Own Your Share Of Revenue In The Trading App For Millions
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Contents of this White Paper
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Quantfury Service Terminology
The financial instruments available on the Quantfury platform allow users to obtain economic exposure to the price movement of an underlying instrument without the need for ownership and physical settlement of the underlying instrument. Although this White Paper refers to “trades” and “trading” as part of the Quantfury platform, such references should be understood as user-directed exposures to positions of various financial instruments that undergo price fluctuations, without ownership of the underlying financial instrument.
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The trading of traditional financial and cryptocurrency markets is executed at a disadvantage for the vast majority of participants: retail traders. Access to traditional financial markets for retail traders is dominated by brokerages that aggressively profit from their clients through artificially adjusted market prices, spreads, unrealistic fees, and commissions, in many cases unbeknownst to the trader. Further to this, crypto buyers and sellers pay maker, taker, and leverage fees to trade cryptocurrencies and are unable to leverage their crypto to trade traditional financial markets.
Traditional brokerages and centralized cryptocurrency exchanges that offer margin trading prey on retail traders worldwide by forcing them into a disadvantageous position against the market. Retail traders buy and sell cryptocurrencies, fiat pairs, stocks and commodities at less favorable prices, paying various commissions and fees, while having zero market edge when deciding what to buy and sell. Traders are obliged to access the markets via brokerages and exchanges whose interests are misaligned with their own, if not in direct opposition. To make matters worse, there is still more value being extracted from retail traders transactions. Their trading data finds its way, on a regular basis, into the hands of hedge funds and banks who use it to build profitable trading strategies that are in direct conflict and therefore detrimental to retail traders.
Quantfury ends these practices once and for all. It makes the trading of traditional and cryptocurrency markets free, fair, and transparent. The Quantfury trading app creates a mutually beneficial ecosystem for both platform traders and platform token holders. Users trade without commissions, leverage fees, maker and taker fees and are offered the best bid and ask prices, while the trade spread revenues and trading data users generate are used for the benefit of Quantfury token holders.
Table of Contents
- Value Proposition
- The Problem
- Trade All Markets with Unmatched Conditions
- The App
- List of Asset Types Traded
- Trading Platform Architecture
- Tokenizing Quantfury’s Economy
- Monetizing Trading Data
- Business Landscape
- Addressable Market
- Quantfury Advantage Matrix
- Token Launch
- Improvement Over Standard Valuation Model
- Budget Allocation
- Key Members
- Notes and Further Reading
Quantfury offers cryptocurrency holders trading traditional financial and cryptocurrency markets with unmatched conditions.
For Cryptocurrency Holders and Retail Traders
• Trade free of commissions, leverage fees, and maker/taker fees;
• Leverage cryptocurrency to buy and sell equities, cryptocurrency, fiat pairs, and commodities;
• Always trade at the best (lowest) available ask price and the best (highest) available bid price.
For QTF Token holders
• Receive the total spread value between the bid and ask prices generated for all positions closed by Quantfury trading app users.
Crypto exchanges only offer cryptocurrency margin trading and charge unnecessary fees, placing the trader at a consistent disadvantage.
Traditional retail brokerages bet against their users and profit from the artificially adjusted market bid and ask prices, trade commissions, and leverage fees.
All retail traders execute their buy orders at the ask price and their sell orders at the bid price; for this reason, they are automatically at a disadvantage for each trade they make. Simply put, retail traders buy higher and sell lower than the real market. When an exchange or brokerage adjusts the spread between the bid and ask prices to make them even wider, charging higher trade commissions and higher fees for leverage and withdrawal, then the edge against the trader will grow exponentially, and trader losses will become greater. These practices represent the core of the business model adopted by the majority of traditional retail brokerages and crypto exchanges offering margin trading. This business model also is known as the “churn and burn” model, where user retention becomes a lesser priority because it is understood that users will eventually lose a substantial amount of their funds (‘burn’) and stop trading (‘churn’). The goal is to burn as much of the trader's capital as possible before the churn occurs.
Although the “churn and burn” model can explain the depth of the user’s losses, it still cannot explain why such a high percentage of retail traders lose money. The fact that the majority of traders lose money is public knowledge 1, yet the industry guards the exact statistics of those losses. Some of those statistics are available from time to time due to some of the retail brokerages having to publish their trader performance reports as a regulatory requirement. These reports provide few details, purposefully so, yet still point to the fact that 65% to 80% of traders lose money (1). Research work conducted by academics from around the world analyzing retail trading for stocks, commodities, and currency pairs points to a much bleaker picture of trader losses. That research shows that 95% of traders lose money, while 4% break even and 1% percent turn a profit. In addition, it takes on average three years for a losing trader to quit speculating the market and stop trading (2).
Therefore, we consider the market to be inefficient in the short term where there is a disproportionate number of market participants (retail traders) constantly misreading short-term market information. A trader’s decision to buy or sell specific financial instruments is not only based on readily available market data, such as the asset price, but also driven by behavioral patterns. These patterns specifically relate to the decision making of the majority of retail traders based on the potential value of losses and gains, as well as evaluating their losses and gains using some heuristics (3). In cognitive psychology and decision theory, loss aversion refers to people's tendency to prefer avoiding losses rather than acquiring equivalent gains: for them, it is better not to lose $5 than to gain $5. The principle is very prominent in the domain of economics and trading. What distinguishes loss aversion from risk aversion is that the utility of a monetary payoff depends on what was previously experienced, or was expected to happen. Some studies have suggested that losses are twice as powerful, psychologically, as gains. In fact, a recently published MIT research article (4) analyzing trade data from more than 80,000 traditional retail brokerage trades concluded that indeed the majority of losing traders held on to their losses twice as long as winning traders.
Quantfury will not change the percentage of retail traders losing money because market spreads and emotions will always exist, however, it will curb the amount of the users’ losses significantly.
Trade All Markets with Unmatched ConditionsThe App
The Quantfury app is the most cost-effective, fair and transparent choice for trading the markets.
Quantfury has developed a smartphone mobile app, which allows cryptocurrency owners to trade through fiat currencies financial instruments, such as, cryptocurrencies, fiat pairs, equities and commodities listed on U.S. and European exchanges, without paying a commission, leverage or other types of fees. All financial instruments that are traded are always quoted at real time market best bid and ask prices (5), which are never artificially adjusted.
Only cryptocurrencies (no “fiat” currencies) are accepted to fund the user collateral to begin trading on the Quantfury app. The users access constant trading power in USD 20x their original collateral value. This trading power remains constant. The cryptocurrency collateral secures potential losses incurred in the fiat currency of the trading power according to the current exchange rate as the user trades. Users are not permitted to incur losses greater than the value of the cryptocurrency collateral according to the most recent exchange rate data. The maximum size of trading power allowed by the Quantfury trading app is $100,000 which requires $5,000 of collateral (in crypto equivalent).
A user funds collateral, in the form of Bitcoin, equivalent to $100 and receives a constant trading power of $2,000 to trade both cryptocurrency and traditional financial markets. This $2,000 trading power remains constant regardless of losses incurred from trades. The losses incurred from trading cannot exceed the fiat equivalent of collateral value; otherwise, trading will be stopped and positions liquidated.
Equities and ETFs
642 US and EU listed mid and large cap stocks
BTC/USD BTC/EUR ETH/USD ETH/EUR EOS/USD EOS/EUR LTC/USD DASH/USD IOTA/USD NEO/USD XMR/USD XRP/USD ETC/USD ZEC/USD
Crude Oil, Natural Gas, Gold, Platinum, Silver, Copper
USD/CAD EUR/USD EUR/GBP USD/JPY GBP/USD AUD/USD NZD/USD USD/CHF EUR/JPY CAD/JPY GBP/JPY AUD/JPY NZD/JPY CHF/JPY
The Quantfury platform uses a microservices architecture to deliver next-generation scalability for its trading app user base. Its platform sets the standard for reliably scalable and high-performance data delivery for transaction-driven services, breaking the work into multiple loosely coupled services that can be independently upgraded and scaled in multiple dimensions.
The Quantfury trading platform is designed to match user trades internally and provide continuous market making activity that delivers guaranteed execution of any user trade order that was not matched. The Quantfury platform is the industry's only “light pool” available for retail traders direct access. The Quantfury platform is the total opposite of a “dark pool” concept which exists primarily for institutional traders. In Quantfury's light pool every participant gets unmatched industry trading conditions, all participants are retail traders, and all trading platform activity can be audited and verified through the blockchain. Anybody can be the owner of the pool, and benefit from its operations through token ownership. Therefore the interests of participants, token holders, and the Quantfury platform itself are always fully aligned.
Quantfury guarantees all its user trades are quoted and executed at the exchanges’ best bid or ask prices, without any trade commissions or leverage fees, regardless of the number of daily active users.
The Quantfury platform's main asset, user trading data, is digitized and published using a smart contract and IPFS, a content-addressable storage system:
1. Once a position is closed the anonymized trade identifier together with transaction data including the bid and ask price, last price and timestamp are stored in an encrypted form in IPFS;
2. A hash of that data is stored on the Ethereum mainnet;
3. Quantfury publishes the key to decrypt that data on a time-delayed (30 days) basis for auditing purposes. The key decrypts the users’ monthly closed positions trading data and is saved into the Quantfury Ethereum smart contract;
4. This key can be seen via the Quantfury DAPP or by executing the Quantfury Smart Contract function to receive it.
Quantfury provides open source scripts that can be found on GitHub under Quantfury to verify the authenticity of the trading data.
Quantfury offers its token holders proportional ownership of its trading data assets and the economy it generates. With logging of its assets and operations to a global, immutable ledger, decentralized ownership and monetary rewards of the economy it generates through tokens, Quantfury aligns the interests of its users, investors and itself, the platform operator.
The Quantfury AI employs multiple quantitative trading strategies driven by machine learning models trained on user trading data to generate income. Such a model is not practical with traditional brokerages where the trading data accuracy is negatively biased due to adjusted spreads, fees, commissions, lack of cap on sizes of user deposits, and user margin requirements for obtaining leverage. The Quantfury ecosystem is driven by the need to create the perfect trading data that is the least biased and represents the “purest” market sentiment of traders. This ecosystem is achieved by having users trade with the best bid and ask prices in real time, without commissions and fees and with constant trading power.
The majority of retail trader decisions are driven by the psychology of gains and losses as described in Prospect Theory (3). Trader responses to market fluctuations can be formulated into quantitative behavioral patterns based on the trader's reactions to his/her trading profits or losses.
It has been common practice for a long time that individual trading data has been monetized by financial institutions, by acquiring or owning (6) significant trade order flows that enable them to formulate profitable trading strategies. Retail traders pay commissions and various fees in order to trade, never getting a share of the monetization of trading data they generate.
Financial institutions analyze client trade order flows in a one-dimensional way, without having the full depth of the client’s trading behavior behind their trade orders. This behavior is largely determined by the size of the client’s accounts, the size of leverage they use, and their previous trading history.
The Quantfury ecosystem is designed to build a textbook-case dataset for its user trading data by:
Grouping the “fear and greed” factor of users effectively by offering constant trading power regardless of trade results;
Fixing the number of user collateral sizes accepted, thus making a number of trader groups with similar fear and greed ratios;
Capturing the least biased or “purest” market sentiment of traders by having users trade with the best bid and ask prices in real time, without commissions and fees; and
Maintaining the full depth and history of user trading decisions.
Quantfury constantly updates and applies machine learning of the trading data generated within its ecosystem. These machine learning algorithms produce automated trading decisions to buy, sell, or do nothing for each of the Quantfury incoming user trade orders. The machine learning of trader behavior, both as individuals and as groups, is conducted at each moment of the current state of the Quantfury ecosystem in real time, thus constantly improving the financial well-being of the platform.
To maintain the efficiency and integrity of Quantfury’s machine learning algorithms and its automated trading decisions, it is a fundamental requirement to provide unmatched trading conditions to the trading app users, which in turn promises the most unbiased data of market sentiment by retail traders. Further, the platform’s predictive powers strengthen continuously as a result of allowing platform activity to occur in this entirely unbiased way.
ML Algorithms used at Quantfury:
A. Multilayer Perceptron (MLP), is a fairly simple form of artificial neural network known as a Feedforward Neural Network (FNN). FNNs are so called because information flows through them in one direction only. MLPs consist of an input layer, one or more hidden layers, and an output layer. Each neuron within the MLP performs a weighted sum of every output from the previous layer (these neurons are therefore known as densely connected), adds an additional number called a bias, and applies a non-linear function to produce its output. The non-linearity is important as it technically allows the MLP to approximate any function.
A simple 4-layer MLP is shown below:
MLPs learn (are trained), in a similar fashion to most other artificial neural networks - with the backpropagation algorithm. This algorithm is a form of supervised learning where an input is allowed to propagate through the network and then the output is compared to the ideal output. The difference between that particular output and the ideal output is known as the error. The algorithm determines which neurons, layer-by-layer, have contributed to the error and by how much and then iteratively adjusts them per example.
An autoencoder is a neural network with the purpose of distilling the important aspects of input data into a shortened form, or code. This shortened form is both useful insofar that it is efficient with respect to data size, but also because the autoencoder removes the least relevant information from the input. This allows for other machine learning algorithms to learn more efficiently if its inputs are first autoencoded.
An autoencoder usually takes the form of an MLP with hidden layers decreasing in size to a point, and then increasing back to the original size with the same number of layers thereafter. The algorithm is then trained such that it attempts to recreate the input at the output. This may seem trivial, but as the size of the hidden layers get progressively reduced, the algorithm must learn to reconstruct the input from missing information. The training process therefore intuitively learns to conserve only the most important aspects of the data.
The first part of the network is known as the encoder, while the second part is known as the decoder. After the training process is complete, only on the encoder is used in order to transform the data. A diagram outlining these parts of the network is shown below:
Q-learning is part of a subset of machine learning techniques known as reinforcement learning. Reinforcement learning concerns itself with optimizing how an artificial agent takes actions within its environment in order to maximize some notion of reward in the long-term - much like how one might train an animal.
In Q-learning, there exists a set of states (S), and a set of actions (A) that may be taken at every possible state. By performing one of these actions in a particular state, the agent transitions from one state to another and earns a particular reward depending on the action and state.
The goal then, of the Q-learning algorithm is to find a function Q that is able to tell the agent what the best action is at any given state. This function is found through a training process where the algorithm is given a high-level objective that it tries to maximize - a cumulative reward from all of its actions. Examples of this include total return on investment for a trading bot, or high score for a bot attempting to learn how to play ‘pong’. The Q function can take many forms, and is commonly an MLP as discussed previously.
The learning process of a Q-learner can be described mathematically as follows:
It can be seen that the function is updated to consider both the reward the agent can earn from a current action, but also how that action might affect the ability to perform future actions which also have corresponding rewards.
Objective and Description of Quantfury’s ML Architecture: A. Objective
The objective of the Quantfury ML architecture is to maximise risk–adjusted returns. By monitoring the activity of traders across the Quantfury platform, as well as integrating technical indicators from all of our instruments, we are able to construct a comprehensive view of the market state, from which our Q-learner can make profitable trading decisions. It is this system that allows Quantfury to remain profitable while offering users a fair trading environment and low margins.
B. Data Streams
Quantfury incorporates several different data streams into its ML architecture in order to accurately assess its universe. The data streams can broadly divided into trader data and financial instrument data.
Trader data is comprised of the behaviour of traders on the platform. This data is anonymised and is simply used to model actions of agents on the platform, not track individuals. Trader data consists of a full trade history, including ROI per position, long or short, exposure etc. This data is then further processed into rolling time windows and rolling position windows - giving an idea of how a trader’s actions have evolved per position opened and time elapsed.
The other datastream contains collated information about all the financial instruments being traded on Quantfury. This includes a large number of technical indicators such as moving averages and volatility and how these indicators have evolved over time for that particular instrument.
C. Quantfury’s AI architecture is comprised of 3 main elements - a system of deep autoencoders, a position prediction network, and a system of Q-learners.
The autoencoders are trained to intelligently extract the most vital information contained in the datastreams above. A different autoencoder is needed for each datastream as every datastream has different characteristics that must be encoded. Not only does this allow for improved performance, the autoencoders allow for compression of data, relaxing bandwidth restrictions and improving system latency - vital for accurate and timely trades.
After all the data streams are autoencoded, a MLP makes a prediction on the performance of a particular trader’s current position, taking all current and historical information into account. This is performed for every position opened for every single instrument and a separate model.
The prediction described above is then appended to all of the autoencoded datastreams at the current timestep. This creates a state that fully describes the Quantfury universe. Such a state may then be used by the Q-learners to dictate an action that Quantfury should take - in this case what position to take on a particular instrument and how large of a position to take. A Q-learner is trained for each instrument such that it can learn to deal with the specific dynamics of that particular instrument.
In this way, all of the models work together in order to create a real-time autonomous list of decisions that Quantfury should take at any given moment, taking into account the entire platform and overall systemic risk.
Quantfury operates in compliance with the Securities and Investment Business Act, 2010 “SIBA” (7) of the
British Virgin Islands.
Quantfury’s app users are subject to AML and KYC procedures in accordance with the Anti-Money Laundering and Terrorist Financing Code of Practice, 2008 (8) of the British Virgin Islands.
The Quantfury app is available to users located in the following markets: Asia-Pacific; Central and South America; Central and Eastern Europe; and the Middle East.
Citizens and or residents of the United States and Canada will not be initially allowed to use the Quantfury trading app.
Quantfury’s target market over the next 12 months is 500,000 users globally. The key product value for this audience is trading equities, cryptocurrency, fiat pairs, and commodities without trade commissions and leverage fees. This value is not offered anywhere else on cryptocurrency or traditional financial markets.
The three largest exchange platforms that offer crypto margin trading in the world (BitMEX, BitForex, and OKEx) are estimated to generate $1.46 trillion in volume this year. Excluding 20% of the volume that is generated by US users, $1.168 trillion of trading volume would be generated by the addressable market of Quantfury users worldwide. In April 2017, Japan's Financial Services Agency (FSA) reported that there were 142,000 crypto margin traders in Japan out of the total 3 million crypto holders, which generated 90% of the country’s total cryptocurrency trading volume of $543 billion that year (9). Given that there are approximately 22,000,000 crypto holders outside of the US, and that the ratio between crypto margin traders and total crypto holders is 1:21 (see stats for Japan above), there are approximately 1,047,000 crypto margin traders worldwide. Additionally, given the reported relationship between the number of crypto margin traders and their yearly volume of trades in Japan, there are estimated to be approximately 340,000 crypto margin traders on BitMEX, BitForex, and OKEx. There are dozens of other, smaller volume crypto margin trading platforms, such as Deribit and Quedex, which are excluded from the estimate. A conservative estimate based on the three leading crypto margin trading platforms and the statistics reported from Japan would put Quantfury’s target audience for the first 12 months at 500,000 users.
Quantfury’s serviceable available market (SAM) is estimated to be 5,500,000 users globally. The key product value for this audience is the availability of the mobile app. A survey of institutional traders performed by J.P. Morgan found that 61% of them are likely to use a trading app, up from 31% in 2017 (10). A similar trend toward mobile trading is also being seen in the retail brokerage industry (11). Cryptocurrency holders have very limited options to utilize their holdings. The ease of accessibility of the the Quantfury app and being able to trade all markets is a powerful value proposition to the user anticipated to result in high conversion.
The SAM for Quantfury represents the estimated available market that Quantfury will be able to reach with its message, and serve, over the first 12 months. In addition to the initial target market audience, Quantfury’s SAM may expand to:
• Сryptocurrency holders who are not active traders;
• A small fraction of traditional brokerage clients – retail traders, who trade cryptocurrencies
as well as equities, commodities, and fiat pairs.
The majority of cryptocurrency holders store their assets on exchanges or personal wallets with the goal of value appreciation. In Quantfury, they will use their cryptocurrency assets by leveraging these assets to trade traditional financial and cryptocurrency markets with unmatched conditions. According to a study performed by the University of Cambridge in 2017, there are between 2.9-5.8 million unique users who are actively using their cryptocurrency wallets, with 70% residing outside of North America, but the number is likely to be much higher due to the exponential growth the cryptocurrency market experienced at the end of 2017 (12). Recent reports from Japan and Korea suggest that there are at least 5 million crypto holders in those two countries alone (9, 13). Binance has reported that its platform has 9 million users, and continues to grow (14). A conservative estimate puts this target audience at 22,000,000, with Quantfury targeting 25% of the audience – 5,500,000 users.
Quantfury estimates its total available market (TAM) is 65,500,000 users globally. The key value proposition for this audience is Quantfury’s unmatched trading conditions, which are vastly superior to any traditional brokerage on the market.
In addition to early adopters, cryptocurrency holders, and the first wave of retail traders, the TAM includes the remainder of the retail trader audience worldwide that fits Quantfury’s target demographics. This retail trader audience that fits Quantfury’s target demographic is estimated to be 60 million people worldwide out of 200 million total retail traders. The early adopter retail traders will have to learn about the basics of owning cryptocurrency and, specifically, how to buy cryptocurrency to fund their collateral account with Quantfury and start trading.
Retail traders that currently use traditional brokerages are paying commissions, leverage fees, and trade using adjusted market spreads all of which puts them at a constant disadvantage against the market. The Quantfury app offers superior trading in terms of cost to trade, real unadjusted market prices, and a flawless user experience.
While Quantfury focuses on cryptocurrency holders to be the early adopters, it envisions that a significant share of retail traders using traditional retail brokerages will seek to buy cryptocurrency, onboard, and take advantage of Quantfury’s unmatched trading conditions. Having a good understanding of the obstacles that each of the groups is facing, Quantfury will use a comprehensive go-to-market strategy and provide appropriate tools and facilities to onboard users.
Quantfury operates on the assumption that it will have 70,000 daily active users or more within the 12 months following its trading app launch. It is estimated that this number of users may generate approximately $700,000,000 of daily trading volume, based on a comparison of “number of users/volume generated” data available for BitMEX, BitForex, and OKEx.
The Quantfury trading app was made to be the platform of choice for cryptocurrency and traditional markets trading, to offer an undeniable value proposition for crypto traders and holders, as well traditional brokerage clients.