Signals Network — Trading Algorithms For Everyone

For the past decade, exchange have been dominated by algorithmic and/or high-frequency trading strategies that are able to make instantaneous trading decisions based on data science techniques. A human trader has very little chance of beating a sophisticated trading algorithm for short-term trading, and thus, retail investors are being pushed further and further away from short-term trading. As machine learning techniques for processing time-series data continue to grow in sophistication, the market grows increasingly powered by trading bots. This is not inherently a bad thing, but since the average investor has no access to such techniques and is not statistics-savvy enough to create their own, it further exacerbates the issues retail investors face.

Even if a retail investor were educated on the topic and wished to create their own algorithmic strategy, they still face several hurdles. First, high-fidelity historical market data is often not accessible or costs a prohibitively-large amount. It is impossible to properly backtest a strategy if the data you use does not offer the precision that your strategy requires. Next, even if a retail investor is able to access data and backtest a strategy, they require low-latency access to successfully execute their strategy in real-time. Trading firms pay top dollar for the lowest possible time to execution, so even if a retail investor were to use the exact same strategy, a trading firm would win out since they would execute their trades before the competition. Finally, even if an investor were somehow able to access both high-quality data and a high-quality pipeline to the exchange, there is very little knowledge-sharing in the process. In other words, the success of one retail investor will most likely not result in the success of others, as they would probably keep their strategy a secret.

Signals Network

The Signals Network project aims to fix all of these problems with its innovative platform. With its decentralized marketplace for data-powered trading strategies, it offers:

Programming-free model building: People using Signals will be able to create models without needing to program. They will offer a GUI containing common technical indicators, so users can easily and quickly translate their trading logic into algorithms without the hassle of programming pipelines to obtain such data.

Free access to backtesting data: Signals will automatically provide an environment to test out strategies. People will not have to pay for this data, so they will be able to easily determine which of their strategies work and under which circumstances.

Model hosting: The Signals platform will host and run your models, using an off-chain computation platform like iExec. You do not need to pay for a server to host your trading bot, and you will not need to worry about latency issues.

Model sharing: Once users have created models, they can share these with others and monetize their sharing. Users not interested in developing their own trading strategies can choose to copy-trade other users’ successful models. This facilitates knowledge-sharing in the platform and allows people to share their success with others in exchange for compensation.

Hopefully, platforms like Signal’s will be able to fix the current inequality present in trading. Giving everyone access to high-powered algorithmic trading strategies is a great step toward ensuring that retail investors are able to feasibly compete with large trading firms. With the built-in social features and an easy-to-use interface for developing algorithms, this platform removes many of the barriers to entry that retail investors previously faced.

If this all sounds interesting and useful to you, I highly encourage making an account and joining their platform. They are also scheduled to begin their ICO on March 12th, so consider investing in their platform’s tokens as well.

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Signals Network — Trading Algorithms For Everyone was originally published in Hacker Noon on Medium, where people are continuing the conversation by highlighting and responding to this story.