MIPT: Testing Cryptocurrency Trading Strategies in Real Market Conditions

At MIPT, a system has been introduced for testing cryptocurrency trading strategies that is based not on an idealized price history, but on modeling conditions close to real trading. The development takes into account commissions, slippage, liquidity, the cost of financing perpetual contracts, and other factors that can make a strategy that appears profitable on paper weak after launch.

Development Card

• Developer: Moscow Institute of Physics and Technology (MIPT).
• System premiere date: July 30, 2026.
• Industry: Financial services, investments, and auditing.
• Technologies: Data Mining.

The system belongs to the tools of intelligent data analysis and is designed for a more rigorous evaluation of cryptocurrency strategies. Its task is to show how the algorithm would behave not in laboratory conditions, but in an environment where trades are executed with costs, delays, and market constraints.

What is Backtesting and Why is it Needed

Backtesting is the process of testing a trading strategy on historical data. Simply put, the algorithm goes through a past segment of the market as if it were new: it receives prices, opens and closes trades according to specified rules, and then shows what the result could have been.

Such testing is necessary to assess the logic of the strategy in advance: whether it was profitable, how much capital drawdown occurred, how often losing periods arose, and whether the idea withstands different market phases. For a trading robot, backtesting is especially important: it helps identify weaknesses before launching with real money.

How to Conduct a Backtest of a Strategy or Robot

Typically, the testing is built on a clear algorithm:

• Prepare historical data: prices, volumes, commissions, and other market parameters.
• Set up strategy rules: entry and exit conditions, position size, and risk limits.
• Incorporate real trading conditions: commissions, slippage, liquidity, and financing costs for perpetual contracts.
• Run the test so that the algorithm does not use future data.
• Analyze the results through reports, charts, and trade tables.

MetaTrader, TradingView, Amibroker, and similar platforms are often used for backtesting. If a custom testing system is needed, it is usually written in Python, C++, or R.

Optimizing a trading robot involves iterating and comparing parameters: for example, the indicator period, exit rules, or position size. However, choosing only the option with the maximum profitability is risky—it's important to see how stable the result is across different market segments.

There are also additional ways to verify. Forward testing runs the strategy on new data after setup to check it outside the historical segment where parameters were tuned. Visual testing shows trades directly on the chart step by step: this makes it easier to see where the algorithm enters, exits, and makes mistakes.

Metrics, Factors, and Common Backtest Errors

The result of a backtest is evaluated not only by profitability. Usually, several indicators are considered at once:

• Return: shows how much the strategy earned over the selected period.
• Maximum drawdown: shows how much capital decreased from peak to minimum.
• Sharpe ratio: helps assess return adjusted for risk.
• Volatility: shows how sharply the strategy's results changed.
• Profit/risk ratio: helps understand whether the potential profit is worth the possible losses.

The success of the testing is most influenced by the quality of data, accounting for commissions, liquidity, slippage, realism of modeling, length of the testing period, and risk of overfitting.

The most common mistakes include using future data, overfitting to a successful segment of history, ignoring trading costs, and having too short a testing period. Due to such errors, a strategy may appear strong on the chart but quickly lose effectiveness after launch.

What Exactly Did the MIPT Scientists Check

The focus of the research was on momentum strategies—one of the popular approaches in algorithmic trading. The idea behind them is simple: if the price of an asset is already moving in a certain direction, it may continue to move in that direction for some time. Such approaches have been used for a long time in traditional financial markets, but in cryptocurrencies, they are often tested too simplistically.

There are different types of strategies in the crypto market:

• Momentum: betting on the continuation of an already initiated price movement.
• Arbitrage: searching for price differences between platforms or instruments.
• Scalping: frequent short-term trades with a small profit target.
• Trend strategies: working in the direction of the main market movement.
• Counter-trend strategies: attempting to profit from a pullback against the current movement.
• Market making: placing buy and sell orders to profit from the spread.

The main problem with such tests is the disconnection from reality. A quote on a historical chart shows the price but does not reveal all the conditions of the trade: what the liquidity was, how much execution cost, what slippage occurred, and how market cycles changed. Because of this, a strategy may appear strong on past data but quickly lose expected returns after being launched.

The team from the MIPT Blockchain department decided to check how robust momentum strategies are with a more realistic approach. To do this, the work was divided into several practical steps:

• Creating a testing system.
• Collecting hourly data from the spot market.
• Collecting hourly data from perpetual futures over 8 years.

How the Testing System Works

The development step-by-step simulates the operation of the trading strategy as if it were launched in real-time. At each step, the system checks several conditions:

• The algorithm receives only the market data that could have been available at the moment of decision-making.
• The result of the trade is calculated taking into account commissions.
• Slippage is included in the calculation.
• Available liquidity is taken into account.
• For perpetual contracts, the cost of financing is considered.

After calculating one time segment, the algorithm moves to the next and repeats the procedure on the updated data. This approach helps avoid the common mistake of using information from the future. As a result, the test shows not an abstract return on history but a more plausible picture of the strategy's behavior in trading.

The system reproduces the strategy's operation step by step: it uses only the data available at that moment, considers real trading costs, and then moves to the next period. This method allows assessing how the strategy could behave under real market conditions," explained Maxim Egorov, a graduate of the MIPT Blockchain department.

For a market where traders often test ideas in MetaTrader, write their own models in Python, or transfer high-load algorithms to C++, the logic of entering and exiting positions is not the only important factor. The quality of the testing relies on three things:

• Accounting for costs incurred during trade execution.
• Accounting for execution constraints, including liquidity and slippage.
• Comparing the final assessment with how the strategy could have performed in real trading.

Results: Profitability Turned Out Not to Be the Main Advantage

The obtained results were additionally verified using statistical methods. This was necessary to separate the stable effect from the random coincidence of parameters with a favorable market segment. Such control showed that many strategies that look good under standard testing significantly lose effectiveness when real trading conditions are taken into account.

The main conclusion was not that momentum strategies yield maximum profit. A more important advantage was their ability to limit losses during periods of sharp market declines. In realistic testing, capital protection during crashes came to the forefront.

A total of 132 strategy variants were run through the system. The research showed that one cannot rely solely on the one variant that performed best on historical data. After adjusting for random selection, its result was statistically no different from luck. However, the momentum effect itself did not disappear.

If the strategy is evaluated not by the single most successful scenario, but on average, and a portfolio is formed from several options, it maintains stable profitability. Its key property is the reduction of drawdown during crisis periods. According to calculations from MIPT, the maximum drawdown was reduced by more than 2 times: from 67% to 29%.

Why Such Testing is Needed for the Crypto Market

The developers believe that the system can serve as a basis for evaluating and creating new investment products in the cryptocurrency market. Its value lies in the fact that the product can be pre-tested under conditions close to real trading and understand how well it can protect capital during significant downturns.

Such a system can form the basis for evaluating new investment products for the cryptocurrency market. It allows one to see in advance how the strategy performs during periods of significant decline and how controlled the risk remains. Against the backdrop of forming a regulatory framework for digital financial assets in Russia, such tools may become particularly in demand," noted Maxim Egorov.

The next step for MIPT scientists is to test other classes of trading strategies. They also want to expand the research to new types of crypto assets to assess how realistic testing changes perceptions of risk and return in different market segments.