Calculating the impact of risk and uncertainty in financial, project management, cost, and other forecasting models is part of modern investment and institutional forecasting. In both traditional and cryptocurrency markets, institutions need a planning model for profit and loss planning. The Monte Carlo statistical model can play the role of risk analysis. Let’s look at what it entails.

**A Bit of History**

The name Monte Carlo raises eyebrows even when unrelated to statistics. *James Bond *movies often depict Monte Carlo, an administrative area in the tiny country of Monaco on the coast of France, as a fancy gambling and dice destination. Incidentally, the Monte Carlo statistical method can actually make gambling more efficient. However, gambling is neither what the method is most applied to nor the focus of this article.

Notwithstanding, this model has been around since the 40s. The earliest record of the term “Monte Carlo model” was among a group of scientists working on the atom bomb. The scientists used the term to refer to a method for determining a range of possible outcomes. Ever since, the Monte Carlo model has helped in a variety of physical and conceptual systems. In contemporary market trading, the method essentially involves running multiple simulations to determine the range of possible performance for a market or product.

Trading, in general, is dependent on having solid facts and making solid projections. Some traders do indeed navigate the trading market based on what they see or by following the supply–demand dichotomy, but traders who have a lot at stake often read graphs and conduct fundamental and technical analyses before making their investments. This is where the Monte Carlo statistical method can really come into play and offer credible market projections. Applied correctly and more realistically, this statistics-based simulation technique can help traders estimate the probability of profitability or loss of their trading strategy.

**The Monte Carlo Method Application in Planning**

In the contemporary world, this model uses a computerized mathematical technique for quantitative analysis and decision-making. The Monte Carlo model is not limited to trading and finance but is also applied in project management, energy, manufacturing, engineering, and research, among other fields. Statisticians use this mathematical technique to better account for risk in quantitative risk analysis, thereby improving their decision-making.

Accordingly, decision-makers can obtain a range of possible outcomes and their probabilities for any iteration, often picked randomly from a given set of inputs. This way, you can see a comprehensive spectrum of possibilities for broke, conservative, and even middle-of-the-road decisions.

Let’s take a real-life example of a sports league where 30 teams play for a playoff berth. In a hypothetical draw for the playoffs where all teams have equal strength, the odds of making the playoffs is 1/30. If team names were simply drawn from a hat, the odds of any given team remain the same.

Similarly, if you took a box with five hundred quarters ($125) and poured out the quarters on a table, what would be the number of coins with heads up? Tails up? In a purely statistical environment, you could assume 1:2, since there’s a 50/50 chance of getting heads or tails. The Monte Carlo model plays a role similar to calculating the odds of a team being drawn from a hat or a coin flip. Taking into account the likelihood of all possible outcomes, such as the likelihood of both outcomes for a coin flip, this model can help formulate the probability of each outcome.

This is obviously a simplistic explanation of how the Monte Carlo model works. However, it operates using the same fundamental models to take into account different possible outcomes. In market prediction, this model analyzes all possible results by substituting a range of values for a probability distribution for any factor that has inherent uncertainty.

The model then runs simulations over and over again with different sets of values from the probability functions. The uncertainties and ranges give the Monte Carlo simulations thousands or tens of thousands of recalculations to work on. The end result is a distribution of possible outcome values.

**Monte Carlo Model in Crypto Markets**

Cryptocurrency markets are notoriously volatile. A great deal of intrinsic and extrinsic factors are involved in formulating a model for profit and loss estimation. The Monte Carlo model can ease the process of planning investments by factoring all possibilities.

The ups and downs of the crypto market are a familiar story in these markets. In 2018, the bear market in crypto was dramatic and wiped out more than half of the total crypto market capitalization by the year’s end. However, 2019 has been relatively good for Bitcoin trading in particular. Bitcoin (BTC) is obviously a market leader, and there is indeed a correlation between its prices and altcoin performance. In general, these market swings are something investors should be wary of.

So, how can the Monte Carlo method make cryptocurrency trading more efficient? Let’s find out.

**Monte Carlo Model in Practice**

In market trading, backtesting a strategy essentially comprises a simple list of trades. How can the Monte Carlo model chip in? In any trading track record, the order of past trades is relatively random. If your past trades have had a profitability of 70 percent, it is logical to assume that the remaining 30 percent are losing trades. However, it is not easy to predict the order of the profitable and losing trades.

Here is where simulations can come in. If you reshuffle these trades, the final profit stays the same, but the losses or drawdown can significantly change. Drawdown essentially refers to the reduction of one's capital after a series of losing trades. For example, the drawdown can rise from 20 percent to 30 percent simply with a change in trade order. What does this mean? Which numbers can we trust? The answer lies in the statistics, and that is where the Monte Carlo method can come in handy. The Monte Carlo method can reshuffle the order hundreds or even thousands of times, allowing you to check the level of drawdown for each iteration. Accordingly, you can see the best, worst, and average drawdowns for each group of orders.

This allows investors to make better forecasts more often to make trades. Bitcoin trading obviously relies heavily on market cycles, and by far, Bitcoin is the largest cryptocurrency by market capitalization. Despite a torrid 2018, BTC prices managed to recover resoundingly in 2019. As a matter of fact, Bitcoin has taken an even bigger share of total crypto market capitalization: more than 65 percent. This means it’s definitely a hot commodity in crypto markets.

So, for maximum profit, you can rearrange your list of Bitcoin trades and run Monte Carlo simulations to determine the drawdown peaks, which can generally reduce the possibility of drawdown compared to confidence trading, which does not use advanced statistics. In summary, there are two possible examples of using the Monte Carlo model with Bitcoin trading:

- Change the order of trades – either through random shuffling or pooling the trades historically. However, the latter can be problematic, as the Monte Carlo method relies on randomness.
- Run simulations to determine periods of higher drawdown to inform the timing of your Bitcoin trades. Ideally, this should create strategy development and an estimation of profit expectations. About a hundred simulations should give investors a great expectancy level.

**Relevance of the Monte Carlo Method**

In crypto markets, price prediction using traditional market techniques has proven to be ineffective. When used correctly, the Monte Carlo technique can be a game-changer. You can use it to determine the most plausible outcomes and work with probability.

For example, in the past two years, crypto markets have had a cycle of bull and bear seasons, which means, for example, a random entry in a bear market will bring less likelihood on a long as opposed to in a bull market. Crypto markets also have mini-cycles and trends that affect prices. Generally, having knowledge of when a coin may periodically go up and down improves your trading odds. Additionally, the simulations can give investors a general idea as to the volatility of different coins.

Moreover, investors can use the Monte Carlo method to determine entries, profit targets, and stop-loss targets to improve their planning. It goes without saying that the Monte Carlo statistical method is not a trading style: Rather, it is an application to predict the outcomes of possible trades. Therefore, traders have to have a decent understanding of the different trade options available. Whether you’re engaged in contract trading, spot trading, or any other kind of trading, having an indicator of market possibilities is important.

The Monte Carlo method may simulate a large range of disparate outcomes, but the range of outcomes relies heavily on initial assumptions. This means that investors have to make a proper analysis of the market before running simulations.

**To Sum It Up**

In conclusion, the Monte Carlo statistical method serves the general purpose of showing market possibilities within a stipulated time frame. This is not a pinpointed prediction of what the markets will do but rather a general picture of possible outcomes. Therefore, traders in the market have to make their own decisions even when using this method. In the grand scheme of things, a tool to forecast coin returns is a great asset in crypto investments.

Cryptocurrency trading has been made easy on Xena Exchange. Thanks to the Xena Pro Desktop Terminal, there is more freedom in bot writing and strategy backtesting. This enables traders to test strategies on historical data.

For more experienced traders, there is the easy automation of trading strategies using C# and Visual Studio Code plugins, with the added efficiency of one-click trading for synthetic instruments. Traders looking to plan can therefore fine-tune their strategy upon drawing historical prices, using the Monte Carlo statistical method as detailed above for better results.