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CopyFunds work by bundling together financial assets of any kind, under one chosen strategy or theme, and are created and traded on eToro . CopyFunds will be divided into Top Trader CopyFunds; comprising the best performing and most sustainable traders on the network and Market CopyFunds made up of specially-selected instruments such as stocks, commodities or ETFs allowing investors to track a wide array of sectors around a defined market strategy. Top Trader CopyFunds is built using machine learning technology that selects the best performing traders on the eToro network. CopyFunds enable investors to invest in a bundled group of high-performing traders as well as predefined market strategies. CopyFunds aim to help investors minimise long-term risk and promote opportunities for growth by creating a diversified investments.
Autonomous trading robots have been studied in ar-tificial intelligence area for quite some time. Many AI techniqueshave been tested in finance field including recent approaches likeconvolutional neural networks and deep reinforcement learning.There are many reported cases, where the developers are suc-cessful in creating robots with great performance when executingwith historical price series, so called backtesting. However, whenthese robots are used in real markets or data not used intheir training or evaluation frequently they present very poorperformance in terms of risks and return. In this paper, wediscussed some fundamental aspects of modelling autonomoustraders and the complex environment that is the financialworld. Furthermore, we presented a framework that helps thedevelopment and testing of autonomous traders. It may also beused in real or simulated operation in financial markets. Finally,we discussed some open problems in the area and pointed outsome interesting technologies that may contribute to advancein such task. We believe that mt5b3 may also contribute todevelopment of new autonomous traders.
Investors try to predict returns of financial assets to make successful investment. Many quantitative analysts have used machine learning-based methods to find unknown profitable market rules from large amounts of market data. However, there are several challenges in financial markets hindering practical applications of machine learning-based models. First, in financial markets, there is no single model that can consistently make accurate prediction because traders in markets quickly adapt to newly available information. Instead, there are a number of ephemeral and partially correct models called "alpha factors". Second, since financial markets are highly uncertain, ensuring interpretability of prediction models is quite important to make reliable trading strategies. To overcome these challenges, we propose the Trader-Company method, a novel evolutionary model that mimics the roles of a financial institute and traders belonging to it. Our method predicts future stock returns by aggregating suggestions from multiple weak learners called Traders. A Trader holds a collection of simple mathematical formulae, each of which represents a candidate of an alpha factor and would be interpretable for real-world investors. The aggregation algorithm, called a Company, maintains multiple Traders. By randomly generating new Traders and retraining them, Companies can efficiently find financially meaningful formulae whilst avoiding overfitting to a transient state of the market. We show the effectiveness of our method by conducting experiments on real market data.
If you'd like to learn more about analyzing financial data with Python, check out Python for Finance by Yves Hilpisch. Algorithmic trading refers to the computerized, automated trading of financial instruments (based on some algorithm or rule) with little or no human intervention during trading hours. Almost any kind of financial instrument -- be it stocks, currencies, commodities, credit products or volatility -- can be traded in such a fashion. Not only that, in certain market segments, algorithms are responsible for the lion's share of the trading volume. The barriers to entry for algorithmic trading have never been lower.