Empirical Smart Investor Syndicate
An Investor Syndicate Backing An Incubator For The Development And Trading Of Artificial Intelligence Machine Learning High-Frequency Algorithmic Trading Systems Exclusively Focused On High-Frequency Trading In Currency Markets.
Earn 6% Annual Interest As A Convertible Note Holder Plus Share In 50% Of Trading Profits.
“People no longer are responsible for what happens in the market, because computers make all the decisions.” - Michael Lewis, Flash Boys.
High-Frequency Algorithmic Trading Investor Syndicate
High-frequency algorithmic trading is irreversibly changing the structure of financial markets and is a natural result of the evolution of financial markets and the development of technology. The main aim of high-frequency trading is to perform trades based on market behaviors as fast and as scalable as possible.
Today it is estimated that more than 80% of trading is done by algorithmic trading. Computers make all the decisions and algorithms are trading against algorithms.
High-frequency algorithmic trading is where trade orders are managed by high-speed algorithms which typically involve two-sided order placements to benefit from short-term price volatility fluctuations and order flow of the market auction process, and momentum trading strategies where the algorithm starts execution based on a given spike or given moment to 'buy high to go higher' and 'sell low to go lower.'
Empirical is offering the opportunity to be a member of an investor syndicate of an artificial intelligence machine learning high-frequency algorithmic trading system incubator, exclusively focused on high-frequency trading in currency markets.
For members of the investment syndicate we are offering convertible notes as a flexible loan scheme that pay a fixed return of 6% annually plus a share of 50% of trading profits. This flexible loan scheme enables new partners to buy in or existing partners to increase their capital stake.
If the algorithms generate 1% per month (12% p.a.) the return to members would be 12% per annum (6% fixed plus half of 12% profits). If the algorithms generate 5% per month (60% p.a.) the return to members would be 36% per annum (6% fixed plus half of 60% profits). If the artificial intelligence machine learning algorithms learn from experience the high-frequency trading profits are expected to be much higher.
Investment syndicate capital will be used for new algorithmic and machine learning software development, and trading capital for an institutional proprietary trading account with direct market access. Members will have transparency to the proprietary account and share in the profit generation from high-speed algorithms executing thousands of daily transactions in the currency markets.
Concept, initial startup and trading capital, research and back-testing of trading algorithms, development of machine learning algorithms, and oversight of algorithm performance is led by Robert Nash, a professional trader with over 29 years experience trading futures, options, currencies and gold, who previously founded and managed a proprietary trading company, a licensed and regulated fund management company, a boutique hedge fund, and an algorithmic system incubator.
Supported by a team of quant developers with experience in implementing algorithmic trading programs for investment banks, proprietary trading firms, brokerage firms, and hedge funds.
The future for the Empirical Smart Investor Syndicate is where everything is automated and all income is generated from the underlying artificial intelligence machine learning high-frequency trading algorithms with the ability to automatically learn and improve from experience resulting in automation that captures frequent pieces of price action each day.
This is an opportunity for members of the investment syndicate to make a very high return on a modest amount of capital within the first year and a continuous income for themselves and future generations.
What Is High-Frequency Algorithmic Trading?
Put simply high-frequency algorithmic trading, is about finding effective ways to carry out a lot of trades in a very short space of time.
High-frequency algorithmic trading is carried out by professional traders employing computerized trading using proprietary algorithms acting in a proprietary capacity whom engage in a large number of trades on a daily basis. The time it took you to read that last sentence could be used to make hundreds and even thousands of trades by a computer. On those numbers, you can see why humans don’t stand much of a chance in competition.
Algorithms are used for decision making, order generation, and execution without any human intervention, employing aggressive short-term trading strategies on short time frames for opening and closing positions that capitalize on the benefits of automation and speed.
The term algorithmic trading is often used synonymously with automated trading, algo trading, black box trading, and quantitative or quant trading, that are heavily reliant on complex mathematical formulas and high-speed computer programs that follows a defined set of instructions (an algorithm) to place a trade. The trade, in theory, can generate profits at a speed and frequency that is impossible for a human trader.
The defined sets of instructions are based on timing, price, quantity, or any mathematical model. Apart from profit opportunities for the trader, algo trading renders markets more liquid and trading more systematic by ruling out the impact of human emotions on trading activities.
It’s easy to see why high-frequency trading is of great use in the forex market, the world’s largest and most liquid market.
High-frequency trading programs are designed to be able to act upon small movements in currency prices, the sort of movement that would make a few dollars at a small scale but can deliver bigger returns when the trades are numbered in the hundreds or thousands. This is how a small move up or down can result in large returns when the right algorithm can identify and react to many different opportunities in a single day, let alone a week or month.
High-frequency trading in forex is, therefore, an exaggerated form of standard forex trading, using complicated algorithms to make big trading opportunities from the smallest of movements. Forex is a market defined by the constant flow of such movements, making it the perfect choice to apply these principles.
Basically, the idea is similar to that of a casino: sustain profitability through taking a small expected profit as many times as possible.
Artificial Intelligence Machine Learning and Deep Learning
Let’s define Machine Learning vs Artificial Intelligence. These two terms are always used side by side of each other, but they are different. With different, we mean that Machine Learning is a subset of Artificial Intelligence. Artificial Intelligence refers to create intelligent machines. Machine Learning refers to a system that can learn from experience.
Machine Learning algorithms build a mathematical model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to perform the task. Machine Learning is closely related to computational statistics, which focuses on making predictions using computers. In the past decade, machine learning has given us self-driving cars, practical speech recognition, and effective web search. Many researchers think it is the best way to make progress towards human-level artificial intelligence.
Many hedge funds started to utilize Artificial Intelligence within the algorithmic trading world. It’s understandable that most of them do not disclose the details and mechanism of their approaches in applying Artificial Intelligence in their trading algorithms, but it’s understood that they use methods of Machine Learning and Deep Learning. There is also a wide appliance of sentiment analysis on the market in which the result can be used in trading. The main objective of applying sentiment algorithms is to obtain knowledge about the psychology of the market.
Machine Learning hedge funds already significantly outperform generalized hedge funds, as well as quant funds. Machine Learning systems can be incredibly helpful tools for humans navigating the decision-making process involved with trading and risk assessment. The impact of human emotions on trading decisions is often the greatest hindrance to performance. Algorithms and computers make decisions and execute trades faster than any human can, and do so free from the influence of emotions.
There are multiple strategies which use Machine Learning to optimize algorithms, including linear regressions, neural networks, Deep Learning, support vector machines, and naive Bayes, to name a few.
Machine Learning and Deep Learning are playing an increasingly important role in real-time trading decisions for market participants. Deep Learning models also have been widely applied in trading. Deep Learning models with multiple layers have shown a promising architecture that can be more suitable for predicting financial time series data.
Traditional linear regression models are gradually becoming outdated, and the opportunity for artificial intelligence to revolutionize this industry cannot be overstated. Academic researchers have already begun assessing the potential implementation of Deep Learning in high-frequency trading, so it is likely well-known funds and industry front-runners such as Citadel, Renaissance Technologies, Bridgewater Associates, Two Sigma Investments, and Virtu Financial are doing the same and are pursuing Machine Learning and Deep Learning strategies as part of their investment approach.
If a machine’s output can be effectively paired with superior investment strategy through algorithms, the impact of Deep Learning throughout the financial services industry beyond high-frequency trading may be unprecedented. As firms continue to seek increased profitability, Machine Learning and Deep Learning will likely be their next transition.
The future for members of the Empirical Smart Investor Syndicate is a continuous income from high-frequency trading utilizing artificial intelligence based foreign exchange trading signal prediction algorithms using Machine Learning and Deep Learning technology to predict the short-term price movements of major foreign exchange currency pairs.