Distributed systems R&D
During the last decade blockchain and cryptocurrencies have exploded into a new market. Traditional technical analysis has adopted new algorithmic strategies for price prediction implementing them in autonomous algo-trading systems. The main strategy creation steps are:
Design autonomous quant algorithms which take long/short positions in cryptocurrencies using trading indicators and risk management.
All strategies are prototyped and backtested against historical data for multiple assets finding where the strategy has its strengths and weaknesses.
Production development is the next step when a strategy is fully validated, the strategy is enriched with resilient and secure software architectures.
Crypto currencies autonomous trading
Cryptocurrencies are volatile assets, very volatile. They are the best case scenario for a trader with proper strategies. With manual trading is impossible to manage very volatile multiple assets every day, automatic trading using computed algorithms fixes this problem scaling it out, being able to invest at lower prices when the market is having FUD and offloading risk when the market is in FOMO.
Cryptocurrency algo trading automatically buys and sells various cryptocurrencies at the right time with the goal of generating a profit. Profit is a balance between positive gains and negative gains the system accumulated.
Trading indicators and risk management
Indicators helps to predict market movements. Indicators are build ingesting collateral price data and analysing it in real time. Based on these calculations they raise sell and buy signals.
Risk management is one of the most important parts, decide how much capital allocation in every trade is key to be resilient while market remains irrational.
Automatic trading allows to operate avoiding human emotions: fear and greed without being tired. It operates and reacts faster to multiple market events.
A strategy is composed by a correct selection of trading indicators and risk management strategies generating an automated decision machine.
Distributed architecture using microservices and serverless architecture facilitates scalability, efficiency, reliability.
Multi-paradigm development languages and parallel computing development facilitates versatility to achieve maximum performance.
DevOps practices gives to development life cycle flexibility, agility and continuous deployment features.
An hybrid approach using Azure cloud services and on-premises assets which suits the cost/redundancy/privacy balance.
Our multi-strategy crypto trading autonomous system. It operates taking long/short positions at scalping and swing trading time frames.
CryptoTrader uses our self design backtested strategies based on traditional and self designed indicators. All transactions are distributed to scalable services and redundant exchanges.
SocialHype is new generation trading indicator that can measure the social crypto hype when crypto news are continously discussed in mass media, socialnetworks newspapers and other sources.
It helps to identify real-time movements in any crypto asset at several time scales, from minutes to months.
Trading strategies are fed using multiple sources of data. Aside from the basic: price and volume, in the crypto currency scene there are other sources like: on-chain data, mining data, new generation indicators and derivated data that needs to be stored to design and backtest stategies.
OmniInput is a data store engine that accumulates and serves historical data in different time resolutions.