Hybrid Intelligence Generates Annual Return of 166% in Bitcoin
Today it is more challenging than ever to generate consistent alpha by self-selecting cryptocurrencies — Cindicator’s Hybrid Intelligence is a predictive analytical technology which provides today’s crypto traders and investors with a priceless edge.
Cindicator’s Hybrid Intelligence combines collective forecasts from over 105,000+ analysts, 5,000+ traders and AI. Over a period of eight weeks, an experiment was conducted where 45 traders executed 1,170 trades based on Cindicator’s Hybrid Intelligence Indicators. On average, the traders generated a return of 21.53% in BTC or 21.54% in USD in eight weeks. The traders outperformed the HOLD 10 Private Index Fund, the common crypto benchmark, which was up 12.97% in USD over the same period. Together, the 45 traders achieved a cumulative return of 16.26% over the period, or a 166% annualised return in Bitcoin, i.e. 2.66x. Within six months of the launch of Cindicator’s analytical product, the experiment validated Hybrid Intelligence as a profitable tool for trading and investing in the highly volatile crypto markets.
From 19 March until 13 May, 45 traders and investment professionals competed in a contest that invited them to test different strategies using Hybrid Intelligence indicators. The group of 45 traders was selected from over 5,000 of Cindicator’s token holders who use the analytical products on a daily basis. All traders started new accounts on the same crypto exchange. Traders were also required to record every trade in a spreadsheet and provide access to their trading accounts for results verification.
Traders were invited to a private Telegram chat, where they could freely communicate with each other, discussing different approaches and strategies. They could choose any strategy as long as their trades were based on Hybrid Intelligence indicators.
Cindicator’s indicators combine human insights from the collective mind and AI to predict the likelihood of market events. First, 105,000 independent analysts use Cindicator’s mobile app to answer questions about prices for specific assets and various financial and economic events.
Over 20 machine learning models then aimed to isolate systematic biases by assigning different weightings to answers, taking into account analysts’ past performance and other data. Finally, a neural network identifies non-linear relationships between different models. The resulting indicators show the probability of different events, for example whether a specific asset will reach a certain price by a given date. Since Q1 2017, indicators have achieved an average accuracy of over 60% and it continues to improve as the neural network learns from additional data.
As the experiment showed, indicators are flexible instruments that could be creatively applied to a range of different strategies.
Some traded based on indicators with a high likelihood.
“What I did first was to start with 100% ETH stack, on general principles, then trade 10% of my stack to a long position whenever any indicator was above 65%,” said one of the top 10 traders.
Others preferred more nuanced indicators.
“On indicators, I should avoid high percentages: they mean everybody is seeing a big green candle… I prefer seeing the potential in the future than in the past. I’m more interested by indicators between 50 and 65%, if possible with a goal above 10%.”
One of the top traders, Vyom Mahadevia, decided to join the Cindicator team after the experiment, so we asked him about his strategies.
“My trading strategy is based on wave theory which is loosely based on the mass psychology of traders depicted through charts. Cindicator’s indicators are a perfect compliment to this style of trading (or any style of trading actually) as you get direct access to the psychology and sentiments of market participants. You can gauge the sentiments of the entire market based on the indicators provided by them,” he said.
However, the indicators could be used by themselves, without any technical analysis, Vyom explained. And indicators could also be used to find new ideas.
“Their internal team of analysts does a really good job in finding the right opportunities and catching the pulse of the market,” he added.
As an early supporter and token holder, Vyom was already contributing to the ecosystem, but the experiment finally tipped the scales in favour of joining the team.
“Being a trader in traditional as well as cryptocurrency markets, once Cindicator’s Hybrid Intelligence came out and showed promise, my mind was racing with possibilities. I felt really invested and engaged with their vision and the project and I wanted to do more to help.”
Findings show top traders made +66% in BTC in eight weeks
By the end of the contest, 29 traders turned a profit, five ended with losses, and 11 didn’t trade. On average, each trader made 30 trades. The average return per single trade was 0.70%. The top trader made a return of 96.33% while the worst individual result was -39.22%. The top five traders made over 66% each. On average, traders made a total return of 21.53%.
The return was measured in Bitcoin, which means that it’s isolated from fiat/crypto exchange rate fluctuations. These results were generated during a time of significant volatility but with no prevailing market trend. On 19 March, the first day of the experiment, Bitcoin was trading on Bitfinex in the range of $8,085–8,717 and on the last day, 13 May, it was trading between $8,318–8,760. Theoretically, strategies based on indicators would work in any market.
On average traders generated a simplified alpha of 8.57% in USD over eight weeks. This is calculated by subtracting the 12.97% return of the HOLD 10 index from the total average return of 21.54% in USD generated by the traders. The HOLD 10 is used as benchmark since it’s a basket of the largest crypto assets, priced in USD, weighted by five-year diluted market capitalisation and rebalanced monthly.
Here is how Cindicator Co-founder and CTO Yuri Lobyntsev commented:
“These results are phenomenal. We already knew that our technology was delivering a high accuracy. Now our token holders have also figured out how to apply indicators in their trading to increase profits. Yet this is only the beginning — we have over 5,000 traders in our ecosystem and we’re going to make more experiments to spread the knowledge between them. The value everybody is extracting from Collective Intelligence will rise significantly when these insights are further verified, refined, codified and available for all of the contributors to the ecosystem.”
Key outcomes and insights
The experiment validated the value of Cindicator’s existing products and the ecosystem’s technology. It confirmed the benefits of integrating the ecosystem’s traders with the team’s internal analysts. Trading records and discussions gave valued materials for creating new, even more interesting approaches for training the entire Hybrid Intelligence to manage capital. The experiment built the foundations for a new socio-technological part of the ecosystem: traders and token holders who use hybrid indicators to manage a hybrid fund in a decentralised manner.
“We see how the ecosystem gives rise to the newest form of hybrid approach to collective market analysis and decentralised capital allocation. This has enormous potential for disrupting the asset management industry in the new economy. We now plan to test indicators with a group of collective microfunds. We’ll select teams of traders and each group will jointly manage assets that we’ll allocate to them. This is a whole new paradigm in asset management where we blur the boundaries between technologists, clients, and traders,” said Yuri Lobyntsev, Cindicator Co-founder and CTO.
“With this second stage of our experiment, we’re essentially modeling a new format of a decentralised investment fund that in future could successfully compete with traditional funds. In this new format, diverse individuals with different unique professional skills are united in a decentralised structure for effective asset management. Their unique intellectual skills are utilised with maximum efficiency without any centralised oversight. In the long-term, this structure could not only deliver a superior return, but could also have a decentralised model that is more resilient to various risks and changing market conditions, such as financial crises and artificial bubbles,” added Mike Brusov, Cindicator Co-founder and CEO.
Experiments in traditional markets
A year and a half ago the group validated a similar model for decentralised asset management. In a single “investment fund” Cindicator united diverse motivated traders and a collective mind of tens of thousands of analysts. The analysts generated data for traders’ decisions. While the Cindicator team has deep expertise in crypto markets, earlier experiments also proved that the technology could also be effectively applied to traditional assets.
In early 2017, Cindicator carried out a pilot study with the Moscow Exchange during which 863 participants made forecasts for futures contracts for USD/RUB, oil, gold, and silver. It’s noteworthy that 40% of participants had no prior trading experience. Based on collective forecasts, Cindicator’s robot modeled 27 trades, generating 47% return per annum. That experiment attracted significant attention from professional and institutional investors.
Today we are witnessing the birth of a new entity in the world of investments and distributed autonomous organisations — a symbiotic decentralised hybrid investment fund. It’s inspiring that our team and active community are right at the heart of these tectonic shifts.
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