How to Optimize Your Cryptocurrency Trading Strategy Through Backtesting

Introduction

Market Background
The cryptocurrency market is widely considered to be one of the most volatile financial markets. Cryptocurrencies like Bitcoin and Ethereum experience dramatic price fluctuations within just days, hours, or even minutes. This volatility offers huge profit opportunities for traders, but it also comes with high risks.

For example, during the global stock market crash in early March 2020, Bitcoin's price plummeted from nearly $10,000 to under $4,000, a drop of almost 60%. Such drastic market movements make the cryptocurrency market a paradise for speculators but also expose ordinary traders to significant risks of loss. Therefore, backtesting has become a crucial tool for cryptocurrency traders to reduce risks and increase profits by validating the effectiveness of their trading strategies.

The Importance of Backtesting
Backtesting involves testing a trading strategy using historical data to evaluate its effectiveness, helping traders assess how a strategy would have performed in real market conditions before using it in actual trades. An optimized strategy can help reduce mistakes and avoid blindly following trends in complex markets. By backtesting, traders can clearly identify which strategies have been successful in past market conditions, thereby improving their chances of success in real-time trading.

1. What is Backtesting?

Backtesting Definition
Backtesting is the process of simulating and testing a trading strategy using historical data. Through backtesting, we can assess how a strategy performed in past market conditions and speculate on its potential for the future. In simple terms, backtesting is like giving a trading strategy a "health check" to see if it can withstand the pressures of various market environments.

Basic Backtesting Process
The basic steps involved in backtesting include the following:

Selecting Historical Data: Choose market data from a specific time period, commonly including open prices, close prices, high prices, low prices, and trading volume. This data reflects how the market performed during that period.
Choosing a Trading Strategy: This is the core of backtesting. You select a strategy that suits the current market conditions, such as trend-following, mean-reversion, or breakout strategies.
Running the Backtest: Apply the chosen strategy to the selected historical data, simulate the trading behavior, and observe its performance during that period.
Analyzing the Results: Evaluate the strategy's performance during the backtest, including metrics like total profit, maximum drawdown, win rate, and others to determine whether the strategy is suitable for actual trading.

Benefits of Backtesting
Through backtesting, traders can:

Validate Strategy Feasibility: Determine whether a strategy can perform well under different market conditions.
Discover Potential Profit Opportunities: By backtesting, traders can identify potential profit opportunities that they may have overlooked.
Avoid Risk: If a strategy performs poorly in the backtest, traders can avoid using it in real trading, thus minimizing risk.

Case Study:
Suppose you want to test a simple trend-following strategy: buy when Bitcoin's price breaks above the 30-day moving average, and sell when the price falls below the 30-day moving average. You could choose the historical data from January 2019 to January 2020 for Bitcoin and backtest this strategy to see how it performed during that period. If the backtest shows that the strategy was consistently profitable during this time, you might consider using this strategy in real trading.

2. Why Backtesting is Crucial for Cryptocurrency Trading

Characteristics of the Cryptocurrency Market
The characteristics of the cryptocurrency market include:

High Volatility: Due to market sentiment and news events often causing significant price fluctuations, the cryptocurrency market offers abundant trading opportunities, but also comes with substantial risks.
24/7 Market: The cryptocurrency market does not have fixed opening and closing times; it operates 24 hours a day, 7 days a week. This requires traders to be able to respond to market changes at any time.
Different Market Cycles: The market typically goes through bull markets, bear markets, and consolidation phases, each of which demands different strategies for adaptation.

Key Insights Revealed by Backtesting
Through backtesting, traders can analyze the following:

Which strategies are profitable during high-volatility periods: For example, during the market crash in March 2020 due to the pandemic, traders using stop-loss strategies and trend-following strategies could close their positions in time to avoid losses.
Which strategies perform better in different market cycles: For example, during Bitcoin's bull market in 2017, trend-following strategies performed particularly well, whereas in the bear market of 2018, mean-reversion strategies might have been more effective and stable.

3. How to Backtest a Crypto Trading Strategy

Backtesting is a crucial step when engaging in cryptocurrency trading. It allows traders to evaluate how their trading strategies would have performed using historical data. In simple terms, backtesting is like “test-driving” your strategy in past markets to see if it could generate profits and handle market volatility.

Choosing a Backtesting Platform

Selecting the right backtesting platform is the first step to successful backtesting. Different platforms offer various features and tools. Below are some commonly used backtesting platforms:

TradingView

Features: TradingView is one of the most popular online charting tools globally, supporting stock, forex, cryptocurrency, and other markets. It provides robust backtesting features that allow users to write their strategies and backtest them with historical data.
Use Case: Suppose you design a simple trend-following strategy based on the crossover of the 10-day and 50-day moving averages. On TradingView, you can input these conditions, select the time range (e.g., Bitcoin data from 2018 to 2020), and run the backtest to view how the strategy performed during that period.

3Commas

Features: 3Commas is an automated trading platform focused on the cryptocurrency market, offering both automated trading and backtesting functions. It allows traders to backtest their strategies across multiple cryptocurrency exchanges and implement automated trading after backtesting.
Use Case: Suppose you want to test a strategy combining the MACD and RSI indicators. In 3Commas, you can set up these indicators and select a cryptocurrency pair (e.g., ETH/BTC). Then, choose a backtesting period (e.g., March 2020 to March 2021), and after the backtest, the platform will provide data on the strategy's returns, maximum drawdown, and more.

Cryptohopper

Features: Cryptohopper is an automated trading platform dedicated to the cryptocurrency market, offering an easy-to-use interface, backtesting, and automation features. It provides various technical indicators to help traders design and backtest their strategies.
Use Case: Suppose you want to test a breakout strategy, where you buy Bitcoin when its price breaks a key level. You can set this strategy on Cryptohopper and choose an appropriate backtesting period (e.g., May 2019 to May 2020) to see if the strategy accurately captures breakout signals.

Data Selection and Quality

The success of backtesting depends not only on the strategy itself but also on the quality of the historical data. Since the cryptocurrency market is highly volatile, choosing the right data source is critical.

Data Sources Common cryptocurrency data sources include:

CoinMarketCap: Provides global cryptocurrency market data, including price, trading volume, market depth, and more.
CryptoCompare: Offers extensive historical cryptocurrency data, supporting various timeframes for data queries.
Binance API: Many traders use the Binance API to retrieve historical data for high-frequency backtesting.

Data Quality and Importance The quality of data directly affects the accuracy of backtest results. If the data you use is inaccurate or incomplete, your backtesting results may be misleading, which could impact your trading decisions. For example, missing price data or incorrect timestamps could cause the strategy to appear too successful, offering you false success signals.
How to Evaluate Data Quality

Accuracy: Ensure the data source is reliable. Use popular sources like CoinMarketCap or CryptoCompare to guarantee the accuracy of the data.
Completeness: Ensure the data covers a sufficiently long period. For instance, if you're backtesting a trend-following strategy, it’s best to use at least 2 to 3 years of data.
High-frequency Data: For high-frequency trading strategies, choose platforms that offer 1-minute, 5-minute, or shorter data intervals to capture short-term market fluctuations.

Building a Trading Strategy

Building a trading strategy is a crucial part of the backtesting process. Different traders may use various strategies. Here are some common cryptocurrency trading strategies:

Trend Following Strategy

Overview: A trend-following strategy assumes that the market price is trending in a specific direction. When the market is in an uptrend, the trader buys; when the market is in a downtrend, the trader sells.
Technical Indicators: Common indicators include Moving Averages (MA) and MACD (Moving Average Convergence Divergence).
Use Case: Suppose you’re using a 10-day and 50-day moving average crossover strategy. When the 10-day moving average crosses above the 50-day moving average, it triggers a buy signal, and vice versa for a sell signal. You can set these conditions in the backtest and observe how the strategy performs.

Mean Reversion Strategy

Overview: A mean reversion strategy assumes that market prices will revert to a mean. When prices deviate too far from the mean, the market will return to it. Hence, you sell when prices are too high and buy when prices are too low.
Technical Indicators: Common indicators include RSI (Relative Strength Index) and Bollinger Bands.
Use Case: Suppose you sell when RSI is above 70 (overbought) and buy when RSI is below 30 (oversold). You can use historical Bitcoin data from 2018 and analyze how this strategy performs.

Breakout Strategy

Overview: A breakout strategy assumes that when market prices break key support or resistance levels, it indicates the start of a new trend. Traders can enter positions at these points.
Technical Indicators: Common indicators include Support and Resistance Levels, and Bollinger Bands.
Use Case: Suppose you set a price breakout strategy where you buy Bitcoin when its price exceeds $7,000 and sell when it drops below $7,000. You can backtest this strategy using Bitcoin data from early to mid-2019.

Executing the Backtest

The final step in backtesting is executing the test, which involves combining historical data with the strategy to see how it performed over a period. Here are the basic steps to execute a backtest:

Input Historical Data Choose appropriate historical data in the backtesting platform. For example, on TradingView, you can select Bitcoin’s daily data and backtest it over the past 2 years.
Choose a Trading Strategy Select the trading strategy you’ve designed, such as trend following, breakout, or mean reversion.
Set Strategy Parameters Configure the necessary parameters for the strategy, such as moving average periods, stop-loss and take-profit ratios, and capital allocation.
Run the Backtest Once all parameters are set, click the backtest button. The platform will run the backtest and generate a report showing the strategy’s returns, maximum drawdown, win rate, etc.
Analyze Backtest Results After the backtest is completed, analyze the results to assess the strategy's effectiveness. Check how it performed under different market cycles (e.g., bull markets, bear markets), whether it was profitable, and if the maximum drawdown is within acceptable limits.

Conclusion

Backtesting is an essential tool for verifying and optimizing trading strategies. By selecting the right backtesting platform, ensuring data quality, building effective trading strategies, and executing the backtest, traders can significantly improve the accuracy and effectiveness of their trading decisions. Continuous backtesting and optimization allow you to refine your strategy based on historical data, making it more successful in future markets.

4. How to Choose the Right Backtesting Period and Timeframe

In cryptocurrency trading, selecting the appropriate backtesting period and timeframe is crucial. These choices directly affect the effectiveness of your strategy and risk management. By selecting the right timeframe and backtesting period, you can assess how a strategy performs in various market environments and adapt to market fluctuations and trends more effectively.

Timeframe and Strategy Matching

Short-Term Trading Strategy: Suitable for 1-Minute to 1-Hour Charts

Short-term trading strategies are often part of day trading, where traders enter and exit the market multiple times within a day. The core of short-term strategies is quick reactions to market volatility, making them ideal for traders who are willing to spend significant time on trading and can make fast decisions.

Applicable Markets: Short-term strategies are ideal for highly volatile markets. Since the cryptocurrency market operates 24/7, there are periods when the market experiences significant price swings. Short-term strategies are particularly useful in these high-volatility periods to find opportunities.

Example: Suppose Bitcoin has risen from $40,000 to $42,000 over the past few hours. This might be a good short-term trading opportunity. You can use 1-minute or 5-minute charts to gauge short-term trends and execute quick buy or sell strategies to capitalize on price fluctuations.

Long-Term Strategy: Suitable for Daily or Weekly Charts

Long-term strategies focus on the broader market trends and usually don't require frequent trades. Instead, decisions are made based on data over a longer time frame. These strategies are ideal for investors who aren’t in a rush to trade every day, particularly those interested in long-term trends.

Applicable Markets: Long-term strategies work well when the market trend is clear, particularly in bull or bear markets. They are relatively more tolerant of market volatility and are suitable for traders who aren’t focused on short-term profits.

Example: Suppose Bitcoin has risen from $30,000 to $70,000. A long-term strategy can capitalize on this sustained upward trend, holding the asset until the trend changes significantly. Here, daily or weekly charts provide enough time to assess the overall market direction.

Summary:

Short-Term Strategy: Suitable for traders who like quick responses and seek short-term profits, relying on minute and hourly charts.
Long-Term Strategy: Ideal for those focusing on broader market trends and looking for steady profits over long-term fluctuations, relying on daily or weekly charts.

Backtesting Duration and Market Cycles

Choosing the right backtesting period is essential to ensure your strategy is adaptable to various market conditions. Different market cycles (such as bull, bear, or ranging markets) have different impacts on strategy performance. A longer backtesting duration helps you fully understand how your strategy performs in various market scenarios.

The Importance of Backtesting Duration

The duration of backtesting determines how long you can assess the effectiveness of your strategy. To better evaluate a strategy, it is recommended to use at least two to three years of historical data, covering different market cycles. The benefits of this approach include:

You can see how the strategy performs in various market conditions, including sharp rallies, sharp declines, and long-term consolidations.
It ensures the strategy is not only effective in a specific market environment but also maintains some level of profitability in other conditions.

Matching Backtesting Period with Market Cycles

Let’s take Bitcoin as an example. Over the past few years, Bitcoin has gone through several different market cycles:

2017 Rally: Bitcoin's price surged from around $1,000 to nearly $20,000.
2018 Bear Market: Bitcoin’s price dropped from $20,000 to $3,000, entering a deep bear market.
2020-2021 Recovery: As the market warmed up, Bitcoin broke new highs, exceeding $60,000.

If we only select data from 2017 to backtest a strategy, it might lead to overfitting the strategy to a bull market and ignore risks in a bear market. Therefore, it is advisable to backtest at least two to three years of data, for example, from 2017 to 2020. This period covers a bull market, a bear market, and a recovery phase, allowing a comprehensive assessment of strategy performance in extreme market environments.

The Benefits of Long-Backtesting Periods

By testing your strategy over a long time frame, you can understand how it performs in different market environments, such as:

Bull Market: A clear upward trend, where the strategy will likely rely on sustained upward momentum, potentially showing good profitability.
Bear Market: A continuous downward trend, where the strategy needs to adjust to manage losses in a declining market.
Ranging Market: The market has no clear trend in the short term, requiring the strategy to be flexible to avoid excessive trading losses.

How to Choose a Backtesting Period

When selecting a backtesting period, consider the following factors:

Market Environment Changes: Ensure that the backtesting period includes at least one full bull and bear cycle. This will test the strategy’s adaptability to different market environments.
Strategy Complexity: If your strategy is complex, requiring extensive data for technical indicators or parameter adjustments, the backtesting period should be longer to ensure the data is representative.
Data Quality: The data used for backtesting must be accurate and detailed. Incomplete or low-quality data could distort the results.

By carefully selecting the right backtesting period and aligning it with market cycles, you can optimize your strategy for better performance in a range of market conditions.

5. How to Optimize Your Crypto Trading Strategy

Optimizing a crypto trading strategy is a process of trial and error, with the goal of identifying potential problems and making improvements based on backtesting results. These results provide concrete data and metrics, helping you assess the performance of your strategy and make adjustments accordingly. This process not only improves the profitability of the strategy but also reduces risk, allowing you to trade more confidently in real market conditions.

Analyzing Backtest Results

The data generated during the backtest contains valuable insights to help you evaluate the strength of your strategy. Below are several key backtest metrics and their significance in strategy optimization:

Return on Investment (ROI)
Definition: ROI is the total profit generated by the strategy during the backtest period, usually expressed as a percentage. It measures the overall return an investor receives by executing the strategy.
Importance: ROI is one of the most direct indicators of a strategy's effectiveness. If the strategy consistently shows good returns, it suggests the strategy is working well in the selected market and timeframe.
Optimization Tip: By comparing the ROI of different strategies, you can select the best-performing one or find the right adjustments among similar strategies.
Maximum Drawdown
Definition: Maximum drawdown refers to the greatest loss in account value from the highest to the lowest point during the backtest period. This metric reflects the potential risk a strategy might face in live trading.
Importance: Understanding maximum drawdown is crucial for risk management. If a strategy's drawdown is too large, it suggests poor performance in downtrends or unfavorable market conditions, which could lead to significant capital loss.
Optimization Tip: If the maximum drawdown is too large in your backtest, consider adjusting your stop-loss, take-profit, or money management rules to reduce risk and prevent large losses.
Sharpe Ratio
Definition: The Sharpe ratio is a metric that measures the return earned per unit of risk. The formula is: Sharpe Ratio = (Strategy’s Average Return - Risk-Free Rate) / Standard Deviation of the Strategy. It helps traders understand the reward-to-risk ratio.
Importance: A higher Sharpe ratio means the strategy offers a higher return for each unit of risk taken. Generally, a Sharpe ratio greater than 1 indicates a good strategy, while above 2 is considered excellent.
Optimization Tip: Increase returns or reduce volatility (risk) to improve the Sharpe ratio. If the Sharpe ratio is low, you may need to reassess the strategy's risk management or adopt more robust indicators and money management techniques.

Strategy Adjustments

After analyzing the backtest results, it’s often necessary to optimize the trading strategy based on these metrics. Below are some common adjustments you can make:

Stop-Loss and Take-Profit Adjustments
Tight or Loose Stop-Loss: If the backtest shows that your stop-loss is too tight, resulting in frequent stop-outs during market fluctuations, you might need to widen the stop-loss to avoid missing out on significant profit opportunities. Conversely, if the stop-loss is too loose, it could result in excessive losses, in which case, tightening the stop-loss is essential to ensure losses are kept within acceptable limits.
Take-Profit Settings: If the backtest indicates that you’re closing positions too early and missing bigger profits, you can consider extending the take-profit distance. However, be cautious not to set the take-profit too loosely, as it may cause you to miss market retracements.
Example: Suppose your stop-loss is set at 5%. The backtest shows that you’re frequently stopped out during market fluctuations, resulting in missed profit opportunities. You could try increasing the stop-loss to 8% to reduce premature stop-outs and increase overall returns.
Money Management
Proper Fund Allocation: Backtest results might reveal that you’re overexposed to a single trade, resulting in excessive risk for the strategy. If that trade fails, it could have a substantial impact on the account. Therefore, sound money management is crucial.
Risk Diversification: Based on backtest data, allocate funds across multiple trades or different strategies to reduce the impact of a single trade failure on the overall account.
Example: If the backtest shows that your strategy has a high win rate but large losses when you lose, consider adjusting the percentage of capital allocated to each trade. For example, reducing the capital allocated to each trade from 10% to 5% can help control risk.

Avoiding Overfitting

Overfitting is a common issue in backtesting where a strategy performs well on historical data but fails to replicate that success in the live market. This typically happens when strategy parameters are overly adjusted to optimize the strategy for a specific data set, lacking the ability to adapt to unknown market conditions.

What is Overfitting?
Overfitting is like a student who studies too much for a specific exam question, only to find that the actual exam covers different material. As a result, their exam performance suffers.
In backtesting, overfitting occurs when you fine-tune strategy parameters (e.g., stop-loss, take-profit points, or technical indicators) too precisely, which results in excellent performance on historical data, but this strategy is not adaptable to the realities of live trading.
How to Avoid Overfitting?
Simplify the Strategy: Keep your strategy simple. Avoid overly complex technical indicator combinations or parameter fine-tuning. Simple strategies tend to adapt better to different market conditions.
Test Strategy Stability: During backtesting, not only should you test historical data, but also validate your strategy under different time periods and market conditions (bull markets, bear markets, ranging markets, etc.). This will ensure that the strategy is versatile.
Avoid Over-Optimization: During backtesting, refrain from excessive optimization of every detail, particularly when it comes to technical indicator parameters. Over-optimization can lead to overfitting on historical data, making it ineffective in the live market.

Example: During backtesting, you notice that a strategy performs exceptionally well after adjusting the moving average parameters. However, if you keep tweaking the moving average period to find the “perfect” fit for the backtest data, the strategy will only work on historical data, and it may fail in different market environments. Therefore, it’s crucial to keep moving average settings simple and avoid relying on a single indicator.

Conclusion

Optimizing a crypto trading strategy is an ongoing process of learning and adjusting. By analyzing backtest results and identifying key metrics such as ROI, maximum drawdown, and Sharpe ratio, you can find your strategy’s strengths and weaknesses and make appropriate adjustments. Adjusting stop-loss, take-profit settings, and money management rules can help better control risk and improve overall returns. At the same time, avoiding overfitting is essential to ensure the strategy remains effective in real market conditions. Keeping the strategy simple and stable allows you to adapt to changing market environments and ultimately increase your trading success and profitability.

6. Optimizing Strategies with Real-World Trading Experience

In actual trading, psychology often plays a more significant role in determining profits and losses than the strategy itself. Even if a strategy performs well in backtesting, it can be challenging to achieve ideal results in a live market if the trader cannot strictly follow the strategy. Therefore, when optimizing a strategy, it is crucial to consider human psychology as a variable.

For example, during periods of market volatility, traders often fall into psychological traps such as fear (selling too early due to the fear of loss) or greed (holding on for too long in hopes of a larger profit). These emotional reactions can lead to deviating from the originally set strategy and result in irrational trading behavior.

To address this issue, it is recommended to incorporate a "psychological intervention model" or "behavioral simulation parameters" when backtesting strategies:

Set emotional triggers, such as whether three consecutive losses will impact the next trading decision;
Simulate execution biases, for example, delaying entry/exit at certain signals;
Tie the strategy to a disciplined system, such as setting mandatory stop-loss and take-profit mechanisms to avoid human intervention.

Additionally, traders can use their actual trading logs as a feedback tool. By reviewing their trading records, emotional fluctuations, and strategy execution, traders can continuously refine their strategies to align with their personal trading style and psychological tolerance.

A mature trading strategy is not only logically sound and well-tested but also one that can be psychologically "handled" in live trading environments.

7. Case Study: Backtesting to Optimize a Crypto Trading Strategy

Case Study: How to Optimize a Crypto Trading Strategy Using Backtesting

Let’s walk through a simple trend-following strategy case to demonstrate how backtesting can optimize a crypto trading strategy.

Scenario:
The trader chooses Bitcoin (BTC) as the trading instrument, and the strategy is based on moving averages to determine entry and exit points.

Moving Averages are a commonly used technical indicator that helps traders assess market trends. Generally, when a short-term moving average (e.g., 10-day MA) crosses above a long-term moving average (e.g., 50-day MA), it is considered a buy signal. Conversely, when the short-term moving average crosses below the long-term moving average, it signals a sell.

Backtesting Process:

Select Historical Data: The trader selects Bitcoin data from the past year, including price, volume, etc., as the foundation for backtesting.
Set Trading Rules:

Buy Bitcoin when the 10-day MA crosses above the 50-day MA.
Sell Bitcoin when the 10-day MA crosses below the 50-day MA.

Run the Backtest: The trader runs the strategy on a backtesting platform, simulating trades over the past year to evaluate its performance.
Backtest Results Analysis:

The strategy performed well in the bull markets of 2019 and 2020, generating significant profits.
However, during the choppy market of 2021, the strategy led to frequent trades, incurring high transaction costs and resulting in losses due to market volatility.

Based on the backtest results, the trader can conclude the following:

In a bull market, this strategy is effective since prices generally rise and trends are more visible.
In a sideways market, the strategy underperforms, with frequent trades leading to larger losses.

Key Factors for Strategy Optimization

Risk Management: Risk management is a crucial part of any trading strategy. Even if a strategy performs well in backtesting, lacking proper risk management can lead to account losses. Examples include:

Stop-Loss Settings: Set a stop-loss point for each trade to prevent losses from exceeding a certain percentage. For instance, if Bitcoin falls more than 5%, automatically sell to limit the loss.
Position Sizing: Set a maximum position size per trade. For example, use only 5% of account funds for a single trade, so even if the strategy fails, losses are kept within a reasonable range.
Capital Allocation: After backtesting, if the strategy underperforms in a sideways market, traders can optimize it through capital allocation:

Market Environment Assessment: Traders can adjust position sizes based on the market environment. For instance, reduce capital allocation during sideways markets to minimize large losses, and increase allocation during bull markets to maximize profits.
Asset Diversification: In addition to Bitcoin, traders can explore other cryptocurrencies like Ethereum (ETH) or Litecoin (LTC). Diversifying assets helps reduce risks related to the volatility of a single asset.

Stop-Loss Strategy:
Besides basic stop-loss settings, traders can adjust their stop-loss strategies based on market volatility. Backtesting may reveal that fixed percentage stop-losses (e.g., selling after a 5% loss) do not perform well during certain market phases. In such cases, optimization can include:

Dynamic Stop-Loss: Set the stop-loss range according to market volatility. For example, increase the stop-loss during high volatility and reduce it during stable market conditions.
Trailing Stop-Loss: Use a trailing stop-loss when in profit. This means the stop-loss point will move up as the price increases, helping lock in profits.

Optimized Strategy:

Add Market Environment Judgment: Introduce a market environment assessment, such as using the Average True Range (ATR) to determine market volatility and decide whether to use the trend-following strategy. For instance, only apply the trend-following strategy during low-volatility periods and pause trading during high-volatility periods.
Dynamic Position Sizing: Adjust position size based on market trend strength. Increase position size during bull markets and reduce it during sideways or bear markets to minimize risk.

Optimized Backtest Results:
By introducing dynamic stop-losses and market environment assessments, the backtest results show that the optimized strategy reduces losses in sideways markets while generating higher profits in bull markets and trending markets. Additionally, risk control becomes more effective.

Start Backtesting Your Crypto Trading Strategy Today!

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