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Paradoxes in Crypto Quant Models: The Retraining Dilemma

Outlier events make the retraining of predictive models in crypto a non-trivial challenge.

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Outlier events make the retraining of predictive models in crypto a non-trivial challenge.

In a recent article published in CoinDesk, I outlined some of the key challenges of quant strategies for crypto assets. Creating predictive models and quant strategies for crypto assets is a fascinating challenges and one that present very novel difficulties compared to traditional capital markets. As we have been building more machine learning(ML)-based predictive models at IntoTheBlock, we have encountered several hurtles that fall outside traditional machine learning and quant methodologies. One of those challenges is what I referred to in the article as the “retraining dilemma”.

ML-based predictive models for financial assets such as cryptocurrencies are fundamentally based in supervised learning methods. The essence of supervised learning states that models are trained in a given labeled dataset such as “Bitcoin trades in Coinbase” or “Ethereum orders in Binance” and they learned patterns or features in that dataset with the goal to forecast a specific target value such as price or volatility. Training is not a one-time event and should be performed regularly. Figuring out the correct retraining frequency is an important challenge for any prediction model. Model retraining is one of those aspects that looks very differently in predictive models for crypto assets compared to traditional capital markets.

Suppose that we have a machine learning model that attempts to predict the price of Microsoft’s stock(MSFT). The model has been trained in MSFT historical order book at the NASDAQ for the last decade. Given that the performance of MSFT has been relatively stable compared to market conditions, the rules for retraining our predictive model are based on two fundamental criteria:

1) Frequency-Based: The model could be retrained every few months to capture the knowledge of the latest trading activity.

2) Model-Drift Based: The model could be retrained when its performance starts degrading. This is known in machine learning as model drifting and its illustrated in the following figure:

Those two steps seem relatively straightforward. Either we retrained a model regularly or when its performance degrades. That methodology works incredibly well on predictive models for traditional asset classes but regularly failed when applied to crypto. This is the essence of what we like to call the retraining dilemma.

The main reason why regular or drift-based retraining methods works in regular asset classes is because their stability an efficiency. Going back to our MSFT stock example, a predictive model might encounter some outlier events but they should be few and far between and certainly unlikely to have an impact in the long term. Crypto is the exact opposite.

The essence of the retraining dilemma can be illustrated by expanding on our sample scenario and assume that we are training a similar predictive model to our MSFT example but this time to predict the price of Bitcoin based on Coinbase order book records. Just this year, we will encounter outlier events such as the March crash, consistent weeks of no volatility in June-July, a crazy spike in volatility at the end of July followed by another sharp drop in early September. Given the young history of most crypto assets, the sequence of many of these outlier events is relatively new and will affect the performance of most predictive models. At that point, we need to decide whether retraining our predictive model is the appropriate solution.

In the face of an outlier event, the decision of retraining a predictive model is far from trivial. If we proceed to retrain, we are assuming several risks such as overfitting the model for the outlier records or even degrading its performance. Furthermore, there is no way to validate if the model learned anything new given that the outlier even just happened. If we decide to not retrain the model then we are risking to see further performance degradations. Now imagine that our predictive model is faced with these outlier events regularly and you get an idea of the magnitude of the challenge. That’s the essence of the retraining dilemma.

There are no silver bullets to deal with the challenges of the retraining dilemma in crypto asset predictions. At IntoTheBlock, we regularly faced those challenges and have explored different creative solutions which will be the subject of a future post 😊

Source: https://medium.com/intotheblock/paradoxes-in-crypto-quant-models-the-retraining-dilemma-35c3abe4385f?source=rss——-8—————–cryptocurrency

Blockchain

BitGo To Introduce Crypto Custodial Services To New York Clients

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Digital asset trust company, BitGo has added another feather to its cap. The company announced today that it has received a Trust Charter that will enable it to operate as an independent qualified custodian in the United States’ commercial hub, New York. The license was issued by the New York State Department of Financial Services.

Go, BitGo

California-based digital asset solutions provider, BitGo has expanded its horizon. It will now render cryptocurrency custodial services to New York-based institutional clients. The news was confirmed after the state’s Department of Financial Services issued the New York Trust charter to the company.

The news reiterates the company’s motive to exhaust the possibilities of the new financial landscape. It announced in December last year that it had over $16 billion in assets in its custody. With the surge in institutional interest, BitGo now has its eyes set on the world’s financial and commercial epicenter. Speaking on the latest milestone, BitGo CEO Mike Belshe said:

“We are extremely proud to receive the approval for a trust charter from NYDFS to serve the world’s premier financial organizations that are based in New York State. The past year has been exceptional for BitGo and the digital asset markets overall, primarily due to the influx of large financial services institutions that bring a new level of credibility, liquidity and stability to the crypto ecosystem.”

The license will enable New York’s premier institutions to key into the digital revolution while staying within the state’s regulatory framework. BitGo’s list of reputable partners adds an extra layer of trust. It claims to be backed by Goldman Sachs, Craft Ventures, Digital Currency Group, Redpoint Ventures, and Valor Equity Partners.

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What’s In Store?

BitGo intends to go all-in for its prospective clients. Asides from its fully secured custodial services, it outlined several benefits of the New York Trust.

It will adopt a sophisticated, independently verified system control – SOC 2 Type 2. The SOC 2 Type 2 compliance records how a company safeguards customer’s data. This system control type verifies compliance with standard procedures and often gives a competitive edge.

Prospective clients will benefit from BitGo’s “comprehensive insurance coverage,” which it says covers up to $100 million in digital assets. Its multi-signature technology is yet another selling point.

New York – The New Crypto Hub?

The city of New York is becoming a destination for mainstream cryptocurrency companies. This week, leading blockchain sports and entertainment platform, ChiliZ announced that it would set up an office in New York.

Perhaps this is fueled by the state’s unwavering efforts to maintain a regulated framework that attracts investment and prioritizes asset protection. The New York Financial Services Law, which was enacted five years ago, necessitates the regulation of companies in the digital asset space. The state’s Department of Financial Services approved the sale and trading of eight cryptocurrencies in August last year.

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Source: https://cryptopotato.com/bitgo-to-introduce-crypto-custodial-services-to-new-york-clients/

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Co-founder of Floyd Mayweather-promoted ICO sentenced to 8 years

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Sohrab Sharma, the co-founder of the notorious celebrity-spruiked initial coin offering of 2017, Centra Tech, has been sentenced to eight years in prison for his leading role in the fraudulent scheme that duped investors out of more than $25 million.

Shama had previously pled guilty to conspiring to commit wire fraud, securities fraud, and mail fraud for material misrepresentations made by him and his co-conspirators to solicit investors to participate in the scheme.

United States attorney, Ilan Graff, described Sharma as having “led a scheme to deceive investors by falsely claiming that the start-up he co-founded had developed fully functioning, cutting-edge cryptocurrency-related financial products.”

“In reality, Sharma’s most notable inventions were the fake executives, fake business partnerships, and fake licenses that he and his co-conspirators touted to trick victims into handing over tens of millions of dollars.”

The court found that Sharma and co-defendants Robert Farkas and Raymond Trapani founded Centra around July 2017, claiming that the firm offered a crypto debit card and other digital asset-related products. The group conducted an ICO from July 2017 until October 2017, distributing unlicensed securities in the form of CTR tokens.

To promote the offering, the group issued materials falsely claiming the team had partnerships with Visa, Mastercard, and Bancorp, money transmitter licensing in 38 U.S. states, along with an entirely fictional CEO boasting more than 20 years experience in the banking sector and a master’s degree from Harvard University.

While the team raised $25 million at the completion of the ICO, authorities found those crypto assets were worth more than $60 million at certain times during 2018.

The U.S. Marshals Service seized 100,000 Ether from Centra which it sold for roughly $33.4 million earlier this year. The proceeds will be made available to potential use in a remission program to compensate victims of the fraud.

Sharma was also sentenced to three years of supervised release and ordered to forfeit more than $36 million.

In December, Centra co-founder Robert Farkas was sentenced to one year in prison for his role in the scheme. Trapani has also pleaded guilty.

Celebrities DJ Khaled and Floyd Mayweather, who promoted the offering on social media in 2017, agreed to six-figure settlements without admitting to wrongdoing in 2019 after Centra’s investors filed a lawsuit against the pair.

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Source: https://cointelegraph.com/news/co-founder-of-floyd-mayweather-promoted-ico-sentenced-to-8-years

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Spike in digital land and NFT sales push Axie Infinity (AXS) price to new highs

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The popularity and sale of NFTs have exploded over the past few months as many artists and collectors have been consistently selling entire NFT collections for millions of dollars.

One platform that has emerged as a fan favorite is Axie Infinity (AXS), a blockchain-based trading and battling game inspired by games like Pokémon and Tamagotchi. In the game, players collect, breed, raise, battle and trade token-based creatures known as Axies. 

In the past two months, the market cap for AXS has increased 600% from $19.25 million to its current value of $115 million as users rush to the platform for a chance to win a rare and valuable Axie. 

Data from Cointelegraph Markets and TradingView shows that the price of AXS has surged 74% over the past 24-hours, going from $1.78 on March 3 to a new all-time high of $3.10 on March 4 on the back of a 1,000% surge in the 24-hour trading volume. 

AXS/USDT 4-hour chart. Source: TradingView

While AXS has been in an uptrend for months, the altcoin really started to gain momentum at the beginning of February following the launch of Ronin, an Ethereum (ETH) network sidechain designed to help AXS users escape high transaction costs and network congestion on the Eethereum network.

Since Ronin’s launch on Feb. 1, the number of active users on the platform has skyrocketed as NFTs began to explode in popularity and mainstream news channels reported on record-setting sales for one-of-a-kind pieces of digital art.

Axie Infinity active users. Source: Axie Infinity

Digital land in Lunancia, the player-controlled virtual realm of the Axie universe, is also attracting increased attention with one user recently spending a total of $1.5 million to purchase nine digital land plots.

VORTECS™ data from Cointelegraph Markets Pro began to detect a bullish outlook for AXS on March 3, prior to the recent price rise.

The VORTECS™ score, exclusive to Cointelegraph, is an algorithmic comparison of historic and current market conditions derived from a combination of data points including market sentiment, trading volume, recent price movements and Twitter activity.

VORTECS™ Score (green) vs. AXS price. Source: Cointelegraph Markets Pro

As seen in the chart above, the VORTECS™ score for AXS reached a high of 76 on March 3, just hours before the price began to rise from $1.84 to its new all-time high at $3.10.

Despite the recent pullbacks experienced in the wider cryptocurrency market, key on-chain metrics like Glassnode’s Reserve Risk indicator show that the Bitcoin rally is still in its early, suggesting that there is plenty of room for BTC to appreciate before it reaches the peaks seen in previous bull markets.

Continued strength for Bitcoin price is likely to translate into an increased interest in NFTs and as the nascent sector expands, projects like AXS could continue to rise in popularity.

The views and opinions expressed here are solely those of the author and do not necessarily reflect the views of Cointelegraph.com. Every investment and trading move involves risk, you should conduct your own research when making a decision.

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Source: https://cointelegraph.com/news/spike-in-digital-land-and-nft-sales-push-axie-infinity-axs-price-to-new-highs

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