternative: a performant database. I went through several databases, and all of them always lacked something I wound up needing to continue my project.

I have started using the bot about 6 months ago. To be honest, the first 4 months I took a lot of losses and it was an expensive learning lesson on how to use to bot, what are the parameters that I need get right and so on. For the past 2 months, I have been making constant profits with NO losing trade over trades currently. How does the bot work?

As soon as my series started to grow exponentially to higher levels, the server didn't have enough memory to handle them all in real-time.

I have a large number of unique metrics, so the process ran out of available memory quickly, I handle somewhat large amounts of data every day, especially on days with many market movements.

Photo by M. This is a programming tutorial aimed at teaching you how to build a trading bot for learning purposes. Any decisions, investments, or risks you take as a result of building a trading bot are your responsibility. I cannot be held responsible for any decisions you make as a result of reading this tutorial.

This is where TimescaleDB started to shine for me. In addition to this raw market my trading bot, a common use case for me is to analyze the data in different time frames e. I maintain these records in a pre-computed aggregate to increase my query performance and allow me to make faster decisions about whether or not to enter a position. To develop my bot and all its capabilities, I used Node.

Photo by Nicolas Hoizey on Unsplash Having trouble thinking of a strategy? Not sure which APIs and packages to use?

The process of re-evaluating a market requires a second instance of my bot that runs in the background and uses my main strategy to simulate trades in all Bitcoin markets. When it detects that a market is doing well, based on the metrics I track, that market enters the main bot instance and starts live trading.

  1. How I Power My Trading Bot with TimescaleDB | Hacker Noon
  2. How to create and test a crypto trading bot - walk-through How to create and test a crypto trading bot - walk-through Create and test a crypto bot in minutes with no coding - text guide Written by Katerina Petkovska Updated over a week ago How to create and test a crypto trading bot You like what CLEO.

The same applies to those that are performing poorly; as soon as the main instance of my bot detects things are going badly, the market is removed from the main instance and the second instance begins tracking it. If it improves, it's added back in.

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As every developer likely knows all too well, the process of building a software is to always improve it. Why TimescaleDB? The TimescaleDB websitecore documentationand this blog post about managing and processing huge time-series datasets is all pretty easy to understand and follow — and the TimescaleDB team is responsive and helpful and they always show up in community discussions, like my Reddit AMA.

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And, as an SQL user, TimescaleDB adds very little maintenance overhead, especially compared to my trading bot or maintaining a new database or language. This post is slightly modified from original post on the TimescaleDB blog read it here.

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