I was recently asked, “What win percentage do your algos achieve?” This is a broad and open-ended question—and a good one but not a great one – it would not be a question a pro trader or a fund manager would ask me -they would ask what NAV and %  profit, drawdown etc, or better what expectancy your systems have -then dive into preferences of win ratio and RR, etc. Ok so Let’s take a moment to put it into perspective.

Note to self: I need to get online the baseline algo stats website again!, as they’re a helpful guide. However, that’s just the starting point—there’s much more to the story. While baselines are useful to understand, let’s dive deeper into the possibilities.

AFT (Algo Trading Framework) is a comprehensive tool designed with preconfigured turnkey systems, giving traders a ready-made solution to start trading effectively. However, it also offers the flexibility for traders to invent, configure, or even code their own algo rules. With its vast range of off-the-shelf features, there isn’t just one answer to what a system can achieve—it’s simply too expansive. The permutations are nearly endless, allowing traders to experiment and refine their setups for years if they choose.

For many, the journey begins with our turnkey systems, offering a solid foundation to start trading. As traders master the basics, they can then optimize, experiment, and create unique combinations to suit their style. Some prefer to stick with the turnkey setups, live trading and fine-tuning them to achieve consistent results. Whatever the approach, AFT adapts to the trader’s needs and skill level.

Trading system win rates can vary widely, ranging from 35% to 95%, depending on the trader’s preference, the type of trading system, and their style. While high win rates may seem appealing, it’s important to understand what truly defines a successful trading system.

Baselines and Real-World Performance

At Algo Trading Systems (ATS), baseline systems typically achieve win rates between 50% and 65%. However, live traders in the VIP Group often average 66% to 80% win rates by leveraging a combination of strategies and tools, such as the Hybrid Trading Approach.

Why Win Percentage Alone Is Misleading

While win percentage is an essential metric, it cannot determine profitability on its own. For instance, a trading system with a 95% win rate could still lose money after factoring in commissions and costs if its losses are disproportionately large compared to its wins.

A more meaningful metric combines win ratio and risk-reward into a measure of expectancy. For example:

• A 65% win rate with a 1:1 risk-reward ratio is excellent.
• A 50% win rate with a 1:2 risk-reward ratio would be exceptional.
• A 50% win rate with a 1:1.25 risk-reward ratio is more typical and sustainable.

Example: High Win Ratio Scalper System (ES Futures)

Consider a scalping system with the following parameters:

• Stop: 24 ticks
• Target: 8 ticks

If we assume random price movement without any edge or strategy, the likelihood of hitting the target or stop is inversely proportional to their respective distances from the entry point. The shorter distance (the target) has a higher probability of being hit.

Probabilities:

• Target Being Hit: 75%
• Stop Being Hit: 25%

This means the win percentage could be 75% based purely on chance, assuming equal price movement probabilities.

Adjusted Example: Smaller Target

Now, consider a system with:

• Stop: 28 ticks
• Target: 6 ticks
• Total Distance: ticks

Probabilities:

• Target Being Hit:  82.35%
• Stop Being Hit: 17.65%

The target is 82.35% likely to be hit because it is much closer to the entry point than the stop. The stop, on the other hand, has a 17.65% probability of being hit.

Caveats of High Win Ratios

While such systems heavily favor hitting the target, they require a very high win rate (>82.35%) to overcome their poor risk-reward ratio (approximately 4.6:1). For every 1 loss, you would need at least 5 wins to break even.

Is This Tradeable?

For me, no. I prefer baseline systems designed around a 65% average win ratio, with targets that start at 1:1, 1:2, or even 1:5 risk-reward ratios, and potentially 1:10 for runners.

Improving the System

To improve such systems, align trading with high-probability times to increase the win ratio without compromising the risk-reward ratio. For example:

• Stop: 20 ticks
• Targets: 20, 40, 100 far runner

With trading skill and careful selection of high-probability conditions, it’s possible to move the win ratio from 50% to 65%, or even 85%, over time.

Introduction to Expectancy: Combining Win Percentage and Risk-Reward

Expectancy is the cornerstone of any successful trading system. It combines two critical metrics—Win Percentage and Risk-Reward Ratio—to provide a complete picture of a strategy’s profitability over time. While Win Percentage reflects how often a trade is successful, it doesn’t account for the size of wins versus losses. Similarly, Risk-Reward Ratio quantifies the relationship between potential profits and losses but says nothing about how frequently those profits occur. Expectancy bridges this gap by calculating the average profit or loss per trade, considering both the probability of winning and the magnitude of gains and losses. A balanced approach, such as a moderate Win Percentage paired with a favorable Risk-Reward Ratio, often leads to consistent and sustainable trading outcomes. This metric allows traders to evaluate whether their system can generate profits over the long term, regardless of occasional losing streaks.

Example of expectancy for a system trading ES E-mini Futures with the given parameters:

System Parameters:

• Average Loss (Stop): 24 ticks
• Average Reward (Target): 12 ticks
• Win Percentage (Win %): 66% (0.66)
• Loss Percentage (Loss %): 1−0.66=34%(0.34)1 – 0.66 = 34\% (0.34)1−0.66=34%(0.34)
  

Step-by-Step Calculation:

1. Win Component:
   $0.66\times 12=7.92$
2. Loss Component:
   $0.34\times 24=8.16$
3. Expectancy:
   $7.92-8.16=-0.24$

Expectancy Result:

The system has an expectancy of -0.24 ticks per trade, meaning that over time, you lose 0.24 ticks on average for each trade.

Interpretation:

This system, with a 66% win rate and a risk-reward ratio of 2:1 (24 ticks risk vs. 12 ticks reward), is not profitable over the long term because the losses outweigh the gains despite a high win percentage.

To make this system profitable:

1. Increase the Reward: Aim for a reward of at least 16 ticks (closer to a 1:1.5 risk-reward ratio).
2. Increase the Win Rate: Improve the win rate beyond 66% to compensate for the current risk-reward imbalance.
3. Reduce the Stop Loss: Tighten the stop loss to 18 ticks to align better with the 12-tick reward.

System 2 Parameters:

• Average Loss (Stop): 24 ticks
• Average Reward (Target): 20 ticks
• Win Percentage (Win %): 66% (0.66)
• Loss Percentage (Loss %): $1-0.66=34\%(0.34)$

Expectancy=(0.66×20)−(0.34×24)

Expectancy Result:

The system has an expectancy of +5.04 ticks per trade, meaning that over time, you gain 5.04 ticks on average for each trade.

Interpretation:

This system is profitable over the long term. Despite risking slightly more (24 ticks) than the potential reward (20 ticks), the 66% win rate compensates for the slightly unfavorable risk-reward ratio.

Why It Works:

• The win percentage (66%) is high enough to offset the imbalance in the risk-reward ratio.
• The average reward (20 ticks) is close to the stop loss (24 ticks), meaning the system doesn’t rely too heavily on an extreme win rate to stay profitable.

Performance Insights:

This system has a positive expectancy, which makes it viable for consistent trading. To enhance it further:

• Improve the Reward: Target closer to 24 ticks for a 1:1 risk-reward ratio.
• Optimize the Win Rate: A slight increase in win percentage (e.g., 70%) would boost profitability significantly.

Combining Systems for Success

Using AWT alongside AFT has allowed traders to achieve win rates of 85% to 100% during certain trading periods, even when the system itself averages around 65%. Some traders have consistently averaged 87% win rates over extended periods, but this level of performance requires skill, practice, and time.

Achievably, realistically for day trading if you are hauling in 66% on average and your RR is around 1:1 and find that to be sustainable you are definitely on the right track to excellence -way above 80%  to 90%+ of the rest of the herd!.

By combining classic trading systems theory with the hybrid edge, traders can achieve maximum profit, minimum drawdown, and reduced emotional strain. This is the approach we teach, coach, and provide the tools and systems to implement.

Conclusion
With Hybrid – high probability trading methods and tools.  Why do we beat traditional algo traders into dust?
We can do what classic trading automated trading and systematic traders are unable to do – increase the win % without changing the system profile… eg they would change the RR  to increase the Win % or keep looking for the next holy grail settings and optimizing to get them through the next phase never settling etc. but we use adaptive systems and 1 setting and instead nail the bullseye with far more consistency than any other method – with time and skill and commitment… “Bruce Lee” trading if you will- economy of thought and action practicing the same way 10000s of times not practicing 1000s of ways.

A Simple Analogy

Think of it as a Tesla driving around a racetrack on autopilot, achieving an okay lap time. With man and machine mode, a skilled driver at the wheel, navigating the nuances of S-bends and traffic, the performance could be far superior. Trading isn’t a Route 66 cruise; it’s more like a Swiss Alps rally. Anyone who believes a robot alone can outtrade a hybrid trader has yet to experience the real rewards and success that VIP traders achieve. To get funded and to trade our approach allows a trader the maximum control on the smallest targets so as not to fall foul of the prop firm trading rules. ATS Traders share their stories and stats, straight from the horse’s mouth in the VIP Trading Group.