Crypto has democratized finance in ways previously unimaginable, making trading accessible to the masses and welcoming a plethora of inexperienced individuals with minimal trading and financial education.

It's estimated that 95% of new crypto traders lose money, a daunting statistic newcomers must grapple with. Even more unsettling is the fact that many persist in trading for months and even years without ever turning a profit.

Unprofitable traders tend to make various mistakes, which broadly fall into three categories: inadequate trading education, weak trading psychology, and incorrect application of a well-defined trading system that possesses an edge. The last aspect is particularly insidious because many traders do not know how to evaluate edge. They might be applying a strategy correctly, but if it lacks an edge, they won't achieve profitability in the long term.

Edge, also known as alpha, is a distinct advantage over other market participants, resulting in long-term profitability. Being a profitable trader over the long term requires consistently using a well-defined system with an edge. This critical element is something many traders lack, frequently without even realizing it.

There are different kinds of trading edges. Transient edges in the market often emerge as secondary consequences of regulatory frameworks (systemic edge) or periodically unpredictable events, such as price disparities for identical securities across different exchanges (cyclical edge - arbitrage). Enduring advantages, also known as sustainable trading edges, appear at the expense of less adept market participants, whose trading mistakes typically manifest with reliable consistency. This article will focus on sustainable edges, which are easier to spot and validate through statistical analysis.

A strategy is said to possess a sustainable trading edge when it maintains a positive Expected Value (EV) over a designated period. The expected value is computed as follows:

EV= (WinRate × Average Winning Trade)- (LossRate × Average Losing Trade)

A successful trader strategically stacks the odds in their favor, ensuring that there exists a statistically significant probability to make money per trade.

A real trading edge is hard to find. Even when found, there is no guarantee of how long it will last as markets change all the time. If enough people are trying to exploit the same edge, it will stop working. That's why most traders are very secretive about their top trading strategies. Furthermore, it’s why much of the advice dispensed in articles centered around "How to build an edge" tends to be generic. Intrinsically, if a strategy or insight is readily accessible, it's unlikely to offer a genuine edge.

Many traders are deceived by "random reinforcement," mistakenly believing they have an edge when, in reality, they do not. Random reinforcement in trading refers to the psychological phenomenon where the outcomes of non-strategy-based trades—whether losing or winning trades—are erroneously attributed to a trader’s perceived skill or strategy. In other words, mistaking luck for edge.

A novice trader might experience a series of winning trades purely by chance without employing a viable, tested strategy. This incidental success can fortify a false belief in their trading approach or intuition, persuading them that they have discovered an edge. Consequently, the trader may continue to employ the same haphazard approach, often leading to substantial financial losses in the long run.

This random reinforcement effect entices traders to continue trading without an edge as they become ensnared in a cycle of chance, mistaking luck for skill and perpetually seeking the elusive win that once was. It remarks the critical importance of understanding, identifying, and conscientiously applying a well-defined trading edge, which is rigorously, systematically tested, and proven reliable.

Now that you know what edge in trading is and why we need it, it's time to develop a trading edge for yourself. This process is much more challenging and time-consuming than many traders anticipate, often taking months to complete. Also, remember that finding an edge is only part of the equation to become a profitable trader; other aspects required include proper trading education, managing risk, and trading psychology.

Before we embark on the journey of finding an edge, you must ponder a fundamental question: What kinds of edges are you willing to trade?

While they must all have positive EV, not all edges are created equal. Some are built on strategies that require holding positions for a more extended period, some have a high number of consecutive losses before the one big win, some require strong trading psychology to withstand a high drawdown, and some have very unevenly distributed returns. As you can see, positive expectancy doesn't always mean smooth sailing.

New traders underestimate the power of emotions when the going gets tough. Maybe you found a strategy with a great return but a low win rate, with up to 8 consecutive losses. When looking at the information in hindsight and seeing the big win after the chain of losers, it's tempting to think you will maintain composure should history repeat itself. However, as the 4th, 6th, or 7th losing trades materialize, your conviction may waver, especially with your hard-earned money on the line.

All strategies have risk, but depending on your trading style, you may be better suited to cope with specific risk metrics over others. Take a moment to think about them and write them down. If you haven't done this exercise before, don't worry too much about getting it perfect now; you will have to modify it later as you learn more and develop your trading skills. I am showing you my metrics for your reference; feel free to use them as a starting point, but remember that we don't have the same goals in life, so make sure you adapt them at some point. What's important now is setting a criteria on paper that you can review later.

**Risk**

- Drawdown: Less than 30%
- Average trade duration: Less than 72 H (I am a lot more relaxed about this for the coins I accumulate, BTC and ETH, and more strict for lower cap tokens)
- Maximum consecutive losing trades: Less than 6

**Reward**

- Daily ROI: more than 0.1%
- Consistency of returns: no preference

These metrics are readily available in Gainium's backtester.

Once you have clear goals, it's time to generate ideas that may provide the edge that will fit your criteria. Generating a hypothesis requires a lot of brain power and creativity; remember that the apparent play has no edge. However, sometimes, the obvious play with a few twists can be used to generate a good hypothesis.

There are four main ways to come up with a hypothesis:

- Observation: Actively observing price action to notice technical analysis patterns or correlations that could lead to predictable price movements.
- Ideas: Logical arguments that you can backtest or forward test.
- Reverse-engineering: Looking for past price moves and elucidating the factors that could predict them.
- Other traders: learned from trading groups or mentors.

After you formulate a trading idea, you can test its effectiveness in historical data, a process known as backtesting. Backtesting is a great way to determine whether your hypothesis may have an edge when used correctly.

Through backtesting, you can get crucial data such as the strategy's Win Rate, the Average winning trade, and the Average losing trade. These elements are necessary to calculate the Expected Value, as discussed before. But you will also get other essential risk metrics such as drawdown, maximum consecutive wins and losses, the average time in trade, etc. All these metrics are great to give you an indication of what the performance of the strategy might be in the real world.

**Can you trust your backtest?**

An important topic that many traders overlook or misunderstand is the reliability of the backtest result. Testing how a strategy has performed in the past is straightforward; the challenge is extrapolating past results to future profitability.

To solve this challenge, we need the help of statistics. In order to reasonably expect a strategy to perform in the future as it did in the past, you need **statistical significance**. When you make a statistically significant prediction, you claim that the observed data result from an underlying cause, not just chance.

The confidence interval accompanies Statistical significance. According to Investopedia, "A confidence interval, in statistics, refers to the probability that a population parameter will fall between a set of values for a certain proportion of times."

Let's review an example. Imagine that a trader predicts that the win rate for a strategy is 50%, with a 5% margin of error and 95% confidence interval. This means that based on his analysis, he expects with a 95% probability that the actual value of the strategy's win rate is in the range of 47.5%-52.5%

For a strategy to have an edge, it must have a positive EV. However, not all strategies with positive EV have a true edge. A widespread mistake traders make is to rely on a small sample size to determine their strategy's performance. In order to be able to confidently say your hypothesis is causing the results and not just random noise, you need statistical significance. You can only achieve statistical significance with a large enough sample (the number of trades in your backtest result).

**How many trades do you need?**

So, how many trades do you need on your backtest to consider it reliable? The short and easy answer is that the more, the better, but no less than 100. The more nuanced answer requires a formula known as **Cochran's Sample Size**. I will not bore you with the mathematical details, but in summary, see the trades needed for a certain confidence with a 5% margin of error (the typical margin of error commonly used in tests like this).

- Confidence level 95% -> 385 trades
- Confidence level 90% -> 273 trades
- Confidence level 85% -> 208 trades
- Confidence level 80% -> 164 trades
- Confidence level 75% -> 133 trades
- Confidence level 70% -> 107 trades

As you can observe, you need many trades to make reliable predictions. If your entry condition doesn't fire often, if you are testing a high timeframe, or if the market doesn't have a lot of historical price data, you will find that the obtained sample size is too small to achieve statistical significance. In such cases, you may want to try on a lower time frame to generate more entries or a token with more historical price data.

Once we have identified a strategy with a statistically significant edge that fits our criteria, we will put it through another test: paper trading. Paper trading, or forward testing, involves running your strategy on a demo account with real market data and fake funds.

Paper trading is a great way to get familiar with the strategy and observe how it behaves with real market data to ensure it meets your expectations. Unlike backtesting, which typically uses candle data, paper trading uses real-time market data. This is a crucial factor in strategies using trailing take profit or stop loss, as the trailing simulation will be more accurate than using candle data, where we don't know the intra-candle price movement.

Paper trading can also help us overcome the problem of overfitting, which is a term used to describe a strategy that has been excessively optimized to perform well in the historical data provided for backtest but not for future live data.

There is no golden rule on how long you should paper trade a strategy before taking it into a live account. It depends on many factors, including the reliability of the initial backtests and your risk tolerance. It's generally advisable to run it on paper trading at least for a few weeks, while more conservative traders prefer to wait for a few months.

If your strategy made it this far, it's ready to meet the real world. But remember that so far, we have only emulated results, and as great tools as backtesting and paper trade are, they also have limitations that can only be overcome by live trading. This is especially important if your strategy relies on market orders, as the effects of slippage can't be easily emulated.

During the trial run, you deploy your strategy live with a smaller portion of the funds you initially planned to allocate to the strategy. Again, there is no golden rule for how long to run the trial, but I believe it doesn't need to run for as long as the paper trade test. In most cases, you will be able to assess the trial within a few weeks.

Don't be discouraged if the strategy doesn't perform as well as you anticipated. Adjustments at this stage are normal, and as long as they are not significant changes, you may not have to repeat the whole process from the beginning. Use common sense to determine how much time to invest in retesting the adjustments.

Backtesting and paper trading are tools meant to help us infer what a trading strategy might do in the real world, but they are far from perfect.

Just because you ran a backtest and it gave you an excellent result, it doesn't mean you should run the strategy with all your funds right away. Always consider the following factors:

No matter how carefully developed, software can always contain bugs. Always double-check as many trades as possible directly on the chart.

Backtesting and paper trading are just an emulation, and trading on exchanges can sometimes have unpredictable results. Slippage, for example, is a problem that often affects users, and it can drastically vary the result of a trade when using market orders, especially during high volume or in low liquidity markets. You may also encounter issues with partially filled limit orders and unforeseen exchange errors.

The trial run is designed to avoid these pitfalls.

The risk of excessively optimizing a strategy to perform in a past period of data is that it becomes overfitted to that data. Overfitting gives the false illusion of an edge in a strategy. Here is what you can do to avoid overfitting:

- Don't overly optimize the settings. If a strategy performs well with a 1.23% take profit but fails miserably with any other value, it's a sign that it is overfitted. When running many cycles, software that automatically runs backtest and optimizes settings will likely produce overfitted results.
- Perform out-of-sample testing. One of the most common techniques to avoid overfitting involves testing the strategy in a different dataset from the one that was trained. For example, you could backtest and optimize your strategy for 2020-2022 (your in-sample), leaving the year 2023 for out-of-sample testing. If your strategy still performs well in 2023 after optimizing it for 2020-2022, then it is not overfitted. Another way to avoid overfitting is using paper trading and trial runs as the out-of-sample data.

Slippage is the difference between the expected price of a trade and the price at which it is actually executed. It can significantly affect your trading edge and profitability, mainly when using market orders in fast-moving or illiquid markets.

To account for slippage in your trading strategy, consider using limit orders instead of market orders and factor in the potential impact of slippage when calculating your risk-reward ratio. In Gainium, you can easily simulate slippage when running a backtest from the backtest settings screen.

Trading edges can become obsolete for a number of different reasons, including fluctuating market conditions, regulatory changes, overuse of a trading strategy, etc. It's essential to monitor the trading results and check whether the current trading performance substantially differs from what you expected, indicating that the edge may no longer exist.

I hope this article has helped you understand the importance of developing a trading edge and given you a blueprint for how to find yours. While finding and refining your trading edge is long and labor-intensive, it will be the best investment you can make in your trading career.

Before closing this topic, I would like to share a few last thoughts on this subject.

Developing a successful trading edge takes time, effort, and an ongoing learning process. Resist the temptation of skipping or shortcutting the steps. With patience and persistence, you can create a unique trading edge that will serve you well in the long run.

People sometimes use tools as clutches for their lack of work. They think that the latest bot feature or magic indicator is what they need to become profitable. They keep switching trading software and indicators, thinking they need that one tool that instantly unlocks a world of profit.

In reality, tools are great and can unlock new edges, but you can't skip the hard work. You must still backtest, forward test, and trial run the strategy.

You can even get away with free software to find an edge. At Gainium, we provide a user interface with charting, many different indicators, risk management options, and unlimited free backtesting and paper trading, so the process of finding and refining an edge won't cost you a single dime.

If you are using someone else's edge, it may be tempting to skip the testing phase, especially if they already provided backtesting and paper trading data. Don't make the mistake of blindly relying on someone else's data, even if you trust and respect the source.

There are many reasons why the data you obtain might be different from the provider. Maybe you must adjust the strategy to fit your risk and reward criteria. Or perhaps you have a different way to calculate returns. Always re-run the backtest and forward test on your own, as that's the best way to ensure someone else's strategy fits your criteria.

Continuous learning is vital to making more money in trading. The trading world keeps changing. As the market keeps changing, there’s always something new to learn. Continuous learning helps you stay ahead, understand new concepts, and make smarter moves.

A trading journal can help. By writing down why you made a trade, what happened during the trade, and how it turned out, you can look back and see patterns in what works and what doesn’t. This means doing more of what makes you money and less of what doesn’t. A trading journal also helps you remember and stick to your trading plans instead of making decisions based on feelings. So, it's like a helpful tool that guides you to make better choices and keep growing in your trading journey.

Being part of a community can also be a big help. In trading communities, people share tips, strategies, and stories about their trading journeys. This sharing helps everyone in the group learn more and avoid common pitfalls. So, to keep improving and earning in trading, never stop learning and consider being active in trading groups such as Ganium's telegram and discord groups.