The world’s top social investment community, DNKG, enables consumers to expand their financial literacy and experience. Since ancient civilizations, humans have used gold as money and a store of value. Since then, and especially in times when the overall macromarket has been in distress, gold has been used as a hedge.
We offer Trading with 20 of the best crypto currencies. At DNKG, you can easily trade with 20 of the world’s top crypto currencies and earn up to 15% in profits!
Discover why millions of investors from over 100 countries joined DNKG
See your trades get settled in real time. Our trades get settled in as low as 10 seconds!
Trade from over 50 cryptocurrencies to choose from and the flexibility to trade from anywhere in the world.
Our customer service representatives will be available to answer all your concerns and queries 24×7.
We maintain complete transparency. At any point in time, you can verify your crypto allocation in our vaults.
Audited timely and regulated by various financial institutions all over the world! Over 3 million customers trust us.
Our quantitative adopts the x*y = k rule to ensure that the number of tokens that you add to the platform remain unchanged.
Welcome to DNKG, your premier destination for crypto trading and mining. With years of experience in the industry, we offer a user-friendly platform that simplifies the complexities of cryptocurrency. Our team of experts is dedicated to providing you with the best trading tools, real-time market analysis, and secure transactions. Join DNKG today and unlock the potential of digital assets with ease. Start your crypto journey with us and experience seamless trading and profitable mining like never before.
Our team of expert analysts ensure that you always make a profit by following their signals! If you follow their signals and make a loss, get 30% compensation of your trade amounts!
Natalie Brunell is a Polish-American television news personality, investigative journalist, podcast host and educator.
Her work at Spectrum News was nominated for an LA Press Club Award and for an Emmy in Investigative Reporting. In 2021, Natalie launched the Coin Stories podcast, which is focused on cryptocurrency and Bitcoin.
Jameson Lopp is an American cypherpunk, software engineer, columnist, and Bitcoin advocate. Lopp is co-founder and CTO of Bitcoin security provider Casa. Prior to joining Casa, Lopp served as a software engineer at BitGo.
Lopp has been publicly involved in cryptocurrency dating back to 2012 and involved with Casa since 2018.
Andreas M. Antonopoulos is a best-selling author, speaker, educator, and highly sought after expert in Bitcoin and open blockchain technologies. He is known for making complex subjects easy to understand and highlighting both the positive and negative impacts these technologies can have on our global societies. As an educator, his mission is to educate as many people as possible, in as many places as possible, in as many languages as possible, about the historical, technological, and socio-economic impacts of Bitcoin and open blockchain technologies.
Our user interface is not filled with unnecessary clutter so you can focus on the most important thing at all times!
In the DNKG dApp input, enter the official website of AI Quantitative Trading, click Open Account, and then complete the connection between Coinbase wallet and Quantitative Trading to start trading.
Check your USDT balance in your personal assets centre, initiate a withdrawal, enter the address of the wallet you want to withdraw your coins from, and the smart contract will automatically run and process this withdrawal request within 30 minutes. However, before initiating a withdrawal, the user must undergo KYC authentication to ensure the safety of the funds.
Quantitative trading rewards are paid 4 times a day, 0:00/6:00/12:00/18:00 (GMT+8)
You can receive the earnings bonus four times a day. The total pledge earnings can be viewed in the pledge history to see the history of your orders and earnings payments.
Special Note: Quantitative Trading rewards are paid out within 48 hours, if you failed to collect your pledged earnings within 48 hours, Quantitative Trading will withdraw back your rewards.
Quantitative trading is growing with the rise of technological trends like machine learning. With further innovation and evolution in modern technology, various markets and organizations now utilize quantitative trading strategies to make a profit. In the case of cryptos, high frequency trading (HFT) is one of the quantitative trading strategies that is sometimes used in dark pool trades.
Cryptocurrency is a unique asset that incorporates various technologies allowing for decentralization and programmable functions. Crypto-based quantitative trading has three categories, which are alpha, primitives, and risk models.
Alpha: Alpha is described as the excess returns of an investment relative to the intended benchmark index. Quantitative strategies look for alphas within datasets of assets like currencies or commodities. In the traditional market, alphas such as spot and earning reports are used in quantitative models. In crypto, however, blockchain data serve as the native alpha. Blockchain datasets contain information about participant behaviours in the crypto space. These participants include miners, HODLers, whales, and so on. There is a lot of potential in the formation of strategies based on blockchain addresses due to its various advantages. For example, relevant blockchain addresses can provide data that allows for capital flow tracking. Therefore, blockchain technology is ideal for quantitative strategies as they rely on statistics and data.
Primitives: In traditional markets, the quantitative strategies formed are primarily based on speculation on the market and third parties can influence financial functions such as liquidity and arbitrage. On the other hand, in the crypto world, such intermediaries are replaced with smart contracts, where transaction records are transparent and hence accessible to quantitative models. Quantitative models in decentralized finance (DeFi)can collect data from crypto primitives, which can be categorized into governance (voting, staking), tokens (ERC20, NFTs), regulations (security protocols), and more. Therefore, primitives are another source of data that quantitative traders can factor into their quantitative strategies.
Risk models: Risk management is typically needed in trading strategies. In traditional markets, risk management models are mostly related to prices such as hedging and fluctuations. However, quantitative strategies in crypto are complex because the DeFi space has risks such as forks, protocol hacks, liquidity attacks, new competitors, and so on. Although the information on crypto risks is inadequate for quantitative trading, quantitative models are still applicable because of their statistical nature. In the future, risk management will grow to become more mature as crypto and quantitative strategies develop.
Algorithmic trading is also called algo trading. Algo traders utilize automated systems to analyse charts and execute trades on their behalf. In comparison, quantitative traders identify opportunities through statistics and do not always trade (despite the opportunity). Algorithmic trading is typically regarded as a subset of quantitative trading. Regardless, the differences between algorithmic trading and quantitative trading are as follows:
Manual/Automated trading: Quantitative trading involves using models to capture opportunities that are then traded manually in general. On the other hand, algorithmic trading fully utilizes the system to execute trading for the trader.
Quantitative trading typically utilizes advanced mathematics models, while algorithmic trading relies on traditional data and chart analysis.
Operating in a highly regulated environment, DNKG takes extra steps to ensure customer data is protected, which is not even accessible to DNKG’s own data scientists and engineers. Any code running on DNKG production servers must undergo code review by multiple groups of people before it goes into production. One of our core principles is security first, as all digital assets stored on our platform belong to our customers.
Tether Gold (XAUT) Is a token that provided ownership of physical Gold. By putting Gold on an exchange, its equivalence on the market presently ranges at US$1643 On the minimum and is currently Growing rapidly.
In recent years, the use of blockchain and cryptocurrency has grown rapidly. While the main investing strategy for crypto is to purchase and hold cryptocurrencies until they increase in value, there are several additional methods you can use to earn passive income. One such strategy involves liquidity mining, which takes advantage of the immense hype behind decentralized finance (DeFi) while allowing investors to use their holdings to generate additional income.
Quantitative trading, also known as quantitative analysis or quant trading, is a trading strategy that is highly dependent on mathematical models and historical data. Quantitative trading strategies are formed using various technologies, databases, and mathematical concepts. For example, price and volume are two common factors that traders can use as inputs for mathematical models. Alternatively, some traders build systems to monitor public sentiment about assets or sectors on social media. Other traders may also use alternative or public databases to identify current and future patterns to ensure that the mathematical model for quantitative trading is sufficiently advanced.
In quantitative trading, traders can build software or programs that combine mathematical models and trading concepts. The programmed mathematical models are automated software that help traders identify historical data patterns to make rational trading decisions. The trading concept selected for a program is based on the trader’s preferences and research scope. Traders try to evaluate only the desired parameters for the specified asset to save time and resources. For example, suppose a trader only wishes to trade during an upward trend; they would program the software specifically to detect upward momentum and purchase the shares of an asset when there is an upward trend. Alternatively, traders can make the trades manually rather than allowing the program to trade automatically. In that case, the trader only intends for the program to capture the profit opportunities and they can make the trades accordingly. To ensure that the programs work as intended, traders typically apply their mathematical model to past market data for back testing before implementing it on the real market.
Quantitative trading involves deciding on strategy, back testing the system prototype, executing the system, and managing risks. The steps that quantitative traders typically take to create a program for efficient trading are as follows:
1.The trader researches the various trading concepts and tools before selecting a trading strategy that fits their investment purposes and risk tolerance.
2.The trader selects a simple or complex trading strategy or analysis tools like moving averages (MA) or oscillators.
3.The trader develops a system based on the selected strategy.
4.The trader back tests, customizes, and improves the system as needed. Nevertheless, a positive back testing outcome does not necessarily mean the system can perform well in actual markets.
5.The trader assesses the outcomes using risk management tools such as stop loss and scenario analysis.
6.Once the trader deems that the program is ready, they utilize the quantitative trading system in the live market.
7.The trader observes and assesses the money-making potential of the program. They can make changes to the strategy or further customize the system if needed.
8.In cases where profit opportunity exists, the system will signal traders who can choose to execute trading manually or automatically.
Systems created may range from entirely manually to fully automated. Moreover, the different programs and systems developed by different traders will compete to generate higher profits. Thus, traders will need to improve their trading systems persistently to remain competitive. Moreover, as machine learning or deep learning gains traction, quantitative trading can become even more complex. As a result, traders can make more efficient trades with these types of trading systems as they can learn from the available data, thereby becoming more aware of different profit opportunities over time.
DNKG is a regulated financial institution on a mission to create a global, frictionless economy. By building infrastructure to enable the movement between physical and digital assets, DNKG is creating a future where all assets—from money to commodities to securities—are digitized and can move instantaneously, 24/7.
DNKG is proud to announce it has secured its formal license from the Monetary Authority of Singapore (MAS) for its Singapore entity. This is a Major Payments Institution License, making DNKG the first US company to secure full Major Payments Institution licensing in Singapore.
With offices in New York, London, and Singapore, DNKG takes a global view of modernizing the financial system.