Topic: GRAYLL [IEO] Simple Automated Investment App Driven by AI & ML - page 12. (Read 7950 times)

Let's go straight to the point : How much does GRAYLL cost?
Depending on the algorithms used, fees between 0.3%-1.8% are charged for starting and stopping the algorithms.

Arkady 0.3% | Balthazar 0.9% | Kaspar 1.3% | Melkior 1.8%
When your algorithmic positions are closed with profits a standard 18% fee is charged in the GRAYLL GRX digital asset on the profits only, not the principal amount. There are no other costs for using the GRAYLL System, of course you must have the GRAYLL GRX digital asset which can be purchased directly in the GRAYLL App with XLM (Stellar Lumens). Transferring GRX and XLM between accounts over the Stellar Network has a negligible cost of 0.00001 XLM (~$0.00000045) per transaction.

Arkady, also referred to as the GRZ algorithm, counteracts the inflation of the US economy and devaluation of the United States dollar, it helps to maintain purchasing power and can also be used as a ultra high yield savings account or as an alternative to an Exchange Traded Fund [ETF] or in particular a Pension Plan. The US dollar has lost 95-97% of its original value since its inception despite having attained the world’s reserve currency status in 1944, additionally unfunded liabilities of the US Government are estimated to be $65 to $220 trillion depending on the source and method of analysis conducted. Arkady seems to be a superior alternative to the traditional long-term options available and is perpetual.

Balthazar, Kaspar, Melkior: 3 algorithms that automatically produce high returns in any market condition. These algorithms are shorter term and each have a specific time-frame for maximimizing profits. The Arkady algorithm differs from the 3xMagi algorithms. The 3xMagi algorithms are also referred to as the GRY algorithms - GRY 1, GRY 2 and GRY 3.

You can either keep them in your GRAYLL App wallet or other wallets, many people buy digital assets in the hope that they will increase in value over time. You may also use the GRAYLL System algorithms to increase the value of your digital assets holdings, that’s what we created the GRAYLL App and System for.

Algorithms are taking a lot of flak from those in financial circles. They’ve been blamed for a recent flash crash in the British pound and the greatest fall in the Dow in decades. They’ve been called a cancer and linked to insider trading.

Government agencies are taking notice and are investigating ways to regulate algorithms. But the story is not simple, and telling the “good” algorithms from the “bad” isn’t either. Before we start regulating we need a clearer picture of what’s going on.

The ins and outs of trading algorithms
Taken in the widest sense, algorithms are responsible for the vast majority of activity on modern stock markets. Apart from the “mum and dad” investors, whose transactions account for about 15 to 20% of Australian share trades, almost every trade on the stock markets is initiated or managed by an algorithm.

There are many different types of algorithms at play, with different intentions and impacts.

Institutional investors such as super funds and insurance companies rely on execution algorithms to transact their orders. These slice up a large order into many small pieces, gradually and strategically submitting them to the market. The intention is to minimise transaction costs and to receive a good price – if a large order were submitted in one go it might adversely move the entire market.

Human market makers used to provide quotes to buy or sell a given stock and were responsible for maintaining an orderly market. They have been replaced by algorithms that automatically post and adjust quotes in response to changing market conditions.

Algorithms drove the human market makers out of business by being smarter and faster. Most market-making algorithms, however, don’t have an obligation to maintain an orderly market. When the market gets shaky, algorithms can (and do) pull out, which is where the potential for “flash crashes” starts to appear – a sudden drop and then recovery of a securities market.

Further concerns about algorithmic trading are focused on another kind – proprietary trading algorithms. Hedge funds, investment banks and trading firms use these to profit from momentary price differentials, by trading on statistical patterns or exploiting speed advantages.

Rather than merely optimising a buy or sell decision of a human trader to minimise transaction costs, proprietary algorithms themselves are responsible for the choice of what to buy or sell, seeking to profit from their decisions. These algorithms have the potential to trigger flash crashes.

Fast vs. slow algorithms
Proprietary algorithmic traders are often further divided, between “slow” and “fast” (the latter also referred to as “high-frequency” or “low-latency”).

Many traditional portfolio managers use mathematical models to inform their trading. Nowadays such strategies are often implemented using algorithms, drawing on large datasets. Although these algorithms are often faster than human portfolio managers, they are “slow” in comparison to other algorithmic traders.

High-frequency algorithmic trading (HFT) is on the other end of the spectrum, where speed is fundamental to the strategy. These algorithms operate at the microsecond scale, making decisions and racing each other to the market using an array of different strategies. Winning this race can be highly profitable – fast traders can exploit slower traders that are yet to receive, digest or act on new information.
Proponents of HFT argue that they increase efficiency and liquidity because market prices are faster to reflect new information and fast market makers are better at managing risks. Many institutional investors, on the other hand, argue that HFTs are predatory and parasitic in nature. According to these detractors, HFTs actually reduce the effective liquidity of the stock market and increase transaction costs, profiting at the expense of institutional investors such as superannuation funds.

The effects of algorithms are complicated
A recent study by Talis Putnins from UTS and Joseph Barbara from the Australian Securities and Exchange Commission (ASIC) investigated some of these concerns. Using ASIC’s unique regulatory data to analyse institutional investor transaction costs and quantify the impacts of proprietary algorithmic traders on these, the study found considerable diversity across algorithmic traders.

While some algorithms are harmful to institutional investors, causing higher transaction costs, others have the opposite effect. Algorithms that are harmful, as a group, increase the cost of executing large institutional orders by around 0.1%. This ends up costing around A$437 million per year for all large institutional orders in the S&P/ASX 200 stocks.

But these effects are offset by a group of traders that significantly decrease those costs by approximately the same amount. The beneficial algorithms provide liquidity to institutional investors by taking the other side of their trades.

They do so not out of the goodness of their little algorithmic hearts, but rather because they earn a “fee” for this service (for example, the difference between the prices at which they buy and sell). What makes these algorithms beneficial to institutions, is that “fee” they charge is lower than the “fee” institutions would face if these algorithmic traders were not present and instead had to trade with less competitive or less efficient liquidity providers, such as humans. The ability for algorithms to provide liquidity more cheaply comes from the use of technology, as well as increased competition.

What distinguishes the algorithms is that the beneficial ones trade against institutional investors (serving as their counterparties), whereas the harmful ones trade with the institutions, competing with them to buy or sell. In doing so, the beneficial algorithms reduce the market impact of institutional trading. This allows institutions to get into or out of positions at more favourable prices.

The study also found that high-frequency algorithms are not more likely to harm institutional investors than slower algorithms. This suggests institutional investor concerns about HFT may be misdirected.

We shouldn’t stamp out the ‘good’ algorithms
ASIC is now using the tools developed in the Putnins and Barbara study to detect harmful algorithms in its surveillance activities. These are identified by looking for statistical patterns in the trading activity of individual algorithmic traders and the variation in institutional transaction costs. The result is an estimated “toxicity” score for every algorithmic trader, with the highest-scoring traders attracting the spotlight.

So, we know the affect of algorithms is complicated and we can start to tell the harmful apart from the beneficial. Regulators need to be mindful of this diversity and avoid blanket regulations that impact all algorithmic traders, including the good guys. Instead, they should opt for more targeted measures and sharper surveillance tools that place true misconduct in the cross-hairs.

The increased use of the algorithmic services increases demand for the GRAYLL GRX digital asset and in turn should increase the price. Our focus will also be on marketing GRAYLL and the outstanding results achieved. Since the GRX digital asset has been listed on decentralized Stellar Network exchanges there have been instances of +700% value increases due to early speculative demand. You may check the current value of the GRX digital asset and the performance of the 3xMagi GRY and GRZ algorithms in your GRAYLL App - https://app.grayll.io

We use these disciplines and technologies to continuously improve the GRAYLL System as well as our service levels and support. Machine Learning in particular will provide a method of service and performance improvement.

Quantitative trading isn't accessible solely to institutional traders; retail traders are getting involved as well. While programming skills are recommended if you want to produce algorithms, even those aren't always required. Programs and services are available that write the programming code for a strategy based on the inputs you provide. The code produced by the program/service is then plugged into the trading platform and trading commences. But before any of this can occur, want-to-be algorithmic traders progress through several steps deciding exactly what they want to accomplish with the algorithm, and how.


Time Frame and Constraints
While a well-programmed algorithm can run on its own, some human oversight is recommended. Therefore, choose a time frame and a trade frequency that you are able to monitor. If you have a full-time job and your algorithm is programed to make hundreds of trades a day on a one-minute chart while you are at work, that may not be ideal. You may wish to choose a slightly longer-term time frame for your trades, and less trade frequency so you can keep tabs on it.


Profitability in the testing phase of the algorithm doesn't mean it will continue to produce those returns forever. Occasionally you will need to step in and alter the trading algorithm if the results reveal it isn't functioning well anymore. This is also a time commitment that anyone who undertakes algorithmic trading must accept.


Financial constraints are also an issue. Commissions rack up very quickly with a high-frequency trading strategy, so make sure you're with the lowest-cost broker available, and that the profit potential of each trade warrants paying those commissions, potentially many times a day. Starting capital is also a consideration. Different markets and financial products require different amounts capital. If day trading stocks, you'll need at least $25,000 (more is recommended), but trading forex or futures you can potentially start with less.

Market constraints are another issue. Not every market is suited to algorithmic trading. Choose stocks, ETFs, forex pairs or futures with ample liquidity to handle the orders the algorithm will be producing.

Develop or Fine Tune a Strategy
Once the financial and time constraints are understood, develop or fine tune a strategy that can be programed. You may have a strategy you trade manually, but is it easily coded? If your strategy is highly subjective, and not rule based, programming the strategy could be impossible. Rule-based strategies are the easiest to code—strategies with entries, stop losses and price targets based on quantifiable data or price movements.

Since rule-based strategies are easily copied and tested, there are plenty freely available if you don't have ideas of your own. Quantpedia is one such resource, providing academic papers and trading results for various quantitative trading methods. The rules outlined can be coded and then tested for profitability on past and current data. Coding an algorithm requires programming skill or access to software or someone who can code for you.

Testing a Trading Algorithm
The most important step is testing. Once a trading strategy has been coded, don't trade real capital with it until it has been tested. Testing includes letting the algorithm run on historical price data, showing how the algorithm performed over thousands of trades. If the historical testing phase is profitable, and the statistics produced are acceptable for your risk tolerance—such as maximum draw down, win ratio, risk of ruin, for example—then proceed to test the algorithm in live conditions on a demo account. Once again, this phase should produce hundreds of trades so you can access the performance.

If the algorithm is profitable on historic price data and trading a live demo account, use it trade real capital but with a watchful eye. Live conditions are different than historic or demo testing, because the algorithm's orders actually affect the market and can cause slippage. Until it is verified the algorithm works in the real market, as it did in testing, maintain a watchful eye.

Continual Maintenance
As long as the algorithm is operating within the statistical parameters established during testing, leave the algorithm alone. Algorithms have the benefit of trading without emotion, but a trader who constantly tinkers with the algorithm is nullifying that benefit. The algorithm does require attention though. Monitor performance, and if market conditions change so much that the algorithm is no longer working as it should, then adjustments may be required.

The Bottom Line
Algorithmic trading isn't a set-and-forget endeavor that makes you rich overnight. In fact, quantitative trading can be just as much work as trading manually. If you choose to create an algorithm be aware of how time, financial and market constraints may affect your strategy, and plan accordingly. Turn a current strategy into a rule-based one, which can be more easily programed, or select a quantitative method that has already been tested and researched. Then, run your own testing phase using historic and current data. If that checks out, then run the algorithm with real money under a watchful eye. Adjust if required, but otherwise let it do its job.

read the whole article here: https://www.bloomberg.com/news/articles/2020-02-04/as-virus-spirals-markets-bullish-bent-proves-tough-to-dislodge