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You will find that the level of success you have using Neural Networks (NN) as a tool for financial market prediction is strongly dependent on what initially appear to be some quite subtle factors. In particular: Input data: You mention using "certain technical indicators". I assume that you mean the standard TA set of price-based indicators such as Moving ...


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Can take a look the other pointers from wikipedia http://en.wikipedia.org/wiki/Algorithmic_trading Another list is here: http://algotradingindia.blogspot.it/2012/05/open-source-trading-platforms-list.html For hedge funds there is a famous top solution publicly available (referenced by wiki), but not "open source". ("Open source" stuff is usually put ...


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No. All you need is to be naturally smart, and not lack common sense. Well, clearly, a bare minimum of education (the 4 operations should suffice) ;-) Also sometimes, too much specialized education may really be detrimental (it's common the case of Physics PhDs, who have an harmful tendency towards predictive models). Usually, and generally speaking, a ...


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Haim Bodek wrote a lot of research, not all those order types still exist, but it's a fascinating read: http://haimbodek.com/research.html


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In addition to @madilyn's answer, there is one point that needs to be addressed and that is often called an unfair advantage although it is merely a competitive advantage. Take the US Equities market. There are now several venues on which the same symbols are traded. If one HFT acquires information about one symbol in one venue - e.g. due to a limit order ...


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I have heard of several allegations in the recent days, but they are mostly baseless. However, there are a rare, few trading venues whose matching rules are most often accused of giving unfair order execution advantages to certain firms. These usually arise from violations of the standard price-time priority: IEX's broker priority rule. "All orders will ...


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std(PPS) PPS = Packets Per Second (wiki article: network packets) The standard deviation of packets per second received from a liquidity source are directly related to the number of quotes per second, or the number of trades per second occurring on that liquidity source. Thus, the higher the number of network / data packets per second, the more volatility ...



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