Crypto Investment Signals With A 75%-100% Hit Rate.

Which signals work best depends on the currency and the timeframe. We have some signals that we call our Catalyst signals that have near-100% success rates. These work as well across all currencies and are something that, unless we have an AI advisor warning, we would always trade on.


Coinist was recently fortunate enough to chat with the folks at Smart Investment Fund, an AI powered, human supported, smart blockchain trading and managed investment portfolio. The ICO is currently located in our ICO calendar in the finance section. Let’s jump into the interview to find out more about what Smart Investment Fund does.

Hey guys, first and foremost thanks for chatting with us and our readers about your project and upcoming ICO. Let’s kick off the interview by allowing you to tell our readers what Smart Investment Fund is all about.

[GP] Thank you for inviting us to talk with you. We are a way for people to get involved with trading crypto-fiat pairs (for example Bitcoin-USD, Ethereum-USD, etc.) without having to do the trading themselves. We take the money raised during the ICO and use that for trading. The gains in the fund are distributed in two ways – half is reinvested in to the fund to grow the underlying asset value and the other half is paid to investors as dividends in Ether. Investors should see a 40% ROI per year split equally between asset growth and dividend payments. Unlike most other crypto funds that are around we think we’re pretty different. We have existed for 10 years offering forex and commodity trading algorithms that plug in to popular Metatrader 4 trading software and have thousands of existing customers. We don’t just trade based on what our traders believe to be right, but we trade based on a combination of their experience and our algorithms. Investors in the fund will be allocated a single SIFT for each 0.01 ETH invested. Each SIFT is representative of a share in the fund and each SIFT is weighted equally ensuring no investors value is worth more or less than others.

Our algorithms started off based on a technique known as volume spread analysis (VSA). Loosely this is a technique not used by many smaller traders but one that has great benefit. Rather than looking at price movements it looks at volume movements in a market. The idea is to see what the big money is doing. In forex this could be banks settling huge volumes of currency whereas in crypto this could be asset sell-offs by large mining groups. VSA aims to spot these moves and get in before the rest of the market responds. Our systems have developed a lot since then and are now based on a number of proprietary algorithms loosely based on the same concepts – volume is key to us.

Our proprietary algorithms are known as Smart Volume Analysis and combine a number of different trading signals that show both strength and weakness in a market with various AI systems. When a trading signal is detected (based purely on the constituent makeup of a particular chart bar and those before it) we have a couple of other systems that kick in. Our AI-based advisor can look at the market and know whether or not the market conditions make sense for that signal. It can give advice, potential risk or clear “avoid this trade” warnings. Without giving too much away of the internals of our system a fairly obvious example here is that if we have a weakness signal and the market has already been in decline for a period we will get a warning that we could be reaching a floor and the trade is not worth the risk. We also have a concept known as expectation of movement that gives us how far we think a signal will move in the direction we expect, the expected drawdown of the trade and the win-to-loss ratio in similar situations of this particular trade. All this information, combined with proprietary volume-based support and resistance levels, provides our traders with the ability to make informed decisions. We never trade unless our AI agrees with us, but sometimes we overrule it if we think there are other inherent risks – we err on the side of caution.

That caution is important to us as risk mitigation is present throughout our systems. We have the risk mitigation in individual trades but also of the entire fund – spreading our trades across exchanges, only allocating a percentage of the fund to each trading asset and keeping a separate prudent reserve. This means that even in a worst case scenario we only ever risk a small amount of the overall fund for each trade. We support this with a number of acts of transparency ensuring that all our account balances are readily accessible to investors and externally audited.

The backbone of your project runs off of AI trading algorithms that you started to develop 10 years ago. You’ve been using these trading algorithms in both forex and commodities markets. You’ve mentioned you’ve been using the same algorithms in the crypto sphere. How well do you feel the algorithms translate into the world of crypto? What are some notable adjustments you’ve had to make when applying these algorithms to a new asset class like cryptocurrencies? For example, I’ve heard traders say that due to the newer nature of the crypto space you have fewer professional investors which changes the nature of the game (and charts) and makes your approach for looking for signals a little different. How have you and your algorithms had to adjust?

[GP] The different nature of the market is one reason we didn’t just apply everything verbatim. We started looking at crypto back in 2013 with Bitcoin-USD pairs and we had some success but the nature of the trades definitely had a lower success level than we were used to in Forex. As our algorithms are based on the nature of large trade volumes we need pairs that have a reliable volume. We feel the crypto markets have started to mature and provide that volume (and the liquidity we require to trade larger amounts) over the last couple of years and that is part of the problem solved immediately. Without going into too much detail about the internals of how our software works we also needed to tailor specific parts of the algorithm to differences in trading patterns and have our systems learn and understand the context. If we look at forex there are definitive sessions – the Asian trading session, the European trading session and the Americas trading session. These three sessions have some overlap and are largely based around the traditional working hours of banks. There are very quiet times where market data is incredibly unreliable (last thing on Friday, first thing on Monday as examples) and other times where there are inherent volume changes are two different trading sessions are active at once. Understanding this context is important and we’ve had to understand similar context in crypto. When do miners make moves, what patterns are there in the movements and what is normal versus what is abnormal.

There are definitely some odd trading patterns that take place in crypto and the low timeframes (anything under 1 hour bars) are extremely volatile compared to many traditional instruments, but without relying solely on price and traditional technical analysis our systems still work exceptionally well once they have understood the context of the market. The large players in crypto act for different reasons but once that context is analysed (which we do continuously) our systems have no trouble whether the big player is a bank or a Chinese mining group.

Our fund is looking at trading crypto-fiat pairs to start with but we’re also currently working on crypto-crypto pairs. This gets a little more complicated as it opens it up to much more volatility. Here we have a lot of low-volume pairs, without big movers and with less history. There are many pairs that have completely flat bars. Where there are multiple exchanges with these currencies we also have a need to aggregate the data between them. This does bring difficulties and is something we’re working on. We’re happy with where we are with our success rates on these pairs at the moment but we are looking to continue to improve their win-loss ratios before proposing that we trade these as well in our funds. An asset without much community interest is difficult to trade and we’re analysing hundreds of different pairs at the moment.

You mention on your site that your algorithms are designed to spot what “big money is doing before the rest of the market plays catch-up”. On your site it’s also mentioned that you have 30 + proprietary signals. How many of these signals have been deployed in the cypto sphere and have you found the majority of the them useful or are only a few useful? Are the signals you’ve found most helpful mostly focused on volume analysis?

[GP] We use all these signals in crypto as well as fiat. They are loosely split in two – strength and weakness signals of a market (indicating buy or sell respectively). Which signals work best depends on the currency and the timeframe. We have some signals that we call our Catalyst signals that have near-100% success rates. These work as well across all currencies and are something that, unless we have an AI advisor warning, we would always trade on. For our standalone trading product we recommend newer traders to only ever trade on these as it’s pretty difficult to lose using this system.

Supporting our catalyst are a couple of dozen other signals. These take more understanding of what they are trying to say about the market and what will come next. Some of these work incredibly well on certain pairs and others do not work at all on the same pair. Our data analysis of each pair helps show us what does and does not work in crypto and this, combined with the knowledge of market changes mentioned above, is where the human aspect of trading comes in when blended with our AI systems.

With regards to whether the signals we’ve found most useful focus on volume analysis – our entire system to some extent depends on volume analysis. Traditional VSA has lots of issues though and times that it doesn’t work. We do find volume incredibly useful but without giving away the secret sauce that’s not the only component we use. Our most helpful signals (our catalyst signals) come down more to overall market behaviour than a single factor.

You also mention that the majority of your signals have a “75%-100% hit rate”. A couple of questions about this. What markets are you achieving this hit rate in? For example, is this your hit rate in forex markets? If so, have you established what your hit rate is in the cypto world yet?

[GP] This is for the crypto work and should more accurately be expanded as “Of the signals we trade for an individual instrument, based on our analysis and back testing, each of those signals has a 75%-100% hit rate when never traded in disagreement with our AI-based advisor systems”. For our crypto product we have some signals that just do not work in the certain markets – and we never trade on these – but this range is reliable for all of the signals we have identified as appropriate for crypto assets. Getting this level of confidence is how we can offer a higher ROI. We don’t take 100 trades a day, we just take very good trades that we perceive to be much lower risk. We have a similar win rate in other markets (when the signals are used appropriately as described above). Ensuring we could be this reliable in crypto is where our analysis has gone.

You mention in your white paper that the pairs you have “selected to trade with at launch are those that we consider that we hold significant data on, allowing us to perform reliable, low-risk trades.” Based on the chart I saw in your white paper these include BTC, ETH, XRP, LTC, DASH and ETH. Is there a particular pair you feel confident trading? You often hear traders say they feel very comfortable and have great success trading one or two certain currency pairs. Not just because a certain currency pair maintains above average trading volume, but for some other reason. Is there a cryptocurrency pair your team and algorithm has had unusual success with recently? If so, why do you think that is?

[GP] We’re happy with all of these pairs – it’s why we’ve selected them. They all operate differently at different times. Some are less reliable than others in certain conditions and it’s important to understand the context. Dash works really well when analysing the data over four-hour time periods but we do not find the win-rates we expect outside of that for Dash. This means we’re happy to trade Dash but only in those situations. We’ve been calling trades in our Telegram room for the duration of the Initial Coin Offering and have our best moves on BTC and ETH. So far every trade on those charts has been a success without a single loss and several times we’ve seen 10% in a day. Our systems act differently than a lot of trading systems so where we’ve had huge single day increases and decreases our AI systems actually kept us out of the markets due to significant warnings about instability and lack of pricing support levels. Where we have seen these moves has been even outside of the recent market conditions.

With that being said we’ve had successful trades on all of these pairs and that’s why we’ve selected them. The number of trades is higher for certain pairs but they all have successful trading opportunities within them. Since we have our systems analysing all the pairs and a number of timeframes on each pair we can consider far more data in real-time than a single trader can. When a valid trade opportunity is detected our trader can then be alerted to its presence and make a decision by looking in more depth at trade information and the market.

There is no hard and fast rule of “only trade this” – there are opportunities across the board and for us its about spotting those with the lowest risk. Risk mitigation is important and because each instrument acts differently and has a different number of opportunities we weight the amount of the fund we invest in any instrument. This varies based on up-to-date market information from our systems so is not fixed for perpetuity – but we never put 100% of our trading capital into a single pair and even if we had allocated, for example, 60% of our fund to BTC/USD trading we would never trade all 60% of that at once.

You mention that your internal token SIFT will give users voting rights. Can you explain what types of votes users will be given access to? I’ve heard in other crypto projects that voting can sometimes do more harm than good especially if there is a big knowledge gap between the developers (or in your course investors) and the average user of the platform. How do you plan to deal with the issue of “knowledge gap” within your voting system?

[GP] Voting will be to cover a few key areas with regards to the direction of the fund rather than technology or business practices. Examples of these are:

– Making a decision to open for further rounds of investment and how to do that

– Changes to dividend process (we’ve had some people discuss a way to re-invest more than 50% in to the fund)

– Addition of new asset types to the fund (for example crypto-crypto pairs)

– Winding down the fund or providing cash-out opportunities for investors

– Us acting as market-makers in any capacity (such as providing liquidity at certain prices, buying SIFT ourselves or opening our own exchange to allow for trading of SIFT)

Each of these are areas that directly affect the fund and the ROI for investors as well as the relative value of their shareholding. In the case of changes to investment strategy that impacts the risk profile that investors have signed up for. In all of these cases we would only make changes that a majority of shareholders agree on. We’ve had many discussions in our Telegram room about this and floated a lot of future ideas around already but it all comes down to what investors are happy with. Since these are not technical issues we don’t see this being any different than a company giving its shareholders a vote at an AGM on certain issues. In fact, we actually consider it of paramount importance to our fund’s ethos that we offer these opportunities.

Examples of issues that we would not put to a vote would include:

– Individual trading decisions

– Changes to technological systems or technological processes

– Day to day business operations

You made the decision to launch on the Ethereum platform. Tell me more about why you made this decision over the other options available? What does Ethereum offer you that other technologies / platforms couldn’t offer you?

[GP] As a technology company first and foremost it’s about adopting a platform that is easy to work with and most mature. When it comes to smart contract technology we believe that Ethereum offers that. There may be other options out there but in terms of toolset, community support and ancillary tools we believe Ethereum is the best option at the moment. As a platform it has a great community and we personally agree with the direction it has taken. It’s never just about raw technology that underlies something but how easy is it to work with? From a practical standpoint in Ethereum how quickly can you compile and deploy a dozen contracts to a new private blockchain and begin a test cycle?

A question that ties in to this though is why an ICO in the first place and why your own token? There are business and technological reasons for this. From a business perspective an ICO opens us up to investors that know about crypto and are interested in it at a very low cost. A traditional share-offering or fund attracts completely different investors and not those that are interested in our product. The real benefit, however, comes in smart contracts. Let’s say we opened a traditional fund – in that case we have to manage:

– Receiving funds and tracking who they are from

– Dealing with the various transaction fees charged at different rates depending on how we’ve received money and from whom

– Directly processing customer identification documents and ensuring we have appropriate systems for securely storing customer data

– Setting up a system that allows people from across the globe to securely vote on fund-related issues

– Deciding how to pay out dividends and dealing with any associated costs

– Either creating our own buy-back system or integrating with a secondary market for people to trade with the fund

Each of these requires its own technological solution to manage. That involves cost, complexity and infrastructure. This costs us money and that ultimately costs our investors their money as well.

Smart Contracts not only offer a way to get all of the above set up in weeks, rather than months or years, but give a degree of certainty to our investors since they can clearly see how our contracts work and know that we cannot suddenly surprise them (for example by announcing a surprise partner is investing a large sum and being awarded additional tokens and rights). We get to have a system that works straight away, is auditable and secure whilst our investors get these benefits plus additional transparency. Many companies are creating all kinds of technologies on top of the blockchain and some are really exciting but do not underrate how important these advances are to much more traditional industries as well.

What are some of the downfalls you’ve experience with using the Ethereum platform so far?

[GP] We do think it’s the best of the platforms out there but compare it to a normal development workflow and it’s difficult to work with. The example of a new compile-build-testnet cycle comes in at minutes, rather than seconds and the development environments and test processes are more like something from 1990s software development than 2017. This will all get solved in time and as a developer it’s one of the exciting parts of the platform – but from a business any new technology lacks the efficiencies and productivity that more established technologies come with.

Personally I think one of the biggest problems with Ethereum and all smart contract platforms is the lack of connectivity in to the network and out from the network. The ingress/egress of data is where we lose trust and yet blockchain technology is not established enough (nor affordable enough) that all systems can run inside it. Thinking about a more general problem I’ve seen cited several times – an insurance policy based on weather that pays out automatically based on weather conditions. How does that weather condition get in to the network? In Ethereum we push data in, which can be unreliable and untrustworthy, in another model we could connect out at certain times – but both rely on external data. We’ve come up with business practices (including audits) to provide this certainty but this is a huge overhead. I think the person that cracks the link between off-chain and on-chain trust will move us into mass adaptation. There are solutions until then, but they’re all a little clunky.

Thank you for taking the time to chat with us today guys! To our readers, if you’d like to learn more about Smart Investment Fund you can visit their website here:

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