Follow-Up on Monte Carlo Simulations

Last week, I wrote a post on Monte Carlo simulations. This is a follow-up post as I spent the past week running a strategy that modeled the trade statistics I inputted into the "calculator."

Here's a recap of the trade statistics I inputted.

Monte Carlo Simulation: Inputs

These inputs are based on learnings from my last post:

• Move stop loss to breakeven aggressively to help minimize drawdowns - this assumes 20% of my trades will be stopped out at breakeven
• Although we want to strive for a high win rate, we assume a more realistic win rate of just 30%
• Reward-to-risk ratio needs to be at least 2.5; otherwise, the worst case scenario can mean a bigger drawdown

Here's the simulation graph of the outcomes below.

Monte Carlo Simulation: Outcomes

By keeping the reward-to-risk ratio of at least 2.5, the stress tested worst case outcome indicated a potential maximum drawdown of -25.2% with 14 consecutive losses. The percentage of winning trades was 26.4%.

Of course, the win rate will fluctuate in real-world live trading. This is why looking for setups and only taking the highest quality ones that have the most potential to hit the 2.5 or higher reward-to-risk ratio is crucial.

Using these risk and target settings, I ran an experiment using Tradingview's paper trading account environment. I looked for exponential moving average (EMA) trend squeezes on the hourly (H1) timeframe. From there, I placed buy or sell stops based on the price structure on the 15-minute (M15) timeframe.

Let's start off with the H1 timeframe.

AUDJPY 1-Hour

If we take a look at the H1 timeframe on the AUDJPY pair, there were four potential entry points. This was based on the following observations:

• Price was consistently trading below the EMAs
• Faster EMAs (15 and 30) were trending below EMA60
• Potential entry point was marked when price pulled up moving counter-trend

That actual entries can be plotted by observing the M15 timeframe.

AUDJPY 15-Minute

The sell stop entires were placed on the bearish bar after it has crossed the low of the counter-trend green bar. By setting a take profit that is approximately 2.5 times more than the stop loss, three of these trades emerged winners and the fourth trade would've been carried-forward over the weekend.

After taking a look at my paper trading stats, here are the numbers:

• Total Trades = 12
• Winning Trades = 5
• Breakeven Trades = 3
• Losing Trades = 4
• Winning Percentage = 41.6%
• Breakeven Percentage = 25%

Admittedly, 12 trades is a really small sample size. Perhaps I will write a follow-up post once my stats become more robust. For the time being, using what I have, here's what the Monte Carlo simulation stress test says:

Monte Carlo Simulation

This graph shows some pretty crazy exponential account growth with a starting balance of $5,000. When the number of trades increases, I expect my winning and breakeven percentages to nerf. However, a reward-to-risk ratio does help significantly to mitigate the risks of negative account equity.