u/Opening-Row7409

Profit factor 1.55. Win rate 55.1%. Expected payoff 1.67 per trade.
Modest numbers. On its own it impresses nobody, and it isn’t meant to run on its own.
Data: 2010-2026, (Mean Reversion system)

The point is what it contributes to the whole.

Chasing the big winner is a selection problem. If you generate thousands of strategies and keep the best one, that spectacular result isn’t measuring edge — it’s measuring the maximum of the noise. The harder you search, the better the winner looks and the less it means. A modest but explainable strategy is more honest evidence than an exceptional one found among ten thousand.

A single strategy is a single point of failure. When it starts underperforming, you won’t be able to tell whether the edge is gone or it’s just a normal bad stretch — separating those two takes decades of data. With one strategy, that uncertainty leaves you with nothing. With several, the system keeps running while you investigate.

What’s scarce isn’t edges, it’s edges that fail at different times. Finding something with an edge isn’t that hard. Finding two things with an edge that don’t collapse on the same day is. That’s where the real work is, and it’s the part almost nobody measures.

So this strategy doesn’t interest me for what it does alone. It interests me for when it loses, and whether that lines up with when the others do.

If you have any questions, I have no problem answering

I had two strategies running on USTEC(NASDAQ) and US500(S&P 500). I measured how much they move together: over 0.90. They weren’t two strategies, they were the same bet twice. I fixed it at the time, but it made me look at something more fundamental.

There are two classic methods for deciding how much to put on each trade.

Turtles (1980s): they size positions based on how much the asset moves on a normal day. If gold moves $30 a day and EURUSD moves 80 pips, you enter with different sizes in each — but calculated so a normal day moves your account by the same amount in both. If volatility rises, the position shrinks automatically.

It doesn’t care whether your strategy is any good. It just makes all your trades weigh the same. That’s why it works even when your numbers are wrong.

Kelly (1950s): calculates how much to risk to grow as fast as possible, but it needs to know exactly what your edge is:

* Win rate 55% → says 25%
* Win rate 50% → says 16.7%

Five points that any optimistic backtest hides from you. Over 200 trades on the identical sequence, that error leaves you with ×21.7 instead of ×59.3 — you lose 63% of your final capital. Push a bit further and you cross into negative territory, where the strategy has an edge but the sizing destroys the account anyway.

Which one to use: little data → Turtles, and split capital evenly. Years of real data → Kelly, but fractional. Kelly isn’t for the trader who wants to earn more, it’s for the trader who already measured well.

And here’s the worst part: all of this assumes correlations stay stable. That 0.90 is the number on normal days. On bad days everything moves together, and diversification disappears exactly when you need it.
(The images are made with AI)

One of the metrics I consider most relevant when evaluating a systematic strategy is mathematical expectancy (Expected Value / EV).

In trading, it is quite common to use the win rate as an initial measure of a strategy’s quality. However, the percentage of winning trades by itself tells us very little about its expected profitability.

A strategy can have a high win rate and still have negative mathematical expectancy. Likewise, a strategy with a relatively low win rate can have a positive expectancy.

Mathematical expectancy aims to quantify the average expected outcome per trade if a strategy were executed a sufficiently large number of times under comparable conditions.

In simplified terms:

EV = (P(win) × Avg. Win) − (P(loss) × Avg. Loss)

To make this more tangible, rather than keeping it purely theoretical, I wanted to show it using a real backtest example.

At first glance, there is one number that immediately stands out: only 35.92% of the trades were winners, while 64.08% were losers.

If we only looked at the win rate, we could quickly conclude that the strategy does not work. However, once we take the average size of the winning and losing trades into account, the picture changes.

The average winning trade was 2,756.75, while the average losing trade was 1,082.42.

In other words, the winning trades were, on average, considerably larger than the losing trades. The report shows an Expected Payoff of 1.43, indicating a positive expected value per trade within this backtest.

And this is, in my opinion, one of the most interesting aspects of quantitative analysis:

A strategy does not need to win the majority of its trades to have positive expectancy.

The win rate is only one part of the distribution of outcomes. To properly evaluate a strategy, we need to consider the win rate, average win, average loss, number of observations, and the stability of the results together.

Of course, a positive Expected Payoff in a backtest is not enough to conclude that a strategy has a robust statistical edge. We would still need to analyze factors such as the period tested, out-of-sample data, overfitting, execution costs, slippage, parameter stability, and how the strategy behaves under different market conditions.

One of the metrics I consider most relevant when evaluating a systematic strategy is mathematical expectancy (Expected Value / EV).

In trading, it is quite common to use the win rate as an initial measure of a strategy’s quality. However, the percentage of winning trades by itself tells us very little about its expected profitability.

A strategy can have a high win rate and still have negative mathematical expectancy. Likewise, a strategy with a relatively low win rate can have a positive expectancy.

Mathematical expectancy aims to quantify the average expected outcome per trade if a strategy were executed a sufficiently large number of times under comparable conditions.

In simplified terms:

EV = (P(win) × Avg. Win) − (P(loss) × Avg. Loss)

To make this more tangible, rather than keeping it purely theoretical, I wanted to show it using a real backtest example.

At first glance, there is one number that immediately stands out: only 35.92% of the trades were winners, while 64.08% were losers.

If we only looked at the win rate, we could quickly conclude that the strategy does not work. However, once we take the average size of the winning and losing trades into account, the picture changes.

The average winning trade was 2,756.75, while the average losing trade was 1,082.42.

In other words, the winning trades were, on average, considerably larger than the losing trades. The report shows an Expected Payoff of 1.43, indicating a positive expected value per trade within this backtest.

And this is, in my opinion, one of the most interesting aspects of quantitative analysis:

A strategy does not need to win the majority of its trades to have positive expectancy.

The win rate is only one part of the distribution of outcomes. To properly evaluate a strategy, we need to consider the win rate, average win, average loss, number of observations, and the stability of the results together.

Of course, a positive Expected Payoff in a backtest is not enough to conclude that a strategy has a robust statistical edge. We would still need to analyze factors such as the period tested, out-of-sample data, overfitting, execution costs, slippage, parameter stability, and how the strategy behaves under different market conditions.