Trading Strategy Optimizing

Trading Strategy Optimization: Example, Definition, Backtesting

Trading Strategy Optimization, is it good for trading? Are optimizers error maximizes? Do small input errors result in large output errors? Stay tuned, and you’ll find out why trading strategy optimization is smart – when you know what you are doing.

Strategy optimization in trading is good if done correctly. Below, you can read how to optimize a trading strategy. Optimization has negative associations for many traders, just like curve fitting, but correct optimization can give you valuable inputs and a better understanding of your strategy. If you know what you are doing, you should run an optimization test of all your backtests. We give you two simple (and naive) examples of how to optimize.

When you start live trading with a backtested strategy, you frequently get disappointed: the results are far away from the indicated results in the backtest. How could this happen after spending days backtesting your strategy?

This might come as a surprise to new traders, but it hardly comes as a surprise to seasoned traders. The reason might be the optimization and curve fitting of your backtest.

Table of contents:

Key Takeaways

  • Strategy optimization in trading is a mathematical process designed to increase the effectiveness of a trading system by fine-tuning its parameters, using historical data for backtesting, and aiming to improve net profit and risk management.
  • Historical data is essential in strategy optimization for identifying optimal settings, which helps in understanding the potential risks and profitability of strategies before they go live.
  • The right use of strategy optimization tools, including technical analysis and genetic algorithms, coupled with measures like walk forward testing, ensures the development of robust trading strategies that adapt to different market conditions and improve trading performance.

What is trading strategy optimization?

Trading Strategy Optimization

Trading strategy optimization is a process or methodology of finding the optimal parameter settings of your strategy. In addition, you can learn how sensitive the parameters are to chnages in the settings.

With today’s computing power, a software program can easily be coded to find the best optimization for a certain strategy. It’s literally done in seconds and the software needed is cheap and good. For example, in this article we have used Amibroker, which you can read more about in our Amibroker review.

Is strategy optimization good in trading?

Yes, trading optimization is good when it’s done correctly and you know what you are doing. It’s good because you get a better understanding of what is making the trading strategy perform well, and you get to understand how the settings and parameters of the strategy influence.

Trading optimization lets you easily understand if your backtest is based on luck or randomness. For example, if you have a trading strategy that goes long when the price breaks above the 25-day moving average, you can optimize it by changing the number of days in the moving average. If you find out that the results are much worse close to 25 days, you get a pretty strong indication that the original strategy most likely is down to chance and randomness. Hence it’s unlikely to be very successful in the future.

Instead of being ignorant to optimization, you should optimize every backtest you do. An optimization is, in reality, a test of how robust your strategy is. When you examine the variables, you get a much better understanding of your strategy: is it due to chance/luck or might it be something worth trading or testing out of sample?

An example of strategy optimization in trading

Here is an example of strategy optimization in trading: you might want to find the best moving average crossover system for the gold price. The system works like this:

  • When the close crosses above the moving average, you go long at the open the next day.
  • When the close crosses below the moving average, you sell at the open the next day (close the long position).

As you can imagine, this is a very simple trend-following system. Despite its simplicity, a long crossover system like this has worked pretty well for the gold price over decades:

However, we can try to optimize such a crossover system by backtesting the number of days in the moving average.

For example, we can start with a minimum number of 5 days and continue up to 500 days. If we test every single one of the days it means 496 tests. To minimize the number of tests we can use intervals of 10 days thus reducing the number of tests by 90% to 50.

If  we perform this test on GLD, the ETF that tracks the gold price, we get the following result from inception to September 2021:

trading strategy optimization
Trading Strategy Optimization

Each row shows the result for each input in the variable (the number of days in the moving average). The second column shows the number of days used in the system. Not all tests are included in the table above – about 40% didn’t fit.

However, we clearly see that such a crossover system works best with a high number of days in the moving average. The shorter the moving average, the worse the results. But because of the small number of trades, as shown in column 3, the results vary quite much.

An RSI strategy optimization in the S&P 500 (example)

Let’s show you another example:

The RSI indicator is a widely popular indicator and has performed well as a mean reversion strategy on the S&P 500 over the last 30 years. But what works best – a short or long RSI? And which thresholds are best – low or high?

Let’s backtesttest to find out. For such a test we need three optimization parameters:

  1. For the number of days in the RSI calculation (from 2 to 5 days with intervals of 1)
  2. For the threshold of when to buy (from 10 to 35 with intervals of 5)
  3. For the threshold of when to sell (from 65 to 90 with intervals of 5)

We backtest SPY, the ETF that tracks the S&P 500, from inception in 1993 until today.

The optimization involves 144 tests (4 x 6 x 6). This is a lot of tests and it might be difficult to find out where the sweet spot is. We can set some minimum variables, for example, that we should have at least 250 trades over the period in order to have a significant number of trades/observations. This reduces the number of tests down to 53. We then sort on the profit factor.

We copied the results from Amibroker into OpenOffice:

strategy optimization
Example of Optimization

Clearly, an RSI based on two or three days seems to give the best result. The balance of where to set the buy and sell thresholds might be less obvious from the results, however. To judge that you might want to have a look at the columns for CAR/CAGR, exposure (time spent in the market), and max. system drawdown.

Code for trading strategy optimization

Amibroker is a very powerful tool despite its cheap price. It works both for backtesting and live trading, especially with Interactive Brokers. How you can learn to code, do backtests, and live trading is described in our Amibroker course.

Understanding Optimization in Trading Systems

Illustration of mathematical procedures and technical analysis

Understanding optimization in trading is like fine-tuning a high-performance sports car. It’s about refining every component to ensure the vehicle runs at peak performance.

Similarly, in trading, optimization includes refining a trading system using mathematical procedures to better its effectiveness. It’s about tweaking the ‘engine’ of your trading strategy – the rules, input values, and parameters – to ensure they’re performing at their best under different market conditions.

This process is crucial as it not only provides knowledge into the strategy’s performance but also identifies the key influences on the end result. Using tools for technical analysis, traders can find optimal settings for these key variables, thus improving the strategy.

Defining Strategy Optimization

You define strategy optimization by testing various input value combinations against historical data to identify the most effective settings. This iterative process recalculates strategy rules with different input values until the final specified value is tested.

Following optimization, the most effective combination of input values is applied, with details available in the Strategy Optimization Report, providing a comprehensive overview of the strategy’s performance. In essence, strategy optimization directly influences net profit and risk management by assessing market risk and the potential for investments to outperform others.

The Importance of Historical Data

Historical market data is imprortant because it directly impacts the results. It’s like a time machine, allowing traders to evaluate how a strategy would have performed in the past. Perhaps nedless to say, if the data is inaccurate your conclusions might lead you astray.

This process, known as backtesting, lets you analyze the potential risks and profitability of strategies before they’re implemented in live trading. By analyzing past market data, traders can spot historical market behaviors such as trends, volatility, and price patterns, offering valuable lessons for strategy development.

Moreover, strategies derived from this historical analysis can be fine-tuned through simulation of various hypothetical market scenarios to ensure they remain effective under different conditions. Thus, the adaptation of strategies based on historical analysis can help mitigate losses during market downturns and maximize gains when the market condition is favorable.

Key Elements of a Robust Trading Strategy

Key elements of a robust strategy is important, and strategy optimization lets you understand what drives the results with over optimization.

Over-optimization, similar to overengineering a bridge, occurs when a trading strategy is excessively fitted to historical data, which can result in underperformance in live trading environments – a phenomenon known as curve fitting.

Although technical and quantified analysis might be a powerful tool in strategy optimization, it has its limitations, including the potential for creating overfitted strategies that don’t perform well when market conditions change.

Applying Technical Analysis for Strategic Edge

Illustration of technical analysis tools for trading strategies

Technical analysis serves for most traders as the compass of trading strategy optimization. It evaluates securities based on price movements and trading signals, contrasting with fundamental analysis that looks at intrinsic value. By optimizing trade entry and exit points, technical analysis helps manage risk and maintain trading discipline.

Traders can leverage advanced tools like charting software and backtesting platforms to enhance the precision of their analysis and refine their strategies. Incorporating techniques like chart patterns and technical indicators, such as the Relative Strength Index (RSI), into algorithmic trading can optimize trading strategies based on market condition analysis.

Incorporating Genetic Algorithms

Genetic algorithms involve populations of potential strategies that specifically evolve, incorporating adjustable parameters such as RSI-based entry and exit thresholds. They evolve a population of potential strategies across generations, improving strategy parameters for enhanced performance. This process requires a fitness function to assess strategy performance, alongside iterative processes, including selection, crossover, and mutation for strategy enhancement.

In essence, genetic algorithms are the ‘survival of the fittest’ applied to trading strategy optimization.

Balancing Risk with Genetic Optimization

Genetic optimization plays a critical role in mitigating risks. By optimizing trading parameters, genetic algorithms help in achieving more stable returns and potentially reduced volatilities.

However, while they adapt trading systems to market changes, genetic optimization demands significant computational resources, which must be balanced against the benefits.

The Role of Walk Forward Testing

The role of walk forward testing is impoirtant because trading involves making money on unkown future price changes. It’s a process designed to assess the predictive performance of a trading strategy by applying optimized parameters derived from historical data to a future out-of-sample dataset. It paints a realistic picture of how a strategy would perform under real market conditions, imitating the conditions of real market scenarios.

This method plays an important role in mitigating the risks of overfitting, which can result from optimizing trading strategies on historical data alone, without considering varying market conditions.

An option to walk forward is to put your strategies in incubation in a demo account for several months before you start trading live.

Steps in Walk Forward Testing

Walk forward testing is a multi-step process:

  1. Defining input optimization values
  2. Selecting the type of optimization to guide the subsequent analyses
  3. Dividing the data used for walk forward testing into distinct in-sample periods used for optimization and out-of-sample periods to test the strategy parameters.

Evaluating Test Results for Strategy Refinement

Traders must evaluate the test results for strategy refinement after walk forward testing. The goal is to ensure that the trading strategy is consistently profitable, adheres to a predefined maximum drawdown limit, and performs efficiently in out-of-sample periods. While one walk forward analysis provides preliminary insights, executing multiple walk forward analyses lends stronger evidence towards the robustness of a trading strategy.

Performance data from walk forward testing can reveal patterns that suggest the need for strategy refinement, either due to signs of longevity or signals of diminishing effectiveness.

Enhancing Efficiency with Algorithmic Solutions

Algorithmic solutions for trading strategy optimization

Algorithmic solutions play a pivotal role in pinpointing optimal solutions by iteratively tuning decision variables to enhance the value of the objective function.

Decision-making processes are streamlined through the use of algorithmic methods that utilize models and data from experiments or simulations, solving problems with greater efficiency. In essence, optimization through algorithmic methods iteratively approaches solutions that are deemed satisfactory when they sufficiently converge towards an ideal outcome.

Selecting the Right Algorithm for Your Strategy

In trading, selecting an appropriate optimization algorithm requires analysis of the application characteristics, including:

  • the type of models
  • determinism of responses
  • consideration of integer or discretized variables
  • the presence or absence of constraints

The role of various algorithms like Gated Recurrent Unit (GRU) neural networks and branch-and-bound algorithms in strategy optimization is crucial. Understanding the role of these algorithms and how they can be applied to your strategy can unlock the door to effective trading strategy optimization.

Implementing Algorithms to Minimize Errors

Implementing algorithms to minimize errors can be optained by doing this:

  • Bias-free state estimates
  • High convergence speed
  • Perfect tracking abilities
  • Short computation times

All of these are essential for minimizing errors and calculating optimal strategy input values.

Maximizing Performance Through Constraint Management

Constraint management for maximizing performance

In trading strategy optimization, each component of the strategy – from entry and exit points to stop loss and take profit levels – must work together to create a successful trading outcome.

This is where constraint management comes in. It involves reducing risk per trade and increasing average profit per trade.

Identifying and Managing Constraints

Traders must identify and manage the constraints of their trading strategies. This involves focusing on the fundamental limitations rather than visible symptoms and conducting root cause analyses. After identifying constraints, traders can exploit them by optimizing processes and making the best use of available resources.

One method for integrating constraints into the optimization strategy is using a substitution method for simple problems, where the constraint is integrated into the objective function to create a composite function reflecting the constraint’s impact.

Alternatively, for more complex problems, traders can use tools like the Lagrange multipliers to convert a constrained optimization problem into an unconstrained one.

Navigating Market Conditions with Adaptive Strategies

Traders need to adapt their trading strategies to respond to current market conditions. An adaptive trading system provides guidance on when to trade and the level of aggression suitable for current market conditions, enabling decisions to match market volatility. This requires ongoing, informed adjustments to trading rules, made outside of the live trading sessions to avoid hasty decisions.

Continuous Evaluation and Adaptation

Traders must continuously evaluate and adjust their trading strategies in response to changing market conditions. This involves a constant cycle of assessment and adjustment, ensuring strategies evolve through consistent market feedback and observations of changing trading conditions. Trading is a process of feedback and learning.

By continuously reviewing their trading strategies, traders can recognize patterns in successful trades, informing ongoing strategy refinement. This continuous adaptation involves refining trade rules based on analysis and live trading experiences to minimize losses and increase profitability.

Examples of Adaptive Strategy Success

These are examples of successful adaptive trading strategies:

  • Preventing losses during market upheavals
  • Identifying profitable patterns for gains
  • Selectively trading during clear market trends
  • Avoiding trades in choppy price movements

The use of advanced machine learning techniques has led to the outperformance of traditional strategies and more stable returns in volatile markets. These examples serve as a beacon of hope for traders navigating the turbulent seas of the market.

Why is trading strategy optimization crucial for success?

Trading strategy optimization is crucial for sucess because it enhances the performance of a trading strategy by adjusting the system to be more effective.

Regular optimization of a trading strategy keeps it responsive to market conditions, helps in reducing drawdowns, and improves the potential for returns. It is an important element in understanding a strategy’s robustness and adaptability, understanding how it performs under various market scenarios and reducing reliance on emotional decision-making.

How does strategy optimization enhance trading performance?

Strategy optimization enhance trading performance because you better understand which settings are best for the strategy. By testing different signal input values, optimization selects those that historically correspond to better strategy outcomes. It helps in understanding the characteristics of a strategy and creating new entry and exit criteria.

Some benefits of strategy optimization include:

  • Improved trading performance
  • Increased profitability
  • Reduced risk
  • Enhanced understanding of market behavior
  • Ability to adapt to changing market conditions

Traders can choose between exhaustive search and genetic algorithms for optimization, based on their specific requirements and constraints of their strategy.

What factors influence strategy optimization in trading?

Several factors influence strategy optimization, such as the robustness of a trading strategy and its sensitivity. Effective optimization can help distinguish between strategies that are successful due to skill and those based on luck or randomness.

Trading optimization can uncover if a backtested strategy is likely to perform well in the future or if its past performance is not indicative of future results. Types of optimization include exhaustive, genetic, and walk-forward testing, each offering unique advantages for evaluating strategy performance.

Can strategy optimization mitigate trading risks effectively?

Strategy optimiztion heklps you better mitigate risks effectively. Risk optimization in investment refers to maximizing returns while minimizing potential risks through various techniques and measures. By diversifying across asset classes and sectors, traders can significantly reduce the overall volatility in a portfolio, a fundamental strategy for risk optimization.

Furthermore, hedging strategies, including the use of options and futures contracts, are employed to protect against specific risks such as market volatility or currency fluctuations. Therefore, strategy optimization serves as a protective shield, safeguarding traders from potential risks and volatility in the market.

When should traders consider strategy optimization?

Traders should always consider strategy optimization as a performance tune-up for their trading strategy. This process should be employed when a trader aims to understand the effectiveness of a strategy and evaluate its robustness by examining how different variables influence the strategy’s outcome.

Optimization tests should be run on all backtests to provide insights into whether a strategy’s past performance is likely due to chance or if it has potential for future success.

What tools are useful for strategy optimization in trading?

Tools useful for strategy optimization in trading includes:

  • NakedMarkets
  • TradingView
  • Forex Tester
  • AmiBroker
  • Spreadsheet

These tools offer a combination of manual and automated backtesting features with a no-code module for strategy building.

Other tools like MetaTrader 4 and 5 provide built-in backtesting functionalities mainly for automated trading strategies. By leveraging these tools, traders can refine their strategies, much like a sculptor uses various tools to carve a masterpiece.

How does historical data aid in strategy optimization?

Historical data aid in strategy optimization because it is the cornerstone of effective strategy optimization. By analyzing past trends and patterns, traders gain valuable insights that can inform their future strategies. This process, known as predictive analytics, applies statistical, data mining, and machine learning techniques to analyze past facts for future predictions.

What role does backtesting play in trading strategy optimization?

Backtesting in trading strategy optimization is the essence of what optimization is all about. It’s a simulation process where historical data is used to evaluate a strategy’s performance, providing insights into its effectiveness and potential pitfalls. Traders utilize backtesting to fine-tune and improve their strategies by analyzing performance metrics such as:

  • Net profit
  • Return
  • Volatility
  • Drawdown

The practice of backtesting aids in identifying and mitigating potential biases within trading strategies, such as overfitting and survivorship bias. In essence, backtesting is a rehearsal that prepares traders for the live performance in the market.

Are there common pitfalls to avoid in strategy optimization?

Yes, there are common pitfalls to avoid in strategy optimization such as lack of knowledge and the temptation to curve fit.

Traders must avoid over-optimization, also known as curve fitting, which can produce strategies that are too tailored to past data and may not perform well when market conditions change.

How do traders measure the effectiveness of strategy optimization?

Traders measure the effectiveness of strategy optimization by analyzing the optimization results, which can include various strategy performance fields. The optimization report provides a detailed output where each row delineates a set of test results for each combination of input parameters that were optimized.

By filtering optimization outputs based on multiple criteria such as maximum net profit and minimum maximum drawdown, traders can identify the most effective strategies.

What are the benefits of incorporating machine learning in strategy optimization?

The benefits of incorporating machine learning in strategy optimization lets you analyze vast amounts of data and make better predictions. By identifying patterns and trends within large datasets, machine learning can inform you the possitibility of improvement.

Furthermore, machine learning automates the price optimization process, thereby reducing manual labor and the chance of human error, which increases operational efficiency.

Is strategy optimization adaptable to different market conditions?

Yes, strategy optimization is adaptable to different market conditions if you backtest a long enough dataset. No matter your type of trading, you must ensure you cover different market regimes. Bull and bear markets are two examples of that.

Strategy optimization involves customizing strategy logic or parameters to align with a specific market, assuming that historical market behavior will continue into the future. However, traders must be cautious to avoid over-optimization, which can result in a strategy too rigidly aligned with historical data and may not perform well when market conditions change.

How can traders optimize strategies for volatile markets?

Traders optimize strategies for volatile markets by incorporating a volatility filter. For example, you might want to change the settings if the volatility is above a certain level. Traders can optimize strategies for volatile markets by:

  • Setting smaller trades to manage risks
  • Placing stop orders further from the current market price to reduce the likelihood of premature stop-outs due to price fluctuations
  • Focusing on stocks that maintain a consistent trend, either upward for buying or downward for short selling
  • Trading smaller size (recommended)

By following these strategies, traders can benefit despite the increased risk.

What are the key parameters to adjust during strategy optimization?

The key parameters to adjust during strategy optimization are the parameters’ settings and the risk management settings. For example, it could be the number of periods in a moving average, the percentage of equity to risk on each trade, and the values for other technical indicators used in the strategy.

By tweaking these parameters and observing the resulting changes in strategy performance, traders can find the optimal settings for their strategy, much like a sound engineer adjusting the knobs on a control panel to achieve the perfect sound balance.

Can social sentiment analysis improve strategy optimization outcomes?

Social sentiment analysis can increase strategy optimization outcomes by making customized sentiment indicators. In the age of social media, the voice of the trader is louder than ever before.

How do macroeconomic factors impact strategy optimization decisions?

Macroeconomic factors impact strategy optimization decisions. For example, factors like

  • inflation
  • monetary policy
  • GDP growth
  • commodity prices

significantly influence asset class performance. By implementing these macroeconomic currents, traders can utilize filters in their strategies.

For instance, interest rates are a key driver of banks’ net interest margins, while commodity sectors like Oil & Gas are heavily influenced by oil prices. Therefore, considering these macroeconomic factors in strategy optimization could be very helpful.

What role does behavioral finance play in strategy optimization?

Behavioral finance play a significant role in strategy optimization because humans are driven by greed and fear. Investors can improve their strategic asset allocation by understanding and addressing biases such as confirmation bias, anchoring bias, and loss aversion bias. By focusing on diversification and using objective data and analysis, investors can mitigate the influence of biases on portfolio construction and investment decisions.

Are there ethical considerations in algorithmic strategy optimization?

There are no specific ethical considerations in algorithmic strategy optimization. However, all traders must treat other people like they would treat themselves, and they must follow the rules and the law.

How can traders balance complexity and simplicity in strategy optimization?

Traders balance complexity and simplicity in strategy optimization by being very careful when you add parameter to a strategy. The more you have, the more likely you are curve fitting.

On the one hand, a strategy needs to be complex enough to capture the nuances of the market. On the other hand, it needs to be simple enough to be practical and manageable.

Optimizing trading strategies for simplicity involves:

  • Setting a clear objective
  • Choosing a limited number of indicators
  • Establishing straightforward rules
  • Conducting performance evaluations

We recommend our article about simple vs. complex trading strategies. Quantified Strategies’ trading library should be a decent example of making trading as simple as possible.

What are the future trends in trading strategy optimization?

There are no specific trends in trading strategy optimization, except for the increased use of artificial intelligence.

How do you optimize a trading strategy?

You optimize a trading strategy by backtesting, evaluating, and improving the strategy by adjusting the parameter settings. Trading strategy optimization is an ongoing process that requires consistent testing, evaluation, and improvement to align with evolving market conditions and performance objectives.

What is optimize strategy?

To optimize a strategy, you need to adjust the settings and parameters to find the best values that maximize the results, but by balancing curve fitting and simplicity vs. complexity. The more you add to a strategy, the more likely you are curve fitting.

When to use trading strategy optimization?

We believe you should always use trading strategy optimization because optimization is a great way to determine the strategy’s robustness and sensitivity to changes in settings. Put simply, optimization helps you understand the strategy better.

Traders should consider strategy optimization as a performance tune-up for their trading strategy. This process should be employed when a trader aims to understand the effectiveness of a strategy and evaluate its robustness by examining how different variables influence the strategy’s outcome.

Optimization tests should be run on all backtests to provide insights into whether a strategy’s past performance is likely due to chance or if it has potential for future success.


Trading strategy optimization involves a series of steps, including backtesting, technical analysis, genetic algorithms, and walk-forward testing (incubation) to increase the effectiveness of trading strategies. We believe you should always use trading strategy optimization because optimization is a great way to determine the strategy’s robustness and sensitivity to changes in settings. Put simply, optimization helps you understand the strategy better.

Strategy optimization in trading might have a bad “reputation” because a backtest done incorrectly results in a system that has performed well in the past but is unlikely to perform well on unknown future data. The predictive value is poor. Because of this, many traders shy away from doing any optimization.

However, as we have argued in this article, optimization done correctly can give you valuable information about your strategy’s properties. We believe you should ALWAYS look at optimizations of your backtests to understand better how robust or not robust your parameters are.

If you understand how to optimize a trading strategy properly, you increase your chances of having a successful out-of-sample test.


What is trading strategy optimization?

Trading strategy optimization is the process of refining a trading system using mathematical procedures to enhance its effectiveness, which involves testing various input value combinations against historical data to identify the most effective settings.

How does backtesting contribute to strategy optimization?

Backtesting contributes to strategy optimization by providing insights into a strategy’s performance and potential pitfalls, helping traders to fine-tune and improve their strategies.

What role do genetic algorithms play in strategy optimization?

Genetic algorithms play a vital role in strategy optimization by evolving potential strategies over generations to improve performance through adjustable parameters. They involve populations of evolving strategies with adjustable parameters for enhanced performance.

How can traders optimize strategies for volatile markets?

In volatile markets, traders can optimize their strategies by setting smaller trades to manage risks and placing stop orders further from the current market price to reduce the likelihood of premature stop-outs due to price fluctuations. This approach helps them navigate market volatility effectively.

Is strategy optimization good in trading?

Yes, strategy optimization is good when done correctly. It provides a better understanding of what makes a strategy perform well and helps in evaluating the robustness of the strategy. Proper optimization can reveal whether a backtest result is based on luck or randomness.

How can I optimize a trading strategy?

You can optimize a trading strategy by exploring and adjusting key variables or parameters. Utilizing software like Amibroker, you can conduct optimization tests to find the best settings for these variables and enhance the overall performance of your strategy.

What are the risks of curve fitting in strategy optimization?

The risks of curve fitting in strategy optimization is huge, hence you need to keep all strategies as simple as possible. Curve fitting occurs when a trading strategy is excessively tailored to historical data, and optimizing increases that risk, resulting in poor performance in live trading. It’s a risk associated with improper optimization. Understanding and avoiding curve fitting is crucial for developing strategies adapting to changing market conditions.

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