The trading market reflects the time. The changes are directly proportional to the seasons, global movements, economic conversions, etc. for instance, the markets tend to become quieter in summers and are charged up in the fall season increasing the volatility. A trader needs to adapt itself to the changing nature of the market.
A robust and good strategy to stay upbeat in the trade market is to uproot yourself with the respectable changes influencing the market from time to time. Monte Carlo simulation stock trading systems are the perfect example to devise a strategy concerning the everchanging nature of the trading market.
Monte Carlo simulation is used to analyze the trading markets to improve your trading strategies to adapt to the changes.
Monte Carlo Simulation Stock Trading Systems
A Monte Carlo simulation stock trading system is a simulating technique that is applied over random historical changes that occurred in the trade market to calculate an equity curve. The calculated equity curve is further utilized to verify the trading strategy and to acknowledge whether it is good enough to withstand the challenging trading market or not.
The simulation technique is widely known by market analysts and professionals in different fields like science, biology, physical science, energy, transportation, project management, insurance, oil and gas, environment, research, development, etc.
This simulation technique is popular because of providing a wide range of possible answers for a single parameter making it easier for the researcher to work on the possibility and probability of a model becoming successful. This greatly helps in decisionmaking.
Although Monte Carlo simulation helps a lot in lining out different probabilities of the outcomes none of them are deterministic. It is an excellent stimulation and estimation tool for the decisionmakers to analyze the different possibilities in the future.
Specifically, in trading markets; the Monte Carlo Simulation technique is used to analyze how well a strategy will perform in the market. It analyzes different models of trade and further gives its prediction about which trade techniques hold future growth and which do not.
How was the Monte Carlo simulation technique invented?
The Monte Carlo simulation technique was invented by Stanislaw Ulam; a renowned mathematician while he was working on the Manhattan atom bomb project. He suffered a nearfatal brain surgery after World War II and decided to keep himself occupied by playing solitaire unlimitedly.
This was the point that got him curious about the possible outcomes and the way possible outcomes can be predicted as well as the probability of these possible outcomes. This churned up his brain to devise a new technique to find out the distribution of all the possibilities that can be predicted for the future.
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He shared this brilliant idea with his friend John von Neumann who decided to collaborate with him in deriving a new formula and both of these geniuses invented the Monte Carlo simulation technique.
The simulation technique was named after the famous Monte Carlo; the renowned gambling hub of the world in Monaco. The main idea was derived from gambling; as the basic concept of gambling is to take chances and predict random possible outcomes. The Monte Carlo model uses the same probability and possibility techniques to determine the possible outcomes.
How does the Monte Carlo simulation model work?
The Monte Carlo simulation model works by taking the trade deals which you have done in the past and then random changes are made in these trades to have different possible outcomes in hand.
For example; some of the changes which can be made are:
 The order of making these trades
 Fees or commissions
 Exempting some trades
 The entries and exits of the trade deals
Once these small changes are applied to the trades, these changes will determine a new equity curve for you. This new equity curve will calculate the new trading strategies along with the survival chances of the new strategy in the market.
A single simulation technique doesn’t need to provide an accurate result. At one time, more than 100 techniques were applied while using the Monte Carlo simulation model to attain one robust trading strategy which is foolproof.
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Random changes are made in the trades which will invent several simulations by making changes in the possible equity curve to determine the risk factor along with the survival chances in the trade market.
The Monte Carlo simulation model uses the distribution method of probability to define the uncertainty of the variables while it performs a deep risk analysis of a said situation to attain the right decision which will be fruitful in the future.
While using the possible distribution probable; the variables are used which yield higher chances of giving results in different probability outcomes this is where the Monte Carlo simulation method comes in handy and derives the desired data to calculate the possibility of all the future outcomes.
Monte Carlo simulation calculates different probability distribution methods to yield different outcomes. Some of the common practices are:

Uniform
The name says it all. All the values considered in this probability distribution method have an equal chance of winning. The only perspective to consider by the user is the maximum and minimum values which yield the same results.
An excellent example of uniform probability will be to use it in the calculation of any company’s production costs by comparing and predicting the sales revenues of the past and future of any particular product or service.

Lognormal
The lognormal concept of the Monte Carlo simulation technique is the opposite of normal probability distributions. In these methods; the probable values are positively marked rather than being skewed up positively. This method is precisely used to analyze the values which are likely to stay infinitely positive or have the potential to never drop down below zero.
A lognormal method of Monte Carlo simulation technique is mostly used to predict the unforeseen future of changing variables of stock market rates of shares, oil reserves, and the real estate price inflation.

Bell curve
The bell curve probability distribution is also known as the normal Monte Carlo simulation technique. The main aim of the user is to find out the mean and deviation of the product to define the variation.
The bell curve allows all the values which are gathered in the middle and near to the mean to have a very high probability of occurrence and recurrence. Bel curve represents asymmetric variation which is opposite of the lognormal as It considers a very large variety of features of natural occurrences.
The best usage of the bell curve method is to check out the unpredictable outcomes of the inflation rates along with the prices of energy levels which are said to remain in a concentrated place.

Discrete
A discrete probability distribution is where the user is especially interested in defining its outcomes which have the potential of occurring repeatedly with a possibility of every variable in the account.
For example, a user is interested in predicting the outcomes of a lawsuit which seems like endorsing some different possible outcomes for instance; 30 % chance of a positive decision whereas a 10% chance of negative rule out while there is a possibility of 15% mistrial and bright 45% chances of settlement between the two parties.

Pert
Pert is specifically used by those users who are aiming towards determining the maximum, most likely, and minimum values of a certain commodity. In part destitution, there are heavy chances that they will likely and the extremely more likely will occur in the future.
The Pert probability method is largely used by the analyst to analyze the task duration of a given project and its management model.

Triangular
The triangular probability distribution method is defined the same way as pert but with a slight difference. In this scenario; the category producing the highest yield is considered to have the highest potential for recurrence. Product inventory levels and previous sales history is considered at the time of probability calculation to analyze all the variables involved in business management.
Monte Carlo simulation aids in predicting and determining the future risks and possible benefits in trading. The Monte Carlo simulation techniques samples work on different variables collectively and simultaneously to devise a probability distribution model.
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The different sets of samples are termed iterations. The outcomes from these sets are further recorded carefully to be compared later on with other outcomes from the repeated simulations obtained from different iterations. At times these repeated simulations can cross hundreds and thousands in numbers.
The trader closely interprets and analyses the data calculated from the Monte Carlo simulation calculation so that the possibility of risk can be minimized and a trading strategy can be planned out which will aid in the future while making decisions related to the business.
Advantages of Monte Carlo simulation
There are various advantages of Monte Carlo simulation techniques that benefit the users in the long run. It helps in determining a foolproof trading strategy that will be good enough to survive market pressures and small changes in trading decisions. Devising policies according to Monte Carlo simulation will improve the trading performance of the business as well as deliver more revenue than anticipated because of the multiple outcomes already foreseen via Monte Carlo simulation techniques.
Here are some of the advantages:

Expectations of winning and losing
It is human nature to lose interest in the business if multiple losses are incurred. The person will lose confidence in his business or trading strategy resulting in losing more than anticipated. The losing streak will inevitably continue if new strategies are not applied.
That is when the Monte Carlo simulation comes to the rescue. One of the biggest advantages of Monte Carlo simulation is that it will tell you exactly what the winning and losing will be like – beforehand. Once you know the equation you will not lose confidence in your trading technique nor will you freak out waiting for your trading technique to work or materialize.
One of the best things to remember in trading is that winning and losing are both parts of the business just like 2 sides of a coin.

Risk of ruin
Monte Carlo simulation tells the user his risk of ruin before he experiences it. It is one of the most accurate trading metrics that every trader should keep in mind. The risk of ruin can be calculated easily by different formulae such as the Kaufmanns or Raul Vince’s concept of risk of ruin.
Monte Carlo simulation will provide you with an accurate outcome since the result is always based upon hundreds of simulations taken into account and any sort of small changes are noted to be applied in the business model.

Maximum drawdown
The maximum drawdown in simpler words means that the user of the Monte Carlo simulation knows beforehand the maximum benefit he will acquire even before actually encountering it.
For instance, the Monte Carlo simulation will determine that the user might experience a drawdown somewhere in the future of about 30% so based on this information; the trader should be prepared to utilize this advantage beforehand.
It also optimizes the trading strategy to keep the expected drawdown to maximum levels.
Disadvantages of Monte Carlo simulations

Cannot incorporate big fundamental changes
One of the major drawbacks of Monte Carlo simulation is that it does incorporate big fundamental changes into the markets it is analyzed. A financial market might be going through a drastic recession due to a naturally occurring disaster such as the war or the recent coronavirus pandemic.
The Monte Carlo simulation will unsee it. When things like these affect the market then the Monte Carlo simulation analysis doesn’t predict a valid result for the future. The reason is as the market has changed its course drastically over some time thus the variables do not work in the same way to determine possible outcomes.
 Trading data cannot be inconsistent
Another downside of the Monte Carlo simulation is that it wants the trader to have a profitable trading strategy without changing the trading units. This makes the historical trading data go obsolete.
If a trader wants to have a fruitful Monte Carlo simulation analysis; the historical trading needs to be consistent and not broken down to rejuvenate a robust strategy for the business.
How is Monte Carlo simulation used in trading systems?
Monte Carlo simulation strategy is overly used in trading systems; it enables a trader to completely understand whether a particular trading strategy will be robust enough to sustain small minute changes in the market or not. This is the best way to stay informed about the market and the quantified market data which is obtained over some time.
Monte Carlo simulation analysis helps the trader to analyze the trading strategy and the attributes attached to it to have a better understanding of the performance metrics like the annual rate of returns, risk of ruin, maximum and median drawdowns, and the famous drawdown ratios.
A trader must have all this vital information in their hands before starting the actual trading practice. It is important to have this information at their disposal to make wise and wellinformed decisions that will make them grow and they will choose the most suitable trading strategy by allocating the designated amount of capital to capture the market and do the correct position sizing of the product.
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Several strategies can be used to perform Monte Carlo simulation analysis on a given trading system but obviously, they differ from one another in their implementation techniques.
Let’s have a look at some of the most popular prevalent methods for Monte Carlo simulation analysis:

Randomized Monte Carlo simulation analysis method
The randomized Monte Carlo simulation method is widely used to find the overfitting objects in the creation process of a said trading strategy. This method is also called the bootstrapping method. It is applied by randomly sampling the retradeable variables from each strategy.
Every entry will be tested upon its signal and is replaced with any other random but most appropriate variable which will produce a similar signal. This process is repeated almost 1000 times to find out 1000 new equity curves for performance analysis in great depth.
The randomized Monte Carlo simulation analysis method is mostly built on assumptions that are derived from the trading signals. The signals should be strong enough to generate enough profit irrespective of the number of variables chosen.
If the random exits from different variables are used, then the trader should keep in mind that it is not necessary to get a smooth equity curve and that it may be different from the original exits rather than the randomly selected ones.
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Whichever the case is, the trading system cannot maintain a profitability level if the variables are randomly chosen from the trading strategy sporting different exit levels.
The high chance of having an original entry signal by a given variable is derived from an overfit from the background data which is already strapped in the strategy method considering the dropping and collection of particular trading strategies. This may seem to be a risky decision for the investment.
While using the randomized Monte Carlo simulation analysis; two key trading properties should be kept in mind; they are:

Change of trades order
when the order of the trades is changed; it’s very much possible that the random shuffling will produce the original order of the trade. The randomized strategy requires resampling of the variables so that the trades can be shuffled well enough to produce a new equity curve.
Furthermore, this way it will pick the other variables randomly and the overall number of trades to be shuffled will be increased.
It is important to keep in mind that the two trades using the same variables don’t need to have an identical list of the trades. There is a potential that one trade is selected multiple times whereas another one is not picked up at all.

Skipping of trades
Some trades are skipped or deliberately left behind to achieve a newer probability for trades that are likely to be utilized in the future. It helps a trader get an idea about how the equity curve will shape up.
In the real world or a given trading scenario; a trade is missed or skipped if it’s not performing so well or if there is a system failure to retrieve its benefits.
There is a set of steps to be followed when trying the bootstrap test of testing your trade system which will help you analyze the trading strategy performance while strengthening under different markets.
 Start by the creation of an input method
 Very important to backtest the trading system you have selected to attain the original sets of trades
 Vital to repeat the test for as many as 1000 ties to get newer trade outcomes
 Now, pick random trades from the original trades to formulate newer random sets of trades
 Do the loss and earn calculations on the randomly selected trades while you keep on switching the positions.
 Very important to record the system’s equity which can be further utilized for distribution
It is now time to start the postprocessing of the data which is readily available to you to generate the exact distribution of analytical stats and charts which will further determine the risk involved in the newer trade strategy
Original and Resample Monte Carlo Simulation method
The most commonly used Monte Carlo simulation method in trading systems is often deemed to be the most straightforward. This is one of the most historically prevalent methods of Monte Carlo simulation. It consists of utilizing the trade result and reorganizing their order to obtain newer results for the equity curve.
Like other methods, the simulation is done more than 1000 times to get new 1000 equity curve variables which will give the information related to the potential risks to the trading strategy. These risks can be negated or avoided by applying a different approach to the said trading strategy. When this particular simulation method is used, the trader assumes that the trade results will remain the same no matter how many times the order of the variables is rearranged.
After the simulation results are retrieved, the said information is utilized to calculate all the performing trading metrics which will help in compiling the 1000 new equity curves. These key trading metrics can be the average of the drawdown or the maximum drawdown depending upon the variable applied.
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The data gathered from the original drawdown analysis will always be higher compared to the backtest. It will largely rely on it to gather an average drawdown of analytical data.
The data which is compiled from the average drawdown analysis will help the trader to make the perfect decision to let its trading strategy grow in the right direction while allocating the perfect size and variables.
These kinds of simulation analytical tests help the trader to implement and improve their trading strategy. For example; if a certain trading system is having a backtest drawdown of almost 10% and that is the size of the trade data based upon the research and market observance then in live trading the drawdown will have to be shifted to a 15% as in the market will force you to adopt a newer trade strategy.
By using the original and resampling Monte Carlo simulation analysis method, the trader can have a beforehand awareness regarding the changes in drawdown levels and will prompt a change in trade sizing to avoid this mishap.
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Another specific advantage of this type of simulation method is that it gives the correct insight regarding the profit expectation. For example, if a trader is casually entering the market, and is making general guesses regarding its trading strategy which according to the system will not be profitable in the long run even after making more than 30 consecutive trades.
In such a situation the trader will be compelled to turn off the trading strategy as it is giving a loss and is unable to produce a prediction for the future. In such a scenario it is advisable to use this original Monte Carlo simulation analysis method so that it will help you make a new trading strategy that will prove to be fruitful.
This will take time to be generalized but it is better to invest time in devising a new trade strategy rather than losing money as well as energy.
Drawdowns of Monte Carlo for ideal sizing
The Monte Carlo simulation technique helps in quantifying and recognizing the risks involved in the trading strategy; it also aids in sizing the trading strategy. A trader can seek help from the Monte Carlo model to size its trading technique by performing a Monte Carlo drawdown test which will allow the trader to assign a small level of confidence to the projected risks which have the potential to affect the new trading strategy.
Monte Carlo simulation drawdown test is carried out by performing the following steps:
 The user needs to specify the initial capital amount he plans to invest in trade
 In the next step, the Monte Carlo simulation original or resample method is carried out
 The creation of more than 1000 drawdowns is recorded on the initial capital invested in the trade.
 Now record another set of 1000 drawdowns according to the frequency distribution of the starting trade capital.
 On the Yaxis, map the cumulative data distribution which will go all the way up to the 95%
 While on the xaxis, you have to find the corresponding value of the drawdown percentage which will match the 95% on the yaxis.
According to the example mentioned above in the steps; it is easily concluded that 95% of almost all the Monte Carlo simulations are termed under 20% drawdown and are automatically indicated on the xaxis. Therefore, it can confidentially have determined that there is only a 5% chance of having a drawdown that will be bigger than the said 20%.
As a trader, it is very important to be comfortable with these numbers and be able to redo the tests while increasing the initial capital for investment and compare the results which will earn the most accurate drawdown result; as it will increase the confidence in your trade size.
The drawdown data enables the trader to size his trade strategy accurately. If the size of the trading strategy is properly done, then it will also be able to calculate the risk quotient attracted to the trading strategy; determining whether to continue the trading strategy or turn it off and move to other testing strategies.
How many times should a Monte Carlo simulation be run?
As explained in this article, the Monte Carlo simulation analysis is created to aid traders in determining the effectiveness of their trading strategy by testing it on small variables and whether the strategy is good enough to withstand small changes in the market. The question arises in the mind of the trader how many times he should run the Monte Carlo simulation to analyze the performance of its trading strategy.
Ideally, it is said to repeat the simulation analysis technique multiple times to stay informed about the potential risks and the growing changes in the market. This theory is determined on a very large scale as we are talking about more than 1000 different equity curves. The more count there is; the more accuracy you will gain regarding your trade strategy.
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Confidence and accuracy are the key players to determine a trading strategy. If the confidence is little the value estimation will be less and vice versa.
The most confident simulation level while testing the robustness of a trading strategy should sit between 95% to 99%.
Generally, a good trading system is said to be that one which has simulations ranging from 1000 to about 500,000 times. Of course, this requires time and deep analysis of the variable attached to a particular trade business but as the equity curve increases so do the complex nature of the Monte Carlo simulation model’s algorithm.
Monte Carlo simulation analysis usage
It is advised for all retail traders to perform or run a Monte Carlo simulation program on their trading strategy before putting it into practice in the real world. This will enable the trader to have a good insight into the expected profits and drawdowns as well as potential risks.
Monte Carlo simulation also helps in attaining the expectancy levels of the number of simulations required for a particular trading size. If the trade model is giving out 95% expectancy, then it is advisable to perform 100 simulations with a minimum amount as well to get the median approach for the said trade strategy.
Monte Carlo simulations are widely used to find out the probability of different results of any given complex trading system that is very hard to be predicted.
Conclusion
Monte Carlo simulation analysis is one of the greatest tools made to aid the traders who are new to the business and want to invest in different instruments to upgrade their trading system’s performance as well as upgrade their portfolio by making the right decisions.
It also gives an insight to the traders about the strategy being robust and good enough to survive different challenges of the market. These insights help traders to build a sustainable trading strategy to perform well in the future and combat the potential risks. This simulation model aids in controlling the trading size as well as disregards any poorperforming trading strategies.