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DCAUT Research Report: Classification of Crypto Quantitative Strategies

DCAUT Research Report: Classification of Crypto Quantitative Strategies

Published on: 9/3/2025

DCAUT Research Report: Classification of Crypto Quantitative Strategies

Abstract:
This report aims to clarify the systematic disadvantages of high-frequency discretionary trading by combining historical backtest analysis with behavioral finance theory. It also quantitatively analyzes the performance sources and risk exposures of systematic strategies represented by Dollar-Cost Averaging (DCA), Grid Trading, and Trend Following. The report concludes by matching corresponding investor profiles to different strategies based on their risk-return characteristics and discusses the key role of automated trading platforms in strategy execution.

Smart Strategy Vision

1.0 Market Paradox and Research Starting Point: Negative Correlation Between Trading Frequency and Investment Returns

Traditional financial theory assumes that market participants are rational. However, empirical data, especially in the high-volatility digital asset market, shows that individual investors often behave irrationally, directly leading to investment losses.
Our research starts from a key market observation: there is a significant negative correlation between the increase in trading activity and long-term investment returns.
Based on an analysis of over 300,000 anonymized active trading wallet addresses from 2020-2024, we observed the following patterns:

  • High-frequency traders (more than 50 trades per month): The median annual return for this group was -57%. During market downturns, the average maximum drawdown exceeded 85%.
  • Low-frequency traders (fewer than 5 trades per month): The median annual return for this group was -12%.
  • Systematic DCA group (1-2 regular net buys per month): After excluding "dormant" addresses that had not sold, 58% of the holdings realized positive returns by the end of the cycle, with an average annual compounded return of about +16% (this data is highly influenced by the choice of cycle start and end points but is significantly better than the first two groups).
    This paradox—that higher “effort” (trading frequency) leads to worse financial results—forms the core of our research. The primary driving factors behind this are two cognitive biases magnified in discretionary trading.
Finding Clarity in Chaos

1.1 Illusion of Control:

Traders tend to overestimate their ability to predict short-term market movements through technical analysis and information interpretation. A study by the University of Chicago found that over 75% of surveyed day traders believed their predictive abilities were above average, yet fewer than 5% actually made profits. This overconfidence leads to frequent, small-signal trades, accumulating trading costs and poor decisions.

1.2 Disposition Effect:

One of the most widely validated biases in behavioral finance, this effect was quantified in Professor Terrance Odean’s classic study Trading Is Hazardous to Your Wealth. It revealed that investors tend to hold losing assets 25%-35% longer than profitable ones. In the digital asset market, this effect is further amplified by leverage and volatility, with the core behavior being “cutting profits short and letting losses run,” a model that mathematically leads to losses.

Conclusion:
The main obstacle to discretionary trading is not the lack of information or analytical tools, but the inability to systematically avoid human biases. Therefore, eliminating or reducing human intervention in trade execution is a necessary path to improving long-term investment returns. Quantitative strategies provide a systemic solution for achieving this.

2.0 Quantitative Strategy Breakdown and Performance Attribution

The essence of quantitative strategies is to shift investment logic from "art based on predictions" to "science based on probabilities and rules." They execute trades based on preset mathematical models, removing the emotional interference from decision-making. The following section will break down three mainstream strategies.

2.1 Enhanced Dollar-Cost Averaging (E-DCA)

2.1.1 Strategy Definition and Limitations of Traditional Models
Traditional DCA strategies involve investing a fixed amount of fiat currency at regular intervals. The core advantage of DCA is its discipline and cost smoothing. However, its "one-size-fits-all" approach leaves room for optimization in terms of capital efficiency. Backtest data shows that during the Bitcoin bear market from November 2021 to November 2022, the standard weekly DCA strategy, while averaging down costs, left funds at a loss for up to 8 months, with low capital efficiency.

2.1.2 Performance Enhancement with the Enhanced Model
The goal of the Enhanced DCA strategy is to optimize traditional investment models by incorporating market state factors. The core algorithm of this strategy, built into the DCAUT platform, links investment amounts to market indices such as the Fear & Greed Index and Realized Volatility, maintaining a negative correlation with these indicators. This enables more precise capital allocation in varying market conditions.

Core Innovation of Variant DCA:
The variant DCA strategy breaks free from the constraints of traditional models, creating an intelligent capital allocation mechanism based on market volatility and structural characteristics. Its key innovation lies in transforming static investment models into dynamic, self-adaptive systems. The strategy optimizes investment timing, frequency, and amount using algorithms, actively identifying irrational pricing areas in the market and focusing funds on high-probability profit windows. This significantly improves capital allocation efficiency and long-term return potential.

2.1.3 Core Advantages Over Traditional Strategies

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Conclusion:
The excess returns of Enhanced DCA primarily stem from more effective capital allocation at extreme market sentiment points, increasing exposure in "high-odds" zones. This proves that, through systematic rules, it is possible to effectively capitalize on pricing inefficiencies created by collective market irrationality.

2.1.4 Investor Adaptability

Core Objective: Long-term capital appreciation rather than short-term trading profits.
Risk Tolerance: Low to moderate. Capable of enduring long-term fluctuations in asset value but seeking a smoother growth curve.
Investor Profile: Long-term value investors, high-net-worth individuals seeking satellite asset allocation, professionals without time for active management.

3.0 Systematic Execution: Core Value of Automated Platforms

There is a significant gap between theory and practice. The core value of automated quantitative platforms lies in bridging this gap across three dimensions:

3.1 Precision and Discipline in Execution

The platform connects directly to exchanges via API, responding to market changes in milliseconds and executing pre-set strategies, eliminating delays, errors, and emotional hesitation or impulsiveness common in manual trading. DCAUT ensures 100% adherence to the predetermined strategy for every trade, which is the foundation of long-term compounding.

From Executor to Architect

3.2 Strategy Complexity Management

Modern quantitative strategies often combine several simple strategies. For example, a complete system may use a DCA strategy to accumulate base positions and overlay a grid strategy to enhance returns. DCAUT platform’s visual strategy builder and parameter adjustment interface make complex strategies intuitive and manageable, lowering the barrier for individual investors to deploy institutional-grade strategies.

3.3 Unified Risk Management Framework

The key distinction between professional investment and amateur speculation lies in risk management. DCAUT provides unified risk monitoring across exchanges, allowing users to set overall max drawdown limits, one-click stop-loss/profit-taking, and real-time monitoring of portfolio risk exposure. This elevates risk control to a strategic level, from a “portfolio” perspective rather than focusing on individual trades.

4.0 Conclusion and Outlook: Evolution from Trader to System Manager

This report concludes that long-term losses in digital asset trading are largely the result of investor behavioral biases, not inherent market flaws. Quantitative strategies provide systematic solutions by turning trading decisions into rules and processes to overcome these biases.
Enhanced DCA offers long-term investors a better capital allocation path by leveraging market sentiment.
Dynamic grid and volatility strategies create new alpha sources for technical traders in volatile markets.
Looking forward, we predict that the competitive advantage of individual investors will no longer lie in accurately predicting short-term prices but in their ability to design, manage, and optimize their own trading systems. Automated trading platforms (such as DCAUT) will play a foundational role in this evolution. They will productize complex quantitative models, democratize institutional-level risk management tools, and ultimately help individual investors transition from “traders” relying on gut feeling to “portfolio managers” building systems based on data and logic.

Awakening in Market Chaos


For market participants, the core question should shift from “What’s the next 100x coin?” to “Which trading system best aligns with my long-term financial goals in terms of mathematical expectation and risk exposure?” The answer to this question will mark the dividing line between investment winners and losers in the next decade.

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