FAQ
Quantitative, or "quant," trading—also known as algorithmic or automated trading—involves utilizing mathematical models and algorithms to inform trading decisions. By leveraging computer programs to analyze financial data, traders can automatically execute trades based on predetermined rules across various financial instruments, including stocks, bonds, futures, options, and currencies. This approach is particularly favored by institutional investors, such as hedge funds and proprietary (prop) trading firms.
One of the key advantages of quant trading lies in its reliance on objective and data-driven criteria, eliminating subjective judgment or emotional impulses. This methodology enables traders to swiftly and accurately analyze large volumes of data, implementing complex trading strategies that may be challenging or impossible to execute manually. Ultimately, quant trading offers a more efficient and effective approach to investing grounded in rigorous analysis and rule-based decision-making.
The term "alpha" generally denotes the measure of an investment strategy's ability to generate returns beyond what is anticipated based solely on market movements. This excess return is measured concerning a market index or benchmark, which is considered representative of the overall market movement. In the context of crypto markets, the movements of dominant cryptocurrencies like BTC, ETH, XRP, and ADA, or their average, are commonly employed as market indices.
In the realm of quant trading, "alpha" also refers to mathematical models assessing statistical probabilities of price movements in the market. These models aim to achieve an excess return on investment by identifying and exploiting opportunities that can potentially outperform the market. Essentially, the alpha itself constitutes the quant strategy.
As a prediction model, alpha is typically designed to analyze extensive data, generating trading signals that inform trading decisions. Each alpha incorporates diverse factors, including technical indicators (e.g., price patterns, trading volume, trend lines), fundamental data (e.g., financial statements, economic indicators, industry trends), and market sentiment towards a specific asset or the broader market (e.g., sentiment analyses from news and social media).
Note that the term "alpha" is often used interchangeably with related terms like "alpha model," "prediction model," "algorithms," "alpha strategy," and "quant strategy."
1. Idea Generation:
Begin by brainstorming and generating ideas for potential trading strategies. This can be based on various factors, such as market inefficiencies, statistical patterns, fundamental analysis, or quantitative models.
2. Data Collection:
Gather relevant data from various sources, including market prices, economic indicators, news feeds, and social media sentiment.
3. Model Development:
Use mathematical and statistical techniques to create a prediction model. This process may include hypothesis testing, statistical analysis, time series analysis, identification of pre-existing trading ideas, as well as machine learning (ML) approaches such as gradient boosting, neural networks, dimensionality reduction, ensemble methods, and reinforcement learning. The model is then trained using a designated dataset, which may involve manual parameter optimization for rule-based models or ML-based training for applicable approaches.
4. Backtesting:
Evaluate the trained model's performance by simulating its past performance using historical price data. This process, referred to as "backtesting," allows for the assessment of the strategy's profitability, risk, and other performance metrics. It essentially examines how the trading strategy would have performed under various market conditions, identifying strengths, weaknesses, and areas for further refinement.
5. Model Development:
If the model demonstrates decent and robust performance, it is handed over to the model registry for deployment in live trading.
Note: While the above provides a general outline of the process of creating a quantitative trading strategy, it is important to note that the specifics may vary depending on the chosen approach and individual preferences.
Quant trading can exhibit a spectrum of human intervention, influenced by factors like strategy complexity, market conditions, and trader preferences.
Even in highly automated strategies utilizing predefined rules and algorithms for data analysis, trade identification, and execution, a certain level of human involvement remains essential. This involvement includes overseeing the trading system's operation and making necessary adjustments. For example, human traders may monitor the system to prevent and address unforeseen technical issues and malfunctions. Additionally, they may tweak algorithm parameters to optimize trading performance and intervene in response to unexpected market disruptions.
Quant trading tends to thrive in crypto markets due to the advantageous presence of high volatility, a characteristic more pronounced than in well-established traditional financial (TradFi) markets.
In equity markets, fundamental factors, such as industry dynamics, earnings, debt, and analysts' consensus, exert significant influence on price movements. This results in an anticipated price range for company stocks, and any deviations from this range are typically promptly corrected by market observers.
On the contrary, crypto markets operate differently, lacking traditional fundamental indicators that determine fair prices. Crypto prices are primarily driven by social consensus, heavily influenced by people's sentiment, leading to the absence of an established expected or appropriate price range. Consequently, abnormal price movements in crypto markets can be self-perpetuating, driven by positive feedback loops, and may eventually reach unreasonable levels.
The notable high volatility in crypto markets creates more frequent trading opportunities and the potential for significant profits through substantial price swings when predictions align with market movements.
It's crucial to recognize that no trading strategy can be expected to maintain effectiveness indefinitely. Two primary reasons contribute to this:
1. Changing Market Conditions: Market dynamics are ever-evolving, and strategies that prove successful in one set of conditions may lose effectiveness as the market evolves or shifts. What works well in one situation may not be applicable or profitable in a different environment.
2. Over-Exploitation: A previously successful strategy, once widely known and adopted by many traders, can lead to over-exploitation. As more traders execute the same strategy, its effectiveness diminishes, reducing the potential for consistent profits.
These factors underscore the necessity for traders to continuously adapt and evolve their strategies in response to changing market conditions. It's crucial to avoid relying solely on strategies that have become widely known or overused to maintain a competitive edge and achieve long-term success.
In our approach, we embrace a human-AI/ML hybrid methodology. While human researchers contribute the majority of alpha model ideas, we move beyond traditional rule-based code with fixed predetermined parameters. Instead, our models are fully parameterized and capable of learning. This approach serves two important purposes:
Detecting Overfitting: Overfitting is a major concern for quant hedge funds. Fixed parameter models rely on observing the model's performance on validation data, providing a single observation lacking statistical significance. With AI/ML, we easily identify overfitting by examining whether the validation loss consistently decreases with iterations, in addition to the train loss.
Enhancing Adaptability Over Time: Our AI/ML-powered models excel in adapting to dynamic market conditions. They are designed to continuously learn and update their parameters even after deployment for actual trading. This ongoing learning process sets them apart from human-created quant models, which often rely on fixed rule-based code without flexibility for parameter modifications. Consequently, when market conditions change and human-created models lose effectiveness, they are often abandoned. In contrast, our AI/ML approach empowers models to evolve and perform well over time.
To sum things up, by combining human insights with the learning capabilities of machine learning, we aim to overcome the challenges of overfitting and enhance the adaptability of our alpha models, ultimately improving their performance and longevity in real-world trading scenarios.
In quant trading, the "square-rooting" concept applied to a portfolio, sqrt(portfolio), serves as a risk management technique. We named our company SQRT as a testament to our commitment to helping users build wealth through crypto in the most secure manner possible.
For those curious about sqrt(portfolio), let's delve deeper.
Consider a portfolio with two assets, A and B, in a 20:1 investment allocation ratio. Despite historical backtests favoring asset A, the portfolio faces risks if A experiences a significant decline. With sqrt(portfolio), the allocation adjusts to an approximate 8:2 ratio. While a larger proportion still goes to asset A, there's a substantial increase in the allocation to asset B. This reallocation process reduces risk metrics such as maximum drawdown (MDD) while simultaneously enhancing the Sharpe Ratio (SR), a measure of risk-adjusted return.
Nope!
We can't impose a lock-up anyway, as users' exchange accounts are linked to our automated trading system through restricted API keys. What that means is that users will always have full control over their funds, including when to initiate and terminate trades.
However, we suggest users to stick with our trading program for at least 3 months, as early intervention in a trading strategy may potentially disrupt the strategy and adversely affect its performance.
We believe in the wealth creating power of crypto, if used well, for the masses. After all, crypto is the biggest killer app of blockchain, and building wealth through trading the biggest incentive of using crypto.
However, today's crypto trading landscape is fundamentally flawed, as seen in the collapses of various services and platforms over the past few years.
We're here to fix that.
We want to help people build financial wealth safely through crypto by providing a central hub, where anyone, regardless of their starting point, can access sophisticated trading strategies personally customized to suit their needs and preferences.
Going forward, our goal is to evolve into a platform where users can integrate all aspects of their lives. They can invest using our quant models, create their own strategies with our AI/ML toolkits, share experiences, connect with others, and be matched with meaningful offers and opportunities. This will be based on their blockchain-verified investor profiles and trading history. Essentially, we aim to broaden the scope of wealth-building through crypto to include social aspects, helping individuals prepare for the digital future.
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