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12 Reasons Why Trading Strategies Stop Working

Last Updated on 17 February, 2024 by Abrahamtolle

12 Reasons Why Trading Strategies Stop Working.

Trading strategies perform poorly in live trading mainly because of behavioral mistakes, curve fitting, survivorship bias, and improper trading size changes. It’s hard to replicate an unbiased backtest to live trading.

If you have spent considerable time developing a strategy, it’s frustrating if it performs poorly in live trading. What is happening? Why does it perform so poorly when it worked so well in backtests?

In this article, we’ll briefly mention some arguments for why trading strategies don’t work in live trading or why many perform worse than in your backtests.  Much for the same reasons trading strategies stop working.

As a rule of thumb, you should always expect your strategies to perform worse than on paper. All backtesting, no matter how cautious you are, involves an element of curve fitting.

Why do trading strategies stop working?

It happens to every trader sooner or later: the strategies stop working or they experience a deep drawdown.

What do you do? Continue trading and hope for the best? Should you change strategy or give up?

Below we give some examples of the most obvious reasons why trading strategies don’t work in live trading and why they stop working (eventually):

Backtests compress time and thus you ignore drawdowns while backtesting

You can test a strategy over many decades in just one second, thus compressing time. In live trading, you spend years doing the daily grind of buying and selling, while a backtest is done in seconds or minutes.

You lose a lot of information when just crunching numbers. Moreover, what looks easy on paper, is not as easy in live trading. You can ignore drawdowns in a backtest, but not when you are risking real money.

Strategies do poorly in live trading because you do behavioral mistakes

A trading backtest never replicates live trading, even though we would argue that backtesting works pretty well. The problem is that we constantly fool ourselves constantly by our cognitive errors and behavioral mistakes. If your strategy has shown a 20% drawdown, how do you deal with it in live trading? In a backtest, you know the strategy performed well after the drawdown. But in actual trading, where the future is uncertain, you don’t know that. Do you keep on trading, do you start tweaking or adding variables, or do you stop trading?

Our biases make it very difficult to do what the strategy tells us to do. Markets have an uncanny habit for shaking out the faint at heart – at the exact wrong time.

How do you deal with behavioral mistakes?

Victor Niederhoffer says that a bad system is better than no system at all. Stick to your systems until you either abandon them or put them back to paper trading.

Trading strategies stop working because you quit in the middle of a drawdown

The biggest drawdowns are yet to come. One of the reasons you chose a particular trading strategy is most likely because it has small drawdowns. This is kind of curve-fitting. Thus, you can expect any strategy to have a bigger drawdown than in your backtest.

Trading strategies stop working because you curve fit

What is curve fitting?

Curve fitting is when you use variables and parameters that fit the past but is unlikely to predict future prices. The future is never like the past. Despite this, we change variables and parameters until we get the results we want.

To elaborate, when we curve fit, we don´t fit our models to market behavior. We fit them to market data. That is a huge difference since market data consists of market behavior and random market noise. For that model to be profitable in the future, historical data’s random patterns must repeat themselves. However, the one primary trait of random patterns is that they do not hold any predictive value, since they are random.

Too many variables make your trading strategies stop working

The more you put into your strategy, the more likely you are to curve-fit your strategy. The simpler you make it, the better. A system might be so complex that it has no predictive value. Just a slight change in the market might turn the strategy into a loser. Moreover, be on the lookout for trades that might explain a significant part of the profitability. Such trades could be due to chance and noise and are unlikely to repeat.

The world changes and thus your trading strategies stop working

Such an obvious fact is easy to forget. No strategy lasts forever.

The markets are mostly random

Because markets are mostly random, many of your strategies and edges are the result of noise. It’s genuinely not an edge, but just something that happened to be profitable.

Correlation is not the same as causation

Because markets are predominantly random and evolving, most correlations in trading are spurious. Most relationships are indirect, not direct. Whatever you do and conclude, the result might come from chance and is not proof of causation. Any strategy that seems statistically significant might be so due to noise or hidden factors.

There are many false positives in the markets.

Your trading strategies don’t work in live trading because you ignore survivorship bias

Survivorship bias is more prevalent and important than you think. For example, in March 2021, Seeking Alpha published a strategy that outperforms the S&P 500 by holding 40 stocks based on holdings of the best hedge funds (the article is behind a paywall).

How did the author conclude this?

He started in 2021 by picking 40 large hedge funds that outperformed the S&P 500 from 2008 until 2021. He then used the quarterly holdings of these funds going back to 2008.

Needless to say, the result is fantastic, obviously because he has picked the winners during this period. This is, of course, unlikely to be repeated. The problem is that if you used the same criteria back in 2010 to pick the 2010 stocks, the strategy would have chosen other hedge funds and holdings. Among the 23 comments about the results, just one mentioned the flaw of survivorship bias.

Almost all traders neglect survivorship bias.

Trading costs make your trading strategies unprofitable

Backtests require realistic entry and knowing when to exit a trade. However, these are often based on “after the fact”. Thus, a strategy that enters on the close needs to buy seconds before the close (or in after-hours). This, of course, might change the results significantly.

Trading strategies perform poorly in live trading because you read news

The world is bombarding you with news from all angles. It’s challenging to ignore the news. An abundance of information and free commissions make a recipe for poor trading results. We suggest you keep both news channels and social media at a distance.

Improper size and money management make trading strategies stop working

Make a rational plan before you start on how to allocate your capital – you have to find the optimal capital allocation. It’s how you deal with losses that are paramount for your survival as a trader. How do your strategies perform together as a portfolio? How much capital should be allocated to each trading style?

The pendulum between pessimism and optimism makes you change the size. After a good run, you increase the size, and after a bad run, you decrease the size – only to find out the market turned around. Then you return to your original size. It doesn’t matter how good your strategy is if you can’t execute it properly.

The best advice we can give is always to trade smaller than you like.

All in all – there is no perfect strategy

Many are looking for the perfect strategy with minimal drawdowns. It doesn’t exist. The price you pay for making money in the markets is pain from drawdown and temporary setbacks. You can’t expect to make money without risk. As we have written numerous times: It’s better to have many “imperfect” strategies and let diversification take care of the drawdowns:

How to reduce the risk of poor live trading

Below we briefly mention some methods to minimize disappointment when you go live with your strategies:

Number stability

By changing your variables’ values, you get to measure how the results change from even small modifications. For example, if one of the variables is ADX(5)>40 try changing it to ADX(5)>45 and so on. Does it improve because of just a few winners?

Out of sample

Out-of-sample testing involves testing your strategy on data not included in the backtest. This can be done by splitting your data in two: one part for developing your strategy, for example, from the year 2000 until 2017, and then testing out of sample from 2018 until today.

Incubation period

An even better method than out of sample is to use an incubation period.

How do you do an incubation period?

You open a demo account with live or delayed quotes and run your strategies as if it was live. This is the best out-of-sample test you can get. You get to see how it performs and get to see the drawdowns live.
We suggest you do an incubation period for several months, preferably at least six months.

Monte Carlo simulation

Monte Carlo simulations are used to model different outcomes of the variables and the parameters in your strategy. It makes random sequences to evaluate your trading system’s robustness to find how the element of risk and randomness might influence the forecasting abilities.

It works by reshuffling the order of the trades in a backtest and can expose weaknesses that otherwise had would be hidden in the backtest. The simulation then gives you a list of potential outcomes – CAGR, drawdowns, risk of ruin, etc with probabilities.

The best medicine for avoiding trading strategies that stop working: Trade smaller than you like

Most traders are too optimistic about how much pain they tolerate. Everything looks easy in a backtest, but when real money is at stake, we tend to make many behavioral mistakes. Greed makes you count the chips before they are won and makes you trade sizes that are too big for your bankroll.

But success is about building up small and frequent profits over time. Prepare for a daily grind. To manage that, you need to trade a smaller size than you would like. This is the only way to detach you from money.

We recommend keeping a journal where you record all your trades (and just as important the trades you skip). Trading is a continuous feedback loop! We have provided a trading journal example for your convenience.

Don’t despair – some ideas work better than others

We have been making quantified strategies for 20 years. Some of the strategies we made prior to 2018 have been recently published as a paid service:

Conclusion: why trading strategies don’t work in live trading

Trading strategies don’t work in live trading, or they stop working completely, for a number of reasons: you curve-fit data, ignore data, or you might do behavioral mistakes and not follow your system at all!

To minimize this risk always do an incubation period of your strategies and trade a smaller size than you’d like.


What is curve fitting in the context of trading strategies, and how does it impact their performance?

Curve fitting occurs when variables and parameters are tailored to fit past data, potentially leading to poor predictions in future market conditions. It is a common reason why trading strategies may not work as expected in live trading.

Why is it essential to trade smaller sizes than desired when implementing trading strategies?

Trading strategies face challenges in live trading due to factors such as behavioral mistakes, curve fitting, survivorship bias, and discrepancies in time. Trading smaller sizes is crucial to managing the psychological aspects of trading, such as greed and optimism. It allows traders to build profits gradually and detach themselves emotionally from the monetary aspect of trades.

How can Monte Carlo simulation be utilized to enhance the robustness of trading strategies?

Monte Carlo simulation involves reshuffling the order of trades in a backtest to expose weaknesses in a trading system. It provides a probabilistic analysis of potential outcomes, including CAGR, drawdowns, and risk of ruin, contributing to a more robust evaluation of the strategy.

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