Author: BTC believers, sandalwood Qiqi
This article briefly reviews the development of cryptographic funds over the past five years, and introduces several well-known crypto fund profiles around the world. Secondly, it focuses on the quantitative trading strategies in the field of digital asset management, quantifying the types and status of trading platforms, and quantifying the risks of trading. The benefits and the comparison between quantitative transactions and manual transactions; finally summarized and forecasted the status quo and development trend of digital asset management.
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- Success or failure of investment in the encryption market
2. Encryption fund
Encrypted funds are also known as blockchain funds or digital asset funds, and the data show that since 2013, especially since 2017, rapid growth. In 2017, nearly 200 encryption funds were launched. In contrast, about 700 hedge funds were launched at the same time. Although 14% of new hedge fund circulation may not seem like much, there are data showing that crypto funds account for less than 0.1% of total hedge fund assets. In fact, crypto funds are the fastest growing segment of the hedge fund industry.
As shown in the above chart, 2017 is the record year for the launch of the new crypto fund, which is almost the sum of the number of crypto funds in the past five years. Entering the 2018, the number of newly launched encryption funds continues to record high, and the encryption fund industry is ushered in an outbreak. However, due to the bear market that lasted for more than a year in 2018, the number of newly launched encryption funds in 2019 has slowed down. Existing hedge funds incorporate crypto assets into their portfolios. Similarly, existing venture capital firms will continue to increase blockchain investments and launch separate crypto funds. The Pandora's Box has been opened and the trend is irreversible.
One of the well-known head-encryption fund companies is Gray Scale Investments, which was founded in 2013 by its parent company, Digital Currency Group, and is an investment company for digital asset trust funds. Up to now, nine trust funds have been launched. The targets of the investment are: BTC, BCH, ETH, ETC, ZEN, XLM, LTC, XRP and Zcash. The asset management scale has once increased to 3.5 billion US dollars. Gray Scale's share of Bitcoin Investment Trust (BIT) is regularly sold by GBTC. The total supply is fixed. Investors can trade their shares in the OTCQX (US over-the-counter market) under the ticker symbol GBTC. The purpose of the BIT is to obtain and guarantee the bitcoin of the trust institution, and then issue the shares to the investor GBTC, the initial price is 1GBTC=0.1BTC, and the annual management fee is 2%.
The general situation of several representative encryption funds for qualified investors is shown in the above table. The brief introduction is as follows:
Blockchain Capital is a closed-end fund owned by traditional venture capital firms, one of the first traditional venture capital firms to enter the blockchain market. Primarily invested in Fintech and blockchain startups such as coinbase, Gem, wave, Tierion, etc. The minimum investment amount is 500,000 USD, the closing period is 5-7 years, the annual fee is 2.5%, and the success fee is 25%.
Pantera Capital was one of the first traditional venture capital firms to enter the digital asset field. It was established in 2003 and entered the digital asset market in 2013. It is mainly engaged in blockchain venture capital and digital asset trading, such as 0x, auger, bitcoin, etc. One of the institutions with the largest amount of assets. The minimum investment is 50,000, the annual fee is 3%, and the profit is 30%.
Founded in 2019 and registered in Hong Kong, SW Fund is a Tokenized distributed self-owned fund, an open-end fund that uses a pass and smart contract deployment to achieve investor profit distribution.
In short, with the rise of digital assets, the growth of investor demand for digital asset management has spawned a large number of crypto funds, some of which come from traditional hedge funds and venture capital institutions, in fund types, investment composition, and management rates. The existing hedge funds are fully borrowed from the factors such as profit rate and re-investment. From January 2016 to January 2019, the managed digital assets (AUM) grew rapidly from $190 million to $14.35 billion. Encryption funds have become the main force in the field of digital asset management.
3. Quantitative transactions
3.1 Introduction to Quantitative Trading
I think there are quite a few novice investors who don't understand quantitative trading. Here is a brief introduction. Quantitative transactions refer to the use of advanced mathematical models to replace artificial subjective judgments, using computer technology to select strategies from a large number of historical data that can bring excess returns to large-scale, to greatly reduce investor sentiment. The impact of volatility, avoiding irrational investment decisions in the face of extreme fanaticism or pessimism.
3.2 Introduction to Quantitative Trading Strategies
This section combines the characteristics of quantification and currency, and focuses on some trading strategies commonly used in currency trading. Some quantitative transactions require analysis and back-testing of historical data to continually mine historical patterns that are expected to be repeated in the future and to make use of them. Some quantitative transactions require the use of analyst-developed strategies to profit from real-time execution strategies without the need for historical data. Quantifying trading strategies requires constant iteration and optimization. If you think that it can be done once and for all, it is the beginning of failure.
3.2.1 Grid trading strategy
Grid trading strategy is one of the most used strategies in the current currency trading platform. Grid trading method, applicable to the shock city, is low buy high. The specific method is to divide the funds into n shares, a fixed amount of a single transaction, first establish a position, then set a percentage or spread to determine the grid width, such as 5%, the price of the currency falls 5% to buy a copy, up 5% Just sell one, so repeated trading.
The above grid trading strategy requires the use of analysts to set parameters without the need to rely on historical data. The analyst sets the upper edge of the grid, the lower edge of the grid, the grid center, the grid width, and the amount of the single transaction, and then automatically executes the grid plan to complete the repeated low buy and sell high profits.
Some quantitative platforms support the unequality of funds in the grid trading strategy setting process. For example, for every grid dropped, the single transaction amount increases by a certain percentage, for example, 10%, thus extending into the Martin strategy.
3.2.2 Arbitrage Strategy
a moving brick arbitrage
In the currency circle, moving bricks and arbitrage is also one of the most used strategies for quantitative trading platforms. This is a low-risk arbitrage with a low risk of arbitrage. The software captures the spread of the same currency on different trading platforms, buys on low-priced platforms, sells on high-priced platforms, and earns the difference.
Phase b and intertemporal arbitrage
In the currency circle, there will be a spread between the spot and futures of a certain trading target of the same trading platform and the current week, sub-week and quarterly contracts of the target. The current arbitrage is to use the spread of spot and futures, reverse operation in futures and spot, and earn the difference. The same intertemporal arbitrage is to use the price difference between different contracts to reverse the operation in the contract and earn the difference.
c statistical arbitrage (cross-currency arbitrage)
Statistical arbitrage is an arbitrage that uses the historical statistical law of asset prices. It is a kind of risk arbitrage. The risk lies in whether this historical statistical law will continue to exist in the future.
The main idea of statistical arbitrage is to first find out some of the most relevant types of investment products, such as BTC and ETH are highly correlated. When the BTC and ETH spreads deviate to a certain extent, they start to open positions, buy relatively undervalued varieties, and sell short-selling varieties. The equivalence returns to equilibrium and profit-taking.
As shown in the following figure, a typical example in the near future is that from April 2 to May 12, 2019, BTC has increased by 66.4%, while ETH has only increased by 31.5%. According to the historical performance of ETH/BTC, ETH is seriously underestimate. Therefore, on May 12th, ETH began to make up the market, and through statistical arbitrage analysis, it can capture this part of the profit well.
In addition to the statistical arbitrage, the above strategies need to backtest historical data, and other strategies do not require historical data to analyze. In terms of whether manual intervention is required, grid trading strategies require software users to have trading experience to manually set parameters. Current and intertemporal arbitrage also requires manual trading teams to set parameters to optimize trading strategies.
In addition to the above-mentioned quantitative trading strategies commonly used in the currency circle, quantitative trading strategies include CTA strategies, alpha strategies, high-frequency trading and algorithmic trading, etc. Since these strategies are currently not common in the currency circle, they are not detailed, and interested investors may wish to Baidu. Extended reading.
3.3 Platform for quantitative trading
With the rise of digital assets, digital asset management needs are huge. In addition to the above mentioned encryption funds has become the main force in the field of digital asset management, managing tens of billions of digital assets. Since the beginning of 2018, quantitative trading has started to rise in the field of blockchain digital asset management. This year has emerged in an endless stream. According to incomplete statistics, there are already hundreds of quantitative platforms. The author randomly selected several analyses.
The above table is the representative of the earliest digital asset management platform. Because there is no trading strategy engine driven, or the declared quantitative strategy is not open opaque, we classify it as a pure asset management platform. The first two platforms have launched their own fixed-income products, similar to the traditional P2P financial management, the monthly interest rate of products is as high as 6%-12%, the average monthly interest rate is about 9%, and the assets are managed by hosting, and are managed to the platform. The other platforms use APIkey to import assets, and the user's assets are controlled by themselves, but there are no asset management functions such as financial management and quantification. These products are gradually replaced by the quantitative platform shown in the table below.
The above table is a typical representative of the very popular quantitative platform this year. There is a quantitative strategy that is clear or set by the user or the manual intervention parameter setting by the platform. The user assets are controlled by themselves, because the strategy is transparent and transparent. Words are more credible, less risky, and more marketable.
In addition, since the last year, there have been a large number of strategies in the currency circle that are not open and opaque. By borrowing the quantitative wealth management wallet, in order to quantify the name, the money has been circled. Since 2019, there has been a running tide. WeChat search "digital wallet running, Token wallet runs, "EOS eco wallet, DOGX and polka dot wallet are suspected to be running. Angeltoken and the nearest Token store, MGC token, Sum token, etc. have been run. It is recommended that investors have fixed-income wealth management wallet products. Be vigilant, stay away from high interest temptations, and stay away from high-risk wallets with models and multi-level distribution.
3.4 Quantify the risk of trading
1. The integrity of historical data. Incomplete market data may result in a mismatch between the model and the market data, and insufficient backtesting data, which greatly reduces the accuracy of the model.
2. Quantitative transactions treat the financial market as a steady-state structure and then mine the rules from historical data. But in the final analysis, the financial market is the human market, and human nature is unpredictable. The black swan event is also likely to happen. The conversion of market data itself may lead to model failures, such as transaction flow, price fluctuations, price fluctuation frequency, etc., which is currently difficult to overcome in quantitative transactions.
3, network interruptions, hardware and software failures may also have an impact on quantitative transactions, such as the famous Everbright Oolong refers to the incident caused by the failure of traders and software models.
4. The homogenous model generates the risk caused by the phenomenon of competitive trading, and leads to the risk that the profit margin of the transaction is greatly reduced.
5. The risk of gambling transactions caused by the disclosure of APIkey information of individual investors is as follows: APIkey is uploaded to the centralized server or stored in the local computer, and there are hidden dangers. Once the criminals steal the trading authority, they can use the hedging method. The “empty” of the victim’s exchange assets.
In order to avoid or reduce the potential risks of quantitative transactions, the strategies that can be adopted are: ensuring the integrity of historical data, adjusting model parameters online, selecting model types online, and monitoring online risk and avoidance.
3.5 Comparison of quantitative transactions and manual transactions
Quantitative transactions have arisen in the currency circle. Many investors have begun to get involved and learn quantitative transactions. Can quantitative transactions replace the human brain and achieve a lie? With our analysis and understanding of quantification, the author compares the advantages and disadvantages of quantification and manual trading, and provides a reference for everyone's cognition.
Table 1 Comparative analysis of quantitative transactions and manual transactions
Disadvantage (Weakness )
Advantage (Strength )
1. The black swan event or the market steady-state structure is broken, causing the model to fail;
2. Lack of qualitative analysis ability, especially the investment direction of policy control;
3. Poor overall decision-making ability and risk control capability;
4. The overall market price performance reflects the ability of human brain and human emotions to read this market. Quantification is only a means and a tool. Quantitative models still need to rely on analysts to develop quantitative strategies;
5. Unlike qualitative investments, most of the quantitative trading efforts are spent on which varieties are undervalued, buying undervalued, and selling overvalued arbitrage scenarios. Scenes that require deep decision-making by the human brain are currently not applicable.
1. Quantification is much more efficient than manual trading, and it can overcome some human weaknesses, such as greed, fear, and luck, and can overcome cognitive bias;
2. The command execution capability is relatively fast, decisive and efficient compared to the manual;
3. The processing power of massive data, especially the huge digital asset market with investment targets, and the powerful information processing capability of quantitative transactions can reflect its advantages;
4. Be good at arbitrage, use big data to analyze systematic scans to capture the opportunities brought by wrong pricing and wrong valuation.
From this we can conclude that on the one hand, quantitative transactions have advantages in data mining and scientific execution decision-making; on the other hand, quantitative transactions have their own limitations, especially when dealing with sudden changes in laws. risks of. The combination of quantitative trading and manual intervention may be a better choice. The transaction is still a game that comprehensively reflects human nature supplemented by people-oriented quantification.
3.6 Quantifying trading income
The quantitative transaction income obtained from a quantitative trading super league (the second session, the competition time of 2019.5.20-2019.8.20) is shown in the following figure:
The above chart reflects the overall profitability of 148 quantitative trading strategies in the currency circle. The profitability is not optimistic. The profit rate of the day and the week continued to be negative after one month of operation.
As shown in the above figure, 47 quantitative teams, after nearly one month of operation, 23 companies with negative yields, 14 with yields of 0-5%, and 5 with yields of 5-10%, and the remaining few More than 10% is shown above.
According to the incomplete statistics of the foreign exchange industry, the average monthly return rate of excellent foreign exchange EA and AI quantitative transactions is 5%, 8% and 12%. The comparison shows that the current quantitative trading yield of the currency circle is lower than that of the foreign exchange industry, which is not optimistic.
4. Summary and outlook
In summary, the field of digital asset management is still in its infancy, with a relatively simple business model, mainly based on encrypted funds, supplemented by quantitative transactions. In addition, a number of wallet financing platforms and quantitative transactions have been generated based on quantitative trading tools. platform. At this stage, the quantitative trading of the currency circle is far from replacing the manual transaction; the digital asset quota of the quantitative trading management of the currency circle is far less than that of the encrypted fund, far from the stage of large-scale adoption; the strategy is not open and opaque, fixed-income-based asset management products. It will gradually be rejected by investors and gradually eliminated by the market; the current quantitative trading yield of the currency circle is lower than that of the foreign exchange industry, which is not optimistic; the combination of encrypted funds and quantitative trading tools, the comprehensive use of manual and quantitative strategies The advantage of managing digital assets is the trend in the field of digital asset management.